What Now? How to Proceed When Automatic Forecasting Doesn t Work

Transcription

1 What Now? How to Proceed When Automatic Forecasting Doesn t Work Presented by Eric Stellwagen Vice President & Cofounder estellwagen@forecastpro.com Business Forecast Systems, Inc. 68 Leonard Street Belmont, MA USA (617)

2 On-demand Webinars and Handouts Today s webinar along with a pdf version of the slide set will be posted on our Website next week. Previously presented Webinars are available for viewing on Participants will receive an confirming availability after the Webinar and slide set is posted.

3 Eric Stellwagen Vice President & Cofounder of Business Forecast Systems, Inc. Coauthor of Forecast Pro product line. Over 27 years in forecasting. Currently serving on the board of directors of the International Institute of Forecasters and on the practitioner advisory board of Foresight: The International Journal of Applied Forecasting.

4 What We ll Cover Overview The Role of Judgment Automatic Forecasting Exponential Smoothing New Product Forecasting Top-down Approaches Multivariate Approaches Summary

5 Common Forecasting Methods Judgmental Approaches Simple Time Series Methods: Moving Averages Same as Last Year Percentage Growth Statistical Time Series Methods: Exponential Smoothing Box-Jenkins (ARIMA) Multivariate Methods: Event-index Models Dynamic Regression New Product Methods: Forecast by Analogy (looks-like) Assumption-based Models Diffusion Models (e.g., Bass) Croston s Intermittent Demand Model

6 Evolution of Forecasting Process Phase 1 Phase 2 Phase 3 Judgment & Spreadsheets Automatic Time Series Approaches Customized Approaches

7 The Role of Judgment

8 Judgmental Forecasting Pros: Does not require statistical expertise. Allows forecaster to incorporate domain knowledge. This knowledge can come from many sources including experience with similar products, feedback from sales staff, customer surveys, focus groups, etc. Does not require historical data.

9 Cons: Judgmental Forecasting Is subjective. Can be biased by company politics, sales goals, etc. Is difficult to monitor performance and fine tune future forecasts. Is not automatic can be very time consuming.

10 Judgmental Forecasting Judgment often plays an important role in forecasting, particularly with new products, short product-life-cycle products, rapidly changing environments and instances where the forecaster s domain knowledge is not captured in the statistical forecasting model. A strong recommendation is to add judgment in the form of an override to a statistically generated base-line forecast. This practice provides the ability to track the effectiveness of the judgmental override and introduces more accountability into the process.

11 Automatic Time Series Approaches

12 3 Months of Data

13 12 Months of Data

14 24 Months of Data

15 36 Months of Data

16 6 Years of Data

17 Automatic Time Series Approaches Pros: Simple to understand and explain Widely accepted and used Often quite accurate Adaptive Easy to apply

18 Automatic Time Series Approaches Cons: Requires adequate demand history Assumes continuity between past and future Does not capture response to noncalendar-based events (e.g., promotions) Does not capture response to explanatory variables Implementations vary and some are poor

19 Rejecting Automatic Models When you disagree with the forecasts generated using an automatic time series approach you should reject them. Generally there are three ways to do this: Judgmentally override the forecasted values. Dictate that a different forecasting model be used. Reconfigure the input data.

20 Why is it Wrong? Domain knowledge Your knowledge of the future leads you to reject the forecast. The solution is most often to judgmentally adjust the forecast. Might be done informally or as part of a structured process (e.g., S&OP)

21 Adding Judgment Best practices: Retain statistical forecast and adjusted forecast for comparison Document reason for changes

22 Why is it Wrong? Chose wrong time series model Often a case of misidentifying trends and/or seasonal patterns. Dictating an appropriate exponential smoothing model is often a good solution.

23 Exponential Smoothing

24 Main Characteristics of Exponential Smoothing A family of models. Models three data components level, trend, and seasonality. Assumes that each component is changing in time. Assumes that there is random variation (noise). Uses weights to reflect the relative emphasis given to the recent vs. the distant past. Estimates final values of the components and uses them to construct forecasts.

25 Holt-Winters Exponential Smoothing Constant Level Nonseasonal (SIMPLE) Additive Seasonal Multiplicative Seasonal Linear Trend (HOLT) (WINTERS) Damped Trend (0.95) Exponential Trend (1.05)

26 Why is it Wrong? Inadequate data Data too short (new product) Data too low level (not enough structure)

27 New Product Forecasting and Top-down Approaches

28 Types of New Products There are different types of new products including: Replacement Products Product Line Extensions New-to-Company New-to-World The type of new product you are trying to forecast will often dictate what approaches should be considered.

29 Replacement Products and Product-Line Extensions When forecasting replacement products or product line extensions you will often want to leverage the data that exist for the predecessor products. Approaches can include: Judgment and Market Research Item Supersession (i.e., mapping histories) Top-down Forecasting

30 Item Supersession You create a forecast history for the new product using the demand histories of predecessor products and the new product. For a replacement product this may be as simple as merging the old product s history with the new product s history. More complex mapping may be necessary depending on the circumstances.

31 Item Supersession Old Product New Product

32 Item Supersession Forecast History

33 Top-down Forecasting Cough Syrup SKU 1 SKU 2 Etc. Group-level data are higher volume, will often exhibit more structure and will have a longer demand history than the product line extensions and replacement products. To generate a top-down forecast, you first forecast at the group level using the aggregated history. Then, you forecast at the lower levels. Finally, you apply proportionality factors to lower-level forecasts so that the forecasts sum to the top-level forecast.

34 New Product Forecasting When forecasting new-to-company or new-to-world products you obviously don t have internal data external data may or may not be available. Approaches can include: Judgment and Market Research Forecasting by Analogy (looks-like) Assumption-based Models Market Share Forecasting Diffusion Models

35 Why is it Wrong? Inadequate data Data too low level (not enough structure) Simplify the hierarchy Forecast top-down Aggregate the time buckets (e.g., switch from weeks to months) Use bucket conversions

36 Bucket Conversion It is not uncommon for a company that needs a daily or weekly forecast to discover that the data can t support statistical modeling at these periodicities. One solution is to forecast at an aggregated level and allocate down to the lower level. This process is sometimes referred to as bucket conversion.

37 Bucket Conversion S Bucket conversion can take different forms: Flat allocation Proportional allocation based on historical profiles Proportional allocation based on forecasts (bucket synchronization)

38 Why is it Wrong? Time series model is not appropriate when demand is driven by: Events that move around the calendar Explanatory variables that can change abruptly

39 Multivariate Methods

40 Common Events Product promotions Moveable holidays (e.g., Easter, Rosh Hashanah, Ramadan) Catastrophes (e.g., earthquakes, hurricanes, 9/11) Labor strikes Acquisitions New legislation or regulations

41 Forecasting Event-Driven Data Judgmentally adjust history to remove impact. Separate base demand from event-driven demand. Use a time series extension model (e.g., eventindex model, ARIMA intervention model, etc.). Use a multivariate model (e.g., regression).

42 What is an Event-Index Model? An extension of exponential smoothing. An index-based approach. The model introduces an additional smoothing weight and updating equation. The model requires an event schedule.

43 Example: Sales of Mouthwash Demand for mouthwash is not seasonal and for this brand not trended. Price promotions by both the manufacturer and competitors introduce significant peaks and valleys. The timing of promotions is similar from year to year (but not exactly the same), and thus without domain knowledge the data appear to be seasonal. The introduction of EDLP with Wal-Mart changes the response to promotions.

44 Why is it Wrong? Time series model is not appropriate when demand is driven by: Events that move around the calendar Explanatory variables that can change abruptly

45 Pros: s Dynamic Regression Allows for the introduction of explanatory variables. Lends insight into relationships between variables. Allows for what if scenarios. Can exploit leading indicators.

46 Dynamic Regression Cons: Is not automatic, requires considerable expertise and time. Will produce poor forecasts of the dependent variable if there are difficulties in forecasting the explanatory variables. Requires large amounts of data.

47 Independent Variables Internal Variables External Variables Prices Weather Promotion Economy Competition Demographics

48 Example: Sales of Electricity Temperature is an important driver.

49 Example: Sales of Electricity Time series models cannot capture adequate response to temperature.

50 Example: Sales of Electricity Dynamic regression models capture response to temperature and work well.

51 Summary Judgment is always important and is best applied as an adjustment to a statistically generated base-line forecast. Automatic time series methods work well when you have adequate data and are generally superior to spreadsheet models. When you reject an automatic time series forecast, your options include: Judgmentally overriding the forecasted values. Dictating that a different forecasting model be used. Reconfiguring the input data.

52 Forecast Training and Workshops S BFS offers forecasting webinars and product training workshops. On-site, and remote-based (via WebEx) classes are available. Learn more at

53 Forecast Pro Examples from today s Webinar used Forecast Pro. To learn more about Forecast Pro: Request a live WebEx demo for your team (submit your request as a question right now) Visit Call us at

54 Our Next Webinar Tracking Accuracy: An Essential Step to Improve Your Forecasting Process, April 11, :30 p.m. EDT Eric Stellwagen, Vice President of Business Forecast Systems Highlights include how to apply best practices, the pros and cons of different error measurements and how to spot poor performance. Visit to sign up!

55 Questions?

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