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1 EMBA-2, BUP Forecasting

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M. AsadEMBA-2 EO - 702 Demand Management A B(4) C(2) D(2)E(1) D(3)F(2) Independent Demand: What a firm can do to manage it? Can take an active role to influence demand Can take a passive role -simply respond to demand Independent Demand Dependent Demand

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M. AsadEMBA-2 EO - 702 Components of Demand Average demand for the period Trend Seasonal element Cyclical elements Irregular variation Random variation

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M. AsadEMBA-2 EO - 702 Components of Demand Quantity Average demand for the period Time

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M. AsadEMBA-2 EO - 702 Components of Demand Quantity Time Trend: Data consistently increase or decrease, not necessarily in a linear fashion

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M. AsadEMBA-2 EO - 702 Components of Demand Quantity |||||||||||| JFMAMJJASOND Months Seasonal: Data consistently show peaks and valleys with a one-year cycle Year 1 Seasonal variations 90 89 88

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M. AsadEMBA-2 EO - 702 Quantity |||||| 123456 Years Cyclical: Data reveal gradual increases and decreases over extended periods of more than one-year in length Components of Demand

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M. AsadEMBA-2 EO - 702 Components of Demand Quantity Time Irregular Variation: any event interrupting the regular phenomena Irregular variation Random Variation: are caused by chance events

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M. AsadEMBA-2 EO - 702 Types of Forecasts Qualitative Time Series Analysis Casual Relations Simulations

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M. AsadEMBA-2 EO - 702 Qualitative Methods Grass Roots Market Research Panel Consensus Executive Judgment Historical analogy Delphi Method Qualitative Methods

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M. AsadEMBA-2 EO - 702 Time Series Analysis Time series forecasting models try to predict the future based on past data The analysis can be done through various methods: 1. Simple moving average 2. Weighted moving average 3. Exponential smoothing 4. Regression analysis 5. Others

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M. AsadEMBA-2 EO - 702 Simple Moving Average Formula The simple moving average model assumes an average is a good estimator of future behavior The formula for the simple moving average is: F t = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

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M. AsadEMBA-2 EO - 702 Simple Moving Average Problem Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

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M. AsadEMBA-2 EO - 702 F 4 =(650+678+720)/3 =682.67 F 7 =(650+678+720 +785+859+920)/6 =768.67 Calculating the moving averages gives us:

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M. AsadEMBA-2 EO - 702 Weighted Moving Average Formula While the simple moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average is:

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M. AsadEMBA-2 EO - 702 Weighted Moving Average Problem Data Weights: t-1.5 t-2.3 t-3.2 Question: Given the weekly demand and weights, what is the forecast for the 4 th period or Week 4? Note that the weights place more emphasis on the most recent data, that is time period “t-1”

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M. AsadEMBA-2 EO - 702 Weighted Moving Average Problem Solution F4 = 0.5(720)+0.3(678)+0.2(650)=693.4

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M. AsadEMBA-2 EO - 702 Exponential Smoothing Model Exponential Smoothing assigns exponentially decreasing weights as the observation get older. F t = F t-1 + (A t-1 - F t-1 )

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M. AsadEMBA-2 EO - 702 Exponential Smoothing Problem Data Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using =0.10 and =0.60? Assume F 1 =D 1 Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using =0.10 and =0.60? Assume F 1 =D 1

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M. AsadEMBA-2 EO - 702 Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

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M. AsadEMBA-2 EO - 702 Simple Linear Regression Model Y t = a + bx 0 1 2 3 4 5 x (Time) Y The simple linear regression model seeks to fit a line through various data over time Is the linear regression model a Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line.

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M. AsadEMBA-2 EO - 702 Simple Linear Regression Formulas for Calculating “a” and “b”

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M. AsadEMBA-2 EO - 702 Simple Linear Regression Problem Data Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

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M. AsadEMBA-2 EO - 702 Answer: First, using the linear regression formulas, we can compute “a” and “b”

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