Augmenting energy efficiency in automotive paint ovens: a review of future prospects and potential for lean, six sigma, AI, and IoT integration

Purpose Automobile manufacturing is an energy-conscious business, specifically paint ovens. With energy costs continuously increasing and environmental issues, more work must be conducted to make the paint ovens more energy-efficient. This project identified trends and avenues for adopting a Green Lean Six Sigma Energy Management System (GLSS-EnMS), Artificial Intelligence (AI), and Internet of Things (IoT) for improved energy efficiency in Electrostatic Powder paint ovens. The project aimed to transform the automotive industry by exploring trends and possibilities for implementing GLSS-EnMS, AI, and IoT technologies to improve the energy efficiency of the automotive paint oven process. Design/methodology/approach A literature review was conducted to evaluate the latest trends and problems of automotive paint oven energy efficiency technologies. Top car makers and oven paint producers were studied to find their approach and suggest energy efficiency improvement methods. The project considered the feasibility and application of integrating GLSS-EnMS, artificial intelligence, and Internet of Things technologies in the automobile paint oven processes. A framework is proposed to enhance paint quality, energy use, productivity, and system efficiency as a whole. A model was also developed to adopt GLSS-EnMS, AI, and IoT technologies to realize the optimal energy efficiency of car paint ovens. Findings The results and suggestions of this project will help automotive producers implement cost-saving and environmentally friendly energy management practices, ultimately making the industry more environmentally sustainable and economical. The GLSS-EnMS, AI, and IoT integration technologies into automotive paint oven processes will not save energy; they can also track and shut down the onset of issues from the very start, provide high availability and minimize maintenance expenses to join the new age of predictive maintenance. It can bring value by making smart grid integration and demand response programs within the power system more reliable. Originality/value This research offered an overview of trends and prospects for integrating the Green, Lean, and Six Sigma Energy Management System model with IoT and Deep Learning-based Predictive Energy Modeling (DL-PEM). It offers in-practice guidelines towards ISO 50001:2018 conformance and real-time data analysis, like automated energy-saving measures that form a true solution towards sustainability.