Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

The traditional manufacturing sectors (footwear, textiles and clothing, furniture and toys, among others) are based on small and medium enterprises with limited capacity on investing in modern production technologies. Although these sectors rely heavily on product customization and short manufacturing cycles, they are still not able to take full advantage of the fourth industrial revolution. Industry 4.0 surfaced to address the current challenges of shorter product life-cycles, highly customized products and stiff global competition. The new manufacturing paradigm supports the development of modular factory structures within a computerized Internet of Things environment. With Industry 4.0, rigid planning and production processes can be revolutionized. However, the computerization of manufacturing has a high degree of complexity and its implementation tends to be expensive, which goes against the reality of SMEs that power the traditional sectors. This paper reviews the main scientific-technological advances that have been developed in recent years in traditional sectors with the aim of facilitating the transition to the new industry standard.This research was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under the project CloudDriver4Industry TIN2017-89266-R

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

A bibliography experiment on research within the scope of industry 4.0 application areas in sports: Sporda endüstri 4.0 uygulama alanları kapsamında yapılan araştırmalar üzerine bir bibliyografya denemesi

Developed countries develop their production sites within the scope of industry 4.0 technology components and experience constant change and transformation to establish economic superiority. This situation allows them to produce more in various fields and thus to rise to a more advantageous position economically. Industry 4.0 technology affects areas within the scope of the sports industry such as sports tourism, athlete performance, athlete health, sports publishing, sports textile products, sports education and training, sports management and human resources, and creates an international competition environment in terms of production and performance. In this study, it is aimed to examine the researches about the usage areas of industry 4.0 in sports. From this point on, researches in the context of the subject have been presented with bibliographic method. In the conclusion section, the weaknesses and possibilities of youth sociology were discussed, and efforts were made to present a projection on what to do about the field. In this respect, a youth sociology evaluation has been tried to be made on the prominent topics, forgotten aspects and themes left incomplete in youth sociology studies. ​Extended English summary is in the end of Full Text PDF (TURKISH) file.   Özet Gelişmiş ülkeler endüstri 4.0 teknolojisi bileşenleri kapsamında üretim sahalarını geliştirmekte ve ekonomik üstünlük kurmak amacıyla sürekli değişim ve dönüşüm yaşamaktadır. Bu durum onların çeşitli alanlarda daha fazla üretmelerine dolayısıyla ekonomik yönden daha avantajlı konuma yükselmelerine olanak sağlamaktadır. Endüstri 4.0 teknolojisi spor turizmi, sporcu performansı, sporcu sağlığı, spor yayıncılığı, spor tekstil ürünleri, spor eğitimi ve öğretimi, spor yönetimi ve insan kaynakları gibi spor endüstrisi kapsamındaki alanları etkilemekte üretim ve performans yönünden ülkeler arası bir rekabet ortamı oluşturmaktadır. Bu çalışmada endüstri 4.0’ın sporda kullanım alanları ile ilgili araştırmaların incelenmesi hedeflenmektedir. Bu noktadan hareketle konu bağlamındaki araştırmalar bibliyografik metodla ortaya konmuştur. Sonuç bölümünde ise sporda endüstri 4.0 kullanım alanları tartışılmış, alana olan katkıları ve olumuz etkilerinin değerlendirilmesi yapılmıştır. &nbsp

A Review of Supply Chain Data Mining Publications

The use of data mining in supply chains is growing, and covers almost all aspects of supply chain management. A framework of supply chain analytics is used to classify data mining publications reported in supply chain management academic literature. Scholarly articles were identified using SCOPUS and EBSCO Business search engines. Articles were classified by supply chain function. Additional papers reflecting technology, to include RFID use and text analysis were separately reviewed. The paper concludes with discussion of potential research issues and outlook for future development

Supply Chain Risk Assessment for Perishable Products Applying System Dynamics Methodology - A Case of Fast Fashion Apparel Industry

With the fast progress of science and technology and with the continuously growing customer expectations, share of merchandise exhibiting characteristics of perishability is on the rise. Perishable products, through their own nature, are subject to decay, deterioration or obsolescence. As a result, their usefulness, value or functionality is gradually reduced or even lost in a short window of time and cannot be regained if it is not used or sold within a specific time window. When producing perishable products, all stages of the supply chain are exposed to much higher uncertainty than in the case of durable products, which directly means higher risk. The phases of inventory planning, lead time control, and demand forecasting for perishable products play a critical role in the overall effectiveness of the supply chain. For this reason, the system dynamics methodology, a simulation and modeling technique developed specifically to address the long term and dynamic management issues, is adopted in this study. The focus of the proposed model is on the interaction between physical processes, information flows and managerial policies of a three-level supply chain for perishable products, in general, and fast fashion apparel supply chain, in particular, so as to create the dynamics of the variables of interest. The values of supply chain key factors such as, for example, inventory, backlogs, stock-outs, forecast error, cost, and profit for each time period are some of the outputs of the proposed model. Moreover, the Conditional Value at Risk (CVaR) measure is applied to quantify and analyze the risks associated with the supply chain for this type of product and also to determine the expected value of the losses and their corresponding probabilities. With the focus on three prominent categories of risks including risks of delays, forecast, and inventory, multiple business situations for effective strategic planning and decision making are generated and analyzed

Sustainable supplier selection based on industry 4.0 initiatives within the context of circular economy implementation in supply chain operations

This study proposes a decision framework based on industry 4.0 initiatives within circular economy implementation to evaluate and select sustainable suppliers. In this context, sustainable supplier selection, industry 4.0, and circular economy have emerged as key topics of the contemporary operations management debate. The mix method approach of combining literature review and industrial expert’s inputs was adopted to identify four main categories and twenty-one sub-categories relevant to the supplier selection decision. A multi-criteria decision-making support tool composed of the ‘best-worst method’ (BWM) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) was applied to aid in the evaluation and selection of a sustainable supplier in Pakistan’s textile manufacturing company. The BWM approach was first applied to determine the relative importance weights, and then, VIKOR used to rank the suppliers. The findings of the study suggest that, the Pakistan’s textile manufacturing company places much emphasis and importance on ‘Technological and Infrastructure (TI)’ with weight of 0.356 and ‘a positive organizational culture towards implementation of industry 4.0 and circular economy initiatives’ (OG3) with global weight of 0.139 when embarking on such decisions, and ranked supplier 2 as the top sustainable supplier. Managerial and post-selection benchmarking negotiations and future research directions are also introduced

Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and po-tential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability

Developing prototype cost model for embedded motherboards assembly- A case study

Organizations today need cost estimation in product early conceptual stage to compete in the market. This study aims to examine the impact of types of components, number of components, memory type, memory size, IPC (Institute for Printed Circuits) class of Printed Circuit Board, types of test/acceptance criteria, number of batch lot sizes, and form factor on the estimated cost of Embedded Motherboard (EM) PCBA (Printed Circuit Board Assembly) at the Prototype Build stage. Learning Curve theory is used as underpinning theory. 77 sample size of suppliers’ quotation of different models of EM in the prototype build stage were collected with case study sampling technique used. Multiple regression was performed for data analysis. The results showed that types of components, IPC Class, types of test, number of batch lot sizes, and form factor significantly impacted the total predicted cost. However, number of components, memory type and memory size have insignificant impact on the total predicted cost. The findings furnish significant input to NPD team members to predict prototype PCBA cost despite minimum information at the early design stage. Theoretical implication includes new cost estimation model to improve cost engineering knowledge while practical implication includes cost savings to company in terms of wastage reduction. Practical implication include cost savings to company in terms of wastage reduction. Future research is suggested to embark on an automated parametric cost estimation model to capture, incorporate and store each estimation into a database that can be kept and retrieved for future cost estimation