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

Analysis of judgmental adjustments in the presence of promotions

Sales forecasting is increasingly complex due to many factors, such as product life cycles that have become shorter, more competitive markets and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers add information to the forecast, like future promotions, potentially improving accuracy. Despite the importance of judgment and promotions, the literature devoted to study their relationship on forecasting performance is scarce. We analyze managerial adjustments accuracy under periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models

Rule Based Forecasting [RBF] - Improving Efficacy of Judgmental Forecasts Using Simplified Expert Rules

Rule-based Forecasting (RBF) has emerged to be an effective forecasting model compared to well-accepted benchmarks. However, the original RBF model, introduced in1992, incorporates 99 production rules and is, therefore, difficult to apply judgmentally. In this research study, we present a core rule-set from RBF that can be used to inform both judgmental forecasting practice and pedagogy. The simplified rule-set, called coreRBF, is validated by asking forecasters to judgmentally apply the rules to time series forecasting tasks. Results demonstrate that forecasting accuracy from judgmental use of coreRBF is not statistically different from that reported from similar applications of RBF. Further, we benchmarked these coreRBF forecasts against forecasts from (a) untrained forecasters, (b) an expert system based on RBF, and (c) the original 1992 RBF study. Forecast accuracies were in the hypothesized direction, arguing for the generalizability and validity of the coreRBF rules

Development and Validation of a Rule-based Time Series Complexity Scoring Technique to Support Design of Adaptive Forecasting DSS

Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity

The Influence of Managerial Forces and Users’ Judgements on Forecasting in International Manufacturers: a Grounded Study

Despite the improvements in mathematical forecasting techniques, the increase in forecasting accuracy is not yet significant. Previous research discussed various forecasting issues and techniques without paying attention to users’ forces and behaviours that influence the construction of forecasts. This research investigates this gap through examining the managerial forces that influence the judgements of different users and constructors of forecasts in international pharmaceutical companies. A qualitative research applying Grounded Theory methodology is used to explore the concealed forces in forecasting processes by interviewing different constructors and users of forecasts in international contexts. Using the Coding Matrices, the research identifies the forces which induce users’ judgements, and consequently lead to conflicts. The research adds value by providing assessment criteria of forecasting management in future research

Do experts' SKU forecasts improve after feedback?

We analyze the behavior of experts who quote forecasts for monthlySKU-level sales data where we compare data before and after the momentthat experts received different kinds of feedback on their behavior. Wehave data for 21 experts located in as many countries who make SKUlevelforecasts for a variety of pharmaceutical products for October 2006to September 2007. We study the behavior of the experts by comparingtheir forecasts with those from an automated statistical program, and wereport the forecast accuracy over these 12 months. In September 2007these experts were given feedback on their behavior and they received atraining at the headquartersÂ’ office, where specific attention was given tothe ins and outs of the statistical program. Next, we study the behaviorof the experts for the 3 months after the training session, that is, October2007 to December 2007. Our main conclusion is that in the second periodthe expertsÂ’ forecasts deviated less from the statistical forecasts and thattheir accuracy improved substantially.expert forecasts;model forecasts;cognitive process feedback;judgmental adjustment;outcome feedback;performance feedback;task properties feedback

Judgement and supply chain dynamics

Forecasting demand at the individual stock-keeping-unit (SKU) level often necessitates the use of statistical methods, such as exponential smoothing. In some organizations, however, statistical forecasts will be subject to judgemental adjustments by managers. Although a number of empirical and ‘laboratory’ studies have been performed in this area, no formal OR modelling has been conducted to offer insights into the impact such adjustments may have on supply chain performance and the potential development of mitigation mechanisms. This is because of the associated dynamic complexity and the situation-specific nature of the problem at hand. In conjunction with appropriate stock control rules, demand forecasts help decide how much to order. It is a common practice that replenishment orders may also be subject to judgemental intervention, adding further to the dynamic system complexity and interdependence. The system dynamics (SD) modelling method can help advance knowledge in this area, where mathematical modelling cannot accommodate the associated complexity. This study, which constitutes part of a UK government funded (EPSRC) project, uses SD models to evaluate the effects of forecasting and ordering adjustments for a wide set of scenarios involving: three different inventory policies; seven different (combinations of) points of intervention; and four different (combinations of) types of judgmental intervention (optimistic and pessimistic). The results enable insights to be gained into the performance of the entire supply chain. An agenda for further research concludes the paper

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

Biases of professional exchange rate forecasts: Psychological explanations and an experimentally based comparison to novices

The empirical performance of macroeconomic exchange rate models is more than disappointing. This dismal result is also reflected in the forecasting capabilities of professional analysts: all in all, analysts are not in a position to beat naïve random walk forecasts. The root for this deficient outcome stems from the fact that professional forecasts are to a large extend influenced by actual changes in exchange rates. A reasonable explanation for this behaviour can be taken from the behavioural finance literature. To test whether this characteristic tends to be general human behaviour in an uncertain environment, we analyse the forecasting behaviour of students experimentally, using a simulated currency series. Our results indicate that a topically oriented trend adjustment behaviour (TOTA) is a general characteristic of human forecasting behaviour. Additionally, we apply a simple model to explain professional and students forecasts. --Foreign exchange market,forecasting,behavioural finance,anchoring heuristics,judgement,expertise