Lawn King is a manufacturer of lawn mowers facing a highly seasonal demand for its products. At the present time the demand forecast for the coming year has just been increased. This is causing management to evaluate the accuracy of the forecast, and to construct several different production strategies (level, chase, second shift) for meeting demand.
The purpose of this case is to illustrate the issues typically encountered in aggregate planning. The student is asked to make a demand forecast, to construct alternative production strategies and to recommend a particular strategy. A substantial amount of "pencil pushing" and "computer pushing" is required in this case to develop and evaluate the various strategies. The case illustrates the tradeoffs involved in aggregate production planning.
Discussion Questions
1. Develop a forecast to use as a basis for aggregate production planning.
2. Develop an aggregate production plan by month for fiscal XXXXXXXXXXConsider the use of several different production strategies. Which strategy do you recommend? Hint: Use of Excel will greatly save time in making these plans.
Analysis
The first step in analysis of this case is to evaluate the demand forecast. This can be done by calculating the actual increase in total demand over the past year. The increase from FY09 to FY10 was:
The projected increase from FY10 to FY11 is:
Thus a larger increase is being projected than was experienced last year.
We also observe that forecasts in the past have been very accurate (e.g., FY09 actual compared to forecast and FY10 actual compared to forecast). But, the forecasts by model type have not been nearly as accurate as total demand forecasts. Furthermore, the case states that demand is highly influenced by the economy and the weather. In view of these insights, we can conclude that past forecasts have been remarkably accurate.
For purposes of analysis we will accept the new forecast of 110,000 units. Although the projected demand increase is larger than last year's actual increase, the forecast still appears reasonable. It may be, however, that marketing is attempting to drive production through a higher forecast to avoid stockouts. Therefore, we may wish to evaluate a somewhat lower forecast, as well as the one given in the case.
It is best to evaluate the various production strategies in terms of aggregate demand. Evaluating these strategies by model type results in a tremendous amount of detailed calculation.
To construct an aggregate plan we need to forecast aggregate demand by month. This can be done by assuming the same monthly pattern as last year. From exhibit 4 in the case, the percentage of annual sales by month can be calculated. These percentages are then multiplied by the total forecast (110,000) to a
ive at monthly demand forecasts. (See Exhibit 1 of the teaching note.)
Next, we must decide on the inventory level needed at the end of the year and the stockout policy desired. The cu
ent inventory is 16,460 units. On an annual basis this inventory level represents a turnover of:
While a turnover of 6.7 might be considered good, the inventory level should ideally be compared to the demand at the end of the year. Since the demand is seasonal, our goal should be to have 1 or 2 months of inventory at year-end as a safety stock. More inventory is not necessary, since all models are still in production and we can respond to changing demand conditions. One month of inventory would amount to 1216 units (the projected demand for September). Two months of inventory would be 3698 units, the demand for September and October. By this criterion, a great deal of excess inventory exists. Therefore, we will assume an 8/31/11 goal of 3700 units (2 months supply) of inventory for the remainder of this analysis.
Adjusting for the inventory change, we have a production requirement of 97,240 units.
Forecast
110,000
Beginning Inventory
â€“ 16,460
Ending Inventory
+ 3,700
Production Required
97,240
There are many alternative strategies to consider. For the sake of simplicity we shall consider four strategies.
1. Level production
2. Level production with overtime
3. Chase demand
4. Two shifts