CHAPTER ONE

OVERVIEW OF PRODUCTION MANAGEMENT

1.1 Introduction
The apparel manufacturing firms are facing stiff competition from its local and overseas
counterparts and have been fighting an uphill battle. In fact, quick response and high
flexibility manufacturing has been considered as an important strategy to survive to this
industry. To achieve this goal, the non-traditional manufacturing systems, such as
modular manufacturing, unit production and computer integrated manufacturing
system has been introduced to the industry.

In addition, it is required to have a production planning system which can quickly

estimate the manufacturing cost and lead time for a potential order, develop an accurate
production plan and schedule, and ensure the availability of the required resources so as
to produce and deliver the order promptly. At the decision-making stage of accepting
an order, however, it is difficult to accurately generate a detailed production plan,
prepare a manning schedule, and estimate manufacturing costs. The level of difficulty
increases in a situation where there is a radical change in style, or a large increase in
production is required over a short period of time and the company must substantially
increase hiring and training activities. Without accurate production plans and estimates,
severe delay in delivery and excessive manufacturing cost may result.

There are several factors contributing to this difficulty. Delays on materials delivery,
unexpected absenteeism, high employee turnover, equipment breakdown, and static
production planning methodology are common problems. In additions to these, an
accurate estimate of the production capacity is another serious challenge. Since the
ability to change styles quickly is important to a make-to-order company, it has to rely
on general-purpose machines to maintain its flexibility. Thus extensive hiring, training,

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and retraining activities are required whenever there is a significant change in garment
design or in production quantity. It is hard to measure production capacity in this type
of environment. The limit of training capacity and the learning effect on production
capacity over time are frequently underestimated. These factors usually make a
significant contribution to the delay and cost increase. Production planning and
scheduling practices in small and medium size apparel manufacturing firms, however, do
not fully address the impacts of operations sequence and time phased training effects on
production capacity.

Production planning and scheduling has received significant research attention for many
decades. Production planning and controlling is the process of organizing, choosing,
and timing resource usage to carry out activities required to produce desired outputs at
desired times, while satisfying given constraints on resources, due dates, production
costs, quality requirements and other operational limitations.

1.2 Functions within business organization

Like other manufacturing organizations, garment manufacturing firms are managed by
three main functions: finance, marketing and production. These functions are organized
under the CEO /managing director/ of organizations. Other business functions—such as
accounting, purchasing, human resources, and engineering—support these three major
functions.

As shown in the figure 1-1, finance is responsible for managing cash flow, current assets,
and capital investments. Marketing is responsible for sales, generating customer demand,
and understanding customer wants and needs. For example, ABC is a garment
manufacturingfirm which produces casual apparels. Its marketing function promote the
product, analyses the customer needs, analyses market trends, foresees social and
political changes that influence the nature of the market; finance provide finance for
promotion, raw material, labor, equipment and etc; What about production

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 management function?Operation/production plans and coordinates all resources needed
to design, produce and deliver the garment.

CEO of the Company

Marketing Operations
Finance
Manages: customer
Manages:
demands
people, equipment, technology,
Manages:
materials,
cash and
flow,information
current assets, and capital investments
Generates: sales for goods and services
To produce: goods and/or services

Figure 1-1: Organizational Chart showing the three functions

1.3 Operation/Production Function

Production consists of all activities directly related with creating of goods and/or
services. To many peoples, the term production conjures up images of factories,
machines and assembly lines. Nowadays, production concepts and techniques applied in
service industry such as healthcare, transportation, retailing and so on. The name
production management can be used interchangeably with operation management.
Some literatures argue that operation management refers service industry where as
production management refers manufacturing industry. There is no clear-cut between
manufacturing and service industry as manufacturing industry delivers service along
their product and service organization deliver product at point of service delivery. For
example, some airlines deliver food and drinks in their flight operation and the biggest
car manufacturer General Motor’s profit is from the service it delivered after sale to its
customers. In textile and fashion business, in addition to quality products and low priced
apparels, manufacturers are being requested to provide services like instant responses,
delivery commitments, insurance for shipping and so on. That means garment industry

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provide both product and service to their customer at right time, right quality and with
reduced price of products. Therefore, in this text book, production management and
operation management are used interchangeably regardless of the industry.

The term production is used to indicate a process through which raw materials are
converted into finished product. In other words, it transforms the various inputs, such as
raw materials, labor, money, management, etc. into output, i.e., goods and services.
The place where such transformation takes place is known as factory. So, technically
speaking, all the processes involved in various productions and service departments of a
factory which converts raw materials to a finished product can be designated as
production.

Production is defined as “the step-by-step conversion of one form of material into

another form through chemical or mechanical process to create or enhance the utility of
the product to the user.” Thus production is a value addition process. At each stage of
processing, there will be value addition. For example, raw cotton cannot be used as
such. But when converted into cloth it has utility. However, the finished product of
one organization can become raw material for another organization. For example, the
products of textile industry are raw materials for garment manufacturing firms.

EdwoodBuffa defines production as ‘a process by which goods and services are created’.
Some examples of production are: manufacturing custom-made products like, boilers
with a specific capacity, constructing flats, some structural fabrication works for selected
customers, etc., and manufacturing standardized products like, car, bus, motor cycle,
radio, television, etc.

Productions is the conversion of inputs into outputs using one or more transformational
processes (physical resources are required in transformational processes) as shown in
figure 1-2, so as toprovide the desired utility/utilities of form, place, possession or state
or a combination thereof to thecustomer while meeting the other organizational

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objectives of effectiveness, efficiency and adaptability.It distinguishes itself from other
functions such as personnel, marketing, etc. by its primary concernfor ‘conversion by
using physical resources’.

For example, (a) The physical distribution of items to the users orcustomers, (b) The
arrangement of collection of marketing information, (c) The actual selection
andrecruitment process, (d) The paper flow and conversion of data into information
usable by the judge in a court of law, etc. can all be put under the banner of production
or operations. ‘The conversion’ here is subtle, unlike manufacturing which is obvious.
While in case (a) and (b) it isthe conversion of ‘place’ and ‘possession’ characteristic of
product, In (c) and (d) it is the conversionof the ‘state’ and characteristics. And using
physical resources effects this ‘conversion’. The inputand / or output could also be non-
physical such as ‘information’, but the conversion process usesphysical resources in
addition to other non-physical resources.

The operation/production function is the core of business organization; it is responsible

for producing goods and services. Production is the business function that plans,
organizes, coordinates, and controls the resources needed to produce a company’s goods
and services. Production management is a management function that involves managing
people, equipment, technology, information, and many other resources. The objective
of operations is to add value during transformational process. The term value added is
used to describe the difference between prices of output and input.

In order to ensure the desired outputs are obtained, measurements are taken at various
points in the transformational processes which is called feedback, and then compared
previously established standards to determine whether corrective actions are required
(called control). The decision making related to productionprocess of that the resulting
goods and service is produced according to specifications and at minimum costis called
production planning and control. Several terms are used interchangeably such

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asoperations management, production and operation management, production
planning and control, manufacturing system and management, etc...

Figure 1-2: Schematic Production System

1.4 Strategy, Strategic Planning and Management

Strategy is derived from Greek words, and taken from military
terminology.Stratosmeans army, ago means lead. Strategy means leadership with
appropriate course of action towards which appropriate activities are directed to achieve
anticipated goals.

Business organizations aim to achieve their mission. Mission is the basis of any
organization- the reasons of its existence. The mission statements provide a general
directionof company that the company is going to achieve. Strategy is detail plan how
to achieve company’s mission. Strategy establishes the course and shape of organization
for specific period of time. It aims to establish competitive advantages for the business
entity by adequate configuration of resources and expertise. Different scholars have
defined strategy in different ways but the concept they try to explain is the same.

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According to Alfred Chandler (1962) “Strategy is setting long-term aims and purposes
of a production business system, the choice of course of actions and allocation of devices
necessary to realize its aim”

Kenneth Richmond Andrews (1986) defined as “Strategy consists of aims, purposes and
main policies and plans for achieving these goals, thus giving a precise definition as to
what the company is/is not/ at that moment and what to be in the future”

According to Michael Porter (1988) “Strategy means being different. It refers to the
systemic choices of set of activities aiming to achieve a unique mixture of values.”

Business organization should have long-term plan as well as short-term goals. The
long-range plan of a business, designed to provide and sustain shareholder value, is
called the business strategy. Business strategy describes in simple wayin the firm’s
mission and vision. Business strategy aims to clarify the overall direction of the
business /firm/. Business strategy is all encompassing and all-embracing for the complete
entity to provide top level guidelines the dictate all other operation.

For a company to succeed, the business strategy must be supported by each of the
individual business functions, such as operations, finance, and marketing. Operations
strategy contains long-range plan for the operations function that specifies the design
and use of resources to support the business strategy.

Business strategy determines the nature of organizational structure. That means,

structuring of functions, staffing of departments, design of production system,
equipments and facilities organized based on the strategy. Based on business strategy,
manufacturing firms devise their production strategy.Production strategy is the planthat
specifies the design and use of resources to support the business strategy. This
includesthe location, size, and type of facilities available; worker skills and talents
required;use of technology, special processes needed, special equipment; and

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qualitycontrol methods. The operations strategy must be aligned with the company’s
businessstrategy and enable the company to achieve its long-term plan.

Figure 1-3: Relationship between business strategy and functional strategy

For example, in garment factory business strategy determines:

 Specific segment of consumer- women’s wear, menswear, children’s wear;

 Specify the purpose of the end product- work wear, casual wear, formal wear,
party wear;
 Specify the style – highly fashion, basic fashioned, basic garments

Production/manufacturing/ strategy is cascaded from business strategy as business

strategy guides the overall operations of the company. Manufacturing strategy is
concerned with producing products in the most effective manner. The most effective
way of production is driven from business strategy i.e. types of product, types of market,
delivery policy and so on.This includes location and facility determination, design
process and method of production, quality and control method. Manufacturing strategy
is very crucial to competition as it is the tool used to achieve business strategy. Before
planning a production system, it is necessary to be sure that the manufacturing strategy is

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correct because no matter how well production system is managed, the company will
not be able to compete if manufacturing strategy is wrong /poorly designed/. Steps used
in devising manufacturing strategy are shown in the figure 1-4.

Manufacturing strategy is setting and determining the methods of production (processes)

and required infrastructure to carry out the required processes. Management of
processes and infrastructures to achieve targeted objectives is called
production/operation managements.

Business Strategy Marketing Order Winning Criteria Process Choice Infrastructure

Strategy

Manufacturing strategy

Figure 1-4: How to design manufacturing strategy

Manufacturing/production is the process, which combines and transforms

variousresources used in the production/operations subsystem of the organization into
value addedproduct/services in a controlled manner as per the policies of the
organization. Therefore, it isthat part of an organization, which is concerned with the
transformation of a range of inputs into the required products/services having the
requisite quality level.

XYZ is an apparel manufacturing firm, its business strategy underlines production of basic
garments with large quantity per style, whereas, ABC garment factory aims to produce highly
fashion garments that require high flexibility and responsiveness production system. From types
of product point of view, make through is a manufacturing system used for fashion products but
PBS used for basic /standardize/ product. Make through is suitable for small quantity and highly
variety of products whereas PBS is used for large quantity garment per style. And PBS is mainly
used for products which do not require flexibility make through is suitable when high degree of
flexibility required. Therefore, the type of product (business strategy) highly influences the types
of production system (Production strategy) of any business organizations.

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Strategic Planning is the activity bridging the gap ambitiously from where the factory is
now to what to be at sometime in the future. It is predicting and determining the
required course of activities, finances, infrastructure and facilities used to achieve the
strategy. Strategic planning is primary and essential phase of management process.
Strategic planning is a process which includes; perception of market conditions,
consumer needs, competitive strength and weakness, sociopolitical, legal and economic
conditions, technological developments as well as perception of specific options and
threats faced by firms in their operations. Strategic planning needs external and internal
analysis. In garment business, firms should take into account the target markets (constant
or variable demand, order quantity and fashion styles), their competencies (cheap labor
availability and employs skill, near to raw material, technology advancement), financial
objectives (profit, cash flows) and strategic goals (quality, cost and productivity). Once
strategy is devised and planned, it requires modifications in accordance with changes in
the environment or organization itself. These changes are impossible to predict,
therefore it is necessary for manager to carryout amendments based on the nature of
changes occurred. Such reaction of managers is called strategic management.

1.4.1 Strategy in Apparel Industry

The objective of management in manufacturing firm are planning and controlling the
manufacturing processes to optimize profit, efficient utilization of resources and
implement company’s policies. Immense paradigm shift have taken place in overall
supply chain of textile and garment business which has created a very difficult situation
to management to accomplish the aforementioned objectives in the past few decades.

Garment manufacturing business is the most globalised industry in the world because
most nations are producing for international textile and garment markets. Globalization
poses high worldwide competition among garment manufacturing firms. This condition
obliged garment manufacturers to revise its way of management and production based
on current scenario in order to be competent in the international market. Larger

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industries and low cost rivals such as China, India, Bangladesh and other South East
Asian Countries forced other countries’ garment manufacturers to reduce cost of
production. The production of apparels is being transformed in its inventory
management, production management and manufacturing systems. Manufacturers are
investing in advanced technologies and are using them to change their methods of
planning and production to significantly reduce the amount of inventories and
manufacturing costs.

The current economic crises which started in the United States in 2008 and spread
quickly to most of the industrialized and developing economies, has brought slumping
of customer demand. Today, consumers highly become reserved in their expenses
which result in shrinkage of order quantity to manufacturers. Customers require fewer
quantities but highly varied styles. The shrinking of market for export oriented
developing countries demands high flexibility of production system than ever. There are
constant increasing of new style introduction, product variation and short product life
cycle. In addition, the market demands shorter lead time, stringent delivery date, and
delivery of high quality of apparels with reduced prices.

In this ever increasing competitive pressures and globalized industry, garment

manufacturing firms have to reduce costs and build new opportunities via optimizing
resources. Therefore, garment manufacturing firms are to rely on their core
competencies and outsource their non competencies. Moreover, the adoption of new
concepts of manufacturing system combined with implementation of emergent
technologies is the answer to the needs of rapidly changing markets. Coordination and
effectiveness can only be achieved through the appropriate organization and
management of manufacturing resources. Rapid changes in globalized apparel business
requires companies to adopt new sort of strategic planning that focus on both current
success and to invest in those activates that will strength competitive edges for future
success. In order to capitalize business advantage throughout the supply chain and to

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share benefits equally, textile and apparel firms are being implementing new types
strategic planning. A critical factor that affect garment industries’ respond to the demand
of customer (lower stream) is the response of supplier. To give prompt response to their
customer, garment manufacturing firms are applying new strategic management system
supported by advanced technologies and information system.

1.5 Production Planning and Production Control

Planning and control is concerned with the reconciliation between what the market
requires and what the operation’s resources can deliver. Planning and control activities
provide the systems, procedures and decisions which bring different aspects of supply
and demand together. Consider, for example, the way in production is organized in a in
a garment industry. When a customer gives an order and is received to the garment
producing firm, much of the planning for manufacturing willbe devised. Fabric and trim
buying will be processed. All activities will be scheduled in specific time and production
facilities will be reserved, and the managers, supervisors, quality controllers who staff
production facilities will be provided with all the information regarding the required
product’s quality and silhouette. Appropriate pre-production activities will be organized
and post production audit criteria will be set. All this will involve staff and facilities in
different parts of the garment manufacturing factory. All must be given the same
information and their activities should be coordinated. Soon after the commencement
of production, checking will be carried out to make sure that the condition is as
expected (in much the same way as material is inspected on arrival in a factory).
Whenever required, seal sample will be cross-matched to the produced pieces and any
mending will be made if it deviates from the seal sample. Any last-minute changes may
require some degree of re-planning to keep the delivery promise to the customers. For
example, ifthere were frequent stoppage of production due to breakdown of machine,
more machines will be assigned to compensate the delayed production. This situation,
not only will affect the specific product’s own production schedule, but other styles’

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schedule may also have to be rescheduled. All these activities of scheduling,
coordination and organizationare concerned with the planning and control of the
garment manufacturing factory.

Customer demands are likely to differ in quantities and delivery schedules, and this lead
to large fluctuations in production level. Demands change due to trends, cyclical and
seasonal factors. In addition, production are subjected to variety of uncertainties such as
emergency order, breakdown, material shortages and various other contingencies.

Planning is a formalization of what is intended to happen at some time in the future.

But a plan does not guarantee that an event will actually happen. Rather it is a statement
of intention. Although plans are based on expectations, during their implementation
things do not always happen as expected. There may be innumerable factors which
affect the production system and because of which there is a deviation from the actual
plan. Some of the factors that affect are:
1. Non-availability of materials (due to shortage, etc.);
2. Plant, equipment and machine breakdown;
3. Changes in demand and rush orders;
4. Absenteeism of workers; and
5. Suppliers may not always deliver on time
6. Lack of coordination and communication between various functional areas of
business.
Thus, if there is a deviation between actual production and planned production, the
control function comes into action. Control is the process of coping with changes in
these variables. It may mean that plans need to be redrawn in the short term.Production
control through control mechanism tries to take corrective action to match the planned
and actual production. Thus, production control reviews the progress of the work, and
takes corrective steps in order to ensure that programmed production takes place. The
essential steps in control activity are:

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  Initiating the production,
 Progressing, and
 Corrective action based upon the feedback and reporting back to the production
planning.
It may also mean that an ‘intervention’ will need to be made in the operation to bring it
back ‘on track’ – for example, finding a new supplier that can deliver quickly, repairing
the machine which failed, or moving staff from another part of the operation to cover
for the absentees. Control makes the adjustments which allow the production manager
to achieve the objectives that the plan has set, even when the assumptions on which the
plan was based do not hold true.

It is essential that before starting the work of actual production, production planning is
done in order to anticipate possible difficulties, and decide in advance as to how the
production should be carried out in a best and economical way.

Production Planning and Control (PPC) is concerned with implementing the plans, i.e.,
the detailed scheduling of jobs, assigning of workloads to machines (and people), and
the actual flow of work through the system.

Production Planning and Control (PPC) philosophy is: “First plan your work, and then
work your plan”. Before starting any work, planning is necessary for the effective
utilization of available resources. Planning is the determination phase of production
management.

Production planning is concerned with the determination, acquisition and arrangement

of all facilities necessary for the future operations, whereas control is concerned with the
implementation of a predetermined production plan or policy and the control of all
aspects of operations according to such a plan or policy.

Formally PPC can be defined as the process of planning the production in advance,
setting the exact route of each item, fixing the starting and finishing date for each item,

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giving production orders to shop and lastly following up the progress of products
according to orders. It is also called the ‘nerve center’ of the factory.

1.5.1 Objectives of Production Planning and Control

Production planning and control consists of planning production in amanufacturing

organization before production activities take place andexercising control actions to
ensure that the planned production is realized interms of quantity, quality, delivery
schedule and cost of production. The mainobjectives are:

1. To attain maximum utilization of resources.

2. To produce quality products.
3. To minimize manufacturing cycle time.
4. To maintain optimum inventor y levels.
5. To maintain flexibility in operations.
6. To achieve coordination between labor, machines, and other supporting
departments.
7. To remove bottle-necks at all levels of production.
8. To achieve cost-reduction and cost control.
9. To prepare and maintain the production schedules.
10.To achieve the goals at minimum cost.

1.5.2 Scope of Production Planning and Control

Production planning and control covers the following activities.

1. Procurement of raw materials, components and spare parts in right quantities at

right time from the right source at the right prices.
2. Selecting the best method of processing and finding out the best sequenceof
operations.

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 3. Determining the nature and magnitude of the output in consultationwith
marketing department.
4. Planning the layout of the different operations to be performed.
5. Preparing and maintaining the time schedule.
6. Ensuring continuous inspection over products produced.
7. Imposing controls over costs and to get work done according to the plan.

On account of such a wide and important scope of production planning and control, it
is considered as an integral part of the corporate planning process in modern multi-
product manufacturing organizations.

1.5.3 Comparison of Production Planning and

Production Control

Production Planning

i) It deals with planning the work.

ii) Planning involves collection of data on materials, machines, tools and
equipments, drawings, layouts etc.
iii) Planning is basically a thinking process so, it involves lot of paper work,
preparing necessary forms etc.
iv) Planning needs feedback so as to know whether the actual performance is
taking place according to plan or not.

Production Control

i) It deals with implementing the plan.

ii) Control involves utilization of data, reporting about output, efficiency of
labor and machines, inventor y control, quality control, etc.
iii) Control involves actual use of thee forms for reporting about production
activities to the higher authorities.

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 iv) Control aims at keeping control over actual properness to take place as per
plan. If any deviation is observed then corrective action is taken
v) Planning is forward thinking.
vi) Control involves looking backwards and taking steps to maintain time
schedule.
vii) Control is a decentralized activity that takes place in shop floors.
v) Planning is basically centralized activity controlled by the top management.
vi) The main functions of production planning include estimative output to be
produced, routing or determine sequence of operations, scheduling and
loading. Thus it may be observed that production planning and control are
not only complementary to each other but they are so interrelated that they
are often considered as being one function.
vii) Production control includes the functions of dispatching expediting follow
up, progressing.

1.5.4 Phases Of Production Planning and Control

Production planning and control has three phases namely:

A. Pre-Planning Phase
B. Planning phase
C. Control Phase

1.5.5 Preplanning

This covers an analysis of data and outline of basic planning policy based on sales,
reports, market research and product development and design on the broad aspects of
planning; this stage is connected with problems of equipment policy and replacement,
new processes and materials, layout and work flow.

1.5.6 Planning

When the task has been specified a thorough analysis of the" 4 M's" is first under taken to

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select the appropriate materials, methods and facilities by means of which; the work can
be accomplished, as already mentioned. This analysis is followed by outing, estimating
and scheduling. The more detailed, realistic and precise the planning, the great
conformity to schedules achieved during production and subsequently the greater the
efficiency of the plant. There are two aspects of planning, a short term one, connected
with immediate production programmes, and a long term phase, where plans for the
more distant future are considered and shaped Prominent planning functions are these
dealing with standardization and simplification of products, materials and methods.

Figure 1-5: Functions of Production Planning and Control

Materials: Ensures the availability of raw materials, semi-processed material and

finished parts to start production operation on time. Activities carried out are making
specification, determine delivery dates, setting standards, procurement and inspections.

Methods: Ensures the best method of production compatible with given circumstances.

Machines: Activities related with maintenance, machine replacement, material

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handling, and equipment utilization policy are carried out.

Man: Activities related identifying required skill for particular production operations,
training, andrecruitment are carried out.

1.5.6.1 Routing

Routing can be defined as the process of deciding the path (route) of work and the
sequence of operations. This is related to consideration of appropriate shop layout and
plant layout, temporary storage locations for raw materials, components and semi-
finished goods, and of materials handling systems.

Routing fixes in advance:

1. The quantity and quality of the product.

2. The men, machines, materials, etc. to be used.
3. The type, number and sequence of manufacturing operations, and
4. The place of production.

The main objective of routing is to determine the best and cheapest sequence of
operations and to ensure that this sequence is followed in the factory.Routing leads to
efficient production by optimizing utilization of resources, such as men, machines,
materials, etc and by saving time and space. It leads to division of labor. It ensures a
continuous flow of materials without any backtracking to have smooth production.It
makes the work easy for the production engineers and foremen. It has a great influence
on design of factory's building and installed machines.

1.5.6.2 Loading
Capacity Planning

Capacity is the output that an operation (or a single process) can deliver in a defined
unitof time. It reflects an ‘ability to supply’, at least in a quantitative sense. Capacity

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management is the activity of coping with mismatches between the demand on an
operationand its ability to supply. Demand is the quantity of products or services that
customersrequest from an operation or process at any point in time. A mismatch
between demandand capacity can occur because demand fluctuates over time, or
capacity fluctuates overtime, or both.

Theability to supply depends not only on the limitations of the previous stage in a
supplynetwork, operation or process, but on all the stages up to thatpoint. Capacity
management is concernedwith fluctuations in demand andsupply. It involves coping
with the dynamics of delivering products and services to customers. It is worth noting
that ‘coping’ with mismatches between demand and capacity maynot mean that capacity
should match demand. An operation could take the deliberatedecision to fail to meet
demand, or to fail to fully exploit its ability to supply.

Loading

Loading is the amount of work that is allocated to a work centre. Loading means
assignment of job to a facility, viz: machine, men, dept, etc. Assigning a subject to a
teacher isloading. For example, an operator of a garment manufacturing factory is
available, in theory, 56 hr per week (8hr working time per day). However, this does not
necessarily mean that 56 hr of work can be loaded onto that operator.For some periods
the operator cannot work; for example, he/she may not be available on statutory
holidays and weekends. Therefore, the load put onto the operator must take this into
account. Of the time that the operator is available for work, other losses further reduce
the available time. For example, time may be lost while changing over from making one
style to another, if the sewing machine breaks down, when operator is sick and so on.
Sometimes the operator may be waiting for parts to arrive or be ‘idling’ for some other
reason. The lost time when operator is available is called off standard time. Other
losses could include allowances whenthe performanceof operator is below standard (for
example, because he lacks the necessary skills and experience) and an allowance for the

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‘quality losses’ or defects which the operator may produce. Of course, many of these
losses should be small or non-existent in a well-managed production. However, the
valuable operating time available for productive working, even in the best managed
production, can be significantly below the maximum time available. Therefore, loading
is based on the available valuable operating time of operators and machines.
Loading should be done at the higher level. Frequently, when attempting to decide
how ordersare to be scheduled onto available facilities, one is faced with various
alternative solutions. For example, one operator may be capable of performing various
operations of apparel production. Production manager must then decide which jobs are
to be scheduled onto which facilities inorder to achieve some objective, such as
minimum cost or minimum throughput time.
Finite loading is an approach which only allocates work to a work centre (a person, a
machine, or perhaps a group of people or machines) up to a set limit. This limit is the
estimate of capacity for the work centre (based on the times available for loading). Work
over and above this capacity is not accepted.
Infinite loading is an approach to loading work which does not limit accepting work,
but instead tries to cope with it. The second diagram in Figure 10.7 illustrates this
loading pattern where capacity constraints have not been used to limit loading so the
work is completed earlier.

1.5.6.3 Scheduling
Sequencing
Whether the approach to loading is finite or infinite, when work arrives, decisions must
be taken on the order in which the work will be tackled. This activity is termed
sequencing. The priorities given to work in an operation are often determined by
some predefined set of rules, some of which are relatively complex. Some of these are
summarized below.

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Physical constraints: The physical nature of the materials being processed may
determine the priority of work.
Customer priority: Operations will sometimes use customer priority sequencing,
which allows an important or aggrieved customer, or item, to be ‘processed’ prior to
others, irrespective of the order of arrival of the customer or item.
Due date (DD): Prioritizing by due date means that work is sequenced according to
when it is ‘due’ for delivery, irrespective of the size of each job or the importance of
each customer.
Last-in first-out (LIFO): Last-in first-out (LIFO) is a method of sequencing usually
selected for practical reasons.
First-in first-out (FIFO): Some operations serve customers in exactly the sequence
they arrive in. This is called first-in first-out sequencing (FIFO), or sometimes ‘first
come, first served’ (FCFS).
Longest operation time (LOT): Operations may feel obliged to sequence their
longest jobs first in the system called longest operation time sequencing. This has
the advantage of occupying work centers for long periods. Most operations at some
stage become cash-constrained. In these situations, the sequencing rules may be adjusted
to tackle short jobs first in the system, called shortest operation time sequencing.
Shortest operation time first (SOT):Most operations at some stage become cash-
constrained. In these situations, the sequencing rules may be adjusted to tackle short
jobs first in the system, called shortest operation time sequencing.
Judging sequencing rules: All five performance objectives, or some variant of them,
could be used to judge the effectiveness of sequencing rules. However, the objectives of
dependability, speed and cost are particularly important. So, for example, the following
performance objectives are often used:
 Meeting ‘due date’ promised to customer (dependability);

22
  Minimizing the time the job spends in the process, also known as ‘flow time’
(speed);
 Minimizing work-in-progress inventory (an element of cost);
 Minimizing idle time of work centres (another element of cost).

Johnson’s rule
Johnson’s rule applies to the sequencing of n jobs through two work centers. The rule is
simple. First look for the smallest processing time. If that time is associated with the first
work centre then schedule that job first, or as near first as possible. If the next smallest
time is associated with the second work centre then sequence that job last or as near last
as possible. Once a job has been sequenced, delete it from the list. Carry on allocating
jobs until the list is complete.

Forward Scheduling
With forward scheduling, processing starts immediately when a job is received,
regardless of its due date. Each job activity is scheduled for completion as soon as
possible, which allows you to determine the job’s earliest possible completion date. The
disadvantage to finishing a job early is that it causes an inventory buildup if items are not
delivered before the due date.
Backward Scheduling
With backward scheduling, you begin scheduling the job’s last activity so that the job
is finished right on the due date. To do this, you start with the due date and work
backward, calculating when to start the last activity, when to start the next-to-last
activity, and so forth.
When you are using backward scheduling and forward scheduling together, a difference
between the start time of the first activity indicates slack in the schedule. Slack means
that you can start a job immediately but you do not have to do so.

23
 1.5.7 Control

Having created a plan for the operation through loading, sequencing and scheduling,
each part of the operation has to be monitored to ensure that planned activities are
indeed happening. Any deviation from the plans can then be rectified through some
kind of intervention in the operation, which itself will probably involve some
replanning. The output from a work centre is monitored and compared with the plan
which indicates what the work centre is supposed to be doing. Deviations from this plan
are taken into account through a replanning activity and the necessary interventions
made to the work centre which will (hopefully) ensure that the new plan is carried out.
Eventually, however, some further deviation from planned activity will be detected and
the cycle is repeated.

This stage is affected by means of dispatching, inspection and expediting. Control of

inventories, control of scraps, analysis of work in process, and control of transportation
are essentially links of this stage. Finally, evaluation takes place to complete the
production planning and control cycle.

The control functions have a very important rate in providing the main sources of feed-
back information to ensure necessary corrective actions. Effective communication
systems are prerequisites to efficient control and are, therefore, of great concern to
production planning and control.

1.5.7.1 Dispatching

It is the action, doing or implementation stage. It comes after routing and

scheduling.Dispatching means starting the process of production. It provides the
necessary authority to start the work.

Dispatching includes the following:

1. Issue of materials, tools, fixtures, etc., which are necessary for actual production.

24
 2. Issue of orders, instructions, drawings, etc. for starting the work.
3. Maintaining proper records of the starting and completing each job on time.
4. Moving the work from one process to another as per the schedule.
5. Starting the control procedure.
6. Recording the idle time of machines.

Dispatching may be either centralized or decentralized:

1. Under centralized dispatching, orders are issued directly by a centralized

authority.
2. Under decentralized dispatching, orders are issued by the concerned department.

1.5.7.2 Follow-up

Follow-up or expediting is a controlling device. It is concerned with evaluation of the

results.Follow-up finds out and removes the defects, delays, limitations, bottlenecks,
loopholes, etc. in the production process. It measures the actual performance and
compares it to the expected performance. It maintains proper records of work, delays
and bottlenecks. Such records are used in future to control production.

Follow-up is necessary when production decreases even when there is proper routing
and scheduling. Production may be disturbed due to break-downs of machinery, failure
of power, shortage of materials, strikes, absenteeism, etc.Follow-up removes these
difficulties and allows a smooth production.

1.5.7.3 Inspection

It is a major control tool. Though the aspects of quality control are theseparate function,
this is of very much important to PPC both for the execution of the currentplans and its
scope for future planning. This forms the basis for knowing the limitations withrespects
to methods, processes, etc., which is very much useful for evaluation phase.

25
 1.5.7.4 Evaluation

This stage though neglected is a crucial to the improvement of productiveefficiency. A

thorough analysis of all the factors influencing the production planning and controlhelps
to identify the weak spots and the corrective action with respect to pre-planning
andplanning will be effected by a feedback. The success of this step depends on the
communication,data and information gathering and analysis.

1.6 The Relationship between PPC and Planning horizons

The nature of planning and control activities changes over time. In the very long term,
operationsmanagers make plans concerning what they intend to do, what resources they
need, and whatobjectives they hope to achieve. The emphasis is on planning rather than
control, becausethere is little to control as such. They will use forecasts of likely demand
which are describedin aggregated terms. For example, a garment industry will make
plans for ‘2,000,000 Birr’ revenue withoutnecessarily going into the details of the types
of garment it will manufacture. Similarly, the factory might plan to have 100 machines
and 160 workers but again without deciding on the specific attributes of the staff.
Operations managers will be concerned mainly to achieve financial targets. Budgets will
be put in place which identifies its costs and revenue targets.

Medium-term planning and control is more detailed. It looks ahead to assess the
overalldemand which the operation must meet in a partially disaggregated manner. By
this time,for example, the garment manufacturing company must distinguish between
different types of demand. Customer orders of higher quantities need to be
distinguished from those having fewer quantities. Similarly, different categories of staff
will have been identifiedand broad staffing levels in each category set. Just as important,
contingencies will have beenput in place which allow for slight deviations from the
plans. These contingencies will act as‘reserve’ resources and make planning and control
easier in the short term.

26
In short-term planning and control, many of the resources will have been set and it
willbe difficult to make large changes. However, short-term interventions are possible if
thingsare not going to plan. By this time, demand will be assessed on a totally
disaggregated basis, with types of production unit (cell) will be treated as individual
activities. More importantly, each production line will be identified by the style they
produced, and the time takes to finish the particular style. In making short-term
interventions and changes to the plan, operations managerswill be attempting to balance
the quality, speed, dependability, flexibility and costs of their operation on an ad hoc
basis. It is unlikely that they will have the time to carry out detailed calculations of the
effects of their short-term planning and control decisions on all these objectives, but a
general understanding of priorities will form the background to their decision making.
Figure 2-1 shows how the control aspects of planning and control increase
insignificance closer to the date of the event.

Figure 1-6: The balance between planning and control changes in short, medium and
long term

27
 1.7 The Relationship between PPC and Product variety
Production facilities which produce a high variety of products or services in relatively
low volume willclearly have customers that require a different set of factors and use
processes which have adifferent set of needs from those facilities which create
standardized products or servicesin high volume (figure 2-2).

Figure 1-7: Volume Variety effect on production planning

Take two contrasting types of production – highly stylized garment manufacturer and
sugar manufacturer. The fashion garment manufacturers produce high variety of
product means that their product will have little standardization; thereforemanufacturers
produce apparels in advance of customers requesting them. Because of this, the time it
willtake to respond to customers’ requests (from request to delivering of product) will
be relatively slow. The details and requirements of eachjob will emerge only when the
customer gives order, soplanning occurs on a relatively short-term basis. The individual
decisions which are taken inthe planning process will usually concern the timing of
activities and events – for example, when garments are to be delivered, when
production should start, when sourcing of fabric and trims are to be completed, where
to source, what types of resources are required and so on are decided by examining
customer order specification and details. Control decisions also will be at a relatively
detailed level.

A small delay in one process could have significant implications to the overall
performance of the firm that could lead its product to obsolete. For these types of

28
manufacturing firms planning and control cannot be totally routinized;rather, it will
need managing on an individual order basis. However, the robustness of theoperation
(that is, its vulnerability to serious disruption if one part of the operation fails) willbe
relatively high. There are probably plenty of alternatives to get on with if a garment
manager confronted with a problem in one part of the job.

The sugar manufacturer, on the other hand, is very different. Volume is high,
production iscontinuous, and variety is virtually non-existent. Customers expect instant
‘delivery’ wheneverthey need sugar. The planning horizon in manufacturing of sugar
can be very long.Major decisions regarding the customers’ demand of sugar are made
many years in advance.Even the fluctuations in demand over time can be forecast in
advance. The individual planning decisions made by the manufacturer will beconcerned
not with the timing of output, but rather the volume of output. Control decisionswill
concern aggregated measures of output such as the total tones of sugar
produced,because the product is more or less homogeneous. However, the robustness
of the operationis very low, insomuch as, if one machine fails, the production’s
capability of manufacturing of sugar from that firm also fails.

1.8 The Relationship between PPC and Demand

Characteristics

If planning and control is the process of reconciling demand with supply, then the
natureof the decisions taken to plan and control an operation will depend on both the
nature ofdemand and the nature of supply in that operation.
Uncertainty in supply and demand: Uncertainty makes both planning and control
more difficult. Demand may be unpredictable. Fashion industry is known in its high
uncertainty of demand. It would be difficult to be certain about the type of color,
silhouette, trims and accessories ahead of the season. Conversely, otheroperations are
reasonably predictable, and the need for planning and control is minimal. The planning
horizon also affected by the nature of predictability of demand. Predictable demand will

29
have long term planning as the demand can be accurately predicted, whereas in
uncertain condition, it is possible to plan for short term only.
Dependent and independent demand: Dependent demand is a demand which is
relatively predictable because it is dependentupon some factor which is known. For
example, fabric demand of a particular garment factory is easily predicted based on the
quantities of garment to be produced. However, there are demands which are difficult
to predict. For example, a fashion designer has little visibility of the future demand that
he is sketching. Such demand is called independent demand. Planning and control
requirement affected by the predictability of demand as discussed above.
Requirement to respond to demand: Theplanning and control necessary for
operation can be resource-to-orderplanning and control, create-to-order or make-to-
order planning and control and make-to-stock planning and control. Most of small and
medium garment manufacturing firms are engaged in make to order production, which
is quite different from make to stock production in that 1) no inventory is available to
offset demand fluctuation, and 2) the capacity of manufacturing plant largely relies on
product structure and worker’s momentary skill. The survival of a make-to-order
company thus depends of its ability to quickly respond to an order inquiry and its
flexibility in production operations.

30
 Exercise
1. What do you mean by production planning and control?
2. Why do you need production planning and control?
3. What are the objective of production planning and control?
4. What are the differences between operation and production?
5. Differentiate and service and manufacturing industry.
6. Why the traditional manufacturing system has become obsolete in today’s
garment production strategy.
7. Define Production in different ways.
8. What is strategy
9. What are the factors need to be considered to develop a strategy for garment
manufacturing firm.
10.Discuss the characteristics of production planning and control in today’s fashion
business
11.What types of strategic planning suitable for fashion manufacturing firms?
12.Differentiate Business strategy, Marketing Strategy and Production strategy.
13.What are the objectives of Production Strategy
14.Develop a business strategy for a garment manufacturing firm
15.Develop a production strategy for a garment manufacturing firm
16.Discuss the basic functions of business organization.
17. Discuss the phases of production planning and control.
18.Discuss the difference between planning and Control
19.What is capacity planning?
20.What is routing?
21.What is loading?
22.What is sequencing?
23.Discuss different parameters considered for sequencing of tasks.

31
24.Discuss Johnson rule of sequencing with appropriate examples.
25. What is scheduling?
26.Discuss the relationship between planning horizon and Production planning and
control (ppc).
27.Discuss the relationship between product proliferation and Production planning
and control (ppc).
28.Discuss the relationship between demand certainty and Production planning and
control (ppc).
29.Discuss the relationship between response to demand and Production planning
and control (ppc).
30.Discuss the different between independent and dependent demand with
appropriate examples.

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 CHAPTER TWO
FORECASTING

2.1 Introduction

One of the most fundamental and important elements of planning for garment and
fashion manufacturing firms is forecasting.  In manufacturing firms, product demand is
forecast over a period of time, and production is planned to approximate the forecast.  If
demand is greater than the planned production, then backorders can occur and extra
product needs to be produced.  If demand is less than the forecast then the company
may be stuck with excess inventory.  The company may likely reduce future production
and may have to reduce the sales price to clear inventories.

Forecasting is a very important aspect in determining an uncertain future. Anticipation

is the key to capitalize a market and establish a company in the same. By anticipating the
future, we can plan our present and set a smooth sail into the future. In recent times
increase in variety of a product forces a company to have a flexible manufacturing
system. Supply chain planning is evolving into becoming very complex due these
variations. Customers expect the introduction of new fashion garments in every season.
In the race for meeting the customer expectation by providing them with variety of
products has increased the consumption levels of products almost exponentially.
Companies to stay in the race, they are investing huge capital in the design and product
development departments consequential to the discovery of new ideas in short time.
This increases the customer expectation is shortening the life of a product. The process
of forecasting and implementing the forecast data for production plan has become
complex.

The following are the characteristics of a fashion product:

1. Less/No historical data.

33
 2. Random variation in the data
As the customer preference changes, the life of product shortens. This results in the
dearth of historical data, making the process of forecasting very difficult.

Fashion industry is broadly a synonym for Short life products. There are many factors
influencing the life of a product viz. climate, movies, sports etc. As the demand for
products of the fashion industry is not stable, prediction becomes difficult. By utilizing
the traditional methods, forecasting of products results in the stock-out or markdown
phenomena. Companies trying to meet the increasing customer demands have
identified that the lead time of products increase. The increase in lead time may be
caused by both external and internal processes.

2.2 Characteristics of forecasts

 Forecasts are rarely perfect. Forecasting the future involves uncertainty.
Therefore, it is almost impossible to make a perfect prediction. Forecasters know
that they have to live with a certain amount of error, which is the difference
between what is forecast and what actually happens. The goal of forecasting is to
generate good forecasts on the average over time and to keep forecast errors as low as
possible.
 Forecasts are more accurate for groups or families of items rather than for
individual items. When items are grouped together, their individual high and low
values can cancel each other out. The data for a group of items can be stable even
when individual items in the group are very unstable. Consequently, one can obtain
a higher degree of accuracy when forecasting for a group of items rather than for
individual items. For example, you cannot expect the same degree of accuracy if you
are forecasting sales of long-sleeved hunter green polo shirts that you can expect
when forecasting sales of all polo shirts.

34
 Forecasts are more accurate for shorter than longer time horizons. The
shorter the time horizon of the forecast, the lower the degree of uncertainty. Data
do not change very much in the short run. As the time horizon increases, however,
there is a much greater likelihood that changes in established patterns and
relationships will occur. Because of that, forecasters cannot expect the same degree
of forecast accuracy for a long-range forecast as for a short-range forecast. For
example, it is much harder to predict sales of a product two years from now than to
predict sales two weeks from now.

2.3 Types of forecasts

Forecasting is an estimate of the future level of some variable. The forecasting

framework starts with the historical data available. By using this it is possible to
determine the Mathematical model (quantitative forecasting) that can be used to forecast
the demand of a product. After applying and obtaining the number, the most important
aspect of the Human Input (qualitative forecasting) is integrated into the system.
Humans in the business for a long time are able to estimate a number.

Therefore, forecasting methods can be classified into two groups: qualitative and
quantitative. Table 2-1 shows these two categories and their characteristics.

Table 2-1: Characteristics of qualitative and quantitative forecasting

35
Qualitative forecasting methods, often called judgmental methods, are methods in
which the forecast is made subjectively by the forecaster. They are educated guesses by
forecasters or experts based on intuition, knowledge, and experience. Because
qualitative methods are made by people, they are often biased. These biases can be
related to personal motivation (“They are going to set my budget based on my forecast,
so I’d better predict high.”), mood (“I feel lucky today!”), or conviction (“That pitcher
can strike anybody out!”).
Quantitative forecasting methods, on the other hand, are based on mathematical
modeling. Because they are mathematical, these methods are consistent. The same
model will generate the exact same forecast from the same set of data every time. These
methods are also objective. They do not suffer from the biases found in qualitative
forecasting. Finally, these methods can consider a lot of information at one time.
Both qualitative and quantitative forecasting methods have strengths and weaknesses as
shown in table2-2.

Table 2-2: Characteristics of qualitative forecasting methods

The integration of the Human experience (qualitative forecasting) and the statistically
obtained number (quantitative forecasting) results in a reduction the forecast error.

36
2.4 Qualitative forecast
Executive Opinion: Executive opinion is a forecasting method in which a group of
managers meet and collectively develop a forecast. This method is often used for
strategic forecasting or forecasting the success of a new product or service.
Market Research: Market research is an approach that uses surveys and interviews to
determine customer likes, dislikes, and preferences and to identify new-product ideas.
The Delphi Method: The Delphi method is a forecasting method in which the
objective is to reach a general agreement among a group of experts while maintaining
their anonymity.

2.5 Quantitative Methods

Quantitative methods are different from qualitative ones because they are based on
mathematics. Quantitative methods can also be divided into two categories: time series
models and causal models. Although both are mathematical, the two categories differ in
their assumptions and in the manner in which a forecast is generated.

2.5.1 Time series forecasting

A time series measures an attribute, like sales, at equal intervals of time. Time series
models assume that all the information needed to generate a forecast is contained in the
time series of data. A time series is a series of observations taken at regular intervals over
a specified period of time. Time series analysis assumes that we can generate a forecast
based on patterns in the data. When a forecaster plots the sales history of a product, the
plot often has a recurring pattern. Usually time is on the horizontal axis, and sales in
dollars or units are on the vertical axis. The forecaster looks for patterns in the data and
tries to obtain a forecast by projecting that pattern into the future. The easiest way to
identify patterns is to plot the data and examine the resulting graphs. If we did that,
what could we observe? There are four basic patterns. Any of these patterns, or a
combination of them, can be present in a time series of data:

37
 A. Level or horizontal
A level or horizontal pattern exists when data values fluctuate around a constant mean.
This is the simplest pattern and the easiest to predict. An example is sales of a product
that do not increase or decrease over time. This type of pattern is common for products
in the mature stage of their life cycle, in which demand is steady and predictable.
A. Seasonality
A seasonal pattern is any pattern that regularly repeats itself and is of a constant length.
Such seasonality exists when the variable we are trying to forecast is influenced by
seasonal factors such as the quarter or month of the year or day of the week. Seasonal
processes prevail in almost every sales time series. Usually we assume a seasonal time
series consists of monthly data, but the data may also be grouped in quarterly
information or weekly reports. In many cases the usual assumption is that the seasonal
period (pattern) repeats itself in one complete year.
Some causes of seasonal effects on sales appear below.

Weather:
Sunshine increased demand for ice cream, sunglasses, beer, soft
drinks, camera film, and lotion
Cool weather heating fuels, clothing, anti-freeze, ski equipment,
snow tires, and tire chain
Rainfall raincoats, umbrellas, and taxi service
Holidays:
Christmas food, toys, trees, cards, travel, hotel accommodation,
sporting equipment, and clothing of all types
Easter baskets, eggs, and fashion clothing
Mother's Day confectionery, certain clothing, cards,
and appliances

38
In each of the examples, the forecaster expects sales to increase during the period, as
compared to the rest of the year. Marketing people refer to "consumption holidays" and
run special sales on Labor Day or on President's Day. Some people who have a day off
from work because of the holiday spend the time for shopping. This shopping raises
average sales for months with holidays. Knowing this, forecasters must accommodate
the seasonal pattern in their forecasts. A first step in forecasting is to plot the data and
observe if a seasonal pattern exists. Next the forecaster must find any natural, repetitive
factors that influence the sales of the product and cause it to sell in a seasonal pattern.
Holidays, weather and promotions sales can create the seasonal pattern. Many personal
care products have a fall seasonal pattern because some individuals "stock up" for the
winter.

The seasonal pattern in figure - fits many clothing and fashion products. Shoe retailers
typically run January and July shoe sales every year, causing sales to be greater in these
two months than in other months. Many retail stores have annual white sales every
January and July, which also causes sales to surge during these two months.

A. Trend
In addition to seasonality, a second prominent pattern exhibited by most time series is
trend. Trend refers to a pattern where sales seem to increase or decrease over a period
of time.

39
Figure shows a time series with a trend pattern. Sales grow at a very consistent rate.
The trend shown above can be described as positive and linear. Some time series have a
nonlinear pattern.

Both linear and nonlinear trends can be expressed in a mathematical formula. For
example: Sales = constant + (slope x months) represents the mathematical pattern of
linear trend in figure 3-2. The following values apply:

constant = 2400
slope = 100
If the forecaster wants to know sales at any period, he just substitutes it into the formula.
For example, for the 10th month, October:

Sales = 2400 + 100 x 10 = 3400 1

And for the 22nd month, October, one year later:

Sales = 2400 + 100 x 22 =4600 2

Several factors cause trend. One basic factor is population growth. As the number of
potential buyers increases, the sales can increase. Trend can also be the result of more
people becoming aware of the product. More or fewer competitors can cause a time

40
series to develop a trend pattern. When the time series is expressed in dollars, the trend
is often caused by inflation.
B. Cycles
Patterns that are created by economic fluctuations such as those associated with the
business cycle are called cycles. These could be recessions, inflation, or even the life
cycle of a product. The major distinction between a seasonal pattern and a cyclical
pattern is that a cyclical pattern varies in length and magnitude and therefore is much
more difficult to forecast than other patterns.

Random variation is unexplained variation that cannot be predicted. So if we look at

any time series, we can see that it is composed of the following:
Data=Level + Trend + Seasonality + Cycle + Random Variation
Data= Trend + Random Variation
The first four components of the data are part of a pattern that we try to forecast.
Random variation cannot be predicted. Some data have a lot of random variation and
some have little. The more random variation a data set has, the harder it is to forecast
accurately. As we will see, many forecasting models try to eliminate as much of the
random variation as possible.

2.5.1.1 Forecasting Level

Naïve Method
The naïve method is one of the simplest forecasting models. It assumes that the next
period’s forecast is equal to the current period’s actual. For example, if your sales were
500 units in January, the naïve method would forecast 500 units for February. It is
assumed that there is little change from period to period. Mathematically, we could put
this in the following form:
Ft+1=At
Where, Ft+1 = forecast for next period, t+1
At = actual value for current period, t

41
 t =current time period
Example
A Shoe store is forecasting sales of sport’s shoe for the month of April. Total sales of
sport’s shoe for March were 3000 for the particular store. If management uses the naïve
method to forecast, what is the store forecast of sales for the month of April?
Solution
Forecast for next month=Sales of current month
= 3000 Shoes
The naïve method can be modified to take trend and seasons into account. One
advantage of the naïve method is that it is very simple. It works well when there is little
variation from one period to the next.
Simple Mean or Average
One of the simplest averaging models is the simple mean or average. Here the forecast
is made by simply taking an average of all data:

Ft +1=
∑ At = At + At −1+ At−2+ …+ At−n
n n
Where, Ft+1=forecast of demand for next period, t + 1
At =actual value for current period, t
n =number of periods or data points to be averaged
Example
Tommy Hilfiger Corporation is forecasting sales for its shirt sales. Shirt sales have been
steady, and the company uses a simple mean to forecast. Weekly sales over the past five
weeks are 51, 53, 48, 52, and 50 sequentially. Use the mean to make a forecast for week
6.
51+53+48+52+50
Salesforecastfor 6 t h week= =50.8
5
Simple Moving Average
The simple moving average (SMA) is similar to the simple average except that we are
not taking an average of all the data, but are including only n of the most recent periods

42
in the average. As new data become available, the oldest are dropped; the number of
observations used to compute the average is kept constant.

Ft +1=
∑ At = At + At −1+ At−2+ …+ At−n
n n
Where, Ft+1=forecast of demand for next period, t + 1
At =actual value for current period, t
n =number of periods or data points to be averaged
Example
What will be Tommy’s forecast in the above example for week 6 using simple moving
average technique?
48+52+50
Salesforecastfor 6 t h week= =50
3
Weighted Moving Average
In the simple moving average each observation is weighted equally. For example, in a
three-period moving average each observation is weighted one-third. In a five-period
moving average each observation is weighted one-fifth. Sometimes a manager wants to
use a moving average but gives higher or lower weights to some observations based on
knowledge of the industry. This is called a weighted moving average.

Ft +1=∑ CtAt=C 1 A 1+C 2 A 2+…+CnAn

Example
What will be Tommy’s forecast in the above example for week 6 using weighted
moving average technique? The manager has decided to weight Week 3 (0.25), Week 4
(0.25), and Week 5 (0.50) and to use three weeks sales data.
Salesforecastfor 6 t h week=0.25∗48+ 0.25∗52+0.5∗50=50
Exponential Smoothing
The exponential smoothing model is a forecasting model that uses a sophisticated
weighted average procedure to obtain a forecast. Even though it is sophisticated in the

43
way it works, it is easy to use and understand. To make a forecast for the next time
period, you need three pieces of information:
1. The current period’s forecast,
2. The current period’s actual value
3. The value of a smoothing coefficient, α, which varies between 0 and 1
The equation for the forecast is quite simple:
Next period’s forecast = α (current period’s actual) +(1-α) (current period’s forecast)
In mathematical terms:

Ft +1=α At + ( 1−α ) Ft

Where, Ft+1= Forecast of demand for next period t+1

At= Actual value of current period t
Ft=Forecast for current period t
α= Smoothing coefficient
Forecast for the current period also calculated in the same way as:
Ft =α At−1+ ( 1−α ) Ft −1

If we substitute the above formula in the first equation, it will be

Ft +1=α At + ( 1−α ) ( α At−1+ ( 1−α ) Ft −1 )

Ft +1=α At + ( 1−α ) ¿

And if we continue substituting Ft-1 in similar manner, we will have a formula:

Ft +1=α At + ( 1−α ) ¿

Selecting α

Note that depending on which value you select for _, you can place more weight on
either the current period’s actual or the current period’s forecast. In this manner the
forecast can depend more heavily either on what happened most recently or on the
current period’s forecast. Values of α that are low—say, 0.1 or 0.2—generate forecasts
that are very stable because the model does not place much weight on the current

44
period’s actual demand. Values of α, those are high, such as 0.7 or 0.8, place a lot of
weight on the current period’s actual demand and can be influenced by random
variations in the data. Thus, how α is selected is very important in getting a good
forecast.
Example
Gap’s eight periods of demand data (in thousands) for products are given for January
through August in Table below.
Month Jan Feb Mar Apr May Jun Jul Aug
Period t-7 t-6 t-5 t-4 t-3 t-2 t-1 t
Deman 45 50 42 46 52 47 41 48
d
The firm wishes to forecast demand for September (period t+1). These calculations are
presented below. Use α=0.2 andα=0.8
Solution
For the first forecast, the manager may take assumption or use other methods of
forecast, like naïve method.
Let’s assume January forecast was 47 thousand.
Month Period Demand αAt+(α-1)Ft Ft αAt+(α-1)Ft Ft
(α=0.2) (α=0.8)
Jan t-7 45 47 47
Feb t-6 50 0.2*45+0.8*47 46.6 0.8*45+0.2*47 45.4
Mar t-5 42 0.2*50+0.8*46.6 47.3 0.8*50+0.2*45.4 49.1
Apr t-4 46 0.2*42+0.8*47.3 46.2 0.8*42+0.2*49.1 43.4
May t-3 52 0.2*46+0.8*46.2 46.2 0.8*46+0.2*43.4 45.5
Jun t-2 47 0.2*52+0.8*46.2 47.3 0.8*52+0.2*45.5 50.7
July t-1 41 0.2*47+0.8*47.3 47.3 0.8*47+0.2*50.7 47.7
Aug T 49 0.2*41+0.8*47.3 46 0.8*41+0.2*47.7 42.3
Sep t+1 0.2*49+0.8*46 46.4 0.8*49+0.2*42.3 47.7

45
2.5.1.2 Forecasting Trend
There are many ways to forecast trend patterns in data. Most of the models used for
forecasting trend are the same models used to forecast the level patterns, with an
additional feature added to compensate for the lagging that would otherwise occur.
Here we will look at two of the most common trend models.
Trend-adjusted exponential smoothing
Trend-adjusted exponential smoothing uses three equations. The first smooths out the
level of the series, the second smooths out the trend, and the third generate a forecast by
adding up the findings from the first two equations. Because we are using a second
exponential smoothing equation to compute trend, we have two smoothing
coefficients. In addition to α, which is used to smooth out the level of the series, we
have a second coefficient, β, which is used to smooth out the trend of the series. Like
α,βcan theoretically vary between 0 and 1, though we tend to keep the value
conservatively low, around 0.1 or 0.2.
Three steps must be followed to generate a forecast with trend:
Smoothing the level of the series
St =αAt+(1−α )(St −1+Tt −1)
Smoothing the trend
Tt=β (St−St−1)+(1−β )Tt−1
Forecast including trend
FITt+1=St +Tt
Where, FITt+1= Forecast of demand including trend for next period t+1
At= Actual value of current period t
St=exponentially smoothed average of time series in period t
Tt= exponentially smoothed trend of time series in period t
α= Smoothing coefficient
β= Smoothing coefficient
Example

46
Weakly sales of fashion store are shown in the following table. If α = 0.8 andβ = 0.3,what
is the sales forecast for Week 22?

Week Sales F T FIT

20 2100 2500 100  

21 2450      

22       ????
Solution
1st smooth the level using the formula shown above.
S t=αA t+(1−α )(S t−1+T t−1)
S t =0.8∗2450+ ( 0.2 ) (2500+ 100 )=2480
Then smooth the trend using the formula
Tt=β (St−St−1)+(1−β )Tt−1
Tt =0.3 ( 2480−2500 )+ 0.7∗100=64
Then find forecast for week 22,
FITt+1=St +Tt
FIT t +1=2480+64=2544
Linear Trend Line
Linear trend line is a time series technique that computes a forecast with trend by
drawing a straight line through a set of data. This approach is useful for computing a
forecast when data display a clear trend over time. The method is simple, easy to use,
and easy to understand.
A linear trend line uses the following equation to generate a forecast:
Y =a+bX
Where, Y= forecast for period X
X= the number of time periods from X= 0
a= value for Y at X= 0 (Y intercept)
The coefficients, a and b are computed using the least-squares method, which
minimizes the sum of the squared errors. Developing the equations for a and b can

47
becomplicated, so we will only provide the equations needed for computation. The
steps for computing the forecast using a linear trend line are as follows:
Compute Parameter b:

b=
∑ XY −n XY
∑ X 2−n X 2
Compute parameter a:
a =Y −b X
Generate the linear trend line:
Y =a+bX
Example,
Weekly data of sales of a fashion store has shown in the following table. What will be
forecast for week 5?
Weeks 1 2 3 4
Sales 4100 4600 4650 4850
Solution
Week X2 Y XY
1 1 4100 4100
2 4 4600 9200
3 9 4650 13950
4 16 4850 19400
1+2+3+ 4
X= =¿2
4 ∑ x 2=¿ ¿3 Y =455 ∑ XY =¿ ¿46
.5 0 0 650

b=
∑ XY −n XY = 46650−4 x 2.5 x 4550 =230
∑ X 2−n X 30−4 x 2.5
2

a =Y −b X=4550−230 x 2.5=3975
Y=3975+230*5=5125

48
2.5.1.3 Forecasting Seasonality
Recall that any regularly repeating pattern is a seasonal pattern. We are all familiar with
quarterly and monthly seasonal patterns. Whether your university is on a quarter or
semester plan, you can see that enrollment varies between quarters or semesters in a
fairly predictable way. For example, enrollment is usually much higher in the fall than in
the summer. Other examples of seasonality include sales of turkeys before Thanksgiving
or ham before Easter, sales of greeting cards, hotel registrations, and sales of gardening
tools. The amount of seasonality is the extent to which actual values deviate from the
average or mean of the data. Here we will consider only multiplicative seasonality, in
which the seasonality is expressed as a percentage of the average. The percentage by
which the value for each season is above or below the mean is a seasonal index.
Here we will show only the procedure for computing quarterly seasonality that lasts a
year, though the same procedure can be used for any other type of seasonality. The
procedure consists of the following steps:
Step 1 Calculate the Average Demand for Each Quarter or “Season.” This is done by
dividing the total annual demand by 4 (the number of seasons per year).
Step 2 Compute a Seasonal Index for Every Season of Every Year for Which You Have
Data. This is done by dividing the actual demand for each season by the average
demand per season (computed in Step 1).
Step 3 Calculate the Average Seasonal Index for Each Season. For each season, compute
the average seasonal index by adding up the seasonal index values for that season and
dividing by the number of years.
Step 4 Calculate the Average Demand per Season for Next Year. This could be done by
using any of the methods used to compute annual demand. Then we would divide that
by the number of seasons to determine the average demand per season for next year.
Step 5 Multiply Next Year’s Average Seasonal Demand by Each Seasonal Index. This
will produce a forecast for each season of next year.
Example:

49
Demand
Week 1 2 3
Mon 91 108 100
Tue 105 110 98
Wed 113 120 125
Thu 150 143 162
Fri 201 194 210
 What s the average seasonal index for all days?
 If the forecast for Week 4 is 720, what is the forecast for Friday of Week 4?

Solution
1. First calculate average demand
Week 1 2 3
Mon 91 108 100
Tue 105 110 98
Wed 113 120 125
Thu 150 143 162
Fri 201 194 210
Average
Demand 132 135 139
2. Compute seasonal index of each day
Week 1 Index 1 2 Index 2 3 Index 3

Mon 91 0.68939394 108 0.8 100 0.71942446

Tue 105 0.79545455 110 0.81481481 98 0.70503597

Wed 113 0.85606061 120 0.88888889 125 0.89928058

Thu 150 1.13636364 143 1.05925926 162 1.16546763

50
 Fri 201 1.52272727 194 1.43703704 210 1.51079137

3. Calculate the average seasonal index

Week Average Index

Mon Index 1+ Index 2 + Index 3= 0.736273

Tue Index 1+ Index 2 + Index 3=0.771768

Wed Index 1+ Index 2 + Index 3=0.88141

Thu Index 1+ Index 2 + Index 3=1.120364

Fri Index 1+ Index 2 + Index 3=1.490185

4. Week four demand forecast is given, 720

720
Averagedailydemand= =144
5
Week Average daily Demand * Daily demand in week 4
Average Seasonal Index

Mon 144*0.736273 106

Tue 144*0.771768 111

Wed 144*0.88141 127

Thu 144*1.120364 161

Fri 144*1.490185 215

51
2.5.2 Causal Methods of Forecasting
Causal models, sometimes called associative models, use a very different logic to
generate a forecast. They assume that the variable we wish to forecast is somehow
related to other variables in the environment. The forecaster’s job is to discover how
these variables are related in mathematical terms and use that information to forecast.
Example
The general manager of apparel production firm feels that the demand for garment
shipments may be related to the amount of money spent on advertizing in different
ways. The manager has collected the data shown in table below.
(a) Compute values for the slope b and intercept a.
(b) Determine a point estimate for apparel shipments when the advertizing cost is 30
thousand birr.
Advertising Cost Shipment
15 6
9 4
40 16
20 6
25 9
15 10
Solution
X X2 Y XY
15 225 6
9 81 4
40 1600 16
20 400 6
25 625 9
15 225 10

52
 1+2+3+ 4
X= =¿2.
4 ∑ x 2=¿ ¿ 3 ∑ XY =¿ ¿466
5 0 Y =4550 50

53
 EXERCISE
1. ABC Retailer’s garments weekly sales are shown below. Forecast demand of
week 7 using 3-week moving ave-rage.
Week Auto Sales
1 800
2 1000
3 900
4 1200
5 1200
6 900
7 -
2. A firm uses simple exponential smoothing with to forecast demand. The
forecast for the week of January 1 was 500 units whereas the actual demand
turned out to be 450 units. Calculate the demand forecast for the week of
January 8.
3. Exponential smoothing is used to forecast automobile battery sales. Two value of
are examined, and Evaluate the accuracy of each smoothing
constant. Which is preferable? (Assume the forecast for January was 22 batteries.)
Actual sales are given below:
Month Actual Battery Sales Forecast
December 21 22
January 20
February 21  
March 15  
April 14  
May 13  
June 16  
4. Use the sales data given below to determine 2008 sales using linear trend method

54
 5. Year 6. Sales (Units)
7. 2001 8. 100
9. 2002 10.110
11.2003 12.122
13.2004 14.130
15.2005 16.139
17.2006 18.152
19.2007 20.164
To minimize computations, transform the value of x (time) to simpler numbers.
In this case, designate year 2001 as year 1, 2002 as year 2, etc.
5. The demand for a product in the past four consecutive years was shown in the
following table.
Quarter Year 1 Year 2 Year 3 Year 4
1 45 70 100 100
2 335 370 585 725
3 520 590 830 1160
4 100 170 285 215
It is estimated total demand for Year 5 will be 2600. How much demand will be
each quarter?

6. The owner of the retail store believes there is a seasonal effect in Souvenir sales.
Data for the last three years was obtained for actual sales.
Season 1996 1997 1998
High 1856 1977 2133
Summer A 1327 1344 1450
Summer B 835 815 983
Medium 1739 1672 1780

55
 7. A manufacturing company has monthly demand for one of its products as follows:
MONTH DEMAND
January 520 develop a three-period average forecast and a
three
February 490 period weighted moving average forecast
March 550 weights of 0.5, 0.3 and 0.2 for the most recent demand
April 580 values for April to July. Indicate which forecast would
May 600 seem to be most accurate. Make a June
420 forecast of September by using both approaches.
July 510
August 610
8. A maintenance engineer has experienced the following request to maintain
“Machine Stoppages”
Week No.
1 56
2 61 Develop an exponential smoothing forecast using
3 55 an alpha value of 0.40
4 70
5 66
6 65
7 72
8 75
9. The head of Business Department at Bahir Dar University wants to forecast the
number of students who will enroll in production/operations management next
semester in order to determine how many sections to schedule. The department has
accumulated the following enrollment data for the past 8 semesters.
Semester Students enrolled in POM
1 80
2 90
3 70
4 84
5 100
6 115

56
 7 98
8 130
a. Compute a 3-semester moving average forecast for semester 4 through 8
b. Compute the exponentially smoothed forecast (alpha=0.20) for the enrollment
data.
c. Compare two forecasts and indicate the most accurate.
d. Make a forecast for the next semester (semester 9) with the most accurate
approach.
10. ABC Hardware handles spare parts for lawn-mowers. The following data were
collected for one week in April when replacement for lawn-mower blades were in
high demand.
Day Demand
10 15
12 16
13 18
15 22
17 21
20 23
21 24
Simulate a forecast using simple smoothing for the week, starting with F = 15 and
alpha=0.2. Find also the forecast for the 8th day.
11. Using total moving average method to forecast the quarterly values of 2010.

Years Quarters Sales (million bottles)

2007 I 18.2
II 29.2
III 22.2
IV 17.4
2008 I 19.2
II 30.8
III 24.2
IV 18.2

57
 2009 I 21.6
II 33.2
III 26.2
IV 20.8
12. Demand for Adidas Shoes at a local sports store for the past eight weeks has been
Week Demand
1 122
2 130
3 98
4 121
5 96
6 152
7 113
8 124
Use a simple exponential smoothing model with alpha=0.6. Assume the forecast
for Period 1 was 120. Make a forecast for period 9.
13. Compute a forecast for the demand in each of the quarters of the following years,
2010.
Year Quarter Demand
2008 1 92
2 82
3 84
4 92
2009 1 90
2 80
3 82
4 90
14. Time 1 2 3 4 5 6 7 8 9 10 11 12
Demand 10 14 19 26 31 35 39 44 51 55 61 54
a. Use a simple four-period moving average to forecast the demand for periods 5-
13.
b. Use a four-period moving average with weights 0.4, 0.3, 0.2 and 0.1 to forecast
demand for time 13.

58
 c. Assume F1 = 8 and α = 0.3 . Use an exponential smoothing factor to forecast
demand in periods 2-13.
d. Compare the above methods. Which one you prefer? Why?
15. The monthly sales for Telco Batteries Inc., were as follows:
Month Sales
January 20
February 21
March 15
April 14
May 13
June 16
July 17
August 18
September 20
October 20
November 21
December 23
Forecast past sales using each of the following;
a. A three-month moving average,
b. a 6-month weighted average using 1,1,2,2,2, and 3 with the heaviest
weights applied to the most recent months.
c. Exponential smoothing using an α = 0.3 and a January forecast of 20.
d. Which method you prefer and why?
e. using the method you chose, forecast January sales of the coming year.
16. Girne Manufacturing Company’s demand for electrical generators over the period
2003 - 2009 is shown in table below.
Electrical
Year Generators
Sold
2003 74
2004 79
2005 80
2006 90

59
 2007 105
2008 142
2009 122
a. Develop a linear trend method.
b. Estimate the demand in 2010 and 2011.
17. The following gives the number of pints of type O (Rh+) blood used at Nalbantoglu
Hospital in the past 6 weeks:

Week of Pints Used

August 4 360
August 11 389
August 18 410
August 25 381
September 1 368
September 8 374
a. Forecast the demand for the week of September 15 using a 3-week moving
average.
b. Use a 3-week-weighted moving average, with weights of 1,3, and 6, using 6
for the most recent week. Forecast demand for the week September 15.
c. Compute the forecast for the above data using exponential smoothing with a
forecast for August 4 of 360 and α =0.2. Forecast the demand for the week of
September15.
d. (Show all your calculations and errors in tabular form.)
18. Quarterly data for the failures of certain aircraft engines at a local military base
during the last two years are
Quarters Engine failures
1 200
2 250
3 175
4 186
5 225
6 285
7 305

60
 8 190
19. The sales manager of a large apartment rental complex feels the demand for
apartments may be related to the number of newspaper ads placed during the
previous month. She has collected the data shown in the accompanying table.
Ads Purchased Apartments leased
15 6
9 4
40 16
20 6
25 13
25 9
15 10
35 16
a. Use causal approach to forecast, if the number of ads is 30
20. Dumlupinar Sports Club wants to develop its budget for the coming year using a
forecast for football attendance. Football attendance accounts for the largest portion
of its revenues, and the Vice Director Mr. T. Turgay believes attendance is
directly related to the number of wins by the team. The Vice Director has
accumulated total attendance figures for the last eight months.
WINS ATTENDANCE
4 3 630
6 4 010
6 4 120
8 5 300
6 4 400
7 4 560
5 3 900
7 4 750

a. Develop causal forecasting method to forecast attendance for at least 7 wins next
year.

61
21. Mr. SalimSelim, sales manager for Magusa Gas Grills Ltd., needs a sales forecast for
the next year. He has the following data from the last 2 years. (Sales are in 000 grills)
Year Quarter Sales Year Quarter Sales
2008 I 60 2009 I 105
II 91 II 130
III 277 III 522
IV 34 IV 73
Compute quarterly sales forecasts for the coming year using seasonality index.

62
 CHAPTER THREE

INVENTORY MANAGEMENT
Any discussion on inventory management must begin with a working definition of
what inventory is. Inventory can be defined simply as a stock or store of goods, or
stock keeping items (SKUs). Garment factories stock fabrics, trims, accessories, tools and
equipment, papers, spare parts, and more. Inadequate controls of inventories can result
in both under- and overstocking of items. Understocking can result in lost sales because
of the dissatisfaction of the customers. For example, customers could source garments
from other countries or factories that can deliver with in stipulated time frame. More
important than lost sales is the risk that under stocking might cause a factory to
bankrupt. From a simply practical viewpoint, on the other hand, overstocking
unnecessarily ties up funds that might be more productive elsewhere. Overstocking
appears to be the lesser of the two evils. How-ever, for excessive overstocking, the price
tag can be staggering for interest, insurance, taxes (in some states), depreciation,
obsolescence, deterioration, spoilage, pilferage, and breakage. Those costs, known as
holding or carrying costs, can be overwhelming if you are dealing with high priced
inventory. As an example of excessive overstocking, it is not unusual for factories carry
obsolete spare parts.

Inventory management has two main concerns: 1) the level of service, that is, having
the right goods, in sufficient quantities, in the right place, and at the right time; 2) the
costs of ordering and carrying inventories. Garment manufacturing firms aims to both
maintain a high level of service and minimize the costs of ordering and carrying
inventory. In other words, the two fundamental decisions are when to order and how
much to order. Welcome to the exciting world of inventory management!

Inventories have several functions. Among the most important are to:

A) meet anticipated patient demand for medical supplies;

63
 B) communicate demand infor-mation upstream on the supply chain (to
distributors, then to suppliers) in order to smooth manufacturers’ production
requirements;
C) to protect against stock-outs;
D) to take advantage of order cycles;
E) to hedge against price increases or to take advantage of quantity discounts; and—
most fundamental—
F) to garment manufacturing organization’s operations to continue.

Let’s put these basic inventory functions into perspective with an example of what any
factory manager would not want to have happen on her or his watch. Imagine the
following scenario, in which the factory manager has to explain to a member of senior
management why the accessory room found itself without buttons and threads.

“Sorry sir, but when the customer placed order for the style we are producing the past
three years, we were out of threads and buttons. Our anticipation stocks were depleted
because we hadn’t corrected the ordering patterns for seasonal variations. Then, the
custom clearance procedures delayed shipments from supplier, and our safety stocks just
weren’t good enough! You know we usually order in bulk to take advantage of large
economic lot size and lower our ordering cycle. Our last order was especially large
because we wanted to hedge against predicted price increases! In the final analysis, our
inventory just wasn’t sufficient to permit smooth operations.”

Requirements for Effective Inventory Management

Besides the basic responsibilities of deciding when and how much to order, the other
basic responsibility is to establish a system for keeping track of items in inventory.
These, then, are the requirements for effective inventory:

1. A system to keep track of the inventory in storage and on order.

2. A reliable forecast of demand.

64
 3. Knowledge of lead times and lead time variability.
4. Reasonable estimates of inventory holding costs, ordering costs, and shortage
costs.
5. A classification system for inventory items in terms of their importance.

Inventory Accounting Systems

Inventory accounting systems can be periodic or perpetual. Under a periodicsystem,

items in inventory are physically counted either daily, weekly, or monthly,for the
purpose of deciding how much to order of each. An advantage of the periodic system is
that orders for many items occur at the same time, which reduces the processing and
shipping of orders. However, this system can also produce dilemmas. In addition to a
lack of control between reviews, the need to protect against shortages between review
periods means carrying extra stock. Health care managers also must decide on order
quantities at each review.

A perpetual system continuously keeps track of removals from inventories, so it can

always give the current level of inventory for each. When the amount on hand reaches a
predetermined mini-mum, a fixed quantity, Q, is ordered. An obvious advantage of this
system is the control provided by the continuous monitoring of inventory withdrawals.
Another major advantage is the fixed order quantity; managers can identify an
economicorder size (discussed later in this chapter). However, even in a perpetual
system,a periodic physical count of inventory must still be performed to verify that the
reported inventory levels equal the effective inventory levels. The difference
betweenwhat is reported and what is actually on hand is caused by errors, theft,
spoilage, and other factors. For perpetual systems, a disadvantage is the added cost of
record keeping and information systems.

Perpetual systems can be either batch or on-line. In batch systems, inventory records
are collected periodically and entered into the system. In online systems, the

65
transactions are recorded instantaneously.

An example of a perpetual on-line system is the computerized checkout system in

grocery stores, where a laser scanning device reads the Universal Product Code (UPC),
or bar code, on an item. Such a system also is now used in many retailing organizations
to track inventories and the as items are used or dispensed for patients.

Universal Product Codes (UPCs). The UPCs have been around since late 1970sand
are used in industry. A UPC can have up to twenty character numbers that uniquely
identify a product using bars with different variety and thickness that can be read by
scanners. The order of the information in UPCs identifies the type of product, its
manufacturer, and the product itself.

Lead Time

Inventories are used to satisfy demand requirements, so reliable estimates of the amounts
and timing of demand are essential. It is also essential to know how long it will take for
orders to be delivered. Now that garment factories increasingly rely on their vendors to
maintain adequate inventory levels in their facilities, their data relevant to demand must
be transferred to their vendors. factory managers also need to know the extent to which
demand and lead time (the time between submitting an order and receiving it) may
vary; the greater the potential variability, the greater the need for additional stock to
avoid a shortage between deliveries.

Cost Information

Three basic costs are associated with inventories: holding, ordering, and shortage costs.
Holding or carrying costs, as mentioned earlier, relate to physically haying the
garment supplies in storage. Such costs include interest on the money barrowed to buy
the items, insurance, warehousing, security, compliance with industry and government
requirements, obsolescence, outdated, deterioration, spoilage, pilferage, theft, and
depreciation. Holding costs can be calculated either as a percentage of unit price or as a

66
dollar amount per unit. In any case, typical annual holding costs range from 20 to 40
percent of the value of an item. In other words, to hold a $10 item for one year could
cost from $2 to $4.

Ordering costs include the time and effort spent to calculate how much isneeded,
prepare invoices, inspect goods upon arrival for quality and quantity, and move goods
to temporary storage. Because those costs are incurred for each order, they are generally
expressed as a fixed dollar amount per order, regardless of order size.

Shortage costs result when an material supply is not on hand.They range from the
opportunity cost of losing customers’ goodwill, to the risk of bankruptcy and becoming
incompetent. Such costs could be extremely high, even threatening the survival
organization. Shortage costs are usually difficult to measure, and are often subjectively
estimated.

Classification System

An important element of inventory management deals with classifying the items in stock
according to their relative importance in terms of dollars invested, volume, utilization,
and profit potential— to say nothing of the disastrous financial consequences that could
result from allowing a stock-out to occur. For instance, a typical garment carries items
such as fabrics, machineries, spare parts and accessories; it would be unrealistic to devote
equal attention to each. Obviously, control efforts should be based on the relative
importance of the various items in inventory.

A classic method of classifying inventory is the A-B-C approach. Inventory items are
placed in one of three classes: A (very important), B (important), and C (somewhat
important), according to a measure of importance such as annual dollar value. That
measure is simply the dollar value per unit multiplied by the annual usage (demand) rate.

With three classes of items, A items generally account for 15 to 20 percent of the items
in total inventory, but for two-thirds of dollar usage. B items are moderate in terms of

67
inventory percentage and dollar usage. Finally, the C items may represent two-thirds of
the items, but only 10 percent of dollar usage. Although those percentages may vary, for
most facilities relatively few items will account for a large share of the value or cost
associated with an inventory, and it is those items that should receive a high share of
control efforts. Because of their high dollar value per unit, A items should receive the
most attention, through frequent reviews of the amounts in stock, as well as close
monitoring of their withdrawals from inventory. The C items should receive looser
control, and B items be controlled with efforts between those two extremes. Factory
manager’s A-B-C analysis should not overemphasize minor aspects of customer service
at the expense of major aspects.

In the following example, items 6, 13, and 14 have relatively high dollar values, so it
seems reasonable to classify them as A items. That classification is supported by the
calculation of percentage shares in annual dollar volume from all the items.

TABLE 1 A-B-C CLASSIFICATION ANALYSIS.

Annual Unit Annual Percent A-B-C

Item Demand Cost Costs of Total Class.
1 20,800 2.50 52,000 1.2% C
2 83,200 0.50 41,600 1.0% C
3 9,100 37.50 341,250 8.0% B
4 13,000 3.50 45,500 1.1% C
5 13,000 1.75 22,750 0.5% C
6 790 1,290.00 1,019,100 24.0% A
7 78,000 2.25 175,500 4.1% B
8 114,400 0.65 74,360 1.8% C
9 66,040 0.95 62,738 1.5% C
10 6,240 12.50 78,000 1.8% C
11 11,440 2.00 22,880 0.5% C

68
12 18,200 1.50 27,300 0.6% C
13 910 1,300.00 1,183,000 27.9% A
14 315 2,700.00 850,500 20.1% A
15 65,000 3.75 243,750 5.7% B
Total Annual Costs 4,240,228

Those three items collectively constitute about 72 percent of the annual expenditure on
all items. Items 3, 7, and 15 are moderate in their percentage values and could be
classified as B items. The remaining items could be classified as C items for their
relatively low shares in annual dollar value.

Economic Order Quantity Model

The economic order quantity (EOQ) model is frequently used to answer the
question of how much to order. EOQ calculates optimal order quantity in terms of
minimizing the sum of certain annual costs that vary with the order costs namely;
inventory’s holding and ordering costs. A few assumptions are important for this model:
that for an individual item the demand for a period (week, month or year) is known,
and that the demand rate is constant throughout the period; that purchase price of the
item does not affect order quantity (no high quantity discounts) and that delivery of the
item (in quantity) is received at once with a constant lead time.

Before we proceed through the EOQ process, it is important to understand the

inventory cycle. As Figure 1 illustrates, the cycle begins when an order for Q units is
received. These units are withdrawn from inventory at a constant

69
 FIGURE 1 THE INVENTORY ORDER CYCLE FOR BASICEOQ MODEL.

rate over time (depletion or demand rate). When the quantity on hand is just
sufficient to meet the anticipated demand during the lead time, a new order for Q units
is submitted to the vendor; that occurs at quantity R, called the reorderpoint (ROP).
Under the assumption that lead time and usage rate are constant, the order will be
received at the precise instant that the inventory on hand falls to zero units. Thus orders
are timed to avoid both excess stock and stock-outs. However, if those conditions were
not the case or if deliveries were expected to be late, as illustrated in cycle 2, the factory
manager should keep safety stocks on hand so operations could safely continue until the
order is received.

The optimal order quantity reflects a trade-off between carrying costs and ordering
costs: as the order size increases, its associated holding cost also increases; on the other
hand, ordering costs decrease when keeping higher quantities on hand reduces frequent
ordering. Looking at this issue in another way, if the order size is relatively small, its
average inventory will be low, and hence have low carrying costs; but the small order
size will necessitate frequent orders, which will drive up annual ordering costs. Figure 2
shows the relationship between ordering and holding costs with respect to the order

70
quantity, Q.

FIGURE 2 THE ECONOMIC ORDERING QUANTITY MODEL.

After observing these two extremes, it should be clear that the ideal solution is an order
size that avoids either a few large orders or many small orders. The basic EOQ model
serves that purpose, but the exact amount to order nevertheless will depend on the
relative amounts of holding and ordering costs for a particular item, as well as the
packaging requirements of its manufacturers and distributors.

The first step of the model is to identify the holding and ordering costs associated with
an item, while keeping the model assumptions in mind. Annual holding cost is
computed by multiplying the average amount of inventory in stock bythe cost to carry
one unit for one year. The average inventory is one half of the order quantity. As can be
observed from Figure 1, the amount on hand depletes at a constant rate from Q to 0
units; here we make one observation at full quantity ( Q ) and one at zero quantity,
when all items are depleted. However, at any given time the average inventory for a
cycle can be calculated by taking the average of these two observations as [( Q + 0)/2], or

71
Q/2. The symbol H is commonly used to represent the average holding cost per unit;
thus the total annual holding cost can be expressed as:
Q
Annual Holding Cost = ×H
2

Holding costs are a linear function of Q: holding costs increase or decrease in direct
proportion to changes in the order quantity Q, as shown in Figure 2.
Ordering costs, commonly labeled as S, are inversely and nonlinearly related to order
size Q. As Figure 2 shows, annual ordering costs will decrease as order size increases.
For a given annual demand level, the larger the order size, the fewer the orders needed.
For instance, if annual demand for T-shirt is 200 thousand units and the order size is 10
thousand units per order, there must be twenty orders over the year. But if we order Q=
40 thousand units, only five orders will be needed, and for Q=50 units, only four orders
will be needed. In general, the number of orders per year, or order frequency, is
computed by dividing annual demand (D) by order quantity (Q ), D/Q. Ordering costs
are relatively insensitive to order size and pretty much fixed, because regardless of the
amount of an order, certain activities (for example, preparing invoices, checking samples
for quality) must be done for each order. Total annual ordering cost is a function of the
number of orders per year and the ordering cost per order and can be expressed as:
D
Annual Ordering Cost= ×S
Q
If we add holding and ordering costs for every point in their respective graphs, we can
determine the total annual cost (TC) associated with inventory management. Figure 2
shows this as the TC curve where holding and order-ing inventory for a given order
quantity (Q ) ordered each time. The total cost can be expressed as the sum of annual
holding cost and annual ordering cost:
Q D
Total Cost = ×H+ ×S
2 Q

where
D = demand, usually in units per year Q = order
quantity, in units
S = ordering cost, in dollars
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 H = holding cost, usually in dollars per unit per year.

(Note: D and H must be in the same units, such as months or years.)

We see in Figure 2 that the total cost curve is U-shaped and that it reaches its minimum
at the quantity where carrying and ordering costs are equal.
Q D
×H= ×S
2 Q
2
Q H =2 DS
2 DS
Q 2=
H

Economic order quantity , Qo=

√ 2 DS
H

That is the optimum solution for Q, given by the minimum total cost of the TC curve.
We will call the point where both costs equal each other, as derived by the above
equations, Q o. It can be used when given annual demand, the ordering cost per order,
and the annual holding cost per unit. One can also compute the minimum total cost by
substituting Q o for Q in the TC formula. Once Q o is known, the length of an order
cycle (the length of a time between orders), or order frequency, can be calculated as:
Qo
Length of order= Year
D

Example

1. RMD Retailers sells 25,000 pair shoes each year (i.e. 52 weeks) and purchases
them at $15 per unit. It costs $50 to process and receive each order, and it costs
$1.10 to hold one pair of shoe in inventory for a whole year. Assume demand is
constant throughout the year. Manager has been ordering 1,000 pair of shoes at a
time, but can adjust his order quantity if it will lower costs.

a. What is the annual cost of the current policy of using a 1,000 pair of shoes
lot size?

73
 b. What is the order quantity that minimizes cost?
c. What is the time between orders for the quantity in part b?
d. If the lead time is two weeks, what is the reorder point, R?
Solution

A) Number of order per year=D/Q=25000/1000=25

Average Inventory Level=Q/2=1000/2=500

Ordering Cost=25*50=$1250

Carrying Cost=500*1.1=$550

Total Cost=1250 +550=$1800

√ √
B) Economic order quantity , Qo= 2 DS = 2∗25000∗50 =1508 shoes
H 1.1
C) Order Cycle= Q/D* Weeks per year=1508/25000*52=3.14 weeks or 22 days
D) Ordering cost=25000/1508*50=$829

Carrying cost=1508/2*1.1=$829

Total Cost=$1658

Example 2

A local distributor for a national tires company expects to sells approximately 9600 steel
belted radial tires of certain size and treated design next year. Annual carrying cost is $16
per tire and ordering cost is $ 75. The distributor operates 288 days a year.

e. What is EOQ Model?

f. How many times per year does the store reorder?
g. What is the length of order cycle?
h. What is the total annual cost if the EOQ quantity is ordered?

When to Reorder

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We used the EOQ model to answer the question of how much to order, but not the
question of when to order. We will now look at a new model that identifies the reorder
point (ROP) in terms of the quantity of an item currently in stock. The reorder point
occurs when the quantity on hand drops to a predetermined amount (see Figure 1 and
ROP level). This trigger amount usually includes the expected demand during the lead
time. There are four conditions that affect the reorder point quantity:
a) the rate of forecast demand;
b) the length of lead time;
c) the extent of variability in lead time and/or demand; and
d) the degree of stock-out risk acceptable to management.
When demand rate and lead time are constant, there is no risk of a stock-out created by
increased demand or lead times longer than expected. Therefore, no cushion stock is
necessary, and ROP is simply the product of usage rate and lead time as:

ROP =D*L
Where,
D = demand per period, and
L= lead time; and demand and lead time must be in the same units.
Example
For the above example 1, if the lead time is one week, determine ROP.
ROP=demand per week* lead time=25000/52*1 weak=481

EOQ for Gradual Replacement

The simple inventory profile shown in Figure 3 assumes that each complete
replacement order arrives at one point in time. However, replenishment may occur
over a timeperiod rather than in one lot, for example where an internal order is placed
for a batch ofparts to be produced on a machine. The machine will start to produce
items and shipthem in a more or less continuous stream into inventory, but at the same
time demand isremoving items from the inventory. Provided the rate at which items are

75
being suppliedto the inventory (P) is higher than the demand rate (D), then the
inventory will increase.

After the batch has been completed the machine will be reset (to produce some
otherpart), and demand will continue to deplete the inventory level until production of
thenext batch begins. The resulting profile is shown in Figure 9.6. This is typical for
inventories supplied by batch processes, and the minimum-cost batch quantity for this
profile is called the economic production quantity(EPQ). It is derived as follows:

Maximum stock level=M

Slope of inventory build-up=P –D

Also, as is clear from Figure 3:Slope of inventory build-up

¿ M ÷ Q/ P=MP /Q

FIGURE 2 INVENTORY PROFILE FOR GRADUAL REPLACEMENT OF

INVENTORY

So, MP/Q=p-d
M=Q(p-d)/p
Average inventory= M/2= Q(p-d)/2p
D
Annual Ordering Cost= ×S
Q
Annual Holding Cost= Q(p-d)/2p*H

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We see in Figure 2 that the total cost curve is U-shaped and that it reaches its minimum
at the quantity where carrying and ordering costs are equal.
D
EPQ=Q(P-D)/2P*H= ×S
Q

√
2 DS
D j
H ( 1− )
P

Example
The manager of a bottle-filling plant that bottles soft drinks needs to decide how long a
‘run’ ofeach type of drink to process. Demand for each type of drink is reasonably
constant at 80,000 permonth (a month has 160 production hours). The bottling lines fill
at a rate of 3000 bottles per hour,but take an hour to clean and reset between different
drinks. The cost (of labor and lost production capacity) of each of these changeovers has
been calculated at $100 per hour. Stock holdingcosts are counted at $0.1 per bottle per
month.

√
2∗80000∗100
EPQ= 500 =13,856
0.1(1− )
3000

The staff who operate the lines have devised a method of reducing the changeover time
from 1 hour to 30 minutes. How would that change the EBQ?Ans=9798, b/c S=50

EOQ in Uncertain Demand and Lead-time

The simple EOQ formula does not consider uncertainties in demand rate or in
replenishment lead time. Each time an order is placed, these uncertainties pose a risk of
stockouts occurring before the replenishment order arrives. To reduce the risk of
stockouts during this time, extra inventory can be held in excess of expected demand
during the lead time. A tradeoff exists between the cost of investing in and holding
excess inventory and the cost of stockouts, however. In any event, except by good luck,

77
either some stock remains in inventory or stockouts have occurred and the shelves are
bare when the replenishment order arrives.

The key to inventory management under uncertainty is the concept of a service level.
This is a customer-oriented term and is defined as the percentage of demand occurring
during the lead time that can be satisfied from inventory. Some analytical approaches for
determining the optimal service level have been suggested, but in practice, selecting a
service level is a policy decision. Consider, for example, a convenience store.
Depending on competition and the patience of customers, cold beer might require a
99-percent service level, but a 95-percent service level might be appropriate for fresh
bread.

The service level is used to determine a reorder point (ROP), which is the level of
inventory on hand when a replenishment order is initiated. The reorder point is set to
achieve a prosperities service level. This, of course, requires information on the
frequency distribution of demand during the replenishment lead time. When we set the
reorder point, we also are determining the safety stock level (SS) which is the excess
inventory that is held during the reorder lead time to achieve the desired service level.
The reorder point equals the safety stock level plus the average demand during the lead
time (dL). That is,

ROP=DL+ SS
The demand during lead time distribution now can be described in the following
general manner, where the daily demand has a mean µ and standard deviation σ:

D L=µ ( ¿ )
σ L =σ √ ¿
The Central Limit Theorem allows us to assume that the demand during lead time
distribution has a normal distribution no matter what the daily demand distribution is.

78
The safety stock now can be calculated using the following equation, where zris the
standard normal deviate for r-percent service level:

SS=Z∗σ L

Example 1

Suppose that the manager of a construction supply house determined from historical
records that demand for sand during lead time averages 50 tons. In addition, suppose the
manager determined that demand during lead time could be described by a normal
distribution that has a mean of 50 tons and a standard deviation of 5 tons. Answer these
questions, assuming that the manager is willing to accept a stock out risk of no more
than 3 percent:

a. What value of z is appropriate?

b. How much safety stock should be held?
c. What reorder point should be used?

Solution: Expected lead time demand = 50 tons, σL= 5 tons, risk = 3 percent.

a. From the probability table of a standard normal distribution, using a service level
of 1 - 0.03 = 0.97, you obtain a value of z = +1.88.
b. Safety stock = zσL= 1.88(5) = 9.40 tons.
c. ROP = expected lead time demand + safety stock = 50 + 9.40 = 59.40 tons.

When data on lead time demand are not readily available, the previous formula cannot
be used. Nevertheless, data are generally available on daily or weekly demand, and on
the length of lead time. Using those data, a manager can determine whether demand
and/or lead time is variable, and if variability exists in one or both, the related standard
deviation(s). For those situations, one of the following formulas can be used:

If only demand is variable, then, and the reorder point is

ROP=DL+ Z r σ √ ¿
Where,

79
SS=Z r σ √ ¿ If only lead timeis variable ,then , and the reorder point is

where

1. If both demand and lead time are variable, then

and the reorder point is

Figure5 illustrates the concept of establishing a demand during lead time distribution for
the case in which daily demand has a mean of 3 and standard deviation of 1.5 and the
lead time is 4 days. Note that the ROP is the stock level that is on-hand when an order
is placed and, thus, should be sufficient to satisfy r percent of demand during the lead
time. We assume that daily demand is an independent variable. The independence
assumption permits the summation of individual daily demand means and variances to
arrive at a total demand during the lead time, which has a normal distribution based on
the Central Limit Theorem.

Example

80
Recall inventory item SKU1341, the glass insulator. RMP's computerized information
system has tracked the daily demand rate for this item. Daily demand appears to be
distributed normally with a mean, µ= 3 and standard deviation, σ=1.5. The
replenishment lead time has been a constant 4 days. Because SKU 1341 is an important
item for utility line maintenance, it is company policy to achieve a 95-percent service
level for such items. What reorder point and safety stock should be recommended?

The demand during lead time for RMP becomes:

Then, we turn to the Standard Normal Distribution in Appendix A to find that z=1.645
leaves 5 percent in one tail to guarantee a 95-percent service level. The safety stock that
is required to ensure the desired service level is calculated using equation

SS=Z r σ √ ¿=1.645∗1.5∗√ 4=5

ROP=DL+ SS=ss+d L=5+ 12=17

Note: Each of these models assumes that demand and lead time are independent.

Example: A restaurant uses an average of 50 jars of a special sauce each week. Weekly usage of
sauce has a standard deviation of 3 jars. The manager is willing to accept no more than a 10
percent risk of stockout during lead time, which is two weeks. Assume the distribution of usage
is normal.

a. Which of the above formula is appropriate for this situation? Why?

b. Determine the value of z.

81
 c. Determine the ROP.

Solution:

a. Because only demand is variable (i.e., has a standard deviation), formula

is appropriate.
b. From the probability of the standard normal distribution, using a service level of 0.90,
you obtain z = +1.28.
c.

Shortage and Service Levels

The ROP computation does not reveal the expected amount of shortage for a given lead time
service level. The expected number of units short can, however, be vary useful to a manager:
This quantity can easily be determined from the same information used to compute the ROP,
with one additional piece of information (see Table 13-3 on page 569). Use of the table assumes
that the distribution of lead time demand can be adequately represented by a normal distribution.
If it can, the expected number of units short in each order cycle is given by this formula:

where

(p.569, Table 13-3 not included here.)

Example: Suppose the standard deviation of lead time demand is known to be 20 units. Lead
time demand is approximately normal.

a. For a lead time service level of 90 percent, determine the expected number of units short
for an order cycle.
b. What lead time service level would an expected shortage of 2 units imply?

Solution: σdLT = 20 units.

a. Lead time (cycle) service level = 0.90. From Table 13-3, E(z)=0.048. E(n)=0.048(20
units)=0.96, or about 1 unit.

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 b. For E(n)=2, we have E(z) = E(n)/σdLT = 2/20 = 0.10. From Table 13-3, this implies a
service level of approximately 81.5 percent.

Having determined the expected number of units short for an order cycle, you can determine the
expected number of units short per year. It is simply the expected number of units short per cycle
multiplied by the number of cycles (orders) per year. Thus,

where

E(N) = expected number of units short per year.

Example: Given the following information, determine the expected number of units short per
year.

D = 1,000    Q = 250    E(n) = 2.5

Solution:

It is sometimes convenient to think of service level in annual terms. One definition of annual
service level is the percentage of demand filled directly from inventory. This is also known as
the fill rate. Thus, if D = 1,000 and 990 units were filled directly from inventory (shortage
totaling 10 units over the year were recorded), the annual service level (fill rate) would be
990/1,000 = 99 percent. The annual service level and the lead time service level can be related
using the following formula:

Using , we have

Example: Given a lead time service level of 90, D = 1,000, Q = 250, and σdLT = 16, determine the
annual service level, and the amount of cycle safety stock that would provide an annual service
level of 0.98. From Table 13-3, E(z) = 0.048 for a 90 percent lead time service level.

Solution:

83
 a.

b.

Note that in the preceding example, a lead time service level of 90 percent provided an annual
service level of 99.7 percent. Naturally, different values of D, Q, and σdLT will tend to produce
different results for a cycle service level of 90 percent. Nonetheless, the annual service level will
usually be greater than the cycle service level. In addition, since the annual service level as
defined relates to the percentage of units short per year. It makes sense to base cycle service
levels on a specified annual service level. This means setting annual level and using that value to
obtain the service level for the order cycles.

AGGREGATE PLANNING AND ITS GOALS

Aggregate planning is the process of developing, analyzing, and maintaining a

preliminary, approximate schedule of the overall operations of an organization. The
aggregate plan generally contains targeted sales forecasts, production levels, inventory
levels, and customer backlogs. This schedule is intended to satisfy the demand forecast
at a minimum cost. Properly done, aggregate planning should minimize the effects of
shortsighted, day-to-day scheduling, in which small amounts of material may be ordered
one week, with an accompanying layoff of workers, followed by ordering larger amounts
and rehiring workers the next week. This longer-term perspective on resource use can
help minimize short-term requirements changes with a resulting cost savings.

84
In simple terms, aggregate planning is an attempt to balance capacity and demand in
such a way that costs are minimized. The term "aggregate" is used because planning at
this level includes all resources "in the aggregate;" for example, as a product line or
family. Aggregate resources could be total number of workers, hours of machine time,
or tons of raw materials. Aggregate units of output could include gallons, feet, pounds of
output, as well as aggregate units appearing in service industries such as hours of
service delivered, number of patients seen, etc.

Aggregate planning does not distinguish among sizes, colors, features, and so forth. For
example, with automobile manufacturing, aggregate planning would consider the total
number of cars planned for not the individual models, colors, or options. When units of
aggregation are difficult to determine (for example, when the variation in output is
extreme) equivalent units are usually determined. These equivalent units could be
based on value, cost, worker hours, or some similar measure.

Aggregate planning is considered to be intermediate-term (as opposed to long- or short-

term) in nature. Hence, most aggregate plans cover a period of three to 18 months.
Aggregate plans serve as a foundation for future short-range type planning, such as
production scheduling, sequencing, and loading. The master production schedule
(MPS) used in material requirements planning (MRP) has been described as the
aggregate plan "disaggregated."

Steps taken to produce an aggregate plan begin with the determination of demand and
the determination of current capacity. Capacity is expressed as total number of units per
time period that can be produced (this requires that an average number of units be
computed since the total may include a product mix utilizing distinctly different
production times). Demand is expressed as total number of units needed. If the two are
not in balance (equal), the firm must decide whether to increase or decrease capacity to
meet demand or increase or decrease demand to meet capacity. In order to accomplish
this, a number of options are available.

85
Options for situations in which demand needs to be increased in order to match
capacity include:

1. Pricing. Varying pricing to increase demand in periods when demand is less

than peak. For example, matinee prices for movie theaters, off-season rates for
hotels, weekend rates for telephone service, and pricing for items that experience
seasonal demand.
2. Promotion. Advertising, direct marketing, and other forms of promotion are used
to shift demand.
3. Back ordering. By postponing delivery on current orders demand is shifted to
period when capacity is not fully utilized. This is really just a form of smoothing
demand. Service industries are able to smooth demand by taking reservations or
by making appointments in an attempt to avoid walk-in customers. Some refer to
this as "partitioning" demand.
4. New demand creation. A new, but complementary demand is created for a
product or service. When restaurant customers have to wait, they are frequently
diverted into a complementary (but not complimentary) service, the bar. Other
examples include the addition of video arcades within movie theaters, and the
expansion of services at convenience stores.

Options which can be used to increase or decrease capacity to match current demand
include:

1. Hire/lay off. By hiring additional workers as needed or by laying off workers not
currently required to meet demand, firms can maintain a balance between
capacity and demand.
2. Overtime. By asking or requiring workers to work extra hours a day or an extra
day per week, firms can create a temporary increase in capacity without the
added expense of hiring additional workers.
3. Part-time or casual labor. By utilizing temporary workers or casual labor
(workers who are considered permanent but only work when needed, on an on-
call basis, and typically without the benefits given to full-time workers).

86
4. Inventory. Finished-goods inventory can be built up in periods of slack demand
and then used to fill demand during periods of high demand. In this way no new
workers have to be hired, no temporary or casual labor is needed, and no
overtime is incurred.
5. Subcontracting. Frequently firms choose to allow another manufacturer or
service provider to provide the product or service to the subcontracting firm's
customers. By subcontracting work to an alternative source, additional capacity is
temporarily obtained.
6. Cross-training. Cross-trained employees may be able to perform tasks in
several operations, creating some flexibility when scheduling capacity.
7. Other methods. While varying workforce size and utilization, inventory
buildup/backlogging, and subcontracting are well-known alternatives, there are
other, more novel ways that find use in industry. Among these options are
sharing employees with counter-cyclical companies and attempting to find
interesting and meaningful projects for employees to do during slack times.

87
 AGGREGATE PLANNING STRATEGIES

There are two pure planning strategies available to the aggregate planner: a level
strategy and a chase strategy. Firms may choose to utilize one of the pure strategies in
isolation, or they may opt for a strategy that combines the two.

LEVEL STRATEGY.

A level strategy seeks to produce an aggregate plan that maintains a steady production
rate and/or a steady employment level. In order to satisfy changes in customer demand,
the firm must raise or lower inventory levels in anticipation of increased or decreased
levels of forecast demand. The firm maintains a level workforce and a steady rate of
output when demand is somewhat low. This allows the firm to establish higher inventory
levels than are currently needed. As demand increases, the firm is able to continue a
steady production rate/steady employment level, while allowing the inventory surplus to
absorb the increased demand.

A second alternative would be to use a backlog or backorder. A backorder is simply a

promise to deliver the product at a later date when it is more readily available, usually
when capacity begins to catch up with diminishing demand. In essence, the backorder
is a device for moving demand from one period to another, preferably one in which
demand is lower, thereby smoothing demand requirements over time.

A level strategy allows a firm to maintain a constant level of output and still meet
demand. This is desirable from an employee relations standpoint. Negative results of
the level strategy would include the cost of excess inventory, subcontracting or overtime
costs, and backorder costs, which typically are the cost of expediting orders and the
loss of customer goodwill.

CHASE STRATEGY.

A chase strategy implies matching demand and capacity period by period. This could
result in a considerable amount of hiring, firing or laying off of employees; insecure and

88
unhappy employees; increased inventory carrying costs; problems with labor unions;
and erratic utilization of plant and equipment. It also implies a great deal of flexibility on
the firm's part. The major advantage of a chase strategy is that it allows inventory to be
held to the lowest level possible, and for some firms this is a considerable savings. Most
firms embracing the just-in-time production concept utilize a chase strategy approach to
aggregate planning.

Most firms find it advantageous to utilize a combination of the level and chase strategy.
A combination strategy (sometimes called a hybrid or mixed strategy) can be found to
better meet organizational goals and policies and achieve lower costs than either of the
pure strategies used independently.

TECHNIQUES FOR AGGREGATE PLANNING

Techniques for aggregate planning range from informal trial-and-error approaches,

which usually utilize simple tables or graphs, to more formalized and advanced
mathematical techniques. William Stevenson's textbook Production/Operations
Management contains an informal but useful trial-and-error process for aggregate
planning presented in outline form. This general procedure consists of the following
steps:

1. Determine demand for each period.

2. Determine capacity for each period. This capacity should match demand, which
means it may require the inclusion of overtime or subcontracting.
3. Identify company, departmental, or union policies that are pertinent. For example,
maintaining a certain safety stock level, maintaining a reasonably stable
workforce, backorder policies, overtime policies, inventory level policies, and
other less explicit rules such as the nature of employment with the individual
industry, the possibility of a bad image, and the loss of goodwill.
4. Determine unit costs for units produced. These costs typically include the basic
production costs (fixed and variable costs as well as direct and indirect labor
costs). Also included are the costs associated with making changes in capacity.

89
 Inventory holding costs must also be considered, as should storage, insurance,
taxes, spoilage, and obsolescence costs. Finally, backorder costs must be
computed. While difficult to measure, this generally includes expediting costs,
loss of customer goodwill, and revenue loss from cancelled orders.
5. Develop alternative plans and compute the cost for each.
6. If satisfactory plans emerge, select the one that best satisfies objectives.
Frequently, this is the plan with the least cost. Otherwise, return to step 5.

An example of a completed informal aggregate plan can be seen in Figure 1. This plan
is an example of a plan determined utilizing a level strategy. Notice that employment
levels and output levels remain constant while inventory is allowed to build up in earlier
periods only to be drawn back down in later periods as demand increases. Also, note
that backorders are utilized in order to avoid overtime or subcontracting. The computed
costs for the individual variables of the plan are as follows:

Output costs:
Regular time = $5 per unit
Overtime = $8 per unit
Subcontracted = $12 per unit
Other costs:
Inventory carrying cost = $3 per unit per period applied to average inventory
Backorders = $10 per unit per period
Cost of aggregate plan utilizing a level strategy:
Output costs:
Regular time = $5 × 1,500 = $7,500
Overtime = $8 × 0 = 0
Subcontracted = $10 × 0 = 0
Other costs:
Inventory carrying cost = $3 × 850 = $2,400
Backorders = $10 × 100 = $1,000
Total cost = $10,900

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A second example, shown in Figure 2, presents the same scenario as in Figure 1 but
demonstrates the use of a combination strategy (i.e., a combination of level and chase)
to meet demand and seek to minimize costs. For this example, let's assume that
company policy prevents us from utilizing backorders and limits our plan to no more
than 50 units of overtime per period. Notice that the regular output level is constant,
implying a level workforce, while overtime and subcontracting are used to meet demand
on a period by period basis (chase strategy). One will notice that the cost of the
combination plan is slightly lower than the cost of the level plan.
Output costs:
Regular time = $5 × 1,200 = $6,000
Overtime = $8 × 100 = 800
Subcontracted = $12 × 250 = 2,500
Other costs:
Inventory carrying cost = $3 × 325 = 975
Backorders = $10 × 0 = 0
Total cost = $10,275

MATHEMATICAL APPROACHES TO AGGREGATE PLANNING

The following are some of the better known mathematical techniques that can be used
in more complex aggregate planning applications.

LINEAR PROGRAMMING.

Linear programming is an optimization technique that allows the user to find a maximum
profit or revenue or a minimum cost based on the availability of limited resources and
certain limitations known as constraints. A special type of linear programming known as
the Transportation Model can be used to obtain aggregate plans that would allow
balanced capacity and demand and the minimization of costs. However, few real-world
aggregate planning decisions are compatible with the linear assumptions of linear
programming. Supply Chain Management: Strategy, Planning and Operation, by Sunil

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Chopra and Peter Meindl, provides an excellent example of the use of linear
programming in aggregate planning.

MIXED-INTEGER PROGRAMMING.

For aggregate plans that are prepared on a product family basis, where the plan is
essentially the summation of the plans for individual product lines, mixed-integer
programming may prove to be useful. Mixed-integer programming can provide a method
for determining the number of units to be produced in each product family.

LINEAR DECISION RULE.

Linear decision rule is another optimizing technique. It seeks to minimize total

production costs (labor, overtime, hiring/lay off, inventory carrying cost) using a set of
cost-approximating functions (three of which are quadratic) to obtain a single quadratic
equation. Then, by using calculus, two linear equations can be derived from the
quadratic equation, one to be used to plan the output for each period and the other for
planning the workforce for each period.

MANAGEMENT COEFFICIENTS MODEL.

The management coefficients model, formulated by E.H. Bowman, is based on the

suggestion that the production rate for any period would be set by this general decision
rule:
Pt = aWt-1 − bIt-1 + cFt+1 + K, where
Pt = the production rate set for period t
Wt-1 = the workforce in the previous period
It-1 = the ending inventory for the previous period
Ft+1 = the forecast of demand for the next period
a, b, c, and K are constants

It then uses regression analysis to estimate the values of a, b, c, and K. The end result
is a decision rule based on past managerial behavior without any explicit cost functions,

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the assumption being that managers know what is important, even if they cannot readily
state explicit costs. Essentially, this method supplements the application of experienced
judgment.

SEARCH DECISION RULE.

The search decision rule methodology overcomes some of the limitations of the linear
cost assumptions of linear programming. The search decision rule allows the user to
state cost data inputs in very general terms. It requires that a computer program be
constructed that will unambiguously evaluate any production plan's cost. It then
searches among alternative plans for the one with the minimum cost. However, unlike
linear programming, there is no assurance of optimality.

SIMULATION.

A number of simulation models can be used for aggregate planning. By developing an

aggregate plan within the environment of a simulation model, it can be tested under a
variety of conditions to find acceptable plans for consideration. These models can also
be incorporated into a decision support system, which can aid in planning and
evaluating alternative control policies. These models can integrate the multiple
conflicting objectives inherent in manufacturing strategy by using different quantitative
measures of productivity, customer service, and flexibility.

FUNCTIONAL OBJECTIVE SEARCH APPROACH.

The functional objective search (FOS) system is a computerized aggregate planning

system that incorporates a broad range of actual planning conditions. It is capable of
realistic, low-cost operating schedules that provide options for attaining different
planning goals. The system works by comparing the planning load with available
capacity. After management has chosen its desired actions and associated planning
objectives for specific load conditions, the system weights each planning goal to reflect
the functional emphasis behind its achievement at a certain load condition. The

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computer then uses a computer search to output a plan that minimizes costs and meets
delivery deadlines.

AGGREGATE PLANNING IN SERVICES.

For manufacturing firms the luxury of building up inventories during periods of slack
demand allows coverage of an anticipated time when demand will exceed capacity.
Services cannot be stockpiled or inventoried so they do not have this option. Also, since
services are considered "perishable," any capacity that goes unused is essentially
wasted. An empty hotel room or an empty seat on a flight cannot be held and sold later,
as can a manufactured item held in inventory.

Service capacity can also be very difficult to measure. When capacity is dictated
somewhat by machine capability, reasonably accurate measures of capacity are not
extremely difficult to develop. However, services generally have variable processing
requirements that make it difficult to establish a suitable measure of capacity.

Historically, services are much more labor intensive than manufacturing, where labor
averages 10 percent (or less) of total cost. This labor intensity can actually be an
advantage because of the variety of service requirements an individual can handle. This
can provide quite a degree of flexibility that can make aggregate planning easier for
services than manufacturing.

WHAT'S NEW IN AGGREGATE PLANNING.

Rudy Hung, in his Production and Inventory Management Journal article entitled

"Annualized Hours and Aggregate Planning," presents a new, useful idea for aggregate
planning called Annualized Hours (AH). Under AH, employees are contracted to work
for a certain number of hours (say 1,800 hours) per year, for a certain sum of money.
Employees can be asked to put in more hours during busy periods and fewer hours in
slow periods. Typically, employees receive equal monthly or weekly payments so that
hourly workers in effect have gained salaried status. Overtime is paid only when
employees have worked beyond their annual hours.

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AH is also known as flexiyear, as it can be seen as an extension of flextime, in which
employees can vary their work hours within limits. This concept is used almost
exclusively in Europe, particularly in the United Kingdom. The Scandinavian pulp and
paper industries pioneered AH in the mid-1970s. Around that time, some West German
firms, particularly those in the retail industry, also used AH.

AH gives employers much flexibility. AH serves to cut labor costs by offering employees
an annual sum less than their previous annual earnings with overtime. Even though
their total earnings may fall, their average earnings per hour would remain the same or
even rise. Effective earnings could rise even more so if the employer is unable to
consume all contracted hours. Employees have greater income security with no worries
about layoffs. There is also increased morale because blue-collar workers are now
salaried.

Another development affecting aggregate planning is postponement. This refers to

delaying the "finish" of a product until the moment of sale. Firms that rely on the
postponement strategy, such as PC-maker Dell Inc. or clothing franchise Benetton
Group Sp.A., depend upon the availability of aggregate inventories of components that
can be assembled to order shortly after, or even immediately, as an order is taken.

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