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

Demand chain planning operations within capacity constraints : Nestlé ZAR.

Master of Commerce in Supply Chain Management. University of KwaZulu-Natal, Durban 2017.Organisations are currently faced with difficulties in effectively aligning demand plans to the volatile environments in which they operate. While operating environments and consumer needs change, capacity capabilities often do not reflect the demand plans. The absence of alignment results in inaccurate forecasts, thus putting the long-term sustainability of a business at risk. The focus and aim of the study is to understand how demand planning operations are aligned with capacity constraints at Nestlé ZAR. A quantitative explorative case study research design is being used and data was collected through a structured self-administered questionnaire in this study. The final sample size is 86, which comprised of employees from Demand and Supply Planning, Finance and Control, Sales and Marketing divisions. The sample includes top management, middle management, first level management and non-management. Data analysis uses descriptive and multivariate statistics. Ethical clearance was obtained from the University Ethics Committee and ethical principles were observed throughout the study. The study findings show that the organisation of interest in this study was perceived as adhering to capacity planning principles. The majority of the respondents perceived positively the capacity demand planning process. An average of 50% scored 22, which indicated a relatively high positive perception with regards to demand chain principles. The majority of the participants responded positively to the statements that information sharing achieves demand chain coordination and improves collaborative demand planning. An average of 50% scored 24, which indicated a relatively high positive perception with regards to information sharing. The results showed few capacity constraints that were perceived or experienced in this organisation. An average of 50% scored 17 which indicated that a moderate number of participants experience capacity constraints. Although the Spearman correlation did not sure any significant relationship between variables of interest in this study, the findings showed a wide range in the distribution of the responses of the participants in some items. This study highlighted that there is no one single approach to improving demand chain performance, but it was evident that allowing collaborative demand plans by using quality information shared can reduce impact of capacity constraints and improve planning performance. This study recommended that top management should provide full support to information sharing initiatives to facilitate the demand planning process. A national study to be conducted as this was conducted in one province, with a limited sample size. Keywords: Demand chain, information sharing, capacity