tailieunhanh - Lecture Managing operations across the supply chain (2/e): Chapter 12 - Swink, Melnyk, Cooper, Hartley

Lecture Managing operations across the supply chain (2/e) – Chapter 13: Sales and operations planning. This chapter include objectives: Describe sales and operations planning, define contents of an aggregate plan, explain relevant aggregate plan costs, contrast aggregate planning strategies, develop alternative aggregate plan, explain differences in service and manufacturing aggregate planning. | Copyright © 2014 McGraw-Hill Higher Education. All rights reserved. CHAPTER 12 Demand Planning: Forecasting and Demand Management McGraw-Hill/Irwin 1 Learning Objectives 12–2 LO12-1 Explain role of demand management LO12-2 Differentiate between demand management and forecasting LO12-3 Describe various forecasting procedures LO12-4 Develop forecast various models LO12-5 Describe forecast measures LO12-6 Explain how improvements make demand planning easier 2 Demand Planning Demand Planning: both forecasting and managing customer demand to reach operational and financial goals Demand Forecasting: predicting future customer demand Demand Management: influencing either pattern or consistency of demand 12–3 LO12-1 3 Demand Forecasting Components of Demand: patterns of demand over time Autocorrelation: relationship of past and current demand Forecast error: “unexplained” component of demand Stable Seasonal Trend Step Change Figure 12-2 12–4 LO12-2 4 Judgment Based Forecasting Grassroots: . | Copyright © 2014 McGraw-Hill Higher Education. All rights reserved. CHAPTER 12 Demand Planning: Forecasting and Demand Management McGraw-Hill/Irwin 1 Learning Objectives 12–2 LO12-1 Explain role of demand management LO12-2 Differentiate between demand management and forecasting LO12-3 Describe various forecasting procedures LO12-4 Develop forecast various models LO12-5 Describe forecast measures LO12-6 Explain how improvements make demand planning easier 2 Demand Planning Demand Planning: both forecasting and managing customer demand to reach operational and financial goals Demand Forecasting: predicting future customer demand Demand Management: influencing either pattern or consistency of demand 12–3 LO12-1 3 Demand Forecasting Components of Demand: patterns of demand over time Autocorrelation: relationship of past and current demand Forecast error: “unexplained” component of demand Stable Seasonal Trend Step Change Figure 12-2 12–4 LO12-2 4 Judgment Based Forecasting Grassroots: input from those close to products or customers Executive Judgment: input from those with experience Historical Analogy: assume past demand is a good predictor of future demand Marketing Research: examine patterns of current customers Delphi Method: input for panel of experts 12–5 LO12-3 5 Statistical Based Forecasting Time Series Analysis: uses historical data arranged in order of occurrence Causal Studies: search for cause and effect relationships among variables Simulation models: create representations of previous events to evaluate future outcomes 12–6 LO12-4 6 Statistical Based Forecasting Moving Average: simple average of demand from some number of past periods 12–7 LO12-4 7 Statistical Based Forecasting Weighted Moving Average: assigns different weights to each period’s demand based upon its importance 12–8 LO12-4 8 Statistical Based Forecasting Exponential Smoothing: a moving average approach that put less weight on further back in time data Smoothing Coefficient: weight given to

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