3,775 research outputs found

    Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review

    Full text link
    [EN] The supplier evaluation and selection process is critical to increase the sustainability and resilience of the agri-food supply chain. Therefore, in this sector, it is necessary to consider sustainability and resilience criteria in the supplier evaluation and selection process. The use of arti¿cial intelligence techniques allows managing of a lot of information and the reduction of uncertainty for decision making. The objective of this article is to analyze articles that address the selection of suppliers in agrifood supply chains that pursue to increase their sustainability and resilience by using arti¿cial intelligence techniques to analyze the techniques and criteria used and draw conclusions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review. IFIP Advances in Information and Communication Technology. 598:501-510. https://doi.org/10.1007/978-3-030-62412-5_41S501510598Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S.: Quantitative models for sustainable supply chain management: developments and directions. Eur. J. Oper. Res. 233, 299–312 (2014)Ocampo, L.A., Abad, G.K.M., Cabusas, K.G.L., Padon, M.L.A., Sevilla, N.C.: Recent approaches to supplier selection: a review of literature within 2006–2016. Int. J. Integr. Supply Manage. 12, 22–68 (2018)Valipour, S., Safaei, A.: A resilience approach for supplier selection: using Fuzzy analytic network process and grey VIKOR techniques. J. Clean. Prod. 161, 431–451 (2017)Amindoust, A.: A resilient-sustainable based supplier selection model using a hybrid intelligent method. Comput. Ind. Eng. 126, 122–135 (2018)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Villalobos, J.R., Soto-Silva, W.E., González-Araya, M.C., González-Ramirez, R.G.: Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agenda. Comput. Electron. Agric. 167, 105092 (2019)Ristono, A., Santoso, P.B., Tama, I.P.: A literature review of design of criteria for supplier selection. J. Ind. Eng. Manage. 11, 680–696 (2018)Torres-Ruiz, A., Ravindran, A.R.: Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. J. Clean. Prod. 172, 4478–4493 (2018)Setak, M., Sharifi, S., Alimohammadian, A.: Supplier selection and order allocation models in supply chain management: a review. World Appl. Sci. J. 18, 55–72 (2012)Ravindran, A.R., Warsing, D.P.: Supplier selection models and methods. In: Supply Chain Engineering: Models and Applications. Taylor and Francis Group, Boca Raton, Florida (2013)De Boer, L., Labro, E., Morlacchi, P.: A review of methods supporting supplier selection. Eur. J. Purch. Supply Manage. 7, 75–89 (2011)De Felice, F., Deldoost, M.H., Faizollahi, M., Petrillo, A.: Performance measurement model for the supplier selection based on AHP. Int. J. Eng. Bus. Manag. 7, 1–13 (2015)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2014)Ali, A., Mahfouz, A., Arisha, A.: Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Manage. 22, 16–39 (2017)Verdecho, M., Alarcón-Valero, F., Pérez-Perales, D., et al.: A methodology to select suppliers to increase sustainability within supply chains. Cent. Eur. J. Oper. Res. (2020). https://doi.org/10.1007/s10100-019-00668-3Rabelo, L., Bhide, S., Gutierrez, E.: Artificial Intelligence: Advances in Research and Applications. Nova Science Publishers, Inc., Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States (2017)Denyer, D., Tranfield, D.: Producing a systematic review. In: The Sage Handbook of Organizational Research Methods. SAGE Publications Ltd., pp. 671–689 (2019)Chen, Y.-J.: Structured methodology for supplier selection and evaluation in a supply chain. Inf. Sci. (Ny) 181, 1651–1670 (2011)Hamdi, F., Ghorbel, A., Masmoudi, F., Dupont, L.: Optimization of a supply portfolio in the context of supply chain risk management: literature review. J. Intell. Manuf. 29(4), 763–788 (2015). https://doi.org/10.1007/s10845-015-1128-3Kumar, V., Srinivasan, S., Das, S.: Optimal solution for supplier selection based on SMART fuzzy case base approach. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems. SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems. ISIS 2014, Institute of Electrical and Electronics Engineers Inc., Department of Computer Science, IISJ Yokohama, Tokai Chiba, Japan, pp. 386–391 (2014)Jahani, A., Murad, M.A.A., bin Sulaiman, M.N., Selamat, M.H.: An agent-based supplier selection framework: Fuzzy case-based reasoning perspective. Strateg. Outsourcing 8, 180–205 (2015)Wang, Q.: Hybrid knowledge-based flexible supplier selection. In: 8th International Conference on Management of e-Commerce and e-Government. ICMeCG 2014. Institute of Electrical and Electronics Engineers Inc., Department of Information Management, Shanghai Finance University, Shanghai, China, pp. 235–239 (2014)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124, 252–264 (2010)Guo, F., Lu, Q.: Partner selection optimization model of agricultural enterprises in supply chain. Adv. J. Food Sci. Technol. 5, 1285–1291 (2013)Azadnia, A.H., Saman, M.Z.M., Wong, K.Y.: Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process. Int. J. Prod. Res. 53, 383–408 (2015)Miranda-Ackerman, M.A., Azzaro-Pantel, C., Aguilar-Lasserre, A.A.: A green supply chain network design framework for the processed food industry: application to the orange juice agrofood cluster. Comput. Ind. Eng. 109, 369–389 (2017)Hajikhani, A., Khalilzadeh, M., Sadjadi, S.J.: A fuzzy multi-objective multi-product supplier selection and order-allocation problem in supply chain under coverage and price considerations: an urban agricultural case study. Sci. Iran. 25, 431–449 (2018)Zhang, H., Cui, Y.: A model combining a Bayesian network with a modified genetic algorithm for green supplier selection. Simulation 95, 1165–1183 (2019)Yadav, S., Garg, D., Luthra, S.: Selection of third-party logistics services for internet of things-based agriculture supply chain management. Int. J. Logist. Syst. Manage. 35, 204–230 (2020)Yazdani, M., Wang, Z.X., Chan, F.T.S.: A decision support model based on the combined structure of DEMATEL, QFD and fuzzy values. Soft. Comput. 24(16), 12449–12468 (2020). https://doi.org/10.1007/s00500-020-04685-2Zhang, H., Feng, H., Cui, Y., Wang, Y.: A fuzzy Bayesian network model for quality control in O2O e-commerce. Int. J. Comput. Commun. Control 15(1), (2020). article number 1003. https://doi.org/10.15837/ijccc.2020.1.3783Amiri, S.A.H.S., Zahedi, A., Kazemi, M., Soroor, J., Hajiaghaei-Keshteli, M.: Determination of the optimal sales level of perishable goods in a two-echelon supply chain network. Comput. Ind. Eng. 139, 106156 (2020)Roy, S., et al.: A framework for sustainable supplier selection with transportation criteria. Int. J. Sustain. Eng. 13(2), 77–92 (2020)Parkouhi, S.V., Ghadikolaei, A.S., Lajimi, H.F.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Camarinha-Matos, L.M., Afsarmanesh, H., Galeano, N., Molina, A.: Collaborative networked organizations – concepts and practice in manufacturing enterprises. Comput. Ind. Eng. 57, 46–60 (2009)Lezoche, M., Panetto, H., Kacprzyk, J., Hernandez, J., Díaz, M.A.: Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103187 (2020)Alikhani, R., Torabi, S., Altay, N.: Strategic supplier selection under sustainability and risk criteria. Int. J. Prod. Econ. 208, 69–82 (2019

    Framework For Effective Resilience Managmenet Of Complex Supply Networks

    Get PDF
    In today\u27s environment with high global and complex supply chains for engineered products, the ability to assess and manage the resilience of supply chains is not a luxury but a fundamental prerequisite for business continuity and success. This is particularly true for firms with deep-tier supply chains, such as the automotive original equipment manufacturers (OEMs) and their suppliers. Automotive supply networks are particularly facing growing challenges due to their complexity, globalization, economic volatility, rapidly changing technologies, regulations, and environmental/political shocks. These risks and challenges can disrupt and halt operations in any section of the supply network. Given that supply chains have become quite lean in the 21st century with relatively little slack, the COVID-19 pandemic has fully exposed these vulnerabilities. According to Allianz\u27s Business Risk Report from 2014, half of all supply chain disruptions stemming from tier-2 and tier-3 suppliers. However, the industry\u27s supply network assessment practice is primarily limited to immediate (i.e., tier-1 ) suppliers with no real consideration for the deep-tiers. The added complication due to poor supplier relations is that there is no visibility to the upstream deeper-tiers of the supply network, which could lead to severe vulnerabilities and impose massive disruption costs. Our research goal is to enhance the resilience of deep-tier automotive supply networks through improved resilience assessment and management mechanisms. In this collaborative study with a global automotive OEM (Ford Motor Company), we seek to develop methods to assess and manage the resilience of deep-tier supply networks. This research considers the multi-dimensional nature of resilience management, focusing on metrics around cost efficiency, effective inventory management, demand fulfillment, capacity management, and delivery performance. We develop and evaluate our proposed resilience assessment and management framework with a real case study supply network for an automotive climate control system. The supply network contains 20 firms (nodes) located in various global regions and 21 connections (edges) between firms. The network includes three tiers of suppliers with different transportation modes, making the network a rich illustrative example for proposed resilience assessment and management methods and analysis. All inventory and shipping policies with related parameters have been defined and set for each supplier and their connections. The proposed resilience assessment framework relies on discrete-event simulation for effectiveness; computational efficiency is maintained by relying on modern open-source packages for modeling, optimization, and analysis. The framework starts by generating a digital supply network model that includes the focal firm and its suppliers and deeper-tiers based on the available visibility. Disruption scenarios, including disruption sources, frequency, and severity, are then efficiently generated using private and public regional risk sources. For illustrative purposes, we primarily relied on public secondary data sources. The secondary regional risk indices that we relied upon aggregate political, economic, legal, operational, and security risks for the given region. Finally, the digital supply network is simulated with an adequate number of replications for reliable assessment. In this research, discrete-event simulation is implemented using NetworkX and SimPy Python packages. We employ the network analysis techniques combined with discrete-event simulation informed by secondary data sources for improving the assessment framework. Our resilience assessment results confirm that visibility into the deeper-tiers of the supply network (through primary or secondary data sources) leads to a more accurate network resilience assessment. Finally, we offer a global sensitivity analysis procedure to determine the supply network players, parameters, and policies that most influence the network performance. We also propose an effective resilience management framework that efficiently leverages simulation-based optimization. For illustrative purposes, we considered the mitigation strategies typical in the automotive industry, such as dual sourcing, reserve capacities (at primary or secondary suppliers), and contracts with backup suppliers besides carrying safety stock. Sourcing and transportation mode decisions can be easily incorporated into the framework. The method seeks to minimize the cost of risk mitigation strategies while attaining the target resilience. The framework is flexible and can entertain other objectives and constraints. Given that simulation-based optimization methods can be computationally expensive, we employ surrogate models that relate supply network resilience performance to network design parameters within our mathematical programming formulation. Without loss of generality, the surrogate models are based on linear regression models that define the relationship between the focal firm and tier-1 suppliers\u27 resilience levels and network design decision variables. The imperfections of the regression models are accounted for in the formulation through constraints with slack (function of the RMSE of the regression model). We demonstrate that optimal resilience management would stem from jointly allocating safety buffers (e.g., capacity, inventory levels) across the network and not by independently applying a simplistic/static set of rules for all nodes/arcs. Our validation experiments with a real-world case study informed by secondary data from public data sources confirm the effectiveness and efficiency of the proposed supply network resilience management method

    Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

    Get PDF
    Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

    Sustainable supply chain management trends in world regions: A data-driven analysis

    Get PDF
    This study proposes a data-driven analysis that describes the overall situation and reveals the factors hindering improvement in the sustainable supply chain management field. The literature has presented a summary of the evolution of sustainable supply chain management across attributes. Prior studies have evaluated different parts of the supply chain as independent entities. An integrated systematic assessment is absent in the extant literature and makes it necessary to identify potential opportunities for research direction. A hybrid of data-driven analysis, the fuzzy Delphi method, the entropy weight method and fuzzy decision-making trial and evaluation laboratory is adopted to address uncertainty and complexity. This study contributes to locating the boundary of fundamental knowledge to advance future research and support practical execution. Valuable direction is provided by reviewing the existing literature to identify the critical indicators that need further examination. The results show that big data, closed-loop supply chains, industry 4.0, policy, remanufacturing, and supply chain network design are the most important indicators of future trends and disputes. The challenges and gaps among different geographical regions is offered that provides both a local viewpoint and a state-of-the-art advanced sustainable supply chain management assessment

    Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

    Get PDF
    The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

    Applications of Contemporary Management Approaches in Supply Chains

    Get PDF
    In today's rapidly changing business environment, strong influence of globalization and information technologies drives practitioners and researchers of modern supply chain management, who are interested in applying different contemporary management paradigms and approaches, to supply chain process. This book intends to provide a guide to researchers, graduate students and practitioners by incorporating every aspect of management paradigms into overall supply chain functions such as procurement, warehousing, manufacturing, transportation and disposal. More specifically, this book aims to present recent approaches and ideas including experiences and applications in the field of supply chains, which may give a reference point and useful information for new research and to those allied, affiliated with and peripheral to the field of supply chains and its management

    Supply chain resilience and risk management strategies and methods

    Get PDF
    Abstract. The changing global market due to Industry 4.0 and the recent pandemic effect has created a need for more responsiveness in an organization’s supply chain. Supply chain resilience offers the firm not only to avoid disruptions but also to withstand the losses due to a disruption. The objective of this research is to find out how resilience is defined so far in other literature and find out the strategies available to gain the resilience fit for an organization. First, in the literature review, the previous studies on resilience were studied to understand what supply chain resilience means. Then, the key results and findings are discussed and conclusions are presented. The research found some interesting strategies for gaining the resilience fit. The benefits and the stakeholders for each strategy are also pointed out. These strategies can be used according to the organization’s business strategy. These strategies aligned with the business strategy can make a huge difference to withstand potential disruption and gaining a competitive advantage against the market competitors

    Supplier selection for smart supply chain: An adaptive fuzzy-neuro approach

    Get PDF
    In recent years, companies have experienced international changes that have occurred as a result of technological advances, market globalization, or natural disasters. So, organizations are trying to improve their performance in order to be more competitive. In other words, organizations’ competitiveness highly depends on their suppliers. At present, companies need to consider and include so-called ‘resilience’, ‘sustainability’, and ‘smartness’ in the supplier’s selection to retain a competitive advantage. In this context, the purpose of this paper is to present an intelligent decision-making model for selecting the appropriate suppliers. For doing so, a set of criteria evaluation was determined to respond to the novel era circumstances. The suggested work is helpful for academics as well as professionals as it emphasizes the importance of resilient-sustainable supplier selection in the digital era.N/

    Exploration of Big Data in Procurement - Benefits and Challenges

    Get PDF
    Emergence of Big Data had positive implications in various industries and businesses. Big Data analytics provides the ability to harness massive amount of data for decision making purposes. One of the important use case of Big Data analytics is in supply chain management. Increased visibility, enhanced bargaining position in negotiations, better risk management and informed decision making are examples of benefits gained from Big Data analytics in supply chain. Although there are advances in analytics application throughout supply chain management, sourcing applications are lagging behind other functions of supply chain. The purpose of this study is to analyse use cases of exploiting Big Data for purchasing and supply purposes, in order to help companies having more visibility over the supply market. Data collection in this study was carried out through the use of semi-structured interviews which then were coded and categorized for comparison. The results pointed out that big data aids in identifying new suppliers. Additionally, having transparency over n-tier suppliers for managing risks were important for companies. Most of the companies are using descriptive analytics. However, they expected to have predictive analytics to become aware of market situation and gain better position in negotiations. Furthermore, this research showed that to prevent supply disruptions, the Big Data analytics should send timely warnings to managers. The main expectations from Big Data analytics are gaining transparency, automation of data collection and analysis, prediction, availability of new data sources, more efficient KPIs and better representation of data. The main hurdle in Big Data initiative is unintegrated and non-homogenous internal data
    corecore