Today, waste reduction strategies and efficient production have become
essential for companies striving to achieve sustainability and remain competitive
globally. In this project, the BDA (Big Data Analytics) comes up with the suggestion
that it is a great technique since it gives a complicated way to find, chase, and fix
problems in lines. This study seeks to investigate in what ways BDA could be applied
to production systems concerning the utilization of vast quantities of operational data to
comb through patterns, trends, and oddities that are usually difficult to observe. The
present study will therefore be an eye-opener with the support that BDA offers to fully
understand the production processes through the use of high-level analytical
methodologies such as predictive analytics and machine learning algorithms, which in
turn ensure that smart interventions toward minimizing wastages are made. The
findings support the hypothesis that BDA has the ability to change things by making
decisions better, allocating resources more efficiently, and encouraging a mindset of
always getting better. The study shows the actual benefits of using data to control
waste, such as lower costs, enhanced operational efficiency, and a minor impact on the
environment. This study contributes to the knowledge of environmentally friendly
manufacturing methods and highlights the importance of BDA in advancing more ecofriendly and effective production processes.
Keywords: Big data analytics, Data analytics, Eco-friendly, Efficiency, Environment, Machine learning, Machine learning algorithms, Operational efficiency, Predictive analytics, Sustainability, Waste minimization.
