Effective forecasting for supply-chain planning: an empirical evaluation and strategies for improvement

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a simple univariate statistical method to produce a forecast and the subsequent judgmental adjustment of this by the company's demand planners to take into account market intelligence relating to any exceptional circumstances expected over the planning horizon. Based on four company case studies, which included collecting more than 12,000 forecasts and outcomes, this paper examines: i) the extent to which the judgmental adjustments led to improvements in accuracy, ii) the extent to which the adjustments were biased and inefficient, iii) the circumstances where adjustments were detrimental or beneficial, and iv) methods that could lead to greater levels of accuracy. It was found that the judgmentally adjusted forecasts were both biased and inefficient. In particular, market intelligence that was expected to have a positive impact on demand was used far less effectively than intelligence suggesting a negative impact. The paper goes on to propose a set of improvements that could be applied to the forecasting processes in the companies and to the forecasting software that is used in these processes

Integration of Statistical Methods and Judgment for Time Series

We consider how judgment and statistical methods should be integrated for time-series forecasting. Our review of published empirical research identified 47 studies, all but four published since 1985. Five procedures were identified: revising judgment; combining forecasts; revising extrapolations; rule-based forecasting; and econometric forecasting. This literature suggests that integration generally improves accuracy when the experts have domain knowledge and when significant trends are involved. Integration is valuable to the extent that judgments are used as inputs to the statistical methods, that they contain additional relevant information, and that the integration scheme is well structured. The choice of an integration approach can have a substantial impact on the accuracy of the resulting forecasts. Integration harms accuracy when judgment is biased or its use is unstructured. Equal-weights combining should be regarded as the benchmark and it is especially appropriate where series have high uncertainty or high instability. When the historical data involve high uncertainty or high instability, we recommend revising judgment, revising extrapolations, or combining. When good domain knowledge is available for the future as well as for the past, we recommend rule- based forecasting or econometric methods.statistical methods, statistics, time series, forecasting, empirical research

Forecasting Methods for Marketing:* Review of Empirical Research

This paper reviews the empirical research on forecasting in marketing. In addition, it presents results from some small scale surveys. We offer a framework for discussing forecasts in the area of marketing, and then review the literature in light of that framework. Particular emphasis is given to a pragmatic interpretation of the literature and findings. Suggestions are made on what research is needed.forecasting, marketing, methods, review, research

Structuring the decision process : an evaluation of methods in the structuring the decision process

This chapter examines the effectiveness of methods that are designed to provide structure and support to decision making. Those that are primarily aimed at individual decision makers are examined first and then attention is turned to groups. In each case weaknesses of unaided decision making are identified and how successful the application of formal methods is likely to be in mitigating these weaknesses is assessed

Scenario of the organic food market in Europe

Scenario analysis is a qualitative tool for strategic policy analysis that enables researchers and policymakers to support decision making, and a systemic analysis of the main determinants of a business or sector. In this study, a scenario analysis is developed regarding the future development of the market of organic food products in Europe. The scenario follows a participatory approach, exploiting potential interactions among the relevant driving forces, as selected by experts. Network analysis is used to identify the roles of driving forces in the different scenarios, and the results are discussed in comparison with the main findings from existing scenarios on the future development of the organic sector

Effective judgmental forecasting in the context of fashion products

We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense. Implications for the theory and practice of building decision support models are discussed

Improving Forecast Accuracy by Guided Manual Overwrite in Forecast Debiasing

We present ongoing work on a model-driven decision support system (DSS) that is aimed at providing guidance on reflecting and adjusting judgmental forecasts. We consider judgmental forecasts of cash flows generated by local experts in numerous subsidiaries of an international corporation. Forecasts are generated in a decentralized, non-standardized fashion, and corporate managers and controllers then aggregate the forecasts to derive consolidated, corporate-wide plans to manage liquidity and foreign exchange risk. However, it is well-known that judgmental predictions are often biased, where then statistical debiasing techniques can be applied to improve forecast accuracy. Even though debiasing can improve average forecast accuracy, many originally appropriate forecasts may be automatically corrected in the wrong direction, for instance, in cases where a forecaster might have considered knowledge on future events not derivable statistically from past time series. To prevent high-impact erroneous corrections, we propose to prompt a forecaster for action upon submission of a forecast that is out of the confidence bounds of a benchmark forecast. The benchmark forecast is derived from a statistical debiasing model that considers the past error patterns of a forecaster. Bounds correspond to percentiles of the error distribution of the debiased forecast. We discuss the determination of the confidence bounds and the selection of suspicious judgmental forecasts, types of (statistical) feedback to the forecasters, and the incorporation of the forecaster’s reactions (comments, revisions) in future debiasing strategies

Organic Farming in Europe by 2010: Scenarios for the future

How will organic farming in Europe evolve by the year 2010? The answer provides a basis for the development of different policy options and for anticipating the future relative competitiveness of organic and conventional farming. The authors tackle the question using an innovative approach based on scenario analysis, offering the reader a range of scenarios that encompass the main possible evolutions of the organic farming sector. This book constitutes an innovative and reliable decision-supporting tool for policy makers, farmers and the private sector. Researchers and students operating in the field of agricultural economics will also benefit from the methodological approach adopted for the scenario analysis

Influence of differentiated roles on group forecasting accuracy

Cataloged from PDF version of article.While behavioral research on forecasting has mostly examined the individual forecaster, organizationally-based forecasting processes typically tend to rely on groups with members from different functional areas for arriving at ‘consensus’ forecasts. The forecasting performance could also vary depending on the particular group structuring utilized in reaching a final prediction. The current study compares the forecasting performance of modified consensus groups with that of staticized groups using formal role-playing. It is found that, when undistorted model forecasts are given, group forecasts (whether they are arrived at through averaging or by a detailed discussion of the forecasts) contribute positively to the forecasting accuracy. However, providing distorted initial forecasts affects the final accuracy with varying degrees of improvement over the initial forecasts. The results show a strong tendency to favor optimistic forecasts for both the staticized and modified consensus group forecasts. Overall, the role modifications are found to be successful in eliciting a differential adjustment behavior, effectively mimicking the disparities between different organizational roles. Current research suggests that group discussions may be an efficient method of displaying and resolving differential motivational contingencies, potentially leading to group forecasts that perform quite well. ⃝c 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved