Transforming big data into supply chain analytics by Alan Milliken.

1. Transferring Big Data into
Supply Chain Analytics
Alan Milliken CFPIM CSCP CPF
Sr. Manager – Supply Chain Capability Development
BASF

2. Transforming Big Data into
Supply Chain Analytics
SAPICS Conference 2015
Alan L. Milliken CFPIM CSCP CPF CSOP
June, 2015

3. Term used to refer to the mass of
information being generated today.
In 2012, it was estimated that 2.5 exabytes
of data were created each day.
(1 exabyte = 1B gigabytes)
The amount of data available is expected
to double every 3 years.
Technology increases data availability,
enables communication of data and
provides the ability to analyze the
information.
What is “Big Data?”
3

4. What is/are Analytics?
The discipline of using math, statistics and models to extract
knowledge from data. The knowledge gained is used to
improve decision making and performance.
Businesses use analytics to describe,
predict and improve performance.
4

5. What are the three major
supply objectives which
analytics help to improve?
5
In a 2012 *SAS-MIT survey with 2,500 respondents from over
20 industries, 67% indicated they are using analytics
to improve overall performance.
?
*Source: “Reimagining the Possible with Data Analytics.” MIT Sloan Management Review,
Spring, 2013 in collaboration with SAS.

6. Gathering and Structuring Data
for Analysis
6
Take care to get what you like,
or you will be forced to
like what you get! «
»
George Bernard Shaw

7. Sample of characteristics used to report
sales and forecast performance:
Material dimensions – SKU, Product, Product Group, etc.
Customer dimensions – Sold-To, Payer, Customer Group, etc.
Accounting dimensions – BU, SBU, Profit Center, etc.
Geographical dimensions – country, region, sub-region, state, customer, etc.
Involve key users and process experts in
definition phase.
Determine
What Characteristics are Needed?
7

8. Determine What Key Figures
are Required?
Note:
All characteristics and key figures are available
for analysis.
Sample of key figures used
to generate a forecast
Sample of key figures used
to report performance.
8

9. Note:
If the sum of F-A = 0
there is no BIAS.
Forecast BIAS%:
Measurement of a continuous under
or over estimation of actual sales
Develop & Program
Key Figure Definitions
Formula
n = # of months = 6
BIAS = -18%
Sales
FCST
Jan 04 Feb 04 Mrz 04 Apr 04 Mai 04 Jun 04
1.000.000
900.000
800.000
700.000
600.000
500.000
400.000
300.000
200.000
100.000
0
9

10. Sample Content:
absolute values & quantities
Sales by BU-Product-Region
Forecast by SKU-Customer
region, sub region, country, company, plant
Organization
OD
BU
SBU
Main
Group
α-code Geography
Material
Company hierachy
Supply
Chain
hierachy
SKU,
Product,
etc.
Material
hierachy
Determine Data Structure
Note: Multi-dimensional data cube provides
key figures to support data mining.
10

11. Central ActivitiesCopy
North America
South America
Europe
Asia Pacific
KPI calculation Data storage
Globally available
Extractions
(raw)
Calculation of KPIs1
Store the data in
aggregation levels to provide
fast access
2
Daily extraction
in the regions
Gather and Process Data
11

12. Data Mining and Sample Reports
Data mining: the process
of extracting information from
a data set and transforming
it into a usable structure,
supports analytics.
Data
Business
under-
standing
Data
under-
standing
Data
preparation
Modeling
Deve-
lopment
Evaluation
12

13. What we want!
Data Mining – the process of extracting
usable information from a dataset
“Big Data” Mine
13

14. Overview reports: Stay on a high level and provide a quick overview
Overview
► Required for most of the report executions
► Only a limited number of characteristics Short response time
► Might be relevant for every user
Specialized
► Required for more special reporting needs
► Cover additional characteristics to allow specialized reporting
► Might only be relevant for certain users
Analysis reports
► Required for special purpose analysis
► High number of characteristics Longer response times
► Detailed information, going down to the lowest level of the
documents
Sample of Reporting Structure
14

15. (1) Select Standard Report
(A) or Custom Query (B):
(2) Enter Query Characteristics Desired
A
B
Sample Standard Report –
User Specific Characteristics
15

16. Sample Standard Report –
User Specific Characteristics
Forecast Accuracy on Customer Level:
Note: Results are in excel format for easy
analysis and charts.
Product area Country Grouped Article Customer Group Calendar Year/Month 04.2009 05.2009 06.2009 07.2009 Over all Result
EMN DE Germany Stat FCA (Art-Cust) 100 100 100 100 100
Sales FCA (Art-Cust) 100 100 100 100 100
Stat FCA (Art-Cust) 100 100 100 100 100
Sales FCA (Art-Cust) 100 100 100 100 100
Stat FCA (Art-Cust) 0 100 0 0 67
Sales FCA (Art-Cust) 0 100 0 0 67
Stat FCA (Art-Cust) 0 100 0 0 67
Sales FCA (Art-Cust) 0 100 0 0 67
Stat FCA (Art-Cust) 0 100 0 0 67
Sales FCA (Art-Cust) 0 100 0 0 67
ES Spain Stat FCA (Art-Cust) 100 100 0 0 0
Sales FCA (Art-Cust) 100 100 0 0 0
Stat FCA (Art-Cust) 100 100 0 0 0
Sales FCA (Art-Cust) 100 100 0 0 0
Stat FCA (Art-Cust) 100 100 0 0 0
Sales FCA (Art-Cust) 100 100 0 0 0
US USA Stat FCA (Art-Cust) 100 100 0 0 0
Sales FCA (Art-Cust) 100 100 0 0 0
Calendar Year/Month 04.2009_07.2009
APO Planning Version 000 – Active Version
Grouped Article
Customer Group
Produkt area
16

17. Analytics – Education & Training
17

18. Descriptive Analytics
Used to measure performance, report what
happened, why it happened and plan for
improvement.
18

19. Standard Analytical Reports: Decision Cockpit:
BASF Decision Cockpit – S&OP Demand Review
All key elements of the Analytical Reports
can be displayed in the Decision Cockpit
to enable management review..
S&OP Level Demand Review Dashboard
Analytics provide management with information
to make better decisions.
Past Performance
Forecast Accuracy for S&OP
Family XYZ: 69% (Target
range: 50% – 70%)
Issues
Customer Demand in
France decreases about 7%
Price acceptance in Spain will
decrease
Planning Information Decisions
Adjust Demand Plan for S&OP
Family XYZ
Decrease sales price target for
S&OP Family XYZ and adjust
budget quantities for Q4 2012
Time Series
Demand Review (ETA based)
1a, b, c. KPI: FCA and BIAS (3 reports for 3 level)
2. Forecast Qty (incl. Budget, Target)
19

20. Business Level –
Forecast Accuracy KPI Report
„Dashboards“
to simplify the
presentation of KPI‘s
are best.
All KPI‘s should
include „Targets“
and a way to identify
those areas needing
immediate attention.
Red-Yellow-Green
light functionality is
quite popular
universally
SBU 2011 YE Nov-12 2012 YTD Target
57% 65% 65% 70%
64% 62% 66% 70%
88% n/a 88% 70%
Total 76% 63% 73% 70%
Forecast Accuracy:
Measures ability to forecast at CM + 2 lag
SBU 2011 YE Nov-12 2012 YTD Target
6% 3% 9% 10%
1% 8% 6% 10%
4% n/a 1% 10%
Total 3% 6% 5% 10%
Bias:
Measures direction of forecast error at CM + 2 lag
SBU 2011 YE Nov-12 2012 YTD Target
36% 38% 42% 50%
45% 40% 41% 50%
55% n/a 59% 50%
Total 39% 39% 42% 50%
Hit Miss:
Measures % of portfolio accurately forecasted
Forecast Accuracy & BIAS are global KPI‘s at BASF.
Hit/Miss based on tolerances are also popular.
EM – 2012 Forecast Accuracy
EM – 2012 Bias
EM – 2012 Hit Miss
20

21. Sample Diagnostic Analytic
Provides exceptions by individual responsibility:
Exceptions:
Sales but no forecast
Forecast but no sales
Difference in sales and forecast > 50%
21
Customer Marketing 03-2010 03-2010 03-2010 03-2010
SBU Article # Article Description Group Manager Actual Sales Statistical FC Sales FC Fin. Reg. Market. FC
ZAC 79345968 Dispersion Bulk ABC 001 J. Smith 0.00 KG 6,803.89 KG 6,803.89 KG 6,803.89 KG
ZAC Result 0.00 KG 6,803.89 KG 6,803.89 KG 6,803.89 KG
ZAC 78459312 Dispersion 190 KG ABC 002 J. Smith 0.00 KG 1,416.00 KG 1,416.00 KG 1,416.00 KG
ZAC ABC 003 J. Smith 0.00 KG 123.43 KG 123.43 KG 123.43 KG
ZAC ABC 004 J. Smith 1,520.00 KG 740.57 KG 740.57 KG 740.57 KG
ZAC ABC 005 J. Smith 190.00 KG 0.00 KG 0.00 KG 0.00 KG
ZAC ABC 006 J. Smith 5,320.00 KG 0.00 KG 0.00 KG 0.00 KG
ZAC Result 7,030.00 KG 2,280.00 KG 2,280.00 KG 2,280.00 KG
ZAC 74698213 Dispersion 25 KG ABC 007 J. Smith 0.00 KG 15.54 KG 15.54 KG 0.62 KG
ZAC ABC 008 J. Smith 0.00 KG 15.54 KG 15.54 KG 1.54 KG
ZAC ABC 009 J. Smith 0.00 KG 21.75 KG 21.75 KG 0.00 KG
ZAC ABC 010 J. Smith 0.00 KG 31.07 KG 31.07 KG 2.78 KG
ZAC ABC 011 J. Smith 0.00 KG 6.22 KG 6.22 KG 0.62 KG
ZAC Result 0.00 KG 90.11 KG 90.11 KG 5.56 KG

22. Sample Exception Report
Analytics should include financial impact when
feasible.
Top 20 Forecast Accuracy Drivers
Region Article Number Article Name ABC Forecast Accuracy Error (kgs) Error ($)
N-NAFTA 5089 A 0% 17775 $159,442
N-NAFTA 5074 A 50% 17584 $193,424
N-NAFTA 5005 A 56% -16000 $199,040
N-NAFTA 5254 A 0% -12350 $469,300
MX 5004 A 34% -11480 $98,269
N-NAFTA 5000 A 0% 11280 $259,891
N-NAFTA 5013 A 50% -8300 $0
N-NAFTA 5508 A 60% -8000 $136,000
MX 5508 A 60% -5600 $94,360
N-NAFTA 5508 A 54% 5050 $99,788
N-NAFTA 5239 B 0% 5000 $61,000
N-NAFTA 5253 A 0% 4500 $832,500
N-NAFTA 5676 A 47% -4460 $38,178
N-NAFTA 5001 C 0% 3750 $46,125
N-NAFTA 5007 C 0% -3500 $0
N-NAFTA 5000 B 0% 3200 $224,000
N-NAFTA 5002 A 0% 3000 $43,590
N-NAFTA 5006 B 39% -2600 $42,172
N-NAFTA 5001 A 0% 2400 $33,528
MX 5002 B 28% -1970 $38,474
Subtotal (721) $837,495
22

23. Forecast Accuracy Measures are
Descriptive
Descriptive Analytics provide feedback
to improve Predictive Analytics.
MPE = ∑ (Actual – FCST) x100 MAPE= ∑ │(Actual – FCST) x100
Actual Actual
No. Of Observations No. Of Observations
Period Sales (Units) FCST (Units) Error % Error Abs. % Error
1 999 1319 -320 -32.03 32.03%
2 1178 1141 37 3.14% 3.14%
3 1247 1168 79 6.36% 6.36%
4 1469 1141 328 22.31% 22.31%
5 1074 1298 -224 -20.86% 20.86%
6 1568 1263 305 19.43% 19.43%
7 1159 1370 -211 -18.23% 18.23%
8 1383 1267 116 8.39% 8.39%
9 1552 1370 182 11.73% 11.73%
10 1174 1365 -191 -16.24% 16.24%
Sum -16.01% 158.72%
MPE -1.60%
MAPE= 15.87%
│
23

24. COV = Std. Dev’n in Period Demand/Average Demand
Using Variance to Test for Forecastability
Descriptive Analytics support key decisions
in supply chain management.
29 of 46 products
are forecastable.
Over 90% of volume
is forecastable.
37% of products represent
6% of sales volume. Check
profitability and place on
make-to-order status.
Product Group A
37%
26%
37%
67%
27%
6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
0-0.50 0.51-1.0 >1.0
% of Total Products
% of Total Sales
COV
Range
24

25. Predictive Analytics
The analysis of current and/or
historical data to make
predictions about the future.
25

26. Sample Forecasting Techniques
(Predictive)
Descriptive Analytics provide feedback
to improve Predictive Analytics.
Period
Actual
Sales ($M)
3-Period
Sum
3-Period
Avg.
(FCST)
1 356
2 372
3 374
4 380 1102 367
5 365 1126 375
6 373 1119 373
7 367 1118 373
8 373 1105 368
9 374 1113 371
10 380 1114 371
11 368 1127 376
12 371 1122 374
Avg. 371
σ 6.6
400
390
380
370
360
350
340
1 2 4 6 83 5 7 9
Units
Period
Sales
FCST
26

27. Statistical
FC
Supply Planning:
Feasibility check
Production quantities
–1 CD
Sales FC
Regional
Marketing
FC
Demand
Validation
Meeting
Qualitative input
may be supported
with quantitative
analyses.
Predictive Analytics May Include
Qualitative Inputs
1–2 CD 3–6 CD 7–9 CD 10–12 CD 13 CD 17 CD
Unit M 09/ 2009 M 10/ 2009 M 11/ 2009 M 12/ 2009 M 01/2010
Statistical FC KG 4,428,853 4,307,289 4,307,289 4,307,289 4,307,289
Final Sales FC KG 4,089,409 3,833,127 2,821,739 3,021,739 3,387,431
Regional
Marketing FC
KG
Final Regional
Marketing FC
KG 4,093,316 3,836,253 2,832,553 3,032,553 3,396,082
Constraint FC KG 3,922,763 3,832,553 2,832,553 3,032,553 3,396,082
27

28. Advanced Predictive Analytic
Leading Indicators include:
Average Weekly Hours Manufacturing
Weekly Claims for Unemployment
New Orders – Manufacturing
Housing Starts
Stock Prices (500 stocks)
Money Supply & Interest Rate
Actual Sales:
Quantities & Dates at Customer
Ship-to Level
3-months EWS indicator
Model includes exponential smoothing
and multi-regression analysis.
120
110
100
90
80
70
60
3- months Index (100 = Avg Actuals 2012)
Jan
10
Jul
10
Jul
11
Jul
12
Jul
13
Jan
11
Jan
12
Jan
13
Jan
14
28

29. Predicting Forecastability
Statistical Forecast
Sales: Review
Marketing: Review
Statistical Forecast
Sales: Proactive Input
Marketing: Review
Statistical Forecast
Sales: Proactive Input only for selective
(large) customers ( 80/20 Rule)
Marketing: Review selective Customers
Only statistical forecast
Sales: No routine action required
Marketing: No routine action required
Not possible to forecast
Switch to MTO or ex-Stock Sales
No routine action by any party
Forecasting RolesDetailed Forecast (Article-Customer
A B C
X
Y
Z
Volume
Variation
of
demand
29

30. Using Analytics in Optimization
of Processes
This application requires advanced math and
statistics enabled by advanced software.
The goal:
Maximize Profits
30

31. Inventory
Develop the Business Objective
Maximize profits while balancing demand & supply within capacity
constraints and inventory limits:
Production
capacity
Variable
demand
Contract
demand
Goal: Maximize EBIT
31

32. Sales targets
for regions
(for Reg. Bus. Mgmt.)
Production plan
(for Production)
External Purchases
Raw material demand
Distribution plan
(for SCM)
Inventory plan
(for information)
Demand forecasts
(from marketing)
Capacities
(from production)
Inventories
(from BW reports)
BOM‘s, Routings
(from production)
Value parameters
(from Controlling
and BW)
Optimization
Optimization: Input-Output Information
Profit optimized
32

33. Sample of Data and Process Structure
Rules:
Sales order requests must be within plan.
Sales order must be provided 4 weeks before expected shipment.
Unplanned sales orders are reviewed by marketing & operations management.
Data & Process Information:
Customer segmentation analysis and results.
Total delivered cost from each plant to customer.
On-line, real-time check of requests versus optimized plan.
Exception report monitoring of planned vs. actual sales.
33

34. More is better
at this point.
What information
is needed?
Multi-dimensional: e.g. sale
by product & customer.
Characteristics (e.g.
Business Unit) and Key
Figures (e.g. Forecast in
Units)
Enable identification of
trends, patterns,
exceptions, etc.. .
Generate KPI’s and
Dashboards.
Summary
1
Gather & Store Data
2
Develop Target Data Implement Data Structure
3
Transform Data
to Information
Transform Info
to Knowledge
Take action
to improve
4 5 6
34

35. IBF Events – Analytics
Please go to:
IBF.org for more details.
!
December 17 – 19, 2014
IBF‘s Demand Planning and Forecasting
Boot Camp w/ Predictive Analytics / Big
Data Workshop New York City, NY
Atlanta, GA
April 25, 2015 Predictive Business
Analytics, Forecasting and Planning
Conference April 23 – 24, 2015
35

36. 36
alan.milliken@basf.com
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37. 37