Reducing Food Wastage and Enhancing Restaurants' Operation in Dhaka City: A Data Driven Strategy For Pizza Demand Forecasting Using Machine Learning

Abstract

In the framework of many pizza businesses in Bangladesh, this thesis examines the crucial problems of food waste. The study adopts a broad strategy to enhance demand forecasting by combining machine learning techniques with data-driven insights. In addition to conducting in-depth interviews with restaurant operators and sales personnel and distributing questionnaires intended to understand customer tastes and behavior, the research comprises the methodical gathering of sales statistics from numerous pizza shops. Through the use of several machine learning models and extensive data preparation, the research seeks to identify the best model for the intricate characteristics of Bangladesh's restaurant business. Using real-world pizza restaurants as a case study, machine learning models are applied in a practical way. Their performance is closely examined in comparison to sales data, and their effects on decreasing food waste and increasing operational efficiency are assessed. The expected results are intended to be both a possible benchmark for comparable situations around the world and a source of useful information for Bangladesh's restaurant industry. This thesis offers a thorough methodology, a thorough analysis, and a thorough discussion in an effort to go beyond simply presenting a data-driven solution. The goal is to provide insightful information that will direct future investigations into the fields of operational optimization and food waste reduction. The research hopes to promote sustainable practices in the restaurant industry by addressing these important factors