Development of a novel method for reducing food waste in the supply chain using predictive analytics

Thesis Abstract

Abstract
Food waste is a significant global issue with both economic and environmental implications. In the food supply chain, various factors contribute to the generation of food waste, including inefficiencies in production, distribution, and consumption processes. This thesis focuses on developing a novel method for reducing food waste in the supply chain using predictive analytics. By leveraging data-driven insights and advanced analytics techniques, this research aims to identify key areas where food waste occurs and implement targeted strategies to minimize waste generation. The introduction provides a comprehensive overview of the research topic, highlighting the importance of addressing food waste in the context of sustainable food systems. The background of the study delves into existing literature on food waste in the supply chain, emphasizing the need for innovative solutions to combat this pervasive issue. The problem statement articulates the specific challenges associated with food waste and sets the stage for the research objectives. The primary objective of this study is to develop a predictive analytics model that can accurately forecast food waste levels at different stages of the supply chain. By analyzing historical data and real-time information, the model aims to provide actionable insights to food producers, distributors, and retailers to optimize their operations and reduce waste. The limitations of the study are also discussed, acknowledging potential constraints and challenges that may impact the research outcomes. The scope of the study outlines the boundaries and focus areas of the research, including the specific sectors of the food supply chain that will be investigated. The significance of the study underscores the potential impact of implementing a novel method for reducing food waste, not only in terms of cost savings but also in promoting sustainability and resource conservation. The structure of the thesis provides a roadmap for the organization of the research work, highlighting the key chapters and sections that will be covered. In the literature review chapter, an in-depth analysis of existing research on food waste, supply chain management, and predictive analytics is presented. Ten key themes are identified and discussed, providing a comprehensive overview of the current state of knowledge in the field. The research methodology chapter outlines the approach and techniques that will be employed to develop and validate the predictive analytics model. Eight key components of the research methodology are detailed, including data collection methods, model development, and performance evaluation metrics. The discussion of findings chapter presents the results of the predictive analytics model and explores the implications for reducing food waste in the supply chain. Key insights and recommendations are provided based on the analysis of the data and model outputs. Finally, the conclusion and summary chapter synthesizes the main findings of the research, highlights the contributions to the field, and outlines potential avenues for future research. Overall, this thesis contributes to the ongoing efforts to address food waste in the supply chain by proposing a data-driven approach that harnesses the power of predictive analytics. By developing a novel method for reducing food waste, this research seeks to promote sustainability, efficiency, and innovation in the food industry, paving the way for a more resilient and responsible food system.