BADM 3601 – Operations Management Assignment # 1 Question 1. a. Define productivity. What is multi‐factor productivity? b. With a diagram explain the transformation process that takes place in operations. c. What are the inputs and outputs of an operations transformation process? d. Why is control important? e. What does it accomplish? ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Question 2. Upton, Inc. makes 1,000 tires per day with the following resources: Labor Raw Material 400 hours per day @ $ 12.50 per hour 20,000 pounds per day @ $ 1 per pound Energy $ 5,000 per day Capital costs $ 10,000 per day a. What is the labor productivity per labor‐hour for these tires at Upton, Inc.? b. What is the multifactor productivity for these tires at Upton? c. What is the percentage change in multifactor productivity if Upton can reduce the energy bill by $ 1,000 per day without cutting production or changing any other input? ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Question 3. 1. How does time‐series forecasting work? What are its components? 2. When can we use moving average method? What is exponential smoothing? How do we choose values of alpha (smoothing constant)? 3. Sunrise Baking Company provided the following data for cookie sales for the past four weeks. The bakery is closed on Saturday, so Friday’s production must satisfy demand for both Saturday and Sunday. Cookies are always made for the following day. Monday Tuesday Wednesday Thursday Friday Saturday Sunday 4 Weeks Ago 2,200 2,000 2,300 1,800 1,900 2,800 3 Weeks Ago 2,400 2,100 2,400 1,900 1,800 2,700 2 Weeks Ago 2,300 2,200 2,300 1,800 2,100 3,000 Last Week 2,400 2,200 2,500 2,000 2,000 2,900 Make a forecast for this week based on the following: a. Daily, using a simple four‐week moving average. b. Daily, using a weighted average of 0.4, 0.3, 0.2, and 0.1 for the past four weeks. Sunrise is also planning its purchases of ingredients for bread production. If bread demand for last week: c. is forecast at 22,000 loaves and only 21,000 loaves are actually demanded, what would Sunrise’s forecast be for this week using exponential smoothing, with alpha = 0.1? d. Supposing, with the forecast made in (c) , this week’s demand actually turns out to be 22,500. What would the new forecast be for the next week? ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Question 4. a. What does associative forecasting technique accomplish? b. What does least‐squares regression method do? c. What is seasonal variation? Explain with examples. d. The demand for electric power at PEPCO over the period 2005‐2011 is given below in megawatts. The firm wants to forecast 2012 demand. Using an associative forecasting technique, help PEPCO to plan for 2012 and 2013. Year 2005 2006 2007 2008 2009 2010 2011 Power Demand (MW) 74 79 80 90 105 142 122 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Question 5. Digital Cell Phone, Inc. Paul Jordan has just been hired as a management analyst at Analog Cell Phone, Inc. Analog Cell manufactures a broad line of phones for the consumer market. Paul’s boss, John Smithers, Chief Operations Officer, has asked Paul to stop by his office this morning. After a brief exchange of pleasantries over a cup of coffee, he says he has a special assignment for Paul: “We’ve always just made an educated guess about how many phones we need to make each month. Usually we just look at how many we sold last month and plan to produce about the same number. This sometimes works fine. But most months we either have too many phones in inventory or we are out of stock. Neither situation is good.” Handing Paul the table below, Smithers continues “Here are our actual orders entered for the past 36 months. There are 144 phones per case. I was hoping that since you graduated recently from the University of Alaska, you might have studied some techniques that would help us plan better. It’s been a while since I was in college—I think I forgot most of the details I learned then. I’d like you to analyze these data and give me an idea of what our business will look like over the next 6 to 12 months. Do you think you can handle this?” “Of course,” Paul replies, sounding more confident than he really is. “How much time do I have?” “I need your report on the Monday before Thanksgiving—that would be November 20th. I plan to take it home with me and read it during the holiday. Since I’m sure you will not be around during the holiday, be sure that you explain things carefully so that I can understand your recommendation without having to ask you any more questions. Since you are new to the company, you should know that I like to see all the details and complete justification for recommendations from my staff.” With that, Paul was dismissed. Arriving back at his office, he began his analysis. Orders Received, by Month Cases Cases Cases Month 2007 2008 2009 January 480 575 608 February 436 527 597 March 482 540 612 April 448 502 603 May 458 508 628 June 489 573 605 July 498 508 627 August 430 498 578 September 444 485 585 October 496 526 581 November 487 552 632 December 525 587 656 DISCUSSION QUESTION a. Prepare Paul Jordan’s report to John Smithers using regression analysis. Provide a summary of the cell phone industry outlook as part of Paul’s response. b. Adding seasonality into your model, how does the analysis change? (use the Multiplicative Seasonal Model only) …
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