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An agile operations management system for green factory

  • Sungkyunkwan University
  • Korea Institute of Industrial Technology

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)131-143
Number of pages13
JournalInternational Journal of Precision Engineering and Manufacturing - Green Technology
Volume1
Issue number2
DOIs
StatePublished - Apr 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Agile operations management
  • AHP
  • DES
  • Green factory
  • Manufacturing intelligence
  • Process mining

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