In this paper, we engage with O'Brien's [O'Brien, F.A., 2004. Scenario planning - lessons for practice from teaching and learning. European Journal of Operational Research 152, 709-722] identification of both pitfalls in teaching scenario planning and proposed remedies for these. We consider...

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 computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these...

Purpose - Hard decision analysis models allow complex decisions to be broken down into easier-to-handle and precisely defined sub-problems and also provide a documented rationale for the decision. This paper aims to investigate why the course of action prescribed by a hard decision analysis...

Restrictiveness and guidance have been proposed as methods for improving the performance of users of support systems. In many companies computerized support systems are used in demand forecasting enabling interventions based on management judgment to be applied to statistical forecasts. However,...

were obtained by using the system's facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of an S & OP process, yet these often only served to reduce forecast accuracy. This study uses...

Research has suggested that outcome feedback is less effective than other forms of feedback in promoting learning by users of decision support systems. However, if circumstances can be identified where the effectiveness of outcome feedback can be improved, this offers considerable advantages,...

Naïve 1 forecasts are often used as a benchmark when assessing the accuracy of a set of forecasts. A ratio is obtained to show the upper bound of a forecasting method's accuracy relative to naïve 1 forecasts when the mean squared error is used to measure accuracy. Formulae for the ratio are...

Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods...