How to Incorporate Market Intelligence into Statistical Forecasting
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A case study on how to improve forecast accuracy by incorporating market or business intelligence into statisitical forecasting and know whether it improves forecast accuracy or not.
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How to Incorporate Market Intelligence into Statistical Forecasting
- 1. Air Products and Chemicals, Inc. Stephen P. Crane, CSCP Director Supply Chain How to Incorporate Market Intelligence into Statistical Forecasting Orlando, Florida October 25-27 A Key to Improving Forecast Accuracy
- 3. Who is Air Products?
- 6. Global Work Processes Metrics Follow the Money!
- 12. Demand planning needs to be based on statistical forecasting and selected market intelligence to increase the accuracy of the forecast. Forecasting segmentation should be the key analysis for prioritizing your forecasting resources. Forecasting Segmentation Forecasting Segmentation Source: Accenture High Low Statistical Forecastability (measured by 1/COV) High Sales Volume/Impact Low Rationalize/Consolidate Collaboration Rationalize SKUs, consolidate stocking locations, make to order Customer Collaboration Gather Majority of Market Intelligence Statistical Forecasting Statistical Forecasting Use statistical forecasting at an aggregate level, minimize safety stock levels Selected Account Review Q1 Q3 Q2 Q4 COV (Coefficient of Variation) = STD Deviation/Average Demand Notes
- 13. Q1 Q2 Q3 Q4 1.0 (July 2004)
- 14. Q2 Q3 1.0 (March 2005) > 98% Demand 74% Reduction Q1 Q4
- 26. Market Intelligence Process Gather and Submit Market Intelligence Information DAY –30 to 0 Information from Customer Contact personnel (Sales, CSO, etc) Information Received Directly From Customers Information Regarding New Products Gather Customer Data Create Demand Change Notification(s) Gather Marketing Data Information Regarding Existing Products, Markets, Segments Create Demand Change Summary Create Demand Change Notification(s) Review Demand Change Notification(s) Assess Impact of Demand Change Notifications Summarize Agreed Demand Change Notifications in Demand Change Summary
- 28. World-Wide Leader in Vinyl Acetate Ethylene Co-Polymer Dispersion Technology, Serving Adhesives, Nonwovens, Coatings, and PSA markets Air Products Polymers, LP
- 32. Pilot - FVA Results (Units in KGS) Statistical Forecast Final S&OP Forecast (Includes Market Intelligence) FVA Impact
- 34. Forecast Accuracy 3 Stages of Improvement Air Products Polymers, LP Data Clean-up Market Intelligence Data Aggregation World Class + 6% + 15% + 7%
- 35. Financial Forecast Accuracy 21% Improvement
- 39. Thank you! Stephen P. Crane, CSCP Director Supply Chain Air Products and Chemicals [email_address]
Notas do Editor
- Introduction There’s an often-quoted saying: The only thing certain about a forecast is that it is always wrong. And yet, companies spend a great deal of time and resources trying to predict as accurately as possible the future demand of their supply chains. It is beneficial to know the future. When companies know their sales for next week, next month, and next year, they only invest in the facilities, equipment, materials, and staffing that 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. So a typical forecasting process involves historical demand data loaded into a computer database, with some form of statistical software used to generate forecasts. However, the statistical package is rarely allowed to operate on its own. Instead, a management team usually reviews and overrides the statistical forecast before giving the revised version its blessing as the official company projection of future demand. It is this process that I’ll be talking about this afternoon, how to utilize market intelligence to minimize the effort and maximize the value of the overall forecasting process.