A big data analytics approach to quality, reliability and risk management

The role of smart technology, managerial initiatives and human factors on sustainable manufacturing: a case study of Indonesian oil and gas workers

This study aims to analyse and determine the effect of Big Data, the Internet of Things (IoT), and physical-cyber system variables on human factors in refinery industry operators and the influence of human factors and managerial initiatives on sustainable manufacturing. The method used in this study is a quantitative method using partial least square-structural equation modelling (PLS-SEM). The respondents in this study were workers of Indonesia's upstream oil and gas sector. The results of this study indicate that Big Data, IoT, and Physical Cyber Systems (PCS) have a positive and significant effect on the human factor. In addition, there is a significant relationship between human factors and sustainable manufacturing. Furthermore, it is also found that there is a relationship between managerial initiatives and sustainable manufacturing. However, the managerial initiative cannot moderate the human factor and sustainable manufacturing.

Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

Purpose

Machine learning (ML) models are increasingly being used in industrial maintenance to predict system failures. However, less is known about how the time windows for reading data and making predictions affect performance. Therefore, the purpose of this research is to assess the impact of different sliding windows on prediction performance.

Design/methodology/approach

The authors conducted a factorial experiment using high dimensional machine data covering two years of operation, taken from a real industrial case for the production of high-precision milled and turned parts. The impacts of different reading and prediction windows were tested for three ML algorithms (random forest, support vector machines and logistic regression) and four metrics (accuracy, precision, recall and F-score).

Findings

The results reveal (1) the critical role of the prediction window contingent upon the application domain, (2) a non-monotonic relationship between the reading window and performance, and (3) how sliding window selection can systematically be used to improve different facets of performance.

Originality/value

The study's findings advance the knowledge of ML-based failure prediction, by highlighting how systematic variation of two important but yet understudied factors contributes to the development of more useful prediction models.

Supply chain quality management 4.0: conceptual and maturity frameworks

Purpose

The research objective is rooted in the principle of providing new insights and a collective perspective regarded as Supply Chain Quality Management 4.0 (SCQM4.0), an integration of all three concepts – Industrie 4.0, quality management and supply chain management.

Design/methodology/approach

A thorough review of historical developments and existing integration trends among Industrie 4.0, quality and supply chain approaches along with future research directions outlined in the main literature, was conducted. This work establishes a knowledge base on research topics, issues of integration and synergies with a concentration on the potential for deeper integration with supply chain operations.

Findings

This article not only introduced the term SCQM4.0 and proposed a definition for it, but also contributed a novel conceptual SCQM4.0 framework and evolutionary perspective through the SCQM4.0 maturation model. Stemming from the gaps, opportunities and benefits identified in the literature, the conceptual SCQM4.0 framework builds on the high potential of the SCQM4.0 constructs to achieve successful governance and implementation. Under the SCQM4.0 maturity framework, it provides a clear evolutionary path underpinned by the SCQM4.0 constructs.

Research limitations/implications

In the effort toward a successful SCQM4.0 implementation, the proposed SCQM4.0 maturity frameworks will be a “road map” for businesses to develop fully and actively in supply chain operations, bringing quality products and services for the company. Industry practitioners are encouraged to perform gap analysis and direct the implementation of the strategy to establish an excellent SCQM4.0.

Originality/value

This is one of the pioneering studies integrating all three concepts (Industrie 4.0, quality management and supply chain management), connecting the link and discovering more synergies to support the future development of more holistic management models. SCQM4.0 is expected to expand on the strengths, synergies and established relationships between technologies 4.0, quality and supply chain, contributing toward a pioneering and quality supply chain.

How does performance vary between early and late adopters of Industry 4.0? A qualitative viewpoint

Purpose

Industry 4.0 is a new trend among organizations. Some organizations have been early adopters or later adopters of Industry 4.0. The purpose of this paper is to investigate how performance effects vary between early and late adopters of Industry 4.0.

Design/methodology/approach

This study applies a qualitative research methodology using grounded theory. 14 senior management professionals who have implemented Industry 4.0 participated in this study through a theoretical and snowball sampling approach. These professionals were from manufacturing and service sectors, from North America, Europe and Asia. The study used semi structured open-ended interviews to capture the organizational performance on operational, financial, environmental and social dimensions.

Findings

The findings were analyzed in terms of four broad themes which emerged from the interviews. In operational performance the operational and implementation cost will be higher for early adopters. The late adopters may enjoy the advantage in terms of improved business models. In terms of financial performance, the early adopters may see a marginal increase in profit and increased stock price compared to late adopters. The performance on the environmental dimension will see early adopters enjoying material efficiency, energy savings and an improved image of the company compared to late adopters. In social performance, the early adopters will provide a better quality of work life, safer manufacturing environment. However, the resistance from labor unions will be higher for early adopters compared to late adopters.

Practical implications

Organizations must decide the timing of implementation of Industry 4.0. This study will act as a guide wherein they can decide to be an early adopter or late adopter based on knowledge of the resulting performance consequences.

Originality/value

This is the first paper that studies the performance effects of early versus late adopters of Industry 4.0.

Integration of continuous improvement strategies with Industry 4.0: a systematic review and agenda for further research

Purpose

The purpose of this paper is to provide a review of the history, trends and needs of continuous improvement (CI) and Industry 4.0. Four strategies are reviewed, namely, Lean, Six Sigma, Kaizen and Sustainability.

Design/methodology/approach

Digitalization and CI practices contribute to a major transformation in industrial practices. There exists a need to amalgamate Industry 4.0 technologies with CI strategies to ensure significant benefits. A systematic literature review methodology has been followed to review CI strategy and Industry 4.0 papers (n = 92).

Findings

Various frameworks of Industry 4.0, their advantages and disadvantages were explored. A conceptual framework integrating CI strategies and Industry 4.0 is being presented in this paper.

Practical implications

The benefits and practical application of the developed framework has been presented.

Originality/value

The article is an attempt to review CI strategies with Industry 4.0. A conceptual framework for the integration is also being presented.