Strategic Management Given the important role of the forecasting function, it is

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Strategic Management

Given the important role of the forecasting function, it is important for managers to understand current methodologies used. However, past forecasting surveys have looked at forecasting practices in all industry segments combined. The management of manufacturing organizations is in many ways different from that of service organizations. Most service organizations frequently face demand levels of high variability within a very short planning horizon. Further, they are unable to inventory their products as manufacturing firms do, and they rely on labor to meet peak demand periods. By contrast, manufacturing planning horizons tend to be longer, and forecasting is often linked to an inventory control system, such as material requirements planning (MRP). Because of these differences, combining information on forecasting practices in manufacturing and service firms can lead only to broad generalizations and is not helpful in understanding practices in a specific industry segment (Sanders, 1997).

Section 10.1 in your Team Member Guide provides you with a basic forecasting method, which is illustrated by the following example:

Last year’s sales can be a good starting point for this year’s forecasts. For example, if the segment growth rate for the upcoming year is 9.2%, you can say, “All things being equal, we can expect to sell 9.2% more units this year than we did last year.”

Assume next year’s growth rate for Traditional is 9.2%, and your Traditional product sold 1,100,000 units last year without stocking out (running out of inventory):1,100,000 x 0.092 = 101,200

Adding 101,200 to last year’s sales of 1,100,000 units gives you a starting forecast for the upcoming year of 1,201,200 units.

In forecasting, it is imperative to consider whether the top products in the segment can meet customer demand. Forecasting is the ability to examine the top product’s capacities. A question to ask yourself is, can the competition manufacture sufficient units? If not, this may be an opportunity to exploit. In this type of industry you need to also keep in mind the intermittent demands of your products as well as inventory control. Ensure you also factor these elements into your forecasting.

What strategy would you use if you had your own company and why? What are the strengths of your approach? What immediate things will you need to focus on to actualize this strategy? What will you need to focus on in the longer term?

Explanation / Answer

Forecasting plays a very important role when we operate in product manufacturing industry. Having a right product at the right place at the right time and in the right quantities is very essential. Accurate forecasts lead to better inventory management. When our forecasts are accurate, we can eliminate problem of losing market share due to stock outs. If excess inventory piles up, it increases inventory handling costs. When stocks are in excess, we would be forced to offer discounts and sell the stocks at a loss in order to liquidate them.

To increase the accuracy of forecasting, it is very much important to understand first nature of the demand.One needs to understand whether there is an increasing demand or the market is saturated. Also, one needs to understand the seasonality of demand. What are the peak seasons and trough seasons for the demand. Once nature of demand is analysed, we need to pick the forecasting technique suitable for that type of demand.

Next step would be to arrive at factors influencing the demand and fina a metric to measure those factors. These factors would be the previous demand, no.of competitor SKUs operating in the market, product attributes, industry growth rate, schemes offered by your comoany to the channel partners,etc.With this approach almost all factors affecting demand can be accounted for and production can be planned accordingly and accuracy of forecasts would increase.

When data handled is small, we can forecast using an inhouse team with softwares like Excel, R etc. When the company grows and data is huge to handle we can outsource forecasting to companies which are good at this. Outsourcing should be done after proper cost benefit analysis.

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