Case StudiesMonth Sales (in no. of bales) 1983 1984. 1985 1986 1987 January 2,000 3,000 2,000 5,000 5,000 February 3,000 4,000 5,000 4,000 2,000 March 3,000 3,000 5,000 4,000 3,000 April 3,000 5,000 3,000 2,000 2,000 May 4,000 5,000 4,000 5,000 7,000 June 6,000 8,000 6,000 7,000 6,000 July . 7,000 3,000 7,000 10,000 8,000 August 6,000 8,000 10,000 14,000 10,000 September 10,000 12,000 15,000 16,000 20,000 October 12,000 12,000 15,000 16,000 20,000 November 14,000 16,000 18,000 20,000 22,000 December 8,000 10,000 8,000 12,000 8,000 78,000 89,000 98,000 115,000 113,000customer requests by use of Merriwell’s own ware-house facilities and routing schedules of the com-pany’s truck line. Heretofore, Ed Merriwell could manage the demand forecasting and production scheduling by “feel.” Because of the ever-growing number of accounts and changes in personnel in customer purchasing departments, the accuracy of Merriwell’s forecasting has been rapidly declining. The percentage of short-shipped accounts for par-ticular types of bags is increasing alarmingly. Con-versely, the warehouse is becoming overstocked with other types of bags. As a result, a severe demurrage penalty on three boxcars of incoming rolls of paper was recently paid because the paper warehouse was partially used to store finished bags that spilled over from the finished-bag ware-house. This caused a delay in unloading the box-cars until space could be created in the raw-material warehouse. Demand forecasting has historically been diffi-cult due to the seasonal nature of the product. There is always a surge in demand for bags prior to a holiday season. The exact timing of the surge in demand for particular types of bags depends upon customer stocking policies and the dates that holi-day promotional activities begin. The Merriwell family needs a forecasting meth-od that would take this seasonal factor into consid-eration. Moreover, they want a method that exhib-its stability, because their market is relatively stablewith a large number of repeat customers. Finally, they want a forecasting method that anticipates the growth patterns of their respective customers. A forecasting method with these specifications would greatly enhance the company’s ability to service its market profitability. It is believed that if such a method could be applied to forecasting aggregate demand, the same method could be used to gain additional accuracy by forecasting demand of its larger customers. By having an accurate forecast of aggregate demand and demand of larger custom-ers, the requirements of the smaller customers could be processed within the existing warehous-ing and shipping flexibility. To develop such a method, the Merriwell family compiled the aggregate demand data shown above. This data shows the monthly sales of bags for the past 5 years.
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