transforming big data into supply chain analytics

Transferring Big Data into Supply Chain Analytics

Alan Milliken CFPIM CSCP CPFSr. Manager – Supply Chain Capability DevelopmentBASF

Transforming Big Data into Supply Chain AnalyticsSAPICS Conference 2015

Alan L. Milliken CFPIM CSCP CPF CSOPJune, 2015

� Term used to refer to the mass of information being generated today.

� In 2012, it was estimated that 2.5 exabytesof 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?”

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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.

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What are the three major supply objectives which analytics help to improve?

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In a 2012 *SAS-MIT survey with 2,500 respondents from over 20 industries, 67% indicated they are using analytics to improve overall performance.

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*Source: “Reimagining the Possible with Data Analytics.” MIT Sloan Management Review, Spring, 2013 in collaboration with SAS.

Gathering and Structuring Data for Analysis

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Take care to get what you like, or you will be forced to

like what you get! «

»

George Bernard Shaw

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?

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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.

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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

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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

SupplyChain

hierachy

SKU, Product, etc.

Material hierachy

Determine Data Structure

Note: Multi-dimensional data cube provides key figures to support data mining.

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Central Activities Copy

� North America

� South America

� Europe

� Asia Pacific

KPI calculation Data storage

Globally available

Extractions(raw)

Calculation of KPIs1

Store the data inaggregation levels to providefast access

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Daily extractionin the regions

Gather and Process Data

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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

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What we want!

Data Mining – the process of extracting usable information from a dataset

“Big Data” Mine

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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 thedocuments

Sample of Reporting Structure

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(1) Select Standard Report (A) or Custom Query (B):

(2) Enter Query Characteristics Desired

A

B

Sample Standard Report –User Specific Characteristics

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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


Stat FCA (Art-Cust) 0 100 0 0 67






ES Spain Stat FCA (Art-Cust) 100 100 0 0 0






US USA Stat 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

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Analytics – Education & Training

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Descriptive Analytics

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

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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 PerformanceForecast 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)

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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 Target57% 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


1% 8% 6% 10%

4% n/a 1% 10%

Total 3% 6% 5% 10%

Bias:Measures direction of forecast error at CM + 2 lag


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

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Sample Diagnostic AnalyticProvides exceptions by individual responsibility:

Exceptions: � Sales but no forecast

� Forecast but no sales

� Difference in sales and forecast > 50%

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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 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 Result 0.00 KG 90.11 KG 90.11 KG 5.56 KG

Sample Exception Report

Analytics should include financial impact when feasible.

Top 20 Forecast Accuracy DriversRegion 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

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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%

│

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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

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Predictive Analytics

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

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Sample Forecasting Techniques (Predictive)

Descriptive Analytics provide feedback to improve Predictive Analytics.

PeriodActualSales ($M)

3-Period Sum

3-PeriodAvg. (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

3401 2 4 6 83 5 7 9

Units

Period

Sales

FCST

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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

RegionalMarketing 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

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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)

Jan10

Jul10

Jul11

Jul12

Jul13

Jan11

Jan12

Jan13

Jan14

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Predicting Forecastability

Statistical ForecastSales: ReviewMarketing: Review

Statistical ForecastSales: Proactive InputMarketing: Review

Statistical ForecastSales: Proactive Input only for selective(large) customers (� 80/20 Rule)Marketing: Review selective Customers

Only statistical forecastSales: No routine action requiredMarketing: No routine action required

Not possible to forecastSwitch to MTO or ex-Stock SalesNo routine action by any party

Forecasting RolesDetailed Forecast (Article-Customer

A B C

X

Y

Z

Volume

Variation of demand

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Using Analytics in Optimization of Processes

This application requires advanced math and statistics enabled by advanced software.

The goal: Maximize Profits

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Inventory

Develop the Business Objective

Maximize profits while balancing demand & supply within capacity constraints and inventory limits:

Productioncapacity

Variable demand

Contract demand

Goal: Maximize EBIT

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Sales targetsfor 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

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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.

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� 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

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IBF Events – Analytics

Please go to: IBF.org for more details. !

December 17 – 19, 2014IBF‘s Demand Planning and Forecasting Boot Camp w/ Predictive Analytics / Big Data Workshop New York City, NY

Atlanta, GAApril 25, 2015 Predictive Business Analytics, Forecasting and Planning Conference April 23 – 24, 2015

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[email protected] ?

transforming big data into supply chain analytics

Business

data analytics

data available

process data

big data

communication of data

data set

structuring data

exabytes of data