Impact of the retail environment drivers on sales and demand forecasting

The Ph.D. dissertation, Impact of the retail environment drivers on sales and demand forecasting, aims to explore and study the influence of the retail environment patterns on sales and demand forecasting. The uncertainty of the retail environment influenced by the complexity of the supply chain and demand management put out the range of drivers that can influence the dynamics of demand and sales and the tendency to develop more functional tools to capture them. In the first chapter, The concept of the environment and external impact on the forecasting in retailing, it is outlined the main research on the environment in the literature, focusing on the concept of uncertainty, complexity especially in the domain of retailing. The network analysis on citation data benefits the research on certain trends in the literature about environmental patterns on forecasting activities. Results are outlined in the theoretical framework of research trends and the concept of the retail environment for the decision-making process in forecasting. In the second chapter, Influence of the internal and external drivers on consumers’ visits and sales productivity, the explorative analysis focuses on analyzing the effect of the internal retail environment, as the retail format and assortment level, on the consumers’ visits and it is hypothesized whether the external (competition distance, public holidays) and internal drivers (promotions, assortment level, customer visits) affect the sales productivity in different store’s formats. Results show that both promotions and competition distance positively influence sales productivity and it variates based on the store format. The third chapter, The causal effect of the environmental and promotional variables on the sales forecasting, studied the type of relationships between the environmental and internal drivers on the sales forecasting, looking for the causality and functional links. Using the linear and additive models shows that, additive models can allow the together nonlinear and linear functions between the macro variables (CPI, Fuel Price, Unemployment and Temperature) and internal micro (promotions) capturing better the dynamics of effects on sales and perform better in terms of prediction. In the previous explorative analysis, the focus is on looking at possible causal links between the external, internal drivers and sales. In the fourth chapter, The added value of the competition information in demand forecasting, matching closer the business reality, it has been studied the effect of the external variables in the short-term demand forecasting. Using DIY (Do-It-Yourself) stores’ demand and sales data it is analyzed the influence of the competitions’ promotional discounts on the weekly demand at SKUs (Stock Keeping Unit) level. Results show that if the SKUs have high sold quantity and promotions more weeks in the year the inclusion of competitors’ promotional discounts may improve the demand-forecasting model, while SKUs that have the stable demand and are not often discounted the simple linear model without external competition’s variables works better. The final chapter, Conclusions, limitations and future research, discuss the main conclusions, academic, practical implications and future research. The research contributes both to the literature and retail practice. There is, still, the lack of studies of the external influence on the demand and sales forecasting. Retail managers may use these insights to extract important information from the environment and to apply different solutions based on the complexity of the business problem. Focal promotions and competitive promotional actions create a significant impact on consumer demand. The complex nonlinear model with the external variables may work better in a situation of enlarging retail business and where its high impact on the market creates dynamics interactions, while the simple linear models can provide efficient solutions in an already stable and easily predicted competitive market. The limitation of this research is the missing variety of external and internal data that can help to find the optimal model. The future research will be in direction of creating the optimal model with external and internal variables that may be the efficient solutions to the large amount of SKUs and useful managerial tool for scanning the environment.