A collaborative demand forecasting process with event-based fuzzy judgements

Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy

Sale Forecasting of Merck Pharma Company using ARMA Model

This study aims to develop a stochastic framework of model to forecast future sales for pharmaceutical industry. In this regard, the study focuses on Merck Pharmaceutical monthly sales data. This study examines the Sale forecasting models. The study includes monthly data published in the annual reports of the company from Jan. 2008 to Dec. 2012.The time series diagram shows unequal means over the time period that suggests the data is stationary. Having transformed the data, ARMA (1, 1) model is applied which shows that there will be increase in sales by $6.784m given that in the last month sales were$1bn. On the contrary, last month’s residual has an adverse effect on current month sales up to the extent of $432.942m. In this study AR (1) and MA (1) both the processes are significant at 1% Keywords: Sales forecast, ARMA (1, 1), Pharma Industr

Wiley Interdiscip Rev Comput Stat

Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This review surveyed recent literature on aggregating expert-elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace.R35 GM119582/GM/NIGMS NIH HHSUnited States/U01 IP001122/IP/NCIRD CDC HHSUnited States/2022-03-01T00:00:00Z33777310PMC799632111017vault:3684

Wisdom of group forecasts: Does role-playing play a role?

Forecasting plays a special role in supply chain management with sales forecasts representing one of the key drivers for collaborative planning and decision making in the organisations involved. We review the important role played by judgemental forecasts in this area, focusing on group predictions. Noting the scarcity of research using group forecasts, we present the results of an experiment where consensus forecasts are elicited from structured groups with and without role-playing. Comparisons with groups without any assigned roles show that getting into tailored organisational roles does have a significant effect in the resultant forecasts. In particular, members of the role-playing groups show less agreement with consensus forecasts and display a strong commitment to their assumed roles and scripts. Furthermore, role-playing groups leave a higher percentage of model-based forecasts unadjusted and when they do make an adjustment, it is significantly less than the groups, whose members are not assigned roles. Differences between the role-playing conditions are interpreted as highlighting the importance of role framing on forecast adjustment and group forecasting behaviour. Future research directions are proposed to improve the accuracy and acceptance of group forecasts. © 2011 Elsevier Ltd

Information use in supply chain forecasting

Demand forecasting to support supply chain planning is a critical activity, recognized as pivotal in manufacturing and retailing operations where information is shared across functional areas to produce final detailed forecasts. The approach generally encountered is that a baseline statistical forecast is examined in the light of shared information from sales, marketing and logistics and the statistical forecast may then be modified to take these various pieces of information into account. This experimental study explores forecasters’ use of available information when they are faced with the task of adjusting a baseline forecast for a number of retail stock keeping units to take into account a forthcoming promotion. Forecasting demand in advance of promotions carries a particular significance given their intensive supply chain repercussions and financial impact. Both statistical and qualitative information was provided through a forecasting support system typical of those found in practice. Our results show participants responding to the quantity of information made available, though with decreasing scale effects. In addition, various statistical cues (which are themselves extraneous) were illustrated to be particularly important, including the size and timing of the last observed promotion. Overall, participants appeared to use a compensatory strategy when combining information that had either positive or negative implications for the success of the promotions. However, there was a consistent bias towards underestimating the effect of the promotions. These observed biases have important implications for the design of organizational sales and operations planning processes and the forecasting support systems that such processes rely on