Forecasting Essay Example | Topics and Well Written Essays - 1000 words

Forecasting - Essay Example Forecasting is therefore a complex business in the company, which effectively has more than one personality. Seasonal forecasting is less significant and used at Daimler Chrysler than for example in the energy industry, in which the seasons make for dramatic and cyclical shifts in demand. It is not as though automobile sales do not peak at certain times of the year, but our regression models are able to take care of these models. We can also use moving annual totals for monthly future projections, rather than a calendar method, to ensure that we account for relatively minor seasonal fluctuations in demand. Seasonal forecasting has some applications for our finance business. Customers of the Daimler Chrysler Bank may have seasonal fluctuations in demand for their products and services. The financial services wing of the company has to evaluate the validity of revenue forecasts in applications for loans in such cases. There are also some key materials that we use in production, which experience seasonal fluctuations in pricing and availability. Energy is an example for plants in the northern hemisphere. Leather, though relatively small in total cost, is another example, the purchase and production support services functions of the company have to keep seasonal forecasting methods in mind, when planning for supplies at optimal prices and in time. Delphi Delphi is a key component of forecasting methodology in the automobile business. Product demand is significantly affected by cultural and geo-political matters, which lie outside the domain of expertise of a car and truck maker. The company has long gestation periods for developing new products. Hence, there is an important and frequent need to use third-party consultant from diverse fields to understand future automobile needs in various parts of the world. Daimler Chrysler has also to use Delphi as it spreads its wings to new territories. Russia, China and India are amongst the new markets with high growth potential for automobile sales, but which have driving, traffic and customer needs very different from the traditional strong holds of Western Europe and the United States. Relevant trend data are lacking for these key opportunities, and Delphi provides important learning inputs for entry and consolidation strategies for such markets. The Delphi technique has a number of drawbacks (Evans, 2002, p 366). The kind of experts selected for the study affect the outcome, and there is inevitable subjectivity in their forecasts. Most of them are highly biased and may take extreme positions to suit their private agendas. The questionnaires we deploy during the interviews are a bit vague, and the responses are therefore not universally comparable. There is no valid measure of error as with regression models. Nevertheless, this method is the best when we need ideas about the distant future and how customer choices and competitive technologies are likely to change. Daimler Chrysler cannot do without Delphi, its weaknesses notwithstanding, because innovation lies at the heart of our competitive position (Daimler-Chrysler Creates New Management Model, 2006). We need expert inputs on matters such as future consumer needs and energy security. The jatropha project in India and the driver assist system are examples of