The contribution of product and industry knowledge to the accuracy of sales forecasting was investigated by examining the company forecasts of a leading manufacturer and marketer of consumable products. The company forecasts of I8 products produced by a meeting of marketing, sales, and production pe
Forecasting for business planning: A case study of IBM product sales
β Scribed by L. S.-Y. Wu; N. Ravishanker; J. R. M. Hosking
- Publisher
- John Wiley and Sons
- Year
- 1991
- Tongue
- English
- Weight
- 842 KB
- Volume
- 10
- Category
- Article
- ISSN
- 0277-6693
No coin nor oath required. For personal study only.
β¦ Synopsis
This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two timeseries methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.
KEY WORDS Adaptive smoothing ARIMA models Transfer-function models FORECASTING AND BUSINESS PLANNING
Business planning often starts with an annual financial goal from which annual targets or plans for financial measurements and sales of individual products must be generated. Business planners take each annual plan and further apportion it into monthly numbers which represent their expectation and a feasible way of reaching the annual plan, in terms of manufacturing schedules and marketing support. The monthly planning numbers are called the track. They are based on historical patterns of seasonality in the data and on the planner's judgment of such matters as manufacturing capability, product announcements and price cuts. One important aspect of business planning is assessing, as the year progresses, whether the annual plan is attainable or whether action needs to be taken to achieve the target; and, when
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