Spare Inventory
Spare Inventory
Spare Inventory
Faculty of Engineering
Industrial Engineering Department
8
Literature Review (cont)
Research Author Date About
Inventory models to Gelders and van 1978 Which were clustered in classes using
control slow and fast Looy ABC analysis together with criticality
moving items. and value considerations.
Forecasting methods Ghobbar and Friend 2003 They present a comparative study of
for the management 13 different forecasting methods for
of spare parts . the management of spare parts in the
aviation industry. (No inventory models
are included)
9
Problem Statement
In this Project we present a case study in inventory management
of spare parts at Al Sarawi for Mercedes Spare Parts; a local
Palestinian company.
The companys core business is selling spare parts for Mercedes
Cars to consumers. The company does not give adequate
importance to inventory management.
As a result, there is an inefficient deployment of inventory.
This study focuses on the inventory management of spare parts
for a specific model of Mercedes Cars which is 416.
It is important for the company has a well-planned inventory
management process for spare parts to control cost and service
customer needs.
Proposed Solution
We need to minimize the total costs of the inventory
in the company through developing and optimizing
various inventory management models of the
companys various spare parts
1. Phase 1
Determine and classify the spare part items by using ABC analysis.
2. Phase 2
Forecasting the demand by analyzing the historical sales data available
3. Phase 3
Collecting Relevant Cost Data ( Holding Cost , Ordering Cost, Transportation
cost, Backordering cost ).
4. Phase 4
Building Inventory Models for each category of the classified spare parts ( A
,B,C)
5. Phase 5
Evaluate the previous conditions and compare them empirically with the new
results based on our inventory models.
ABC Inventory Classification
(A) category items: Helps one identify these stocks as high value items
and ensure tight control in terms of process control, physical security as
well as audit frequency. It helps managers and inventory planners to
maintain accurate records and draw managements attention to the
issue on hand to facilitate instant decision-making.
Usage
ID Demand Cum Value/Unit Value 100% Cum Category
Sample of B 26800038 27 21.177 250.00 6750 1.414214 71.55775 B
items 72.9405
20300020 120 22.3535 55.00 6600 1.382787 4 B
Usage
Sample of C ID Demand Cum Value/Unit Value 100% Cum Category
items 90.7889
F0017856 30 47.06 75.00 2250 0.471405 6 C
0.43997 91.2289
E0762821 21 48.2365 100.00 2100 8 4 C
Cont
Cont..
Total number Percentage of Cumulative Percentage of Cumulative of
Description of parts items in the usage value annual sales annual sales
inventory value value
A 17 20% 20% 70% 70%
Sample of A
items D 1- D 6- D 6- D
6\201 12\20 D 1- 12\20 forcast1- D forecast 6- D forecast 1-
# ID ABC 0 10 6\2011 11 6\2011 12\2011 6\2012
2520001
1 2 A 40 30 30 20 35 30 25
FNS000
2 07 A 65 120 120 90 93 120 105
Sample of B
items
D 1- D 6- D 1- D 6- D F 1- D F 6- D F 1-
# ID ABC 6\2010 12\2010 6\2011 12\2011 6\2011 12\2011 6\2012
1 S0011097 B 3 7 5 6 5 6 6
2 25000024 B 9 7 10 18 8 9 14
A weighted moving average
Sample of A
items D 1- Forecast Forecast
6\201 D 6- D 1- D 6- D1- Forecast D D 1-
# ID ABC 0 12\2010 6\2011 12\2011 6\2011 6-12\2011 6\2012
2520001
1 2 A 40 30 30 20 34 30 24
FNS000
2 07 A 65 120 120 90 98 120 102
Sample of B
items D 1- Forecast Forecast
6\201 D 6- D 1- D 6- D1- Forecast D D 1-
# ID ABC 0 12\2010 6\2011 12\2011 6\2011 6-12\2011 6\2012
S001109
1 7 B 3 7 5 6 6 6 6
2500002
2 4 B 9 7 10 18 8 9 15
Criteria for choosing time series methods
Sample of B
items error6-
error1- error6- error1- 12\201 Sum MAD
# ID ABC 6\2010 12\2010 6\2011 1 Error avg MAPE
1 S0011097 B 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2 25000024 B 0.00 0.00 2.00 9.00 11.00 2.75 17.50
Sample of C
items error6-
error1- error6- error1- 12\201 Sum MAD
# ID ABC 6\2010 12\2010 6\2011 1 Error avg MAPE
1 99800001 C 0.00 0.00 0.00 5.00 5.00 1.25 6.58
2 F0007517 C 0.00 0.00 9.00 0.00 9.00 2.25 18.75
Forecasting error for weighted average method
Sample of A
items Error6-
Error1- Error6- Error1- 12\201 sum MAD
# ID ABC 6\2010 12\2010 6\2011 1 error W.Avg MAPE
Sample of B
items Error6-
Error1- Error6- Error1- 12\201 sum MAD
# ID ABC 6\2010 12\2010 6\2011 1 error W.Avg MAPE
1 S0011097 B 0.00 0.00 -1.00 0.00 1.00 0.25 5.00
2 25000024 B 0.00 0.00 2.00 9.00 11.00 2.75 17.50
Sample of C
items Error6-
Error1- Error6- Error1- 12\201 sum MAD
# ID ABC 6\2010 12\2010 6\2011 1 error W.Avg MAPE
1 99800001 C 0.00 0.00 0.00 5.00 5.00 1.25 6.58
2 F0007517 C 0.00 0.00 9.00 -1.00 10.00 2.50 21.88
Forecasting accuracy for demand
Forecasting Method Percentage of
Item Classes Best Forecasting Method Accuracy Measures Accuracy the total
items
E: 3 items E:17.6%
Weighted moving average A: 11 items A:29.4%
A items MAD
and simple average method W: 15 items W:64.7%
17 items MAPE N: 0 items N: 0%
E: 2 items E:9.5%
Weighted moving average A: 13 items A:61.9%
B items MAD
and simple average W: 15 items W:71.4%
21 items method. MAPE N: 0 items N: 0%
E: 10 items E: 22.2%
Weighted moving average A: 26 items A:57.7%
C items MAD
and simple average W: 32 items W:71.1%
45 items method. MAPE N: 0%
N: 0 items
Total Items E:ExponentiaSmoothing Method
85 items W: Weighted moving average
A: simple average method
N: Nave Method
Inventory Costs
Calculating cost of holding inventory and
ordering cost and the measurement of various
management practices.
1- Non Capital Costs: The non capital cost of inventories varies from
business to business. Generally non-capital cost is identified as:
Warehousing rental
Transportation
Obsolescence
Pilferage/theft
Damage
Insurance
Tax and duty
Administration cost (accounting, management)
29% 0 29%
Inventory Management Models
Good management of inventory is required to manage the supply
of product, its spares or consumables and satisfy the customers
needs. The inventory management is to meet the customers
demands and requirements at a minimum cost to the supplier.
For the Sarrawi Company, the number of items offered and the
volume of the car parts sold has increased over the years and
this has in turn created a need for extensive service
commitments and more spare parts to be held. However,
because inventory is expensive, the company does not want to
hold excessive amounts of stock unnecessarily. Thus, to establish
balance it becomes essential to strike a proper trade off between
the companys cost considerations and customer service
requirements.
36
Inventory Management Models
There are two basic types of inventory system
that we used:
I. Continuous review
II. Periodic review
37
Model Formation
Finding The Optimal ordering quantity : The EOQ
formula was used to determine the optimal Q to
be ordered.
Where:
Q = order quantity
EOQ = optimal order quantity
D = annual demand quantity
S = fixed cost per order
H = annual holding cost per unit
38
Model Formation for EOQ
Sample of A
items Unit D Forecasted Q Safety Setup
# ID ABC Value multiplied by 2 EOQ 2012 Current Stock Cost
Sample of B
items Unit D Forecasted Q Safety Setup
# ID ABC Value multiplied by 2 EOQ 2012 Current Stock Cost
Sample of C
items
Unit D Forecasted Q Safety Setup
# ID ABC Value multiplied by 2 EOQ 2012 Current Stock Cost
Where:
t= standard deviation of daily demand.
L = Lead time.
40
Model Formation
Periodic Review System
1-Reorder point = Average demand during lead time and the protection period +
Safety Stock.
Where:
t= standard deviation of daily demand.
p+L= Standard deviation for daily demand + Protection time
41
Model Formation
Calculating the total costs for the new ordering quantity and current one
for the two systems.
Total Cost = Annual holding cost + Setup Cost + Safety stock holding
cost.
Where
C = Total cost per year.
Q = Lot size, in units for the new and current quantity.
H = cost of holding one unit is inventory for a year.
D = Annual demand, in units per year.
S = Cost of ordering or setting up one lot.
42
Model Formation
Daily demand, Service level, and the lead time for A items
Average z
daily (service
# ID ABC Lead time(L) demand level) d.L L
1 25200012 A 3 0.160 1.65 0.480 0.094
2 FNS00007 A 3 0.680 1.65 2.040 0.307
Sample of A
items
Average z
daily (service
# ID ABC Lead time(L) demand level) d.L L
1 S0011097 B 3 0.040 1.65 0.120 0.020
2 25000024 B 3 0.040 1.65 0.120 0.056
Sample of B
items
43
Model Formation
Continuous Review System for first a sample
of A and B items
D/Q*S Q/2* H Cost
Safety Reorde SS Q Curren Curren Curren
# ID ABC stock r point Q new Q/2 * H D/Q * S Cost current current t t t
252000
1 12 A 1 2 55 1436 1396 2884 10 100 768 2610 3900
FNS00
2 007 A 1 4 187 1762 1745 3527 20 200 1632 1885 3894
F00152
3 61 A 1 2 15 1958 1920 4139 2 5 5760 652.5 6934.5
700007
4 2 A 1 3 49 4263 4114 8551 20 20 10080 1740 15300
45
Results and Discussion
Results for class A
The results clearly show that the chosen continuous review system model has
marked improvement over the existing method; the inventory cost savings are
97,640 NIS with percentage of 12.21 %, but also it shows how the periodic
review system is saving money for the A items but due to the high amount of
inventory and the long period to restock, so its clearly that is not applicable in
this company.
Old current New Inventory model New inventory
(Continuous Review model (Periodic
System) Review System)
Total Parts 17 17 17
40000.00
35000.00
30000.00
25000.00
C.system
20000.00 Current
15000.00
10000.00
5000.00
0.00
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85
Results
12000.00
10000.00
8000.00
6000.00 Continous-S
Periodic.S
4000.00
2000.00
0.00
1 5 9 13172125293337414549535761656973778185
Conclusion
Spare parts supply chains are in fact very different from those of
finished goods supply chain. The fundamental driving forces are
balancing between having a low inventory of spare parts to
decrease the cost and service fulfillment with short response
time.
The company in this study lacks of expertise in the area of
inventory management. This has resulted in severe shortcomings
in the business process of their company. After reviewing and
analyzing the data collected we proposed a cost effective solution
for them to manage their inventory optimally.
By implementing an effective inventory management system, Al-
Sarrawi Company in this study will be able to save a lot of money
and keeping their services as it is to their customers.
Recommendation
Applying the inventory model successfully depends on
the effective implementation of every stage of the
framework of inventory management which includes
ABC analysis, demand forecasting and implementation
development of an inventory model. Inaccurate data
going into a perfect model will give inaccurate or even
misleading results. Perfect data going into an
unsuitable model similarly will give inaccurate results.
Limitations
The limitation in this project has been the
amount of data available. Clearly the data
obtained does not cover a long enough time-
frame to provide accurate forecast so in a more
few years of stored data will give better results
and accurate assumptions, and the need to the
system to be continually updated or it will
become invalid.
Any
Questions