Are smart manufacturing systems beneficial for all SMEs? Evidence from Korea

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

A Multi-Criteria Assessment of Manufacturing Cell Performance Using the AHP Method

Research of manufacturing cell design problems is still pertinent today, because new manufacturing strategies, such as mass customization, call for further improvement of the fundamental performance of cellular manufacturing systems. The main scope of this article is to find the optimal cell design(s) from alternative design(s) by multi-criteria evaluation. For this purpose, alternative design solutions are mutually compared by using the selected performance criteria, namely operational complexity, production line balancing rate, and makespan. Then, multi-criteria decision analysis based on the analytic hierarchy process method is used to show that two more-cell solutions better satisfy the determined criteria of manufacturing cell design performance than three less-cell solutions. The novelty of this research approach refers to the use of the modification of Saaty’s scale for the comparison of alternatives in pairs based on the objective assessment of the designs. Its benefit lies in the exactly enumerated values of the selected criteria, according to which the points from the mentioned scale are assigned to the alternatives.

Rescheduling of Distributed Manufacturing System with Machine Breakdowns

This study attempts to explore the dynamic scheduling problem from the perspective of operational research optimization. The goal is to propose a rescheduling framework for solving distributed manufacturing systems that consider random machine breakdowns as the production disruption. We establish a mathematical model that can better describe the scheduling of the distributed blocking flowshop. To realize the dynamic scheduling, we adopt an “event-driven” policy and propose a two-stage “predictive-reactive” method consisting of two steps: initial solution pre-generation and rescheduling. In the first stage, a global initial schedule is generated and considers only the deterministic problem, i.e., optimizing the maximum completion time of static distributed blocking flowshop scheduling problems. In the second stage, that is, after the breakdown occurs, the rescheduling mechanism is triggered to seek a new schedule so that both maximum completion time and the stability measure of the system can be optimized. At the breakdown node, the operations of each job are classified and a hybrid rescheduling strategy consisting of “right-shift repair + local reorder” is performed. For local reorder, we designed a discrete memetic algorithm, which embeds the differential evolution concept in its search framework. To test the effectiveness of DMA, comparisons with mainstream algorithms are conducted on instances with different scales. The statistical results show that the ARPDs obtained from DMA are improved by 88%.

Parallel simulation and optimization framework of supplies production processes for unconventional emergencies

The outbreak of unconventional emergencies leads to a surge in demand for emergency supplies. How to effectively arrange emergency production processes and improve production efficiency is significant. The emergency manufacturing systems are typically complex systems, which are difficult to be analyzed by using physical experiments. Based on the theory of Random Service System (RSS) and Parallel Emergency Management System (PeMS), a parallel simulation and optimization framework of production processes for surging demand of emergency supplies is constructed. Under this novel framework, an artificial system model paralleling with the real scenarios is established and optimized by the parallel implementation processes. Furthermore, a concrete example of mask shortage, which occurred at Huoshenshan Hospital in the COVID-19 pandemic, verifies the feasibility of this method.

A Novel Data Analytics Oriented Approach for Image Representation Learning in Manufacturing Systems

In the era of digital manufacturing, huge amount of image data generated by manufacturing systems cannot be instantly handled to obtain valuable information due to the limitations (e.g., time) of traditional techniques of image processing. In this paper, we propose a novel self-supervised self-attention learning framework—TriLFrame for image representation learning. The TriLFrame is based on the hybrid architecture of Convolutional Network and Transformer. Experiments show that TriLFrame outperforms state-of-the-art self-supervised methods on the ImageNet dataset and achieves competitive performances when transferring learned features on ImageNet to other classification tasks. Moreover, TriLFrame verifies the proposed hybrid architecture, which combines the powerful local convolutional operation and the long-range nonlocal self-attention operation and works effectively in image representation learning tasks.

Cognitive Digital Twins for Resilience in Production: A Conceptual Framework

Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in production, i.e., to enable manufacturing systems to identify and handle anomalies and disruptive events in production processes and to support decisions to alleviate their consequences. Through analyzing five real-life production cases in different industries, similarities and differences in their corresponding needs are identified. Moreover, a connection between resilience and cognition is established. Further, a conceptual architecture is proposed that maps the tools materializing cognition within the DT core together with a cognitive process that enables resilience in production by utilizing CDTs.