Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis

Manufacturing has adopted technologies such as automation, robotics, industrial Internet of Things (IoT), and big data analytics to improve productivity, efficiency, and capabilities in the production environment. Modern manufacturing workers not only need to be adept at the traditional manufacturing technologies but also ought to be trained in the advanced data-rich computer-automated technologies. This study analyzes the data science and analytics (DSA) skills gap in today's manufacturing workforce to identify the critical technical skills and domain knowledge required for data science and intelligent manufacturing-related jobs that are highly in-demand in today's manufacturing industry. The gap analysis conducted in this paper on Emsi job posting and profile data provides insights into the trends in manufacturing jobs that leverage data science, automation, cyber, and sensor technologies. These insights will be helpful for educators and industry to train the next generation manufacturing workforce. The main contribution of this paper includes (1) presenting the overall trend in manufacturing job postings in the U.S., (2) summarizing the critical skills and domain knowledge in demand in the manufacturing sector, (3) summarizing skills and domain knowledge reported by manufacturing job seekers, (4) identifying the gaps between demand and supply of skills and domain knowledge, and (5) recognize opportunities for training and upskilling workforce to address the widening skills and knowledge gap.     

An inquiry into human nature and the cost of the wealth of nations

Current economic ontology development has failed to confront two important errors associated with historicism. Embracing the linearity of economic value being directly attributed to the labor applied to natural resources taken together with efficiency arguments used to justify monetary policy on both the microlevel (transaction) and macrolevel (global trade), we know these legacies of the scientific method applied to economic systems have left the G-20 paralyzed to deal with structural failings evidenced from banking to business to economic policy. An exploration of the structural modalities that impair our current capacity for adaptation and alternative methods for accounting for value is the basis for this inquiry. Integral accounting is proposed as a more suitable method to transition from scarcity-based market models to abundance-based modes of sustainable engagement.     

Expert systems and human knowledge: A view from the sociology of science

After the setbacks suffered in the 1970s as a result of the Lighthill Report (Lighthill, 1973), the science of Artificial Intelligence (AI) has undergone a dramatic revival of fortunes in the 1980s. But despite the obvious enormity and complexity of the problems tackled by AI, it still remains rather parochial in relation to the import of alternative though potentially fruitful ideas from other disciplines. With this in mind, the aim of the present paper is to utilise ideas from the sociology of science in order to explore some current issues in AI and, in particular, the branch of expert systems.It is argued that the sociology of sciences shares a common focus of enquiry along with AI — namely, the nature of knowledge — and has a role to play in the understanding, design and future development of expert systems.     

A novel algorithmic approach to the integration of posterior knowledge into condition-monitoring systems

This paper considers the problem of the integration of 'posterior knowledge' into condition-monitoring systems from both the theoretical and practical points of view. The work is presented in the context of aircraft engine maintenance. A methodology for updating posterior probabilities is proposed for cases where fault conditions are rejected or retained on the basis of external knowledge supplied by an end user, that is the posterior knowledge. A possible fault class ranking is generated following the specification of fault class posterior probability functions. Context-free simulations are used to show the effect of posterior knowledge as part of a maintenance strategy. The simulations are independent of any specific condition-monitoring situation. Preliminary results indicate that posterior knowledge reduces the number of subunit inspections required for isolation of all faults. This has the potential to result in real maintenance cost savings.     

Data mining and knowledge discovery in materials science and engineering: A polymer nanocomposites case study

In this study, data mining and knowledge discovery techniques were employed to validate their efficacy in acquiring information about the viscoelastic properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites solely from data derived from a designed experimental study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. SOMs also showed how to prepare different VGCNF/VE nanocomposites with the same storage and loss modulus responses. A clustering technique, i.e., fuzzy C-means algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens (i.e., specimens containing no VGCNFs) in separate clusters. Most importantly, the results from data mining are consistent with previous response surface characterizations of this nanocomposite system. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.     

An insight into the expert-seisd: A knowledge based system for structural design

The research reported in this paper addresses the application of artificial intelligence towards the automation of the engineering design process, in particular the structural design process. The application involves the development of the knowledge based expert system (KBES). The resulting KBES, called Expert-Seisd, is developed in Common Lisp on IBM PS/II system. In this paper, examples demonstrating the implementation of various aspects of the Expert-Seisd as applied to structural design are presented. These include user interface, database, knowledge base, inference engine, and knowledge acquisition.     

Toward an art and science of knowledge engineering: a case forbelief networks

The knowledge engineering of belief networks is discussed. Several design issues that arose during the construction of two belief network-based systems, Pathfinder and ARCO1, are described. The issues of accuracy, consistency, and calibration as they emerged during the design of these systems are addressed, and the ways in which compatibility of all networks designed for the same domain suggests an architecture for combining the recommendations of independently designed knowledge bases into a single, consensus recommendation are discussed     

On the learning of rule uncertainties and their integration into probabilistic knowledge bases

We present a natural and realistic knowledge acquisition and processing scenario. In the first phase a domain expert identifies deduction rules that he thinks are good indicators of whether a specific target concept is likely to occur. In a second knowledge acquisition phase, a learning algorithm automatically adjusts, corrects and optimizes the deterministic rule hypothesis given by the domain expert by selecting an appropriate subset of the rule hypothesis and by attaching uncertainties to them. Then, in the running phase of the knowledge base we can arbitrarily combine the learned uncertainties of the rules with uncertain factual information.Formally, we introduce the natural class of disjunctive probabilistic concepts and prove that this class is efficiently distribution-free learnable. The distribution-free learning model of probabilistic concepts was introduced by Kearns and Schapire and generalizes Valiant's probably approximately correct learning model. We show how to simulate the learned concepts in probabilistic knowledge bases which satisfy the laws of axiomatic probability theory. Finally, we combine the rule uncertainties with uncertain facts and prove the correctness of the combination under an independence assumption.     

Using social power and influence to mobilise the supply chain into knowledge sharing: A case in insurance

The paper explores the antecedent effects of social influences arising from buyer power and supplier competition on knowledge sharing behaviours within a horizontal supply chain. A 2-year long empirical study examining web posts from a dedicated social supplier platform (SSN), together with interview and ‘conversational’ data over a similar time period was conducted within insurance claims. The findings show social power and influence play a powerful role in supporting knowledge sharing even in typically competitive supply chains where information and knowledge exchange is usually guarded.     

A collaborative approach for the evaluation and assessment of the computer science curriculum

It is critical that program curriculums are periodically reviewed and modified to reflect the ongoing changes in technology and job market demands. A process used to evaluate and assess the computer science curriculum at prince sultan university （PSU） is the curriculum review workshop （CRW）. As part of its program self-study process and in meeting the requirements of the Saudi national commission on academic accreditation and assessment （NCAAA）, the computer and information sciences department at PSU organized and hosted the first computer science curriculum review workshop in Saudi Arabia. Faculty from academic institutions across the country, were provided a valuable opportunity to share experiences and identify best practices in computer education. They identified areas of improvements and developed actions plans for courses. They also discussed incorporating job market needs into the curriculum. This paper will describe the process of planning, executing and evaluating the CRW. It will discuss the workshop goals, derived from the NCAAA Quality Standards for Post Secondary Institutions： these goals are to define relevant learning outcomes, evaluate effective teaching strategies and verify standards of student assessments. A model visualizing the CRW process is presented and explained. This model is composed of three components; pre-workshop planning activities, the discussion topics during the workshop, and post-workshop activities such as evaluating the CRW and defining areas of improvement, and publishing the results on the CRW website.     

Instructional designs for the development of transferable knowledge and skills: A cognitive load perspective

This paper analyzes the main points and results of a set of the previous papers in this Special Issue from the point of view of developing characteristics of flexible—transferable—expertise. It focuses on cognitive load issues related to the acquisition of deep transferable knowledge structures and developing metacognitive and self-regulation skills. The contributions to this Special Issue demonstrate that appropriate instructional support and optimal levels of control over the learning processes, enhanced by self-explanation and self-visualization techniques, may enhance learners’ abilities to transfer their knowledge and skills. Better understanding of the role of germane cognitive load, as well as our abilities to measure different types of load and high-level cognitive processes are essential for further progress in this area.     

An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry

Globally, supply chains compete in a complex and rapidly changing environment. Hence, sustainable supplier selection has become a decisive variable in the firm’s financial success. This requires reliable tools and techniques to select the best sustainable supplier and enhance understanding about how supplier behavior evolves with time. System dynamics (SD) is an approach to investigate the dynamic behavior in which the system status alterations correspond to the system variable changes. Fuzzy logic usually solves the challenges of imprecise data and ambiguous human judgment. Thus, this work presents a novel modeling approach of integrating information on supplier behavior in fuzzy environment with system dynamics simulation modeling technique which results in a more reliable and responsible decision support system. Supplier behavior with respect to relevant sustainability criteria in the past, current and future time horizons were sourced through expert interviews and simulated in Vensim to select the best possible sustainable supplier. Simulation results show that an increase in the rate of investment in sustainability by the different suppliers causes an exponential increase in total sustainability performance of the suppliers. Also, the growth rate of the total performance of suppliers outruns their rate of investment in sustainability after about 12 months. A dynamic multi-criteria decision making model was presented to compare results from the systems dynamics model.     

Critical IS professional activities and skills/knowledge: A perspective of IS managers

This study utilized the activity competency model to investigate the perceived importance of critical professional activities and skills/knowledge required by three levels of information system (IS) managers. Our findings indicated that the perceived importance of critical IS professional activities were significantly different among the management levels, but not significantly different for various industry types. Carrying a critical IS activity out involves the use of a number of different skills and/or knowledge. A portfolio of the professional activities and the needed skills/knowledge at each management level are identified. The results of this study have implications for IS professional development and training. They can also serve as a guideline for recruiting the right IS managers and developing IS curriculums.     

IS integration and knowledge sharing in multi-unit firms: the winner's curse

Knowledge sharing across business units (BUs) is paramount for enterprises that aim to exploit latent cross-BU synergies. This paper examines how information systems (ISs) integration and two forms of cross-BU knowledge complementarities (workflow interdependence and BU relatedness) affect an important dimension of a firm's absorptive capacity – cross-BU knowledge sharing. The analysis, performed on survey data from 75 enterprises, supports all of our hypotheses. First, we hypothesized that cross-BU workflow serves as a conduit for knowledge transfer as participants continually engage in spontaneous adaptations. Second, IS application integration and cross-BU relatedness should both directly contribute to knowledge sharing. Third, IS application integration should positively moderate the impacts of cross-BU relatedness. Finally, and most interestingly, we hypothesized a redistribution of the effects of IS integration by which it becomes a key contributor to knowledge sharing, but also causes a reduction in the impact of workflow interdependence. Thus, while the seamlessness of high IS application integration has obvious benefits, it paradoxically also reduces the occasions for cross-BU workflow adaptations, which is a key mechanism for knowledge transfer. That is the phenomenon we call the ‘winner's curse’.     

An integration of mixed VND and VNS: the case of the multivehicle covering tour problem

The multivehicle covering tour problem (m‐CTP) is a transportation problem with different kinds of locations, where a set of locations must be visited while another set must be close enough to planned routes. Given two sets of vertices V and W, where V represents the set of vertices that may be visited and W is a set of vertices that must be covered by up to m vehicles, the m‐CTP problem is to minimize vehicle routes on a subset of V including T, which represents the subset of vertices that must be visited through the use of potential locations in V. The variant of m‐CTP without a route‐length constraint is treated in this paper. To tackle this problem, we propose a variable neighborhood search heuristic based on variable neighborhood descent method. Experiments were conducted using the datasets based on traveling salesman problem library instances.     

An integration of Lagrangian split and VNS: The case of the capacitated vehicle routing problem

In this paper, we propose an efficient and novel Lagrangian relaxation method which incorporates a new integer linear programming (ILP) formulation to optimally partition a giant tour in the context of a capacitated vehicle routing problem (CVRP). This approach, which we call Lagrangian split (Ls), is more versatile than the ILP which, in most cases, can be intractable using a conventional solver. An effective repair mechanism followed by a local search are also embedded into the process. The mathematical validity of the repair mechanism and its time complexity are also provided. An integration of Ls into a powerful variable neighbourhood search (VNS) is also presented. Computational experiments are conducted to demonstrate that Ls provides encouraging results when applied on benchmark instances and that the integration of Ls into a metaheuristic scheme produces good results when compared to those found by state-of-the-art methods.     

Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit

The factors influencing KMS usage are of major concern to the MIS community. Among the diverse theories employed to help understand this is task technology fit (TTF), which considers the needed technological characteristics of the task as a major factor determining usage. This theory, however, ignores the personal cognition dimension, which has been found to affect the use of an IS. By integrating TTF and social cognitive theory (SCT), we attempted to determine the key factors affecting KMS usage in IT, the organizational task, and personal cognition. Through a survey of 192 KMS users, task interdependence, perceived task technology fit, KMS self-efficacy, and personal outcome expectations were found to have substantial influences on KMS usage. Among the key factors, KMS self-efficacy was found to be especially important as it was substantially and positively correlated to perceived task technology fit, personal and performance-related outcome expectations, and KMS usage.