124,741 research outputs found

    An Information Management Conceptual Approach for the Strategies Alignment Collaborative Process

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    [EN] This paper proposes an information management approach to deal with the strategies alignment collaborative process. Much attention has been given to the information management in collaborative networks (CNs), resulting in a wide variety of information management approaches and frameworks. The treatment, estimation, and collection of data are key issues that still need to be addressed, due to the complexity associated with the information exchange and the need to build trust relationships within the CN. In order to address this literature gap, this paper presents an approach to manage information in the specific collaborative process of strategies alignment. The approach is composed of a methodology, that enables to identify the roles participating in the application of the collaborative process, select the collaborative application context, determine the level of collaboration to be applied, and estimate and gather the data required to feed the strategies alignment process. The proposed information management approach bridges the conceptual model of strategies alignment process, with its application in real-world CNs.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872548 "Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs" (DIH4CPS) (http://dih4cps.eu/).Andres, B.; Poler, R. (2020). An Information Management Conceptual Approach for the Strategies Alignment Collaborative Process. Sustainability. 12(10):1-22. https://doi.org/10.3390/su12103959S1221210Camarinha-Matos, L. M., & Afsarmanesh, H. (2005). Collaborative networks: a new scientific discipline. Journal of Intelligent Manufacturing, 16(4-5), 439-452. doi:10.1007/s10845-005-1656-3Cheikhrouhou, N., Pouly, M., & Madinabeitia, G. (2013). Trust categories and their impacts on information exchange processes in vertical collaborative networked organisations. International Journal of Computer Integrated Manufacturing, 26(1-2), 87-100. doi:10.1080/0951192x.2012.681913Andres, B., & Poler, R. (2016). A decision support system for the collaborative selection of strategies in enterprise networks. Decision Support Systems, 91, 113-123. doi:10.1016/j.dss.2016.08.005Blome, C., Paulraj, A., & Schuetz, K. (2014). Supply chain collaboration and sustainability: a profile deviation analysis. International Journal of Operations & Production Management, 34(5), 639-663. doi:10.1108/ijopm-11-2012-0515Soosay, C. A., & Hyland, P. (2015). A decade of supply chain collaboration and directions for future research. Supply Chain Management: An International Journal, 20(6), 613-630. doi:10.1108/scm-06-2015-0217Chen, L., Zhao, X., Tang, O., Price, L., Zhang, S., & Zhu, W. (2017). Supply chain collaboration for sustainability: A literature review and future research agenda. International Journal of Production Economics, 194, 73-87. doi:10.1016/j.ijpe.2017.04.005Transforming Our World: The 2030 Agenda for Sustainable Development https://sustainabledevelopment.un.org/post2015/transformingourworldFonseca, L. M., Domingues, J. P., & Dima, A. M. (2020). Mapping the Sustainable Development Goals Relationships. Sustainability, 12(8), 3359. doi:10.3390/su12083359Horan, D. (2019). A New Approach to Partnerships for SDG Transformations. Sustainability, 11(18), 4947. doi:10.3390/su11184947Andres, B., & Marcucci, G. (2020). A Strategies Alignment Approach to Manage Disruptive Events in Collaborative Networks. Sustainability, 12(7), 2641. doi:10.3390/su12072641Andres, B., & Blanes, V. J. (2020). A Negotiation Approach to Support the Strategies Alignment Process in Collaborative Networks. Sustainability, 12(7), 2766. doi:10.3390/su12072766Provan, K. G., & Kenis, P. (2007). Modes of Network Governance: Structure, Management, and Effectiveness. Journal of Public Administration Research and Theory, 18(2), 229-252. doi:10.1093/jopart/mum015Pilbeam, C., Alvarez, G., & Wilson, H. (2012). The governance of supply networks: a systematic literature review. Supply Chain Management: An International Journal, 17(4), 358-376. doi:10.1108/13598541211246512Alemany, M. M. E., AlarcĂłn, F., Lario, F.-C., & Boj, J. J. (2011). An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Computers in Industry, 62(5), 519-540. doi:10.1016/j.compind.2011.02.002Schneeweiss, C. (2003). Distributed decision making in supply chain management. International Journal of Production Economics, 84(1), 71-83. doi:10.1016/s0925-5273(02)00381-xAlemany, M. M. E., Boj, J. J., Mula, J., & Lario, F.-C. (2009). Mathematical programming model for centralised master planning in ceramic tile supply chains. International Journal of Production Research, 48(17), 5053-5074. doi:10.1080/00207540903055701Saiz, J. J. A., Rodriguez, R. R., Bas, A. O., & Verdecho, M. J. (2010). An information architecture for a performance management framework by collaborating SMEs. Computers in Industry, 61(7), 676-685. doi:10.1016/j.compind.2010.03.012AndrĂ©s, B., & Poler, R. (2013). Relevant problems in collaborative processes of non-hierarchical manufacturing networks. Journal of Industrial Engineering and Management, 6(3). doi:10.3926/jiem.552Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Campuzano, F., Mula, J., & Peidro, D. (2010). Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, 161(11), 1530-1542. doi:10.1016/j.fss.2009.12.002Mula, J., Peidro, D., & Poler, R. (2014). Optimization Models for Supply Chain Production Planning Under Fuzziness. Studies in Fuzziness and Soft Computing, 397-422. doi:10.1007/978-3-642-53939-8_17Da Piedade Francisco, R., Azevedo, A., & Almeida, A. (2012). Alignment prediction in collaborative networks. Journal of Manufacturing Technology Management, 23(8), 1038-1056. doi:10.1108/17410381211276862Savastano, M., Amendola, C., Bellini, F., & D’Ascenzo, F. (2019). Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review. Sustainability, 11(3), 891. doi:10.3390/su1103089

    A methodology to select suppliers to increase sustainability within supply chains

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    [EN] Sustainability practice within supply chains remains in an early development phase. Enterprises still need tools that support the integration of sustainability strategy into their activity, and to align their sustainability strategy with the supplier selection process. This paper proposes a methodology using a multi-criteria technique to support supplier selection decisions by taking two groups of inputs that integrate sustainability performance: supply chain performance and supplier assessment criteria. With the proposed methodology, organisations will have a tool to select suppliers based on their development towards sustainability and on their alignment with the supply chain strategy towards sustainability. The methodology is applied to an agri-food supply chain to assess sustainability in the supplier selection process.The authors of this publication acknowledge the contribution of Project GV/2017/065 'Development of a decision support tool for the management and improvement of sustainability in supply chains', funded by the Regional Valencian Government. Also, the authors acknowledge 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 according to funding scheme H2020-MSCA-RISE-2015.Verdecho SĂĄez, MJ.; AlarcĂłn Valero, F.; PĂ©rez Perales, D.; Alfaro Saiz, JJ.; RodrĂ­guez RodrĂ­guez, R. (2021). A methodology to select suppliers to increase sustainability within supply chains. Central European Journal of Operations Research. 29:1231-1251. https://doi.org/10.1007/s10100-019-00668-3S1231125129Agarwal G, Vijayvargy L (2012) Green supplier assessment in environmentally responsive supply chains through analytical network process. In: Proceedings international multiconference of engineers and computer scientists, Hong KongAgeron B, Gunasekaran A, Spalanzani A (2012) Sustainable supply management: an empirical study. Int J Prod Econ 140(1):168–182Akarte MM, Surendra NV, Ravi B, Rangaraj N (2001) Web based casting supplier evaluation using analytical hierarchy process. J Oper Res Soc 52:511–522Alfaro Saiz JJ, RodrĂ­guez R, Ortiz Bas A, Verdecho MJ (2010) An information architecture for a performance management framework by collaborating SMEs. Comput Ind 61:676–685Alfaro JJ, Ortiz A, RodrĂ­guez R (2007) Performance measurement system for enterprise networks. Int J Prod Perform Manag 56(4):305–334Awasthi A, Govindan K, Gold S (2018) Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int J Prod Econ 195:106–117Azadnia AH, Ghadimi P, Zameri M, Saman M, Wong KY, Heavey C (2013) An integrated approach for sustainable supplier selection using fuzzy logic and fuzzy AHP. Appl Mech Mater 315:206–221Azimifard A, Moosavirad SH, Ariafar S (2018) Selecting sustainable supplier countries for Iran’s steel industry at three levels by using AHP and TOPSIS methods. Resour Pol 57:30–44Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Econ 124:252–264Bhagwat R, Sharma MK (2007) Performance measurement of supply chain management: a balanced scorecard approach. Comput Ind Eng 53(1):43–62Bititci US, Mendibil K, Martinez V, Albores P (2005) Measuring and managing performance in extended enterprises. Int J Oper Prod Manag 25(4):333–353Brewer PC, Speh TW (2000) Using the balanced scorecard to measure supply chain performance. J Bus Logist 21(1):75–93Bullinger HJ, KĂŒhner M, Hoof AV (2002) Analysing supply chain performance using a balanced measurement method. Int J Prod Res 40(15):3533–3543Chan FTS (2003) Interactive selection model for supplier selection process: an analytical hierarchy process approach. Int J Prod Res 41(15):3549–3579De Boer L, Labro E, Morlacchi P (2001) A review of methods supporting supplier selection. Eur J Purch Supply Manag 7(2):75–89Degraeve Z, Labro E, Roodhooft F (2000) An evaluation of supplier selection methods from a total cost of ownership perspective. Eur J Oper Res 125(1):34–58Dobos I, Vörösmarty G (2014) Green supplier selection and evaluation using DEA-type composite indicators. Int J of Prod Econ 157(11):273–278Dou Y, Sarkis J (2010) A joint location and outsourcing sustainability analysis for a strategic offshoring decision. Int J Prod Res 48(2):567–592Dyllick T, Hockerts K (2002) Beyond the business case for corporate sustainability. Bus Strategy Environ 11:130–141Falatoonitoosi E, Leman Z, Sorooshian S (2013) Modeling for green supply chain evaluation. Math Probl Eng 2013:1–9Farzad T, Rasid OM, Aidy A, Rosnah MY, Alireza E (2008) AHP approach for supplier evaluation and selection in a steel manufacturing company. JIEM 1(2):54–76Ferreira LMDF, Silva C, Garrido Azevedo S (2016) An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM). Benchmark Int J 23(6):1398–1422Figge F, Hahn T, Schaltegger S, Wagner M (2002) The sustainability balanced scorecard: linking sustainability management to business strategy. Bus Strat Env 11:269–284Folan P, Browne J (2005) Development of an extended enterprise performance measurement system. Prod Plan Control 16(6):531–544Freeman J, Chen T (2015) Green supplier selection using an AHP-entropy-TOPSIS framework. Supply Chain Manag 20:327–340Genovese A, Koh L, Bruno G, Esposito E (2013) Greener supplier selection: state of the art and some empirical evidence. Int J Prod Res 51(10):2868–2886Ghodsypour SH, O’Brien C (1998) A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. Int J Prod Econ 56–57:199–212Glock CH, Grosse EH, Ries JM (2017) Decision support models for supplier development: systematic literature review and research agenda. Int J Prod Econ 194:246–260Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod 47:345–354Govindan K, Rajendran S, Sarkis J, Murugesan P (2015) Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J Clean Prod 98:66–83Gunasekaran A, Patel C, Tirtiroglu E (2001) Performance measures and metrics in a supply chain environment. Int J Oper Prod Manag 21(1/2):71–87Ho W, Xu X, Dey PK (2010) Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur J Oper Res 202:16–24Hsu CW, Hu AH (2009) Applying hazardous substance management to supplier selection using analytic network process. J Clean Prod 17(2):255–264Hsu CW, Kuo TC, Chen SH, Hu AH (2013) Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. J Clean Prod 56:164–172Huan SH, Sheoran SK, Wang G (2004) A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Manag Int J 9(9):23–29Hutchins M, Sutherland JH (2008) An exploration of measures of social sustainability and their application to supply chain decisions. J Clean Prod 16(15):1688–1698Igarashi M, Boer L, Magerholm Fet A (2013) What is required for greener supplier selection? A literature review and conceptual model development. J Purch Supply Manag 19(4):247–263Jimenez-Jimenez D, MartĂ­nez-Costa M, Sanchez Rodriguez C (2019) The mediating role of supply chain collaboration on the relationship between information technology and innovation. J Knowl Manag 23(3):548–567Kaplan RS, Norton DP (1992) The balanced scorecard: measures that drive performance. Harvard Bus Rev 70(1):71–79Luthra S, Govindan K, Kannan D, Kumar Mangla S, Prakash Garg C (2017) An integrated framework for sustainable supplier selection and evaluation in supply chains. J Clean Prod 140:1686–1698Maestrini V, Luzzini D, Maccarrone P, Caniato F (2017) Supply chain performance measurement systems: a systematic review and research agenda. Int J Prod Econ 183A:299–315Masella C, Rangone A (2000) A contingent approach to the design of vendor selection systems for different types of co-operative customer/supplier relationships. Int J Oper Prod Manag 20(1):70–84Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97Mohammed A, Harris I, Govindan K (2019) A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. Int J Prod Econ 217:171–184Motevali-Haghighi S, Torabi SA, Ghasemi R (2016) An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). J Clean Prod 137:579–597Nawaz W, Koç M (2018) Development of a systematic framework for sustainability management of organizations. J Clean Prod 171:1255–1274Nie X (2013) Green suppliers selecting based on analytic hierarchy process for biotechnology industry. In: Zhong Z (ed) Proceedings of the international conference on information engineering and applications. Springer, London, pp 253–260Nielsen IE, Banaeian N, GoliƄska P, Mobli H, Omid M (2014) Green supplier selection criteria: from a literature review to a flexible framework for determination of suitable criteria. In: Golinska P (ed) Logistics operations, supply chain management and sustainability. Springer, Cham, pp 79–99Noci G (1997) Designing ‘green’ vendor rating systems for the assessment of a supplier’s environmental performance. Eur J Purch Supply Manag 3(2):103–114Petersen KJ, Handfield RB, Ragatz GL (2005) Supplier integration into new product development: coordinating product, process and supply chain design. J Oper Manag 23:371–388Pishchulov G, Trautrims A, Chesney T, Gold S, Schwab L (2019) The voting analytic hierarchy process revisited: a revised method with application to sustainable supplier selection. Int J Prod Econ 211:166–179Popovic T, Kraslawski A, Barbosa-PĂłvoa A, Carvalho A (2017) Quantitative indicators for social sustainability assessment of society and product responsibility aspects in supply chains. J Int Stud 10(4):9–36Qorri A, Mujki Z, Kraslawski A (2018) A conceptual framework for measuring sustainability performance of supply chains. J Clean Prod 189:570–584Reefke H, Trocchi M (2013) Balanced scorecard for sustainable supply chains: design and development guidelines. Int J Prod Perform Manag 62(8):805–826Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkSaaty RW (1987) The analytic hierarchy process: what it is and how it is used. Math Model 9(3–5):161–176Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98Saaty TL, Ozdemir MS (2003) Why the magic number seven plus or minus two. Math Comput Model 38(3–4):233–244Seuring S, MĂŒller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16:1699–1710Shaik M, Abdul-Kader W (2011) Green supplier selection generic framework: a multi-attribute utility theory approach. Int J Sustain Eng 4(1):37–56Shi P, Yan B, Shi S, Ke C (2015) A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Inf Technol Manag 16(1):39–49Superdecisions (2018) Tutorial on hierarchical decision models. Creative Decisions Foundation. https://www.superdecisions.com/sd_resources/v28_man03.pdf. Accessed 7 Jan 2018Thakkar J, Kanda A, Deshmukh S (2009) Supply chain performance measurement framework for small and medium scale enterprises. Benchmark Int J 16(5):702–723Theißen S, Spinler S (2014) Strategic analysis of manufacturer–supplier partnerships: an ANP model for collaborative CO2 reduction management. Eur J Oper Res 233(2):383–397Tseng ML, Lim M, Wong WP (2015) Sustainable supply chain management: a closed-loop network hierarchical approach. Ind Manag Data Syst 115(3):436–461Uysal F (2012) An integrated model for sustainable performance measurement in supply chain. Proc Soc Behav Sci 62:689–694Valenzuela L, Maturana S (2016) Designing a three-dimensional performance measurement system (SMD3D) for the wine industry: a Chilean example. Agric Syst 142:112–121Verdecho MJ, Alfaro-Saiz JJ, Rodriguez-Rodriguez R, Ortiz-Bas A (2012) A multi-criteria approach for managing inter-enterprise collaborative relationships. Omega 40:249–263Virender P, Jayant A (2014) A green supplier selection model for an agriculture-machinery industry. Int J Appl Eng Res 9(5):597–605Weber CA, Current JR, Benton WC (1991) Vendor selection criteria and methods. Eur J Oper Res 50(1):2–18Xu L, Kumar DT, Madan Shankar K, Kannan D, Chen G (2013) Analyzing criteria and sub-criteria for the corporate social responsibility-based supplier selection process using AHP. Int J Adv Manuf Technol 68(1–4):907–916Xu Z, Qin J, Liu J, MartĂ­nez L (2019) Sustainable supplier selection based on AHP Sort II in interval type-2 fuzzy environment. Inf Sci 483:273–293Zaklad A, McKnight R, Kosansky A, Piermarini J (2004) The social side of the supply chain. Ind Eng 36(2):40–44Zhe S, Wong NT, Lee LH (2013) Using data envelopment analysis for supplier evaluation with environmental considerations. In: International systems conference, OrlandoZimmer K, Fröhling M, Schultmann F (2016) Sustainable supplier management: a review of models supporting sustainable supplier selection, monitoring and development. Int J Prod Res 54(5):1412–144

    A conceptual framework for crop-based agri-food supply chain characterization under uncertainty

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    [EN] Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.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-2015Alemany DĂ­az, MDM.; Esteso, A.; Ortiz Bas, Á.; HernĂĄndez Hormazabal, JE.; FernĂĄndez, A.; Garrido, A.; Martin, J.... (2021). A conceptual framework for crop-based agri-food supply chain characterization under uncertainty. Studies in Systems, Decision and Control. 280:19-33. https://doi.org/10.1007/978-3-030-51047-3_2S1933280Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. Supply Chain Manage. 11, 379–384 (2006)Matopoulos, A., Vlachopoulou, M., Manthou, V., Manos, B.: A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry. Supply Chain Manage. 12, 177–186 (2007)Ahumada, O., Villalobos, J.R.: Application of planning models in the agri-food supply chain: a review. Eur. J. Oper. Res. 196, 1–20 (2009)Iakovou, E., Vlachos, D., Achillas, C., Anastasiadis, F.: A methodological framework for the design of green supply chains for the agrifood sector. Paper presented at the 2nd international conference on supply chains, Katerini, 5–7 Oct 2012Manzini, R., Accorsi, R.: The new conceptual framework for food supply chain assessment. J. Food Eng. 115, 251–263 (2013)Shukla, M., Jharkharia, S.: Agri-fresh produce supply chain management: a state-of-the-art literature review. Int. J. Oper. Prod. Manage. 33, 114–158 (2013)Lemma, Y., Kitaw, D., Gatew, G.: Loss in perishable food supply chain: an optimization approach literature review. Int. J. Sci. Eng. Res. 5, 302–311 (2014)Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosyst. Eng. 120, 47–64 (2014)Van der Vorst, J.G., Da Silva, C.A., Trienekens, J.H.: Agro-industrial Supply Chain Management: Concepts and Applications. FAO (2007)Hernandez, J., Mortimer, M., Patelli, E., Liu, S., Drummond, C., Kehr, E., Calabrese, N., Iannacone, R., Kacprzyk, J., Alemany, M.M.E., Gardner, D.: RUC-APS: enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems. In: 11th International Conference on Industrial Engineering and Industrial Management, Valencia, Spain (2017)Miles, M.B., Huberman, A.M.: Qualitative Data Analysis: An Expanded Sourcebook. Sage Publications, Thousand Oaks (1994)Alemany, M.M.E., AlarcĂłn, F., Lario, F.C., Boj, J.J.: An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Comput. Ind. 62, 519–540 (2011)Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M.: A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: a system dynamics approach. Comput. Electron. Agric. 93, 37–45 (2013)Kusumastuti, R.D., van Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)Zhang, W., Wilhelm, W.E.: OR/MS decision support models for the specialty crops industry: a literature review. Ann. Oper. Res. 190, 131–148 (2011)Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of mathematical models for supporting the order promising process under lack of homogeneity in product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Blanco, A.M., Masini, G., Petracci, N., Bandoni, J.A.: Operations management of a packaging plant in the fruit industry. J. Food Eng. 70, 299–307 (2005)Grillo, H., Alemany, M.M.E., Ortiz, A., Fuertes-Miquel, V.S.: Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Appl. Math. Model. 49, 255–278 (2017)Verdouw, C.N., Beulens, A.J.M., Trienekens, J.H., Wolferta, J.: Process modelling in demand-driven supply chains: a reference model for the fruit industry. Comput. Electron. Agric. 73, 174–187 (2010)Amorim, P., GĂŒnther, H., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. Econ. 138, 89–101 (2012)Nahmias, S.: Perishable inventory theory: a review. Oper. Res. 30, 680–708 (1982)Mowat, A., Collins, R.: Consumer behavior and fruit quality: supply chain management in an emerging industry. Supply Chain Manage. 5, 45–54 (2000)Kazaz, B., Webster, S.: The impact of yield-dependent trading costs on pricing and production planning under supply uncertainty. M&SOM Manuf. Serv. Oper. Manage. 13, 404–417 (2011)Van der Vorst, J.G.: Effective food supply chains: generating, modelling and evaluating supply chain scenarios (2000)Fuertes-Miquel, V.S., Cuenca, L., Boza, A., Guyon, C., Alemany, M.M.E.: Conceptual framework for the characterization of vegetable breton supply chain sustainability in an uncertain context. In: 12th International Conference on Industrial Engineering and Industrial Management, XXII Congreso de IngenierĂ­a de OrganizaciĂłn, Girona, Spain, 12–13 July 2018Kummu, M., de Moel, H., Porkka, M., Siebert, S., Varis, O., Ward, P.J.: Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. Sci. Total Environ. 438, 477–489 (2012)Hoekstra, S., Romme, J.: Integral Logistic Structures: Developing Customer-Oriented Goods Flow. Industrial Press Inc., New York (1992)Borodin, V., Bourtembourg, J., Hnaien, F., Labadie, N.: Handling uncertainty in agricultural supply chain management: a state of the art. Eur. J. Oper. Res. 254, 348–359 (2016)Handayati, Y., Simatupang, T.M., Perdana, T.: Agri-food supply chain coordination: the state-of-the-art and recent developments. Logist. Res. 8, 1–15 (2015)Mintzberg, H.: The Structuring of Organisations. Prentice-Hall, Upper Saddle River (1979)Keuning, D.: Grondslagen Van Het Management. Stenfert Kroese, Houten (1995) (in Dutch)Esteso, A., Alemany, M.M.E., Ortiz, A.: Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. Int. J. Prod. Res. (2018)Backus, G.B.C., Eidman, V.R., Dijkhuizen, A.A.: Farm decision making under risk and uncertainty. Neth. J. Agr. Sci. 45, 307–328 (1997)Esteso, A., Alemany, M.M.E., Ortiz, A.: Conceptual framework for managing uncertainty in a collaborative agri-food supply chain context. In: IFIP Advances in Information and Communication Technology, vol. 506, pp. 715–724 (2017)Mundi, I., Alemany, M.M.E., Poler, R., Fuertes-Miquel, V.S.: Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model. Int. J. Prod. Res. (2019)Grillo, H., Alemany, M.M.E., Ortiz, A., De Baets, B.: Possibilistic compositions and state functions: application to the order promising process for perishables. Int. J. Prod. Res. (2019)Soto-Silva, W.E., Nadal-Roig, E., GonzĂĄlez-Araya, M.C., Pla-Aragones, L.M.: Operational research models applied to the fresh fruit supply chain. Eur. J. Oper. Res. 251, 345–355 (2016)Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S.: Competitive supply chain network design: an overview of classifications, models, solution techniques and applications. Omega 45, 92–118 (2014)Banasik, A., Bloemhof-Ruwaard, J.M., Kanellopoulos, A., Claassen, G.D.H., van der Vorst, J.G.: Multi-criteria decision making approaches for green supply chains: a review. Flex. Serv. Manuf. J. 1–31 (2016)Paam, P., Berretta, R., Heydar, M., Middleton, R.H., GarcĂ­a-Flores, R., Juliano, P.: Planning models to optimize the agri-fresh food supply chain for loss minimization: a review. In: Reference Module in Food Science (2016)Soysal, M., Bloemhof-Ruwaard, J.M., Meuwissen, M.P., van der Vorst, J.G.: A review on quantitative models for sustainable food logistics management. Int. J. Food Syst. Dyn. 3, 136–155 (2012

    A social network-based organizational model for improving knowledge management in supply chains

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    Purpose: This paper aims to provide a social network-based model for improving knowledge management in multi-level supply chains formed by small and medium-sized enterprises (SMEs). Design/methodology/approach: This approach uses social network analysis techniques to propose and represent a knowledge network for supply chains. Also, an empirical experience from an exploratory case study in the construction sector is presented. Findings: This proposal improves the establishment of inter-organizational relationships into networks to exchange the knowledge among the companies along the supply chain and create specific knowledge by promoting confidence and motivation. Originality/value: This proposed model is useful for academics and practitioners in supply chain management to gain a better understanding of knowledge management processes, particularly for the supply chains formed by SMEs. © Emerald Group Publishing Limited.CapĂł-Vicedo, J.; Mula, J.; CapĂł I Vicedo, J. (2011). A social network-based organizational model for improving knowledge management in supply chains. Supply Chain Management: An International Journal. 16(5):379-388. doi:10.1108/13598541111155884S379388165Archer, N., Wang, S., & Kang, C. (2008). Barriers to the adoption of online supply chain solutions in small and medium enterprises. Supply Chain Management: An International Journal, 13(1), 73-82. doi:10.1108/13598540810850337Arend, R. J., & Wisner, J. D. (2005). Small business and supply chain management: is there a fit? Journal of Business Venturing, 20(3), 403-436. doi:10.1016/j.jbusvent.2003.11.003BERNARDES, E. S. (2010). THE EFFECT OF SUPPLY MANAGEMENT ON ASPECTS OF SOCIAL CAPITAL AND THE IMPACT ON PERFORMANCE: A SOCIAL NETWORK PERSPECTIVE. Journal of Supply Chain Management, 46(1), 45-55. doi:10.1111/j.1745-493x.2009.03185.xBORGATTI, S. P., & LI, X. (2009). ON SOCIAL NETWORK ANALYSIS IN A SUPPLY CHAIN CONTEXT. Journal of Supply Chain Management, 45(2), 5-22. doi:10.1111/j.1745-493x.2009.03166.xBorgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323(5916), 892-895. doi:10.1126/science.1165821Boschma, R. A., & ter Wal, A. L. J. (2007). Knowledge Networks and Innovative Performance in an Industrial District: The Case of a Footwear District in the South of Italy. Industry & Innovation, 14(2), 177-199. doi:10.1080/13662710701253441Cadilhon, J.J. and Fearne, A.P. (2005), “Lessons in collaboration: a case study from Vietnam”,Supply Chain Management Review, Vol. 9 No. 4, pp. 11‐12.Carter, C. R., Ellram, L. M., & Tate, W. (2007). THE USE OF SOCIAL NETWORK ANALYSIS IN LOGISTICS RESEARCH. Journal of Business Logistics, 28(1), 137-168. doi:10.1002/j.2158-1592.2007.tb00235.xChen, I. J., & Paulraj, A. (2004). Understanding supply chain management: critical research and a theoretical framework. International Journal of Production Research, 42(1), 131-163. doi:10.1080/00207540310001602865Cheng, J., Yeh, C., & Tu, C. (2008). Trust and knowledge sharing in green supply chains. Supply Chain Management: An International Journal, 13(4), 283-295. doi:10.1108/13598540810882170CHOI, T. Y., & WU, Z. (2009). TRIADS IN SUPPLY NETWORKS: THEORIZING BUYER-SUPPLIER-SUPPLIER RELATIONSHIPS. Journal of Supply Chain Management, 45(1), 8-25. doi:10.1111/j.1745-493x.2009.03151.xCrone, M., & Roper, S. (2001). Local Learning from Multinational Plants: Knowledge Transfers in the Supply Chain. Regional Studies, 35(6), 535-548. doi:10.1080/00343400120065705Egbu, C. O., Hari, S., & Renukappa, S. H. (2005). Knowledge management for sustainable competitiveness in small and medium surveying practices. Structural Survey, 23(1), 7-21. doi:10.1108/02630800510586871Fong, P. S. W., & Kwok, C. W. C. (2009). Organizational Culture and Knowledge Management Success at Project and Organizational Levels in Contracting Firms. Journal of Construction Engineering and Management, 135(12), 1348-1356. doi:10.1061/(asce)co.1943-7862.0000106Giannakis, M. (2008). Facilitating learning and knowledge transfer through supplier development. Supply Chain Management: An International Journal, 13(1), 62-72. doi:10.1108/13598540810850328Giuliani, E. (2007). The selective nature of knowledge networks in clusters: evidence from the wine industry. Journal of Economic Geography, 7(2), 139-168. doi:10.1093/jeg/lbl014Giuliani, E., & Bell, M. (2005). The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research Policy, 34(1), 47-68. doi:10.1016/j.respol.2004.10.008Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71-87. doi:10.1108/01443570110358468Hogarth‐Scott, S. (1999). Retailer‐supplier partnerships: hostages to fortune or the way forward for the millennium? British Food Journal, 101(9), 668-682. doi:10.1108/00070709910288865Javernick-Will, A. N., & Scott, W. R. (2010). Who Needs to Know What? Institutional Knowledge and Global Projects. Journal of Construction Engineering and Management, 136(5), 546-557. doi:10.1061/(asce)co.1943-7862.0000035Johnsen, T. E., Johnsen, R. E., & Lamming, R. C. (2008). Supply relationship evaluation: European Management Journal, 26(4), 274-287. doi:10.1016/j.emj.2007.10.001Kinder, T. (2003). Go with the flow—a conceptual framework for supply relations in the era of the extended enterprise. Research Policy, 32(3), 503-523. doi:10.1016/s0048-7333(02)00021-5Lambert, D. M., Cooper, M. C., & Pagh, J. D. (1998). Supply Chain Management: Implementation Issues and Research Opportunities. The International Journal of Logistics Management, 9(2), 1-20. doi:10.1108/09574099810805807Lamming, R., Caldwell, N., & Phillips, W. (2006). A Conceptual Model of Value-Transparency in Supply. European Management Journal, 24(2-3), 206-213. doi:10.1016/j.emj.2006.03.010Lamming, R., Caldwell, N., Phillips, W., & Harrison, D. (2005). Sharing Sensitive Information in Supply Relationships: European Management Journal, 23(5), 554-563. doi:10.1016/j.emj.2005.09.010Levy, M., Loebbecke, C., & Powell, P. (2003). SMEs, co-opetition and knowledge sharing: the role of information systems. European Journal of Information Systems, 12(1), 3-17. doi:10.1057/palgrave.ejis.3000439McCarthy, T. M., & Golicic, S. L. (2002). Implementing collaborative forecasting to improve supply chain performance. International Journal of Physical Distribution & Logistics Management, 32(6), 431-454. doi:10.1108/09600030210437960Malhotra, A., Gosain, S. and El Sawy, O.A. (2001), “Absorptive capacity configurations in supply chains: gearing for partner‐enabled market knowledge creation”,MIS Quarterly, Vol. 29 No. 1, pp. 145‐87.Matopoulos, A., Vlachopoulou, M., Manthou, V., & Manos, B. (2007). A conceptual framework for supply chain collaboration: empirical evidence from the agri‐food industry. Supply Chain Management: An International Journal, 12(3), 177-186. doi:10.1108/13598540710742491Mentzas, G., Apostolou, D., Kafentzis, K., & Georgolios, P. (2006). Inter-organizational networks for knowledge sharing and trading. Information Technology and Management, 7(4), 259-276. doi:10.1007/s10799-006-0276-8Morrison, A. (2008). Gatekeepers of Knowledgewithin Industrial Districts: Who They Are, How They Interact. Regional Studies, 42(6), 817-835. doi:10.1080/00343400701654178Morrison, A., & Rabellotti, R. (2009). Knowledge and Information Networks in an Italian Wine Cluster. European Planning Studies, 17(7), 983-1006. doi:10.1080/09654310902949265Newell, S., Bresnen, M., Edelman, L., Scarbrough, H., & Swan, J. (2006). Sharing Knowledge Across Projects. Management Learning, 37(2), 167-185. doi:10.1177/1350507606063441Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5(1), 14-37. doi:10.1287/orsc.5.1.14Ozkul, A., & Barut, M. (2009). Measuring supply chain relationships: a social network approach. International Journal of Integrated Supply Management, 5(1), 38. doi:10.1504/ijism.2009.026204RamĂ­rez-Pasillas, M. (2010). International trade fairs as amplifiers of permanent and temporary proximities in clusters. Entrepreneurship & Regional Development, 22(2), 155-187. doi:10.1080/08985620902815106Sanderson, J., & Cox, A. (2008). The challenges of supply strategy selection in a project environment: evidence from UK naval shipbuilding. Supply Chain Management: An International Journal, 13(1), 16-25. doi:10.1108/13598540810850283Seggie, S. H., Kim, D., & Cavusgil, S. T. (2006). Do supply chain IT alignment and supply chain interfirm system integration impact upon brand equity and firm performance? Journal of Business Research, 59(8), 887-895. doi:10.1016/j.jbusres.2006.03.005Soosay, C. A., Hyland, P. W., & Ferrer, M. (2008). Supply chain collaboration: capabilities for continuous innovation. Supply Chain Management: An International Journal, 13(2), 160-169. doi:10.1108/13598540810860994Vaaland, T. I., & Heide, M. (2007). Can the SME survive the supply chain challenges? Supply Chain Management: An International Journal, 12(1), 20-31. doi:10.1108/13598540710724374Venters, W., Cornford, T., & Cushman, M. (2005). Knowledge about Sustainability: SSM as a Method for Conceptualising the UK Construction Industryïżœs Knowledge Environment. Journal of Computing and Information Technology, 13(2), 137. doi:10.2498/cit.2005.02.05Wagner, B. A., Fillis, I., & Johansson, U. (2003). E‐business and e‐supply strategy in small and medium sized businesses (SMEs). Supply Chain Management: An International Journal, 8(4), 343-354. doi:10.1108/13598540310490107Walter, J., Lechner, C., & Kellermanns, F. W. (2007). Knowledge transfer between and within alliance partners: Private versus collective benefits of social capital. Journal of Business Research, 60(7), 698-710. doi:10.1016/j.jbusres.2007.01.026Wu, C. (2008). Knowledge creation in a supply chain. Supply Chain Management: An International Journal, 13(3), 241-250. doi:10.1108/13598540810871280Zheng, W., Yang, B., & McLean, G. N. (2010). Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management. Journal of Business Research, 63(7), 763-771. doi:10.1016/j.jbusres.2009.06.00

    A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain

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    [EN] The challenges of global economies foster supply chains to have to increase their processes of collaboration and dependence between their nodes, generating an increase in the level of vulnerability to possible impacts and interruptions in their operations that may affect their sustainability. This has developed an emerging area of interest in supply chain management, considering resilience management as a strategic capability of companies, and causing an increase in this area of research. Additionally, supply chains should deal with the three dimensions of sustainability (economic, environmental, and social dimensions) by incorporating the three types of objectives in their strategy. Thus, there is a need to integrate both resilience and sustainability in supply chain management to increase competitiveness. In this paper, a systematic literature review is undertaken to analyze resilience management and its connection to increase supply chain sustainability. In the review, 232 articles published from 2000 to February 2020 in peer-reviewed journals in the Scopus and ScienceDirect databases are analyzed, classified, and synthesized. With the results, this paper develops a conceptual framework that integrates the fundamental elements for analyzing, measuring, and managing resilience to increase sustainability in the supply chain. Finally, conclusions, limitations, and future research lines are exposed.This study was supported by the Valencian Government in Spain (Project AEST/2019/019).Zavala-AlcĂ­var, A.; Verdecho SĂĄez, MJ.; Alfaro Saiz, JJ. (2020). A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain. Sustainability. 12(16):1-38. https://doi.org/10.3390/su12166300S1381216Roberta Pereira, C., Christopher, M., & Lago Da Silva, A. (2014). Achieving supply chain resilience: the role of procurement. Supply Chain Management: An International Journal, 19(5/6), 626-642. doi:10.1108/scm-09-2013-0346Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK. Journal of Business Logistics, 31(1), 1-21. doi:10.1002/j.2158-1592.2010.tb00125.xPettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool. Journal of Business Logistics, 34(1), 46-76. doi:10.1111/jbl.12009Ponis, S. T., & Koronis, E. (2012). Supply Chain Resilience: Definition Of Concept And Its Formative Elements. Journal of Applied Business Research (JABR), 28(5), 921. doi:10.19030/jabr.v28i5.7234Seuring, S., & MĂŒller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699-1710. doi:10.1016/j.jclepro.2008.04.020Qorri, A., Mujkić, Z., & Kraslawski, A. (2018). A conceptual framework for measuring sustainability performance of supply chains. Journal of Cleaner Production, 189, 570-584. doi:10.1016/j.jclepro.2018.04.073Verdecho, M.-J., AlarcĂłn-Valero, F., PĂ©rez-Perales, D., Alfaro-Saiz, J.-J., & RodrĂ­guez-RodrĂ­guez, R. (2020). A methodology to select suppliers to increase sustainability within supply chains. Central European Journal of Operations Research, 29(4), 1231-1251. doi:10.1007/s10100-019-00668-3Edgeman, R., & Wu, Z. (2016). Supply chain criticality in sustainable and resilient enterprises. Journal of Modelling in Management, 11(4), 869-888. doi:10.1108/jm2-10-2014-0078Marchese, D., Reynolds, E., Bates, M. E., Morgan, H., Clark, S. S., & Linkov, I. (2018). Resilience and sustainability: Similarities and differences in environmental management applications. Science of The Total Environment, 613-614, 1275-1283. doi:10.1016/j.scitotenv.2017.09.086Ahern, J. (2012). Urban landscape sustainability and resilience: the promise and challenges of integrating ecology with urban planning and design. Landscape Ecology, 28(6), 1203-1212. doi:10.1007/s10980-012-9799-zRamezankhani, M. J., Torabi, S. A., & Vahidi, F. (2018). Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach. Computers & Industrial Engineering, 126, 531-548. doi:10.1016/j.cie.2018.09.054Shashi, Centobelli, P., Cerchione, R., & Ertz, M. (2019). Managing supply chain resilience to pursue business and environmental strategies. Business Strategy and the Environment, 29(3), 1215-1246. doi:10.1002/bse.2428Ivanov, D. (2017). Revealing interfaces of supply chain resilience and sustainability: a simulation study. International Journal of Production Research, 56(10), 3507-3523. doi:10.1080/00207543.2017.1343507Fahimnia, B., & Jabbarzadeh, A. (2016). Marrying supply chain sustainability and resilience: A match made in heaven. Transportation Research Part E: Logistics and Transportation Review, 91, 306-324. doi:10.1016/j.tre.2016.02.007Ruiz-Benitez, R., LĂłpez, C., & Real, J. C. (2019). Achieving sustainability through the lean and resilient management of the supply chain. International Journal of Physical Distribution & Logistics Management, 49(2), 122-155. doi:10.1108/ijpdlm-10-2017-0320Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. doi:10.1007/s10479-019-03182-6Khot, S. B., & Thiagarajan, S. (2019). Resilience and sustainability of supply chain management in the Indian automobile industry. International Journal of Data and Network Science, 339-348. doi:10.5267/j.ijdns.2019.4.002Roostaie, S., Nawari, N., & Kibert, C. J. (2019). Sustainability and resilience: A review of definitions, relationships, and their integration into a combined building assessment framework. Building and Environment, 154, 132-144. doi:10.1016/j.buildenv.2019.02.042Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40, 101074. doi:10.1016/j.jocs.2019.101074Carvalho, H., Duarte, S., & Cruz Machado, V. (2011). Lean, agile, resilient and green: divergencies and synergies. International Journal of Lean Six Sigma, 2(2), 151-179. doi:10.1108/20401461111135037Wang, Z., & Zhang, J. (2019). Agent-based evaluation of humanitarian relief goods supply capability. International Journal of Disaster Risk Reduction, 36, 101105. doi:10.1016/j.ijdrr.2019.101105Alikhani, R., Torabi, S. A., & Altay, N. (2019). Strategic supplier selection under sustainability and risk criteria. International Journal of Production Economics, 208, 69-82. doi:10.1016/j.ijpe.2018.11.018Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109-142. doi:10.1016/j.tre.2017.04.009Aboah, J., Wilson, M. M. J., Rich, K. M., & Lyne, M. C. (2019). Operationalising resilience in tropical agricultural value chains. Supply Chain Management: An International Journal, 24(2), 271-300. doi:10.1108/scm-05-2018-0204Statsenko, L., & Corral de Zubielqui, G. (2020). Customer collaboration, service firms’ diversification and innovation performance. Industrial Marketing Management, 85, 180-196. doi:10.1016/j.indmarman.2019.09.013Duong, L. N. K., & Chong, J. (2020). Supply chain collaboration in the presence of disruptions: a literature review. International Journal of Production Research, 58(11), 3488-3507. doi:10.1080/00207543.2020.1712491Bhamra, R., Dani, S., & Burnard, K. (2011). Resilience: the concept, a literature review and future directions. International Journal of Production Research, 49(18), 5375-5393. doi:10.1080/00207543.2011.563826Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk – Definition, measure and modeling. Omega, 52, 119-132. doi:10.1016/j.omega.2014.10.004Hohenstein, N.-O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90-117. doi:10.1108/ijpdlm-05-2013-0128Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133. doi:10.1016/j.ijpe.2015.10.023Ali, A., Mahfouz, A., & Arisha, A. (2017). Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Management: An International Journal, 22(1), 16-39. doi:10.1108/scm-06-2016-0197Umar, M., Wilson, M., & Heyl, J. (2017). Food Network Resilience Against Natural Disasters: A Conceptual Framework. SAGE Open, 7(3), 215824401771757. doi:10.1177/2158244017717570Stone, J., & Rahimifard, S. (2018). Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework. Supply Chain Management: An International Journal, 23(3), 207-238. doi:10.1108/scm-06-2017-0201Colicchia, C., Creazza, A., NoĂš, C., & Strozzi, F. (2019). Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA). Supply Chain Management: An International Journal, 24(1), 5-21. doi:10.1108/scm-01-2018-0003Annarelli, A., & Nonino, F. (2016). Strategic and operational management of organizational resilience: Current state of research and future directions. Omega, 62, 1-18. doi:10.1016/j.omega.2015.08.004Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21-42. doi:10.1016/j.omega.2017.07.005Kochan, C. G., & Nowicki, D. R. (2018). Supply chain resilience: a systematic literature review and typological framework. International Journal of Physical Distribution & Logistics Management, 48(8), 842-865. doi:10.1108/ijpdlm-02-2017-0099Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307. doi:10.1016/j.tre.2019.03.001Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207-222. doi:10.1111/1467-8551.00375Rousseau, D. M., Manning, J., & Denyer, D. (2008). 11 Evidence in Management and Organizational Science: Assembling the Field’s Full Weight of Scientific Knowledge Through Syntheses. Academy of Management Annals, 2(1), 475-515. doi:10.5465/19416520802211651Zimmer, K., Fröhling, M., & Schultmann, F. (2015). Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5), 1412-1442. doi:10.1080/00207543.2015.1079340Natarajarathinam, M., Capar, I., & Narayanan, A. (2009). Managing supply chains in times of crisis: a review of literature and insights. International Journal of Physical Distribution & Logistics Management, 39(7), 535-573. doi:10.1108/09600030910996251Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116(1), 12-27. doi:10.1016/j.ijpe.2008.07.008Kleindorfer, P. R., & Saad, G. H. (2009). Managing Disruption Risks in Supply Chains. Production and Operations Management, 14(1), 53-68. doi:10.1111/j.1937-5956.2005.tb00009.xChristopher, M., & Peck, H. (2004). Building the Resilient Supply Chain. The International Journal of Logistics Management, 15(2), 1-14. doi:10.1108/09574090410700275Wu, T., Huang, S., Blackhurst, J., Zhang, X., & Wang, S. (2013). Supply Chain Risk Management: An Agent-Based Simulation to Study the Impact of Retail Stockouts. IEEE Transactions on Engineering Management, 60(4), 676-686. doi:10.1109/tem.2012.2190986Fang, H., & Xiao, R. (2013). Resilient closed-loop supply chain network design based on patent protection. International Journal of Computer Applications in Technology, 48(1), 49. doi:10.1504/ijcat.2013.055566Gong, J., Mitchell, J. E., Krishnamurthy, A., & Wallace, W. A. (2014). An interdependent layered network model for a resilient supply chain. Omega, 46, 104-116. doi:10.1016/j.omega.2013.08.002Mari, S., Lee, Y., & Memon, M. (2014). Sustainable and Resilient Supply Chain Network Design under Disruption Risks. Sustainability, 6(10), 6666-6686. doi:10.3390/su6106666Bueno-Solano, A., & Cedillo-Campos, M. G. (2014). Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts. Transportation Research Part E: Logistics and Transportation Review, 61, 1-12. doi:10.1016/j.tre.2013.09.005Costantino, F., Gravio, G. D., Shaban, A., & Tronci, M. (2014). Replenishment policy based on information sharing to mitigate the severity of supply chain disruption. International Journal of Logistics Systems and Management, 18(1), 3. doi:10.1504/ijlsm.2014.062119Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014). A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39-49. doi:10.1016/j.eswa.2013.07.009Raj, R., Wang, J. W., Nayak, A., Tiwari, M. K., Han, B., Liu, C. L., & Zhang, W. J. (2015). Measuring the Resilience of Supply Chain Systems Using a Survival Model. IEEE Systems Journal, 9(2), 377-381. doi:10.1109/jsyst.2014.2339552LOH, H. S., & THAI, V. V. (2015). Cost Consequences of a Port-Related Supply Chain Disruption. The Asian Journal of Shipping and Logistics, 31(3), 319-340. doi:10.1016/j.ajsl.2015.09.001Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22-48. doi:10.1016/j.tre.2015.03.005Cardoso, S. R., Paula Barbosa-PĂłvoa, A., Relvas, S., & Novais, A. Q. (2015). Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty. Omega, 56, 53-73. doi:10.1016/j.omega.2015.03.008Salehi Sadghiani, N., Torabi, S. A., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95-114. doi:10.1016/j.tre.2014.12.015Dixit, V., Seshadrinath, N., & Tiwari, M. K. (2016). Performance measures based optimization of supply chain network resilience: A NSGA-II + Co-Kriging approach. Computers & Industrial Engineering, 93, 205-214. doi:10.1016/j.cie.2015.12.029Liu, F., Song, J.-S., & Tong, J. D. (2016). Building Supply Chain Resilience through Virtual Stockpile Pooling. Production and Operations Management, 25(10), 1745-1762. doi:10.1111/poms.12573Fahimnia, B., Jabbarzadeh, A., & Sarkis, J. (2018). Greening versus resilience: A supply chain design perspective. Transportation Research Part E: Logistics and Transportation Review, 119, 129-148. doi:10.1016/j.tre.2018.09.005Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52. doi:10.1016/j.tre.2015.12.009Azhmyakov, V., FernĂĄndez-GutiĂ©rrez, J. P., Gadi, S. K., & Pickl, S. (2016). A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization. IFAC-PapersOnLine, 49(31), 137-142. doi:10.1016/j.ifacol.2016.12.175Ivanov, D., Sokolov, B., Solovyeva, I., Dolgui, A., & Jie, F. (2016). Dynamic recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research, 54(23), 7245-7258. doi:10.1080/00207543.2016.1161253Jabbarzadeh, A., Fahimnia, B., Sheu, J.-B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149. doi:10.1016/j.trb.2016.09.004Babich, V., Burnetas, A. N., & Ritchken, P. H. (2007). Competition and Diversification Effects in Supply Chains with Supplier Default Risk. Manufacturing & Service Operations Management, 9(2), 123-146. doi:10.1287/msom.1060.0122Bogataj, D., Aver, B., & Bogataj, M. (2016). Supply chain risk at simultaneous robust perturbations. International Journal of Production Economics, 181, 68-78. doi:10.1016/j.ijpe.2015.09.009Wang, X., Herty, M., & Zhao, L. (2015). Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective. International Transactions in Operational Research, 23(4), 775-796. doi:10.1111/itor.12151Zeng, B., & Yen, B. P.-C. (2017). Rethinking the role of partnerships in global supply chains: A risk-based perspective. International Journal of Production Economics, 185, 52-62. doi:10.1016/j.ijpe.2016.12.004LĂŒcker, F., & Seifert, R. W. (2017). Building up Resilience in a Pharmaceutical Supply Chain through Inventory, Dual Sourcing and Agility Capacity. Omega, 73, 114-124. doi:10.1016/j.omega.2017.01.001Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176-200. doi:10.1016/j.tre.2017.02.004Kırılmaz, O., & Erol, S. (2017). A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks. Journal of Purchasing and Supply Management, 23(1), 54-65. doi:10.1016/j.pursup.2016.04.002Li, H., Pedrielli, G., Lee, L. H., & Chew, E. P. (2016). Enhancement of supply chain resilience through inter-echelon information sharing. Flexible Services and Manufacturing Journal, 29(2), 260-285. doi:10.1007/s10696-016-9249-3Otto, C., Willner, S. N., Wenz, L., Frieler, K., & Levermann, A. (2017). Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate. Journal of Economic Dynamics and Control, 83, 232-269. doi:10.1016/j.jedc.2017.08.001Rezapour, S., Farahani, R. Z., & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259(3), 1017-1035. doi:10.1016/j.ejor.2016.11.041Ledwoch, A., Yasarcan, H., & Brintrup, A. (2018). The moderating impact of supply network topology on the effectiveness of risk management. International Journal of Production Economics, 197, 13-26. doi:10.1016/j.ijpe.2017.12.013Al-Othman, W. B. E., Lababidi, H. M. S., Alatiqi, I. M., & Al-Shayji, K. (2008). Supply chain optimization of petroleum organization under uncertainty in market demands and prices. European Journal of Operational Research, 189(3), 822-840. doi:10.1016/j.ejor.2006.06.081Ivanov, D., Dolgui, A., & Sokolov, B. (2017). Scheduling of recovery actions in the supply chain with resilience analysis considerations. International Journal of Production Research, 56(19), 6473-6490. doi:10.1080/00207543.2017.1401747Das, K. (2019). Integrating Lean, Green, and Resilience Criteria in a Sustainable Food Supply Chain Planning Model. International Journal of Mathematical, Engineering and Management Sciences, 4(2), 259-275. doi:10.33889/ijmems.2019.4.2-022Das, K. (2018). Integrating resilience in a supply chain planning model. International Journal of Quality & Reliability Management, 35(3), 570-595. doi:10.1108/ijqrm-08-2016-0136Arora, V., & Ventresca, M. (2018). Modeling topologically resilient supply chain networks. Applied Network Science, 3(1). doi:10.1007/s41109-018-0070-7Almeida, J. F. de F., Conceição, S. V., Pinto, L. R., de Camargo, R. S., & JĂșnior, G. de M. (2018). Flexibility evaluation of multiechelon supply chains. PLOS ONE, 13(3), e0194050. doi:10.1371/journal.pone.0194050Mancheri, N. A., Sprecher, B., Deetman, S., Young, S. B., Bleischwitz, R., Dong, L., 
 Tukker, A. (2018). Resilience in the tantalum supply chain. Resources, Conservation and Recycling, 129, 56-69. doi:10.1016/j.resconrec.2017.10.018Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2017). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339-2360. doi:10.1080/00207543.2017.1370149Rozhkov, M., & Ivanov, D. (2018). CONTINGENCY PRODUCTION-INVENTORY CONTROL POLICY FOR CAPACITY DISRUPTIONS IN THE RETAIL SUPPLY CHAIN WITH PERISHABLE PRODUCTS. IFAC-PapersOnLine, 51(11), 1448-1452. doi:10.1016/j.ifacol.2018.08.311Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657-672. doi:10.1016/j.cie.2018.10.001Zavitsas, K., Zis, T., & Bell, M. G. H. (2018). The impact of flexible environmental policy on maritime supply chain resilience. Transport Policy, 72, 116-128. doi:10.1016/j.tranpol.2018.09.020Mitra, K., Gudi, R. D., Patwardhan, S. C., & Sardar, G. (2009). Towards resilient supply chains: Uncertainty analysis using fuzzy mathematical programming. Chemical Engineering Research and Design, 87(7), 967-981. doi:10.1016/j.cherd.2008.12.025LĂŒcker, F., Seifert, R. W., & Biçer, I. (2018). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. International Journal of Production Research, 57(4), 1238-1249. doi:10.1080/00207543.2018.15041

    The Future of Electronics Consumption and the Role of the Sustainable Consumer

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    The Future of Electronics Consumption and the Role of the Sustainable Consumer Jennifer D. Henderson University of South Alabama ABSTRACT A recent area of ethical, environmental, and social responsibility concern is in regard to smartphones and related electronic devices. Specifically, these products have been linked to generating large quantities of electronic waste (e-waste) and are increasingly produced under questionable working conditions. These social and environmental impacts can potentially be mitigated through the adoption of sustainably produced devices. However, although we are years into the modern sustainability movement, there still exists an attitude-behaviors gap where many consumers report that they are concerned about environmental and social responsibility issues, but they fail to translate these concerns into purchase behaviors. This paper examines the factors that could drive consumers to purchase electronic devices that have been manufactured by sustainable processes. A conceptual model is proposed that is comprised of two primary constructs: consciousness for sustainable consumption and likelihood to purchase. The value consciousness of consumers is proposed as a factor that moderates consumers’ likelihood to engage in purchase behaviors. While the constructs have been examined in various ways in previous research, they have not been joined together to study behavioral implications in the electronics industry. These insights will prove valuable to marketers as they navigate the new, muddy waters of closed-loop supply chains and sustainable devices. ABOUT THE AUTHOR Jennifer Henderson is a PhD student at the University of South Alabama. She is an Instructor of Information Systems and Decision Sciences at Louisiana State University. She received a Master of Business Administration degree from Southeastern Louisiana University, specializing in Management Information Systems. Her research interests include supply chain management, sustainability, digital disruption, and technology adoption

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

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    [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 artiÂżcial 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 artiÂżcial 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-AlcĂ­var, A.; Verdecho SĂĄez, 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-AlcĂ­var, 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., GonzĂĄlez-Araya, M.C., GonzĂĄlez-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., AlarcĂłn-Valero, F., PĂ©rez-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., DĂ­az, 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

    Eco‐Holonic 4.0 Circular Business Model to  Conceptualize Sustainable Value Chain Towards  Digital Transition 

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    The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

    Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

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    The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field
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