A high-end skiwear producer makes parkas for men, women, juniors, and preschoolers. For each “gender,” there are tens of styles, and for each style approximately five colours and four sizes. This results in hundreds of parka SKUs for each “gender.” This large variety plus the long lead time from design to distributions make the production planning of the skiwear producer very difficult.
The design starts in February, concepts are finalized by May, prototypes are made by July, designs are finalized by September, first-half production quantity is determined and fabrics are ordered by' November, fabrics are received and production starts by February' of next year, retailers place their orders by March, shipments of products arrive from manufacturers in Asia by July, retailers receive their orders by August, sales pick tip by December, and unsold goods are discounted by' February of the third year.
It is now November and the VP of operations has to decide on the first-half production quantities. The buying committee has met and each of the six members forecasted the first-half demand for the parkas. For each parka, the VP has averaged these forecasts and calculated the standard deviation. To be more conservative, he lias doubled the standard deviation.
As an illustration, a specific women’s parka has average forecast value of 2,150 units and standard deviation of 807 units (after doubling). Assume a Normal distribution for tire demand. Each parka will be sold for $173 to retailers. The unit cost will be approximately $ 130. If any parkas are left after January two years later, it will be sold at $ 115 each. Determine the best production quantity' for this parka.