Retail forecasting: research and practice

This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts. But no research has yet put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 12.6 percent over the model using only the SKU's own predictors. But of the improvements achieved, 95 percent of it comes from the intra-category information, and only 5 percent from the inter-category information. The substantive marketing results also have implications for promotional category management

PRODUCT VARIETY, SERVICE VARIETY, AND THEIR IMPACT ON DISTRIBUTORS

Despite considerable research relating to product variety, few studies have analyzed the impact of product variety on distributors. Furthermore, compared to research on product variety, service variety has been overlooked in the literature. This dissertation consists of three essays: Essay One examines the direct effect of product variety on sales, its indirect effect on sales through stockouts, as well as the total impact of product variety on sales performance. Essay Two develops a moderated mediation model to investigate how service quality and market performance are affected by service variety and the interaction impacts of different types of services. Essay Three analyzes a dynamic system, which includes influences of product and service variety on demand and costs and their reverse impacts on variety decisions

Customer Behaviour Forecasting in FMCG Retail Industry; Golpakhsh Avval Co. Case Study

Objective: Providing that data mining has been an effective solution of improving the efficiency and the effectiveness of the retail industry, this industry has been the subject of data mining science due to the nature of its data. In this study, the prediction of customer behavior in the retail industry of Fast Moving Consumer Goods is aimed at increasing the quantity and quality of sales in the study of Golpakhsh Avval Co. Methods: The present study is applied in terms of purpose, using data survey to collect data. The research is based on the CRISP-DM process, which uses the RFMCL clustering model, regression classification and regression techniques as well. Eventually, a collaborative recommendation method has been applied for recommendation. Results: The result is a forecasting model recommended to the best customers goods that they have not bought on a particular date and to a certain amount, so that, the order-based sale is changed to hot sale method. The final solution involves three sub models of customer clustering, sale forecasting and a recommendation system. The five variables model –with MSE/Range accuracy of 2.24% – is solved for recommendation of sales amount. Conclusion: By implementing the developed recommender system in Golpakhsh Avval Co., the proactive production master plan would be possible to execute. In addition, the marketing approach could be transformed from visiting sales to hot sales in the future which provides considerable savings in shipping and personnel costs

The impact of sales and operations planning on supply chain performance:an investigation of contingency and organisational culture in S&OP implementations

S&OP is a set of business processes and technologies that enable an enterprise to respond effectively to demand and supply variability, with insight into the optimal market deployment of resources and most profitable supply chain mix. S&OP can also be described as a means for internal coordination in which a cross-functional team reaches consensus on sales forecasts, capacity and/or production plans. This thesis aims to develop a deeper understanding of S&OP and in turn the specific means through which supply chain performance is impacted, via a combination of the case study and survey methods. The results from the first phase of this study show that in the case studies of the two separate companies, both cases show significant quantifiable improvements in supply chain performance from implementing S&OP. In the second phase of this study, a large-scale survey was conducted to test the efficacy of six coordination mechanisms of S&OP and the effect of contingency factors. Results based on 568 respondents indicate that Strategic Alignment and Information Acquisition/Processing are the mechanisms that most significantly enable superior S&OP outcomes. However, the survey dataset strongly suggests that a highly formalised S&OP Procedure inhibits supply chain performance. Furthermore, from a contingency theory perspective, increasing firm size and increasing experience in S&OP amplify the negative effect of a standardised S&OP Procedure upon supply chain performance. In the final phase of this study, the effect of organisational culture as an antecedent to S&OP coordination mechanisms is explored. Results show that a strong S&OP culture leads to better overall coordination outcomes, but a strong S&OP culture may concurrently suppress Supply Chain Performance via the S&OP Procedure/Schedule pathway due to competitive mediation