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Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
Using Key Metrics to Supercharge Your Demand Management and S&OP Process
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Using Key Metrics to Supercharge Your Demand Management and S&OP Process

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Forecast accuracy is the single most important metric for demand planning, and quite possibly for the entire sales and operations planning (S&OP) process. If you start with an accurate forecast, the …

Forecast accuracy is the single most important metric for demand planning, and quite possibly for the entire sales and operations planning (S&OP) process. If you start with an accurate forecast, the rest of the process is a lot easier. Improvements in forecast accuracy have a cascade effect, leading to reductions in inventory, improvements in customer service/reductions in stock outs, and eventually to increases in gross revenues and/or margins. But do you really understand forecast accuracy, and how to properly leverage it for results?

In this webinar, we will focus on explaining the foundations of forecast accuracy metrics:

How to measure forecast accuracy
Selecting the most meaningful offset or lag period
Units vs. revenue
Aggregation levels
Time buckets: Weeks, months or quarters
Impacts to functional groups: Sales, Marketing, Demand Planning
Strategies for transparent and effective tracking for improved results

Published in: Software, Business, Technology
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  • 1. 1© 2014 Steelwedge Software, Inc. Confidential. Single Line of Sight: Plan, Perform, Profit Measuring Forecast Accuracy Improved S&OP through Analysis of Forecast Accuracy
  • 2. 2© 2014 Steelwedge Software, Inc. Confidential. Today’s Presenter Background Rick Blair VP, Solutions Design and Analytics Steelwedge Software Inc. 3825 Hopyard Rd Pleasanton, CA 94588 Tel : (925) 460-1700 rblair@steelwedge.com • Over 25 years of experience in supply chain consulting and operations management. • Works directly with customers and team members to optimize solution design, drive improvements and apply best practice methodologies. • Leverages supply chain management practitioner experience in various roles across S&OP, demand and supply planning
  • 3. 3© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? Examples of forecast accuracy metrics How is MAPE calculated? Beyond a number: Key Considerations Accuracy measures in action Agenda
  • 4. 4© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? • What is Forecast Accuracy? • A measure of deviation between plan and actual • Less deviation = greater accuracy • What motivates an organization to track accuracy? • Hold individuals and groups accountable • Benchmark against other companies • Manage inventory levels • Improve management of business
  • 5. 5© 2014 Steelwedge Software, Inc. Confidential. Quick Poll #1 Which reason is the most important?
  • 6. 6© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? • While each reason has its merit, I suggest that #3 and #4 are most critical 1. Hold individuals and groups accountable 2. Benchmark against other companies 3. Manage inventory levels 4. Improve management of business 5. Something else
  • 7. 7© 2014 Steelwedge Software, Inc. Confidential. Manage Inventory Levels • If demand fluctuates substantially from period-to-period, should you carry more safety stock? • Conventional supply chain planning says ‘Yes’ Carry buffer inventory to handle demand fluctuations • But…what if demand variability is predictable? – Buy or make to forecast – Reduce safety stock to cover just unpredictable variability High forecast accuracy translates into reduced inventory requirement
  • 8. 8© 2014 Steelwedge Software, Inc. Confidential. Improve Management of Business • Questions worth asking • What are we trying to accomplish? • What are we trying to improve? • If we measure forecast accuracy… – At a high level such as Family or Business Unit – Using most recent forecast values • Our accuracy metric looks much better, so why not make ourselves look good? Are you trying to look good or improve the business?
  • 9. 9© 2014 Steelwedge Software, Inc. Confidential. More Reasons to Improve Forecast Accuracy Benefits of Better Forecasting: Improved customer service levels Increased sales Reduced inventory carrying costs Improved cash flow projections Production smoothing (level loading) Reduced employee costs Increased ROI Balancing Supply and Demand 0 10 20 30 40 50 60 70 80 90 100 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Time Quantity Demand Supply Excess Inventory Lost Opportunity
  • 10. 10© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? Examples of forecast accuracy metrics How is MAPE calculated? Beyond a number: Key Considerations Accuracy measures in action Agenda
  • 11. 11© 2014 Steelwedge Software, Inc. Confidential. Accuracy Metrics • MAD = Mean Absolute Deviation • RMSE = Root Mean Square Error • MAPE = Mean Absolute Percent Error • WMAPE = Weighted Mean Absolute Percent Error • MAD/Mean = Mean Absolute Deviation/Average • Yields same result as WMAPE • Bias = Tendency to over or under forecast • % of forecasts over actual • % of forecasts under actual
  • 12. 12© 2014 Steelwedge Software, Inc. Confidential. Which accuracy metric does your company use? Quick Poll #2
  • 13. 13© 2014 Steelwedge Software, Inc. Confidential. Accuracy Metrics • MAD = Mean Absolute Deviation • RMSE = Root Mean Square Error • MAPE = Mean Absolute Percent Error • WMAPE = Weighted Mean Absolute Percent Error • MAD/Mean = Mean Absolute Deviation/Average • Yields same result as WMAPE • Bias = Tendency to over or under forecast MAPE & WMAPE are the most widely used forecast accuracy measures
  • 14. 14© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? Examples of forecast accuracy metrics How is MAPE calculated? Beyond a number: Key Considerations Accuracy measures in action Agenda
  • 15. 15© 2014 Steelwedge Software, Inc. Confidential. How is MAPE calculated? • Mean Absolute Percent Error = MAPE = ∑│PE│/N • N = number of periods for which we have PE values • │PE│ = absolute value of the PE (Percent Error) • Weighted MAPE = average of individual MAPEs weighted by actual shipments = ∑item MAPE * │(item actual / total actual)│ Let’s consider an example…
  • 16. 16© 2014 Steelwedge Software, Inc. Confidential. MAPE Example 1. Error: The difference between Forecast and Actual 2. Absolute Error: Convert negatives to positives 3. Absolute Percent Error = Absolute Error / Actual Error SKU 1 Actual 100 Forecast 90 Error (10) Absolute Error SKU 1 Actual 100 Forecast 90 Error (10) Abs Error 10 Absolute Percent Error SKU 1 Actual 100 Forecast 90 Error (10) Abs Error 10 APE 10.0%
  • 17. 17© 2014 Steelwedge Software, Inc. Confidential. MAPE Example • Mean Absolute Percent Error (MAPE) • Average of APEs for multiple items or multiple periods or both MAPE Calculation SKU 1 2 3 4 5 6 7 8 9 10 Family A Actual 100 90 8 1,000 1 90 10 11 50 76 1,436 Forecast 90 100 10 970 3 80 13 10 50 80 1,406 Error (10) 10 2 (30) 2 (10) 3 (1) - 4 (30) Abs Error 10 10 2 30 2 10 3 1 - 4 30 APE 10.0% 11.1% 25.0% 3.0% 200.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.1% MAPE 30.5% MAPE = 30.5% = Average of 10 SKU APE values MAPE for Family A: At Family level = 2.1% At SKU level = 30.5%
  • 18. 18© 2014 Steelwedge Software, Inc. Confidential. MAPE and WMAPE Calculations SKU 1 2 3 4 5 6 7 8 9 10 Family A Actual 100 90 8 1,000 1 90 10 11 50 76 1,436 Forecast 90 100 10 970 3 80 13 10 50 80 1,406 Error (10) 10 2 (30) 2 (10) 3 (1) - 4 (30) Abs Error 10 10 2 30 2 10 3 1 - 4 30 APE 10.0% 11.1% 25.0% 3.0% 200.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.1% Wtd MAPE 0.7% 0.7% 0.1% 2.1% 0.1% 0.7% 0.2% 0.1% 0.0% 0.3% MAPE 30.5% WMAPE 5.0% WMAPE Example • Weighted Mean Absolute Percent Error (WMAPE or WAPE) • Average of individual MAPEs weighted by actual shipments WMAPE = 5.0% = Weighted Average of 10 SKU APE values WMAPE for Family A: At Family level = 2.1% At SKU level = 5.0%
  • 19. 19© 2014 Steelwedge Software, Inc. Confidential. MAPE and WMAPE Calculations SKU 1 2 3 4 5 6 7 8 9 10 Family A Actual 100 90 8 1,000 2 90 10 11 50 76 1,437 Forecast 90 100 10 970 3 80 13 10 50 80 1,406 Error (10) 10 2 (30) 1 (10) 3 (1) - 4 (31) Abs Error 10 10 2 30 1 10 3 1 - 4 31 APE 10.0% 11.1% 25.0% 3.0% 50.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.2% Wtd MAPE 0.7% 0.7% 0.1% 2.1% 0.1% 0.7% 0.2% 0.1% 0.0% 0.3% MAPE 15.5% WMAPE 4.9% MAPE & WMAPE: Impact of Small Changes • What if SKU5 Actual was 2 (not 1)? MAPE changes from 30.5% to 15.5% WMAPE changes from 5.0% to 4.9% A one unit change can have a huge impact on MAPE. WMAPE is less sensitive.
  • 20. 20© 2014 Steelwedge Software, Inc. Confidential. MAPE and WMAPE Calculations SKU 1 2 3 4 5 6 7 8 9 10 Family A Actual 100 90 8 1,000 - 90 10 11 50 76 1,435 Forecast 90 100 10 970 3 80 13 10 50 80 1,406 Error (10) 10 2 (30) 3 (10) 3 (1) - 4 (29) Abs Error 10 10 2 30 3 10 3 1 - 4 29 APE 10.0% 11.1% 25.0% 3.0% 0.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.0% Wtd MAPE 0.7% 0.7% 0.1% 2.1% 0.0% 0.7% 0.2% 0.1% 0.0% 0.3% MAPE 10.5% WMAPE 4.9% MAPE & WMAPE: Zero Actual Values • What if SKU5 Actual was 0 (not 1)? MAPE changes from 30.5% to 10.5% WMAPE changes from 5.0% to 4.9% Pay close attention to instances where Actual = 0. Dividing by zero causes an error, so the MAPE formula may assign 0%. This can be misleading since 0% should represent zero deviation between forecast and actual.
  • 21. 21© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? Examples of forecast accuracy metrics How is MAPE calculated? Beyond a number: Key Considerations Accuracy measures in action Agenda
  • 22. 22© 2014 Steelwedge Software, Inc. Confidential. Key Considerations • Error vs Accuracy • Metrics measure degree of error • Accuracy can be defined as (1 – MAPE) or (1 – WMAPE) – If MAPE = 25%, then Forecast Accuracy = 75% – If MAPE = 100%, then Forecast Accuracy = 0% – If MAPE = 200%, then Forecast Accuracy = 0% Notice that Forecast Accuracy is not negative Loss of visibility to error magnitude if MAPE > 100%
  • 23. 23© 2014 Steelwedge Software, Inc. Confidential. Key Considerations • Aggregation Level • Higher aggregation levels usually yield lower MAPE and WMAPE values – Variation is dampened as peaks and valleys get smashed together • Ask: What are we trying to improve? – Measure accuracy at level where you can affect change – You may decide to measure accuracy at multiple levels – For example, Product Mix (SKU) & Product Line (Family)
  • 24. 24© 2014 Steelwedge Software, Inc. Confidential. Key Considerations • Time Buckets • Examples: Month, Quarter, Rolling 3 Months • The bigger the time bucket, the lower the MAPE MAPE Calculation Month Jan Feb March Q1 Actual 105 92 75 272 Forecast 95 100 82 277 Error (10) 8 7 5 Abs Error 10 8 7 5 APE 9.5% 8.7% 9.3% 1.8% MAPE 9.2% Monthly deviations are eliminated in Q1 MAPE value
  • 25. 25© 2014 Steelwedge Software, Inc. Confidential. Key Considerations • “Lag” or “Offset” • Use the forecast at time of decision – May be production or raw material lead time – Measure accuracy of forecast that manufacturing could actually use • The most recent forecast provides little to no value in making business decisions – What can we do with a forecast for February provided in February? In this example, the forecast from Month 2 of prior quarter -1 is compared to actual results from prior quarter. For example, forecast created in May for Q3 (July, August and September) is compared to actual results from Q3 (July, August and September). Previous Quarter - 1 Previous Quarter Current Quarter month 1 2 3 1 2 3 1 2 3 "as of date" Quarter to be measured
  • 26. 26© 2014 Steelwedge Software, Inc. Confidential. Key Considerations Forecast Period: As Of: Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Oct-12 124 140 90 120 120 85 120 120 100 120 100 120 Nov-12 110 130 100 100 100 100 100 100 90 110 100 100 Dec-12 120 125 100 100 115 90 85 90 95 100 110 110 Jan-13 90 95 100 130 120 125 100 120 115 90 95 80 Feb-13 100 105 110 120 100 100 110 100 90 80 80 Mar-13 120 95 100 120 120 100 120 100 120 115 Apr-13 100 95 115 123 85 90 95 100 110 May-13 85 103 113 102 85 90 95 100 Jun-13 100 96 88 118 120 100 120 Jul-13 111 112 115 112 85 90 Aug-13 120 102 115 120 85 Sep-13 100 102 115 97 Oct-13 95 106 135 Nov-13 95 100 Dec-13 115 Actual 95 105 120 105 90 95 101 130 95 95 100 120 MAPE Offset Jan-10 Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 0 5% 5% 0% 5% 6% 5% 10% 8% 5% 0% 5% 4% 1 26% 10% 13% 10% 6% 8% 5% 14% 7% 7% 6% 17% 2 16% 19% 17% 5% 11% 21% 12% 32% 21% 21% 15% 13% 3 31% 24% 17% 24% 33% 26% 22% 22% 24% 18% 20% 19% In this example, monthly MAPE values are shown over time with various offsets. Tracking MAPE trends is a great way to see improvement. Multiple offsets allow insight into how accuracy may improve as better information is available nearer to current period.
  • 27. 27© 2014 Steelwedge Software, Inc. Confidential. Key Considerations • Units vs Revenue • Accuracy measures need not be limited to Units • $ may be more meaningful if Average Selling Prices vary considerably – Use WMAPE and weight by $
  • 28. 28© 2014 Steelwedge Software, Inc. Confidential. Why measure forecast accuracy? Examples of forecast accuracy metrics How is MAPE calculated? Beyond a number: Key Considerations Accuracy measures in action Agenda
  • 29. 29© 2014 Steelwedge Software, Inc. Confidential. MAPE & Bias Analysis Actual and Forecast values MAPE by Item Bias MAPE and WMAPE Excel Slicers
  • 30. 30© 2014 Steelwedge Software, Inc. Confidential. Targeted MAPE Analysis MAPE by Functional Group WMAPE by Functional Group
  • 31. 31© 2014 Steelwedge Software, Inc. Confidential. Q&A
  • 32. 32© 2014 Steelwedge Software, Inc. Confidential.

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