Forecasting - Lecture - BUS 370 Forecasting Donavon Favre...

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BUS 370: Forecasting Donavon Favre Poole College of Management North Carolina State University 1
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Chapter Objectives Be able to: Discuss the importance of forecasting and identify the most appropriate type of forecasting approach, given different forecasting situations. Apply a variety of time series forecasting models, including moving average, exponential smoothing, and linear regression models. Develop causal forecasting models using linear regression and multiple regression. Calculate measures of forecasting accuracy and interpret the results. 2
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Cheeznax Snack Foods Three Products: Puffed Cheese Balls, Cheese Nachos, Cheese Flavored Potato Chips Assignment – 2017 Forecast of 2017 Demand Suppliers Upstream Downstream Retailer - Gas N’Grub Manufact urer 3
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How many new stores in 2017? How will new stores affect demand? Any advertising campaigns or promotions? How do I break out sales into the three products? Other issues? 2016 monthly sales data is a good start, but what is missing? Month Sales ($) January $ 230,000 February $ 230,000 March $ 240,000 April $ 250,000 May $ 240,000 June $ 250,000 July $ 270,000 August $ 260,000 September $ 260,000 October $ 260,000 November* $ 280,000 December* $ 290,000 TOTAL: $ 3,060,000 Cheeznax Snack Foods 4 2016 History
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Forecast Demand Firm-level Market-level Supply Materials Labor supply Price Cost of supplies and services Cost of money — interest rates, currency rates Market price for firm’s product or service Definition : An estimate of the future level of some variable. Common variables include: 5
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Why forecast? Assess long-term capacity needs Develop budgets, hiring plans, etc. Plan production or order materials Get agreement within company and across supply chain partners 6
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Forecasting “Laws” Law 1 – Forecasts are almost always wrong (however are still useful) Law 2 – Forecasts for the near term tend to be more accurate Law 3 – Forecasts for groups of products or services tend to be more accurate Law 4 - Forecasts are no substitute for calculated values (Future Chapter on MRP) 7
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Used when situation is ‘stable’ with measurable, historical data to generate forecasts Existing products Current technology Heavy use of math models – time series and causal Example - forecasting sales of a mature products Quantitative Methods Used when situation is vague and data are scarce, not available, or irrelevant New products New technology Involves intuition, or informed opinion Example - forecasting sales to a new market Qualitative Methods Forecasting Approaches 8
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Selecting a Forecasting Method Quantitative historical data available. Evidence of a relationship between the variable of interest and some other variable(s). Qualitative Techniques Market surveys “Build-up” forecasts Life cycle analogy Panel consensus Delphi method Little or no quantitative data available Relationship between past events and future events difficult or impossible to model quantitatively Which statements best describe the forecasting situation?
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