lect3 - Demand Forecasting I Time Series Analysis Chris...

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Demand Forecasting I Time Series Analysis Chris Caplice ESD.260/15.770/1.260 Logistics Systems Sept 2006
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© Chris Caplice, MIT 2 MIT Center for Transportation & Logistics – ESD.260 Agenda Problem and Background Four Fundamental Approaches Time Series Methods “Predictions are usually difficult – especially for the future” Yogi Berra
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© Chris Caplice, MIT 3 MIT Center for Transportation & Logistics – ESD.260 Demand Processes Demand Forecasting ± Predict what will happen in the future ± Typically involves statistical, causal or other model ± Conducted on a routine basis (monthly, weekly, etc.) Demand Planning ± Develop plans for creating or affecting future demand ± Results in marketing & sales plans – builds unconstrained forecast ± Conducted on a routine basis (monthly, quarterly, etc.) Demand Management ± Make decisions in order to balance supply and demand within the forecasting/planning cycle ± Includes forecasting and planning processes ± Conducted on an on-going basis as supply and demand changes ± Includes yield management, real-time demand shifting, forecast consumption tracking, etc.
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© Chris Caplice, MIT 4 MIT Center for Transportation & Logistics – ESD.260 Four Fundamental Approaches Subjective Judgmental ± Sales force surveys ± Delphi techniques ± Jury of experts Experimental ± Customer surveys ± Focus group sessions ± Test Marketing ± Simulation Objective Time Series ± “Black Box” Approach ± Uses past to predict the future Causal / Relational ± Econometric Models ± Leading Indicators ± Input-Output Models Often times, you will need to use a combination of approaches
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© Chris Caplice, MIT 5 MIT Center for Transportation & Logistics – ESD.260 Cost of Forecasting vs Inaccuracy Cost of Forecasting Forecast Accuracy Cost Cost of Errors In Forecast Total Cost Å Overly Naïve Models Æ Å Excessive Causal Models Æ Å Good Region Æ
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© Chris Caplice, MIT 6 MIT Center for Transportation & Logistics – ESD.260 Time Series The typical problem: ± Generate the large number of short-term, SKU level, locally disaggregated demand forecasts required for production, logistics, and sales to operate successfully. Predominant use is for: ± Forecasting product demand of . . .
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This note was uploaded on 12/06/2011 for the course ESD 1.260j taught by Professor Chriscapliceesd during the Fall '06 term at MIT.

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lect3 - Demand Forecasting I Time Series Analysis Chris...

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