Cyber-Physical Production Networks and Advanced Digitalization in Industry 4.0 Manufacturing Systems: Sustainable Supply Chain Management, Organizational Resilience, and Data-driven Innovation.

Author:Nica, Elvira
  1. Introduction

    Big data analytics can be adopted to collect important data and obtain operative manufacturing intelligence. The growing employment of multimode sensors, smart machinery, and robotics has facilitated big data analytics and the Internet of Things for Industry 4.0. (Khakifirooz et al., 2018) Sensors and machine information capturing systems are progressively being designed for the production and gathering of data. (Rauch et al., 2019) Smart manufacturing attempts to upgrade concept generation, manufacturing, and product trades from conventional procedures to digitized and self-governing systems. (Oztemel and Gursev, 2018)

  2. Conceptual Framework and Literature Review

    The swiftness of technological change generated by Industry 4.0 has led to an important breach between present capability of workforce and the quickly varying exigencies of its roles. To attain any noticeable consolidation of talent pipelines, reorganization rather than progress in talent management routines is needed. (Whysall et al., 2019) While production is the multi-phased operation of fabricating commodities by using raw materials, smart manufacturing represents the division that adopts computer supervision and significant degrees of flexibility accordingly (Balica, 2017; Bolton et al., 2018; Chijioke et al., 2018; Ionescu, 2018; Popescu, 2018), seeking to harness cutting-edge data and production technologies (Berloffa et al., 2017; Chessell, 2018; Havu, 2017; Nelson, 2018; Stroe, 2018) for the purpose of facilitating adjustability in physical operations to perform in an industriously worldwide market. (Oztemel and Gursev, 2018)

  3. Methodology and Empirical Analysis

    Using and replicating data from Active Idea, AI Group, BDO, Capgemini, CIO, DAA, EY, IoT Analytics GmbH, McKinsey, and PwC, I performed analyses and made estimates regarding top Industry 4.0 business goals (%), top technologies being considered in-line with organizations' strategic plan (%), digital marketing activities with the greatest commercial impact (%), and average realized quality gain from smart factories so far (%). Data were analyzed using structural equation modeling.

  4. Results and Discussion

    In Industry 4.0, production systems transcend simple linkage, to also disseminate, inspect, and employ gathered data to set off further smart operations. (Rao and Prasad, 2018) The capacity to consolidate a worldwide business by employing Industry 4.0 is pivotal, in addition to the resourcefulness to provide a comparable technological service internationally relevant. (Telukdarie et al., 2018) A smart factory...

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