A Roadmap To World Class Forecasting Accuracy

Stephen P. Crane, CSCP, Director Strategic Supply Chain Management Institute of Business Forecasting & Planning Conference Phoenix, AZ February 22-24, 2009 A ROADMAP TO WORLD CLASS FORECASTING ACCURACY Six Keys to Improving Forecast Accuracy

CONTENT WACKER Company Overview Why Forecast? Forecasting Background and 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

Wacker Chemie AG WACKER Group (2007) OVER 90 YEARS OF SUCCESS Founded in 1914 by Dr. Alexander Wacker Headquartered in Munich 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 Basic chemicals 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 co- and terpolymers

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, 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

WHAT IS A GOOD FORECAST? World class forecasting accuracy performance 95% currency by product line 90% by product line 85% product mix level Source: Buker Management Consulting Goal

FORECASTING BACKGROUND & 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 difficult and thankless endeavor with high inaccuracies Companies react to inaccuracies with investments in technology New investments do not guarantee any better forecasts There are often fundamental issues that need to be addressed before improvements can be achieved Forecastin g Process

FORECASTING BACKGROUND & 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!

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 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

STEP 1: DEFINING THE PROCESS, PEOPLE & TOOLS People People make organizations and 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: DEFINING THE PROCESS, PEOPLE & TOOLS 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

STEP 2: STATISTICAL FORECASTING CAPABILITY 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 with 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 STEP 2: STATISTICAL FORECASTING CAPABILITY 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 2: STATISTICAL FORECASTING CAPABILITY

Step 3: Forecasting Segmentation - 80/20 Analysis SIX KEYS TO IMPROVING FORECAST ACCURACY Exceptions Collaboration Business Intelli g ence Data 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 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 COV (Coefficient of Variation) = STD Deviation/Ave. Demand Notes

STEP 3: FORECASTING SEGMENTATION EXAMPLE

Step 4: Data Aggregation SIX KEYS TO IMPROVING FORECAST ACCURACY

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 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

Step 5: Sales Adjustments to Statistical Forecast SIX KEYS TO IMPROVING FORECAST ACCURACY

STEP 5: SALES ADJUSTMENTS TO STATISTICAL FORECAST Accurate forecasting is not just getting a forecast from the customer. The customer isn’t always right!

Since statistical forecasting is based on previous sales, it is necessary to take into account elements that can affect sales Seasonality of the business State of the economy Competition and market position Product 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? STEP 5: SALES ADJUSTMENTS TO STATISTICAL FORECAST

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 STEP 5: SALES ADJUSTMENTS TO STATISTICAL FORECAST Decide where adjustments should be made

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

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

HIGH IMPACT EXCEPTIONS FOR SALES REVIEW Identify High Impact “Exceptions” to focus forecast review Decreasing or increasing sales rates Aggressive statistical forecast based on historical run rates

ACCURACY OF FORECAST ADJUSTMENTS (Units in KGS) Statistical Forecast Final S&OP Forecast (Includes Forecast Adjustments) Impact +/- Ship-to Customer Jun06 Stat Fcst Stat Fcst Abs Error Jun06 S&OP Final S&OP Abs Error Impact +/- Customer A 2,167,904 1,465,968 701,936 1,691,155 476,749 225,187 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

ACCURACY OF FORECAST ADJUSTMENTS Accurate customer intelligence provided by Sales can significantly improve forecast accuracy (Units in KGS) If adjustments to the statistical forecast do not improve forecast accuracy, why make them Month Actual Demand Stat Forecast Stat Forecast Error S&OP Forecast 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%

CURRENT MONTH VARIANCE ANALYSIS These items have the biggest overall impact to forecast accuracy results Determine the source of t h e variances

FORECAST BUT NO SALES Zero out the history for these customers in the forecast model

SALES BUT NO FORECAST Add forecasts for these customers

FORECAST ACCURACY IMPROVEMENT (Product Mix Level) Process, People, Statistical Forecasting Exception Analysis Forecast Adjustments Forecast Segmentation Data Aggregation World Class + 6% + 15% + 7%

38% Improvement 2004 2007 PRODUCTION PLAN ADHERENCE SUPPLY PLANNING ACCURACY

21% Improvement 2004 2007 FINANCIAL FORECAST ACCURACY

ACTUAL FORECAST DEVIATION TREND Forecast deviation at product level APO Go-Live Target .0 .1 .2 .3 .4 .5 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Forecast Deviation AP WACKER

CONCLUSIONS If you follow the Roadmap to improve your companies 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 be willing to commit the necessary resources to becoming world class.

The Wacker Group THANK YOU FOR YOUR ATTENTION Stephen P. Crane Director Strategic Supply Chain Management [email_address] CREATING TOMORROW'S SOLUTIONS

A Roadmap To World Class Forecasting Accuracy

More Related Content

What's hot (20)

Viewers also liked (15)

Similar to A Roadmap To World Class Forecasting Accuracy (20)

More from President Stephen Crane Consulting, LLC (9)

Recently uploaded (20)

A Roadmap To World Class Forecasting Accuracy

Editor's Notes