Lecture 2: Time Series Forecasting Forecasting and production Data and demand patterns Stationary demand forecasting model Naïve method Moving average method Exponential smoothing method Summary Readings: Page 68-87 Assignment 1 Part 1: 4.24, 4.30, 4.34, 4.43 Part 2: Select one article from SR-2. Write a half page summary (using Win-word) to describe the forecasting procedure of the company and point out what you think is the most important aspect for forecasting in this company Sep 6 ISMT 162/Stuart Zhu

Forecasting and Production Business planning is based on forecast (and strategy) Is this new product going to sale? What is the potential market for this new product? Will customer accept this new technology? How much to produce in each period? Availability of raw materials? Changes in interest rates, exchange rates, material prices? Bad forecasts are costly Sony’s video technology, Apple computer (customer/tech) IBM’s notebooks (new product sales potential) Stock-out and markdown can cost more than manufacturing cost (Fisher et al. 1994) Sep 6 ISMT 162/Stuart Zhu

Making and Using Forecasts Forecasts are usually made by marketing and sales Forecasts are decision inputs for marketing and production/operations Forecasting horizon in operation planning short term product sales forecast in days or weeks for inventory management and production plan (MRP) intermediate term forecast of sales patterns in weeks or months of product family for labor and resource requirement long term demand trend forecast in months or years for capacity planning Sep 6 ISMT 162/Stuart Zhu

Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Obtain, clean and analyze data Step 5 Make the forecast Step 6 Monitor the forecast “The forecast” Sep 6 ISMT 162/Stuart Zhu

Subjective Forecasting Methods Sales force composites sales manager aggregates salesmen’s individual sales estimates could be biased Consumer survey (market research) for signals of future trend and shift of preference patterns, survey and sampling design needs specialist Executive opinion no data, expert opinion is the only source of information interview or consensus meeting The Delphi method formal and iterative method of coming up a forecast from experts’ opinion, a group of experts and a facilitator Sep 6 ISMT 162/Stuart Zhu

Sport Obermeyer, an Example 6 managers look at the new styles and estimate sales (short life-cycle products) Forecasts Member Pandora Parka Entice Jacket Carolyn 1,200 1,500 Laura 1,150 700 Tom 1,250 1,200 Kenny 1,300 300 Wally 1,100 2,075 Wendy 1,200 1,425 Average Std. Dev 1,200 1,200 70.7 627.1 Sep 6 ISMT 162/Stuart Zhu

Objective Forecasting Methods Time Series models past data contains future demand information and can be used to project future demands used for operation planning Associative models uses explanatory variables to predict the future Sep 6 ISMT 162/Stuart Zhu

Data and Demand Patterns Data Analysis We need to know the demand pattern before selecting an appropriate forecasting model How? Plot the data to examine the pattern Example data sets: forecast-s1 Also Figure 3.1 (P. 73) What are the common patterns? Why is it important to determine the pattern first? Sep 6 ISMT 162/Stuart Zhu

Demand Patterns Stationary/constant Linear trend Cyclic/seasonal Cyclic/seasonal with trend εt : a random fluctuation; a, b: constant; ct : time-varying coefficient forecast-s1 Sep 6 ISMT 162/Stuart Zhu

Forecasting Model for Stationary Demand For a stationary demand pattern, there is only one parameter a that we need to estimate from the past demand data Time series forecasting model Use Ft to denote the estimate of a made at time t, i.e., At Sep 6 ISMT 162/Stuart Zhu

Time Series Forecasting Methods to obtain Ft Naïve N-period moving average Exponential smoothing Purpose: To estimate the parameters of the demand model, using past data To filter out the random element from the past data Sep 6 ISMT 162/Stuart Zhu

Naive Forecasts Ft = At –1 (1) Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value. Ft = At –1 (1) Sep 6 ISMT 162/Stuart Zhu

Naïve Forecasts Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy Sep 6 ISMT 162/Stuart Zhu

N-Period Moving Average Select only the recent data Example 1 Passed demand data: 106 110 118 105 115 100 112 106 118 102 112 110 What is the forecast for period 13? Or for period 15? With N =3 With N =5 Choice of N: ≥3 forecast-s2 (2) Sep 6 ISMT 162/Stuart Zhu

Exponential Smoothing Use Ft to denote the estimate of a at period t, is the smoothing constant Data: 106 110 118 105 115 100 112 106 118 102 112 110 What is the forecast for period 13? forecast-s2 (3) Sep 6 ISMT 162/Stuart Zhu

Information Content All past data are used The weight to the data of i periods old is It decreases exponentially as the data gets older Sep 6 ISMT 162/Stuart Zhu

Choice of Smoothing Constant  Between 0 and 1 (why?) If the demand is stable, choose a small ; if the demand is rapidly increasing or decreasing, choose a large . (why?)  determines the weight on the most recent data We should test the forecast model to fit a good  Usually  is from 0.1 to 0.3 Sep 6 ISMT 162/Stuart Zhu

Summary Distinguish three things in time series forecast: the underlying process parameter estimation and forecasting formula/model Equations (1), (2), (3) Keys to good forecast Good data Right model Proper balance of forecasting stability and sensitivity to the recent change in data, through selection of N and Sep 6 ISMT 162/Stuart Zhu

Forecast Variations Trend Cycles Irregular variation 90 89 88 Seasonal variations Sep 6 ISMT 162/Stuart Zhu

Review Problems Problem 1 at page 112 Problem 3 at page 113 Sep 6 ISMT 162/Stuart Zhu

A Caveat on Forecasts “Man won’t fly for 1000 years” Wilbur Wright - 1901 “No woman in my time will be Prime Minister” Margaret Thatcher – 1969 “I think there is a world market for about five computers” Thomas J. Watson - 1958 The last slide shows you some of the worst forecasts have ever been made in the history. What do I try to say here? It is important to have planning and rely on scientific methods. However, forecasts are always just forecasts. We will never know what is going to happen for sure in the future. We have to learn to live with uncertainty and inaccurate forecasts. For some people, that is exactly what is making life exciting. Sep 6 ISMT 162/Stuart Zhu