MGT 3900 PLAN REQUIREMENTS FOR MIYAOKA LITTLEFIELD SIMULATION

MGT 3900 PLAN REQUIREMENTS FOR MIYAOKA LITTLEFIELD SIMULATION Clemson University MGT 3900 PLAN REQUIREMENTS FOR MIYAOKA LITTLEFIELD SIMULATION Team Name: Questions about the game set up: 1) The cost of a single raw kit is: 2) The lead time to obtain an order of raw kits is: 3) The amount of interest earned on the cash balance is (choose one): a. Current market rate. b. 5% c. 10% d. 10% minus taxes Forecast of demand: Either enter your demand forecast for the weeks requested below, or use Excel to create a projected demand graph and import that into a Word document. The forecast is to be based on information given in the instructions AND data you have at the start of the simulation from the first 50 days of operation. Period Forecast (on average per day) 0-50 12.24 50-100 16 100-200 16 200-268 16 Explain how you made your forecast. If you used a forecasting rule, state which rule and show the calculations. While our demand could drop to around 8.8, we need to be able to account for uncertainty and the potential increase in demand. Therefore, we are going to forecast demand at 16 to buffer against uncertainty in demand and to be able to plan our appropriate capacity Customer Response Strategy For the days given below, state what contract you expect to have and your rationale for selecting that contract. Period Contract 1 Contract 2 Contract 3 0-50 Contract 1 50-100 Contract 2 100-200 Contract 3 By downloading the completed job lead time data, we can see if our production is capable of fulfilling the contractual lead time parameters. If average lead time for completed jobs drops below three days, we will move to Contract 2, and if lead time then drops below 0.5 days we will move to Contract 3. Our goal is to build capacity over 50 increments of time so that we can slowly move into Contract 3 and generate maximum revenue. Forecast of capacity needs: Enter the projected capacity requirements into the table below (expressed as the number of machines) for the weeks shown in the table below. Period Work Station 1 Work Station 2 Work Station 3 0-50 3 1 1 50-100 4 2 2 100-200 4 2 2 Explain how you made your forecast by stating the formulas you used, where you obtained the data and show your calculations: From our spreadsheet we found the average processing time in hours at station 1 to be 5.88 hours. If we multiply this value by our forecasted job demand we can find how many hours it will take to complete all the jobs. Next, if we divide that value by 24 we can find the number of machines that will fulfill our capacity/demand needs. For station 1, the forecasted capacity came to 3.92, which gets rounded up to 4 machines. For station 2, the forecasted capacity came to 1.33, which gets rounded up to 2 machines. For station 3, the forecasted capacity came to 1.3, which gets rounded up to 2 machines. While our capacity could change depending on which contract we our using, for now we can use our current data and forecasts to create a general plan. State your initial ROP and initial EOQ. Show your work: Our initial forecasted EOQ is 22,684 units. We found this by forecasting total demand in kits (forecasted order demand * number of kits per order * total product life). Then we plugged this value into the EOQ equation with a fixed order cost of $1000, and a carrying cost per unit of $1, ($10/unit * 10% cost of capital) and found our final optimal ordering quantity to be 22,684 units. Our initial forecasted ROP was found by multiplying leaditme demand and safety stock. To develop our ROP we need to choose a desired service level and work it into our safety stock calculation. At this point we would then multiply the standard deviation of the leadtime demand by the z score of our service level. Then, we would add this value to our leadtime demand. However, we don’t know how to do this because we are unsure of how to get daily demand to go with our current 4 day lead time. Also, we don’t know how to find the standard deviation of leadtime demand.