Responsive Forecasting and Planning Process in the High-Tech Industry

By Aertsen, Freek; Versteijnen, Edward | The Journal of Business Forecasting, Summer 2006 | Go to article overview

Responsive Forecasting and Planning Process in the High-Tech Industry


Aertsen, Freek, Versteijnen, Edward, The Journal of Business Forecasting


The forecasting and planning process in the electronics industry requires special attention because of its cyclical demand pattern, shorter life cycle, and unreliable forecast-related information ... for an effective process, information transparency, speed, and efficiency are the key ... to improve efficiency, companies should concentrate only on those items that are high value drivers.

Forecasting and planning are an important part of any business process, and are regarded by practitioners and academics as key to an efficient supply chain management. Over the last decade, companies are working feverishly to improve their forecasting and planning process. According to a recent Aberdeen survey, 70% of the companies are actively engaged in enhancing their existing sales and operations planning capabilities.

In the high-tech electronics industry, planning and forecasting are extremely challenging. Here the market is characterized by cyclical demand patterns with a high level of uncertainty, shorter product life cycles, prices under constant pressure, changing distribution channels, and unreliable forecast information. Furthermore, the products must be supplied through long, fragmented supply chains with a high level of pressure on capacity utilization and a difficult process to bring products to a stage of mass production.

This article describes how to design a forecasting and planning process to deal with the specific needs of the high-tech electronics industry. Information is based on face-to-face interviews with 25 large multinational companies. Participants in the interview included equipment manufacturers, component suppliers, distributors, providers of electronic manufacturing services, OEM manufacturers, and operators.

One of the key findings is that although forecast accuracy in this high-tech industry is very poor, investment in improving forecast accuracy is not perceived as waste. Many companies are engaged in programs to improve their forecasting and planning process. Companies are spending considerable amounts of time and resources on it.

THREE TYPES OF DECISIONS

Forecasting is a crucial input to decision making, whether the decision is operational, tactical, or strategic. Operational decisions are very short term and deal with issues such as production scheduling, sales order management, and inventory management. In this industry, management's ability to change the course of the company is limited. As such, the focus is mainly to make the most from the infrastructure they have to satisfy customers.

Tactical decisions deal mainly with the issue of whether the company is still on track to achieve its strategic goals; if not, what corrective actions can be taken. Here we asked questions such as: Are the engineers working on the correct products, are the right customers being targeted, do we have adequate capacity at manufacturing sites, have we planned the right promotions, and would we be able to deliver the expected financial value? The keys to all these are reliable sales forecasts.

Strategic decisions have a longer-term focus and concentrate on the markets the company wants to compete in, technology, customer priorities, required organizational set-up, and required investments in the company's infrastructure. In each case, forecasts are needed. But for an effective forecasting and planning process, one needs information transparency, speed, and efficiency. (see Figure 1)

INFORMATION TRANSPARENCY

Information transparency means that best information is available to make the decisions. There is no information distortion among different functions within an organization or among the different supply chain partners. In the past, different functions, such as marketing, sales, logistics, and finance, made decisions in isolation, using their own information sources and assumptions. At the same time, demand forecast information was not shared. …

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