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How to Improve Forecast Accuracy to Drive
Measurable Trading Partner Results
Jason Murphy & Scott Trevena
General Manager & Chief Technologist
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Presenters
Jason Murphy, General Manager, Wipro Promax [email protected]
Scott Trevena, Chief Technologist, Wipro Promax [email protected]
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Agenda
• What are we covering today • The Challenges • The Current State of Play • Some Sample Data • Measuring Accuracy • Movement to More Sophisticated • Leads To • Case Studies • The Take Aways • Q&A
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What we are going to cover today..
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The Challenges…
▪ Assortment ▪ Large SKU counts ▪ Often large number of NPDs ▪ Multiple units of measure, cans, bottles, kegs, litres….
▪ Data ▪ Huge volume of data ▪ Cleansing and interpretation ▪ Deciding on what is relevant
▪ Promotions ▪ Special packs and seasonal SKUs ▪ Regional variations e.g. England football team branded products
for stores in Scotland ▪ Cannibalisation / Halo affects
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The Challenges…
▪ Channels ▪ Direct to customer DC ▪ Direct to customer store ▪ Indirect ▪ Forecasting levels in product hierarchies – determined by finance,
executed on by supply chain and planned for by sales
▪ Forecasting methodologies ▪ Statistical models versus gut feel and experience versus hybrid approach
▪ Market Intelligence ▪ Competitor activity ▪ Marketing activity ▪ Macro economic trends
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The Current State of Play….
▪ Estimate accuracy varies between companies ▪ 10% very accurate ▪ 35% reasonably accurate ▪ 40% somewhat accurate ▪ 15% not very accurate
▪ Only 30-40% of companies use POS data ▪ Forecasting methodologies range in sophistication
▪ Informal Spreadsheets ▪ Tools using averages ▪ Time series analysis ▪ Regressions ▪ Game theory technologies
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Some Sample Data
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Sample Data – EDLP Account C
Retailer Stock Levels
Price Levels
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Sample Data – High / Low Account D
Multi Week
Features
Some Price
Variation
Variations in Off
Weeks
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Sample Data – High / Low Account B
High Frequency
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Sample Data – High / Low Account B - POS
Little Post Effect
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Sample Data – High / Low Account B Sales Shipments Only
Weeks Affected
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Measuring Accuracy
There are many measures to forecast accuracy. One of the most common is Mean Absolute Percentage Error (MAPE) which technically is a measurement of error not accuracy; the lower the MAPE the more accurate the prediction. It is a measure of error in a fitted time series value in statistics, specifically trending and is defined by the formula
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Measuring Accuracy
MAPE 345 MAPE 115
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Movement to More Sophisticated
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Leads to…
• A 50% improvement in control of Trade Spend $ due to visibility to all key stakeholders in the business. No ability for KAMs to hide money for next year
• Total Trade Spend reduced by 3% of sales over the past 4 years
• Reduced promotional forecast error by 16% which helped to reduce inventory by over $3 million (despite growing sales over the period).
• Forecast Accuracy has improved from 82% to 95%.
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Case Study #1
▪ Appreciating context and external factors……
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The Take Aways…
▪ Understand first of all how much you need to improve ▪ Accuracy measurements, which ones, what level?
▪ Define a plan for how to improve ▪ Set a target incremental improvement against each step of the
plan ▪ Educate the organisation. Forecast accuracy is not just a
Demand Planning problem ▪ Pilot new ideas in realistic environments
▪ Don’t use statistical modelling for the slow, lump demand or the new products with no history
▪ Embrace pareto. 80% of the improvement will come from 20% of your forecast skus
▪ Acknowledge that forecasting is not all science and adapt your business to tune in to market signals
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Questions…
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Helping youDo Business Better
www.wipro.com/promax
Thank You!