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Demand Management

Demand Management. What does a firm want in its demand and production patterns?What drives demandTry to influence the timingOr to reduce the uncertaintyDowWhy???. Demand Management for Service Firms. 1. Maintain sufficient capacity to serve every customer immediatelyAnyone see a problem with

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Demand Management

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    1. Demand Management Demand Management Demand Forecasting Demand Tracking

    2. Demand Management What does a firm want in its demand and production patterns? What drives demand Try to influence the timing Or to reduce the uncertainty Dow Why??? What does a firm want in its demand and production patterns? Constant, predicable (maybe steady increase as a sign of increased sales What does a firm want in its demand and production patterns? Constant, predicable (maybe steady increase as a sign of increased sales

    3. Demand Management for Service Firms 1. Maintain sufficient capacity to serve every customer immediately Anyone see a problem with that? 2. Influence the demand pattern to make it match the firm’s capacity Make “wait time” information available to customers Use appointment system These strategies are not mutually exclusiveThese strategies are not mutually exclusive

    4. Demand Management for Service Firms 3. Price to stimulate demand during slack periods/ lessen demand during busy periods variant: ripe-banana pricing strategy What is the problem in “bid” industry when firm “turns down” work? 4. Service-delay management make people wait for service Text describes this as “dangerous” - has it ever happened to you? Waiting management program Examples? These strategies are not mutually exclusive Waiting management program Examples? video tapes at DOS any place that has you fill out a form while waiting in line Ask question 2 about customer schedulingThese strategies are not mutually exclusive Waiting management program Examples? video tapes at DOS any place that has you fill out a form while waiting in line Ask question 2 about customer scheduling

    5. Demand Management for Manufacturers Promised delivery date (Make to Order, Assemble to Order) Inventory management (Make to Stock) Assign Order Priority Maintain Slack

    6. Demand Forecasting Process Melnyk & Denzler via Wight 1. Identify people and processes that need the forecast. 2. Identify the best data inputs 3. Select proper forecasting techniques 4. Apply the techniques. State your assumptions. 5. Monitor the performance of the forecasting process.

    7. Types of Forecast Demand Forecast Supply Forecast Purchase of Supply Source Price Forecast Exchange Rate Fluctuation

    8. Planning Horizons (1st Influencing Factor) Long Range Demand-- 5 Years Out Intermediate Demand-- 1-2 Years Out Short Range Demand--Less Than 1 Year Buildup Breakdown Homogenous Corporate

    9. Demand Forecasting Influencing Factors Makridakis and Wheelwright, 1989 Time Horizon (next slide) Level of Detail Number of Demand Segments Control vs. Planning Constancy Existing Business Processes Time Horizon Level of Detail aggregate to individual data Number of Demand Segments one critical product vs. mass of products expend forecasting resources with Pareto rule in mind Control vs. Planning i.e., what is forecasting system to be used for Constancy more constancy = easier to forecast Existing Business Processes politics reliance on current toolsTime Horizon Level of Detail aggregate to individual data Number of Demand Segments one critical product vs. mass of products expend forecasting resources with Pareto rule in mind Control vs. Planning i.e., what is forecasting system to be used for Constancy more constancy = easier to forecast Existing Business Processes politics reliance on current tools

    10. Qualitative Forecasting Methods Grass-Roots Forecasting Focused Forecasting Historical Analogy Market Research Forecasting Tools Delphi Method Grass-Roots Forecasting ask the people closest to the phenomenon for their input What is the problem with this method? Highly subjective Focused Forecasting Use grass-roots forecasting data as input to computer simulation process How does this improve grass-roots? Adds a level of objectiveness to the proceedings Historical Analogy Use the past to predict the future What is risk? Things change Market Research Forecasting Tools various marketing techniques to analyze purchasing patterns and attitudes of potential buyers - Take a marketing class Delphi Method compile forecasts through sequential, independent responses from a group of experts to a series of questionnaires Where does the name come from? Oracle of Delphi in mythologyGrass-Roots Forecasting ask the people closest to the phenomenon for their input What is the problem with this method? Highly subjective Focused Forecasting Use grass-roots forecasting data as input to computer simulation process How does this improve grass-roots? Adds a level of objectiveness to the proceedings Historical Analogy Use the past to predict the future What is risk? Things change Market Research Forecasting Tools various marketing techniques to analyze purchasing patterns and attitudes of potential buyers - Take a marketing class Delphi Method compile forecasts through sequential, independent responses from a group of experts to a series of questionnaires Where does the name come from? Oracle of Delphi in mythology

    11. Delphi Example

    12. Quantitative Forecasting Historical time-series Rough Cut / Trends Seasonal Adjustments Casual studies correlation Mathematical or simulation models Ask question 3 about golf ball warmerAsk question 3 about golf ball warmer

    13. Demand Tracking Methods Naive Forecasts Weighted / Moving Averages What is a problem with weighted averages? Know how to calculate Exponential Smoothing (emphasize recent data) Adaptive Forecasting (use recent forecast errors) Naive Forecasts assume that the current results as the next period’s forecast Weighted / Moving Averages Exponential Smoothing -combination of moving average and weighted average where weighting coefficients are systematically smaller for older data Adaptive Forecasting weighting coefficients are based on forecast error Ask question 11 about seasonally adjusted unemployment Naive Forecasts assume that the current results as the next period’s forecast Weighted / Moving Averages Exponential Smoothing -combination of moving average and weighted average where weighting coefficients are systematically smaller for older data Adaptive Forecasting weighting coefficients are based on forecast error Ask question 11 about seasonally adjusted unemployment

    14. Forecast Error The algebraic difference between the current actual sales and the projected sales that were extrapolated one sales period earlier MAD - Mean Average Deviation MSE - Mean Square Error Need to understand Don’t need to calculate

    15. Forecast Error Trumpet of DOOM

    16. Kmart Demand

    17. Next Time Problem 16. (on p484) a and e only Goal

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