The Operations Management Club organizes industry mixers, seminars, technical workshops, and conferences for students with an interest in Operations Management and Management Science. If you are interested in joining the OM Club, or are considering a major in Operations Management and have any questions about the degree, we would like to hear from you. For more information on the club, membership, and events, visit or Meeting: Tuesday, January 16 at 5:00 PM, Bus 4-10

Announcements HW 1 due Wednesday, 11:59 PM OM Club Excel workshops –Jan 20, 11 AM – 1 PM –Free –Watch for a sign up link on the course page Don’t have course pack yet? –Get one Friday in Lab

MGTSC 352 Lecture 2: Forecasting Why forecast? Types of forecasts “Simple” time series forecasting methods Including SES = Simple Exponential Smoothing Performance measures

Plant Site Selection Alberta Manufacturer Has one old plant, in Calgary Planning to build new plant, but where? –Edmonton or Calgary?

Recent Demand Figures

What Would you Do?

Perspectives on Forecasting Forecasting is difficult, especially if it's about the future! Niels Bohr Rule #0: Every forecast is wrong! –Provide a range More sarcastic quotes about forecasting:

What is the Driver Doing?

Forecasting Technological forecasts –New product, product life cycle (Ipod, Blackberry) –Moore’s Law –Gates’ Law Economic forecasts –Macro level (unemployment, inflation, markets, etc.) Demand forecasts –Focus in MGTSC 352

Moore's Law: Computing power doubles about every two years. Data from ftp://download.intel.com/museum/Moores_Law/Printed_Materials/Moores_Law_Backgrounder.pdf Gates’ Law: “The speed of software halves every 18 months.”

Economic Forecasts An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today. Evan Esar Why do economists make forecasts? “We forecast because people with money ask us to.” Kenneth Galbraith

Forecasting – Quantitative Time series analysis: uses only past records of demand to forecast future demand –moving averages –exponential smoothing –ARIMA Causal methods: uses explanatory variables (timing of advertising campaigns, price changes) –multiple regression –econometric models

Active learning Groups of two Recorder: person that is born closest to Telus 150. Task: think of three quantities that you’d like to forecast 1 minute

Choosing a Forecasting Method

Simple models Notation –D t = Actual demand in time period t –F t = Forecast for period t –E t = D t - F t = Forecast error for period t Problem: Forecast the TSX index 4 simple models Excel

(Simple) Exponential Smoothing Generalization of the WMA method Uses a single parameter for weights 0  LS  1 Three steps –Initialization... F 2 = D 1 –Calibration... F t+1 = LS D t + (1 - LS) F t –Prediction... same formula Note the formula is a weighted average of Demand and Forecast from last period Excel

SES weights Decrease “exponentially” as data age Most recent data gets a weight of LS F t+1 = [LS D t ] + [(1 - LS) F t ] Rearrange... F t+1 = F t + LS (D t - F t ) = F t + LS E t A learning model

How do we choose LS Active learning (1 min.): –High LS (≈ 1) results in.... –Low LS (≈ 0) results in.... Suggested range for LS: (0.01,0.3) Performance measures (formulas in course pack, pg. 21) –BIAS –MAD –SE –MSE –MAPE Excel

Famously Incorrect Forecasts “I think there is a world market for maybe five computers.” Thomas Watson, chairman of IBM, 1943 “There is no reason anyone would want a computer in their home.” Ken Olson, president, chairman and founder of Digital Equipment Corp., 1977 “The concept is interesting and well-formed, but in order to earn better than a 'C,' the idea must be feasible.” A Yale University management professor in response to Fred Smith's paper proposing reliable overnight delivery service. (Smith went on to found Federal Express Corp.)