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• Q1:Discussion Questions: 1. In the lower levels of the pyramid forecasting system, how would you prevent abdication of the responsibility for forecasting? 2. Can a grocery store capture true demand data? How might a warehouse capture demand data? 3. Some experts have argued it's more important to have low bias (mean error) than to have a low MAD. Why would they argue this way? Problems: 1. Demand for stereo headphones and MP3 players for joggers has caused Nina Industries to grow almost 50 percent over the past year. The number of joggers continues to expand, so Nina expects demand for headsets to also expand, because, as yet no safety laws have been passed to prevent joggers from wearing them. Demand for the stereo units for last year was as follows: Demand Month (Units) January 4,200 February 4,300 March 4,000 April 4,400 May 5,000 June 4,700 Demand July August September October Month (Units) 5,300 4,900 5,400 5,700 November 6,300 December 6,000 a. Using least squares regression analysis, what would you estimate demand to be for each month next year? Using a spreadsheet, follow the general format in Figure 3.3. Compare your results to those obtained by using the forecast spreadsheet function. b. To be reasonably confident of meeting demand, Nina decides to use three standard errors of estimate for safety. How many additional units should be held to meet this level of confidence?See Answer
• Q2:2. Historical demand for a product is Demand Months January 12 February 11 March 15 April 12 May June 16 15 a. Using a weighted moving average with weights of 0.60, 0.30, and 0.10, find the July forecast. b. Using a simple three-month moving average, find the July forecast. c. Using single exponential smoothing with Alpha=0.2 and a June forecast = 13, find the July forecast. Make whatever assumptions you wish. d. Using simple linear regression analysis, calculate the regression equation for the preceding demand data. e. Using the regression equation in d, calculate the forecast for July.See Answer
• Q3:4. Zeus Computer Chips Inc. used to have major contracts to produce the Centrino-type chips. The market has been declining during the past three years because of the dual-core chips, which it cannot produce, so Zeus has the unpleasant task of forecasting next year. The task is unpleasant because the firm has not been able to find replacement chips for its product lines. Here is demand over the past 12 quarters: 2007 I II III IV 4,800 3,500 4,300 3,000 2008 I Year Sales (in 1,000 units) 3,500 2,700 3,500 II III IV 2,400 2009 I II III IV 8. Talbot Publishing Company's production planning manager has provided the following historical sales data for its leading textbook on forecasting: 3,200 2,100 2,700 1,700 4 5 6 21 18 20 7 17 The firm is considering using a basic exponential smoothing model with Alpha= 0.2 to forecast this item's sales. a. Use the sales average of 20,000 units through year 3 as the forecast for period 4. Prepare forecasts for years 5 through 7 as of the end of year 4. b. Calculate the average error and MAD value for the three forecasts using the actual sales data provided. Estimate the standard deviation of the forecast errors using the calculated MAD. c. Redo the forecasts and MAD calculations, updating the forecasts for years 6 and 7 at the end of years 5 and 6, respectively. What do you observe?See Answer
• Q4:12. Five individual products in a product family of the Cumberland Company have identical sales patterns. Each averages 100 units per month, with a standard deviation of 10 units. Assuming normal distributions and independent demands: a. What is the expected yearly sales distribution of each product? b. What is the expected monthly aggregate sales distribution for all products together? c. What is the expected yearly aggregate sales distribution for all products together?See Answer
• Q5:13. Using standard deviations for the values obtained in parts a, b, and c of problem 12, compare your results to the results of a Cumberland Company survey of its managers concerning the accuracy of the company's forecasts (shown below). Expected Forecast Error for a Period of: 1 Month 1 Quarter 1 Year Level of Detail Total Volume Family Type in Family Grade in Type SKU *+12%. *NF no forecast. *Stockkeeping unit. 12* 15 15 30 8 10 NF+ NF 8 8 12 NFSee Answer
• Q6:14. MacRonald's Restaurant uses a monthly exponential smoothing forecast for demand of each of its products. MacRonald's has four product families: burgers, chicken, hoagies, and pizza. MacRonald's also asks the shift managers to come up with a forecast for each product family. The exponential forecast for each product and the family forecast are given below. Family Burgers Chicken Hoagies Pizza Family Burgers Chicken Hoagies Pizza Product Regular Super Super-Duper Regular Cajun Italian French American Cheese Pepperoni Forecast 1,200 2,700 2,100 1,800 2,700 2,250 1,650 1,350 750 1,200 S/Unit 1.00 1.50 1.80 2.50 2.75 3.50 3.00 3.25 1.75 2.25 S Sales 10,000.00 15,000.00 20,000.00 5,000.00 a. Calculate a roll-up of the individual forecasts and compare it to the product family forecast. b. Roll up the individual product forecast to the top level and compare it to an overall corporate forecast of \$65,000. Roll the forecast back down to families and individual forecast (dollars and units).See Answer
• Q7:1. Define the objectives for the study.See Answer
• Q8:2. Collect input data on arrival times, service times, and any other input data that you feel is relevant. Data should be collected on at least two representative days.See Answer
• Q9:3. Collect output data to verify your model; e.g., waiting times, and total time in the system, total customers served, queue lengths, etc.See Answer
• Q10:4. Fit the data to appropriate distributions.See Answer
• Q11:5. Build a simulation model using SIMIO to simulate your system. Capture the key aspects of the system. State all assumptions in your write up (e.g., input distributions). 6. Verify and validate your model 7. Run the model as a terminating simulation. 8. Compare your model's output to the actual output and explain any discrepancies. 9. Determine the number of replications you will need to run in order to obtain your desired accuracy for the terminating model. Demonstrate that you have obtained your desired accuracy.See Answer
• Q12:10. Increase the demand by 25% and 50% and describe what happens to the system. What happens if additional resources are added? How much extra capacity will this provide? 11. Suggest at least two alternative configurations that should improve the system and make a statistical comparison between the original system and each of your two alternatives. 12. Analyze and write-up all your results.See Answer
• Q13: Read the article "Time Tells All" by: George Bishop (a copy is posted on Blackboard) and write a 1 paragraph summary of the key points of the article.See Answer
• Q14: 1. Little Brownie Bakers, the Girl Scout Cookie Baker, must deliver 300,000 boxes of cookies per month. The shop produces batches of cookies requiring a setup time of 45 minutes for each production batch. Average standard time for each cookie in a batch is 10 seconds, and there is an average of 600 cookies in a batch. Each box has 22 cookies per box. The bakery workforce for the pressing department consists of two workers per press, two supervisors,and one clerical support staff. There are 8 hours worked per day and an average of 21 days per month at one shift per day. a. Determine how many cookie presses are needed to satisfy production requirements for the month. There are 8 hours worked per day and an average of21 days per month at one shift per day. b. Determine the number of workers for the presses, the number of supervisors and the number of clerical support staff that would be needed.See Answer
• Q15: 2. The six flavors of Girl Scout Cookies(TM-Thin Mints, SAM-Samoas, Tre-Trefoils, SS-Savannah Smiles, SM - Smores and TT - Toffee Tastic) that do not contain peanuts are processed through a sequence of five operations (1-mixing dough, 2-baking, 3-liquidtoppings, 4-dry toppings, 5-packaging) at one facility. Not all cookies are processed in all operations. TM, which has weekly quantities of 3250 boxes, is processed through operations 1, 2, 3 and 5 in that order. SAM, which has weekly quantities of 2900 boxes, is processed through operations 1, 2, 3, 3 and 5 in that order. Tre, which has weekly quantities of 1900 boxes, are processed through operations 1, 2, and 5 in that order. SS,which has weekly quantities of 950 boxes, is processed through operations 1, 2, 4, and 5 in that order. SM, which has a weekly quantities of 400, is processed through operations 1, 2,and 5 in that order. Finally, TT, weekly quantities of 250 boxes, is processed through 1, 2,and 5 in that order. (a) Draw the network diagram for this work system. (b) Prepare the From -To table for this work system.See Answer

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