White Paper

2021 Planning Tip:

Eight Methods to Improve
Forecast Accuracy
Forecasting best practices for varying
supply chain scenarios

© 2021 American Software, Inc. All rights reserved.

 Executive Summary
Most companies recognize the importance of a repeatable and accurate forecasting process. Accurate
forecasts help minimize inventory, maximize production efficiency, streamline purchasing, optimize
distribution, maximize customer service and ensure confidence in company projections. However,
developing accurate product forecasts at all stages of a product’s life cycle can be very challenging. Gartner
places demand forecasts at the top of their Hierarchy of Supply Chain Metrics to highlight its impact back
through the supply chain.1 After all, a forecast is not simply a projection of future business; it is a request
for product and resources that ultimately impacts almost every business decision the company makes
across sales, finance, production management, logistics and marketing.

Typically, a variety of forecasting methods are applicable to any particular type of supply chain scenario.
Smart supply chain planners use multiple methods tuned to perform well at different phases of the product
life cycle, chosen to best exploit the available historical data and degree of market knowledge. The key is to
pick the most effective and flexible methods and models, blend their best features, and shift between them
as needed to keep forecast accuracy at its peak.

In this paper we take a brief look at the three categories of forecasting models and the eight methods
that have produced superior results for Logility’s many clients in a variety of industries and market
conditions around the world. We also discuss how Multi-Variate Demand Signal Management can help you
incorporate internal and external demand data to improve forecast quality and uncover insights to make
better and faster decisions.

1
The Hierarchy of Supply Chain Metrics: Diagnosing Your Supply Chain Health
Refreshed: 15 November 2016 | Published: 26 June 2015
Analyst[s]: Debra Hofman

Table of Contents
Three Categories of Forecasting Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Eight Forecasting Methods that Improve Supply Chain Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Best-fit Statistical Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Derived Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Modeling for Intermittent Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Attribute-based Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Demand Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Multi-Variate Demand Signal Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

© 2021 American Software, Inc. All rights reserved.

 Advanced forecasting
systems employ a flexible
combination of qualitative
and quantitative techniques to
generate reliable forecasts.

© 2021 American Software, Inc. All rights reserved.

 Three Categories of Forecasting Models
Forecasting models classically fall into three categories: qualitative, quantitative and hybrid. The primary
differences between them include the type of input data and the mathematical and statistical methods
employed to generate forecasts.

1 Qualitative models are experience-driven, relying on subjective inputs from knowledgeable personnel,
such as salespeople, account managers, and the like. This approach typically sets up formal procedures
for data review, and requires a consensus to determine the value of various forms of information.
Consensus among forecasters may be obtained by aggregating individual estimates or through
structured polling methods.

2 Quantitative models are statistically driven, drawing

heavily on historical performance data as the basic data Forecasting models
input. The calculating logic is defined and operations are classically fall into
purely mathematical.
three categories:
Time series models employ a time-ordered sequence of qualitative,
observations of a particular variable, and use only the history
of that variable to determine future values. For example, if quantitative and
monthly sales volumes of lawnmowers sold in the Southeast
United States display a linear pattern, a linear trend model
hybrid.
would provide the best basis for the forecast.

Derived models create new forecasts based on existing forecasts. When a new item’s forecast is thought
to be fundamentally the same as an existing item, characteristics can be used in creating the new
forecast, which may be factored up or down by a percentage. This preserves the overall trend and
seasonal characteristics of the item, providing a good starting point for the new item.

3 Hybrid models typically draw on historical demand information as a starting point, then use empirical
data to further refine the forecast.

Attribute-based models employ user-defined attributes to model new product introductions, seasonal or
fashion driven products, and product end-of-life retirement based on a demand profile.

Causal models use a causal relationship between a particular time series variable and other time series
factors to calculate the forecast. Causal techniques are useful in forecasting ‘lift’ during promotional
campaigns, where demand caused by promotional factors has an established relationship to base
demand. Additional factors can be used, such as end-cap displays, seasonality of the product, etc.
Factors are not additive but are used together to calculate the expected lift for your product.

© 2021 American Software, Inc. All rights reserved.

 Eight Forecasting Methods that Improve
Supply Chain Performance
For many supply chain scenarios, it’s typically best to employ a
variety of methods to obtain optimal forecasts. Ideally, managers A best practice
should take advantage of several different methods and build
them into the foundation of the forecast. The best practice is to
approach must
use automated method switching to accommodate selection and include the ability
deployment of the most appropriate forecast method for optimal
results. to incorporate
personal expertise
Advanced demand planning and forecasting systems automate
many of the functions required to select, model and generate and weight the
multi-echelon forecasts, lifting the burden of manually intensive various factors in
approaches and accelerating sensitivity to model changes as market
conditions evolve. A best practices approach also must include the generating
ability to incorporate personal expertise and weight the various
factors in generating forecasts.
forecasts.

In Logility’s experience working with more than 1,200 organizations ranging across dozens of industries,
eight specific forecasting methods stand out. Their unique strengths combine to deliver powerful, flexible
and accurate results.

1 Modified Holt is a best-fit statistical technique used when demand is trended, but does not vary by
[1]
the time of the year. A Holt-Winters variant is often used when demand is seasonal.

2 Moving Average is used for products whose demand histories have random variations, including no
[2]
seasonality or trend, or a fairly flat demand.

3 Inhibited is a type of derived model used to produce a zero forecast.

[3]

4 Modified Parent-Child is a derived model technique used to forecast products as a percent of the
[4]
forecast for another product [dependent demand].

5 Modified Croston is an intermittent demand technique used for products such as slow-moving parts
[5]
that have low demand or some zero demand periods.

6 The Demand Profile technique is attribute based. It employs user-defined attributes to model new
[6]
product introductions and product end-of-life retirement.

7 Proportional Profiling is another attribute-based technique used to disaggregate higher-level

[7]
forecasts into lower-level forecasts using user-defined attributes.

[8]
8 Demand Sensing techniques provide real-time visibility and insights into short-term demand,
and are enabled through down-stream data sources such as POS and syndicated scanner data and
by advanced technologies such as machine learning driven pattern recognition and natural
language processing algorithms, simulation, and optimization.

© 2021 American Software, Inc. All rights reserved.

 Best-fit Statistical Modeling
For most levels of management within an organization, aggregated demand history for product family,
brand category, country and/or selling region are good predictors of future performance. Such demand
history also serves as a baseline for effectively forecasting Stock Keeping Units [SKUs]. When there are
more than four-to-six periods of sales history, SKUs can be effectively forecast with moving average and
basic trend methods. SKUs with at least one year of sales history offer sufficient information to incorporate
a seasonal profile into the projected trend.

A modified Holt-Winters decomposition model with best-fit analysis can generate forecasts based on
demand history that incorporate trends and seasonal information. The method “senses” the amount of
history available for each time series or segment to create a basic model that best fits the history. Then it
uses the best combination of smoothing factors to enable the model to react to changing conditions going
forward without overreacting to anomalies in demand [such as unplanned seasonal events, transportation
disruptions, and so on].

For factors relating to seasonality, planners need the ability to weight the historical demand. Under the
assumption that the previous year is the best indicator of what will happen next year, most forecast
systems apply a higher weighting factor to the previous year’s demand, less to the year before and even
less to the years before that. But if the previous year was unusual in any significant way, the planner
must have the capability to change the historical weighting factors [so that the history two years ago
has more impact on the current forecast than last year, for instance] so as not to under- or over-forecast
the business.

Seasonal methods can be effective with less than 24 months of history; the minimum required is twelve
months. An effective approach for expected seasonal items with less than twelve months of history is to
assign a seasonal curve that has been captured from a similar item or item group.

A powerful best-fit statistical method should include flexible features such as trend, seasonal-with-trend,
moving average and low-level pattern fitting, as well as trend models for products with sporadic, low-
volume demand. The method should provide limiting and damping, as well as seasonal smoothing,
demand filtering, reasonability tests, tracking signals and tests for erratic nature that evaluate the validity of
each element, determining which are anomalous and should be filtered. These parameters give the planner
the flexibility to tune the process to best fit conditions at any element of the organization.

“Best fit” refers to the ability to change forecast methods as a product evolves.
The process may start out as a demand profile method, evolve to a modified
Holt-Winters method as the product becomes stable, and ultimately transition to a
demand profile method again as the product life cycle comes to an end.

© 2021 American Software, Inc. All rights reserved.

 Parameter Description

Limiting Confidence Limits describe the spread of the distribution above and below the point forecast.

Removes random variation [noise] from the historical demand, enabling better identification
Smoothing of demand patterns [primarily trend and seasonality patterns] and demand levels. Results in a
closer estimate of future demand.

Applies various “weights” to each period to achieve the desired results. These weights are
Damping
expressed as percentages, and the total of all weights for all periods must add up to 100%.

Forecast error, viewed as the difference between forecast value and actual value, is usually
normally distributed. A Demand Filter is usually set to ±4 Mean Absolute Deviation against
Filtering
the forecast value. Whenever the deviation is more than that, the adequateness of the
forecast model should be reviewed by analyzing the actual data.

The difference between actual demand and forecast demand. Error can occur in two ways:
bias and random variation. Bias is a systematic error that occurs when cumulative actual
demand is consistently above or below the cumulative forecast demand.
Forecast Error Type 1 Bias is subjective and occurs due to human intervention.
Type 2 Bias is a manifestation of a business process that is specific to the product [for
instance, persistent demand trend and forecast adjustments don’t correct fast enough for
items specific to a few customers].

An important type of reasonability measure is the tracking signal, which can be used to
Reasonability monitor the quality of the forecast. There are several procedures used, but one of the simpler
Tests is based on a comparison of the cumulative sum of the forecast errors to the mean absolute
deviation.

“Best fit” refers to the ability to change forecast methods as a product evolves. The process may start out as
a demand profile method, evolve to a modified Holt-Winters method as the product becomes stable, and
ultimately transition to a demand profile method again as the product life cycle comes to an end.

© 2021 American Software, Inc. All rights reserved.

 Derived Modeling
One method of generating new product forecasts is to use demand
When combined
variations or extensions from existing products, families or brands. with causal effects
Consequently, they draw on the historical data of existing products
or families. When combined with causal effects or management-
or management-
selected overrides to accommodate introductory promotions, selected overrides
derived modeling can provide a realistic and dynamic forecast for
new products. to accommodate
introductory
Using this approach, new products are assigned a percentage of
the parent, family and/or brand, enabling them to proportionately promotions,
inherit a forecast that contains the base, trend and seasonal derived modeling
elements of the associated category. As the forecast for the
associated category is adjusted to reflect changing conditions can provide a
over time, so too is the derived product’s forecast. If the derived
product’s point-of-sale [POS] or demand levels deviate from the
realistic and
forecast and exceed a user-defined tolerance, the system can dynamic forecast
generate a performance management alert to notify forecast
analysts to take corrective action.
for new products.
Once the product has accumulated sufficient demand history of its own, the link to the derived model’s
source model is severed and the product is then forecasted on its own using multiple best-fit statistical
methods.

Modeling for Intermittent Demand

Slow-moving parts typically exhibit irregular demand that may include periods of zero or excessively lumpy
demand. A Modified Croston Method handles low and lumpy demand that exhibits either a patterned
variation or no pattern.

The patterned variation looks at available history and classifies each demand element relative to those
around it. It classifies the periods into peaks, valleys, plains, plateaus, up-slopes and down-slopes. It
measures the duration of plateaus and plains, as well as the severity of peaks and valleys. It then conducts
pattern-fitting analysis to find regularity over time, attempting to fit the pattern to the history and
averaging for low and high points. The patterned forecast is put in context of future periods with the
average trend, and the pattern is re-evaluated using demand history of subsequent periods.

If no pattern is present, the unpatterned variation method attempts to use averaged highs and lows to
create a step-change forecast for future demand.

Both techniques permit zero demand to reside in the history, and will acknowledge such in the future
demand forecast. In forecasting for spare parts, for example, the demand is frequently low-level and spotty,
containing many periods of zero demand interspersed with low-level demand. This forecasting technique
allows patterns of zero demand to be forecast into the future.

© 2021 American Software, Inc. All rights reserved.

 Attribute-based Modeling
What if lack of data, short-life cycle or other mitigating The attribute-based
factors make it difficult to forecast using time series or
qualitative techniques? Forecast creation for new product
model provides a
introductions, short-life or seasonal products, and end-of-life wide variety of
products calls for attribute-based modeling techniques.
demand profiles by
The attribute-based model provides a wide variety of demand which to characterize
profiles by which to characterize the product, and can adjust
the product’s plan dynamically in response to early demand the product, and can
signals. The method will analyze historical sell-in and/or adjust the product’s
sell-through data to develop a wide variety of demand and
seasonal profiles. These profiles are assigned to individual plan dynamically in
planning records. Then, as actual demand information is response to early
captured, the current profile is validated or alternate profiles
identified to dynamically adjust the product’s plan. demand signals.

Attribute-based modeling consists of four unique processes.

1 Creation of Demand Profiles.

Demand profile creation is based on mathematical concepts known as Chi-squared analysis. The
demand planner selects products to be included based on attributes such as color, fabric type, region
of the country, etc., and multiple attributes can be used at once. Planners can efficiently realign history
for events like Easter, which does not occur during the same period each year.

2 Assigning Demand Profiles.

New, seasonal and end-of-life products can now be assigned to Demand Profiles. Advanced attribute-
based models offer ‘user-defined attribute’ matching capabilities, allowing the planner to set criteria for
how a new product’s attributes must match the attributes of a demand profile.

3 Automatic Revision of the Forecast Based on Demand Signals.

Forecast accuracy must be monitored continually using data such as Point-of-Sale [POS] to accurately
monitor customer buying patterns. Other demand signals such as syndicated data is used to check the
accuracy of the forecast. Correctness-of-fit modeling adjusts the forecast to reflect and quickly react to
real-world changes.

4 Assess Accuracy of Demand Profile Based on Demand Signals.

New products never sell exactly the same way as other products with similar attributes. But by using
point-of-sale or other demand signals, the accuracy of the assigned curve can be checked against
other demand profiles that have similar attributes. Relative-Error-Index [REI] calculations quickly show
planners which demand profile has the most accurate fit based on current demand trends. The current
demand profile can be switched to the profile that has the lowest REI.

© 2021 American Software, Inc. All rights reserved.

 Demand Sensing
Demand sensing is the translation of market based demand information to detect short term buying
patterns. Demand sensing leverages new mathematical techniques and near real-time Big Data to improve
a supply chain organization’s capability to respond to unplanned demand changes.

Demand sensing involves the import of short-term demand data such as Point of Sale [POS] scan-based
data, point of use device data, weather data, or social media data on an hourly/daily basis to immediately
sense demand signal changes. Through advanced algorithms the statistical significance of demand changes
are evaluated and short term forecast adjustments made to drive short term supply chain responses.

• POS • Warehouse Depletions

Internal Downstream • Price/Promotion Moves • Customer/channel

• Weather • Precipitation by Month

Environmental • Seasonality • Temperatures

Economic • CPI • Commodities Pricing

• Promotion actions • Pricing adjustments

Competitor • Store openings • Store closings
• New product intros

• “Moneyball” • New metrics derived by

Synthetic • E.g., Shipments/ combining your array into
Depletions/Lags new ratios and indices

• Trade areas • Demographics

Regional Factors • Housing starts • Car Registrations, etc.

Syndicated Sources[IRI/ • POS by brand • Store-level dynamics

Nielsen/VIP] • Distributors Depletions • Consumer Preference

The typical performance of demand sensing can reduce near-term forecast error by 30% or more
compared to traditional time-series forecasting techniques. However, since demand sensing usually
takes place inside of the supply planning time fence, the greatest value gained from demand sensing
is the ability to optimize inventory and resource deployment. Demand sensing can lead to a 5%—10%
improvement in customer service.

© 2021 American Software, Inc. All rights reserved.

 Demand sensing is most successful when the following capabilities are already in place.

● Prior experience using customer POS data in ad-hoc ways

● Strong collaborative relationship between commercial and supply chain groups
● Strong supply chain visibility capabilities
● Statistical based demand, inventory, and replenishment planning
● Workflow and exception-based alerts
● Constrained/profit-based “what-if” scenario analysis
● Agile manufacturing capabilities [short change overs]
● Agile distribution capabilities [inventory visibility and agility to reroute shipments]
● An integrated platform to collect and analyze demand signals and to enable optimized response

An evolving demand sensing trend is the use of Artificial Intelligence [AI] to automate the process of
analyzing Big Data to recognize complex patterns and to separate actionable demand signals. Used
together, machine learning and natural language processing algorithms can be used to analyze information
like social media text to determine the “Sentiment” of the text and to predict the impact of that sentiment
on demand. Today’s natural language processing algorithms have the ability to correctly categorize the
sentiment of most social media content. Machine learning algorithms can quickly learn the differences
between humor, sarcasm, irony and so on to improve categorization capabilities.

Companies are using social media insights today to make significant operational impacts.

● Evaluate the Health of a Brand—An understanding of how your target market feels about your
company, product and services through analysis of overall sentiment can provide valuable insight into
the health of your brand.
● Address a Crisis—Analysis of social sentiment might reveal a spike in negative posts and provide an
early warning to a potential product or service issue. Through alerts and analysis, the root cause of the
issue can be uncovered and corrected.
● Research the Competition—Social sentiment analysis can help you understand how to position against
the competition.
● Improve Demand Prediction—Companies can now use the ‘Voice of the Consumer’ to drive
improvements in forecasting and inventory positioning.

An evolving demand sensing trend is the use of Artificial Intelligence [AI] to

automate the process of analyzing Big Data.

© 2021 American Software, Inc. All rights reserved.

 Multi-Variate Demand Signal Management
MDSM augments
Leading companies are turning to Multi-Variate Demand Signal
Management [MDSM] to capture data from multiple demand
streams and translate it into demand data insights used to provide demand
input for future planning activities, identify and pre-empt service
disruptions, and generate measurable sales and profit growth. forecasting,
MDSM integrates all relevant demand signals into a single source of enables demand
truth and enables predictive analytics to uncover insights to make
decisions ahead of the demand curve. sensing and
answers the
MDSM is a more proactive approach than basing inventory and
replenishment on only shipment or order data—it gives better question of how
access to downstream data, analysis and insights to make more
accurate decisions, faster. More than forecasting trends and
to react to
seasonality, it is about identifying and measuring market signals, external factors
then using those signals to shape future demand. MDSM augments
demand forecasting, enables demand sensing and answers the
that drive demand
question of how to react to external factors that drive demand for for buy decisions.
buy decisions.

Typical demand signals to boost demand sensing

Captured Demand Signals Analyze Demand Predict Demand

Demand Signal Repository Demand Signal Visualization Causal Forecasting

Store, Organize and Blend Descriptive analytics to Quantify Direction and
Diverse Data Sources develop downstream, market Importance of Demand
serve Data to DS and and demand insights Signals
Planning Systems Ad hoc analysis

LOGILITY ADAPLINK
CRM ERP CDM Suppliers Customers

© 2021 American Software, Inc. All rights reserved.

 MDSM includes the tools and services to aggregate, cleanse and harmonize disparate downstream data
streams. It also drives a stronger link between sales, marketing and brand teams with their supply chain
counterparts. Leading users of MDSM incorporate short-term fast response planning technologies like
demand sensing to improve their ability to optimize a supply response to short-term demand. MDSM
works best when organizations have a technology structure in place that supports a quick supply planning
response and can use demand signals updated daily or weekly.

Typical MDSM Benefits:

● Demand planners are better prepared to quantify variability in demand through increased visibility
downstream by capturing and analyzing external demand signals
● Planners can improve forecast accuracy by incorporating information from external demand signals and
predicting mid-term demand patterns
● Operations improve lead time because of the improved use of real-time forecast to drive enhanced
scheduling and procurement
● Sales can boost sales by predicting and reducing stock outs with demand pattern recognition
● Sales can Improve new product introduction through better customer insight at POS
● Operations can lower expediting and inventory costs
● Logistics build transport and deployment scheduling efficiency because of improved visibility of
customer requirements

Where does MDSM fit in?

Demand Forecasting
Multi-Variate Demand Sensing Management
Foundation of Demand Planning
Aggregate Time Series Demand
DemandSensing
Sensing
Augmentation
Orders/Shipments Integrates all relevant demand
Mid/Long-range Horizon signals to single source of truth Uses POS data
“What am I going to buy/make” Machine Learning with minimal latency to
know what is being
Market and external demand sold, where it is being
signals sold, and to whom
“How do I react to external Daily Forecasting
factors driving demand for
buy decisions” “What am I going to
ship”

© 2021 American Software, Inc. All rights reserved.

 Conclusion
Supply chain organizations routinely rank demand planning immaturity as a major obstacle in meeting
their supply chain goals. Accurate forecasts are the foundation for profitable business growth. Optimal
demand planning and forecasting requires comprehensive modeling capabilities plus the flexibility and
ease-of-use to shift methods as life cycles progress and market conditions change. Logility Demand
Planning™ provides a combination of qualitative, quantitative and hybrid techniques to generate reliable
forecasts.

Attribute-based methods that use demand profiles are often suited to new product introduction and end
of product life cycles, at times when reliable historical demand data is lacking or the available data is less
relevant.

At the more mature stages of the product life cycle, five different time-series statistical models come into
play, including modified Holt, Holt-Winters, moving average, and intermittent or low demand, whether
patterned or unpatterned. These models are used to create retrospective forecasts that cover prior periods
[typically three years] of documented demand.

Derived models can be used to create a Parent-Child

relationship in which forecasts for closely related
products are driven as a percentage of the forecast
for a ‘leader’ product. This ensures that when the
forecast is modified for the ‘parent’ all the ‘child’
forecasts would be updated accordingly. Logility’s powerful software solutions
help planners leverage the best
The ability to sense and quickly react to demand methods, spot trends and forecast
changes has become a critical capability to meet ever demand signal changes more quickly,
increasing customer service requirements. Through and sense and respond to market
the use of machine learning and natural language changes and inventory investments
processing algorithms Big Data can be systematically and deployments.
mined to gain new insights to improve operational
capabilities.

To prevail in a business economy shaped by uncertain demand and rapid market changes, all of these
forecasting methods must be harnessed within one practical, comprehensive solution suite. Multi-variate
demand signal management sorts out the flood of data in a structured way to recognize complex patterns
and to separate actionable demand signals from a sea of “noise”. A best-in-class forecasting system is one
that provides flexibility for users to weight elements and override key parameters in the forecast calculation
based on their intuitive knowledge and market expertise.

Logility’s powerful software solutions help planners leverage the best methods, spot trends and
forecast demand signal changes more quickly, and sense and respond to market changes and inventory
investments and deployments.

© 2021 American Software, Inc. All rights reserved.

About Logility
Accelerating the sustainable digital supply chain, Logility helps companies seize new opportunities, sense
and respond to changing market dynamics and more profitably manage their complex global businesses.
The Logility® Digital Supply Chain Platform leverages an innovative blend of artificial intelligence [AI] and
advanced analytics to automate planning, accelerate cycle times, increase precision, improve operating
performance, break down business silos and deliver greater visibility. Logility is a wholly owned subsidiary
of American Software, Inc. [NASDAQ: AMSWA].

To learn how Logility can help you make smarter decisions faster, visit www.logility.com.

For more information, contact Logility:

Worldwide Headquarters 800.762.5207
United Kingdom +44 [0] 121 629 7866
asklogility@logility.com

© 2021 American Software, Inc. All rights reserved.