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1 Dickson K.W. Chiu PhD, SMIEEE Text: Ballou - Business Logistics Management, 5/E (Chapter 8)Business Logistics Management, 5/E Demand and Forecast.

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Presentation on theme: "1 Dickson K.W. Chiu PhD, SMIEEE Text: Ballou - Business Logistics Management, 5/E (Chapter 8)Business Logistics Management, 5/E Demand and Forecast."— Presentation transcript:

1 1 Dickson K.W. Chiu PhD, SMIEEE Text: Ballou - Business Logistics Management, 5/E (Chapter 8)Business Logistics Management, 5/E Demand and Forecast

2 Dickson Chiu 2006Demand-2 Learning Objectives To understand some basic concept of demand and forecasting To anticipate typical problems involved in demand and forecasting

3 Dickson Chiu 2006Demand-3 What’s Forecasted in the Supply Chain Demand, sales or requirements Purchase prices Replenishment and delivery times

4 Dickson Chiu 2006Demand-4 Some Forecasting Method Choices Historical projection Moving average Exponential smoothing Causal or associative Regression analysis Qualitative Surveys Expert systems or rule-based Collaborative

5 Dickson Chiu 2006Demand-5 Typical Time Series Patterns: Random

6 Dickson Chiu 2006Demand-6 Typical Time Series Patterns: Random with Trend

7 Dickson Chiu 2006Demand-7 Typical Time Series Patterns: Random with Trend and Seasonal

8 Dickson Chiu 2006Demand-8 Typical Time Series Patterns: Lumpy Time Sales

9 Dickson Chiu 2006Demand-9 Is Time Series Pattern Forecastable? Whether a time series can be reasonably forecasted often depends on the time series’ degree of variability. Forecast a regular time series, but use other techniques for lumpy ones. How to tell the difference: A time series is lumpy if where regular, otherwise.

10 Dickson Chiu 2006Demand-10 Analysis Details See textbook if you are interested Moving Average Exponential Smoothing Formulas Regression Analysis Combined Model Forecasting Note data requirements and timeliness requirement Tracking signal monitors the fit of the model to detect when the model no longer accurately represents the data => events

11 Dickson Chiu 2006Demand-11 Actions When Forecasting is Inappropriate Seek information directly from customers Collaborate with other channel members Apply forecasting methods with caution (may work where forecast accuracy is not critical) Delay supply response until demand becomes clear Shift demand to other periods for better supply response Develop quick response and flexible supply systems, e.g., order-to-build of Dell

12 Dickson Chiu 2006Demand-12 Collaborative Forecasting Demand is lumpy or highly uncertain Involves multiple participants each with a unique perspective—“two heads are better than one” Goal is to reduce forecast error The forecasting process is inherently unstable

13 Dickson Chiu 2006Demand-13 Collaborative Forecasting Key Steps Establish a process champion Identify the needed information and collection processes Establish methods for processing information from multiple sources and the weights assigned to multiple forecasts Create methods for translating forecast into form needed by each party Establish process for revising and updating forecast in real time Create methods for appraising the forecast Show that the benefits of collaborative forecasting are obvious and real

14 Dickson Chiu 2006Demand-14 Summary Again much domain knowledge is required. Note the data / information requirements and how IT helps to collect / integrate the data for calculations and decision making. Capture forecasting signals (either determined by a business analyst or automatically by a sub-system) as events / exceptions / alerts and forward them to the appropriate system and personnel for decision / action. Collaborative forecasting as well as quick response and flexible supply systems requires much new IT in the process and information integration.


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