Tracking Accuracy: An Essential Step to Improve Your Forecasting Process

Transcription

1 Tracking Accuracy: An Essential Step to Improve Your Forecasting Process Presented by Eric Stellwagen President & Co-founder Business Forecast Systems, Inc. Business Forecast Systems, Inc. 68 Leonard Street Belmont, MA USA (617)

2 On-Demand Webinars & Materials S A recording of today s Webinar will be posted next week on forecastpro.com along with the slide set (in.pdf format) All previously presented Webinars are archived and available for viewing on-demand at forecastpro.com Attendees will receive an notifying them when the recording and materials are available

3 Eric Stellwagen President, CEO & Co-founder of Business Forecast Systems, Inc. Co-author of Forecast Pro product line. More than 30 years of dedicated business forecasting experience. Served on the. board of directors of the International Institute of Forecasters for 12 years. Is currently serving on the practitioner advisory board of Foresight: The International Journal of Applied Forecasting.

4 What We ll Cover Introductions Why Track Accuracy? How Do We Measure Error? How Do We Track Accuracy? How Do We Spot Problems? Summary Q&A

5 Why Track Forecast Accuracy? To improve your forecasting process o Forecasting should be a continuous improvement process. o Improving your forecasting requires knowing what s working and what s not. To gain insight into expected performance To benchmark To spot problems early

6 How do we measure error?

7 Form of Error Measurement Tracking forecast accuracy requires measuring forecast error. Error measurements generally take one of three forms: Percentage-based measurements Unit-based measurements Relative-based measurements

8 MAPE and MAD MAPE: Mean Absolute Percent Error Tells you the average error size as a percent. MAD: Mean Absolute Deviation Tells you the average error size in units.

9 Error Measurements: MAPE The MAPE (Mean Absolute Percent Error) measures the average size of the error in percentage terms. Step 1: Calculate the absolute size of the error in each forecast period Step 2: Calculate the size of the error as a percentage of actual Step 3: Take the average percent error across periods Month Actual Forecast Absolute Error Absolute % Error % % % % MAPE 17.6%

10 Error Measurements: MAD The MAD (Mean Absolute Deviation) measures the average size of the error in units. Mean = Average of Absolute = Magnitude of (doesn t matter if it s positive or negative) Deviation = The error Step 1: Calculate the absolute size of the error in each forecast period Step 2: Take the average across periods Month Actual Forecast Absolute Error MAD 22.08

11 Error Measurement Considerations The MAPE is easy to interpret, even when you don t know a product s demand volume; however, the MAPE is scale sensitive and becomes meaningless for lowvolume data or data with zero demand periods. The MAD is a good statistic to use when analyzing a single product s forecast and you know the demand volume.

12 Measuring Error Across Products Aggregating error measurements across products can be problematic. When aggregating MAPEs, low-volume products can dominate the results. When aggregating MADs, high-volume products can dominate the results. When aggregating across products some corporations establish weighted error measurements to properly reflect the various products relative importance to the corporation. This is an excellent practice.

13 How do we track accuracy?

14 Types of Accuracy Measures Within-sample statistics (a.k.a. goodness-of-fit statistics) tell you how accurately a forecasting method tracks the historical data.

15 Within-sample Statistics

16 Within-sample Statistics Can aid the model-building process. Are NOT a good indicator of expected performance.

17 Types of Accuracy Measures Within-sample statistics (a.k.a. goodness-of-fit statistics) tell you how accurately a forecasting method tracks the historical data. Out-of-sample statistics tell you how accurately a forecasting method actually forecasted: Hold-out analysis Wait and See : Real-time tracking Out-of-sample statistics yield a better measure of expected forecast accuracy than within-sample statistics, with real-time tracking providing the best error accuracy measurements.

18 Hold-out Analysis

19 Hold-out Analysis Allows you to compare different approaches Provides insight into expected accuracy May not be able to simulate your true forecasting process

20 Real-time Tracking

21 Real-Time Tracking Tracks the actual forecast process Allows you to compare different forecasts (e.g., statistical vs. adjusted vs. salesperson s, etc.) Can be used to determine the value add (if any) of judgment Provides the most accurate insight into expected accuracy Is the strongest of all approaches

22 Building a Forecast Archive We begin with historic data through December 2016 and generate a forecast Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Actual Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8,211

23 Building a Forecast Archive Once January's demand is known we generate a new forecast Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Actual 18,468 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9,726 6,780

24 Building a Forecast Archive Once February's demand is known we generate a new forecast Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Actual 18,468 9,720 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9,726 6, Feb 13,265 12,913 6,654 9,102 6,574 8,493

25 Building a Forecast Archive Once March's demand is known we generate a new forecast Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Actual 18,468 9,720 15,552 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9,726 6, Feb 13,265 12,913 6,654 9,102 6,574 8, Mar 9,623 4,364 6,983 4,801 6,901 14,710

26 Building a Forecast Archive Once April's demand is known we generate a new forecast Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Jan-17 Oct-17 Actual 18,468 9,720 15,552 10,692 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9,726 6, Feb 13,265 12,913 6,654 9,102 6,574 8, Mar 9,623 4,364 6,983 4,801 6,901 14, Apr 4,367 6,994 4,802 6,905 14,725 17,624

27 Building a Forecast Archive Once May's demand is known we generate a new forecast Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Jan-17 Oct-17 Nov-17 Actual 18,468 9,720 15,552 10,692 6,804 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9,726 6, Feb 13,265 12,913 6,654 9,102 6,574 8, Mar 9,623 4,364 6,983 4,801 6,901 14, Apr 4,367 6,994 4,802 6,905 14,725 17, May 6,873 4,800 6,858 14,554 17,527 15,184

28 Building a Forecast Archive Once June 2017 sales are known, we can compare the forecasts in the red box to what actually happened--this is the basis for a "waterfall" report Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Jan-17 Oct-17 Nov-17 Actual 18,468 9,720 15,552 10,692 6,804 7,776 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9,726 6, Feb 13,265 12,913 6,654 9,102 6,574 8, Mar 9,623 4,364 6,983 4,801 6,901 14, Apr 4,367 6,994 4,802 6,905 14,725 17, May 6,873 4,800 6,858 14,554 17,527 15,184

29 A Waterfall Report Adjusted forecast Showing forecasts Date Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Actual 18,468 9,720 15,552 10,692 6,804 7,776 Origin 2016-Dec 25,950 11,808 12,429 11,302 6,033 8, Jan 12,697 14,114 13,535 6,837 9, Feb 13,265 12,913 6,654 9, Mar 9,623 4,364 6, Apr 4,367 6, May 6,873 Lead time Series Analysis No. observations Avg. Forecast 12,129 12,811 13,373 13,778 14,061 13,474 Avg. Error 627 1,309 1,871 2,276 2,559 1,972 MAD 2,859 2,862 3,226 2,785 3,070 2,298 Avg. Perc. Error -0.1% 5.3% 12.7% 17.1% 19.6% 15.7% MAPE 23.9% 23.6% 23.5% 20.4% 25.0% 18.5% CMAPE 6.0% 6.0% 6.5% 5.3% 6.3% 5.0%

30 Relative Absolute Error MAD: Mean Absolute Deviation Tells you the average error size in units. MAPE: Mean Absolute Percent Error Tells you the average error size as a percent. RAE: Relative Absolute Error Tells you the error size relative to the error from a Naïve model (same as last period)..

31 Error Measurements: RAE The RAE (Relative Absolute Error) is the ratio of the absolute error from the current method to the absolute error from a Naïve model. A geometric mean can be used to average RAEs. Actual Forecast Actual Naive _ Forecast Month Actual Forecast Absolute Error Naïve Forecast Naïve Absolute Error GMRAE 0.77 RAE

32 FVA Stair Step Report The RAE provides an indication of the value added (or destroyed) by your current forecasting model. This concept can be extended to generate a Forecast Value Add (FVA) report Process Step Naïve Forecast Statistical Forecast Demand Planner Override Forecast Accuracy 60% FVA vs. Naïve 65% 5% FVA vs. Statistical 62% 2% -3% You can report on an individual time series, or for an aggregation of many (or all) time series. If you are doing better than a Naïve forecast, your process is adding value. If you are doing worse than a Naïve forecast, you are simply wasting time and resources. (Slide courtesy of Mike Gilliland, SAS Institute, Inc.)

33 Exception Reports

34 Exception Reports Reduce the need for manual review. Allow you to focus on the items where human attention is most needed.

35 Summary

36 Conclusions Tracking forecast accuracy allows you to improve your forecasting process, gain insight into expected performance, benchmark and spot problems quickly. All error measurement statistics have strengths and weaknesses and care should used when selecting which ones to focus on. Out-of-sample performance provides a better measure of expected forecast accuracy than within-sample performance. Exception reports are a useful tool to zero in on forecasts that need human attention.

37 Tracking Accuracy: Best Practices Establish a forecast archive and routinely track accuracy. Ideally, track every step in your forecasting process to determine what is adding/destroying value. Establish a feedback loop to allow participants to learn and improve. Monitor for changes in forecast accuracy and take action when necessary. Understand the differences among error measurements and choose appropriate metrics for the task at hand.

38 Our Next Webinar How to Boost Your Forecast Accuracy by Modeling the Impact of Promotions and Other Events October 26, 1:30 pm EDT Presented by Sarah Darin, Senior Consultant, Business Forecast Systems, Inc. Visit to sign up!

39 On-Demand Webinars & Materials S A recording of today s Webinar will be posted next week on forecastpro.com along with the slide set (in.pdf format) All previously presented Webinars are archived and available for viewing on-demand on forecastpro.com Attendees will receive an notifying them when the recording and materials are available

40 User Conference Forecast Pro User Conference 2017 October 2-4, 2017 in Boston, MA USA Designed to help you get the most out of Forecast Pro and to improve your forecasting. Is structured as an intimate event where you can make valuable connections with other Forecast Pro users (60 people max). Features tutorials, user case studies, product training, panels and networking events. More info at: forecastpro.com/userconference2017/index.html

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43 Questions?

44 Thank you for attending!