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Steven Crane, CSCP, is Director Strategic Supply Chain Management NAFTA, Wacker Chemie AG, where he is responsible for strategic supply chain management for the Wacker Polymers Division. This includes demand planning, supply planning, and sales and operations planning. Currently, Crane is working on the implementation of APO and S&OP for several businesses in the division.

A Roadmap to World Class Forecasting Accuracy

Six Keys to Improving Forecast Accuracy

Stephen P. Crane, CSCPWacker Chemical CorporationDirector Strategic Supply Chain Management

WACKER Company OverviewWhy Forecast?Forecasting ChallengesSix Keys to Improving Forecast Accuracy– Process, People, & Tools– Statistical Forecasting– Forecasting Segmentation– Data Aggregation– Sales Adjustments to Statistical Forecast– Measurement & Exception Reporting

Forecasting Accuracy ResultsConclusions

Content


Forecasting Accuracy ResultsConclusion

Content

Wacker Chemie AG

WACKER Group (2007)

Over 90 Years Of Success

• Founded in 1914 by Dr. Alexander Wacker • Headquartered in Munich, Germany

• Sales: €3.78 billion

• EBITDA: €1.00 billion

• Net income: €422 million

• Net cash flow: €644 million

• R&D: €153 million

• Capital expenditures: €699 million

• Employees: 15,044

We Are Committed To Benchmark-Quality Products Designed For Our Focus Industries

Industries• Adhesives• Automotive and transport• Construction chemicals • Gumbase• Industrial coatings and printing inks• Paper and ceramics

Products: • Polymer powders and dispersions for the

construction industry• Polyvinyl acetate solid resins, polyvinyl

alcohol solutions, polyvinyl butyral and vinyl chloride terpolymers



Content

Forecast Types

Most companies use three types of forecasts:

Sales or channel forecast

Corporate planning forecasts

Supplier forecasts

These forecasts are very different in their use, frequency, and definition

Need consistent demand signal across these three forecasting processes, but most companies don’t know how to align them

Why Forecast?

The forecast drives supply planning, production planning, inventory planning, raw material planning, capital planning, and financial forecasting

Companies that are best at demand forecasting average:

– 15% less inventory

– 17% higher perfect order fulfillment

– 35% shorter cash-to-cash cycle times

– 1/10 the stockouts of their peers

1% improvement in forecast accuracy can yield 2% improvement in perfect order fulfillment

3% increase in forecast accuracy increases profit margin 2%

Source: AMR Research 2008

Inventory Level Vs. Forecast Accuracy

Source: Aberdeen Group 2008

0

20

40

60

80

100

120

20% 30% 40% 50% 60% 70% 80% 90% 100%

Forecast Accuracy at SKU Location

Inve

ntor

y D

ays

of S

ale

World class forecasting accuracy performance

– 95% currency by product line

– 90% by product family

– 85% product mix level

What Is A Good Forecast?

Source: Buker Management Consulting

Goal



Content

Management Is Often the Problem

Forecasting Challenges

Typical forecasting process involves historical demand dataloaded into a database using software to generate statistical forecastsStatistical software is rarely allowed to operate on its ownManagement usually overrides the statistical forecast before agreeing to final forecastForecasting is often a difficult and thanklessendeavor with high inaccuraciesCompanies react to inaccuracies with investments in technology, but do not guarantee any better forecastsThere are often fundamental issues that need to be addressed before improvements can be achieved

Forecasting Process

Forecasting Challenges

When businesses know their sales for next week, next month, and next year, they only invest in the facilities, equipment, materials, and staffing they need

There are huge opportunities to minimize costs and maximize profits if we know what tomorrow will bring –but we don’t.

Therefore we forecast!



Content

World Class Forecasting Accuracy Requires Making The Right Decisions

Step 1: Defining the Process, People, & Tools

Six Keys To Improving Forecast Accuracy

Step 1: Defining the Process, People & Tools

ProcessForecast accuracy improvement occurs with the proper blending of process, people, and IT tools

Overemphasis on any one leads to an imbalance that can defeat the desired result

The process should be defined first, then followed by roles and responsibilities, and then IT applications

The more people that touch a forecast, the greater the bias and the greater the forecast error

Sales & Operations Planning ProcessSupply Networking Planning

WD 1- 6

Submit Supply Plan with

Documented Options

Approve Supply Plan

Sequential Business Unit

Planning[WD 1-5]

Finalize & Approve

Supply Plan [WD 6]

Upload WD 1 Inventory

Develop Updated

Supply Plan Proposal

Review Supply Shortage &

Capacity Overload Alerts

Resolve Supply Alerts

Adjust Target Stock Levels

Fix SNP Planned orders

Enter Adjusted Demand

Supply Planning WD 13 -End

Adjust Supply Planning

Constraints

Supply Network Planning Preparation Phase

Analyze Draft Supply /

Capacity Plan

Update Supply Change

Summary

Submit Off System Demand Figures

Update Loc Shift Table For Future Source

Changes

Release FC to R3 For MRP

Update Master Data & Upload

Inventory

Refresh Inactive Version /

Release DP to SNP

Review Supply Planning Metrics

Preferred Sources

Assigned to Demand

Forecasting & Demand Planning WD 0 -16

Add New Business

Entries in R3

Upload New Sales History &

Business Combos

Apply Future Demand Changes

Create Statistical Forecast

DP Passed to CO for

Financial Forecast

Demand Planning Data Preparation Phase [WD 0-7]

Review Historical Sales

Data

Create Unconstrained Demand Plan [WD 8-16]

Review Final Sales Manager

Figures

Review Demand Metrics

Review FC Adjustments with Sales Managers

BT/BU Review & Approve

Unconstrained Demand Plan

Update Demand Change

Summary

Apply Planning Type

Assignments

Apply Historical Sales Data

AdjustmentsAPO Data

Passed to BW

Publish Unconstrained Demand Plan

Approve Supply Chain Plans WD 7-8

Agree & Communicate

Approved Plans

Communicate Implications to

Financial & Sales Plans

Partnership Meeting[WD 7]

Executive S&OP [WD 8]

Review Action Items from Last

Month

Review Revenue

Projections

Review Unconstrained Demand Plan

Exceptions

Review Supply Options & Cost

Projections

Review Performance

Metrics

Summarize Supply Chain

Plans

Review Financial Plan

Key Business Issues &

Resolution

Review Supply Chain Plans

Review Performance

Metrics

Review Action Items from Last

Month

Automated Jobs

PeoplePeople are critical to the forecasting process and how it’s used within the organization

They need to understand how their role fits with the work process and how to make improvements

Position descriptions need to be defined with clear responsibilities that are accepted by the organization

Full-time positions are essential

Limit number of people making decisions about final forecast


IT Tools

Forecasting software is sometimes “sold”as the answer to forecasting issues

Software does not solve forecasting problems. Processes and people solve problems

Implementing forecasting software should not be considered an IT project, but a business process improvement project

Forecasting applications can eliminate much of the manual work associated with forecasting if configured properly


Step 2: Establish Statistical Forecasting Capability

Six Keys To Improving Forecast Accuracy

Many supply chains are too complex to manually generate forecasts for all products and customers

Forecasting engines are widely used to improve forecast accuracy by generating statistical forecasts

Statistical forecasting uses sales history to predict the future by identifying trends and patterns within the data to develop a forecast

Need to decide at what level the forecasting should be done

– Product family– Individual product– Product/customer – Product/customer ship-to– Plant/product/customer ship-to

Step 2: Statistical Forecasting Capability

Need to decide how often to forecast– Quarterly– Monthly– Weekly– Daily

Determine how much sales history is required for meaningful statistical forecast (minimum 2 years)

Sales history master data must be correct especially when migrating from legacy systems. Very difficult to do correctly

Analyze the forecast error associated with using available forecasting algorithms to optimize accuracy of forecast


Typical statistical forecasting methods include

– Multiple regression analysis– Trend analysis

– Seasonal

– Simple moving average

– Weighted moving average– Exponential smoothing

– Automatic selection Recommended


It is critical to make appropriate sales history adjustments to generate an accurate statistical forecast

Adjustments Necessary to History

– Data errors– Lost product volume– Lost customers– One time customer outages– Discontinued products– Non-optimal sourcing


Step 3: Forecasting Segmentation (80/20 Analysis)

Exceptions Collaboration

Business IntelligenceData Aggregation


Step 3: Forecasting Segmentation - 80/20 Analysis

It is crucial to distinguish the “high-value” items for special attention while automating the “not-as-valuable” items

COV (Coefficient of Variation) = STD Deviation/Ave. Demand

Notes

High

Low

StatisticalForecastability(measured by

1/COV)

HighSales Volume/Impact

Low

Manage by Exception using Exception Reports

Collaborate with Customer (if possible)

High Impact ItemsNon-High Impact Items

Use Data Aggregation in Statistical Model for all

Non-HI Items

Gather Business Intelligence for all HI

items

~ 80% Total Volume

Step 3: Forecasting Segmentation Example

Step 4: Data Aggregation


SAP APO 4.1 used for forecasting and demand planning

Forecasting done at product/customer ship-to level

Thousands of unique customer / product combinations exist to manage

Too much data for Planners to review monthly

Sales history for many combinations (~80%) was sporadicand difficult to forecast, i.e., high forecasting errors

So how do you get a good forecast for sporadic combinations?

– Forecast at a more aggregate level


Getting a good forecast for sporadic combinations

Compile non-high impact items from segmentation analysis

Program forecast model to aggregate non-high impact items to logical planning source (plant, warehouse, prod. unit, etc.)

Generate statistical forecast at aggregate planning source

Disaggregate statistical forecast to lowest forecast level in model based on past history (plant/product/customer ship-to)

Aggregation to the plant/product level reduced number of combinations to review by 80%

Aggregation produces a more accurate forecast for sporadic items allowing more time to focus on high impact items


Step 5: Sales Adjustments to Statistical Forecast


Accurate forecasting is not just getting a forecast from the customer. The customer isn’t always right!

Source: Agere Systems Inc.; Operations Management Roundtable Research


Since statistical forecasting is based on previous sales, it is necessary to account for factors that can affect future sales

Seasonality of the businessState of the economyCompetition and market positionProduct trends

It’s also important to ask customers the right sales questions to validate forecast assumptions

Where does this project rank today?When are you looking to make a purchasing decision?When will you be implementing?What would you like to see happen as a next step?


Adjustments Made to ForecastPre-buyingPromotional impactsUpside and downside volumesCustomer plants expected to be downNew businessNew customers

Adjustments Made to HistoryData errors Lost product volumeOne time customer outagesPackaging changesDiscontinued products

Decide where adjustments should be made


Step 6: Measurement & Exception Reporting



If you can not effectively measure forecasting performance, you can not identify whether changes to the process are improving forecast accuracy

Effective measures should evaluate accuracy at different levels of aggregation (plant, warehouse, sales region, etc.)

Measuring and reporting forecast accuracy helps to build confidence in the forecasting process

Once people realize that sources of error are being eliminated, the organization will begin to use the forecast to drive business operations decisions

A variety of analyses and exception-based measurements are needed to understand where the biggest sources of forecast error are– Identifying High Impact exceptions for Sales review

– Accuracy of adjustments made to statistical forecast

– Identifying non-High Impact exceptions

– Current month variances (Forecast – Actual)

– Forecast but no sales

– Sales but no forecast

– No sales in last 12 months


Identify High Impact “Exceptions” to focus forecast review

– Decreasing or increasing sales rates

– Aggressive statistical forecast based on historical run rates

High Impact Exceptions for Sales Review

Sold To Loc Product MAY 2006 JUN 2006 JUL 2006 JUL 2006 AUG 2006 SEP 2006Customer A MO Product 1 142,800 80,948 101,251 88,863 80,000 80,000 Customer B GA Product 2 - 20,276 - - 10,000 10,000 Customer C TX Product 3 101,577 101,378 81,284 82,009 86,990 87,060 Customer D AZ Product 4 60,899 81,166 39,208 60,000 60,000 60,000 Customer E TN Product 5 60,954 20,348 81,247 53,012 60,520 60,582 Customer F TX Product 6 141,131 121,971 141,321 120,000 140,000 120,000 Customer G NY Product 7 101,496 81,438 82,019 81,210 87,681 87,738 Customer H FL Product 8 100,761 120,302 100,788 103,993 104,148 104,239 Customer I GA Product 9 121,753 142,392 121,817 116,505 117,554 117,554 Customer J NJ Product 10 163,003 142,709 61,217 155,800 155,800 155,800

History S&OP Forecast

(Units in KGS)

Statistical ForecastFinal S&OP Forecast (Includes Forecast Adjustments)Impact +/-

Accuracy of ForecastAdjustments

Ship-to CustomerJun06 Stat

FcstStat FcstAbs Error

Jun06 S&OP Final

S&OP Abs Error Impact +/-

Customer A 2,167,904 1,465,968 701,936 1,691,155 476,749 225,187Customer B 286,650 494,531 207,881 281,859 4,791 203,090Customer C 316,315 454,387 138,072 313,809 2,506 135,566Customer D 743,619 906,002 162,383 680,000 63,619 98,764Customer E 20,266 0 20,266 50,000 29,734 (9,467)Customer F 244,023 370,466 126,443 205,698 38,325 88,117Customer G 332,211 252,729 79,482 330,000 2,211 77,271Customer H 20,312 40,547 20,236 113,400 93,088 (72,853)Customer I 50,000 130,416 80,416 80,000 30,000 50,416Customer J 301,221 111,502 189,719 159,757 141,464 48,255Customer K 40,479 56,471 15,992 102,000 61,521 (45,529)Customer L 142,709 84,879 57,830 155,800 13,091 44,739Customer M 163,592 237,196 73,604 193,000 29,408 44,196Customer N 0 1,603 1,603 40,000 40,000 (38,397)Customer O 40,615 0 40,615 37,800 2,815 37,800Customer P 0 52,893 52,893 16,000 16,000 36,893Customer Q 152,434 194,153 41,719 140,000 12,434 29,285

Jun06 Act

Accurate customer intelligence provided by Sales cansignificantly improve forecast accuracy

(Units in KGS)

Accuracy of ForecastAdjustments

If adjustments to the statistical forecast do not improve forecast accuracy, why make them?

MonthActual

DemandStat

Forecast

Stat Forecast

ErrorS&OP

Forecast

S&OP Forecast

Error ImpactJan06 6,091,955 7,593,962 5,196,370 5,918,751 1,886,262 54.3%Feb06 9,147,987 8,661,173 4,497,213 9,061,139 3,013,993 16.2%Mar06 11,570,962 11,932,441 3,992,114 12,448,817 2,885,691 9.6%Apr06 10,625,650 12,512,901 5,348,524 13,477,086 4,550,418 7.5%May06 10,815,034 9,840,902 3,791,501 11,297,905 3,059,782 6.8%Jun06 8,817,693 9,041,067 3,697,356 9,311,634 2,569,887 12.8%Last 6 Month Ave. 57,069,281 59,582,446 26,523,078 61,515,332 17,966,033 15.0%

These items have the biggest overall impact to forecast accuracy resultsDetermine the source of the variances

Current Month Variance Analysis

Product Customer JULY Actual JULY Forecast JULY Variance JULY Fcst Accuracy

Product 1 Customer A 2,134,314 KG 1,611,990 KG 522,324 KG 67.6%Product 2 Customer B 1,230,867 KG 900,000 KG 330,867 KG 63.2%Product 3 Customer C 433,674 KG 147,542 KG 286,132 KG -93.9%Product 4 Customer D 0 KG 240,000 KG 240,000 KG 0.0%Product 5 Customer E 61,117 KG 300,000 KG 238,883 KG 0.0%Product 6 Customer F 165,799 KG 330,000 KG 164,201 KG 50.2%Product 7 Customer G 431,901 KG 268,665 KG 163,236 KG 39.2%Product 8 Customer H 424,536 KG 262,575 KG 161,961 KG 38.3%Product 9 Customer I 334,660 KG 174,096 KG 160,565 KG 7.8%Product 10 Customer J 20,040 KG 175,167 KG 155,127 KG 11.4%Product 11 Customer K 490,342 KG 355,456 KG 134,886 KG 62.1%Product 12 Customer L 40,642 KG 171,132 KG 130,490 KG 23.7%Product 13 Customer M 264,235 KG 143,679 KG 120,557 KG 16.1%Product 14 Customer N 181,589 KG 77,946 KG 103,644 KG -33.0%

Forecast Variances -- By Ship-to Customer JULY 2006

Zero out the history for these customers in the forecast model

Forecast but No Sales

Last 3 Mo. Stat Fcst Final Fcst Final Fcst Final Fcst

Product Customer Ship-to

Average Monthly Demand

Ave Fcst Next 3 Mo. 09/2006 10/2006 Ave Fcst Next 3

Mo.

Product 1 Customer A 0 0 125,000 125,000 125,000Product 2 Customer B 0 15,694 80,000 123,000 101,333Product 3 Customer C 0 0 87,000 87,000 87,000Product 4 Customer D 0 0 72,000 72,000 72,000Product 5 Customer E 0 34,619 34,485 34,676 34,619Product 6 Customer F 0 0 1 20,000 20,000Product 7 Customer G 0 18,047 18,047 18,047 18,047Product 8 Customer H 0 17,293 17,282 17,298 17,293Product 9 Customer I 0 10,654 10,624 10,667 10,654Product 10 Customer J 0 9,763 9,744 9,772 9,763Product 11 Customer K 0 8,223 8,223 8,223 8,223

Add forecasts for these customers

Sales but No Forecast

Act Sales Act Sales Act Sales Last 3 Mo. Stat Fcst Stat Fcst Final Fcst Final Fcst

Product Customer Ship-to

05/2006 06/2006 07/2006Average Monthly Demand

09/2006 10/2006 09/2006 10/2006

Product 1 Customer A 0 0 84,550 28,183 0 0 0 0Product 2 Customer B 0 0 61,117 20,372 0 0 0 0Product 3 Customer C 29,402 20,330 0 16,577 0 0 0 0Product 4 Customer D 13,789 12,093 20,339 15,407 0 0 0 0Product 5 Customer E 0 0 40,760 13,587 0 0 0 0Product 6 Customer F 0 0 40,587 13,529 0 0 0 0Product 7 Customer G 5,700 11,400 22,800 13,300 0 0 0 0Product 8 Customer H 0 0 39,635 13,212 0 0 0 0Product 9 Customer I 0 0 39,608 13,203 0 0 0 0Product 10 Customer J 0 0 35,150 11,717 0 0 0 0Product 11 Customer K 654 0 34,382 11,679 0 0 0 0



Content

Forecast Accuracy Improvement

40

50

60

70

80

90

100

4Q03

1Q04

2Q04

3Q04

4Q04

1Q05

2Q05

3Q05

4Q05

1Q06

2Q06

3Q06

% F

orec

ast A

ccur

acy

(Product Mix Level)

Process, People,Statistical Forecasting

Exception AnalysisForecast Adjustments

Forecast SegmentationData Aggregation

World Class

+ 6% + 15%+ 7%

50

60

70

80

90

100

Prod

uctio

n Pl

an A

dher

ence

(%)

38% Improvement

2004 2007

Production Plan AdherenceSupply Planning Accuracy

30

40

50

60

70

80

90

100

Dire

ct P

rofit

Acc

urac

y (%

)

21% Improvement

2004 2007

Financial Forecast Accuracy

Forecast Deviation Trend

• Forecast deviation at chemical product levelAPO Go-Live

Target

.0

.1

.2

.3

.4

.5

Jul-0

7

Aug

-07

Sep-

07

Oct

-07

Nov

-07

Dec

-07

Jan-

08

Feb-

08

Mar

-08

Apr

-08

May

-08

Jun-

08

Jul-0

8

Aug

-08

Sep-

08

Oct

-08

Nov

-08

Dec

-08

Jan-

09

Feb-

09

Mar

-09

Fore

cast

Dev

iatio

n

APP Wacker

• Forecast deviation at chemical product level

Target

0%

20%

40%

60%

80%

100%

Sep

-08

Oct

-08

Nov

-08

Dec

-08

Jan-

09

Feb-

09

Mar

-09

Apr

-09

Fore

cast

Dev

iatio

n % Forecast Deviation Reduced 56%

Forecast Deviation Trend

Forecast Deviation Vs Inventory Levels

0%

10%

20%

30%

40%

50%

Nov

-08

Dec

-08

Jan-

09

Feb-

09

Mar

-09

Apr

-09

Fore

cast

Dev

iatio

n %

10

20

30

40

50

60

Inventory DO

S

Forecast Deviation Inventory DOS

Inventory Decreased 29%

Forecast Deviation Vs On-Time Delivery

0%

10%

20%

30%

40%

50%

Nov

-08

Dec

-08

Jan-

09

Feb-

09

Mar

-09

Apr

-09

Fore

cast

Dev

iatio

n %

90

92

94

96

98

100

On-Tim

e Delivery %

LDU Forecast Deviation Calvert On-Time Delivery

On-Time Delivery Increased 2%

Forecast Deviation Vs Revenue Accuracy

0%

10%

20%

30%

40%

50%

Nov

-08

Dec

-08

Jan-

09

Feb-

09

Mar

-09

Apr

-09

Fore

cast

Dev

iatio

n %

60

70

80

90

100

Revenue A

ccuracy %

Forecast Deviation Revenue Accuracy

• Aligning demand plan with financial forecasting process

Revenue Accuracy Increased 29%



Content

If you follow the Roadmap to improve your company’s sales forecasting practices, you will experience

reductions in costs and increases in customer and employee satisfaction. Costs will decline in inventory levels, raw materials, production, and logistics. But the first step a company must take before realizing

these kind of benefits is to recognize the importance of sales forecasting as a management function, and to

be willing to commit the necessary resources to becoming world class.

CONCLUSION

THANK YOU FOR YOUR ATTENTION

The WACKER Group

Stephen P. CraneDirector Strategic Supply Chain [email protected]

CREATING TOMORROW'S SOLUTIONS

Documents

Solution-Driven Integrated Learning Paths - APICSmedia.apics.org/sites/AnnualConference2009/handouts/basics/A... · Solution-Driven Integrated Learning Paths • Educational Sessions