Improving Forecasts with Integrated Business Planning: From Short-Term to Long-Term Demand Planning Enabled by SAP IBP

430 Pages·2019·31.27 MB·English

by  Ganesh Sankaran

Management for Professionals
Ganesh Sankaran
Federico Sasso
Robert Kepczynski
Alessandro Chiaraviglio
Improving
Forecasts with
Integrated
Business Planning
From Short-Term to Long-Term
Demand Planning Enabled by SAP IBP
Management for Professionals
The Springer series Management for Professionals comprises high-level business
and management books for executives. The authors are experienced business
professionals and renowned professors who combine scientific background, best
practice, and entrepreneurial vision to provide powerful insights into how to
achieve business excellence.
More information about this series at http://www.springer.com/series/10101
(cid:1) (cid:1)
Ganesh Sankaran Federico Sasso
(cid:1)
Robert Kepczynski Alessandro Chiaraviglio
Improving Forecasts
with Integrated Business
Planning
From Short-Term to Long-Term
Demand Planning Enabled by SAP IBP
123
Ganesh Sankaran Federico Sasso
Ingolstadt, Germany Deloitte Consulting AG
Zurich, Switzerland
RobertKepczynski
Rothrist, Switzerland Alessandro Chiaraviglio
Polytechnic University ofTurin
Turin, Italy
ISSN 2192-8096 ISSN 2192-810X (electronic)
Managementfor Professionals
ISBN978-3-030-05380-2 ISBN978-3-030-05381-9 (eBook)
https://doi.org/10.1007/978-3-030-05381-9
LibraryofCongressControlNumber:2018964048
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It gives me great pleasure to acknowledge the
contributions of some special people in my
life who have made this moment, of me
enjoying this opportunity to write a
dedication for my book, possible. My wife,
Meena, who has been a pillar of strength,
whohashelpedmestayfocusedthroughthick
and thin, who has kept me sane through all
the deadlines missed and weekends and
nights spent catching up, and who has in no
small measure helped me emotionally and
literally complete this project. My parents for
their boundless love, endless desire and
innumerable sacrifices in pursuit of my
happiness and success. My sister, Meera, for
gifting me my first PC. She had to wade
through customs with great difficulty as a PC
was still an object of curiosity back in the
day. Thank you for kindling my passion in
computers—I wouldn’t be here if not for that
act of sisterly love.
Finally, thanks to my coauthors for their
patience. SpecialthankstoRobertfor inviting
me onboard and for his stewardship. Thanks
Federico for keeping it all together!
—Ganesh Sankaran
Preface
TheideaofthisbookcametolightasanextensionofIntegratedBusinessPlanning,
especiallyaneedonhowtoleveragestatisticalforecasting,demandsensing,howto
set up competencies and demand planning organization, how to leverage technol-
ogy to improve forecasting. My colleagues Robert Kepczynski and Ganesh
Sankaran have encouraged me to shape this book and to manage the overall
“project.” As part of this book, we elaborate on standard and custom methods to
dealwithforecasting,outlierdetectionandcorrection,productseasonality,product
intermittent. We exemplify technology part of the solution with SAP IBP cloud
software enabled on HANA. We did it that way to make it tangible, meaning we
talk about organization, skills, processes as well as about what you can do in
SAP IBP to make it happen.
ThankstoRobertandGanesh’sprovenexperienceinsupplychainprocessesand
technology,wewereabletodefinewhatthisbookshouldfocuson,howtostructure
it and how to link it to previous Springer publications about Integrated Business
Planning. We can say that for S&OP process step called “demand review” this
publicationisanextensionof(Kepczynskietal.,2018)“ImplementingIntegrated
Business Planning” by Springer 2018.
Sincedayone,thisprojecthasbeensupportedandpushed tobirthbytheentire
initial team, with a special contribution provided by Alessandro Chiaraviglio. He
envisioned a new practice toward intermittent demand forecasting, and during the
early stages of the project we discussed and managed to on board his competence
and ideas to enhance and enlarge the coverage of the book.
From that moment on, the full team has exchanged ideas and opinions about
SAP IBP enhancements and SAP IBP out-of-the-box solutions to make the book
useful in hands of demand planning, statistical forecasting and demand sensing
practitioners.
In addition, the final book would not have reached the finish line without our
colleague who provided a great support: Alexei Koifman.
vii
viii Preface
“Integrated Business Planning improves forecasting” brings to you how to:
– Connect short-term with mid- and long-term forecasting
– Prepare organizational structure and capabilities to leverage advanced
techniques
– Understand the value of SAP IBP statistical forecasting
– Leverage IBP functionalities to configure custom methods for intermittent
demand forecasting
– Leverage IBP functionalities to configure custom methods for detecting and
correcting outliers for seasonal products
– Understand the value and algorithms of SAP IBP demand sensing
– Define forecasting value-added measurements and how to improve forecasting
with the use of Six Sigma methodology proven on real projects.
Enjoy reading
Zurich, Switzerland Federico Sasso
Rothrist, Switzerland Robert Kepczynski
Reference
Kepczynski,R.,Jandhyala,R.,Sankaran,G.,&Dimofte,A.(2018).Integratedbusinessplanning—
Howtointegrateplanningprocesses,organizationalstructuresandcapabilities,andleverage
sapIBPtechnology.Switzerland:Springer.
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 The What and Why of Forecasting . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Planning Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Scope of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Statistical Forecasting Process Framework . . . . . . . . . . . . . . . . . . 11
1.5 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.6 Key Challenges of Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6.1 Seasonality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6.2 Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.6.3 Intermittent Demand. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.6.4 Human Judgment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.6.5 Effectiveness and Efficiency . . . . . . . . . . . . . . . . . . . . . . 26
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2 Building Demand Planning Organization and Competencies . . . . . . 35
2.1 Organizational Structures, Placement . . . . . . . . . . . . . . . . . . . . . . 36
2.1.1 Some Insights on Organizational Structures and Roles . . . 36
2.1.2 Some Insights on Organizational Placement. . . . . . . . . . . 45
2.2 Responsibilities, Interactions, Size of a Team. . . . . . . . . . . . . . . . 47
2.2.1 Responsibilities (Incl. Job Profiles) and Impact . . . . . . . . 49
2.2.2 Insights on Role Comparison and Size of the Team. . . . . 63
2.3 Concluding Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3 Efficient and Effective Usage of Out-of-the-Box Statistical
Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.1 Model Class Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.2 Gathering, Analyzing and Cleansing Data . . . . . . . . . . . . . . . . . . 72
3.2.1 SAP Use Case: Outlier Detection Variance Method . . . . . 80
3.2.2 SAP Use Case: Outlier Detection Interquartile Range. . . . 84
ix
x Contents
3.3 Forecast Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.3.1 Averages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
3.3.2 SAP Use Case: Moving Average, Moving Weighted
Average. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.3.3 Single Exponential Smoothing (SES) . . . . . . . . . . . . . . . 97
3.3.4 SAP Use Case: SES. . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.3.5 Double Exponential Smoothing (DES) . . . . . . . . . . . . . . 101
3.3.6 SAP Use Case: DES . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.3.7 Triple Exponential Smoothing (TES). . . . . . . . . . . . . . . . 104
3.3.8 SAP Use Case: TES with Solver Parameters . . . . . . . . . . 108
3.3.9 SAP Use Case: Statistical Forecasting on Aggregated
and Product Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
3.3.10 SAP Use Case: Automated Exponential Smoothing
Within SES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
3.3.11 SAP Use Case: Automated Exponential Smoothing
Within DES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
3.3.12 SAP Use Case: Automated Exponential Smoothing
Within TES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3.3.13 SAPUseCase:AutomatedExponentialSmoothingwith
Best Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
3.3.14 Adaptive Response Rate Single Exponential
Smoothing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
3.3.15 SAP Use Case: Adaptive Response Rate Single
Exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . 126
3.3.16 Brown’s Linear Exponential Smoothing . . . . . . . . . . . . . 127
3.3.17 SAP Use Case: Brown’s Linear Single Exponential
Smoothing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
3.3.18 Croston Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
3.3.19 SAP Use Case: Croston Method . . . . . . . . . . . . . . . . . . . 133
3.3.20 Multiple Linear Regression Model . . . . . . . . . . . . . . . . . 134
3.3.21 SAP Use Case: Multiple Linear Regression. . . . . . . . . . . 141
3.3.22 Auto-ARIMA/SARIMA . . . . . . . . . . . . . . . . . . . . . . . . . 146
3.3.23 SAP Use Case: ARIMA/SARIMA . . . . . . . . . . . . . . . . . 149
3.3.24 Automatic Model Selection. . . . . . . . . . . . . . . . . . . . . . . 157
3.3.25 SAP Use Case: Best Fit . . . . . . . . . . . . . . . . . . . . . . . . . 157
3.3.26 SAP Use Case: Weighted Combined Forecast
from SES, DES, TES. . . . . . . . . . . . . . . . . . . . . . . . . . . 160
3.4 Validate Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
3.5 Make Predictions and Observations . . . . . . . . . . . . . . . . . . . . . . . 173
3.6 Evaluate a Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
3.6.1 SAP Use Case: Tracking Signal . . . . . . . . . . . . . . . . . . . 175
3.6.2 SAP Use Case: Forecast with Prediction Interval . . . . . . . 177