Industrial Artificial Intelligence, Smart Connected Sensors, and Big Data-driven Decision-Making Processes in Internet of Things-based Real-Time Production Logistics.

AuthorDavis, Ruth
  1. Introduction

    Technology-intensive sectors enhance the volume of breakthroughs (Andrei et al., 2016a, b; Ionescu, 2019a, b; Lazaroiu et al., 2017; Popescu et al., 2017; Popescu Ljungholm, 2019; Zeman and Bogdan, 2019) by carrying out smart manufacturing, consequently furthering the upgrading of transient financial performance, while the progress of innovation quality detrimentally shapes temporary financial performance. (Yang et al, 2020)

  2. Conceptual Framework and Literature Review

    The effects of the organizational and environmental frameworks on the carrying out of digital manufacturing technologies (Andrei et al, 2020; Krizanova et al., 2019; Lazaroiu et al., 2019; Popescu et al., 2018; Sion, 2019) are mediated by use of the technological context of the company whose performance is affected (Dusmanescu et al, 2016; Lazaroiu, 2017; Lazaroiu et al., 2020a, b; Popescu and Ciurlau, 2019; Wright and Birtus, 2020) as regards adjustability, design, distribution, and quality performance. (Gillani et al., 2020) Perception layer supplies relevant consistency for instantaneous data visibility and traceability as a result of the upsides of pervading identification and connectivity. (Guo et al, 2020) Throughout the product design phase, digital twins are frequently harnessed to improve the coherence and responsiveness of the design operations. (Sun et al, 2020)

  3. Methodology and Empirical Analysis

    Building our argument by drawing on data collected from Catapult, Deloitte, MHI, PwC, SME, Software AG, and ZDNet, we performed analyses and made estimates regarding the relationship between industrial artificial intelligence, smart connected sensors, and big data-driven decision-making processes. Structural equation modeling was used to analyze the collected data.

  4. Results and Discussion

    Production system conditions may be predetermined by input sequence of operations and instantaneous manufacturing data. (Chen et al, 2020) The developments in machine learning techniques bring about innovative prospects for inspecting production system dynamics. (Subramaniyan et al, 2020) The assimilation of the technologies that will improve the capability of digital twin in terms of precise behavior prediction (e.g. data incorporation, monitoring systems, and machine learning) is pivotal. (Mourtzis, 2020) (Tables 1-9)

  5. Conclusions and Implications

    In an extremely automated manufacturing company, the reliability of smart production equipment is essential for standard performance. (Lee et al., 2020) Robots are exemplary alternatives for skilled personnel concerning certain replicable, general, and strategically relevant assignments. (Perez et al., 2020) The collected product data comprise significant design knowledge, thus generating innovative prospects to improve manufacturing coherence and outcome competitiveness. (Feng et al., 2020)

    Survey method

    The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. Sampling errors and test of statistical significance take into account the effect of weighting. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error...

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