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To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied.

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Today, megatrends such as individualization, climate change, emissions, energy, and resource scarcity, urbanization, and human well-being, impact almost every aspect of people’s lives. Transformative impacts on many sectors are inevitable, and manufacturing is not an exception. Many studies have investigated solutions that focus on diverse directions, with urban production being the focus of many research efforts and recent studies concentrating on Industry 4.0 and smart manufacturing technologies. This study investigated the integration of smart factory technologies with urban manufacturing as a solution for the aforementioned megatrends. A literature review on related fields, mass personalization, sustainable manufacturing, urban factory, and smart factory was conducted to analyze the benefits, challenges, and correlations. In addition, applications of smart factory technologies in urban production with several case studies are summarized from the literature review. The integration of smart factory technologies and urban manufacturing is proposed as the urban smart factory which has three major characteristics, human-centric, sustainable, and resilient. To the best of the author’s knowledge, no such definition has been proposed before. Practitioners could use the conceptual definition of an urban smart factory presented in this study as a reference model for enhancement of urban production while academics could benefit from the mentioned future research directions.

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