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Component selection system for green supply chain
Abstract
With the differences of customization attributes, the changes of the implementing stages of rules and different selling countries, the contents of the check value of RoHS (Restriction of the use of certain Hazardous Substance in electrical and electronic equipment) which each enterprise has to comply with and which is more complicated than component selection operation for general products are different. Under such complicated productive production, it is a major test for business decision makers to maintain the most effective operational efficiency and the lowest cost. This study puts forward a set of solutions which integrate group technology and neural network against component selection of green supply chain management (GSCM). First, neural networks are used for grouping products with the similar customized need against the value of the check item of each part of orders. Next, according to the information of existing inventory within the enterprise, available parts are selected and the total production costs are calculated to effectively reduce the complexity of the planning of the production lines for production manager. The above operation mode is established to be an information system of a component selection for green supply chain. Finally, data is analyzed to account for the using situation to ensure the availability of practical operation of the system based on the case of a company.
... Seeking to incorporate environmental and sustainable practices into the supply chain, organizations have demanded a great deal of information from their partners. Thus, information systems (IS) have become important tools for the adoption and management of these new practices (Chen, Tai, & Hung, 2012;Dao, Langella, & Carbo, 2011;Hu, Li, Chen, & Wang, 2014;Khor, Thurasamy, Ahmad, Halim, & May-Chiun, 2015;Sarkis, Koo, & Watson, 2013;Shaft, Sharfman, & Swahn, 2001;Wognum, Bremmers, Trienekens, van der Vorst, & Bloemhof, 2011). IS hold greater promise for addressing environmental issues in organizations; they can support business initiatives in reducing negative environmental impacts (Jenkin, Webster, & McShane, 2011). ...
... Therefore, the financial and social dimensions still need more attention. Moreover, despite the fact that most articles are concentrated in the United States, there are several publications in Asia, demonstrating that this field of study has attracted attention from researchers in that continent too (Chen et al., 2012Lai, Hsub, & Chenc, 2012;Liu, So, Choy, Lau, & Kwok, 2008). ...
... Thus, this IS helps improve efficiency, reduces management costs, and ensures environment protection. Chen et al. (2012) Proposed the development of an IS to select green supply chain members. The selection is made according to environmental laws and regulations imposed by industries. ...
The objectives of this study are to identify and systematize scholarly articles on the use of information system to support sustainable supply chain management and to suggest future research opportunities. Therefore, a structured literature review was conducted. The most relevant studies identified were classified and categorized into seven dimensions: research context, research focus, research method, sector analyzed, information system (IS) beneficiaries, relationship between IS and green supply chain practices, and performance benefits. The main authors and articles on this particular topic were identified. In addition, it was concluded that IS is an important support tool for sustainable supply chain management practices since it brings benefits to the organization, suppliers, and customers. Furthermore, IS positively influences the operational, financial, and environmental performance of the organization. However, further advances in the literature are still needed. The major contribution of this research is related to the recommendations that provide opportunities for future research.
... However, NNs have an intrinsic disadvantage, that is, because the computational results are obtained by repetitive training in a "black box", NNs are unable to explain their reasoning process. 8. Support Vector Machine (SVM): SVMs are able to make up for the disadvantage NNs have, for SVMs have characteristics of simple structure, replacing experience optimum with global optimum [15]. SVMs have the strong generalization ability and strong mathematical interpretability. ...
... With the help of ML, manufacturers could reduce SC latency for components and parts used in their most heavily customized products. For example, faced with the different customization requirements and production regulation of each country, Chen et al. [15] put forward a solution with NNs to group similar customization needs. Then they used the existing inventory information to select the parts for production managers, which hugely reduced the cost during the SC compared to human decision. ...
Research interests in machine learning (ML) and supply chain management (SCM) have yielded an enormous amount of publications during the last two decades. However, in the literature, there was no systematic examination on the research development in the discipline of ML application, in particular in SCM. Therefore, this study was carried out to present the latest research trends in the discipline by analyzing the publications between 1998/01/01 and 2018/12/31 in five major databases. The quantitative analysis of 123 shortlisted articles showed that ML applications in SCM were still in a developmental stage since there were not enough high-yielding authors to form a strong group force in the research of ML applications in SCM and their publications were still at a low level; even though 10 ML algorithms were found to be frequently used in SCM, the use of these algorithms were unevenly distributed across the SCM activities most frequently reported in the articles of the literature. The aim of this study is to provide a comprehensive view of ML applications in SCM, working as a reference for future research directions for SCM researchers and application insight for SCM practitioners.
... Additionally, ML algorithms will improve on the human processes used in the past to balance limitations of production workflows. For instance, Chen et al. [27] proj ected a solution using NNs to group analogous customization necessities when faced with several modification necessities and production protocols of each country . The cost of the SC was then significantly reduced compared to a human decision by using the inventory information that was already available to choose the parts for production managers. ...
... ML can also help to better plan customisation needs. For example, Chen, Tai, and Hung (2012) applied NNs to group similar customisation needs. They then used existing inventory information to select parts for production, which dramatically reduced the cost throughout the supply chain compared to a human decision. ...
Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies’ competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.
... Making decisions based on uncertainty is an important problem in supply chains (Steckel et al., 2004), and machine learning solves this problem to some extent. Machine learning has performed well in production (Chen et al., 2012), demand prediction (Carbonneau et al., 2008), transportation and distribution optimization (Ćirović et al., 2014), inventory management improvement (Gumus et al., 2010), and supply chain risk prediction (Baryannis et al., 2019). ...
Vaccination offers health, economic, and social benefits. However, three major issues—vaccine quality, demand forecasting, and trust among stakeholders—persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.
... Prior studies have identified several benefits which IS offer for GSCM, including assistance in selecting suppliers based on environmental criteria and legislation [46] and allowing for transparency in products and supply chains, which in turn acts as a driver for the consumption of more sustainable products [15]. Furthermore, Green IS can aid supply chain members to make more informed and sustainable decisions [16], [43], integrate various stakeholders and assess products' carbon footprint throughout the supply chain [18]. ...
Anchored in the resource-based view, this research analyzes the relationships between green information systems (Green IS), green human resource management (GHRM), and green supply chains (GSCM). It then examines the effects of these relationships on the environmental and financial performance of organizations. To meet the proposed objectives, a survey was carried out with 211 Brazilian companies, with the data analysis stage employing the structural equation modeling technique. The main findings include that green information systems play an important role in supporting green supply chain practices, and indirectly contribute to environmental and financial performance by facilitating organizational processes. Furthermore, our results reveal that human resources partially mediate the relationship between information systems and green supply chains, through employee engagement in environmental management and rewards based on environmental performance. This study also brings a number of theoretical and practical contributions. In terms of theory, it highlights a previously unexplored relationship between the prevailing concepts of Green IS, GSCM, and GHRM, and their impacts on firm performance, which enriches the body of knowledge in the IS, HR, and engineering management fields. For practitioners, this study reveals how they can strategically articulate HR, IS, and supply chain functions to support sustainability strategies. The findings clarify that blending technical (IS) and soft (HR) skills is pivotal to developing valuable GSCM capabilities to support and improve companies’ performance.
... Manufacturers can apply ML tools to reduce supply chain latency for components and parts used in their most heavily customized products. For example, faced with the different production regulations and customization requirements of each country, Chen et al. [82] applied NNs to group similar customization needs. en, they used the existing inventory information to select the parts for production managers, which hugely reduced the cost during the supply chain compared with human decision. ...
In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.
... , Y. C.Chen et al. (2015) and M.-K.Chen, Tai, & Hung (2012) design a system to deal with the complexity of design and manufacture products for customers in different regions, with different sustainability standards.Guo, Zhou, Li, & Xie (2014) studies a model to use the Enterprise Information System to green design assessment of electromechanical products.Eun, Son, Moon, & Chung (2009) proposes a ...
The applications of information and communication technologies (ICT) for sustainable supply chain is being studied for more than one decade by operations management and information systems scholars. Much investment was made to apply new ICT technologies for western industrial parks to recover their competitiveness, in initiatives like Smart Manufacturing and Industrie 4.0, and improvements in the sustainability of operations are expected by these programs developers and researchers. How the sustainability can be impacted by these technologies, however, is not clear yet. This research uses the systematic review to map the previous research about ICT for sustainable supply chain and analyze it in the Smart Manufacturing perspective. The ICT literature presented some technology applications that are being included in Smart Manufacturing programs and has already some empirical applications that can guide and evaluate the real impact of them. As a result, the proposals of both types of research are very similar, and Smart Manufacturing can be seen as an evolution of previous concepts.
... Não se observa também, no Brasil, de acordo com o observado na literatura e nas entrevistas, o uso de métodos avançados para otimização da seleção de fornecedores e componentes, com uso de modelos matemáticos e ferramentas estruturadas, como os propostos porChen et al. (2012),Kuo e Chu (2013),Dou et al. (2014),Pecht et al. (2016) ouZhou et al. (2016). ...
A evolução tecnológica motivada pela convergência de tecnologias tem se acentuado nas últimas décadas, por um lado, resultando em grandes benefícios à sociedade, mas, por outro, acentuando a obsolescência dos equipamentos eletroeletrônicos (EEE) e, consequentemente, a geração de resíduos a partir desuas utilizações e ao final de seus ciclos de vida. Preocupada com esses resíduos, que vêm sendo denominados de resíduos de equipamentos eletroeletrônicos (REEEs), a União Europeia promulgou a diretiva RoHS (Restriction of HazardousSubstances), restringindo o uso de substâncias perigosas nesses equipamentos. Essa diretiva implicou mudanças e adequações em toda a cadeia produtiva do setor de eletroeletrônicos na Europa e para eventuais exportadores.
... Caniels et al. (2013) studied the drivers of suppliers participation in 'greening' supply chains in German automotive suppliers and found that supplier readiness and customer requirements are significant drivers, whereas, relational norms and customer investments are not. Chen et al. (2012) presented an approach for component selection system for 'green' supply chain management. Bierma and Waterstraat (1999) studied the chemical suppliers and developed shared savings contracts to reduce waste and total costs in the supply chain. ...
Operational practices of ‘green’ supply chain processes within as well as across the firm boundaries
are strongly linked with the firm performance. However, the desire for such practices is governed by
external and/or internal factors. The challenge, therefore, for supply chain and operations managers is to
design and develop an operations strategy that ensures the firms investments in such ‘green’ initiatives.
We develop a theoretical framework for resource allocation strategies for sustainable manufacturing
operations that can be characterized as speculative, responsible, efficient, and sustainable resource
allocation. This can aid managers in characterizing and controlling the firm’s underlying operational
processes and guide resource allocation decisions at strategic level.
... Dekker et al. (2012) presented the integration of environmental aspects into logistics management known as green logistics and highlighted the contribution of operations research models to green logistics. Chen et al. (2012) proposed a component selection system for GSCM which integrates group technology and neural network. Further, they demonstrated the applicability of the system through a case study. ...
The paper aims to examine the practices and performances of the green supply chain management of Indian firms. In addition, we compare Indian firms' practices and performances with Japanese and Chinese firms that were obtained from the previous studies. A self-administrative questionnaire survey method was employed to obtain responses from the supply chain managers across different industries. Descriptive statistics shows that the Indian firms have high active participation in customer cooperation with environmental considerations and eco-design compared to Japanese and Chinese firms. We also found that the practices are similar among Indian, Japanese and Chinese firms, whereas, performances differ. Further, we discuss the results and findings in detail and suggest implications to managers in order to improve the firm performance.
This study evaluates the role and impact of artificial intelligence (AI) in supply chain management (SCM). Following a five-step process, the review covered academic publications from 2000 to 2024, drawing from different databases. The review identified 426 relevant articles for analysis, focusing on AI techniques. The analysis explored their applications, advantages, and barriers to adoption in SCM. The study also discussed key challenges, including financial, organizational, strategic, technological, and legal barriers. The findings suggest that while AI techniques offer significant potential for improving SCM, several obstacles hinder their broader implementation. Addressing these obstacles requires investments in infrastructure, skills development, and effective change management.
The increase in the world population and resource scarcity has led to the introduction of environmental concepts such as sustainability and sustainable supply chain design (SSCD). However, there is a lack of consensus among researchers on how to measure sustainability in SSCD. Therefore, the authors propose a novel approach to measuring sustainability in the context of SSCD by developing an integrated, tractable, and representative metrics framework. The methodology corresponds to a quantitative approach involving bibliographic examination and statistical techniques. First, the authors conducted a systematic literature review by formulating research questions and a search protocol, searched for relevant articles, and conducted a quality assessment on full-text reviews to obtain metrics for measuring sustainability in SSCD from the literature. Then, they defined aggregation criteria representing their inclusion relationship by merging associated metrics. The authors then used Cluster Analysis (CA), a multivariate statistical technique, for grouping the metrics. Consequently, twelve clusters were distinguished from 541 research articles, grouping 51 metrics from different sustainability dimensions. It shows the strong connection among the sustainability dimensions, i.e., they must be assessed holistically. Then, we proposed reducing the 51 metrics to 5 to evaluate sustainability in the SSCD, allowing us to focus on a reduced number of indicators.
Sustainable development refers to an economic, environmental, and social development aspect that meets the demands of the contemporary context and does not prevent future generations from fulfilling their requirements. In this perspective, businesses play an imperative role. However, studies reveal that the progress towards sustainable development has been slow, indicating the need for more concrete guidance that would allow businesses to act strategically and successfully in a sustainable way. There are three distinct, but complementary, dimensions of strategic management as viewed from the perspective of sustainability in order to encourage the integration of sustainability issues into corporate activities and strategies. This chapter looks through various aspects of sustainability towards making a sustainable organization through a vertical literature review. This theoretical discussion contributes to existing research to find relationships between strategic management and sustainable development and tries to provide a schema for further empirical research.
The volume contains latest research on software reliability assessment, testing, quality management, inventory management, mathematical modeling, analysis using soft computing techniques and management analytics. It links researcher and practitioner perspectives from different branches of engineering and management, and from around the world for a bird's eye view on the topics. The interdisciplinarity of engineering and management research is widely recognized and considered to be the most appropriate and significant in the fast changing dynamics of today's times. With insights from the volume, companies looking to drive decision making are provided actionable insight on each level and for every role using key indicators, to generate mobile-enabled scorecards, time-series based analysis using charts, and dashboards. At the same time, the book provides scholars with a platform to derive maximum utility in the area by subscribing to the idea of managing business through performance and business analytics.
The applications of information and communication technologies (ICT) for sustainable supply chain is being studied for more than one decade by operations management and information systems scholars. Much investment was made to apply new ICT technologies for western industrial parks to recover their competitiveness, in initiatives like Smart Manufacturing and Industrie 4.0, and improvements in the sustainability of operations are expected by these programs developers and researchers. How the sustainability can be impacted by these technologies, however, is not clear yet. This research uses the systematic review to map the previous research about ICT for sustainable supply chain and analyze it in the Smart Manufacturing perspective. The ICT literature presented some technology applications that are being included in Smart Manufacturing programs and has already some empirical applications that can guide and evaluate the real impact of them. As a result, the proposals of both types of research are very similar, and Smart Manufacturing can be seen as an evolution of previous concepts.
Green supply chain management is defined as the process of using environmentally friendly inputs and transforming these inputs into outputs that can be reclaimed and re-used at the end of their life cycle thus, creating a sustainable supply chain. GSCM is one of the recent innovations for the enhancement of capabilities of Supply Chain Management. The purpose of this paper is to carry out a theoretical review of Green supply chain management (GSCM). The assessment involved 144 research articles published in 48 selected journals between 1998 and 2013. The methodology of empirical research involved selection and classification of 144 research articles in GSCM. A systematic classification and a critical analysis is carried out so as to identify research gaps in content of GSCM, as well as to recommend directions for future research. It is concluded from the analysis of the results that research in GSCM is increasing at a faster rate than ever; with theory building more in focus than verification. Furthermore, a new taxonomy was proposed on the basis of content and research methodology utilised. Based on this taxonomy, significant trends were observed and some unique inferences were drawn, apart from identifying the directions for future research. There have been a couple of literature reviews in GSCM but none of them focused exclusively on research methodology in GSCM. Also the sample size with respect to the number of papers (144 papers), as well as number of journals (48 journals), is larger than ever considered for literature review in GSCM. The papers spans a longer time period of 16 years (1998-2013).
The objective of this study is to identify the green supply chain management barriers (GSCMBs) and to prioritise the solutions to overcome the barriers of GSCM implementation. Through literature review and expert discussion, total 36 GSCMBs and 20 solutions were identified. The nationwide survey was conducted to rank the GSCMBs. Twenty-nine GSCMBs (Mean≥2.50) out of 36 GSCMBs were selected for analysis. This study proposes a framework based on hybrid fuzzy AHP – VIKOR. The proposed methodology is helpful to overcome these GSCMBs for the success of GSCM implementation. The results show that ‘integrate the reverse material flow in SC’ is the highest ranked solution which provides useful insights for managers to formulate plans for benchmarking and setting targets for GSCM implementation through phase wise organisational priority to overcome the GSCMBs. This study provides an efficient, effective, robust and organised way to overcome the challenges to enhance the competencies of GSCM implementation.
The purpose of this chapter is to identify the dimensions of green supply chain and their impact on manufacturing practices. In this study, the authors used two extended strategies. First thorough review of literature was done considering articles from reputed journals. Second the factors identified from literature review was further refined through experts by forming a problem solving group consisting of seven experts from the manufacturing sector. These factors were used to develop the green supply chain management model using Interpretive structural modeling. Further MICMAC analysis was used to identify the driving and dependence power of the factors. The results of the analysis are very encouraging. Finally, the authors have presented the relationship management strategy for sustainable manufacturing practices.
Due to the rapid technological advances, machine Learning or the ability of a machine to learn automatically has found applications in various fields. It has proven to be a valuable tool for aiding decision makers and improving the productivity of enterprise processes, due to its ability to learn and find interesting patterns in the data. Thereby, it is possible to improve supply chains processes by using Machine Learning which generates in general better forecasts than the traditional approaches.
The goal of delivering a green product starts with green procurement and then continue further with green logistics, green process and green manufacturing specially in a strong supply chain like that in an automobile organization. The purpose of this paper is to identify the green supply chain management enablers (GSCMEs) for Indian automobile industry and establish relationships among them using interpretive structural modelling (ISM) and find out driving and dependence power of enablers, using Matriced' Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis. A survey method is used to get the primary data for this research from fifteen automobile organisations in the Pune-Nashik area. Although currently the research has been conducted only in one industrial region but in future this study can be replicated to other Indian regions as well. The study identifies environmental regulations along with relationship with suppliers as main enablers for a better green supply chain.
This article aims to present the Green Supply Chain Management (GSCM) practices from a comprehensive point of view and to analyze the subject's behaviour in the last ten years, through a systematic literature review/bibliometric analysis in articles published from 2006 to 2016. Through the research profiling method, we identified that (i) the most frequent research contexts were "GSCM financial impact" and "motivations to GSCM implementation", (ii) the automotive, textile / manufacturing and electronic sectors were the most discussed, (iii) the most used research methods were those involving empirical procedures, iv) Web of Science and Scopus databases gathered 96.7% of the articles used in this analysis, (v) there is a high concentration of researches from countries academically established and recognized, while developing countries are also present, (vi) Journal of Cleaner Production was the most cited journal and with more publications about GSCM, (vii) Samir Srivastava’s article had the highest Citation Score, and (viii) there are 11 GSCM research clusters. In addition, we discuss the content covered in the literature review, seeking to extend the understanding of the scenario where the GSCM is inserted nowadays and helping to identify research opportunities for researchers interested in such subject.
Operational practices of ‘green’ supply chain processes within as well as across the firm boundaries are strongly linked with the firm performance. However, the desire for such practices is governed by external and/or internal factors. The challenge, therefore, for supply chain and operations managers is to design and develop an operations strategy that ensures the firms investments in such ‘green’ initiatives. We develop a theoretical framework for resource allocation strategies for sustainable manufacturing operations that can be characterized as speculative, responsible, efficient, and sustainable resource allocation. This can aid managers in characterizing and controlling the firm’s underlying operational processes and guide resource allocation decisions at strategic level.
Mit der wachsenden Bedeutung der Ökologie sind Unternehmen zunehmend gefordert, die ökologische Bilanz ihrer Wertschöpfungsprozesse und Produkte zu verbessern. Einen wesentlichen Beitrag hierzu kann die Logistik leisten, deren Prozesse für die Herstellung jedes physischen Produkts erforderlich sind. Um Ökologie bereits frühzeitig und somit in der Gestaltungsphase logistischer Netzwerke zu berücksichtigen, wird in dieser Arbeit das Supply Chain Design um ökologische Zielgrößen und Kennzahlen, Bewertungsmethoden sowie Standarddaten erweitert. Dies betrifft im Speziellen die Phasen Festlegung der Lieferkettenziele, Handlungsfeldanalyse, Grobkonzeption und Feinkonzeption. Die durchgängige Berücksichtigung ökologischer Kennzahlen und die methodische Erweiterung erlauben, in bestehenden Lieferketten Handlungsfelder zu identifizieren, zahlreiche Handlungsoptionen in der Grobplanungsphase auch ökologisch schnell zu bewerten und die aussichtsreichsten Szenarien in der Feinplanungsphase detailliert zu untersuchen. Zur vollständigen Integration von Ökologie in das Supply Chain Design müssen sich bei der Feinplanung Steuerungsstrategien auf Basis ökologischer Kriterien evaluieren lassen. Hierzu wurde das Konzept der intra-simulativen ökologischen Bewertung entwickelt. Die Erprobung des konzeptionellen Ergebnisses auf Basis prototypischer Implementierungen und der positive Effekt der Berücksichtigung ökologischer Kennzahlen werden anhand von zwei Anwendungsfällen dargestellt.
The purpose of this chapter is to identify the dimensions of green supply chain and their impact on manufacturing practices. In this study, the authors used two extended strategies. First thorough review of literature was done considering articles from reputed journals. Second the factors identified from literature review was further refined through experts by forming a problem solving group consisting of seven experts from the manufacturing sector. These factors were used to develop the green supply chain management model using Interpretive structural modeling. Further MICMAC analysis was used to identify the driving and dependence power of the factors. The results of the analysis are very encouraging. Finally, the authors have presented the relationship management strategy for sustainable manufacturing practices.
Evaluation of the lean status is very crucial for firms in resource constrained manufacturing environments that they operate in today. The information regarding the extent of lean adaptation achieved and relative lean status offers the producing units strong reasons to review the tactical and strategic aspects of their operations and reconfigure their systems in their drive to be/remain globally competitive. Several articles published hitherto have addressed the issue of leanness assessment but the search for even more objective and comprehensive evaluation methods is still on. In this paper, we propose Chang's entropy based fuzzy AHP method for assessing leanness in MCDM environment. The method can deal in a natural manner with complexities inherent in the decision making environment due to imprecision or subjectivity of information. The method employs fuzzy set theory concepts for determination of criteria weights and information entropy approach for ranking the alternatives under considerations. A numerical example is provided to explain, illustrate and verify the practical application and usefulness of the approach. Sensitivity analysis is also carried out to study how certain subjective considerations influence the results. It can be concluded that the suggested method is quite versatile and efficient for solving wide array of MCDM applications.
In supply chain management (SCM), two topics have gained importance over the last years. On the one hand, sustainable SCM (SSCM) has become increasingly relevant and many publications have contributed to the topic. On the other hand, information technology (IT) is being progressively considered as a key enabler for efficiency in supply chains. Several research efforts have contributed to the field of IT for SSCM. However, this paper is the first recent attempt to summarise the current state of the art of how IT can affect SSCM in any structured way and to compare it with IT for ‘general’ SCM to give guidance for future research. This paper surveys 55 peer-reviewed articles that were retrieved through keyword searches (until May 2014). The analysis identifies research deficits as well as a lack of scientific discourse employing empirical techniques and a lack of investigations on the social sustainability. Additionally, possible topics for further research were derived by comparing the survey’s results with the current research on IT for ‘general’ SCM following the analysis of 631 articles. Six fields could be identified, namely output/effects of IT, machine communication and multiagents, inputs and IT-supported processing, IT-enabled interorganisational exchange, quantitative IT approaches and a sector focus.
In the novel electricity monitoring system designed in this study, manufacturing data with energy consumption are transformed in real time in the communication module and analysis module and then sent to such information devices as notebooks, i-Pads, or i-Phones. A fuzzy multiple objective linear programming (FMOLP) model is formulated to integrate the designed system for making decision associated with about environmental regulations and cost-effectiveness of carbon emissions in manufacturing firms given a carbon footprint label (CFL). Implementation results demonstrate that the proposed system is very effective in terms of CFL, cost-effectiveness, and manufacturing capacity. Importantly, the electric monitoring system can be practically installed in specific equipment to acquire electric consumption in real time and then helped decision makers to achieve a satisfactory solution in which manufacturing costs and CFL are simultaneously considered under restrain of electricity uncertainty.
Industrial operations could emit harmful pollutants and degrade natural environment, thereby posing a threat to human beings and wildlife (polar bears, panda, penguins, turtles, whales, walrus etc.). Globally manufacturers must ensure that the operations be done as safely and responsibly as possible keeping in line with the three dimensions of triple bottom line. We develop a framework which analyses the various complex relationships involved in a sustainable supply chain with the aid of interpretive structural
modeling. The key factors influencing sustainable supply chain were identified based on a thorough literature review and in consultation with rubber industry experts. Further MICMAC analysis was applied to identify the autonomous, linkage, dependent and independent factors.
Keywords: Sustainable Supply Chain Management (SSCM), Indian Rubber Industry, Interpretative Structural Modeling, MICMAC.
Materials have very important influence on the environmental impact of the product life cycle from production to disposal and recycling. The environmental factors should be considered when product materials are selected, in order to improve the environmental friendliness of products. Due to the complexity of product material selection considering technical, economic and environmental factors, it is transformed into a decision-making problem. An integrated decision-making index system is presented, and a multi-objective decision-making framework model is established according to the index system. An algorithm integrating grey relation analysis (GRA) and analytic hierarchy process (AHP) is used to aid the decision making process. A material selection case of the housing of an electronic device is introduced, which shows the feasibility and effectiveness of the model and method presented.
In this paper, we analyze how neighborhood size and number of weights in the self-organizing map (SOM) effect quantization error. A sequence of i.i.d. one-dimensional random variable with uniform distribution is considered as input of the SOM. First obtained is the linear equation that an equilibrium state of the SOM satisfies with any neighborhood size and number of weights. Then it is shown that the SOM converges to the unique minimum point of quantization error if and only if the neighborhood size is one, the smallest. If the neighborhood size increases with the increasing number of weights at the same ratio, the asymptotic quantization error does not converge to zero and the asymptotic distribution of weights differs from the distribution of input samples. This suggests that in order to achieve a small quantization error and good approximation of input distribution, a small neighborhood size must be used. Weight distributions in numerical evaluation confirm the result.
This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many real-world problems are too hard to construct the appropriate network that solve them. In such problems, neural network ensembles are a successful alternative. Nevertheless, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative coevolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a multiobjective method. For each network, different objectives are defined, considering not only its performance in the given problem, but also its cooperation with the rest of the networks. In addition, a population of ensembles is evolved, improving the combination of networks and obtaining subsets of networks to form ensembles that perform better than the combination of all the evolved networks. The proposed model is applied to ten real-world classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set. In all of them the performance of the model is better than the performance of standard ensembles in terms of generalization error. Moreover, the size of the obtained ensembles is also smaller.
We present a method for an automated quality control of textile seams, which is aimed to establish a standardized quality measure and to lower costs in manufacturing. The system consists of a suitable image acquisition setup, an algorithm for locating the seam, a feature extraction stage and a neural network of the self-organizing map type for feature classification. A procedure to select an optimized feature set carrying the information relevant for classification is described. key words: neural networks, self-organizing feature maps (SOFM), textile seams, quality control, feature selection 1 Introduction Reliable and accurate quality control is an important element in industrial textile manufacturing. For many textile products, a major quality control requirement is judging seam quality. Presently, this is still accomplished by human experts, which is very time consuming and suffers from variability due to human subjectivity. Consequently, investigations about automated seam...
The European Union enacted legislation, the ROHS Directive, that bans the use of lead (Pb) and several other substances in electronic products commencing July 1, 2006. The legislation recognized that in some situations no viable alternative Pb-free substitute materials are known at this time, and so provided exemptions for those cases. It was also recognized that certain electronic products, specifically servers, storage and storage array systems, network infrastructure equipment and network management for telecommunication equipment referred to as high-performance electronic products, perform tasks so important to modern society that their operational integrity had to be maintained. The introduction of new and unproven materials posed a significant potential reliability risk. Accordingly, the European Commission (EC) granted an exemption permitting the continued use of Pb in solders, independent of concentration, for high-performance (H-P) equipment applications. This exemption was primarily aimed at assuring that the reliability of solder joints, particularly flip-chip solder joints is preserved. Flip-chip solder joints experience the most severe operating conditions in comparison to other applications that utilize Pb in electronic equipment. This paper briefly describes the solder-exempted H-P electronic products, their capabilities, and some typical tasks they perform. Also discussed are the major attributes that differentiate H-P electronic equipment from consumer electronics, particularly in relation to their operational and reliability requirements. Interestingly, other than the special solder exemption accorded to H-P electronic equipment, these products must meet all the other requirements for ROHS compliancy. The EC was aware that issues would surface after the legislation was enacted, so it created the Technical Advisory Committee (TAC) to review industry-generated requests for exemptions. The paper discusses three exemption requests granted by the EC that are particularly relevant to H-P electronic products. The exemptions allow the continued use of lead-bearing solder materials.
The EU's environmental package for the IT sector aims to increase the level of recycling of electrical and electronic equipment. Its broad scope encompasses IT hardware, regardless of whether it is used for commercial or domestic applications. This article provides an overview of how one part of that package, the Restriction of the Use of Certain Hazardous Substances in Electrical and Electronic Equipment ("RoHS") Directive (2002/95/EC), will be applied in the UK from 1 July 2006. © 2006 Herbert Smith LLP. Published by Elsevier Ltd. All rights reserved.
Directive 2002/95/EC, the Restriction of Hazardous Substances or ‘RoHS’ Directive, restricts the use of environmentally hazardous substances in electrical and electronic equipment distributed and sold in the EU from 1 July 2006. The Directive's objectives are to harmonize EU Member States’ national legislation and protect human health and the environment. This article reveals differences in the implementation of the RoHS Directive at the time it took effect. Differences were revealed in the analysis of results obtained from telephone interviews conducted with all but four of the 25 EU Member State RoHS enforcement officials. Differences in implementation created barriers to the achievement of the RoHS Directive's objectives and caused problems for producers of electrical and electronic equipment. The article attributes the principal cause of differences in implementation to insufficiently detailed text in the RoHS Directive. The article also considers how barriers and problems could have been prevented and makes proposals to solve them.
The European Union Restriction on Hazardous Substances has placed electronics and electrical equipment companies under a very challenging requirement to collect, manage, and report materials and substance data. This has resulted in a huge collective effort to standardize the definition and reporting of substance data within industry, in order that each party in the supply chain is able to minimize its legal, financial, and market risk. This paper describes standards efforts within the industry and how those standards can minimize risk, reduce effort, and increase accuracy in requesting and supplying data on material and substance data in electrical and electronic products and parts. THE EUROPEAN UNION RESTRICTION ON HAZARDOUS SUBSTANCES (RoHS) The European Union Directive on the Restriction on Hazardous Substances 1 , along with similar 'copy-cat' laws like that in China, has created perhaps the most challenging data-collection and data communication requirement ever experienced by the electronics industry. The Directive itself (which has been "transposed" into national law in each of the EU Member States) is deceptively simple-sounding: electrical and electronic products sold in the EU must be substantially free of six substances -cadmium, hexavalent chromium, lead, mercury, and two classes of poly-brominated plastics – known to be toxic to human and environmental health. While the Directive lists exemptions for certain classes of products and particular substance applications, it is a 'blanket' restriction covering nearly all types of electrical and electronic products. For those products within the scope of the ban every single material in every single component in every subassembly making up the final product must be free of the prohibited six substances.
Noisy and large data sets are extremely difficult to handle and especially to predict. Time series prediction is a problem,
which is frequently addressed by researchers in many engineering fields. This paper presents a hybrid approach to handle a
large and noisy data set. In fact, a Self Organizing Map (SOM), combined with multiple recurrent neural networks (RNN) has
been trained to predict the components of noisy and large data set. The SOM has been developed to construct incrementally
a set of clusters. Each cluster has been represented by a subset of data used to train a recurrent neural network. The back
propagation through time has been deployed to train the set of recurrent neural networks. To show the performances of the
proposed approach, a problem of instruction addresses prefetching has been treated.
Due to their massively parallel structure and ability to learn by example, artificial neural networks can deal with nonlinear problems for which an accurate analytical solution is difficult to obtain. These networks have been used in modeling the mechanical behavior of fiber-reinforced composite materials. Although promising results were obtained using such networks, more investigation on the appropriate choice of their structure and their performance in the presence of limited and noisy data is needed. On the other hand, polynomials networks have been known to have excellent properties as classifiers and are universal approximators to the optimal Bayes classifier. Not being dependant on various user defined parameters, having less computational requirements makes their use over other methods, such as neural networks, an advantage.In this work, the fatigue behavior of unidirectional glass fiber/epoxy composite laminae under tension–tension and tension–compression loading is predicted using feedforward and recurrent neural networks. These predictions are compared to those obtained using polynomial classifiers. Experimental data obtained for fiber orientation angles of 0°, 19°, 45°, 71° and 90° under stress ratios of 0.5, 0 and –1 is used.It is shown that, even when a small number of experimental data points is used to train both polynomial classifiers and neural networks, the predictions obtained are comparable to other current fatigue life-prediction methods. Also, polynomial classifiers are shown to provide accurate modeling between the input parameters (maximum stress, R-ratio, fiber orientation angle) and the number of cycles to failure when compared to neural networks.
A computer vision system (CVS) was developed for the quality inspection of beans, based on size and color quantification of samples. The system consisted of a hardware and a software. The hardware was developed to capture a standard image from the samples. The software was coded in Matlab for segmentation, morphological operation and color quantification of the samples. For practical application of the software, a user-friendly interface was designed using Matlab graphical user interface (GUI). Length and width of the samples were determined using this system. Then the results of the system were compared to the measurements obtained by a caliper. High correlations (r = 0.984 and 0.971 for length and width, respectively) were obtained between the results of the system and the caliper measurements. Moment analysis was performed to identify the beans based on their intensity distribution. Average, variance, skewness and kurtosis values were determined for each channel of RGB color format. Artificial neural networks (ANN) were used for color quantification of the samples. Samples were classified by human inspectors into five classes and twelve moment values of the 69 samples with their classes were used in the training stage of ANN. Testing of the ANN was performed with other 371 samples. The automated system was able to correctly classify 99.3% of white beans, 93.3% of yellow–green damaged beans, 69.1% of black damaged beans, 74.5% of low damaged beans and 93.8% of highly damaged beans. The overall correct classification rate obtained was 90.6%.
Group Technology (GT) is a useful way of increasing the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation (CF) is a key step in GT. It is used in designing good cellular manufacturing systems using the similarities between parts in relation to the machines in their manufacture. It can identify part families and machine groups. Recently, neural networks (NNs) have been widely applied in GT due to their robust and adaptive nature. NNs are very suitable in CF with a wide variety of real applications. Although Dagli and Huggahalli adopted the ART1 network with an application in machine-part CF, there are still several drawbacks to this approach. To address these concerns, we propose a modified ART1 neural learning algorithm. In our modified ART1, the vigilance parameter can be simply estimated by the data so that it is more efficient and reliable than Dagli and Huggahalli’s method for selecting a vigilance value. We then apply the proposed algorithm to machine-part CF in GT. Several examples are presented to illustrate its efficiency. In comparison with Dagli and Huggahalli’s method based on the performance measure proposed by Chandrasekaran and Rajagopalan, our modified ART1 neural learning algorithm provides better results. Overall, the proposed algorithm is vigilance parameter-free and very efficient to use in CF with a wide variety of machine/part matrices.
In this paper, we review two recently developed kernel self-organising maps (SOMs) for classification and further establish their link with an energy function. We demonstrate that the kernel SOM can be derived naturally by minimising the energy function, and the resulting kernel SOM unifies the approaches to kernelise the SOM and can be performed entirely in the feature space. Different forms of kernel functions can be readily adopted. Various kernel SOMs, as well as the original SOM, are compared on classifying several benchmark datasets. The performance of the kernel SOMs depends on the choices of kernel functions and their parameters, as well as the ways to classify the neurons. Although the proposed energy based kernel SOM can produce better classification results than other SOMs in some cases, there is no clear evidence showing that the kernel SOMs are always superior to the common SOM. The computational cost to kernelise the SOM, however, increases significantly.
The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.
Neural Cell Adhesion Molecule (NCAM) is a well-characterized member of the immunoglobin superfamily. The structure of NCAM is similar to the tumor suppressor Deleted in Colon Carcinoma (DCC). NCAM has been found in some epithelial tissues and plays a role in tumorigenesis of some cancers. The purpose of the present study was to determine if NCAM is present in normal human colon. Once its presence was established, its function as a tumor suppressor was investigated.
Colon tumors and normal proximal margins were processed for reverse transcription-polymerase chain reaction (RT-PCR) of the NCAM-180 message. Immunohistochemistry of the tissue was performed to determine the distribution of NCAM.
RT-PCR analysis demonstrated the presence of the NCAM-180 kD isoform in normal colonic epithelia. Immunohistochemistry showed NCAM on the basolateral surface of colonic epithelial cells of the villous tips. Tumors from 15 patients followed up for 4 years were studied. All seven tumors expressing NCAM-180 were from patients having a benign clinical course. Seven of eight tumors that lacked NCAM-180 were associated with aggressive clinical behaviors (presenting with obstruction, perforation or metastatic disease, or patient death within 18 months of presentation). The sole exception was in a villous adenoma excised from a patient who has had multiply recurrent polyps on follow-up.
We conclude that like DCC, NCAM is an important colonic adhesion molecule that functions as a tumor suppressor.
The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.
Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named "tributary", "urban", "rural" or "background". This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream.
The application of mathematical tools in initial steps of sediment quality assessment frameworks can be useful to provide an integrated interpretation of multiple measured variables. This study reveals that the Self-Organizing Map (SOM) artificial neural network can be an effective tool for the integration of multiple physical, chemical and ecotoxicological variables in order to classify different sites under study according to their similar sediment quality. Sediment samples from 40 sites of 3 estuaries of Cantabria (Spain) were classified with respect to 13 physical, chemical and toxicological variables using the SOM. Results obtained with the SOM, when compared to those of traditional multivariate statistical techniques commonly used in the field of sediment quality (principal component analysis (PCA) and hierarchical cluster analysis (HCA)), provided a more useful classification for further assessment steps. Especially, the powerful visualization tools of the SOM, which offer more information and in an easier way than HCA and PCA, facilitate the task of establishing an order of priority between the distinguished groups of sites depending on their need for further investigations or remediation actions in subsequent management steps.
The upcoming European Union RoHS and WEEE directives are driving new requirements for the management and exchange of information, both across the extended electronics manufacturing value chain, and across the product lifecycle. All electronics OEMs that sell products into the EU will have to comply or they will lose access to this market. Compliance with these two directives will require that OEMs and their supply chains understand the material composition of their products, from bulk materials and individual components to sub-assemblies and finished products. In order to support this, data will have to be created, exchanged and analyzed across all tiers in the supply chain. Efforts are underway to develop standards for the definition and electronic exchange of Material Composition Declarations (IMCD) to minimize the industry impact.
The European Union WEEE and RoHS directives: How are Atlas Copco and CP's handheld industrial tools and assembly systems affected by the WEEE and RoHS directives
- A Relkman
Relkman, A. (2005). The European Union WEEE and RoHS directives: How are Atlas Copco and CP's handheld industrial tools and assembly systems affected by the WEEE and RoHS directives? Linköping University, Environmental Technology and Management Department of Mechanical Engineering.
The EU restriction of hazardous substances directive: problems arising from implementation differences between member states and proposed solutions. Review of European Community and International Environmental Law
- Martin
Supply chain data exchange for material disclosure. SMTA News and Journal of Surface Mount Technology
- Myles