Smart Logistics and Data-driven Decision-Making Processes in Cyber-Physical Manufacturing Systems.

• Date: 01 March 2020
• Author: Sheares, Gavin

1. Introduction

   Cyber-physical systems are instrumental in digitizing manufacturing systems and harmonizing various systems together for shared operations. (Zheng and Sivabalan, 2020) Cyber-physical assimilation of activities in an industrial system complements production via optimized coherence and enhanced quality while furthering customization. (Runji and Lin, 2020) The digital data stream from the factory to the administration through the cyber-physical system facilitates smart manufacturing in a large-scale production setting. (Prathima et al., 2020)

2. Conceptual Framework and Literature Review

   Cutting-edge production technologies (e.g. smart autonomous robotic systems, 3D printing, and cyber-physical systems) are shaping manufacturing operations. (Kumar et al., 2020) The rise of cyber-physical system and big data (Felstead, 2019; Kliestik et al., 2018; Lazaroiu et al., 2017; Pera, 2019) makes it possible for manufacturing to eventually be smart. (Cui et al., 2020) In smart manufacturing, the physical system functions as an information access role with sensors and data systems (Andrei et al., 2016; Gutberlet, 2019; Krizanova et al., 2019; Mihaila et al., 2016; Valaskova et al., 2018) to gather instantaneous input and network with computation modules (that is, cyber layer), which thoroughly inspect and communicate the outcomes to the synchronized physical systems through numerous feedback loops. (Chen et al., 2020) Due to the broad series of applications and the efficiency in manufacturing raw materials adjustably (Groener, 2019; Krech, 2019; Lazaroiu, 2018; Popescu Ljungholm, 2019a, b), machine tools are pivotal industrial components enabled technologically by cyber-physical systems. (Jeon et al., 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from AMG World, The Boston Consulting Group, Deloitte, KSM, MIT Sloan Management Review, PwC, and SME, I performed analyses and made estimates regarding how manufacturers are addressing smart manufacturing (%), adoption level of artificial intelligence in organizations (%), and manufacturing modernization priorities (%). The results of a study based on data collected from 4,200 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire.

4. Results and Discussion

   The advancement of cyber-physical systems offers innovative ways to play a part in the present environment of inconstant market demands. (Napoleone et al., 2020) Cyber-physical production systems progress from industrial automation to the sphere of machine learning, reinforcing smart manufacturing. (Qian et al., 2020) The swift expansion of groundbreaking data technologies enables the consolidation of cyber-physical production systems that expedite the examination of smart manufacturing solutions. (Liu et al., 2020) Smart factories attempt to make the production processes more reactive by using artificial intelligence technologies through the generation, gathering, and storage of massive volumes of data. (Farooqui et al., 2020) (Tables 1-9)

5. Conclusions and Implications

   With the materialization of cutting-edge technologies, the networking between physical resources and virtual ones is feasible in manufacturing systems...