Abstract:
"Efficient management of inventory and accurate demand forecasting play pivotal roles in reducing product wastage across various industries. The research project explores the optimization of inventory and demand forecasting techniques with the aim of minimizing product wastage focusing specifically on a dairy dataset.
Drawing on a synthesis of literature, the study investigates existing strategies and methodologies employed in inventory management and demand forecasting. Leveraging advanced analytical techniques, including time series analysis and machine learning algorithms, the research develops and evaluates forecasting models tailored to the dairy industry context.
Through comprehensive data analysis, the study assesses the performance of different forecasting models in predicting demand patterns and examines the impact of optimized inventory levels on waste reduction. The findings underscore the significance of integrity robust forecasting techniques with streamlined inventory management processes to mitigate product waste effectively.
Furthermore, the project discusses the practical implications of its findings for dairy industry practitioners, highlighting opportunities for enhancing operational efficiency and sustainability. It also identifies avenues for future research aimed at refining and extending the proposed methodologies to other sectors facing similar inventory management challenges.
Overall, the research contributes to advancing knowledge in the field of inventory optimization and demand forecasting while offering actionable insights for minimizing product wastage and promoting sustainability in dairy production and beyond."
