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Chapter 3

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Wilfrid Laurier University
Paul Iyogun

BU385 Chapter 3 – Demand Forecasting Week 2 Introduction -Demand forecast – the estimate of expected demand during a specified future period -Demand forecasting basically involves modelling the past pattern of demand for an item and projecting it into the future while taking new developments into account -Three uses for demand forecasts: -Help managers design the system -Help managers plan the medium-term use of the system -To schedule the short-term use of the system -Supply chain partners have started to collaborate on the forecasting process Features Common to All Forecasts -Forecasting techniques generally assume that the same underlying casual system that existed in the past will continue to exist in the future -Forecasts are rarely perfect; actual results usually differ from predicted values -Forecasts for groups of items tend to be more accurate than forecasts for individual items, because forecasting errors among items in a group usually have a cancelling effect -Forecast accuracy decreases the farther the forecasts time period is into the future -Forecasting horizon – the range of time periods we are forecasting for Elements of a Good Forecast 1. The forecast horizon must be long enough so that its results can be used 2. The degree of accuracy of the forecast should be stated 3. The forecasting method/software chosen should be reliable; it should work consistently 4. The forecast should be expressed in meaningful units 5. All functions of an organization should be using the same forecast 6. The forecasting technique should be simple to understand and use Steps in the Forecasting Process 1. Determine the purpose of the forecast, the level of detail required, the amount of resources that can be justified, and the level of accuracy necessary 2. Establish a forecasting horizon 3. Select a forecasting technique 4. Gather and analyze relevant historical data 5. Prepare the forecast 6. Monitor the forecast Approaches to Forecasting -There are two general approaches to forecasting: judgmental and quantitative -Judgmental methods – use non-quantitative analysis of historical data and/or analysis of subjective inputs from consumers, sales staff, managers, executives, similar products, and experts to help develop a forecast -Time series model – extend the pattern of data into the future -Associative models – se explanatory variables to predict future demand for the variable of interest Overview of Demand Forecasting by Forecasting Horizon -Forecasting long-term demand typically involves annual data, it requires knowledge of the specific market and judgment of experts/managers – 5 years -Forecasting medium-term demand typically involves monthly demand – 12 months -Forecasting short-term demand typically involves daily or weekly demand – 12 weeks BU385 Chapter 3 – Demand Forecasting Week 2 Judgmental Methods -In some situations forecasters may rely solely on judgment and opinion to make forecasts Executive Opinions -A small group of upper-level managers may meet and collectively develop a forecast Sales Force Opinions -The sales staff or the customer service staff is often a good source of information because of their direct contact with customers Consumer Surveys -Because it is the potential consumers who ultimately determine sales, it seems natural to solicit input from them Historical Analogies -Sometimes the demand for a similar product in the past, after some adjustment, can be used to forecast a new product’s demand Expert Opinions -The forecaster may solicit opinions from a number of experts -Delphi method – experts complete a series of questionnaires, each developed from the previous one, to achieve a consensus forecasts Time Series Models: Introduction and Averaging -Time series – a time-ordered sequence of observations taken at regular intervals of time -Analysis of time series data requires the analyst to identify the underlying behaviour of the series -Trend behaviours are as follows: 1. Level (average) – a horizontal pattern of time series 2. Trend – a persistent upward or downward movement in data 3. Seasonality – regular wavelike variations related to the calendar weather, or recurring events 4. Cycle – wavelike variation lasting more than one year 5. Irregular variation – caused by unusual one-time explainable circumstances, not reflective of typical behaviour 6. Random variation – residual variations after all other behaviours are accounted for (also called noise) Naïve Methods -Naïve forecast – for a stable series, the naïve forecast for the period equals the previous period’s actual value Averaging Methods -Three techniques for averaging: moving average, weighted moving average, exponential smoothing -Moving average – technique that averages a number of recent actual values as forecast for current period. It is updated as new values become available -Weighted moving average – a variation of moving average where more recent values in the time series are given larger weight in calculating a forecast -Exponential smoothing – weighted averaging method based on previous forecast plus a percentage of the difference between that forecast and the previous actual value BU385 Chapter 3 – Demand Forecasting
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