Bài giảng môn học Quản trị kinh doanh - Chapter 12: Demand planning: forecasting and demand management

Copyright © 2014 McGraw-Hill Higher Education. All rights reserved.CHAPTER 12Demand Planning: Forecasting and Demand ManagementMcGraw-Hill/IrwinLearning Objectives12–2LO12-1 Explain role of demand managementLO12-2 Differentiate between demand management and forecastingLO12-3 Describe various forecasting proceduresLO12-4 Develop forecast various modelsLO12-5 Describe forecast measuresLO12-6 Explain how improvements make demand planning easierDemand PlanningDemand Planning: both forecasting and managing customer demand to reach operational and financial goalsDemand Forecasting: predicting future customer demandDemand Management: influencing either pattern or consistency of demand12–3LO12-1Demand ForecastingComponents of Demand: patterns of demand over timeAutocorrelation: relationship of past and current demandForecast error: “unexplained” component of demandStableSeasonalTrendStep ChangeFigure 12-212–4LO12-2Judgment Based ForecastingGrassroots: input from those close to products or customers Executive Judgment: input from those with experienceHistorical Analogy: assume past demand is a good predictor of future demandMarketing Research: examine patterns of current customersDelphi Method: input for panel of experts12–5LO12-3Statistical Based ForecastingTime Series Analysis: uses historical data arranged in order of occurrence Causal Studies: search for cause and effect relationships among variablesSimulation models: create representations of previous events to evaluate future outcomes12–6LO12-4Statistical Based ForecastingMoving Average: simple average of demand from some number of past periods 12–7LO12-4Statistical Based ForecastingWeighted Moving Average: assigns different weights to each period’s demand based upon its importance12–8LO12-4Statistical Based ForecastingExponential Smoothing: a moving average approach that put less weight on further back in time dataSmoothing Coefficient: weight given to most recent demand12–9LO12-4Statistical Based ForecastingRegression Analysis: fits an equation to a set of dataB = Base sales (computed y intercept)D = Disposable personal incomeA = Advertising expendituresF = Fuel pricesS = Sales from prior year12–10LO12-4Statistical Based ForecastingSimulation Models: sophisticated techniques that allow for the evaluation of multiple business scenariosFocused Forecasting: combination of computer simulation and input from knowledgeable individuals12–11LO12-4Forecast Process PerformanceForecast Accuracy: measure of how closely forecast aligns with demandBias: tendency to over or under predict future demand (forecast error)12–12LO12-5Forecast Process PerformanceMean Absolute Deviation (MAD): average of forecast errors, irrespective of directMean Absolute Percentage Error (MAPE): the MAD adjusted to measure how large errors are relative to the actual demand quantities12–13LO12-5Improving Demand PlanningCollaborative, planning, forecasting and replenishment (CPFR): supply chain partners share forecast, and demand and resource plans to reduce riskMarket Planning: changes to products, locations, pricing and promotions Demand and resource planning: forecastingExecution: order fulfillmentAnalysis: data on key performance metrics12–14LO12-6Demand Planning SummaryForecasting process choice is influenced by a variety of factorsForecasts are judgment or statistical model basedBoth accuracy and bias should be consideredDemand management involves influencing customer demandSupply chains can be made more responsive to changes in customer demand12–15