A Sustainable Productive Method for Enhancing Operational Excellence in Shop Floor Management for Industry 4.0 Using Hybrid Integration of Lean and Smart Manufacturing: An Ingenious Case Study

Integration of Lean manufacturing and Industry 4.0: a bibliometric analysis

Purpose

The purpose of this study is to identify the prominent research constituents in the domain of integration of Lean manufacturing and Industry 4.0 techniques and analyze the intellectual structure among them.

Design/methodology/approach

A bibliometric analysis of articles based on Donthu et al. (2021a) has been adopted to conduct a systematic review of the integration of Lean manufacturing and Industry 4.0 using the Scopus database.

Findings

The co-citation analysis and bibliographic coupling depicted three clusters and themes around which the research related to the integration of Lean manufacturing and Industry 4.0. Publications related to the topic have majorly focused on the development of conceptual models and frameworks for integrating Lean manufacturing and Industry 4.0, analyzing the compatibility between the two techniques for better implementation of one another and the techniques' combined impact on operational performance.

Originality/value

Most of the review studies related to the domain of integration of Lean manufacturing and Industry 4.0 have adopted a systematic literature review methodology. The present study has tried to infer the intellectual framework of the research being conducted in the said domain using the bibliometric analysis to identify the prominent research constituents in the field and examine the intellectual relationship between them.

Immersive virtual reality application for intelligent manufacturing: Applications and art design

Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.

An Intelligent Logic-Based Mold Breakout Prediction System Algorithm for the Continuous Casting Process of Steel: A Novel Study

Mold breakout is one of the significant problems in a continuous casting machine (caster). It represents one of the key areas within the steel production facilities of a steel plant. A breakout event on a caster will always cause safety hazards, high repair costs, loss of production, and shutdown of the caster for a short while. In this paper, a logic-judgment-based mold breakout prediction system has been developed for a continuous casting machine. This system developed new algorithms to detect the different sticker behaviors. With more algorithms running, each algorithm is more specialized in the other behaviors of stickers. This new logic-based breakout prediction system (BOPS) not only detects sticker breakouts but also detects breakouts that takes place due to variations in casting speed, mold level fluctuation, and taper/mold problems. This system also finds the exact location of the breakout in the mold and reduces the number of false alarms. The task of the system is to recognize a sticker and prevent a breakout. Moreover, the breakout prediction system uses an online thermal map of the mold for process visualization and assisting breakout prediction. This is done by alerting the operating staff or automatically reducing the cast speed according to the location of alarmed thermocouples, the type of steel, the tundish temperature, and the size of the cold slab width. By applying the proposed model in an actual steel plant, field application results show that it could timely detect all 13 breakouts with a detection ratio of 100%, and the frequency of false alarms was less than 0.056% times/heat. It has the additional advantage of not needing a lot of learning data, as most neural networks do. Thus, this new logical BOPS system should not only detect the sticker breakouts but also detect breakouts taking place due to variations in casting speed and mold level fluctuation.

Recent Progression Developments on Process Optimization Approach for Inherent Issues in Production Shop Floor Management for Industry 4.0

In the present industry revolution, operations management teams emphasize implementing an efficient process optimization approach with a suitable strategy for achieving operational excellence on the shop floor. Process optimization is used to enhance productivity by eliminating idle activities and non-value-added activities within limited constraints. Various process optimization approaches are used in operations management on the shop floor, including lean manufacturing, smart manufacturing, kaizen, six sigma, total quality management, and computational intelligence. The present study investigates strategies used to implement the process optimization approach provided in the previous research to eliminate problems encountered in shop floor management. Furthermore, the authors suggest an idea to industry individuals, which is to understand the operational conditions faced in shop floor management. The novelty of the present study lies in the fact that a methodology for implementing a process optimization approach with an efficient strategy has been reported for the first time that eliminates problems faced in shop floor management, including industry 4.0. The authors of the present research strongly believe that this research will help researchers and operations management teams select an appropriate strategy and process optimization approach to improve operational performance on the shop floor within limited constraints.