Mini-Cases • Chapter 7  1

Mini-Cases

Chapter 7: Models and Forecasting

Blast-Away House-Cleaning Service

Blast-Away Housecleaning Service uses powerful water jets to clear loose paint from residential buildings and to clean aluminum siding. The company is trying to arrive at a fast and accurate way of estimating cleaning jobs. The following simple formula is its first attempt. It includes a fixed charge for coming to the job plus time requirements, which are a function of the exterior of the house measured in square feet (sf).

Estimated cost = $15 + ($0.06/sf)(sf)

After one year of experience, Blast-Away has lost $50,000 on sales of $250,000. At first, the owner, Hadley Powers, could not understand the reasons for his losses. His employees worked hard, and Blast-Away could barely keep up with demand. In fact, Powers was planning to add another crew this year, but if he cannot determine the reason for the losses and find a solution, his investors would be reluctant to provide him with additional capital. What caused the loss?

One thing he learned from his accountant is that the model had not included a recovery of his investment in the equipment used on the jobs. Powers had invested $60,000 in equipment at the beginning of the first year and expected it to last three years. His accountant recommended that Powers increase the price charged per job to generate an extra $20,000 to cover equipment costs. Even if Powers were able to do this, his losses would still be $30,000 if all other things remained the same. He had to look further for the problems.

Powers has hired you to carefully examine last year's job tickets, which contain the quoted price; distance from headquarters; size of the house; type of exterior, such as painted wood, aluminum, or brick; and style of the house, such as ranch, two-story, or story-and-a-half. You also have the operator's logbook that lists travel time and the time to do each house. As you analyze the job tickets, you notice that a substantial number of the jobs that Blast-Away gets are for small, story-and-a-half or two-story homes located in the suburbs and surrounding rural area. Many of the homes are wood sided, which is the most difficult type to clean to the customer's satisfaction.

1. In addition to the equipment recovery problem, what is causing Blast-Away to lose money?

2. What would you recommend Powers do to correct the problem?

3. What data would you want to collect to verify your recommendations?

Lucy's Lamps-R-Us

Lucy Mertz has opened a specialty lamp shop in a suburban shopping mall. Mertz's shop has an excellent location next to the entrance to the largest and most popular department store in the surrounding five-county area. After a slow beginning, business picked up nicely, and the lamp shop had made a nice profit. To plan for the next year, Mertz decided to use sales for the last eight months to forecast next year's sales. She has asked you to use the following data to project sales. The forecast listed here, which is for last year, was based on judgment. Mertz wants you to use a quantitative approach.

TIME PERIOD / FORECASTED SALES / ACTUAL SALES
May / $ 5,000 / $ 8,300
June / 5,200 / 10,200
July / 5,600 / 9,900
August / 6,200 / 10,200
September / 6,900 / 9,800
October / 7,800 / 11,400
November / 8,500 / 12,800
December / 9,000 / 14,500

1. How much error existed in the old forecast?

2. Project the sales for January, February, and March of next year.

It is now the end of March, and the actual sales for the first three months are available. The results are disappointing. In January, sales declined because of returns from the Christmas buying season and an increase in bargain hunting. Also, the large department store that anchored Mertz's end of the shopping mall closed at the end of January because of operating losses by the parent company.

TIME PERIOD / ACTUAL SALES
January / 7,500
February / 6,000
March / 6,100

3. Why did the model give Mertz a poor forecast?

4. What would you recommend to Mertz regarding the forecast for the next three months?

Vonderembse and White • Operations Managment