A presentation by Calven van der Byl BCom Economics and Statistics, BCom Honours Mathematical Statistics, Masters Mathematical Statistics, Inventory Optimization Demand Planning Manager, DSV, South Africa. Delivered during SAPICS 2016, a leading event for supply chain professionals, held in Sun City, South Africa. Demand Planning is a complex, yet often de-emphasized function in the supply chain planning function. The demand planning function is often characterized by an over-reliance on off the shelf software as well as a great deal of manual intervention. This presentation will outline the current developments and perspective in big data analytics and how they can be leveraged with the demand planning function to improve forecasting agility and efficiency. A simulation study will be presented in order to illustrate these principles in practice.

1. Data Science in Demand Planning (When the machine is not enough)
Calven van der Byl
Introduction
Demand planning is a complex yet often de-emphasized function within supply chain planning.
The demand planning function is frequently characterized by an over-reliance on off the shelf
software as well as a great deal of manual intervention. Data science suggests that some
business problems can be supported by deriving valuable insight from large, complex data sets.
Demand forecasting for the sales and operations process is the business problem being
considered here. The idea that a data science approach can be used in the forecasting process
is predicated on the idea that the sales and operations planning process needs the best possible
statistical forecast possible before review commences. Only the statistical forecast generation
and demand review will be considered in this paper.
This paper will provide an overview of the requirements for a data intensive approach to
generating a statistical forecast as well as how the demand review can be supported by a data
intensive approach. We will be able to show that a forecasting process can be seen as a
complete analytical model that can initially be trained, tested and continuously improved upon
as part of an ongoing analytics life cycle.
This paper also outlines the current developments and perspective in big data analytics and
shows how they can be leveraged with the demand planning function to improve forecasting
agility and efficiency. A simulation study will also be shown during the presentation of this
paper in order to illustrate these principles in practice.

2. Big Data in Forecasting Demand
Organizations are now widely dependent on software systems. Large amounts of business data
are routinely created by business software. The demand forecasting process is no different. The
table below shows how the data required for the demand planning process can easily be seen
as big data using the four V’s of big data.
If any data that forms part of the demand planning process exhibits any of these characteristics,
alternative data approaches should be considered.
Common Forecasting Process
A common forecasting process set up within an organization usually involves the use of the
forecasting module from an MRP system and Excel.
4 V's of Big Data
Velocity
• Daily forecasting
of SKU's
required to
maintain the
forecast plan
Veracity
• Stock take
uncertainty
• Stock outs
• Business process
controls the
interpretation of
the data
Variety
• Demand history
• Daily stock on
hand
• Customer
master data
Volume
• Customer level
sales history
• Point of sales
history

3. This process may require a fair amount of manual intervention from a demand planner. If each
item in a portfolio requires a lengthy amount of consideration, this forecasting process would
require at least one demand planner per 200 SKU’s. This forecasting process can mean a heavy
reliance on the forecasts generated by a forecasting module with very little flexibility in
selecting the forecasting models that get applied.
Data Science
Data science is the practice of deriving meaningful insight from data in order to drive value in
an organization. The skill sets required are multi-faceted for a data scientist. A data scientist is
required to understand the business problem, be capable of extracting the relevant data and
designing the analytics methods that need to be used. Demand planning is characterized by
predictive analytics.
MRP
Forecast
DP
extracts
report
from
MRP
DP
manual
checks
DP
reports
created
Forecast
Review
Business
Understanding
•Asking the right questions
•Identifying the business
Opportunity
Technology
•Data Architecture
•Calculation efficiency
•Deployment into
production
Math and Statistics
•Understanding the
algorithms and models
used
•Identifying the
appropriate methods

4. Predictive analytics is characterized by assumptions and theory about the data being analyzed.
The forecasting process is no different, the results of which should be continuously assessed for
accuracy, credibility and reliability.
The following questions are asked of the forecasting process:
 How large will prediction errors be?
 What is the bias to be expected from the forecast process?
 How long does it take to run a forecast?
These are very difficult questions to answer in the absence of a formalized and automated
forecasting process that is monitored by a technically capable specialist.
Five years of sales history data is used to train the forecasting process. Use three years to train the
data set and get results for year four. The results are then validated by running a hold out for year 5
to validate the results in training. The idea is to fairly represent the real world forecasting process
as closely as possible. This can be formalized and automated by applying the six phases of the
analytics life cycle to the forecasting process.
Six Phases of the Analytics Life Cycle
The analytics life cycle is a formal framework of the steps covering the investigation,
management and deployment of analytics into a business process. The 6 phases are shown
below.
 Discovery entails the formulation of the business problem as well as understanding if
data is available to support the analytics project.
Discovery
Data
Preparation
Model
Planning
Model
Building
Communicate
Results
Deploy to
Production

5.  Data preparation requires the ability to extract the data required from the production
system as well as the tools needed to transform data into a workable condition. This will
often entail data cleansing.
 Model planning consists of exploratory analysis as well as determining the possible set
of methods and workflows that will be followed during the next phase.
 Model building requires the execution of the analytics models defined in the planning
phase. The performance of the models is assessed in terms of resources required to
execute as well the appropriateness of the model results.
 Results are communicated in order to assess if the analytics models answer business
questions formulated in discovery.
 Production deployment involves the construction and implementation of the
recommended models into the production environment.
Application to Demand Planning
The monthly demand forecasting process can be seen as a complete model that can be built
and calibrated within the analytics life cycle framework.
• Discovery: Identify forecast level. Could be different to data available (Customer level vs
Item level). What KPI’s are going to be used? WAPE, APE, Error etc. Typically required
when setting up a forecasting process but needs to be re-visited.
• Data Conditioning: Identify data sources and the reports required to perform the
monthly forecast, e.g. SAP report, SQL extract, ODBC. The methods to be used to
Discovery
Data
Conditioning
Model Planning
(Segementation)
Model Building
(Execution)
Communicate
Results
Deploy to
Production

6. condition data into a desired, workable format will need to be defined. Data is edited
and re-formatted so that it can be used in analysis.
• Model Planning (Segmentation): Software to be used to generate the statistical
forecast. This should be preferably separate from the software environment that does
the actual MRP calculation. A forecast using solely the forecasting module or forecast
software is created. Rules based exception management is created in order to direct
forecasting models to the most appropriate SKU segments.
• Model Building (Execution): Custom models and methods are applied to the SKU
segments in the segmentation phase. Any items not flagged for segmentation are to
keep the initial software forecast.
• Communicate Results: The statistical forecast is presented to the relevant stakeholders
as part of a demand forecast review before consideration in the rest of the S&OP
process. Recommendations about new data and desired forecasts are received.
• Deploy to production: It is then the responsibility of the forecaster to translate the
input from the demand review into clear mathematical statements that can be taken
back to the discovery, data conditioning and model planning phases.
Three Tiers of Forecasting
This entire process requires the appropriate tools needed to set up a workflow and turn
business knowledge into mathematical statements. This can be described in terms of the three
tiers of forecasting required by a forecasting process. This will require the technology needed to
store data, a sufficient analytics tool to translate business requirements into mathematical
statements and presentation software in order to show results.

7. Analytics Life Cycle in Action
The advantage of defining the forecasting process in this way is that it lends itself to an
analytical framework that can be automated for testing and training of an entire demand
forecasting process. The graph below shows
• BI software: TableauPresentation
• Analytical tool: R
• Forecasting software:
Forecast Pro
Application and
Logic
• Data store: SQL
• Data access:
ODBC
Data

8. Summary
A few important points can be concluded:
 Focus has been on the forecasting portion of the S&OP process.
 Big data complexity suggests that an alternative analytics approach would be required.
 Manually driven forecasting can benefit from a data science approach.
 Three tiers of forecasting require specialist analytics software for presentation, custom
logic and quick data access.
 The forecast process can be seen as an analytics model which can be trained, validated
and automated.
 A data science approach to forecasting can support the S&OP process by allowing the
forecaster full control over the implementation of business logic into the forecasting.
There is huge potential for analytics to add value to the forecasting within an organization
where a “black box” cannot.

9. About The Author
Calven van der Byl is an Inventory Optimization Demand Planning Manager at UTi and leads a
team of statistician demand planners within the SDi division of UTi. He is a data driven demand
planner with a passion for extracting insight from data. He holds a BCom (Economics and
Statistics), a BCom Honours (Mathematical Statistics) and a Masters (Mathematical Statistics)
from the Nelson Mandela Metropolitan University
UTi SDi currently provides an Inventory Optimization service to clients in the Pharmaceutical,
Automotive, Chemical, Manufacturing and Retail industries. SDi Africa have also successfully
completed projects with a number of high profile international
companies across several industries and are the Inventory
Optimization centre of excellence for UTi globally.
Contact details
Email address: cvanderbyl@go2uti.com
Website: http://www.go2uti.com/inventory-optimization
Telephone: 0415012612