Operations Forecasting Review Notes.docx

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Department
Management and Organizational Studies
Course
Management and Organizational Studies 3330A/B
Professor
May Tajima
Semester
Fall

Description
Operations Forecasting Review Notes  Forecasting: a method for translating past experience and present events into predictions of the future o Strategic: future products and markets o Planning: product demand  Forecasting horizon o Long range – longer than 2 years – for strategy o Mid range – weekly/monthly for up to 2 years – for planning o Short range – hourly/daily for up to several months – for scheduling  Forecasting Methods o Qualitative  Based on opinions or judgment of knowledgeable persons  Executive opinions, panel of experts  Market survey, focus group  Delphi method: to develop a consensus among experts  Subjective  Can incorporate a variety of information  Do not require commercial data  Results may be biased  Results may be conflicting o Quantitative  Based on numerical data and mathematical models  Causal methods: based on a known or perceived relationship between the factors  linear regression  Time series methods  Objective  Can incorporate large volumes of information  Do not have to rely on few individuals  Numerical data may not be available  Mathematical models may be too simplistic  Forecasting Process o Identify the purpose o Collect historical data o Examine the data o Select appropriate models o Compute forecasts for historical data and check forecast accuracy o Is accuracy acceptable?  Yes: forecast over planning horizon  No: adjust parameters or select new model o Include qualitative information o Monitor results o Back to is accuracy acceptable?  Time Series Forecasting Methods o Based on statistical analysis of historical data o Time series: a set of observed values measured over successive time periods o Assumptions:  Past demand is a predictor of future demand  Record of past demand is available o Behavior:  Average – stable over time (e.g. toothpaste)  Trend – general increase or decrease in average demand  Seasonality – short term periodic behavior due to time of day, day of week, month, season, etc. (short term) (e.g. Christmas trees)  Cycle – long term periodic behavior due to product life cycles (long term)  Random variation – any remaining variability that cannot be explained; virtually unpredictable  Time Series Forecasting Models o Naïve  Relies only on demand in the current period  Short term  Sensitive to random variation  Ft+1= Dt  Uses previo
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