Cognitive Automation, Business Process Optimization, and Sustainable Industrial Value Creation in Artificial Intelligence Data-driven Internet of Things Systems.

AuthorBell, Edward
  1. Introduction

    Cutting-edge technologies can collect and process big data precisely and instantaneously. By integrating unambiguous, complex, and operational manufacturing data with production system physical characteristics, a smart event-driven feedback monitoring can reorganize the release strategy of activities concomitantly without work-in-process expansion, resulting in enhanced system responsiveness and coherence. (Chen et al., 2020)

  2. Conceptual Framework and Literature Review

    Industry 4.0 may enhance process operations, fault identification, and resource adoption by carrying out data-driven self-governing decision-making (Andrei et al., 2016; Cruciani, 2018; Lazaroiu et al., 2017; Majerova et al., 2020; Nica et al., 2014; Popescu et al., 2017; Popescu Ljungholm, 2018), but huge volumes of operational specifications and product quality data (Andrei et al., 2020; Dusmanescu et al., 2016; Lazaroiu et al., 2019; Mihaila et al., 2018; Nica, 2015; Popescu et al., 2018; Swadzba, 2019) are needed on the instant. (Lewis Bowler et al., 2020) Data-driven decision-making and swiftness to hindrances brought about by the harnessed Industry 4.0 technologies develop coherence of supply chains (Balica, 2018; Heuston, 2019; Lazaroiu et al., 2020; Nelson, 2018; Popescu, 2014; Popescu et al., 2019), resulting in improved customer contentment. (Ghadge et al., 2020) Digitization enables requested data to be distributed in real time to actuators in production plants, generating reduced conversion time and boosted service level. (Schniederjans et al., 2020) Digital twin blends both real and simulated data, supplying extensive input for machine availability prediction, and reinforces multiple-dimension performance assessment. (Zhang et al., 2020)

  3. Methodology and Empirical Analysis

    Building my argument by drawing on data collected from AlphaWise, BDV, Capgemini, Catapult, Globant, Morgan Stanley, PwC, Software AG, and ZDNet, I performed analyses and made estimates regarding the relationship between cognitive automation, business process optimization, and sustainable industrial value creation. The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 4,800 respondents.

  4. Results and Discussion

    Industry 4.0 requires significant alterations in the plant management, the personnel job contentment, and organizational commitment. (Di Nardo et al., 2020) Manufacturing companies require mechanisms able to handle elaborate production systems as regards resource deployment, product mix, part distribution and material managing upgrade, as human labor is pivotal. (Bortolini et al., 2020) The acceleration of digital disruption is stepping up and placing growing pressure on manufacturing companies to implement cutting-edge technologies so as to enhance their performance and end products. (Love et al., 2020) (Tables 1-8)

  5. Conclusions and Implications

    Manufacturing environments are shaped by external determinants associated with products' features and customers' demands. (Martins et al., 2020) Sensor-based and cognitive assistance systems have altered production processes with the development of Industry 4.0. (Rauch et al., 2020) Being decisive in product design and advancement, data are instrumental throughout the complete life cycle of the...

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