Solution-Driven Integrated Learning Paths. Make the Most of Your Educational Experience. Live Learning Center

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Solution-Driven Integrated

Learning Paths

• Educational Sessions

– Lean

– Global Supply Chain

– Basics of Operation Management

– Demand Management, Forecasting, and S & OP – Professional Advancement

– Special Interest Topics

• Plant Tours

• Networking Events/Peer Interaction

• Materials for Sale at the APICS Bookstore

Make the Most of Your

Educational Experience

• Develop a Learning Plan

• Assess your learning needs

• Use teamwork

• Prepare to learn

• Create your own Action Plan

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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, CSCP Wacker Chemical Corporation

Director Strategic Supply Chain Management

WACKER Company Overview

Why Forecast?

Forecasting Challenges

Six Keys to Improving Forecast Accuracy

– Process, People, & Tools

– Statistical Forecasting

– Forecasting Segmentation

– Data Aggregation

– Sales Adjustments to Statistical Forecast

– Measurement & Exception Reporting

Forecasting Accuracy Results

Conclusions

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Why Forecast?

Conclusion

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

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Why Forecast?

Conclusion

Content

Forecast Types

Most companies use three types of forecasts:

9

Sales or channel forecast

9

Corporate planning forecasts

9

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%

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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

In ve nt or y D ays of S al e

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

Why Forecast?

Conclusion

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Management Is Often the Problem

Forecasting Challenges

Typical forecasting process involves historical demand data

loaded into a database using software to generate statistical forecasts

Statistical software is rarely allowed to operate on its own Management usually overrides the statistical

forecast before agreeing to final forecast Forecasting is often a difficult and thankless

endeavor with high inaccuracies

Companies react to inaccuracies with investments in technology, but do not guarantee any better forecasts

There 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.

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Why Forecast?

Conclusion

Content

World Class Forecasting Accuracy

Requires Making The Right Decisions

Step 1: Defining the Process,

People, & Tools

Six Keys To Improving

Forecast Accuracy

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Step 1:

Defining the Process, People & Tools

Process

Forecast 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 Process

Supply 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 Adjustments APO 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

People

People 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

Step 1:

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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 1:

Defining the Process, People & Tools

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:

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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

Step 2:

Statistical Forecasting Capability

Typical statistical forecasting methods include

–

Multiple regression analysis

–

Trend analysis

–

Seasonal

–

Simple moving average

–

Weighted moving average

–

Exponential smoothing

–

Automatic selection Recommended

Step 2:

Statistical Forecasting Capability

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 2:

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Step 3: Forecasting Segmentation

(80/20 Analysis)

Exceptions Collaboration Business Intelligence Data Aggregation

Six Keys to Improving Forecast

Accuracy

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 Statistical Forecastability (measured by 1/COV) High Sales Volume/Impact Low Manage by Exception using Exception Reports

Collaborate with Customer (if possible)

High Impact Items Non-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:

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Step 4: Data Aggregation

Six Keys to Improving Forecast

Accuracy

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 sporadic and 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

Step 4: Data Aggregation

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

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Step 5: Sales Adjustments to

Statistical Forecast

Six Keys to Improving Forecast

Accuracy

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

Step 5:

Sales Adjustments to Statistical Forecast

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

9Seasonality of the business

9State of the economy

9Competition and market position

9Product trends

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

9Where does this project rank today?

9When are you looking to make a purchasing decision?

9When will you be implementing?

9What would you like to see happen as a next step?

Step 5:

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Adjustments Made to Forecast

Pre-buying Promotional impacts Upside and downside volumes Customer plants expected to be down New business

New customers

Adjustments Made to History

Data errors Lost product volume One time customer outages Packaging changes Discontinued products

Decide where adjustments should be made

Step 5:

Sales Adjustments to Statistical Forecast

Step 6: Measurement &

Exception Reporting

Six Keys to Improving Forecast

Accuracy

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

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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

Step 6:

Measurement & Exception Reporting

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 2006

Customer 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 Forecast

Final S&OP Forecast (Includes Forecast Adjustments) Impact

+/-Accuracy of Forecast

Adjustments

Ship-to Customer Jun06 Stat Fcst Abs Error Stat Fcst Jun06 S&OP Final S&OP Abs Error Impact +/-Customer A 2,167,904 1,465,968 701,936 1,691,155 476,749 225,187 Customer B 286,650 494,531 207,881 281,859 4,791 203,090 Customer C 316,315 454,387 138,072 313,809 2,506 135,566 Customer D 743,619 906,002 162,383 680,000 63,619 98,764 Customer E 20,266 0 20,266 50,000 29,734 (9,467) Customer F 244,023 370,466 126,443 205,698 38,325 88,117 Customer G 332,211 252,729 79,482 330,000 2,211 77,271 Customer 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 50,416 Customer J 301,221 111,502 189,719 159,757 141,464 48,255 Customer 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 44,739 Customer M 163,592 237,196 73,604 193,000 29,408 44,196 Customer N 0 1,603 1,603 40,000 40,000 (38,397) Customer O 40,615 0 40,615 37,800 2,815 37,800 Customer P 0 52,893 52,893 16,000 16,000 36,893 Customer Q 152,434 194,153 41,719 140,000 12,434 29,285 Jun06 Act

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Accurate customer intelligence provided by Sales can significantly improve forecast accuracy

(Units in KGS)

Accuracy of Forecast

Adjustments

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

Month

Actual Demand ForecastStat

Stat Forecast

Error ForecastS&OP S&OP Forecast Error Impact Jan06 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 results

Determine the source of the variances

Current Month Variance

Analysis

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

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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 07/2006 Average Monthly Demand 09/2006 10/2006 09/2006 10/2006 Product 1 Customer A 0 0 84,550 28,183 0 0 0 0 Product 2 Customer B 0 0 61,117 20,372 0 0 0 0 Product 3 Customer C 29,402 20,330 0 16,577 0 0 0 0 Product 4 Customer D 13,789 12,093 20,339 15,407 0 0 0 0 Product 5 Customer E 0 0 40,760 13,587 0 0 0 0 Product 6 Customer F 0 0 40,587 13,529 0 0 0 0 Product 7 Customer G 5,700 11,400 22,800 13,300 0 0 0 0 Product 8 Customer H 0 0 39,635 13,212 0 0 0 0 Product 9 Customer I 0 0 39,608 13,203 0 0 0 0 Product 10 Customer J 0 0 35,150 11,717 0 0 0 0 Product 11 Customer K 654 0 34,382 11,679 0 0 0 0

Why Forecast?

Conclusion

Content

Forecast Accuracy

Improvement

40 50 60 70 80 90 100 4Q 0 3 1Q 0 4 2Q 0 4 3Q 0 4 4Q 0 4 1Q 0 5 2Q 0 5 3Q 0 5 4Q 0 5 1Q 0 6 2Q 0 6 3Q 0 6 % F o re ca st A ccu ra cy

(Product Mix Level) Process, People,

Statistical Forecasting Forecast AdjustmentsException Analysis Forecast Segmentation

Data Aggregation World Class

+ 6%

+ 15%

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50 60 70 80 90 100 P roduc ti on P lan A dhe re nc e ( % )

38% Improvement

2004 2007

Production Plan Adherence

Supply Planning Accuracy

30 40 50 60 70 80 90 100 D ire ct P ro fi t A c cu ra cy ( % )

21% Improvement

2004 2007

Financial Forecast Accuracy

Forecast Deviation Trend

• Forecast deviation at chemical product level APO Go-Live Target .0 .1 .2 .3 .4 .5 Jul -0 7 A ug-07 Se p -0 7 Oct -0 7 No v-07 De c-07 Jan -0 8 Feb-08 Ma r-08 Ap r-0 8 M a y-08 Jun-08 Jul -0 8 A ug-08 Se p -0 8 Oct -0 8 No v-08 De c-08 Jan -0 9 Feb-09 Ma r-09 For ecast De vi at io n APP Wacker

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• Forecast deviation at chemical product level Target 0% 20% 40% 60% 80% 100% Se p-08 Oct-08 No v-08 De c-0 8 Jan-09 Fe b -0 9 Ma r-0 9 A p r-09 F o recas t De vi at io n

% Forecast Deviation Reduced 56%

Forecast Deviation Trend

Forecast Deviation Vs

Inventory Levels

0% 10% 20% 30% 40% 50% No v-08 Dec -0 8 Ja n -0 9 Fe b-09 Ma r-0 9 Ap r-0 9 F o re c ast D evi ati o n % 10 20 30 40 50 60 In ve nt or y D O S

Forecast Deviation Inventory DOS

Inventory Decreased 29%

Forecast Deviation Vs

On-Time Delivery

0% 10% 20% 30% 40% 50% Nov -0 8 De c-0 8 Jan-09 Feb -09 Ma r-0 9 Ap r-0 9 Fo re cas t D e v iati o n % 90 92 94 96 98 100 O n -Tim e D e liver y %

LDU Forecast Deviation Calvert On-Time Delivery On-Time Delivery Increased 2%

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Forecast Deviation Vs

Revenue Accuracy

0% 10% 20% 30% 40% 50% No v-0 8 De c-0 8 Ja n -0 9 Feb -09 Ma r-0 9 Ap r-0 9 F o reca st D evi at io n % 60 70 80 90 100 R even u e A ccu racy %

Forecast Deviation Revenue Accuracy

• Aligning demand plan with financial forecasting process

Revenue Accuracy Increased 29%

Why Forecast?

Conclusion

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.

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Solution-Driven Integrated Learning Paths. Make the Most of Your Educational Experience. Live Learning Center