Chapter 7 Forecasting Demand

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1 Chapter 7 Forecasting Demand Aims of the Chapter After reading this chapter you should be able to do the following: discuss the role of forecasting in inventory management; review different approaches to forecasting; use a variety of judgmental forecasting methods; define time series and appreciate their importance for inventory control; calculate forecast errors; describe the characteristics of causal forecasting and use linear regression; describe the characteristics of projective forecasting and use forecasts based on simple averages, moving averages and exponential smoothing; forecasts demand with seasonality and trend; consider the planning needed for forecasts. 1

2 Forecasting supporting management decisions Use of Forecasts Forecasting data: Demand Costs Lead time Inputs to forecasting process: Forecasting model Values for parameters Historical data Subjective inputs 2

3 Information needed for forecasts Use of Forecasts The forecasts should be: accurate with small errors unbiased so they do not always under- or over-estimate demand responsive to changes in demand not affected by the odd unusual figure in time for its purpose cost-effective easy to understand. 3

4 Use of Forecasts Specific areas need management inputs: items that are particularly important or expensive, have large or erratic forecasting errors, have demand that suddenly changes, have a major change of some other type, have no recent demand, or have recently been introduced to stock. Methods of Forecasting There are so many different ways of forecasting, so many different things to forecast and so many different circumstances, that no single method of forecasting is always the best. time covered in the future availability of historical data relevance of historical data to the future type of product variability of demand accuracy needed and cost of errors benefits expected from the forecasts amount of money and time available for the forecast. 4

5 Methods of Forecasting 1. Long-term forecasts look ahead several years the time needed to build a new factory or organize new facilities. They usually look at overall demand which gives enough information to plan budgets and major facilities over the next few years (strategic decisions). 2. Medium-term forecasts look ahead between three months and a year the time needed to replace an old product by a new one or organize resources (tactical decisions). 3. Short-term forecasts cover the next few weeks describing the continuing demand for a product or scheduling operations (operational decisions). The time horizon affects the choice of forecasting method, because of the availability and relevance of historic data, the time available to do the forecasting, the cost involved and the effort considered worthwhile. Methods of Forecasting Qualitative (judgmental) forecasts 1. Personal insight 2. Panel consensus 3. Market surveys 4. Historical analogy 5. Delphi method Quantitative forecasts 1. Projective methods look at the pattern of past demand and extend this into the future. 2. Causal methods look at the factors that affect demand and use these to forecast. 5

6 Methods of Forecasting Forecasting methods Qualitative/ judgmental Quantitative/ statistical Personal insight Panel consensus Market surveys Historical analogy Delphi method Projective methods Causal methods Qualitative Methods of Forecasting 1. Personal insight uses a single expert who is familiar with the situation to produce a forecast based on his/her own judgment. 2. Panel consensus by collecting together several experts and allowing them to talk freely to each other until they reach a consensus. 3. Market surveys collect data from a sample of customers, analyze their views, and then draw inferences about the population at large. a sample of that accurately represents the population; carefully worded, useful, unbiased questions; fair and honest answers; reliable analyses of the answers; valid conclusions drawn from the analyses. 6

7 Qualitative Methods of Forecasting Qualitative (judgmental) forecasts 4. Historical analogy uses the demand of a similar item that was introduced in the past to judge the demand for a new item. Qualitative Methods of Forecasting Qualitative (judgmental) forecasts 5. Delphi method A number of experts are posted a questionnaire; Each reply is anonymous to avoid the influences of status; The replies are analyzed and summaries are passed back to the experts. Now each expert is asked to reconsider their original reply in the light of the summarized replies from others. They may adjust their answers for a second round of opinions. This process is repeated several times usually between three and six. 7

8 Time Series Time series: series of observations taken at regular intervals of time. constant series, where demand continues at roughly the same level over time (such as demand for bread or annual rainfall); trend, where demand either rises or falls steadily (such as demand for 3G phones or the price of petrol); seasonality, where demand has a cyclical component (such as demand for ice cream or electricity). Time Series 8

9 Time Series There are always differences between actual demand and the underlying pattern. These differences form a random noise that is superimposed on the underlying pattern. Actual demand = underlying pattern + random noise The noise is a completely random effect that is caused by many factors, such as varying customer demand, hours worked, speed of working, weather, rejections at inspections, time of year, wider economic influences, errors in available data, delays in updating information, poor communications. It is the noise that makes forecasting difficult. With a good forecast this error should be relatively small. Time Series There are always differences between actual demand and the underlying pattern. These differences form a random noise that is superimposed on the underlying pattern. Actual demand = underlying pattern + random noise The noise is a completely random effect that is caused by many factors, such as varying customer demand, hours worked, speed of working, weather, rejections at inspections, time of year, wider economic influences, errors in available data, delays in updating information, poor communications. It is the noise that makes forecasting difficult. Error = forecast actual demand 9

10 Worked Example 1: Errors in Forecasting Hendra Holidays has compared the actual number of holidays it booked each week with its short-term forecasts. What are the errors? What do these errors show? Week Demand Forecast Error = forecast actual demand Week Demand Forecast Error Abs. Error Error Sq Worked Example 2: Causal Forecasting Kurt Steinman s computer supply business is growing, and sales over the past 10 months have been as follows. Month Demand If Kurt uses linear regression to forecast demand for the next three months, what results will he get? Month Forecast Slope= Intercept=

11 Demand 6/9/2014 Worked Example 3: Causal Forecasting Over the past 16 weeks Burridge Transport Ltd has recorded the following number of loads moved for a particular customer. What can the company learn from these figures? Week Demand y = x R² = Week Worked Example 4: Projective Forecasting (Simple Average) Use simple averages to forecast demand for period 6 of the following two time series. How accurate are the forecasts? What are the forecasts for period 27? Week Series Series Week Avg. Series Series

12 Worked Example 5: Projective Forecasting (Moving Average) Demand for an item over the past 6 months has been as follows: Month Demand The market for this item is unstable, and any data over 3 months old is unreliable. Use a moving average to forecast demand for the item. Month Demand Moving Avg Worked Example 6: Projective Forecasting (Moving Average) Demand for an item over the past 11 weeks is as follows. Use moving averages over different periods to find one week ahead forecasts. Month Demand Month Demand Moving Avg(3) Moving Avg(4) Moving Avg(6)

13 Worked Example 6: Projective Forecasting (Moving Average) Worked Example 7: Projective Forecasting (Exponential Smoothing) An item has the following weekly demand. Use exponential smoothing with α = 0.2 and an initial forecast for week 1 of 102 units to find one period ahead forecasts. Month Demand Month Demand Forecast

14 Worked Example 8: Projective Forecasting (Exponential Smoothing) The following time series has a clear step upwards in demand in month 4. Use an initial forecast of 50 to compare exponential smoothing forecasts with varying values of α. Month Demand Month α= Demand Forecast Forecast Forecast Forecast Worked Example 8: Projective Forecasting (Exponential Smoothing)

15 Worked Example 9: Projective Forecasting (Winter s Model) Over the past 12 quarters the demand for an item has been as follows: Quarter Demand How would you forecast demand for the next five quarters? Worked Example 9: Projective Forecasting (Winter s Model) Quarter Actual Trend and Seasonal Model t A(t) L(t) T(t) S(t) F(t) Deseasonal Initializati on Ex-Post Forecast Forecast a= 0.3 b= 0.4 g=

Forecasting. Dr. Richard Jerz rjerz.com

Forecasting. Dr. Richard Jerz rjerz.com Forecasting Dr. Richard Jerz 1 1 Learning Objectives Describe why forecasts are used and list the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three qualitative