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

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

This paper proposes a novel forecasting method that combines the deep learning method - long short-term memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multi-channel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting

Development of Predictive Analytics for Demand Forecasting and Inventory Management in Supply Chain using Machine Learning Techniques

Forecasting demand effectively and managing inventories efficiently are critical components of modern supply chain management. By understanding full scope of demand possibilities, businesses gain ability to fine-tune inventory levels, navigate situations involving stockouts and overstock, and move toward a more resilient and precise supply chain. This thesis focuses on strategies to enhance these critical functions. We start with examining impact of customer segmentation on forecasting precision by introducing a novel cluster-based demand forecasting framework that harnesses ensemble learning techniques. Our results showcase the effectiveness of the clustered-ensembled approach with minimal forecast errors. However, the constraints related to data availability and segmentation indicate areas that warrant further investigation in future research. The significance of demand accuracy becomes most apparent when we consider its impact on safety stock. In second objective, we explore multivariate time series forecasting for optimal safety stock and inventory management, utilizing deep learning models and a cost optimization framework. This strategy outperforms individual models, demonstrating enhanced forecasting accuracy and stability across diverse product domains. Calculating safety stock based on proposed demand prediction framework leads to optimized safety stock levels. This not only prevents costly stockouts but also minimizes surplus inventory, resulting in reduced overall holding costs and improved inventory efficiency. Although the first two objectives provided optimized results, relying on point predictions to calculate safety stock is not ideal. Unlike traditional point forecasting, distribution forecasting aims to cover the entire range of potential demand outcomes, essentially creating a comprehensive map of possibilities. The third objective of this thesis introduces recurrent mixture density networks (RMDNs) for refined distribution demand forecasting and safety stock estimation. These innovative models consistently outperform traditional LSTM models, offering more precise stockout and overstock predictions. This approach not only reduces inventory costs but also enhances supply chain efficiency. In summary, this thesis provides valuable insights and methodologies for businesses aiming to enhance demand forecasting accuracy and optimize inventory management practices in the retail industry. By leveraging customer segmentation, ensemble deep learning, and distribution forecasting techniques, organizations can enhance decision-making processes, reduce operational costs, and thrive in the dynamic landscape of supply chain operations

New Concepts for Efficient Consumer Response in Retail Influenced by Emerging Technologies and Innovations

The retail industry is continuously confronted with new challenges and experiences a transformation from a supplier’s market to a buyer's market. It is, thus, essential for the retail industry to consequently focus on, anticipate and fulfil consumer’s demands. Technologies and innovative business solutions can help to support to establish a required customer experience and, thereby, gain a competitive advantage. A multitude of new services and products, channels as well as players can already be identified which drive the transformation. Therefore, retailers need to understand current trends and technologies and identify as well as implement relevant solutions for their transformation since otherwise, new players will dominate the market. Hence, this dissertation aims to review and analyse new technologies which are coupled with innovative business activities in order to provide customer-centric retailing. For this purpose, this dissertation consists of five articles and derives four major contributions which introduce different approaches to establishing consumer satisfaction. Firstly, a core technology for retail is artificial intelligence (AI) which can be meaningful applied along the entire value chain and improve retailers’ positions. Two focus areas have been identified in this context which are (i) the optimisation of the entire retail value chain with the help of AI with the aim to derive transparency and (ii) the improvement of consumer satisfaction and relationship. Secondly, focussing on the consumer-retailer relationship in the digital era, a concept with a data architecture is proposed based on a real use case. The outcome was that a specific customer orientation based on data can increase the brand value and sales volume. Thirdly, the work presents that new shopping concepts, named unmanned store concepts, gain continuous growth. Unmanned store concepts employ a variety of new technologies, are characterised by attributes of speed, ease, as well as comfort, and are deemed to be the new ideal of the expectations of modern buyers. Two different directions have been deeper analysed: (i) walk-in stores and (ii) automated vending machines. The critical success factors for the usage of unmanned store solutions are distance as well as high consumer affinity for innovations. In times of the COVID-19 pandemic, which has a huge impact on retail, a continuous innovation capability still needs to be established. Finally, this work introduces a tool for systematic innovation management considering the current circumstances. Taken as a whole, this dissertation with its five articles deals with significant research questions which have not been approached so far. Thereby, the literature is extended by the introduction of novel insights and the provision of a deeper understanding of how retailers can transform their business into a more consumer-oriented way