Sustainability Improvement Studies Using Statistical Engineering and Industrial Informatics Methods

Dear Colleagues,

This Special Issue is devoted to lean and green data-driven methods that help us to recognize and exploit opportunities for the greening of modern industrial operations. The emphasis is on processes that may be improved by implementing modern high-quality tools and techniques, which expedite the uncovering of non-value-added activities and deliver sustainability gains in a manufacturing setting. As a guide, operations that may promote sufficient maturity toward to the UN Sustainable Development Goals 9 (Industries, Innovation, and Infrastructure) and 12 (Ensure Sustainable Consumption and Production Patterns) are particularly welcomed. However, other industry-oriented goals may be also applicable such as UN SDG 6 (Clean Water and Sanitation) and SDG 7 (Affordable and Clean Energy). Therefore, green paradigms that demonstrate how to lower carbon emissions and enable the efficient use of resources in processes, up to the Industry 4.0 era, are desirable. Selection of the proper technologies, processes, and materials that deliver CO₂ savings should be demonstratable. The lean aspect should also be accentuated, since it contributes to reducing environmental impacts without demanding substantial capital investments. The engagement of quality screening and optimization techniques is essential to achieving higher productivity and should be implemented in a fashion that shows the feasibility of such innovative efforts in spite of tight budgetary constraints. In pursuit of the enhancement of environmental performance, the operational excellence toolbox may provide concepts and methods such as those recommended in the Lean Six Sigma initiative, which boost the velocity of value creation efforts and accelerate the waste elimination cycle. The process screening and optimization approaches need not to be limited to any particular improvement philosophy, which means that statistical engineering approaches that support production analytics projects may be paired with leading-edge AI and machine learning deployment in the smart factory facilities. The self-optimization of process improvements to increase flexibility, efficiency, and responsiveness to customers, as well as predictive maintenance to minimize equipment downtime, are anticipated. Diagnostics and informatics may be conducted on routinely gather big data from manufacturing plant sensors, or from the quickly devised and implemented design of experiments for customized operations in order to reduce manufacturing errors that may save money and time. Case studies may be of a broad scope and may include fields as diverse as pharmaceuticals, biomedical applications, additive manufacturing, nanotechnology, biotechnology, waste management, digital-age automotive technologies and aerospace applications.

Dr. George Besseris
Guest Editor

Manuscript Submission Information

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• lean green methodologies
• sustainability improvements
• statistical and algorithmic industrial process screening
• lean products
• process optimization