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After the Industry 4.0 discussion in Germany in 2011, much attention has been paid to smart factory in Korea. Since 2014, smart factories have been established and expanded in Korea. However, about 80% of them were established at a low level. In this paper, we analyze smart factory statuses in detail through an empirical research on 113 manufacturing companies that have established smart factories in Korea. We build a framework based on the resource-based view (RBV) and IT value creation process and analyze the results of five constructs—manufacturing strategy, organization, system, process, and performance—using basic statistical methodologies to derive the current statuses of manufacturing companies that have established smart factories. Our results show that implementing advanced technologies such as AI technology that can implement semi-finished and finished product quality inspection, manufacturing process optimization and product demand forecast is a challenge, particularly for SMEs. We also find that securing and managing facility data is a difficult problem. In addition, while output and material management ranked high, the utilization of integration systems, which is important when building a smart factory, was found to be extremely low. Lastly, the performance indicator results showed that yield management and defect rate were most important, while job creation through the introduction of smart factories was low. Based on the results of this study, the government may be able to determine effective smart factory policies and provide manufacturing companies with a guide on establishing a smart factory.

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In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

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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.

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