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.

Thesis Overview

The research project titled "Development of a novel method for reducing food waste in the supply chain using predictive analytics" aims to address the critical issue of food waste management in the supply chain through the application of predictive analytics. Food waste poses significant environmental, social, and economic challenges globally, making it imperative to develop innovative solutions to minimize waste generation and improve overall sustainability in the food industry. This project seeks to leverage the power of predictive analytics, a data-driven approach that utilizes historical data and statistical algorithms to forecast future trends and outcomes, to optimize supply chain processes and reduce food waste. By analyzing various factors such as demand patterns, inventory levels, production schedules, and external factors like weather conditions and market trends, predictive analytics can help food manufacturers, distributors, and retailers make informed decisions to prevent overproduction, minimize spoilage, and enhance resource efficiency. The research will involve a comprehensive literature review to explore existing methods and technologies for food waste reduction in the supply chain, including traditional approaches and emerging trends in predictive analytics. By synthesizing knowledge from academic research, industry reports, and case studies, the study aims to identify gaps in current practices and propose a novel methodology that integrates predictive analytics into food supply chain management. The methodology section of the research will outline the data collection process, model development, and validation techniques to be employed in the study. By collecting and analyzing relevant data sets from food supply chain operations, the project aims to develop predictive models that can accurately forecast demand, optimize inventory levels, and identify potential risk factors leading to food waste. The research will also assess the feasibility and scalability of the proposed method in real-world supply chain settings through simulation and validation exercises. Furthermore, the discussion of findings section will present the results of the predictive analytics models and their impact on food waste reduction in the supply chain. By evaluating key performance indicators such as waste reduction rates, cost savings, and operational efficiency improvements, the study aims to demonstrate the effectiveness of the novel method in mitigating food waste and promoting sustainability in the food industry. In conclusion, the research project "Development of a novel method for reducing food waste in the supply chain using predictive analytics" offers a valuable contribution to the field of food technology and supply chain management by proposing an innovative approach to address the complex challenge of food waste. By harnessing the potential of predictive analytics, the study aims to empower food industry stakeholders with data-driven insights and tools to optimize their operations, minimize waste generation, and enhance overall sustainability in the supply chain."