Demand forecasting errors in industrial context: Measurement and impacts

Abstract

It is important to know the impacts that sales forecast errors have on the supply chain. Knowing the role of forecasting and the impacts of forecast errors creates a basis for defining a realistic target for forecast accuracy, identifying the most important customers and/or products to be forecasted, and finding a suitable way to measure forecasting performance.

This paper provides a case study about assessing the impacts of sales forecast errors. The analysis steps include defining the planning flow and the role of sales forecasts in production planning and inventory management and analyzing the characteristics of sales forecasting errors of a company. The case company is a large process industry company that seeks out to improve the accuracy of their sales forecasts and to improve control over the inventory policy decisions of different sales divisions.

This case study points out some managerial problems that companies run into when demand forecasting is applied in an industrial context. One of the problems is the insufficiency of traditional error measures. The problem is analyzed and an alternative measurement practice is presented.

Introduction

Demand forecasting is commonly applied in companies that operate in consumer markets. When demand patterns are relatively smooth and continuous, demand forecasts based on historical demand are usually quite accurate. Success stories about demand forecasting typically report lower inventory levels and improved customer service. After the success of forecasting in consumer markets, there has been growing interest in applying demand forecasting in companies operating in industrial markets to apply demand forecasting, despite the fact that environment is different. In industrial markets, the importance of single customers is greater, so demand patterns are more volatile. In this environment, the historical demand does not always predict the future demand sufficiently, so human judgment plays a more important role in the forecasting process.

When the demand forecasting system is implemented in a company, there is a tendency that concepts, targets and principles are imitated from other companies to speed up the implementation. Since most of the forecasting approaches have been developed for consumer products, there is a risk that unrealistic accuracy targets and deceptive error measures are adopted, if the environment is different. Companies operating in industrial markets have special characteristics that should be addressed and understood before any techniques or approaches are applied.

Forecasting should not be considered as an individual function, but as an important part of supply-chain management. However, most research emhasizes producing the forecasts, not their usage in decision making or their impacts in the whole supply chain. Also earlier empirical research points out that in practical work, producing the forecasts is more consciously managed than evaluating its impacts in the supply-chain management (Mentzer and Moon, 2005). For example, in many companies forecast accuracy is measured, but assessing the impacts of forecast errors on supply-chain management is not managed equally well.

Many forecasting studies start from the premise that the use of forecasts and the required forecast accuracy is defined before the approach for producing the forecasts is chosen, see e.g. Heizer (2001). This does not always happen in practice, however, as the nature of sales forecasting management is more iterative. That is why it is important to get a general view about the whole forecasting process in a company, illustrate it, and communicate about it in the organization.

The emphasis of this paper is on the usage of forecasts and on the impacts of forecast errors. Through an explorative case study, we illustrate that forecasting systems in companies are not always rational with respect to the real impacts of forecast errors. The impacts of forecast errors are analyzed in order to find a target, focus, and suitable performance measurement for forecasting that fit the characteristics and needs of the case company.

In the next two sections, the literature on forecast error measurement and setting the targets for forecast accuracy is reviewed. After that, the case company's planning and forecasting operations are analyzed. At the end of the paper, managerial impacts of the analysis are reviewed and the last section provides some concluding remarks.