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Journal articles on the topic 'Mining supply chain'
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1
Statsenko, Larissa, Alex Gorod, and Vernon Ireland. "A supply network governance framework: a case study of the South Australian mining industry." Journal of Global Operations and Strategic Sourcing 11, no. 1 (February 19, 2018): 55–78. http://dx.doi.org/10.1108/jgoss-03-2017-0007.
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Purpose The competitiveness of mining regions largely depends on the performance of the regional supply chains that provide services to mining companies. These local supply chains are often highly intertwined and represent a regional supply network for the industry. Individual companies often use supply chain strategies that are sub-optimal to overall supply network performance. To effectively respond to an uncertain business environment, policy-makers and supply chain participants would benefit by a governance framework that would allow to incentivise the formation of supply networks structures enabling effective operations. The purpose of this paper is to offer an empirically grounded conceptual framework based on Complex Adaptive Systems (CASs) governance principles, which links network governance mechanisms with supply network structure and operational performance to incentivise the formation of adaptive and resilient supply networks in the mining industry. Design/methodology/approach A mixed method research design and a case study of the South Australian mining sector were used to collect empirical data. Qualitative interviews and network analysis of the SA mining industry regional supply network structure were conducted. The relationships between network parameters were interpreted using CAS theory. Findings An empirically grounded conceptual framework based on CAS governance principles is developed. The case study revealed that supply chain strategies and governance mechanisms in the SA mining industry have led to the formation of a hierarchical, scale-free structure with insufficient horizontal connectivity which limits the adaptability, responsiveness and resilience of the regional supply network. Research limitations/implications The findings are drawn from a single case study. This limits generalisability of the findings and the proposed framework. Practical implications The proposed framework draws the attention of the policy-makers and supply chain participants towards the need for utilising CAS governance principles to facilitate the formation of adaptive, responsive and resilient regional supply networks in the mining industry. Originality value The proposed conceptual framework is an attempt to parameterise the governance of the regional supply networks in the mining industry.
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Stefanovic, Nenad. "Proactive Supply Chain Performance Management with Predictive Analytics." Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/528917.
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Today’s business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.
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Tavana, Madjid, Akram Shaabani, Iman Raeesi Vanani, and Rajan Kumar Gangadhari. "A Review of Digital Transformation on Supply Chain Process Management Using Text Mining." Processes 10, no. 5 (April 24, 2022): 842. http://dx.doi.org/10.3390/pr10050842.
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Industry 4.0 technologies are causing a paradigm shift in supply chain process management. The digital transformation of the supply chains provides enormous benefits to organizations by empowering collaboration among multiple internal and external organizations and systems. This study presents a narrative review explaining the existing knowledge on digital transformation in supply chain process management using text mining. It summarizes the existing literature to explain the current state of the art in supply chain digitalization. This comprehensive review identifies the most important topics and technologies and determines the future trends in this emerging field. We investigate the articles published in Web of Science and Scopus databases and use text mining techniques (clustering and topic modeling) on the article contents. Using VOS viewer, a bibliometric analysis of 395 articles with 12,700 references is analyzed. The contents of the articles are explored using text mining approaches. The synthesized results reveal that the most important topics in digital transformation are “sustainable supply chain management” and “circular economy and industry 4.0 technologies”. The study further discovers big data, data analytics, blockchain, artificial intelligence, machine learning, and the Internet of Things as the most critical technologies for facilitating supply chain digital transformation. Finally, an overlay heatmap analysis of the research articles found that digital transformation, supply chain management, industry 4.0, decision-making, and sustainability are emerging trends in supply chain digitalization.
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Jiang, Rui, Chunxue Liu, Xiaowei Liu, and Shuai Zhang. "Supply Chain Resilience of Mineral Resources Industry in China." Discrete Dynamics in Nature and Society 2023 (January 31, 2023): 1–10. http://dx.doi.org/10.1155/2023/1338223.
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Improving the supply chain resilience of the mineral resources industry is crucial for ensuring national economic security in China. Based on the supply and demand data of China’s mineral resources industry from 2002 to 2018, this study adopts system dynamics model to simulate the supply chain resilience of the mineral resources industry, the mining industry, and the smelting and processing industry under the scenario of steady economic development and the scenario of supply chain crisis. From the simulation results, the reserves of the mineral resources industry and the smelting and processing industry under the two scenarios are nearly the same, indicating that they are weakly affected by the foreign market, and both have strong resilience. The mining industry has a high dependence on imports and a lack of supply chain resilience. Under the condition of steady economic development, the output of the mining industry needs to develop at a low speed to reduce production capacity. More attention should be paid to the high level of import dependence and insufficient supply chain resilience of the mining industry. In the stable international trade situation, reserves of important minerals should be increased to alleviate the resource shortage during the supply chain crisis.
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Shah, Sayed Mehdi, Michael Lütjen, and Michael Freitag. "Text Mining for Supply Chain Risk Management in the Apparel Industry." Applied Sciences 11, no. 5 (March 5, 2021): 2323. http://dx.doi.org/10.3390/app11052323.
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Text mining tools are now widely used for the efficient management of information and resources in business, academic and research organizations. This paper provides a comprehensive overview of research articles on the application of text mining techniques in the field of Supply Chain Risk Management and the apparel industry. Research articles published between 2000 and 2020, were obtained from various journals through two online databases, i.e., SCOPUS and IEEE Xplore. Through a systematic approach following PRISMA guidelines, 370 research papers were screened, filtered and finally classified into three main areas: Supply Chain Risk Management and outsourcing in the apparel industry, application of text mining in Supply Chain Risk Management and application of text mining in the apparel industry. In this study, we have identified a comprehensive list of various available data sources for text mining, methodologies and risks associated with outsourcing in the apparel industry. We classify the gaps in expanding the application of text mining in the apparel industry’s Supply Chain Risk Management. Extracting useful information from online newspapers through text mining could vividly enhance the ability to monitor supply chain risks and provide the ability to link data to provide decision makers with the right information at the right time.
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Young, Steven B., Alberto Fonseca, and Goretty Dias. "Principles for responsible metals supply to electronics." Social Responsibility Journal 6, no. 1 (March 9, 2010): 126–42. http://dx.doi.org/10.1108/17471111011024595.
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PurposeThis paper seeks to critically analyse the list of principles on the extractive phase of the electronics supply chains, proposed for consumer electronic companies, by the non‐governmental campaign MakeITfair. The purpose is to understand whether conformance with these principles could positively influence the socio‐environmental conditions at the mining level.Design/methodology/approachThe paper reviews the literature on incorporation of corporate social responsibility in supply chain management. It then examines how metals are mined, traded and used in electronics, as well as how the mining industry has been managing its own socio‐environmental problems. This information underpins the qualitative discussion of the principles.FindingsMakeITfair's principles were found to be constructive insofar as they draw the attention of electronic companies to their shared responsibility for the problems of distant‐tier suppliers. Nevertheless, some principles may lead to potentially undesired outcomes such as biased prioritization of mining companies or regions, adoption of contentious “standards”, and conflicts concerning the sovereign rights of nations over their natural resources. Overall, the principles stress traceability mechanisms as means of influencing the mining phase of supply chains without considering the costs and benefits of overcoming the complexities involved in the metal trade and other barriers. The paper concludes by highlighting the need to consider additional ways of positively influencing metals supply.Research limitations/implicationsThe paper points out specific research priorities in the value chains of metals.Originality/valueThe paper provides a critical analysis of intricate responsibility issues in the supply chain of the world's top electronic companies.
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Son, Jeong-Soo. "A Study on the Establishment of a New Supply Chain and Risk Management According to the Global Transition Period: Focusing on the Urban Mining Industry." Korea International Trade Research Institute 19, no. 3 (June 30, 2023): 277–92. http://dx.doi.org/10.16980/jitc.19.3.202306.277.
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Purpose – This study proposes a strategic direction to respond to deglobalization and trends that are changing due to COVID-19, the war in Ukraine, disruptive factors in the global supply chain, and domestic-centered supply chain establishment, and identifies changes in the global supply chain and the environment to respond. Design/Methodology/Approach – This study analyzes the changes in and structural characteristics of the global value chain, and develops case studies and related policies for the promotion and value of the urban mining industry as a countermeasure to solve problems in Korea through supply chain management. Findings – Utilizing the advantages of urban mining in Korea, the urban mining industry was selected as a strategic industry development plan to create an industrial cluster that encompasses the relevant public sector, research institutes, and companies at each stage of the industry. Research Implications – Through this study, it is possible to preemptively respond to the crisis by preparing an alternative to supply chain risk, and to secure and operate resources through the vitalization of the urban mining industry, which is the most realistic and long-term alternative. It will be possible to review the advantages and strategic development of the urban mining industry, job creation, and development of social enterprises by separately fostering the private and public sectors.
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Pieth, Mark. "Gold Standards in the Supply Chain – A Golden Example." Zeitschrift für europarechtliche Studien 25, no. 2 (2022): 351–58. http://dx.doi.org/10.5771/1435-439x-2022-2-351.
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Sourcing gold is an example of one of the most problematic mineral supply chains. This is undoubtedly the case in artisanal mining (with serious environmental degradation through deforestation, the use of mercury, child labour and frequent violence). However, industrial mining is rarely better (local communities suffer from displacement and toxic emissions). The international community is struggling to regulate, however most of the rules remain soft law and self-regulation is unreliable. Furthermore, a large part of the supply chain evades the regulation altogether, since consumers in the Middle East, India and China rarely insist on clean sourcing.
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Sinaga, Poltak T., Togar M. Simatupang, and Mursyid H. Basri. "Enhancing Supply Chain Resilience in Coal Mining: A Comprehensive Systematic Literature Review to Managing Disruptions." 15TH GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES ON 14 - 15 SEPTEMBER 2023, NOVOTEL BANGKOK PLATINUM PRATUNAM, THAILAND 15, no. 1 (September 14, 2023): 152. http://dx.doi.org/10.35609/gcbssproceeding.2023.1(152).
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Supply chain risk and uncertainty are critical factors that impact coal mining operations. Risks can arise from various sources such as natural disasters, supplier disruptions, market fluctuations, regulatory changes, and geopolitical uncertainties (Marzouk et al., 2022; Pettersen-Sobczyk, 2018). Managing these risks involves identifying, assessing, and implementing measures to mitigate and minimize potential losses. Uncertainties, on the other hand, stem from the lack of predictability or knowledge about future events and outcomes. They represent a level of unpredictability and the potential for surprises or unexpected events that can impact the supply chain. Overcoming uncertainties requires a resilient supply chain capable of preparing for unexpected events, responding to disruptions, and recovering from them while maintaining operational continuity (Adobor, 2020; Gunasekaran et al., 2015). Resilience capability allows a company to restore its operations to their original or desired state after being disturbed (Chowdhury & Quaddus, 2017). However, despite the abundance of literature proposing resilience frameworks in supply chain management, their application within the context of coal mining remains relatively limited (Agrawal & Pingle, 2020; Belhadi et al., 2022). Existing research in the field of coal mining has primarily focused on operational efficiency, cost reduction, and production optimization, with limited attention given to the concept of resilience. Therefore, this research aims to address this gap by developing a comprehensive resilience framework specifically tailored to the mining supply chain, with a focus on coal mining company. By incorporating the unique characteristics and challenges of the mining industry, such as geological uncertainties, complex extraction processes, and environmental regulations, this framework will provide valuable insights and strategies for enhancing the resilience capabilities of coal mining company. Keywords: Supply chain resilience, Coal mining, Disruptioin, Risk mitigation, Systematic literature review
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Du, Wenyi, and Huimin Wang. "Operation decision of competitive mining supply chain based on social responsibility." PLOS ONE 17, no. 12 (December 8, 2022): e0278815. http://dx.doi.org/10.1371/journal.pone.0278815.
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The development of the green economy has significantly impact the traditional mining industry. Mining enterprises must invest in green technology to reduce the environmental pollution caused by flying dust and soil erosion and are subject to increased scrutiny to be socially responsible when conducting their business. To address this issue, we consider a competitive mining supply chain system consisting of two excavators and two exclusive retailers. Among them, the excavators have a certain sense of corporate social responsibility (CSR), that is, in addition to pursuing economic profits, they also consciously pay attention to the interests of consumers. We establish three different game models that two excavators exhibit no CSR behaviour (NN), two excavators exhibit CSR behaviour (SS) and one excavator exhibits CSR behaviour (SN). We examine the optimal decision-making strategies and analyse the impact of social responsibility. Analytical results show that the optimal strategies of mining supply chain are different under different supply chain structures. The optimal decisions of the mining supply chain members are the same in each case under the NN and SS models. In the SN model, the optimal decision strategy value of mining supply chain members is always greater than non-socially responsible supply chain members. In SS model, when the intensity of social responsibility competition is low, two excavators reduce the wholesale price, and retailers reduce the sales price; when the intensity of social responsibility competition is strong, two excavators will increase the wholesale price, and retailers will increase the sales price. These help to promote product sales and increase the profits of the supply chain system. In SN model, with the increase of social responsibility competition intensity, the wholesale price of two excavators and the sales price of retailers first increased and then decreased. Finally, numerical examples illustrated to justify the proposed model.
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Yaping, Qu, and Mary Bossman. "Logistics and Supply Chain Management Efficiency Strategy for Ghana’s Mining Industry." European Journal of Business and Management Research 6, no. 2 (April 26, 2021): 193–202. http://dx.doi.org/10.24018/ejbmr.2021.6.2.779.
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The invaluable contribution of the mining industry in any country’s economy is widely acclaimed and thus not far-fetched. In as much as the mining industry contributes hugely to economic and social growth, its operations are characterized by complex supply chain processes that are relatively capital and cost intensive, and technologically sophisticated to manage. Unfortunately, firms in the mining industry especially for developing countries tend to focus more on procuring modernized equipment and technologies to carry out the complex processes of mining operations to the detriment of applying logistics optimization models.
This research project therefore seeks to analyze the logistics and supply chain operations of mining companies in Ghana, identify the challenges and inefficiencies associated with these operations and their underlying causes, and recommend logistics and supply chain management design, modelling and analysis tools to optimize. With the help of secondary data collected from books, journals, which were further depicted in graphs and tables, the Microsoft Excel Solvers for Vehicle Routing Problem (VRP, Version 3.42), with an embedded Bing Map Key License was used to illustrate and derive optimal solutions for the transportation efficiency of mining firms in Ghana.
The study revealed that the mining industry in Ghana is confronted with supply chain issues like poor road networks in mining communities, variable lead time and exchange rate, strict governmental laws, violent clashes over land royalties, high transport cost due to bad roads, unreliable lead times (caused by port delays), unavailability of local suppliers of international standards, and policy restrictions as the main challenges to supply chain. Clearly, the policy implication emanating from this study is that mining firms in partnership with transport services firms in Ghana must adopt an efficient service delivery and optimal transportation cost reduction strategies, which is designed with the Vehicle Routing Problem Optimization Model in order to benefit mutually in gaining competitive advantage.
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Wincewicz-Bosy, Marta, Małgorzata Dymyt, and Katarzyna Wasowska. "The Supply Chain of the Mining Industry: The Case of Copper Mining." EUROPEAN RESEARCH STUDIES JOURNAL XXIV, Issue 1 (February 1, 2021): 204–25. http://dx.doi.org/10.35808/ersj/1958.
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Chen, Lihua, Liying Wang, and Yingjie Lan. "Auction models with resource pooling in modern supply chain management." Modern Supply Chain Research and Applications 1, no. 2 (May 13, 2019): 120–34. http://dx.doi.org/10.1108/mscra-01-2019-0001.
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Purpose In this paper, the main focus is on supply and demand auction systems with resource pooling in modern supply chain from a theoretical modeling perspective. The supply and demand auction systems in modern supply chains among manufacturers and suppliers serve as information sharing mechanisms. The purpose of this paper is to match the supply and demand such that a modern supply chain can achieve incentive compatibility and economic efficiency. The authors design such a supply and demand auction system that can integrate resources to efficiently match the supply and demand. Design/methodology/approach The authors propose three theoretic models of modern supply chain auctions with resource pooling according to the Vickrey auction principle. They are supply auction model with demand resource pooling, demand auction model with supply resource pooling, and double auction model with demand and supply resource pooling. For the proposed auction models, the authors present three corresponding algorithms to allocate resources in the auction process by linear programming, and study the incentive compatibility and define the Walrasian equilibriums for the proposed auction models. The authors show that the solutions of the proposed algorithms are Walrasian equilibriums. Findings By introducing the auction mechanism, the authors aim to realize the following three functions. First is price mining: auction is an open mechanism with multiple participants. Everyone has his own utility and purchasing ability. So, the final price reflects the market value of the auction. Second is dynamic modern supply chain construction: through auction, firm can find appropriate partner efficiently. Third is resources integration: in business practices, especially in modern supply chain auctions, auctioneers can integrate resources and ally buyers or sellers to gain more efficiency in auctions. Originality/value In the paper, the authors propose three theoretic models and corresponding algorithms of modern supply chain auctions with resource pooling according using the Vickrey auction principle, which achieves three functions: price mining, dynamic modern supply chain construction and resources integrating. Besides, these proposed models are much closer to practical settings and may have potential applications in modern supply chain management.
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Kang, Yong-Shin, Kyounghun Lee, Yong-Han Lee, and Ku-Young Chung. "RFID-based Supply Chain Process Mining for Imported Beef." Korean Journal for Food Science of Animal Resources 33, no. 4 (August 31, 2013): 463–73. http://dx.doi.org/10.5851/kosfa.2013.33.4.463.
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TIEN, JAMES M. "DATA MINING REQUIREMENTS FOR CUSTOMIZED GOODS AND SERVICES." International Journal of Information Technology & Decision Making 05, no. 04 (December 2006): 683–98. http://dx.doi.org/10.1142/s0219622006002167.
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Customized goods and services occur when their respective supply and demand chains are managed in a simultaneous and real-time manner. From a research perspective, we classify the methods that are employed in the management of these chains, based on whether supply and/or demand are flexible or fixed. Interestingly, our classification scheme highlights a critical and rewarding research area at which both supply and demand are flexible, thus manageable. Simultaneous management of supply and demand chains sets the stage for mass customization which is concerned with meeting the needs of an individualized customer market. Simultaneous and real-time management of supply and demand chains, in turn, set the stage for real-time mass customization (e.g. wherein a tailor first laser scans an individual's upper torso and then delivers a uniquely fitted jacket within a reasonable period, while the individual is waiting). The benefits of real-time mass customization cannot be over-stated as products and services become indistinguishable and are co-produced in real-time, resulting in an overwhelming economic advantage. Customized goods and services can only be achieved through sophisticated data mining techniques that can define the customization requirements from both a supply and a demand chain perspective, techniques that can obtain pertinent information from the mass of non-homogeneous data, in order to make informed customization decisions.
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Statsenko, Larissa, Alex Gorod, and Vernon Ireland. "A complex adaptive systems governance framework for regional supply networks." Supply Chain Management: An International Journal 23, no. 4 (June 11, 2018): 293–312. http://dx.doi.org/10.1108/scm-08-2017-0279.
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PurposeThis paper aims to propose an empirically grounded governance framework based on complex adaptive systems (CAS) principles to facilitate formation of well-connected regional supply chains that foster economic development, adaptability and resilience of mining regions.Design/methodology/approachThis study is an exploratory case study of the South Australian (SA) mining industry that includes 38 semi-structured interviews with the key stakeholders and structural analysis of the regional supply network (RSN).FindingsFindings demonstrate the applicability of the CAS framework as a structured approach to the governance of the mining industry regional supply chains. In particular, the findings exemplify the relationship between RSN governance, its structure and interconnectivity and their combined impact on the adaptability and resilience of mining regions.Research limitations/implicationsThe data set analysed in the current study is static. Longitudinal data would permit a deeper insight into the evolution of the RSN structure and connectivity. The validity of the proposed framework could be further strengthened by being applied to other industrial domains and geographical contexts.Practical/implicationsThe proposed framework offers a novel insight for regional policy-makers striving to create an environment that facilitates the formation of well-integrated regional supply chains in mining regions through more focussed policy and strategies.Originality/valueThe proposed framework is one of the first attempts to offer a holistic structured approach to governance of the regional supply chains based on CAS principles. With the current transformative changes in the global mining industry, policy-makers and supply chain practitioners have an urgent need to embrace CAS and network paradigms to remain competitive in the twenty-first century.
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Stefanovic, Nenad. "Collaborative predictive business intelligence model for spare parts inventory replenishment." Computer Science and Information Systems 12, no. 3 (2015): 911–30. http://dx.doi.org/10.2298/csis141101034s.
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In today?s volatile and turbulent business environment, supply chains face great challenges when making supply and demand decisions. Making optimal inventory replenishment decision became critical for successful supply chain management. Existing traditional inventory management approaches and technologies showed as inadequate for these tasks. Current business environment requires new methods that incorporate more intelligent technologies and tools capable to make fast, accurate and reliable predictions. This paper deals with data mining applications for the supply chain inventory management. It describes the unified business intelligence semantic model, coupled with a data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions and to deliver this information to relevant decision makers in a user-friendly manner. Experiments carried out with the real data set, from the automotive industry, showed very good accuracy and performance of the model which makes it suitable for collaborative and more informed inventory decision making.
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Lan, Yong, Ning Miao, Yan Ya Wang, Ming Yong Feng, and Dong Xia Liu. "Research on the Coordinated Development Mechanism of supply chain finance among countries under the development of Internet Era." E3S Web of Conferences 251 (2021): 02017. http://dx.doi.org/10.1051/e3sconf/202125102017.
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This paper studies the international supply chain finance among trading countries under the Internet financial environment. By introducing “One Belt And One Road” and “supply chain finance”, this paper analyzes the main supply chain finance models involved in the “One Belt And One Road” strategy, and uses clustering method to analyze and summarize the financial risks of supply chain. The analysis results are obtained through data mining reasoning. On this basis, it mainly uses Internet information technology, big data analysis and other technologies to deal with the risks in the international supply chain, and puts forward constructive suggestions to promote the coordinated development of supply chain finance among countries.
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Larin, Oleg, Dmitry Tarasov, Leonid Mirotin, Vladimir Rublev, and Denis Kapski. "Resilient Supply Chain Management Model." SHS Web of Conferences 93 (2021): 03005. http://dx.doi.org/10.1051/shsconf/20219303005.
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The strategy for the development of the supply chain should ensure a high level of fault tolerance of all links when exposed to adverse factors. The article analyzes the impact on the stability of the supply chain of two types of influences: failure and disruption. The low stability of the supply chain appears in the stoppage of work in case of any disruptions and failures. With moderate stability, disruptions do not give up a significant impact on the operation of the supply chain, and failures lead to an increase in operating costs to maintain the stability of work processes. With a high level of stability, failures can cause disruptions in the operations of individual links. In case of disruptions, response models are applied based on the control of process parameters, the subsequent analysis of the causes of disruptions and the development of measures to restore the normal operation of the links in the supply chain. Effective disruption response involves the use of proactive response models. For this, it is necessary to ensure flexibility and transparency of processes in all links of the supply chain based on digital services for material flow control and mining of big data.
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Lawrence, Kenneth D., Dinesh R. Pai, Ronald Klimberg, and Sheila M. Lawrence. "Enterprise Information System and Data Mining." International Journal of Business Intelligence Research 1, no. 3 (July 2010): 34–41. http://dx.doi.org/10.4018/jbir.2010070103.
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The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.
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Agarwal, Reshu, and Adarsh Dixit. "Water Supply Chain Resource Management in Cities Using Data Mining Techniques." International Journal of Information Retrieval Research 13, no. 1 (February 3, 2023): 1–14. http://dx.doi.org/10.4018/ijirr.317087.
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This paper presents a comparative research study between a number of data mining techniques, knowledge discovery tools, data analysis and software packages to be used in a Decision Support System (DSS) for Smart water supply chain resources management. The case study deals with the evaluation and comparative research of water quality of city water supply within New Delhi city area. In the case of New-Delhi water supply alternative actions for improving of water supply and quality are defined for efficient supply in distributed area. The real time water quality monitor uses given standards by Water Quality Index (WQI) and Statistical analysis done on it suggests the shortest path between supply station and local area distribution Centre by used WEKA mining tool (decision tree) and OLAP. The results show that the city water isn't supplied efficiently in the city and not within the standard quality criteria of (WHO) standards and Indian standards. Leanings and research challenges observed during this comparative study have also been enumerated.
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Lobsiger-Kägi, Evelyn, Luis López, Tobias Kuehn, Raoul Roth, Vicente Carabias, and Christian Zipper. "Social Life Cycle Assessment: Specific Approach and Case Study for Switzerland." Sustainability 10, no. 12 (November 23, 2018): 4382. http://dx.doi.org/10.3390/su10124382.
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This article proposes a specific social life cycle assessment (S-LCA) approach, to be applied in a case study on energy production in Switzerland. The aim of the present study is to describe the social conditions along the global supply chains and to compare them with the social situation in Switzerland. Therefore, a specific S-LCA methodology was developed that combines a relevance analysis with a performance reference point (PRP) assessment. The relevance analysis is carried out to identify the most relevant unit processes and S-LCA indicators and the Swiss PRPs (SPRPs) are designed to compare the social issues along the value chain to the situation in Switzerland. The methodology was applied to two life cycle stages of the copper supply chain (resource extraction and wire production), relevant for the production of renewable energy technologies, where it was found that the most critical step is mining in the Democratic Republic of the Congo (DRC) due to the artisanal way of mining. The proposed methodology offers a comprehensive overview for the analysis of supply chains and the comparison of different life cycle phases. Nevertheless, the methodology can result in a very coarse resolution with low validity. However, if the data and the results are treated transparently, they provide meaningful information about the social conditions along the supply chain.
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Jokonowo, Bambang, Jan Claes, Riyanarto Sarno, and Siti Rochimah. "Process Mining in Supply Chains: A Systematic Literature Review." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4626. http://dx.doi.org/10.11591/ijece.v8i6.pp4626-4636.
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Performance analysis and continuous process improvement efforts are often supported by the construction of process models representing the interactions of the partners in the supply chain. This study was conducted to determine the state of the art in the process mining field, specifically in the context of cross-organizational process. The Systematic Literature Review (SLR) method is used to review a collection of twenty-one papers that are classified according to the Artifact framework of Hevner, et al. and within the Process Mining framework of Van der Aalst. In the reviewed papers, the authors conducted a variety of techniques to establish the event log, which is then used to perform the process mining analysis. Eight of the reviewed papers focus on the definition of concepts or measures. Five of the papers describe models and other abstractions that are used as a theoretical basis for process mining in the context of supply chains. The majority twenty of papers describe some kind of informal method or formal algorithm to perform process mining analysis. Nine of the papers that propose a formal algorithm also present an accompanying software implementation. Eight papers discuss the data preparation challenges and twelve papers discuss process discovery techniques.
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Kurbatova, Anna, Petr Kurenkov, Anastasia Safronova, and Margarita Kuzina. "Rational Precious Metals Supply Schemes." E3S Web of Conferences 138 (2019): 01039. http://dx.doi.org/10.1051/e3sconf/201913801039.
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Rapidly developing social progress and new conditions for the functioning of all economic entities put industrial enterprises in a special, rigid economic framework. In a tough competitive environment, you can survive and develop only by orienting your business activities to a logistic approach. The gold mining industry plays a key role in the global economy. This industry has a high asset value, large-scale production lines and large infrastructure. One of the main tasks of strategic planning of gold mining enterprises is the choice of a rational marketing system. Planning and selection of schemes for the supply of precious metals is an important condition for the effective functioning of the marketing activities of gold mining enterprises. For these purposes, the need for improving logistics flows and creating an information system for sales planning is growing. The article explores the planning and selection of precious metals supply schemes using the example of PJSC “Vysochaishy”. Much attention paid to the construction of the company’s logistics flows. The main problems in the supply chain of PJSC “Vysochaishy” are identify. The algorithm of the process of introducing a new supply chain planning system for Vysochaishy PJSC is considered, and an improved strategic planning process for PJSC “Vysochaishy” is present. A new scheme of operation of the transport and logistics system for the direction of logistics flows of PJSC “Vysochaishy” proposed.
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Zuñiga, Raul, Thorsten Wuest, and Klaus-Dieter Thoben. "Comparing mining and manufacturing supply chain processes: challenges and requirements." Production Planning & Control 26, no. 2 (December 10, 2013): 81–96. http://dx.doi.org/10.1080/09537287.2013.855335.
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Wu, Mengna, Ke Liu, and Hua Yang. "Supply chain production and delivery scheduling based on data mining." Cluster Computing 22, S4 (February 19, 2018): 8541–52. http://dx.doi.org/10.1007/s10586-018-1894-8.
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Er Kara, Merve, Seniye Ümit Oktay Fırat, and Abhijeet Ghadge. "A data mining-based framework for supply chain risk management." Computers & Industrial Engineering 139 (January 2020): 105570. http://dx.doi.org/10.1016/j.cie.2018.12.017.
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Chatzidimitriou, Kyriakos C., and Andreas L. Symeonidis. "Data-Mining-Enhanced Agents in Dynamic Supply-Chain-Management Environments." IEEE Intelligent Systems 24, no. 3 (May 2009): 54–63. http://dx.doi.org/10.1109/mis.2009.51.
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Jowitt, Simon M. "COVID-19 and the Global Mining Industry." SEG Discovery, no. 122 (July 1, 2020): 33–41. http://dx.doi.org/10.5382/segnews.2020-122.fea-02.
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Abstract The world is currently experiencing a rapid and deep economic slowdown as a result of COVID-19 mitigation efforts. The depth and global nature of this recession, which could turn into a depression, suggests that this pandemic will significantly affect the demand for metals and the global mining sector. The majority of governments consider mining to be essential, meaning that the effect of mitigation on the mining industry and on metal production has been minimal to date. However, increases in metal stocks and decreases in metal prices suggest that the mining industry will be negatively affected by the COVID-19 crisis, at least in the short term. This paper presents an overview of the effects of COVID-19 mitigation on the mining sector to date. That includes variations in metal and commodity prices and stocks during the crisis and the outlining of two possible scenarios for COVID-19 related impacts. The first involves persistent supply-chain disruptions, where metal supply is restricted by logistical or COVID-19–related mitigation impacts on intermediates such as smelters and refiners. This restriction of supply could cause higher metal prices but also could cause issues with demand for ores and concentrates that negatively affect individual mining operations. More likely is a second slower demand growth scenario in which a global decrease in demand for metals causes further lowering of metal prices with associated negative economic impacts on mining operations. However, further research into global metal supply chains and the impact of the COVID-19 crisis on individual metals is needed. Key remaining unknowns include the influence of mitigation efforts on global metal supply and demand, the effect of these efforts on metal prices, and the geography of supply chains.
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Aranoglu, Fatih, Tulay Flamand, and Sebnem Duzgun. "Analysis of Artisanal and Small-Scale Gold Mining in Peru under Climate Impacts Using System Dynamics Modeling." Sustainability 14, no. 12 (June 16, 2022): 7390. http://dx.doi.org/10.3390/su14127390.
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In this paper, we propose a system dynamics (SD) model to examine the dynamics of an informal artisanal and small-scale gold mining (ASGM) supply chain that has interactions with the illegal gold supply chain in the Amazon rainforest region, Madre de Dios (MdD), Peru. In order to examine the system under climate impacts and validate the model, we run it under a flood scenario, which is one of the main climate impacts that causes disruption in mining activities. Our findings suggest that the dynamics of informal mines are highly affected by the illegal mercury supply, fuel supply, and availability of workers. In addition, the model under the flood scenario suggests that any external variable that could directly affect fuel and mercury supply would result in a disruption of informal and illegal gold production.
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Naderi, Reihaneh, Mohsen Shafiei Nikabadi, Akbar Alem-Tabriz, and Mir Saman Pishvaee. "Sustainable Coal Supply Chain Management Using Exergy Analysis and Genetic Algorithm." Management Systems in Production Engineering 29, no. 1 (December 2, 2020): 44–53. http://dx.doi.org/10.2478/mspe-2021-0006.
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Abstract Environmental threats of coal usage in the electricity production combined with the consumption of renewable and non-renewable resources had led to worldwide energy challenges. The cost of coal mining and economical and environmentally sustainable usage of mined coal could be optimized by efficient management of coal supply chain. This paper provides a mathematical model for improving coal supply chain sustainability including the cost of exergy destruction (entropy). In the proposed method, exergy analysis is used to formulate the model considering not only economic costs but also destructed exergy cost, while genetic algorithm is applied to efficiently solve the proposed model. In order to validate the proposed methodology, some numerical examples of coal supply chains are presented and discussed to show the usability of the proposed exergetic coal supply chain model and claim its benefits over the existing models. According to the results, the proposed method provides 17.6% saving in the consumed exergy by accepting 2.7% more economic costs. The presented model can be used to improve the sustainability of coal supply chain for either designing new projects or upgrading existing processes.
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Yu, Bao Qin, and Yue Zhang. "A Method for Evaluation of Green Degree in Green Supply Chain." Key Engineering Materials 450 (November 2010): 433–36. http://dx.doi.org/10.4028/www.scientific.net/kem.450.433.
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Green supply chain management is a method to reduce the ecological impact of the whole supply chain and make the most efficient use of resources. In this paper, the data mining (DM) technique is utilised to evaluate green degree in green supply chain. Then the process of the evaluation is given and a classification model of multivariate decision tree based on rough set is proposed. Finally, a simulation analysis is given to show the application of this method.
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Fu, Yu, Xiaoxi Sun, and Weiwei Wang. "The Optimization of Global Organizational Communication for Enterprise Supply Organization Management by Using Big Data Text Mining." Journal of Global Information Management 31, no. 3 (June 13, 2023): 1–17. http://dx.doi.org/10.4018/jgim.324608.
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The purpose is to optimize supply chain management (SCM) programs in global organizations and promote international economic development. This paper first discusses the development of cloud technology and the international organization supply chain under big data, artificial intelligence, the internet of things, mobile internet and cloud computing (BIIMC). Then, the application design concept of cloud technology in the global organization supply chain is discussed. Finally, based on the current international organization supply chain foundation, the management optimization program is designed using cloud technology and evaluated. The results show that the program performs prominently in the SCM of Taobao and Amazon.
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Jiang, Fang, Jie Zhao, and Han Lin Ge. "Research on the Application of Improved Association Rule Algorithm in Supply Chain Management." Advanced Materials Research 121-122 (June 2010): 309–13. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.309.
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Effective Supply Chain Management (SCM) approach must focus on flexible supply and production processes as well as rapidly respond to change of customer demands. To make up of existing drawbacks of association rule data mining algorithm, the paper brought out an improved algorithm and applies it in the product relativity analysis of SCM. Based on the algorithm, the solution of how the parts be arranged can achieve more cost-effective and higher profits can be achieved by data mining. The mining result can not only guide customers to correctly shopping, but also help manufacturers to design and produce goods, so that the companies can be in a better competitive position.
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Wu, Kuo Jui, Yan Bin, Maomao Ren, Ming-Lang Tseng, Qing Wang, and Anthony S. F. Chiu. "Reconfiguring a hierarchical supply chain model under pandemic using text mining and social media analysis." Industrial Management & Data Systems 122, no. 3 (January 20, 2022): 622–44. http://dx.doi.org/10.1108/imds-06-2021-0358.
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PurposeThis study is to reconfigure a hierarchical supply chain model utilizing databases and text files to understand future pathways due to COVID-19 pandemic has had a bullwhip effect, disrupting the global supply chain, and a mechanism is needed to address this disruptive event under pandemic uncertainties.Design/methodology/approachTo address this mechanism, this study employs bibliometric analysis and text mining to reconfigure a hierarchical supply chain model under pandemic conditions and associates it with social media to conduct an intuitive visual analysis.FindingsThe current academic concerns are related to an overconcentration on risk management and a data-driven approach, generating an enormous gap between the concerns of academics and those of the public. The evidence shows that for both countries with outstanding performance and those that need improvement, the efficiency in terms of preventing the spread of the pandemic should be promoted.Originality/valueThis study contributes to (1) reconfiguring a hierarchical supply chain model under pandemic uncertainties and (2) bridging theory and practice by offering comparable interrelated attributes to guide post-COVID-19 strategies in the supply chain. The findings are that the supply management approach and big data are attributes that involve the concerns of world public and academics under pandemic uncertainties.
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Benalcazar, Pablo, Jacek Kamiński, and Piotr W. Saługa. "The storage location problem in a coal supply chain: background and methodological approach." Gospodarka Surowcami Mineralnymi 33, no. 1 (March 1, 2017): 5–14. http://dx.doi.org/10.1515/gospo-2017-0009.
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Abstract In order to achieve two main objectives: (1) reduce risk and (2) increase the expected rate of return on invested capital, coal mining and coal trading companies have looked for new ways to improve their supply chain networks. Developments in the supply chain design and analysis have helped coal mining and coal trading companies expand their businesses, but at the same time, have forced them to consolidate their assets and downsize any underused storage facilities. In the coal mining industry, the problem of consolidation and downsizing becomes much more complicated due to the variety in quality parameters (hence many coal grades) involved, locational zones and different number of market players. Furthermore, for the last decade, the storage allocation and assignment problem has received a great deal of attention within the Logistics and Operation Research (OR) area. Yet, little attention has been given to the modeling of coal supply chains and the issue of strategic supply chain planning of coal-producing and coal-trading companies. Similar to the generic warehouse consolidation problem (WCP), in specific cases of coal-producing and coal-trading companies, storage facilities that are redundant or underutilized can be eliminated without causing a negative impact on customer and service levels. In this context, this paper discusses the background of the problem and proposes a mixed-integer linear programming (MILP) model mainly intended for storage and distribution network reconfiguration of a coal-producing or trading company. The model, which can be implemented in a high-level mathematical modelling system such as GAMS or AIMMS, captures the essential methodological features of a warehouse restructuring and/or consolidation problem and can be applied in practice.
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Puica, Elena. "Predictive Analytics Functionalities in Supply Chain Management." Proceedings of the International Conference on Business Excellence 17, no. 1 (July 1, 2023): 986–96. http://dx.doi.org/10.2478/picbe-2023-0090.
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Abstract This scientific paper presents a comprehensive analysis of the capabilities of IT solutions for predictive analytics in supply chain management. The study uses a multi-method approach, including a literature review, case studies by applying a machine learning model to technology solutions currently available on the market. The study examines the various software and technology platforms available today and their key features and functionalities, focusing in particular on Scripting, Data Mining, Algorithms, Data Analysis, Modeling, Data Interaction, Data Visualization, Reporting and Data Unification. The study also assesses the potential benefits and challenges associated with implementing these solutions in supply chain management. The results of the study provide valuable information for supply chain professionals, IT managers and researchers interested in the application of predictive analytics in this field. In addition, the paper also discusses the current trend and future direction in this field.
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Ghadge, Abhijeet, Maximilian Weiß, Nigel D. Caldwell, and Richard Wilding. "Managing cyber risk in supply chains: a review and research agenda." Supply Chain Management: An International Journal 25, no. 2 (November 17, 2019): 223–40. http://dx.doi.org/10.1108/scm-10-2018-0357.
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Purpose In spite of growing research interest in cyber security, inter-firm based cyber risk studies are rare. Therefore, this study aims to investigate cyber risk management in supply chain contexts. Design/methodology/approach Adapting a systematic literature review process, papers from interdisciplinary areas published between 1990 and 2017 were selected. Different typologies, developed for conducting descriptive and thematic analysis, were established using data mining techniques to conduct a comprehensive, replicable and transparent review. Findings The review identifies multiple future research directions for cyber security/resilience in supply chains. A conceptual model is developed, which indicates a strong link between information technology, organisational and supply chain security systems. The human/behavioural elements within cyber security risk are found to be critical; however, behavioural risks have attracted less attention because of a perceived bias towards technical (data, application and network) risks. There is a need for raising risk awareness, standardised policies, collaborative strategies and empirical models for creating supply chain cyber-resilience. Research limitations/implications Different types of cyber risks and their points of penetration, propagation levels, consequences and mitigation measures are identified. The conceptual model developed in this study drives an agenda for future research on supply chain cyber security/resilience. Practical implications A multi-perspective, systematic study provides a holistic guide for practitioners in understanding cyber-physical systems. The cyber risk challenges and the mitigation strategies identified support supply chain managers in making informed decisions. Originality/value To the best of the authors’ knowledge, this is the first systematic literature review on managing cyber risks in supply chains. The review defines supply chain cyber risk and develops a conceptual model for supply chain cyber security systems and an agenda for future studies.
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Tan, Kangye, and Fang Xu. "The Importance Evaluation of Weighted Supply Chain Network Nodes Based on Big Data Considering the Degree of Coupling and Coordination." Journal of Physics: Conference Series 2138, no. 1 (December 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2138/1/012001.
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Abstract Through big data and computer intelligent software, it is possible to quickly and accurately calculate the supply chain coordination relationship between global enterprises, and due to comprehensively, quickly and objectively assess the importance and stability of each node in the supply chain network where the company is located in the wave of supply chain globalization, it is convenient for decision makers to effectively and accurately identify the key nodes in the supply chain network system, thereby improving the supply chain of the stability and sustainability of the network. The paper proposes an improved comprehensive evaluation method of weighted node entropy weight based on coupling coordination, supply chain and complex network theories, taking into account the coupling and coordination of nodes in the supply chain network system, as well as the importance and connection of nodes. The importance of edges. With the mining and support of big data technology, this improved method can comprehensively and objectively evaluate the importance of nodes in the supply chain network. Finally, taking 2827 companies as examples, the experimental results verify the rationality and effectiveness of the method.
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Chu, Chih-Yuan, Kijung Park, and Gül E. Kremer. "A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks." Advanced Engineering Informatics 45 (August 2020): 101053. http://dx.doi.org/10.1016/j.aei.2020.101053.
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Rahimpour Golroudbary, Saeed, Nikita Krekhovetckii, Mohammad El Wali, and Andrzej Kraslawski. "Environmental Sustainability of Niobium Recycling: The Case of the Automotive Industry." Recycling 4, no. 1 (January 10, 2019): 5. http://dx.doi.org/10.3390/recycling4010005.
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The recycling of scrap is one of the common approaches aiming at reduction of mining-based production of critical metals and mitigation of their supply risk as well as processing-related environmental impact. The number of currently available end-of-life vehicles (ELVs) indicates—significant potential for critical metals recycling, especially niobium (Nb). Therefore, the quantification of environmental impact of niobium recovery starts to be an important issue in assessment of sustainability of large-scale recycling processes. In this paper, we assess energy consumption and greenhouse gas (GHG) emissions in individual stages of niobium supply chain in the automotive industry over the period 2010–2050. The different stages including mining, production and recycling are analyzed using dynamic simulation. The results show the majority of the consumed energy (45% of energy demand in niobium supply chain) is used in the primary production stage. This stage also contributes to 72% of total gas emissions of supply chain over the period 2010–2050. Mining of niobium consumes up to 36% of energy and generates ca. 21% of GHG emissions. While, in recycling stage, the secondary production of niobium requires 19% of supply chain energy and generates 7% of gas emissions. The detailed calculations show that recycling of niobium could save around 133–161 m GJ energy between 2010 and 2050. The recycling would also contribute to the reduction of 44–53 mt CO2-eq in the same period. It shows around 18% reduction of annual emissions between 2010 and 2050 thanks to reuse of niobium in secondary production rather than primary production.
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Augustine, Sabu, and Sajimon Abraham. "Analysis and Optimization of Supply Chain Traffic using Mobility Mining Techniques." International Journal of Computer Applications 54, no. 6 (September 25, 2012): 40–43. http://dx.doi.org/10.5120/8574-2311.
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Kalinowski, Thomas, Jason Matthews, and Hamish Waterer. "Scheduling of maintenance windows in a mining supply chain rail network." Computers & Operations Research 115 (March 2020): 104670. http://dx.doi.org/10.1016/j.cor.2019.03.016.
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He, Xuefeng, Xingzheng Ai, Yuewu Jing, and Yuanyuan Liu. "Partner selection of agricultural products supply chain based on data mining." Concurrency and Computation: Practice and Experience 28, no. 4 (November 10, 2015): 1246–56. http://dx.doi.org/10.1002/cpe.3625.
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Ngcobo, Abednigo, Chengedzai Mafini, and Welby Okoumba. "Extending Green Supply Chain Management to the Mining Sector in South Africa: Implications for Corporate Performance." African Journal of Inter/Multidisciplinary Studies 4, no. 1 (2022): 311–24. http://dx.doi.org/10.51415/ajims.v4i1.1044.
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The South African mining sector faces numerous challenges that limit the performance of firms, chief among them environmental management, labour, and operational issues. An important research gap exists on how the adoption of green supply chain management (GSCM) can contribute to the mitigation of some of these challenges. This study investigates the connection between GSCM, employee satisfaction, operational and relational efficiency, and enterprise performance in the mining sector in South Africa. The study employed a quantitative method using a cross-sectional survey design. A structured research questionnaire was used to collect data from supply chain professionals at two leading mining firms based in the North-West Province, South Africa. The collected data were analysed using a structural equation modelling procedure based on the partial least squares technique. The study reveals the positive influence of GSCM on employee job satisfaction, operational efficiency, relational efficiency, and enterprise performance. The study also confirms that relational efficiency and operational efficiency both exert a positive influence on enterprise performance. However, employee job satisfaction exerted no influence on enterprise performance. GSCM itself exerted a direct positive influence on enterprise performance. The application of GSCM in the mining sector should be recognised as part of the solutions to motivating supply chain professionals and stimulating important internal and external efficiencies, leading to the enhanced performance of the firm.
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Gao, Boren. "Construction of Knowledge Service Model of Guizhou Supply Chain Enterprises Based on Big Data." International Journal of Information Systems and Supply Chain Management 15, no. 3 (July 2022): 1–11. http://dx.doi.org/10.4018/ijisscm.290016.
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In the era of big data, "knowledge" scope is expanded. To realize the optimization of supply chain collaborative innovation in the era of big data, the platform of the collection, analysis, mining and application of massive data resources is needed. By analyzing the sources of big data of collaborative innovation of supply chain, a basic framework of knowledge innovation platform of Guizhou supply chain enterprises under the environment of big data is proposed. The effect of big data technology on supply chain logistics mode is analyzed, and the current situation of logistics industry modernization in Guizhou province is discussed. The mathematical model of big data processing is designed, and an example is simulated to validate the advantage of the proposed method.
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Qu, Qiao, Cheng Liu, and Xinzhong Bao. "E-Commerce Enterprise Supply Chain Financing Risk Assessment Based on Linked Data Mining and Edge Computing." Mobile Information Systems 2021 (May 19, 2021): 1–9. http://dx.doi.org/10.1155/2021/9938325.
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In recent years, the rapid development of information technology has affected the way the world economy operates. The emergence of e-commerce has greatly shortened the time and space distance between economic participants and maximized the sharing of resources. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. First, this article establishes an evaluation framework for the supply chain of e-commerce companies based on edge computing. Second, according to the distribution of the supply chain, this article adds the member’s predetermined quota, reputation, execution time, and other indicators as parameters to establish a fuzzy neural network model. On this basis, combined with the price regression model, the pricing plan is evaluated. The results show that the financing risk obtained by this model differs very little from the actual risk. The above-mentioned model constructs an e-commerce enterprise supply chain financing risk management model that adapts to the environment of the new era.
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Zong, Chun Mei, Hong Bin Wang, and Xue Juan Du. "The Application of Data Mining in the Food Manufacturing Industry." Applied Mechanics and Materials 20-23 (January 2010): 856–60. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.856.
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The paper first displays the feature of food, then points out the significance of putting into practice the Customer Relationship Management and supply chain administration in selling management, thereby draws out data mining. Finally, the paper describes the application of data mining in food.
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Saravanan, Nishant, Jessica Olivares-Aguila, and Alejandro Vital-Soto. "Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains." Sustainability 14, no. 23 (November 29, 2022): 15943. http://dx.doi.org/10.3390/su142315943.
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The current COVID-19 pandemic has virtually disrupted supply chains worldwide. Thus, supply chain research has received significant attention. While the impacts have been immeasurable, organizations have realized the need to design strategies to overcome such unexpected events. Therefore, the supply chain research landscape has evolved to address the challenges during the pandemic. However, available literature surveys have not explored the power of text analytics. Hence, in this review, an analysis of the supply chain literature related to the impacts of COVID-19 is performed to identify the current research trends and future research avenues. To discover the frequent topics discussed in the literature, bibliometric analysis (i.e., keyword co-occurrence network) and text mining tools (i.e., N-gram analysis and topic modeling) are employed for the whole corpus and the top-three contributing journals (i.e., Sustainability, International Journal of Logistics Management, Operations Management Research). Moreover, text analytics (i.e., Term Frequency-Inverse Document Frequency: TF-IDF) is utilized to discover the distinctive topics in the corpus and per journals. A total of 574 papers published up to the first semester of 2022 were collected from the Scopus database to determine the research trends and opportunities. The keyword network identified four clusters considering the implementation of digitalization to achieve resilience and sustainability, the usage of additive manufacturing during the pandemic, the study of food supply chains, and the development of supply chain decision models to tackle the pandemic. Moreover, the segmented keyword network analysis and topic modeling were performed for the top three contributors. Although both analyses draw the research concentrations per journal, the keyword network tends to provide a more general scope, while the topic modeling gives more specific topics. Furthermore, TF-IDF scores unveiled topics rarely studied, such as the implications of the pandemic on plasma supply chains, cattle supply chains, and reshoring decisions, to mention a few. Additionally, it was observed how the different methodologies implemented allowed to complement the information provided by each method. Based on the findings, future research avenues are discussed. Therefore, this research will help supply chain practitioners and researchers to identify supply chain advancements, gaps in the literature and future research streams.
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Gupta, Sachin, and Anurag Saxena. "Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach." Recent Patents on Computer Science 12, no. 3 (May 8, 2019): 171–79. http://dx.doi.org/10.2174/2213275911666181012121059.
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Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.