A process strategy “is an organization’s approach to transforming resources into goods and services” (Heizer & Render, 2010). The objective of using a process strategy is to “build a production process that meets customer requirements and product specifications within costs and other managerial constraints (Heizer & Render, 2010).
It would be recommended that Shuzworld purchase new equipment in its Shanghai plant in order to be cost effective. This recommendation comes from a cost-volume-profit (CVP) analysis, assuming volume is at 1,000 shoes. When looking at total complete cost effectiveness within the volume analysis, purchasing new equipment has a higher fixed cost at $200,000 with a lower variable cost of $500 per 1,000 shoes. This results in the lowest overall cost of $700,000.
In addition to making the recommendation, breakeven analysis was done as a “means of finding the point, in dollars and units, at which costs equal revenue” (Heizer & Render, 2010). In the event 1,000 shoes are not sold, the cost effectiveness would change at the breakeven points. For example, if Shuzworld only sold 25 shoes, then outsourcing would be the most cost effective route. If they were able to sell more than 25 but less than 300, then reconditioning the existing equipment would be more cost effective; however, if they sell 300 and above, then the original recommendation of purchasing new equipment would remain in effect, as shown below.
Forecasting is the “art and science of predicting future events” (Heizer & Render, 2010). There are different methods available for forecasting; the focus will be on least-squares method and exponential smoothing with trend adjustment.One important thing that will be analyzed is how to measure forecast error by using three of the most popular measures. Forecast error shows “how well the model performed against itself using past data” (Heizer & Render, 2010). One measure will be looking at the mean absolute deviation (MAD) which is “a measure of the overall forecast error for a model” (Heizer & Render, 2010). The second measure is the mean squared error (MSE), which is the “average of the squared differences between the forecasted and observed values” (Heizer & Render, 2010).
The third measure that will be utilized is the mean absolute percent error (MAPE), which is “the average of the absolute differences between the forecasted and actual values, expressed as a percentage of actual values” (Heizer & Render, 2010).Analyzing both methods above, there is a very similar forecast for the next period. Using least-squares method, the forecast for next period is 121,862. Using exponential smoothing with trend adjustment, the forecast for next period is 121,621. It would be recommended that Shuzworld use the least-squares method based on the more favorable results when looking at forecasting error.
