Chapter 4: Supply and Demand Planning and Control

Historical demand for a product is as follows

1. Using a simple four-month moving average, calculate a forecast for October.
2. Using single exponential smoothing witha=0.2 and a September forecast = 65, calculate a forecast for October.
3. Using simple linear regression, calculate the trend line for the historical data. Say the X-axis is April = 1, May = 2, and so on, while the Y-axis is demand.
4. Calculate a forecast for October using your regression formula

Historical demand for a product is as follows

(c) Using simple linear regression, calculate the trend line for the historical data. Say the X-axis is April = 1, May = 2, and so on, while the Y-axis is demand.

(d) Calculate a forecast for October using your regression formula

Ordering exactly what is needed each period without regard to economic considerations.

What forecasting tool is most appropriate when closely working with customers dependent on your products?

How might planning for a special customer affect the personnel schedule in a service?

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 players for last year was as follows:

1. Using linear regression analysis, what would you estimate demand to be for each month next year? Using a spreadsheet, follow the general format in Exhibit 18.8. Compare your results to those obtained by using the forecast spreadsheet function.
2. 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?

JIT is limited to what type of manufacturing environment?

Develop a production schedule to produce the exact production requirements by varying the workforce size for the following problem. Use the example in the chapter as a guide (Plan 1). The monthly forecasts for Product X for January, February, and March are 1,000, 1,500, and 1,200, respectively. Safety stock policy recommends that half of the forecast for that month be defied as safety stock. There are 22 working days in January, 19 in February, and 21 in March. Beginning inventory is 500 units. Manufacturing cost is \(200 per unit, storage cost is \)3 per unit per month, standard pay rate is \(6 per hour, overtime rate is \)9 per hour, cost of stockout is \(10 per unit per month, marginal cost of subcontracting is \)10 per unit, hiring and training cost is \(200 per worker, layoff cost is \)300 per worker, and worker productivity is 0.1 unit per hour. Assume that you start off with 50 workers and that they work 8 hours per day.

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 players for last year was as follows:

Using linear regression analysis, what would you estimate demand to be for each month next year? Using a spreadsheet, follow the general format in Exhibit 18.8. Compare your results to those obtained by using the forecast spreadsheet function.