CHAPTER 8 FORECASTING AND DEMAND PLANNING Have you ever gone to a restaurant and been told that they are sold out of their “special‚” or gone to the university bookstore and found that the texts for your course are on backorder? Have you ever had a party at your home only to realize that you don’t have enough food for everyone invited? Just like getting caught unprepared in the rain‚ these situations show
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INTRODUCTION TO OPERATIONS MANAGEMENT Spring 2012-ASSIGNMENT # 1 Name 1: --------------------------------------------------- ID # ------------------------------------------------ Name 2: --------------------------------------------------- ID # ------------------------------------------------ Question # 1 [15 Marks] Bob Richards‚ the production manager of Zychol Chemicals‚ is preparing his quarterly report‚ which is to include a productivity analysis for his department. One of the inputs
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Quantitative Method- Used when situation is “stable” and historical data exists. Used for existing products and current technology. Involves mathematical techniques. E.G.‚ forecasting sales of color televisions. Naïve approach‚ moving averages‚ exponential smoothing‚ trend projection‚ linear regression. Time Series Forecasting- Set of evenly spaced numerical data. Obtained by observing response variable at regular time periods. Forecast based only on past values‚ no other variables important. Assumes
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MGMT 472 Homework assignment 2 1. According to the text‚ key ingredients for developing successful supply partnerships include all of the following EXCEPT: a. Personal relationships b. Individualized objectives c. Mutual benefits and needs d. Performance metrics 2. The combination of the purchase price of a good and additional costs incurred before or after product delivery can be referred to as: a. Total cost of acquisition b. Total cost of ownership c. Purchase requisition cost d. Total procurement
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increases. 3) 4) T T F F The critical path in a network of activities will be the path with the most number of activities. Forecasts are generally more accurate for individual products rather than for product families. In a simple exponential smoothing model the manager would prefer a large value for alpha if he/she wants to respond well to a system characterized by a low level of random behavior but often subjected to a real change in the
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(b) [i] Naive The coming January = December = 23 [ii] 3-month moving (20 + 21 + 23)/3 = 21.33 [iii] 6-month weighted [(0.1 17) + (.1 18) + (0.1 20) + (0.2 20) + (0.2 21) + (0.3 23)]/1.0 = 20.6 [iv] Exponential smoothing with alpha = 0.3 [v] Trend Forecast = 15.73 + .38(13) = 20.67‚ where next January is the 13th month. (c) Only trend provides an equation that can extend beyond one month 4.23 Students must determine the naive forecast for the four months
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46% 16% -.82 3.75 -4.57 Five week moving average 10.73 15.57 7.9 28% 40% 16% 11.17 5.17 -2.72 Five week exponential smoothing 11.58 18.09 8.57 29% 43% 18% 0.62 1.93 -0.59 Three week exponential smoothing 11.13 17.78 7.89 29% 45% 17% -.27 1.74 -2.66 Aggregate demand model 30.57 14% 0.93 Question 2 Next consider using a simple exponential smoothing model. In your analysis‚ test two alpha values‚ .2 and .4. Use the same criteria for evaluating the model as in
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Chapter 7 Problem Summary Problem Solutions 7.1 See file Ch7.1.xls a. Yes‚ a stationary model seems appropriate b. Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20.16667 1.373732 14.6802 4.3E-08 17.1058 23.22753 17.1058 23.22753 Period -0.07692 0.186653 -0.41212 0.688949 -0.49281 0.338967 -0.49281 0.338967 From regression output‚ t = -.412 and p = .689. A stationary model seems appropriate since the linear term‚ Period
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Doepke’s book. First of all we need to compute the growth rate of real GDP for each period: we will create new variable GRATET it shows us the economic growth in period t. GRATEt=RGDPt+1RGDPt Now we are supposed to apply a method called exponential smoothing (which is described in our Textbook) to get smooth versions of our data: GRATESM1=GRATE1‚ GRATESMt=0.5*GRATEt-1+0.5*GRATESMt-1 for t>1 Now we should apply the same method to real GDP‚ but additionally we will use the smooth growth
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Eight Steps to Forecasting • Determine the use of the forecast □ What objective are we trying to obtain? • Select the items to be forecast • Determine the time horizon of the forecast □ Short time horizon – 1 to 30 days □ Medium time horizon – 1 to 12 months □ Long time horizon – more than 1 year • Select the forecasting model(s) |Description |Qualitative Approach |Quantitative Approach
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