Optimising supermarket promotions of fast moving consumer goods using disaggregated sales data: A case study of Tesco and their small and medium sized suppliers

The use of price promotions for fast moving consumer goods (FMCG’s) by supermarkets has increased substantially over the last decade, with significant implications for all stakeholders (suppliers, service providers & retailers) in terms of profitability and waste. The overall impact of price promotions depends on the complex interplay of demand and supply side factors, which has received limited attention in the academic literature. There is anecdotal evidence that in many cases, and particularly for products supplied by small and medium sized enterprises (SMEs), price promotions are implemented with limited understanding of these factors, resulting in missed opportunities for sales and the generation of avoidable promotional waste. This is particularly dangerous for SMEs who are often operating with tight margins and limited resources. A better understanding of consumer demand, through the use of disaggregated sales data (by shopper segment and store type) can facilitate more accurate forecasting of promotional uplifts and more effective allocation of stock, to maximise promotional sales and minimise promotional waste. However, there is little evidence that disaggregated data is widely or routinely used by supermarkets or their suppliers, particularly for those products supplied by SMEs. Moreover, the bulk of the published research regarding the impact of price promotions is either focussed on modelling consumer response, using claimed behaviour or highly aggregated scanner data or replenishment processes (frameworks and models) that bear little resemblance to the way in which the majority of food SMEs operate. This thesis explores the scope for improving the planning and execution of supermarket promotions, in the specific context of products supplied by SME, through the use of dis-aggregated sales data to forecast promotional sales and allocate promotional stock. An innovative case study methodology is used combining qualitative research to explore the promotional processes used by SMEs supplying the UK’s largest supermarket, Tesco, and simulation modelling, using supermarket loyalty card data and store level sales data, to estimate short term promotional impacts under different scenarios and derive optimize stock allocations using mixed integer linear programming (MILP). ii The results suggest that promotions are often designed, planned and executed with little formalised analysis or use of dis-aggregated sales data and with limited consideration of the interplay between supply and demand. The simulation modelling and MILP demonstrate the benefits of using supermarket loyalty card data and store level sales data to forecast demand and allocate stocks, through higher promotional uplifts and reduced levels of promotional wast

Quantitative Methods and its Contemporary Directions

Field of business and economics has seen a surge in the big data applications in recent years. These advances have impacted the research direction significantly for both academics and practitioners as methods and theory underlying these applications are also changing. This is because factors affecting these applications are dynamic in both regional and international context (Davies & Hughes, 2014)

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Consumer demand information as a re-balancing tool for power asymmetry between food retailers and suppliers

This conceptual paper presents a model that may be used to redress the power balance between retailers and suppliers in the supply chain through better information symmetry and mutual dependence. It explores power dependence and resource dependence theories to conceptualise the use of demand information, by drawing on the diverse viewpoints within the extant literature on the effect of supply chain power asymmetry on exchange relationships and mutual dependence. Co-optation adds stability and reduces uncertainty through the exchange of resources. The dynamic nature of relationships and power between retailers and suppliers requires a multi-theory approach to identify a robust understanding of the interplay of different influence factors. This study has both operational and strategic implications for the food supply chain, as power asymmetry in relationships affects sustainability, especially in sales promotions periods for both retailers and suppliers. Improving power equilibrium between the buyer and supplier through information symmetry with the integration of power and resource dependence theory is novel

Mechanical and comfort properties of Hydroentangled nonwovens from comber noil

Cotton fibre is one of the most important commodity fibre and is widely employed in apparels. At present, the share of natural fibres in production of nonwoven fabrics is low and employed in opt applications. The cotton fibre is conventionally converted into woven and knitted fabrics by short staple spinning methods. The comber noil is short fibre waste during production of combed cotton yarns. The aims of the current study were to employ comber noil for preparation of Hydroentangled cotton nonwovens at varying water jet pressures and conveyor speeds. The effect of these parameters is studied with respect to mechanical and comfort properties of prepared fabrics. The results showed that these variables can help to manufacture fibrous assemblies with engineered properties according to required application area