1. 3-1Forecasting Ghana Institute of Management and Public Administration [GIMPA] McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. Forecasting Dr. Samuel Famiyeh., PhD., MBA., BSc., MGhIG

2. 3-2Forecasting  Learning Outcomes After completing, participants should be able to:  List the elements of a good forecast;  Outline the steps in the forecasting process;  Describe at least three qualitative forecasting techniques with their advantages and disadvantages;  Compare and contract the qualitative and quantitative approaches to forecasting;  Briefly describe averaging and trend techniques, in addition to regression analysis and solve typical problems;  Describe some measures of forecast accuracy; and  Identify the major factors to consider when choosing a forecasting technique.

3. 3-3Forecasting FORECAST:  A statement about the future value of a variable of interest such as demand. The better those predictions are, the more informed decisions can be.  Forecasts affect decisions and activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product / service design

4. 3-4Forecasting Forecasts In operations management, we forecast a wide range of future events, which could significantly affect the long-term success of the firm. Most often the basic need for forecasting arises in estimating customer demand for a firm’s products and services. However, we may need aggregate estimates of demand as well as estimates for individual products. In most cases, a firm will need a long-term estimate of overall demand as well as a shorter-run estimate of demand for each individual product or service.

5. 3-5Forecasting Forecasts Short-term demand estimates for individual products are necessary to determine daily or weekly management of the firm’s activities such as scheduling personnel and ordering materials. On the other hand, long-term estimates of overall or aggregate demand can be used in determining company: Strategy, Planning long-term capacity and Establishing facility location needs of the firm.

6. 3-6Forecasting AccountingCost/profit estimates FinanceCash flow and funding Human ResourcesHiring/recruiting/training MarketingPricing, promotion, strategy MISIT/IS systems, services OperationsSchedules, MRP, workloads Product/service designNew products and services Uses of Forecasts

7. 3-7Forecasting Features common to all forecast  Assumes causal system past ==> future  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs. individuals  Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.

8. 3-8Forecasting Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

9. 3-9Forecasting 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 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

10. 3-10Forecasting Types of Forecasts  Judgmental - uses subjective inputs  Time series - uses historical data assuming the future will be like the past  Associative models - uses explanatory variables to predict the future

11. 3-11Forecasting Judgmental Forecasts  Executive opinions  Sales force opinions  Consumer surveys  Outside opinion  Delphi method  Opinions of managers and staff  Achieves a consensus forecast

12. 3-12Forecasting Time Series Forecasts  Trend - long-term movement in data  Seasonality - short-term regular variations in data  Cycle – wavelike variations of more than one year’s duration  Irregular variations - caused by unusual circumstances  Random variations - caused by chance

13. 3-13Forecasting Forecast Variations Trend Irregular variatio n Seasonal variations 90 89 88 Figure 3.1 Cycles

14. 3-14Forecasting Naive Forecasts 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.

15. 3-15Forecasting  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 Naïve Forecasts

16. 3-16Forecasting Techniques for Averaging  Moving average  Weighted moving average  Exponential smoothing

17. 3-17Forecasting Moving Averages  Moving average – A technique that averages a number of recent actual values, updated as new values become available.  Weighted moving average – More recent values in a series are given more weight in computing the forecast. MA n = n AiAi i = 1  n

18. 3-18Forecasting Moving Averages E.g. MA 3 would refer to a three-period moving average forecast and MA 5 would refer to a five-period moving average forecast. E.g. Compute-three period moving average forecast given the demand for shopping carts for the last five periods. PeriodDemand 142 240 343 440 541

19. 3-19Forecasting Moving Averages F 6 = 43 + 40 + 41 3 = 41.33 If the actual demand in period 6 turns out to be 39, what will be the moving average for period 7? F 7 = 40.00 show working please

20. 3-20Forecasting Moving Averages Weighted moving average – More recent values in a series are given more weight in computing the forecast. Given the following demand data: a) Compute a weighted average forecast using a weight of.40 for the most recent,.30 for the next and.20 for the next, and.10 for the next. b) If the actual demand for period 6 is 39, forecast demand for period 7 using the same weights as in part a. PeriodDemand 142 240 343 440 541

21. 3-21Forecasting Linear Trend Equation  F t = Forecast for period t  t = Specified number of time periods  a = Value of F t at t = 0  b = Slope of the line F t = a + bt 0 1 2 3 4 5 t FtFt

22. 3-22Forecasting Calculating a and b b = n(ty) - ty nt 2 - ( t) 2 a = y - bt n   

23. 3-23Forecasting Calculating a and b Week (t)Sales (y) 1150 2157 3162 4166 5177 Hence or otherwise, forecast the sales for weeks 6 and 7

24. 3-24Forecasting Linear Trend Equation Example

25. 3-25Forecasting Linear Trend Calculation y = 143.5 + 6.3t a= 812- 6.3(15) 5 = b= 5 (2499)- 15(812) 5(55)- 225 = 12495-12180 275-225 = 6.3 143.5

26. 3-26Forecasting Cell phone sales for Mobile Max over the last 10 weeks are shown in the table below. Plot the data, and visually check to see if a linear trend line would be appropriate. Determine the equation of the line and predict sales for weeks 11 and 12. WeekUnit Sales tyt2t2 1700 2724 3720 4728 5740 6742 7758 8750 9770 10775

27. 3-27Forecasting The national Communication Authority sells radio frequency inventory tags. Monthly sales for a seven-month period were as follows: a) Plot the monthly data on a sheet of paper b) Forecast September sales volume using each of the following: 1. A linear trend equation 2. A five-month moving average 3. The naïve approach 4. A weighted average using.60, for Aug,.30 for July and.10 for June. c) What does use of the term sales rather than demand presume? MonthSales (000 units Feb19 Mar18 Apr15 May20 Jun18 Jul22 Aug20

28. 3-28Forecasting Choosing a Forecasting Technique  No single technique works in every situation  Two most important factors  Cost  Accuracy  Other factors include the availability of:  Historical data  Computers  Time needed to gather and analyze the data  Forecast horizon