Cloud-Based Data-Driven Framework for Optimizing Operational Efficiency and Sustainability in Tube Manufacturing  
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undergoing rapid transformation due to increased global competition, shorter product life cycles, customized demands, and digitalization under Industry 4.0. This shift requires manufacturers to achieve high levels of agility, efficiency, and quality. At the same time, they face mounting environmental pressures, including climate change, resource depletion, and pollution. As major energy consumers and waste generators, manufacturers must now address the dual challenge of improving operational efficiency while advancing environmental sustainability to remain competitive, comply with regulations, and support global sustainability goals. The challenges of operational efficiency in manufacturing are complex and long-standing. Production systems, particularly complex ones like tube manufacturing that involve sequential processes across multiple machines and lines, are often plagued by inefficiencies. These manifest themselves as bottlenecks that restrict overall production, excessive cycle times that delay order fulfillment, significant nonproductive idle time for machinery and labor, suboptimal resource allocation, high rates of defects requiring rework or scrap, and inflexible scheduling that struggles to adapt to dynamic changes in demand or unexpected disruptions [1]. Traditional improvement methodologies, such as Lean Manufacturing and Six Sigma, have provided valuable frameworks for waste reduction and process standardization. However, their effectiveness can be limited in highly dynamic and data-rich environments where real-time visibility and adaptive control are crucial. Identifying the root causes of inefficiencies often requires deep, data-driven insights into the actual execution of processes, which may deviate significantly from idealized models. Concurrently, the imperative for environmental sustainability introduces another layer of complexity. Manufacturing processes are inherently resource-intensive. Key areas of environmental concern include high energy consumption, particularly in processes such as extrusion, heating, curing, and machining, which often rely heavily on fossil fuels, contributing significantly to greenhouse gas (GHG) emissions [2]. Water use, especially for cooling, cleaning and processing, can be substantial, requiring local water resources [3]. The consumption of raw materials and the subsequent generation of waste, including process scrap, defective products, and packaging waste, contribute to the depletion of resources and the burden on landfills [4]. Accurately measuring, monitoring, and managing these environmental impacts in intricate production chains is a formidable task. Furthermore, decisions aimed at improving one aspect of sustainability (e.g., switching to a less energy-intensive material) might have unforeseen consequences on other environmental metrics or even operational performance. Historically, manufacturing teams have addressed operational efficiency and environmental performance separately, using different tools and KPIs. Operations might track metrics such as OEE and throughput, while EHS teams focus on energy use and emissions, often with delayed periodic data. This separation overlooks how closely connected these goals truly are. For instance, speeding up machines might boost output but also spike energy use or increase defects. On the flip side, recycling efforts could slow production or increase costs. Without a unified approach, well-intended changes risk causing inefficiencies or damaging sustainability goals [5]. The advent of Industry 4.0 technologies offers a way to overcome these limitations. The proliferation of sensors, the Internet of Things (IoT), Cyber-Physical Systems (CPS), cloud computing, and big data analytics provides manufacturers with the unprecedented ability to collect vast amounts of granular real-time data from the factory floor [6].