Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.Comment: 1o pages, 5 figure

SKU Time Series Forecasting Methods for FMCGs

This research aims at using forecasting algorithm that predicts the demand that is to be needed on a monthly basis while factoring in occasional inconsistent patterns, seasonality, and non-stationary and cyclical patterns of the data. The prediction is to predict around 3000 SKUs in 19 end markets and since the data is necessary for marketing enhancement and strategies, the Forecasting accuracy must be high. Since market strategies will be based on those predictions and revenue will be lost in the case of an error. Hence, we need to keep in mind that the model is not overfitted and that it wouldn’t give a reasonable accuracy when tested on another SKU. In this study, I will use encrypted data from the organization as such the name SKUs are in numbers instead of names where the trends are there while the region and SKUS will remain undisclosed as well as the numbers wouldn’t be the same. The algorithms used were FBProphet and SARIMA for the given SKUs. They were able to forecast at a MAPE accuracy of 77% and 87% respectively

Creation and application of a comparative financial index for the Australian fast-moving consumer goods (FMCG) industry

Fast-moving consumer goods (FMCGs) are food and non-food daily consumer items that are exhausted when used once and thus have short lifespans. Purchase of these items is typically a result of small-scale consumer decisions. Research in this thesis explores the similarities between value sales volatility in the FMCG industry and that of commodities traded on financial markets. The research is based on the imminent need and opportunity to identify and quantify the value sales volatility of brands traded in retail stores in Australia and aims to gain an increased understanding of brands’ overall performance. Specifically, this thesis seeks to answer the question: What are the antecedents of brands’ sales volatility in the Australian retail sector and how do they influence brand performance overall? To this end, a “Brands Index”, analogous to financial market indices such as the S&P500 and All Ordinaries Indices, is created to test the conceptual framework. The importance of the creation of a Brands Index in the FMCG industry in Australia is absolute. The index itself represents an excellent contribution to the management and marketing disciplines as it allows any brand or set of brands to be compared against the overall market (represented by the Brands Index). This thesis theorises a market index for the FMCG industry in Australia and measures and captures its observed volatility clustering by using ARCH-GARCH models and CAPM theory to calculate brand betas. Two competing methodologies will be advanced to calculate returns; namely, with and without the presence of an equivalent risk-free rate of return. From these two methodologies, only returns including the risk-free rate are shown to successfully pass the CAPM test

An Integrated Retail Supply Chain Risk Management Framework: A System Thinking Approach

It is often taken for granted that the right products will be available to buy in retail outlets seven days a week, 52 weeks a year. Consumer perception is that of a simple service requirement, but the reality is a complex, time sensitive system - the retail supply chain (RSC). Due to short product life-cycles with uncertain supply and demand behaviour, the RSC faces many challenges and is very vulnerable to disruptions. In addition, external risk events such as BREXIT, extreme weather, the financial crisis, and terror attacks mean there is a need for effective RSC risk management (RSCRM) processes within organisations. Literature shows that although there is an increasing amount of research in RSCRM, it is highly theoretical with limited empirical evidence or applied methodologies. With an active enthusiasm coming from industry practitioners for RSCRM methodologies and support solutions, the RSCRM research community have acknowledged that the main issue for future research is not tools and techniques, but collaborative RSC system wide implementation. The implementation of a cross-organisational initiative such as RSCRM is a very complex task that requires real-world frameworks for real-world practitioners. Therefore, this research study attempts to explore the business requirements for developing a three-stage integrated RSCRM framework that will encourage extended RSC collaboration. While focusing on the practitioner requirements of RSCRM projects and inspired by the laws of Thermodynamics and the philosophy of System Thinking, in stage one a conceptual reference model, The �6 Coefficient, was developed building on the formative work of supply chain excellence and business process management. The �6 Coefficient reference model has been intricately designed to bridge the theoretical gap between practitioner and researcher with the aim of ensuring practitioner confidence in partaking in a complex business process project. Stage two focused on a need for a standardised vocabulary, and through the SCOR11 reference guide, acts as a calibration point for the integrated framework, ensuring easy transfer and application within supply chain industries. In their design, stages one and two are perfect complements to the final stage of the integrated framework, a risk assessment toolbox based on a Hybrid Simulation Study capable of monitoring the disruptive behaviour of a multi-echelon RSC from both a macro and micro level using the techniques of System Dynamics (SD) and Discrete Event Simulation (DES) modelling respectively. Empirically validated through an embedded mixed methods case study, results of the integrated framework application are very encouraging. The first phase, the secondary exploratory study, gained valuable empirical evidence of the barriers to successfully implementing a complex business project and also validated using simulation as an effective risk assessment tool. Results showed certain high-risk order policy decisions could potentially reduce total costs (TC) by over 55% and reduce delivery times by 3 days. The use of the �6 Coefficient as the communication/consultation phase of the primary RSCRM case study was hugely influential on the success of the overall hybrid simulation study development and application, with significant increase in both practitioner and researcher confidence in running an RSCRM project. This was evident in the results of the hybrid model’s macro and micro assessment of the RSC. SD results effectively monitored the behaviour of the RSC under important disruptive risks, showing delayed effects to promotions and knowledge loss resulted in a bullwhip effect pattern upstream with the FMCG manufacturer’s TC increasing by as much as €50m. The DES analysis, focusing on the NDC function of the RSC also showed results of TC sensitivity to order behaviour from retailers, although an optimisation based risk treatment has reduced TC by 30%. Future research includes a global empirical validation of the �6 Coefficient and enhancement of the application of thermodynamic laws in business process management. The industry calibration capabilities of the integrated framework application of the integrated framework will also be extensively tested

Application of Shallow Neural Networks to Retail Intermittent Demand Time Series

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting methods could improve their predicting accuracy and establish a high-performing baseline for future development. The solution also offers an end-to-end systematic forecasting landscape enabling a lift-and-shift and easy transition from design to deployment. A practical implementation should bring about stable and reliable forecasts, resulting in cost savings, improved customer service, and increased profitability. Lastly, the research findings contribute to the broader academic field of forecasting and ML with a seminal proposal that provides insights and opportunities for future research