Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Abstract

Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a grey area for many engineering managers who are key players in the gravitation of manufacturing and industrial production towards the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via PEST (political, economic, social, and technological) considerations of the broad implications of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this paper. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

“…(4) LLARS shows the worst average and median R 2 of 0.185 and 0.185, respectively, over the 50 independent runs. (5) The average and median R 2 for MOR-SVR and MOR-NuSVR (0.188 and 0.188, and 0.286 and 0.286, respectively) over the 50 independent runs are also small. ( 6) All methods show relatively good robustness according to their standard deviations over all runs: LLARS has the lowest standard deviation of 0.002 and NN has the highest standard deviation of 0.016.…”

“…As the world continues to gravitate to artificial intelligence (AI)-driven societies, where paradigms such as Industry 4.0 [1] and Society 5.0 [2] are gradually and increasingly redefining the manufacturing and production of goods and services, and how we live and interact, respectively, the role of the science of data in the drive for innovation and invention cannot be overemphasized [3,4]. Presently, AI, which in a sense premises on data-driven models and constructs [5], is one of the primary proponents of Industry 4.0 (I4.0) and Industry 5.0 (I5.0) [3]. With a focus on manufacturing, from the first industrial revolution in the 1760s to the present-day I4.0 and I5.0, manufacturing across the globe has always involved workflows and processes that churn out vast amounts of data [3,4].…”

“…In other words, other than process monitoring and regulation that are mostly achieved via traditional process visualization and process control using human-machine interfaces (HMIs), manufacturing data is still yet to be used extensively to support data-driven paradigms such as predictive modeling (usually takes the form of predictive maintenance when machine behavior and the life cycle is the context [11,12]) and ubiquitous monitoring and control (usually incorporated in the industrial internet of things (IIoT) where higher levels of connectivity, interoperability and robustness is the goal [13,14]). This apparent gap can be mainly attributed to the purpose-built or fit-for-purpose nature of most of the computational paradigms and analytic frameworks that support manufacturing processes [5].…”

“…It can be argued that many of the data-driven innovations and inventions expected to be seen in the global manufacturing sector will be mostly based on predictive modeling [3,5,16]. Generally, predictive modeling can be viewed as being synonymous with supervised learning, a form of machine learning or subcategory of AI that can take on the form of classification and/or regression, depending on the nature or type of data that is employed.…”

“…Broadly speaking, predictive analytics is the analysis of available data (e.g., explanatory and response variables in a typical manufacturing process data set as carried out in Section 2.3) and data trends using statistical learning and/or machine learning methods to enable and support the prediction of future trends and/or responses. According to the literature, the global manufacturing industry can be said to be in the early stage of adopting AI in many of their production processes, particularly, machine learning in the form of predictive modeling [5,16]. So, this has necessitated the conceptualization, design, development, and testing of several paradigms by scientists, engineers, and researchers as also carried out in this work.…”