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Agent-based modeling has proven effective in increasing the understanding of complex systems, including social-economical systems. Agoal of modeling complex systems is to distill the system into simple agents with phenotypes guided by simple rules. The model then displays the emergent behavior of these agents interacting with each other and their environment. An agent-based model of innovation and its place in a global economy or ecosystem is presented. The model utilizes simple agents to represent innovating entities such as large corporations and small companies. The results produced by this model reveal the dynamics of innovation and its role in a global economy. The results indicate a large need for partnership in innovation for those entities working within rapidly changing domains. Domains, such as high technology, have constantly changing market expectations, which force innovating entities to seek external sources of assistance to meet these expectations in a timely enough fashion so as to incur benefit.

Decision making in Flexible Manufacturing Systems (FMS) is difficult because of their high complexity level. The operational level of FMS is concerned with the detailed decision making required for real‐time operation. This applies to various control problems such as selection of a transportation path to move parts between stations. Describes a prototype knowledge‐based system for selection of a transport path in real‐time control of FMS. The knowledge‐based system is evaluated with an empirical approach.

Many researchers in cybernetics remain sceptical about the practical applications of expert systems to industrial problems. Apart from the much publicised medical expert systems, little information is available about the use of these systems in such fields as safety and reliability. It was therefore encouraging to see the report in ESRA (European Safety and Reliability Association) Newsletter Vol. 5, No. 1, 1988, pp. 14, of experience with expert systems to perform safety studies. Work carried out in 1985 by Électricité de France (EDF) showed that an important step forward towards automating reliability systems could be taken by using expert systems.

With the current global downturn, companies must develop new and innovative approaches to ensure that economic sustainability is achieved. The purpose of this paper is to propose a Fit Manufacturing Framework (FMF), the adoption of which can help manufacturing companies to become economically sustainable and operate effectively in a global competitive market. This contribution extends the previous work by the authors and provides an evolution on the initial work through enhancing the development of Fit manufacture through developing a more robust framework and a more comprehensive functional testing of the framework.

The proposed FMF provides a new manufacturing management perspective and a new manufacturing management strategy for creating economically sustainable manufacturing organisations. It builds upon the principles of existing manufacturing paradigms, along with innovative management concepts, to set up the conditions necessary for sustainability. A pilot application of the framework in three SMEs shows positive initial results when assessed against four Measures of Performance.

Manufacturing strategies such as Lean and Agility allow companies to deliver bottom‐line savings in production terms, although their effectiveness depends upon the volume and demand profile of their products. The trend towards mass customisation requires companies to provide personalised products and services at mass production prices. This now places a further burden on companies and therefore a holistic manufacturing framework must be developed in order to ensure that the factory of the future is able to meet this new demand. This paper proposes a Fit manufacturing paradigm which integrates the manufacturing efficiencies achieved through Lean and Agility with the need to break into new markets through effective marketing and product innovation strategies to achieve long term economic sustainability. The small‐scale application of the approach in a case company shows the initial results to be positive when measured against key MOPs developed within this paper.

The development of a Fit paradigm aimed at tackling directly the issues of economic sustainability is proposed and is considered by the authors as one of a kind. Fit will also provide a framework for the implementation of sustainable manufacturing operations within organisations.

The purpose of this paper is to identify the qualitative characteristics that can make manufacturing small to medium‐sized enterprises (SMEs) more robust and hence become economically sustainable in this globally competitive environment. The characteristics identified will form the foundations for defining a new manufacturing management perspective to both academics and industrialists.

Through a comprehensive case study approach the authors initially analyse the developmental cycle of the subject company and then identify the key characteristics which enabled the company to become economically sustainable and survive in the changing environment in which it operates. The paper opens with a brief academic analysis of sustainability literature available, before developing the case study.

Traditional business improvement strategies, such as Lean and Agility, which many companies initially follow in an attempt to become more robust and economically stable, allow companies to deliver bottom‐line savings in production terms although their effectiveness depends upon the volume and demand profile of their products. Through the case study outlined in this paper however, a combined approach towards the application of Lean is outlined as a primary means of reducing operating costs alongside the simultaneous implementation of product innovation strategies which allows the company to break into new markets as a means to achieving long‐term economic sustainability and making it more robust to market changes. The development of a business within a business is described as an effective mechanism towards achieving business sustainability.

The paper proposes a novel approach to achieving economic sustainability within a business and can be of benefit to the wider industrial and academic community. The development of work around a single company has its obvious limitations and it is crucial that further work, with a range of companies in the area of business sustainability, is key to developing a comprehensive set of sustainability characteristics.

The paper proposes a set of qualitative characteristics for the development of an economically sustainable manufacturing company. The development of a comprehensive case study with a subject company also directs and enables other companies of similar size and style to apply a similar approach and to achieve economic sustainability in an efficient and effective manner, through reducing production costs, minimising company failure and increasing business efficiency and effectiveness.

The development of a set of sustainability characteristics aimed at tackling directly the issues of economic sustainability is proposed and is considered by the authors as one of a kind. The case study approach also provides for a framework towards the implementation of sustainable manufacturing operations within SMEs.

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.

This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Previous studies have focused on explaining the developmental paths and the relevant skills necessary for smart factories, based on an extensive review of the literature. Unfortunately, there is a deficit of empirical analyses that present an in-depth overview of smart factory development. Although the literature supports the benefits of smart factories, it remains unclear whether there should be government intervention (GI) to facilitate or regulate such adoption. This study will provide an in-depth empirical analysis of smart factory adoption (SFA) and its role in manufacturing performance (MP) and sustainable manufacturing (SM).

This study used non-probability convenience and referral sampling techniques for data collection. This approach considered production managers from each company that participated in the survey questionnaire; thus, each production manager represented one firm. A total of 240 managers from several manufacturing companies participated in the study. This study employed direct and moderating hypotheses tested using PROCESS Macro, which Andrew Hayes developed for SPSS.

The study identified three fundamental elements of a smart factory: manufacturing big data (MBD), process automation (PA) and supply chain integration (SCI) and analyzed them individually to see how they affect MP. According to the results, building a smart factory has positive and significant impacts on MP and SM. Furthermore, this study explores the role of GI in promoting smart factory deployment for both production performance and sustainable production. The study found that GI did not have a significant moderating effect but did have a positive relationship with SM.

This study contributes to the literature on smart factories by examining the impact of SFA on MP and SM. This provides a more comprehensive overview of the potential benefits of smart factories across various aspects, such as the application of big data, adoption of automation technology and integration of the supply chain. This study suggests that managers and decision-makers consider investing in smart factory implementation to improve factory productivity and enhance sustainability. Policymakers and government officials can promote the adoption of smart factories by providing incentives, funding, and resources to manufacturing firms.

There is a scarcity of research measuring the actual performance of manufacturing firms that have already adopted smart factories, and this study seeks to address this gap in the literature. This study focuses on the implementation of manufacturing big data, process automation and supply chain integration and how the adoption of these technologies improves MP and provides a SM environment by conducting a real-time study of manufacturing organizations. This study presents an initial effort to explore the role of government involvement in promoting smart factories.