Technology management model aimed at potentiating the supply chain of chemical fertilizers

Authors

DOI:

https://doi.org/10.5281/zenodo.8260120

Keywords:

Technology Management, Supply Chain, Interpretive Structural Modeling, Chemical Companies, Fertilizers

Abstract

This article shows a technology management (GT) model called FIFE, made up of a set of 30 terms binding to the GT, interrelated with each other in the area of chemical fertilizer production, it was developed through the interpretive structural modeling methodology. The FIFE model contributes mainly to decision-making, policies, systems, procedures, people, alliances, clients, among others. It is made up of 2 large macro levels, the first called "Internal Factors" referring to what the company "does" and "how it does it", the second group called "Environmental Factors" that account for the benefits obtained by the organization. through interest groups; here it intertwines, links and meets short- and long-term needs. In addition, two flows are integrated, one designated "Dynamic Flow" in its trajectory is normally developed in any company in a daily and traditional way, as occurs from the preparation of a good to its reception by a client or consumer and another flow with reverse direction named "Communication Flow", has been that accumulated information data, feedback from the objective and / or subjective appreciation of customers, The FIFE technology management model developed, introduces a new strategic dimension in supply chains in chemical companies, in various aspects: as part of the improvement process, it predicts the research and development activities that are lacking in companies in the fertilizer production sector.

 

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Author Biographies

Juan Rodriguez, Universidad Nacional Experimental Politécnica Antonio José de Sucre (UNEXPO), Venezuela

Ingeniero Químico, Magister Scientarium en Ingeniería en Control de Procesos, Candidato a Doctor en el programa del Doctorado en Ciencias de la Ingeniería Mención Productividad en la Universidad Nacional Experimental Politécnica Antonio José de Sucre (UNEXPO), Profesor Asistente en el Departamento de Ingeniería Química, Sección de Ingeniería, UNEXPO,Barquisimeto, Venezuela. Email: jerodriguez@unexpo.edu.ve

Elisa Verruschi, Universidad Nacional Experimental Politécnica Antonio José de Sucre (UNEXPO), Venezuela

Ingeniero Químico, Doctora en Ingeniería Ambiental, Química y de los Materiales, Profesora Titular (Jubilada), Departamento de Ingeniería Química, Sección de Ingeniería, UNEXPO, Barquisimeto, Venezuela.

References

Abdallah, A. B., & Al-Ghwayeen, W. (2020). Green supply chain management and business performance: The mediating roles of environmental and operational performances. Business Process Management Journal, 26(2), 489-512. doi:https://doi.org/10.1108/BPMJ-03-2018-0091

Abdelmageed, S., & Zayed, T. (2020). A study of literature in modular integrated construction - Critical review and future directions. Journal of Cleaner Production(124044), 124044. doi:https://doi.org/10.1016/j.jclepro.2020.124044

Aboelmaged , M., & Hashem, G. (2019). Absorptive capacity and green innovation adoption in SMEs: The mediating effects of sustainable organisational capabilities. Journal of Cleaner Production, 220, 853-863. doi:https://doi.org/10.1016/j.jclepro.2019.02.150

Ahmad, S., Wong, K. Y., & Rajoo, S. (2019). Sustainability indicators for manufacturing sectors: A literature survey and maturity analysis from the triple-bottom line perspective. Journal of Manufacturing Technology Management, 30(2), 312-334. doi:https://doi.org/10.1108/JMTM-03-2018-0091

Ahmed, W., Ashraf, M., Khan, S., Kusi-Sarpong, S., Arhin, F., Kusi-Sarpong, H., & Arsalan , N. (2020). Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance. Operations Management Research, 13, 4–21.

Alamro, A., Awwad, A., & Anouze, A. (2018). The integrated impact of new product and market flexibilities on operational performance: The case of the Jordanian manufacturing sector. Journal of Manufacturing Technology Management, 29(7), 1163-1187. doi:https://doi.org/10.1108/JMTM-01-2017-0001

AL-Shboul, M., Garza-Reyes, J. A., & Kumar , V. (2018). Best supply chain management practices and high-performance firms: The case of Gulf manufacturing firms. International Journal of Productivity and Performance Management, 67(9), 1482-1509. doi:https://doi.org/10.1108/IJPPM-11-2016-0257

Ashby, A. (2018). Developing closed loop supply chains for environmental sustainability: Insights from a UK clothing case study. Journal of Manufacturing Technology Management, 29(4), 699-722. doi:https://doi.org/10.1108/JMTM-12-2016-0175

Attri, R., Dev, N., & Sharma, V. (2013). Interpretive Structural Modelling (ISM) approach: An Overview. Research Journal of Management Sciences, 2(2), 3-8.

Azzamouri, A., Essaadi, I., Elfirdoussi, S., & Giard, V. (2019). Interactive Scheduling Decision Support System a Case Study for Fertilizer Production on Supply Chain. Baghdadi, Y., Harfouche, A. (eds) ICT for a Better Life and a Better World, 30, 131-146.

Basole, R., & Nowak, M. (2018). Assimilation of tracking technology in the supply chain. Transportation Research Part E: Logistics and Transportation Review, 114, 350-370. doi:https://doi.org/10.1016/j.tre.2016.08.003

Benzidia, S., Makaoui , N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. doi:https://doi.org/10.1016/j.techfore.2020.120557

Bleischwitz, R., & De Giacomo, M. (2020). Business models for environmental sustainability: Contemporary shortcomings and some perspectives. Bus. Strat. Environ., 29(8), 3352–3369. doi:https://doi.org/10.1002/bse.2576

Blichfeldt, H., & Faullant, R. (2021). Performance effects of digital technology adoption and product & service innovation – A process-industry perspective. Technovation, 105(102275), 102275. doi:https://doi.org/10.1016/j.technovation.2021.102275

Boone, T., Ganeshan, R., Jain, A., & Sanders, N. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180. doi:https://doi.org/10.1016/j.ijforecast.2018.09.003

Bruns, B., Becker, T., Riese, J., Lier, S., & Werners, B. (2021). Efficient Production of Specialized Polymers with Highly Flexible Small-Scale Plants. Chemical Engineering Technology, 44(6). doi:https://doi.org/10.1002/ceat.202000591

Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121. doi:https://doi.org/10.1108/JMTM-03-2018-0099

Chen, C.-J. (2019). Developing a model for supply chain agility and innovativeness to enhance firms’ competitive advantage. Management Decision, 57(7), 1511-1534. doi:https://doi.org/10.1108/MD-12-2017-1236

Da Silva, H., Espíndola Ferreira, J., Kumar, V., & Garza-Reyes, J. (2020). Benchmarking of cleaner production in sand mould casting companies. Management of Environmental Quality, 31(5), 1407-1435. doi:https://doi.org/10.1108/MEQ-12-2019-0272

De Giacomo, M., & Bleischwitz, R. (2020). Business models for environmental sustainability: Contemporary shortcomings and some perspectives. Business Strategy and the Environment, 29(8), 3352–3369. doi:https://doi.org/10.1002/bse.2576

De Goey, H., Hilletofth, P., & Eriksson, L. (2019). Design-driven innovation: a systematic literature review. European Business Review, 31(1), 92-114. doi:https://doi.org/10.1108/EBR-09-2017-0160

de Nadae, J., Carvalho, M. M., & Rodrigues Vieira, D. (2019). Exploring the influence of environmental and social standards in integrated management systems on economic performance of firms. Journal of Manufacturing Technology Management, 30(5), 840-861. doi:https://doi.org/10.1108/JMTM-06-2018-0190

Diaz-Martinez, J., Ruiz-Ariza, J., Contreras-Salinas, J., & Hernández-Palma, H. (2017). Technology management to increase the efficiency of the supply chain. Journal of Theoretical and Applied Information Technology, 95(19), 5264-5272.

Dong, C., Akram, A., Andersson, D., Arnäs, P.-O., & Stefansson, G. (2021). The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends. The International Journal of Logistics Management, 32(2), 386-412. doi:https://doi.org/10.1108/IJLM-01-2020-0043

Fachini, R. F., Esposto, K. F., & Camargo, V. C. (2018). A framework for development of advanced planning and scheduling (APS) systems in glass container industry. Journal of Manufacturing Technology Management, 29(3), 570-587.

Famiyeh, S., Adaku, E., Amoako-Gyampah, K., Asante-Darko, D., & Amoatey, C. (2018). Environmental management practices, operational competitiveness and environmental performance: Empirical evidence from a developing country. Journal of Manufacturing Technology Management, 29(3), 588-607. doi:https://doi.org/10.1108/JMTM-06-2017-0124

Fratocchi, L., & Di Stefano, C. (2019). Does sustainability matter for reshoring strategies? A literature review. Journal of Global Operations and Strategic Sourcing, 12(3), 449-476. doi:https://doi.org/10.1108/JGOSS-02-2019-0018

Gaikwad, L., & Sunnapwar, V. (2020). An integrated Lean, Green and Six Sigma strategies: A systematic literature review and directions for future research. The TQM Journal, 32(2), 201-225. doi:https://doi.org/10.1108/TQM-08-2018-0114

Ganji, E. N., Shah, S., & Coutroubis , A. (2018). An examination of product development approaches within demand driven chains. Asia Pacific Journal of Marketing and Logistics, 30(5), 1183-1199. doi:https://doi.org/10.1108/APJML-02-2018-0042

Ghobakhloo, M., Azar, A., & Fathi, M. (2018). Lean-green manufacturing: the enabling role of information technology resource. Kybernetes, 47(9), 1752-1777. doi:https://doi.org/10.1108/K-09-2017-0343

Gonçalves Machado, C., Despeisse, M., Winroth, M., & Ribeiro da Silva, E. (2019). Additive manufacturing from the sustainability perspective: proposal for a self-assessment tool. Procedia CIRP, 81, 482-487. doi:https://doi.org/10.1016/j.procir.2019.03.123

Green, K. W., Inman, R. A., Sower, V. E., & Zelbst, P. J. (2019). Comprehensive supply chain management model. Supply Chain Management, 24(5), 590-603. doi:https://doi.org/10.1108/SCM-12-2018-0441

Green, K. W., Inman, R. A., Sower, V., & Zelbst , P. (2019). Comprehensive supply chain management model. Supply Chain Management, 24(5), 590-603. doi:https://doi.org/10.1108/SCM-12-2018-0441

Huo, B., Wang, K., & Zhang, Y. (2021). The impact of leadership on supply chain green strategy alignment and operational performance. Operations Management Research, 14, 152–165. doi:https://doi.org/10.1007/s12063-020-00175-8

Jawad, S., & Ledwith, A. (2021). Analyzing enablers and barriers to successfully project control system implementation in petroleum and chemical projects. International Journal of Energy Sector Management, 15(4), 789-819. doi:https://doi.org/10.1108/IJESM-08-2019-0004

Jin, Y., & Smith, J. T. (2021). Manufacturer power over suppliers: scale development and validation. Journal of Manufacturing Technology Management, 32(1), 199-218. doi:https://doi.org/10.1108/JMTM-03-2020-0100

Jonsson, P., & Holmström, J. (2016). Future of supply chain planning: closing the gaps between practice and promise. International Journal of Physical Distribution & Logistics, 46(1), 62-81. doi:https://doi.org/10.1108/IJPDLM-05-2015-0137

Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply Chain. Technological Forecasting and Social Change, 163, 120465. doi:https://doi.org/10.1016/j.techfore.2020.120465

Kaswan, M. S., & Rathi, R. (2021). An inclusive review of Green Lean Six Sigma for sustainable development: readiness measures and challenges. International Journal of Advanced Operations Management, 13(2), 129-166. doi:https://doi.org/10.1504/IJAOM.2021.116132

Khan, S. A., Chaabane, A., & Dweiri, F. (2019). A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach. Supply Chain Management, 24(3), 377-396. doi:https://doi.org/10.1108/SCM-06-2017-0197

Kumar, S., & Anbanandam, R. (2019). An integrated Delphi – fuzzy logic approach for measuring supply chain resilience: an illustrative case from manufacturing industry. Measuring Business Excellence, 23(3), 350-375. doi:https://doi.org/10.1108/MBE-01-2019-0001

Kumar, S., Luthra, S., Garg, D., Singh, S., & Mangla, S. (2018). An integrated approach to analyse requisites of product innovation management. International Journal of Business Innovation and Research, 16(1), 36-62. doi:https://doi.org/10.1504/IJBIR.2018.091081

Kumar, V., Lai, K.-K., Chang, Y.-H., Bhatt, P. C., & Su, F.-P. (2021). A structural analysis approach to identify technology innovation and evolution path: a case of m-payment technology ecosystem. Journal of Knowledge Management, 25(2), 477-499. doi:https://doi.org/10.1108/JKM-01-2020-0080

Lazaretti, K., Giotto, O. T., Sehnem, S., & Bencke, F. (2020). Building sustainability and innovation in organizations. Benchmarking: An International Journal, 27(7), 2166-2188. doi:https://doi.org/10.1108/BIJ-08-2018-0254

Li, H., Yang, M., & Evans, S. (2019). Classifying different types of modularity for technical system. International Journal of Technology Management, 81(1-2), 1–23. doi:https://doi.org/10.1504/IJTM.2019.101267

Miemczyk, J., & Luzzini, D. (2019). Achieving triple bottom line sustainability in supply chains: The role of environmental, social and risk assessment practices. International Journal of Operations & Production Management, 39(2), 238-259. doi:https://doi.org/10.1108/IJOPM-06-2017-0334

Mishra, R., Pundir, A. K., & Ganapathy, L. (2018). Empirical assessment of factors influencing potential of manufacturing flexibility in organization. Business Process Management Journal, 24(1), 158-182. doi:https://doi.org/10.1108/BPMJ-07-2016-0157

Modgil, S., Gupta, S., Sivarajah, U., & Bhushan, B. (2021). Big data-enabled large-scale group decision making for circular economy: An emerging market context. Technological Forecasting and Social Change, 166, 120607. doi:https://doi.org/10.1016/j.techfore.2021.120607

Narayanan, A. E., Sridharan, R., & Kumar, P. R. (2019). Analyzing the interactions among barriers of sustainable supply chain management practices: A case study. Journal of Manufacturing Technology Management, 30(6), 937-971. doi:https://doi.org/10.1108/JMTM-06-2017-0114

Nguyen, H., & Harrison, N. (2019). Leveraging customer knowledge to enhance process innovation: Moderating effects from market dynamics. Business Process Management Journal, 25(2), 307-322. doi:https://doi.org/10.1108/BPMJ-03-2017-0076

Núñez, E. (2011). Gestión tecnológica en la empresa: definición de sus objetivos fundamentales. Revista de Ciencias Sociales (RCS), 156-166.

Onufrey, K., & Bergek, A. (2020). Second wind for exploitation: Pursuing high degrees of product and process innovativeness in mature industries. Technovation, 89, 102068. doi:https://doi.org/10.1016/j.technovation.2019.02.004

Palma, R. R. (28 de 02 de 2022). ISM MicMac. Obtenido de Corrupción y Resultados en la Gestión de la Crisis del COVID: https://themys.sid.uncu.edu.ar/rpalma/MBA/ISM/Bases_ISM.html#:~:text=El%20modelado%20estructural%20interpretativo%20(ISM,hacer%20frente%20a%20situaciones%20complejas.

Park, H., Bellamy, M., & Basole, R. (2018). Structural anatomy and evolution of supply chain alliance networks: A multi-method approach. Journal of Operations Management, 63, 79-96.

Pereira, T., Kennedy, J., & Potgieter, J. (2019). A comparison of traditional manufacturing vs additive manufacturing, the best method for the job. Procedia Manufacturing, 30, 11-18. doi:https://doi.org/10.1016/j.promfg.2019.02.003

Poduval, P. S., Pramod, V., & Raj, J. (2015). Interpretive Structural Modeling (ISM) and its application in analyzing factors inhibiting implementation of Total Productive Maintenance (TPM). International Journal of Quality & Reliability Management, 32(3), 308-331.

Pooya, A., & Faezirad, M. (2017). A taxonomy of manufacturing strategies and production systems using self-organizing map. Journal of Industrial and Production Engineering, 34(4), 300-311. doi:https://doi.org/10.1080/21681015.2017.1305996

Prakash, S., Soni, G., & Rathore, A. (2017). A critical analysis of supply chain risk management content: a structured literature review. Journal of Advances in Management Research,, 14(1), 69-90. doi:https://doi.org/10.1108/JAMR-10-2015-0073

Prashar, A. (2020). Adopting Six Sigma DMAIC for environmental considerations in process industry environmen. The TQM Journal, 32(6), 1241-1261. doi:https://doi.org/10.1108/TQM-09-2019-0226

Rodríguez, J. E., & Verruschi, E. M. (2022). La gestión tecnológica en la cadena de suministros en empresas químicas: una revisión sobre términos vinculantes 2011-2021. Publicaciones en Ciencias y Tecnología, 16(2), 81-115.

Rojas, M. (2014). Gestión de la cadena de suminisitro en empresas del sector petroquímico. Maracaibo: Sebirluz.

Routroy, S., & Behera , A. (2017). Agriculture supply chain: A systematic review of literature and implications for future research. Journal of Agribusiness in Developing and Emerging Economies, 7(2), 275-302. doi:https://doi.org/10.1108/JADEE-06-2016-0039

Roy, V., Schoenherr, T., & Charan, P. (2018). The thematic landscape of literature in sustainable supply chain management (SSCM): A review of the principal facets in SSCM development. International Journal of Operations & Production Management, 38(4), 1091-1124. doi:https://doi.org/10.1108/IJOPM-05-2017-0260

Sacristán-Díaz, M., Garrido-Vega, P., & Moyano-Fuentes , J. (2018). Mediating and non-linear relationships among supply chain integration dimensions. International Journal of Physical Distribution & Logistics Management, 48(7), 698-723. doi:https://doi.org/10.1108/IJPDLM-06-2017-0213

Selvaraj , J. J., & Wesley, J. (2020). Modelling performance of supply chain system and its antecedents: an empirical study. Int. J. Bus. Inf. Syst., 34(3), 330-354. doi:https://doi.org/10.1504/IJBIS.2020.108661

Shen, B., Xu, X., Chan, H., & Choi, T.-M. (2021). Collaborative innovation in supply chain systems: Value creation and leadership structure. International Journal of Production Economics, 235, 108068. doi:https://doi.org/10.1016/j.ijpe.2021.108068

Singh, R., Kumar, P., & Chand, M. (2021). Evaluation of supply chain coordination index in context to Industry 4.0 environment. Benchmarking: An International Journal, 28(5), 1622-1637. doi:https://doi.org/10.1108/BIJ-07-2018-0204

Smith Elgesem, A., Skogen, E., Wang, X., & Fagerholt, K. (2018). A traveling salesman problem with pickups and deliveries and stochastic travel times: An application from chemical shipping. European Journal of Operational Research, 269(3), 844-859. doi:https://doi.org/10.1016/j.ejor.2018.02.023

Tarei, P. K., Thakkar, J., & Nag, B. (2020). Benchmarking the relationship between supply chain risk mitigation strategies and practices: an integrated approach. Benchmarking: An International Journal, 27(5), 1683-1715. doi:https://doi.org/10.1108/BIJ-12-2019-0523

Tarei, P., Thakkar, J., & Nag, B. (2018). A hybrid approach for quantifying supply chain risk and prioritizing the risk drivers: A case of Indian petroleum supply chain. Journal of Manufacturing Technology Management, 29(3), 533-569. doi:https://doi.org/10.1108/JMTM-10-2017-0218

Trianni, A., Cagno, E., Neri, A., & Howard, M. (2019). Measuring industrial sustainability performance: Empirical evidence from Italian and German manufacturing small and medium enterprises. Journal of Cleaner Production, 229, 1355-1376. doi:https://doi.org/10.1016/j.jclepro.2019.05.076

Tripathi, S., & Gupta , M. (2021). A framework for procurement process re-engineering in Industry 4.0. Business Process Management Journal, 27(2), 439-458. doi:https://doi.org/10.1108/BPMJ-07-2020-0321

Van Eck, N., & Waltman, L. (23 de Enero de 2023). https://www.vosviewer.com/getting-started. Obtenido de https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.19.pdf:

Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. doi:https://doi.org/10.1108/BPMJ-10-2019-0411

Wankhade, N., & Kundu, G. K. (2018). Supply chain performance management: a structured literature review. International Journal of Value Chain Management, 9(3), 209-240. doi:https://doi.org/10.1504/IJVCM.2018.093885

Wiech, M., Boffelli, A., Elbe, C., Carminati, P., Friedli, T., & Kalchschmidt, M. (2022). Implementation of big data analytics and Manufacturing Execution Systems: an empirical analysis in German-speaking countries. Production Planning & Control, 33(2-3), 261-276. doi:https://doi.org/10.1080/09537287.2020.1810766

Yunus, E. (2018). Leveraging supply chain collaboration in pursuing radical innovation. International Journal of Innovation Science, 10(3), 350-370. doi:https://doi.org/10.1108/IJIS-05-2017-0039

Zimmermann, R., Ferreira, L., & Carrizo Moreira, A. (2016). The influence of supply chain on the innovation process: a systematic literature review. Supply Chain Management,, 21(3), 289-304. doi:https://doi.org/10.1108/SCM-07-2015-0266

Published

2023-06-30

How to Cite

Rodriguez, J., & Verruschi, E. (2023). Technology management model aimed at potentiating the supply chain of chemical fertilizers. Gestión Y Gerencia, 17(1), 85-119. https://doi.org/10.5281/zenodo.8260120