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3-2 You should be able to: LO 3.1List features common to all forecasts LO 3.2Explain why forecasts are generally wrong LO 3.3List elements of a good forecast LO 3.4Outline the steps in the forecasting process LO 3.5Summarize forecast errors and use summaries to make decisions LO 3.6Describe four qualitative forecasting techniques LO 3.7Use a naïve method to make a forecast LO 3.8Prepare a moving average forecast LO 3.9Prepare a weighted-average forecast LO 3.10Prepare an exponential smoothing forecast LO 3.11Prepare a linear trend forecast LO 3.12Prepare a trend-adjusted exponential smoothing forecast LO 3.13Compute and use seasonal relatives LO 3.14Compute and use regression and correlation coefficients LO 3.15Construct control charts and use them to monitor forecast errors LO 3.16Describe the key factors and trade-offs to consider when choosing a forecasting technique

Techniques assume some underlying causal system that existed in the past will persist into the future 2. Forecasts are not perfect 3. Forecasts for groups of items are more accurate than those for individual items 4. Forecast accuracy decreases as the forecasting horizon increases LO 3.1

3-4 Forecasts are not perfect: Because random variation is always present, there will always be some residual error, even if all other factors have been accounted for. LO 3.2

3-5 The forecast should be timely should be accurate should be reliable should be expressed in meaningful units should be in writing technique should be simple to understand and use should be cost-effective LO 3.3

Determine the purpose of the forecast 2. Establish a time horizon 3. Obtain, clean, and analyze appropriate data 4. Select a forecasting technique 5. Make the forecast 6. Monitor the forecast errors LO 3.4

3-7 Mean Absolute Devaiton(MAD) weights all errors evenly Mean Square Error(MSE) weights errors according to their squared values Mean Absolute Percentage Error(MAPE) weights errors according to relative error LO 3.5

3-8 The forecast and the actual demand of a product is given in the table. Determine the forecast accuracy measures.

3-9 Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts Executive opinions a small group of upper-level managers may meet and collectively develop a forecast Sales force opinions members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future Consumer surveys since consumers ultimately determine demand, it makes sense to solicit input from them consumer surveys typically represent a sample of consumer opinions Other approaches managers may solicit 0pinions from other managers or staff people or outside experts to help with developing a forecast. the Delphi method is an iterative process intended to achieve a consensus LO 3.6

Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell....

3-11 Naïve Forecast Uses a single previous value of a time series as the basis for a forecast The forecast for a time period is equal to the previous time period’s value Can be used with a stable time series seasonal variations trend LO 3.7

3-12 Regression - a technique for fitting a line to a set of data points Simple linear regression - the simplest form of regression that involves a linear relationship between two variables The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion) y= ax +b LO 3.14

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3-14 In a factory, the cost of labor is highly related to the products manufactured. The corresponding data is given in the next table. Conduct a regression analysis and find out an estimate for the labor cost if the production units is 310.

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3-16 Coefficient of correlation, r, is the measure how the two variables have linear relation between. If r is close to “ 0 ” then there is no linear relation between the variables. If it is positive and close to “1”, if one is increased the other will also increase. If it is close to negative “-1”, then if one variable is increased the other will be decrease..

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3-18 Technique that averages a number of the most recent actual values in generating a forecast LO 3.8

3-19 The most recent values in a time series are given more weight in computing a forecast The choice of weights, w, is somewhat arbitrary and involves some trial and error LO 3.9

3-20 In the table, the demand of a product for the past year is given. Using 3 period and 5 period moving average forecasting, estimate the demand for 13th month and 20th month. Compare 4-period and 7- period moving average and determine which one is better using accuracy measures

3-21 A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error LO 3.10

3-22 PeriodDemandForecast

3-23 F 3 = (40 -42)=41.8 F 4 = ( )=41.92 F 5 = ( )=41.73

3-24 Compute Seasonal Relatives(SR) 1. Compute season average 2. Compute overall average 3. Compute the SR by dividing each value by overall average Deseasonalize Data 1. Divide season average by SR 2. Obtain trend estimate by linear regression Add seasonality by multiplying seasonal relative

3-25 SeasonWeek 1Week 2Week 3 Tuesday Wed Thurs Fri Sat Sun Mon Estimate the demand for next week.

3-26 LO 3.15

3-27 Factors to consider Cost Accuracy Availability of historical data Availability of forecasting software Time needed to gather and analyze data and prepare a forecast Forecast horizon LO 3.16