Real-Time Sensor Networks, Advanced Robotics, and Product Decision-Making Information Systems in Data-driven Sustainable Smart Manufacturing.

AuthorPopescu, Gheorghe H.
  1. Introduction

    Digitalization undertakings in production settings positively impact the long-term performance of manufacturing operations, sustainable value chain management, and industrial value creation. (Felsberger et al, 2020) The goal of fashioning smart equipment is to improve the sensing and networking capability of machineries (Du?manescu et al, 2016; Kovacova et al, 2019) in the prearranged space of the product-service system supplier. (Wang et al., 2020a)

  2. Conceptual Framework and Literature Review

    Digitalized operations can cut down defects, diminish expenses, boost manufacturing adjustability, and optimize organizational sustainability. (Felsberger et al, 2020) Performance data are expedient assets (Andrei et al, 2016a, b; Kliestik et al, 2018; Majerova et al, 2020; Popescu et al, 2017) in configuring groundbreaking applications (Adami, 2019; Kliestik et al, 2020a, b, c; Lazaroiu, 2018; Nica et al, 2014; Popescu et al, 2018), such as sustainable supply chain management, energy consumption mechanism and production planning upgrade (Allen, 2020; Connolly-Barker et al, 2020; Lazaroiu et al, 2020a, b; Peters et al, 2020), and dynamic preventive maintenance. (Wang et al, 2020a)

  3. Methodology and Empirical Analysis

    We inspected, used, and replicated survey data from BDV, Capgemini, Deloitte, MHI, PwC, Techconsult, we.CONECT, and World Economic Forum, performing analyses and making estimates regarding the link between real-time sensor networks, advanced robotics, and product decision-making information systems. Structural equation modeling was used to analyze the data and test the proposed conceptual model.

  4. Results and Discussion

    The product-service system business pattern can supply improved solutions for equipment users and articulate exemplary and sustainable standards for equipment providers to reinforce the adoption of circular economy. (Wang et al, 2020a) Developing on intelligent design, by use of the matching function of end user demands, terminal memory and simulation learning can anticipate consumer options, and cognitive automation is carried out. (Feng et al., 2020) Digital-networked production configures the adequate network infrastructure for data-driven sustainable smart manufacturing while assimilating the business value chain. (Zhou et al, 2019) (Tables 1-13)

    There is a robust connection between sustainable development, big data technologies, and the harmonization of dynamic capabilities, as Industry 4.0 applications can improve organizational competitiveness. (Felsberger et al., 2020) When embracing Industry 4.0 technologies, it is decisive to thoroughly gather and inspect the operational data of various machines and end users in heterogeneous conditions so as to make the product-service system suppliers set up cutting-edge equipment management services. (Wang et al., 2020a) Standard production resources are customized as smart objects by harnessing advanced technologies to sense, perform, and act throughout a big data-driven setting. (Wang et al., 2020b) In smart production, coherent data transfer is decisive in manufacturing enhancement and resource-efficiency, comprising decrease of waste generation. (Schmetz et al., 2020)

    By configuring an interactive and sustainable product-service system that may assist all stakeholders, the elaborate equipment sector can bring about alterations in relation to big data-driven companies. (Wang et al., 2020a) By implementing digital twin, additional merging between the physical and the virtual realms across Internet of Things-based real-time production logistics can be carried out, significantly articulating dynamic scheduling. (Zhang et al., 2020) Smart manufacturing entails discrete and process-oriented production, while integrating the product life-cycle. (Zhou et al., 2019) To enhance output and adjustability in product decision-making information systems, cognitive automation technologies can be adopted with the advancement of deep learning-assisted smart process planning. (Matsumoto et al., 2020) Cyber-physical system-based smart factories insist on preventive process simulation and monitoring instantaneously. (Lee, 2020)

  5. Conclusions and Implications

    Digital manufacturing companies can thoroughly gain from a significant degree of automation by inspecting the enhancements obtained by use of accelerated adjustability and process quality, resulting in superior product and service quality, while decreasing operational expenses throughout the value chain. (Felsberger et al., 2020) Industry 4.0 has stimulated networking of big data technologies across smart manufacturing, and can improve the capacity to interact among lifecycle stakeholders, and thus the performance status data of product-service system offerings in various circumstances can be assessed by use of artificial intelligence-based decision-making algorithms. (Wang et al., 2020a) The merging of the digital and physical realms facilitates the articulation of smart decisions the whole time during production operations, consolidating the data-driven smart manufacturing...

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