Mbpfc7 - Managing Flow Variability: Safety Inventory:...

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1 Managing Flow Variability: Safety  Inventory: Chapter 7 Managing the Supply Chain Economies of Scale (Chapter 6) Managing Flow Variability: Safety Inventory (Chapter 7) Characteristics of Forecasts Continuous Review System (Reorder Point Policy) Inventory Pooling Accurate Response (News vendor model) Postponement / Delayed Differentiation Service level depends on flow rate variability Operational levers to reduce flow variability
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2 Strategic Positioning Goal Profitably meet market demand Decisions Customer order lead time » Planning lead time » Manufacturing lead time » Delivery time Planning lead time » Product design policies Manufacturing lead time » Inventory policies
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3 Strategic Positioning 1 2 3 Week in planning / production Engineer to Order (ETO) 4 Design 5 Make to Order (MTO) 6 7 Assemble to Order (ATO) 8 Manufacturing I to Market Make to Stock (MTS) When do orders arrive?
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4 Demand uncertainty and forecasting Forecasts depend on historical data “market intelligence” Forecasts are always wrong. A good forecast has at least 2 numbers (includes a measure of forecast error, e.g., standard deviation). Aggregate forecasts tend to be more accurate. The longer the forecast horizon, the less accurate the forecast. Forecast models Time-series analysis Causal models
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Time Series Models Predict future as a function of the past Based on a series of evenly spaced (weeks, months etc.) data points Break data into components and project the components forward Trend (T) Seasonality (S) Cycle (C) Random (R)
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Time Series Models Naive demand in next period = demand this period Moving Averages Assumes Market demand is fairly steady Average Demand of n most recent time periods Moving average = Demand in n periods)/ n
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Example: Moving Average @ Bob’s Hardware Month (t) Actual Mower Sales 3 Month Moving Avg. Forecast (t+1) Jan 10 Feb 12 March 13 (10 + 12 + 13)/3 = 11 2/3 12 April 16 (12 + 13 + 16)/3 = 13 2/3 14 May 19 (13 + 16 + 19)/3 = 16 16 June 23 (16 + 19 + 23)/3 = July 26
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Weighted Moving Average (WMA) Useful with long-run trend in demand Weight most recent demand the heaviest WMA = [ Σ (Weight for n )(Demand in n )]/( Σ Weights) Month Actual
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Mbpfc7 - Managing Flow Variability: Safety Inventory:...

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