2. 1 BABS 502 Lecture 1 February 23, 2009 (C) Martin L. Puterman

3. 2 Bookkeeping Your instructor Course guidelines –Lectures –Assignments –Project – no exam –Contest –Software –NCSS Case Study (C) Martin L. Puterman

4. 3 What is a Forecast? A prediction of the future fore = before + cast = throw Literally planning before you throw. There is some confusion about this point Often organizations refer to direct outputs of decisions as forecasts. (Sometimes it is easier to use this terminology) Example – “production forecasts” are not “forecasts” They are subject to variability but are known to some degree of accuracy by organization members. (C) Martin L. Puterman

5. 4 Course Themes Forecasts are necessary for effective decision making –Forecasting, planning and control are interrelated Forecasts are usually (almost always) wrong –Quantifying forecast variability is as important as determining the forecast; it is the basis for decision making. –Rare events happen and can have significant impact on forecasts Scientific methods improve forecasting (C) Martin L. Puterman

6. 5 Course Objectives To provide a structured and objective approach to forecasting To provide hands on experience with several popular forecasting methods To determine the data requirements for effective forecasting To integrate forecasting with management decision making and planning To introduce you to some advanced forecasting methods (C) Martin L. Puterman

7. 6 Why Forecast? It’s fun To look smart But most importantly: To make better decisions –Investments –Inventory –Staff –Medical treatment timing Fact: Forecasts are usually (always?) wrong! –Why do it then? –Because you have to!! Effect of bad forecasts –Excess costs – too much staff or stock –Poor service –waiting lines and stockouts (C) Martin L. Puterman

8. 7 Knowledge Base for Effective Forecasting Subject Matter Knowledge –Industry –Market –Demand Sources Statistics Statistical software and IT Interpersonal skills –acquiring data –report writing –presentations –team work (C) Martin L. Puterman

9. 8 Forecasting Applications Demand forecasts –Whistler-Blackcomb - staffing –TELUS – capacity expansion –Worksafe BC – staffing, budgeting and reserve planning –Health Authorities – staffing, scheduling and planning –Mike’s Products - production and inventory decisions Price forecasts –Teck- Cominco - production planning, ore purchase –Vancouver Olympic Village – resale value New market forecasts; –Webvan, Petfood.com, Napster Technology forecasts –Intel; Nortel; TELUS; Microsoft; Google (C) Martin L. Puterman

10. 9 Forecasting for a Consumer Product Distribution System (C) Martin L. Puterman

11. 10 The Challenge Enhance the performance of the inventory and distribution system for products in the US market Highly competitive market with highly seasonal demand patterns Client’s Goal - Get the right product in the right quantity to the right customer on time! (C) Martin L. Puterman

12. 11 The Production/Distribution System Co-packers Distribution Centers Retailers (many) Products (C) Martin L. Puterman

13. 12 Modeling A linear programming based planning tool For each SKU it finds for the next 12 months: -Optimal co-packer production levels -Optimal distribution and transshipment plans -Optimal distribution center (DC) inventory levels Developed for operational decisions but first used for tactical/strategic decisions Implemented in Excel using Frontline Solver User friendly interface (C) Martin L. Puterman

14. 13 Using the Model in Practice MonthDateSteps to Take T – 120 th Provide forecasts for month T to T + 12 T5 th Estimate closing inventory at the end of month T, using - Opening inventory of month T, - Production schedule of month T, and - Actual order from distributors and DC re-order suggestions in month T Monthly input data check list, including - Unit costs - Production and inventory capacity - Minimum and fixed production From production and distribution personnel. Document the changes to the data. 6-9 th - Run the tool with updated data, review the output and re-run if necessary. - Set production plan for month T + 1 - Document changes of actual plan from tool output and reasons of changes 10 th Provide co-packers with production plan for month T+1 (C) Martin L. Puterman

15. 14 Forecasts drive the model! Key input – Forecasts by sales region by SKU for next 12 months. –Produced by regional sales representatives –Accuracy declines over 12 month period –Not calibrated but good in aggregate! But model is used in a rolling horizon approach (C) Martin L. Puterman

16. 15 Company logo (C) Martin L. Puterman

17. 16 Model in MS Excel (C) Martin L. Puterman

18. 17 More on Forecasting (C) Martin L. Puterman

19. 18 Forecasting is NOT a Statistical Topic Primary interest is not in hypothesis tests or confidence intervals. Underlying models developed in statistics arena are often used: –regression –time series –neural networks –dynamic Bayesian systems and state space models Forecasts must be assessed on –the quality of the decisions that are produced –their accuracy (C) Martin L. Puterman

20. 19 Types of Forecasting Extrapolation –Based on previous data patterns Assumes past patterns hold in future –Exponential Smoothing, Trend Models, ARIMA models Causal –Based on factors that might influence the quantity being forecasted Assumes past relationships hold in the future –Regression Judgemental –Based on individual knowledge –Sales force composites, expert opinion, consensus methods –Surveys and market research Collaborative –Based on information available to supply chain partners –Information sharing and partnerships (C) Martin L. Puterman

21. 20 Forecasting Considerations Forecasts vs. Targets Short Term vs. Medium Term vs. Long term One Series vs. Many Seasonal vs. Non-seasonal Simple vs. Advanced One-Step Ahead vs. Many Steps Ahead Automatic vs. Manual Exceptions When to update models (C) Martin L. Puterman

22. 21 Forecasting Horizons  Short term  a few days or weeks  Medium term  usually a few months to 1 or 2 years  Long term  usually more than 2 year  Why distinguish between these?  Different methods are more suitable in each case. (C) Martin L. Puterman

23. 22 Some Forecasting Observations He who lives by the crystal ball soon learns to eat ground glass. –Edgar R. Fiedler in The Three Rs of Economic Forecasting-Irrational, Irrelevant and Irreverent, June 1977. Prediction is very difficult, especially if it's about the future. –Nils Bohr, Nobel laureate in Physics –This quote serves as a warning of the importance of testing a forecasting model out-of-sample. It's often easy to find a model that fits the past data well--perhaps too well!--but quite another matter to find a model that correctly identifies those features of the past data which will be replicated in the future There is no reason anyone would want a computer in their home. –President, Chairman and founder of Digital Equipment Corp, 1977 640K ought to be enough for anybody. –Bill Gates, 1981 Our sales forecasts are accurate in aggregate –Many marketing directors (C) Martin L. Puterman

24. 23 Forecasting methods that work Naïve: Last Period or Same Period Last Year Regression –Extrapolation –Causal Exponential Smoothing –Simple –Trend / Damped Trend –Holt-Winters Pooled methods (C) Martin L. Puterman

25. 24 Forecasting methods I don’t recommend Crystal balls Tea leaves Fortune cookies Expert Opinion Complex statistical models –Box-Jenkins / ARIMA Models –Multivariate Econometric Models –Neural Networks (C) Martin L. Puterman

26. 25 Forecasting in Organizations There is no forecasting department! (C) Martin L. Puterman

27. 26 Forecasting Practice in Organizations What quantities do organizations need to forecast? What methods are users familiar with? What methods have been used? What are the impediments to using quantitative techniques? What factors which make forecasting most difficult? (C) Martin L. Puterman

28. 27 What do organizations need to forecast? Costs –raw materials –semi-finished goods –wage rates and overheads –interest rates Sales/ Activities –by industry, by region –by market/product, market share –by product category, by wholesaler, by retailer –new product sales –competitive position - e.g. prices, exchange rates –competitive behaviour –customer service –price (C) Martin L. Puterman

29. 28 What do organizations need to forecast? Technology –new products –new processes –diffusion rates Social and Political trends –demographics –wealth profile –welfare and health provisions –impact of technology Projects –duration –costs –life cycle maintenance (C) Martin L. Puterman

30. 29 Top 10 impediments to effective forecasting 10. Absence of a forecasting function 9. Poor data 8. Lack of software 7. Lack of technical knowledge 6. Poor data 5. Lack of trust in forecasts 4. Poor data 3. Too little time 2. Not viewed as important 1. Poor data (C) Martin L. Puterman

31. 30 Forecasting Challenges Technical Issues –What is the best approach Organizational Issues –reporting structures –accountability –incentive systems Information –historical data not available –timeliness and reliability –what information is required when Users –conflicting objectives (C) Martin L. Puterman

32. 31 Top Down vs. Bottom Forecasting Top Down - Forecast at central office Bottom up - Forecast by sales force Questions – which is more accurate? which should be used? (C) Martin L. Puterman

33. 32 Silos and Forecasting IT Marketing Production Forecaster (C) Martin L. Puterman

34. 33 Responsibilities of Units Production –Acquiring materials –Planning and scheduling production runs Logistics –Delivering products to customers Marketing –Generating orders –Creating product demand IT –Acquiring software –Integrating software –Managing data (C) Martin L. Puterman

35. 34 Scientific Forecasting (C) Martin L. Puterman

36. 35 Scientific Forecasting Requires familiarity with very basic statistical concepts: –Mean, standard deviation, skewness and kurtosisskewnesskurtosis –medians and percentiles –histograms, stem and leaf plots, box plots –scatter plots, correlation, regression If you’re not keeping score you are only practicing! (C) Martin L. Puterman

37. 36 The Forecasting Process - I Determine what is to be forecasted and at what frequency Obtain data Process the data PLOT THE DATA Clean the data Hold out some data (C) Martin L. Puterman

38. 37 The Forecasting Process - II Obtain candidate forecasts Assess their quality –Forecast accuracy on hold out data –Do they make sense? –Do they produce good decisions? Revise forecasts Recalibrate model on full data set Produce forecasts and adjust as necessary Produce report In future - Evaluate accuracy of forecasts (C) Martin L. Puterman

39. 38 Means and Standard Deviations Means and standard deviations are only useful for summarizing data when it looks like it comes from a normal distribution They especially are not appropriate for summarizing time series data with trends or seasonality. (C) Martin L. Puterman

40. 39 Some Normal Distribution Properties Determined completely by its mean  and standard deviation  Its skewness is 0 and its kurtosis is 0 95% of the observations fall within 2 standard deviations (not standard errors!) of the mean –Useful for determining forecast ranges –Usually forecasts are accurate to  2 standard deviations 95% of the observations fall below  + 1.645  –Useful for determining service levels of inventory policies When extreme outliers may occur, the normal distribution may not be appropriate –Such distributions are said to have long tails –These distributions have positive kurtosis. –The book, The Black Swan, by Nicholas Taleb addresses the practical significance of this issue. (C) Martin L. Puterman

41. 40 Data Patterns (C) Martin L. Puterman

42. 41 Basic Modeling Concept An observed measurement is made up of a systematic part and a random part Unfortunately we cannot observe either of these. Forecasting methods try to isolate the systematic part. Forecasts are based on the systematic part. The random part determines the distribution shape and forecast accuracy. (C) Martin L. Puterman

43. 42 Basic Concept Again Observed Value = Signal “+” Noise In non-normal (or non-additive) models the “+” may be inappropriate (C) Martin L. Puterman

44. 43 Forecasting Notation (p.71) ta specific time period ntotal number of observations Y t observed value at time t F t+k forecasted value k periods ahead at time t (C) Martin L. Puterman

45. 44 Correlation Measures the strength of the (linear) relationship between two measurements Often denoted by  XY A number between -1 and +1 Answers question: Does one measurement contain information about another measurement? Theoretically  XY = Cov(X,Y)/  X  Y From a sample r XY (see equation 2.8). (C) Martin L. Puterman

46. 45 Autocorrelation - What is it? Correlation between observations at different time points in a time series - estimated by r k –Lag 1 autocorrelation measures the correlation between Y t and Y t-1 –Lag k autocorrelation measures the correlation between Y t and Y t-k Summarized in terms of an autocorrelation function (ACF) which give the autocorrelations between observations at all lags. –It is often represented graphically as a plot of autocorrelation vs. lag (C) Martin L. Puterman

47. 46 Autocorrelation - Why is it useful? Can the past help predict the future? –if autocorrelations at all lags are near zero then best predictor is historical mean –if all autocorrelations of differences of series are near zero then best predictor of the future is the current value –if autocorrelations at seasonal lags are large - suggests seasonality in data An important component of the ARIMA or Box- Jenkins’ method (C) Martin L. Puterman

48. 47 Autocorrelation Example 1 (C) Martin L. Puterman

49. 48 Autocorrelation Example 2 Difference Original (C) Martin L. Puterman