Causal Discovery in Manufacturing: A Structured Literature Review

Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.

Design and Simulation of Manufacturing Organizations Based on a Novel Function-Based Concept

Historically, researchers and managers have often failed to consider organizations as a sum of functions leading to a set of capabilities that produce a product that can serve society’s needs. Furthermore, functions have increased with the development of industrial revolutions, however, many manufacturing organizations have not realized their full potential. As a result, many industrial organizations do not know why, where, and when the existing functions and projects for implementing new functions fail where tactical and strategic functions of a manufacturing organization are commonly over-seen. Thus, the aim of this research was to propose a holistic approach for manufacturing organizations in order to model their functions enabling the assessment, design, management, and control of operations and performance as well as to identify improvement potentials. For this purpose, a conceptual model was developed based on the evolution of functions along with the industrial revolutions. Moreover, using the conceptual model, manufacturing organizations can be modeled, considering common organizational functions in the respective areas of production, maintenance, and quality, etc., in the three planning horizons—strategic, tactical, and operative. As a result, the model serves as a basis for the integral management and control of manufacturing organizations. Moreover, it can be also used as a basis framework for a digital twin model for organizations. Thus, a system dynamics simulation model based on the conceptual model was developed for a generic organization. The goal of the simulation model is to provide an exemplary digital model of a manufacturing organization in which the different functions are applied with different methods, systems, and/or individuals along the development phases.

Prioritization and ranking of lean practices: a case study

PurposeThe paper aims to identify, prioritize and rank lean practices in the context of an Indian automotive component manufacturing organization using interpretive ranking process (IRP) and interpretive structural modeling (ISM) approaches.Design/methodology/approachLean practices are identified from the literature. Then, two hierarchical models were are developed using two distinct modeling approaches – ISM and IRP with expert opinions from an Indian automotive component manufacturing organization to analyze the contextual relationships among the various lean practices and to prioritize and rank them with respect to performance dimensions.FindingsIn the study, the hierarchical structural models are developed using ISM and IRP approaches for an Indian automotive component manufacturing organization. In ISM-based modeling, lean practices can be categorized into five levels. Top priority should be given to the motivators followed by value chain, system/technology and organization centric practices. IRP model shows the dominance relationship among the various lean practices with respect to performance dimensions.Practical implicationsThe models are constructed from the organizational standpoint to evaluate their impact to the implementation of lean manufacturing. The study leverages the organizations to prioritize limited resources as per the hierarchy. Managers get the inter-linkages and ranking of various lean practices, which leads to a better perspective for the effective implementation of lean. The structural models also assist management to assign proper roles to employees/departments for effective lean implementation.Originality/valueThere is hardly any structural model of lean practices in the literature for clustering, prioritizing and ranking of lean practices. The study fills this gap and develops the hierarchical models of lean practices through IRP and ISM approaches for an Indian automotive component manufacturing organization. The results from both approaches are compared for illustrating the benefits of one over the other.

Mathematical Analysis of Supply Chain Optimization focusing on Artificial Neural Network: A case study in Apparel Manufacturing Organization

In this study, implemented artificial nueral network (Ann) in apparel manufacturing organizations to optimize the supply chain converging on right supplier selection by analyzing their performance criteria.Moreover, data collected from three diffrents factory to analyze the efficiney and profit -loss status of that units. Furthermore, analyze the supplier selection criteria of three suppliers in order to select the right supplier at the real time in apparel manufacturing industry . This study shows that it can be saved 20 % of the total cost.

Systematic Assessment of Product Quality

The article describes an innovative metrizable idea for systemic assessments of product quality within the baking industry. Complex product quality analysis requires the employment of metrizability criteria for factors that impact the quality of the product, and these are called determinants. Therefore, such analysis is only possible with the use of systems engineering. A system represents the potential of a manufacturing process, of major impact on quality. Composites of the manufacturing process make up the determinants of bread quality, grouped into three sets: raw materials, manufacturing technology, and manufacturing organization and technique. This paper also contains methodological implications for the construction of algorithms for manufacturing process potential determinants. Metrizable product quality assessment is a very important issue in the context of its implementation in manufacturing companies. Its use allows for obtaining comprehensive data on the quality status of a product. It is an important tool for analyzing and forecasting modern quality trends. The method presented in the article is new, innovative, and practical; and its vector representation may prove useful in Quality 4.0. The method could be an important point of reference for managers, directors, and decision makers who must determine the best metrizability criteria for systemic product quality assessments, and could prove useful in Industry 4.0 in the bakery industry. The main value of the paper is the presentation of a new, extensive method for systemic assessments of product quality based on vector analysis in industrial organization. We trialed the method in the baking industry. We concluded that the method is a contribution to management science, especially in the field of quality management, because this approach is not used in business and is not described in relevant international literature.

An Industry 4.0 Training Framework Addressing ‘COVID-19 Type’ Disruptions on Manufacturing

Although digitization in the manufacturing industry has been going on for some years, the recent COVID-19 pandemic helped reveal a number of bottlenecks and challenges that still need to be overcome. Joint ongoing research by a number of European Universities aimed at developing a systematic training framework on Industry 4.0 happened to be performed in the midst of the pandemic. COVID-19 meant that suddenly, internal and external workers of different educational backgrounds and in different roles had to rapidly adapt to new working procedures and environments whilst learning to use new technologies. This disruption helped this research group to generate specifications of a Higher Education Industry 4.0 Training Framework (HEI4.0) that is relevant to foster skills and competencies that make manufacturing more resilient to other possible scenarios requiring social distancing limitations. This paper outlines the details of the research performed and contributes the concept and value of establishing what is termed as the ‘flow-cognitive profile chart’ of a manufacturing organization to effectively help it in its transition towards digital manufacturing. Based on this concept, the paper passes on to prescribe a HEI4.0 Training Framework intended to guide manufacturing organizations in addressing ‘COVID-19 type’ manufacturing disruptions that can take place in other future unforeseen circumstances.

How Leader Narcissism Links to Team Voice Behavior: The Mediating Mechanisms of Leader Voice Solicitation and Team Voice Climate

Drawing on the narcissism literature and social information processing theory, we theorized and examined a serial mediation model linking leader narcissism with team voice behavior through leader voice solicitation and team voice climate. We tested our hypotheses using data collected from a time-lagged and multisource survey of 223 frontline employees in 60 teams at a large manufacturing organization. The results indicated that leader narcissism had a negatively indirect effect on team voice climate via leader voice solicitation. Team voice climate positively predicted team voice behavior, and the indirect effects of leader narcissism via leader voice solicitation and team voice climate on team voice behavior were significantly negative. In this paper, we discuss the theoretical implications of our findings for both the narcissism literature and the voice literature, along with their practical implications.

Comparative analysis of ranking the lean supply chain enablers: An AHP, BWM and fuzzy SWARA based approach

PurposeIn the recent scenario, there has been an increasing trend toward lean practices and implementation in production systems for the improvement of an organization’s performance as its basic nature is to eliminate the wastes. The increasing interest of customers in customized products and the fulfillment of customers’ demand with good productivity and efficiency within time are the challenges for the manufacturing organization; that is why adopting lean manufacturing concept is very crucial in the current scenario.Design/methodology/approachIn this paper, the authors considered three different methodologies for fulfilling the objective of our research. The analytical hierarchy process, best–worst method and fuzzy step-wise weight assessment ratio analysis are the three methods employed for weighting all the enablers and finding the priority among them and their final rankings.FindingsFurther, the best results among these methodologies could be used to analyze their interrelationships for successful lean supply chain management implementation in an organization. In this paper, 35 key enablers were identified after the rigorous analysis of literature review and the opinion of a group of experts consisting of academicians, practitioners and consultants. Thereafter, the brainstorming sessions were conducted to finalize 28 lean supply chain enablers (LSCEs).Practical implicationsFor lean manufacturing practitioners, the result of this study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process.Originality/valueThis paper is the first of the research papers that considered deep literature review of identified LSCEs as the initial step, followed by finding the best priority weightage and developing the ranking of various lean enablers of supply chain with the help of various methodologies.

Empowering Flight Exercise: Teaching Students the Design, Underlying Processes, and Organizational Outcomes of Empowering Work Systems

Employee empowerment yields positive outcomes for employees, managers, and organizations. Yet, too many employees feel disempowered at work, and managers, while wanting to empower employees, often do not know how. Contributing to this state of affairs is the lack of published, high-fidelity exercises explicitly designed to instruct students on how empowerment “feels,” how empowerment “works,” and how to practically empower others. In this article, we outline a 90-minute face-to-face classroom exercise that integrates the structural and psychological empowerment perspectives enabling students to “feel” empowerment or disempowerment and see the productivity and quality benefits of an empowered workforce, and teaches students how to empower others at work. While participating in the exercise, students simulate working in an airplane manufacturing organization, working either in an empowered work environment or a traditional hierarchical work environment. The exercise provides instructors with an important classroom tool to instruct students about the importance of empowerment, trust, and performance in organizational life.

An analysis of Industry 4.0 implementation-variables by using SAP-LAP and e-IRP approach

PurposeIn today’s uncertain business environment, Industry 4.0 is regarded as a viable strategic plan for addressing a wide range of manufacturing-related challenges. However, it appears that its level of adoption varies across many countries. In the case of a developing economy like India, practitioners are still in the early stages of implementation. The implementation of Industry 4.0 appears to be complex, and it must be investigated holistically in order to gain a better understanding of it. Therefore, an attempt has been made to examine the Industry 4.0 implementation for the Indian manufacturing organization in a detailed way by analyzing the complexities of relevant variables.Design/methodology/approachSAP-LAP (situation-actor-process and learning-action-performance) and an efficient interpretive ranking process (e-IRP) were used to analyze the various variables influencing Industry 4.0 implementation. The variables were identified, as per SAP-LAP, through a thorough review of the literature and based on the perspectives of various experts. The e-IRP has been used to prioritize the selected elements (i.e. actors with respect to processes and actions with respect to performance) of SAP-LAP.FindingsThis study ranked five stakeholders according to their priority in Industry 4.0 implementation: government policymakers, industry associations, research and academic institutions, manufacturers and customers. In addition, the study also prioritized important actions that need to be taken by these stakeholders.Practical implicationsThe results of this study would be useful in identifying and managing the various actors and actions related to Industry 4.0 implementation. Accordingly, their prioritized sequence would be useful to the practitioners in preparing the well-defined and comprehensive strategic roadmap for Industry 4.0.Originality/valueThis study has adopted qualitative and quantitative approaches for identifying and prioritizing different variables of Industry 4.0 implementation. This, in turn, helps the stakeholder to comprehend the concept of Industry 4.0 in a much simpler way.