Adaptive, Lean and Green Manufacturing: Implementations and Applications in Energy-Intensive Industries 4.0

abstrakt

en

Since the birth of lean manufacturing in vehicle production companies such as Ford and Toyota, production efficiencies were being elevated by engineering heuristics and statistical approaches. This school of thought have been generally successful and relates to many different lean techniques like six sigma, Kaizen and other techniques for process improvement. In recent years, researchers have pointed out that lean manufacturing lacks the consideration of sustainability and various motive of production improvement does not align with environmental consideration. Various implementation of lean manufacturing has produced detrimental effects to the environment despite benefiting production economics. Nevertheless, it was shown that by carefully considering production objectives and implementation, it is possible to align both lean manufacturing and green manufacturing in the same direction. Later, lean and green manufacturing was popularized which considers a complete variety of objectives from materials, economics, machines, environment to social aspects. However, with more production objectives, better data collection systems and various uncertainties due to renewables, simple statistical tools from lean manufacturing are gradually growing less efficient. In the era of Big Data, machine learning and intelligent algorithms are performing multi-variateanalysis much better and effective than traditional statistical lean manufacturing. Hereby, we propose a concept of adaptive, lean and green manufacturing to properly utilize the manufacturing-based Big Data within processing facilities. Our proposition covers a complete range of expert systems, machine learning algorithms, optimization and search algorithms for the purpose of industrial production improvement. This new concept of “adaptive” is designed to transform the energy-intensive industries to cope with varying and dynamic environment with minimal and effective efforts. We also demonstrate some successful case studies which have created tremendous value with partnering companies and facilities.

Since the birth of lean manufacturing in vehicle production companies such as Ford and Toyota, production efficiencies were being elevated by engineering heuristics and statistical approaches. This school of thought have been generally successful and relates to many different lean techniques like six sigma, Kaizen and other techniques for process improvement. In recent years, researchers have pointed out that lean manufacturing lacks the consideration of sustainability and various motive of production improvement does not align with environmental consideration. Various implementation of lean manufacturing has produced detrimental effects to the environment despite benefiting production economics. Nevertheless, it was shown that by carefully considering production objectives and implementation, it is possible to align both lean manufacturing and green manufacturing in the same direction. Later, lean and green manufacturing was popularized which considers a complete variety of objectives from materials, economics, machines, environment to social aspects. However, with more production objectives, better data collection systems and various uncertainties due to renewables, simple statistical tools from lean manufacturing are gradually growing less efficient. In the era of Big Data, machine learning and intelligent algorithms are performing multi-variateanalysis much better and effective than traditional statistical lean manufacturing. Hereby, we propose a concept of adaptive, lean and green manufacturing to properly utilize the manufacturing-based Big Data within processing facilities. Our proposition covers a complete range of expert systems, machine learning algorithms, optimization and search algorithms for the purpose of industrial production improvement. This new concept of “adaptive” is designed to transform the energy-intensive industries to cope with varying and dynamic environment with minimal and effective efforts. We also demonstrate some successful case studies which have created tremendous value with partnering companies and facilities.

Lean and Green; Adaptive Manufacturing; Adaptive Systems; Energy Industry Optimization; Industry 4.0

26.08.2020

Volos

2653-8911

7

1

42–42

1