The environmental impact on forecasting plans in retailing: theoretical research trends

Purpose: Difficulties in prediction are increasing caused by complex environments and high variability of the markets; with a marketing perspective, the aim of this work is to analyze actual research trends in multi-factor forecasting in retailing. The results will be useful to design future research on multi-factor forecasting in retail. Design/Methodology/Approach: Using the bibliometric analysis - in particular web-based social network analysis (SNA) on the citation data and some literature research integrations - after the analysis, the authors interpret the data identifying the research perspectives useful to understand the theoretical research roots and trends with attention toward the published studies in forecasting activities, involving final customers. Findings: In the turbulence of the market, many theoretical perspective and approaches are developed to resolve the problem of observing the factors coming from the complex environment. It is possible going beyond the history of sales in forecasting in retailing observing other factors (external and internal) - such as fuel price, consumer price index (CPI), temperature, markdowns -, because monitoring the variations of many more factors/indicators, could increase the possibilities to reduce or minimize the complexity – eventually finding the optimization of complexity - better align tactics and future strategies. Therefore, this work is useful to integrate the research in multi-factors forecasting in retailing analyzing different theoretical aspects by authors, organizing and systematizing them along with outlining trends in research; that could be the way to start future research, identifying the optimal level of factors to have robust results in predictions. Practical implications: The study shows trends in research for scholars that are considering and discriminating the role of environmental factors in forecasting applicable for the retail industry; that’s interesting for retail industry worldwide adapting management and prediction activities to specific economic environments and markets, calibrating the necessary level of forecasting complexity inside companies. Originality: The work provides an original interpretation of the research trends presenting the inverse relation between the complexity of the environment and optimization of forecasting plans. In the turbulent environment when complexity increases it is difficult to optimize forecasting plans in retailing considering many uncontrolled factors, vice versa, optimization increases when complexity decreases, in other words, it is easy to maintain simple forecasting models, not considering the environmental impact.