An agile operations management system for green factory

• Published in: International Journal of Precision Engineering and Manufacturing-Green Technology, 1(2) Vol. 1; no. 2; pp. 131 - 143
• Main Authors: Jo, Hyunjei, Noh, Sang Do, Cho, Yongju
• Format: Journal Article
• Language: English
• Published: Springer Korean Society for Precision Engineering 01.04.2014; Springer Nature B.V; 한국정밀공학회
• Subjects: Agile manufacturing; Energy Efficiency; Engineering; Globalization; Industrial and Production Engineering; Intelligence; Manufacturing; Operations management; Schedules; Sustainable Development; 기계공학; Process mining; Agile operations management; AHP; Green factory; Manufacturing intelligence; DES
• ISSN: 2288-6206; 2198-0810
• DOI: 10.1007/s40684-014-0018-z

The globalization of suppliers and personalization of customers require new manufacturing strategy for environmental issues, region’s special regulation and energy besides manufacturing factors from manufacturers. In this study, green factory is defined as a manufacturing floor that reduces waste elements such as extra work, energy, time and cost through quickly responding to external uncontrollable changes like a regulation, due date and supply. Therefore, the green factory should respond by establishing and implementing strategies for controlling their production volumes, changing their dispatch rules, adjusting their work schedules, increasing and decreasing the number of their machines and workers based on their own internal data and capacities, and other measures. To achieve such objective, manufacturing companies should realize a manufacturing intelligence that can visualize their shop-floor data, and detect and solve problems at the business planning level. In this study, an agile operations management (AOM) system that can quickly respond to field problems was developed for manufacturing intelligence. The AOM system refers to a system that collects internal data, monitors such data in real time, immediately detects problems, if any, and provides solutions to the detected issues within the shortest time.