Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

Palabras clave: Software; inteligencia artificial; sector bananero; cadena de suministro; transformación digital

Resumen

La inteligencia artificial ofrece grandes oportunidades para la cadena de suministro, siendo esto una ventaja competitiva para el mercado cambiante de hoy en día. Este artículo tiene como objetivo identificar los impactos y oportunidades que puede ofrecer el software con inteligencia artificial para facilitar la operación y mejorar el desempeño de la cadena de suministro en el sector bananero de Colombia. La metodología de trabajo consta de seis pasos en donde se obtuvo un total de 72 investigaciones. Las fuentes de información fueron cuatro bases de datos. Como conclusión principal, la cadena de suministro del sector bananero tiene todo lo necesario para que se implementen soluciones basadas en software inteligente con el fin de lograr una adaptación, flexibilidad y sensibilidad al contexto y dominio de ejecución.

 

Artificial intelligence offers great opportunities for the supply chain, making it a competitive advantage for today's changing market. This paper aims to identify the impacts and opportunities that artificial intelligence software can offer to supply chain in the Colombian banana sector to facilitate the operation and improve the performance. The searching method consists of six steps getting 72 investigations finally. The sources of information were four databases. The main conclusion is the supply chain of the banana sector has everything for implementation of solutions based on intelligent software in order to achieve adaptation, flexibility and context awarenes and execution domain.

Biografía del autor/a

Isabel Cristina Arango-Palacio, Magister Logística Integral, Docente, Politécnico Colombiano Jaime Isaza Cadavid

Medellín, Docente

Citas

Dellino, G., Laudadio, T., Mari, R., Mastronardi, N., & Meloni, C. (2018). A reliable decision support system for fresh food supply chain management. International Journal of Production Research, 56(4), 1458-1485.

Muñoz-Pinzón, D. S., Polo-Roa, A., Sierra-Mantilla, E. J., & Rueda-Uribe, D. (2020). Modelación matemática en estudio de agro-cadenas: una revisión de literatura. REVISTA POLITÉCNICA, 16(31), 110-137.

Nasiri, M., Ukko, J., Saunila, M., & Rantala, T. (2020). Managing the digital supply chain: The role of smart technologies. Technovation, 102121.

Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: a review and impli-cations for future research. The International Journal of Logistics Management.

Zhao, J., Ji, M., & Feng, B. (2020). Smarter supply chain: a literature review and practices. Journal of Data, Information and Management, 1-16.

Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed frame-work for future research. Computers in Industry, 97, 157-177.

Augura (2019). Sector Bananero en Colombia. Obtenido de Sector Bananero en Colombia: Recupe-rado de https://sac.org.co/sector-bananero-colombiano-crecio-en-2018/

Ehie, I., & Ferreira, L. M. D. (2019). Conceptual Development of Supply Chain Digitalization Frame-work. IFAC-PapersOnLine, 52(13), 2338-2342.

MinAgricultura. (2018). Cadena de Banano Indicadores e Instrumentos 2018. Recuperado de https://www.minagricultura.gov.co/paginas/default.aspx

Giarratano, J., & Riley, G. (2001). Sistemas expertos: principios y programación. Thomson.

Cruz, P. P. (2011). Inteligencia artificial con aplicaciones a la ingeniería. Alfaomega.

Plinere, D., & Merkurvev, Y. (2019, November). Designing A Multi-Agent System For Improving Supply Chain Performance. In 2019 IEEE 7th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) (pp. 1-7). IEEE.

Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algo-rithms. Cambridge university press.

Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.

Korpela, K., Hallikas, J., & Dahlberg, T. (2017, January). Digital supply chain transformation to-ward blockchain integration. In proceedings of the 50th Hawaii international conference on system sci-ences.

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194.

Lezoche, M., Hernandez, J. E., Díaz, M. D. M. E. A., Panetto, H., & Kacprzyk, J. (2020). Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Computers in Industry, 117, 103187.

Yinyun, L. (2015, August). Enterprise Logistics Cost Research Based on Optimized Supply Chain Model. In 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applica-tions (ISDEA) (pp. 380-383). IEEE.

Zhang, Y., Liu, S., & Zhang, X. (2017). An optimized supply chain network model based on modi-fied genetic algorithm. Chinese Journal of Electronics, 26(3), 468-476

Yang, H., Chung, J. K., Chen, Y., Pan, Y., Mei, Z., & Sun, X. (2018). Ordering Strategy Analysis of Prefabricated Component Manufacturer in Construction Supply Chain. Mathematical Problems in Engi-neering, 2018.

Zhang, Y., Jiang, Y., Zhong, M., Geng, N., & Chen, D. (2016). Robust optimization on regional WCO-for-Biodiesel supply chain under supply and demand uncertainties. Scientific Programming, 2016

Akinade, O. O., & Oyedele, L. O. (2019). Integrating construction supply chains within a circular economy: An ANFIS-based waste analytics system (A-WAS). Journal of Cleaner Production, 229, 863-873.

Guo, F., Liu, Q., Liu, D., & Guo, Z. (2017). On production and green transportation coordination in a sustainable global supply chain. Sustainability, 9(11), 2071.

Kusolpuchong, S., Chusap, K., Alhawari, O., & Suer, G. (2019). A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains. Procedia Manufacturing, 39, 1139-1148.

Bank, M., Mazdeh, M., & Heydari, M. (2020). Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain. Uncertain Supply Chain Management, 8(1), 77-92.

Hamontree, C., Prompakdee, S., & Koiwanit, J. (2019, October). Resource Scheduling Problem in Distribution Center. In IOP Conference Series: Materials Science and Engineering (Vol. 639, No. 1, p. 012017). IOP Publishing.

Ahmadizar, F., Zeynivand, M., & Arkat, J. (2015). Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach. Applied Mathematical Modelling, 39(22), 7065-7081.

Boru, A., Dosdoğru, A. T., Göçken, M., & Erol, R. (2019). A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem. Symmetry, 11(5), 717.

Rabbani, M., Navazi, F., Farrokhi-Asl, H., & Balali, M. (2018). A sustainable transportation-location-routing problem with soft time windows for distribution systems. Uncertain Supply Chain Management, 6(3), 229-254

Moncayo-Martínez, L. A. (2017). Supply chain design using a modified IWD algorithm. Revista Fa-cultad de Ingeniería Universidad de Antioquia, (84), 9-16.

Gong, G., Deng, Q., Gong, X., Zhang, L., Wang, H., & Xie, H. (2018). A Bee Evolutionary Algo-rithm for Multiobjective Vehicle Routing Problem with Simultaneous Pickup and Delivery. Mathematical Problems in Engineering, 2018.

Rahman, A., Shahruddin, N. S., & Ishak, I. (2019, November). Solving the Goods Transportation Problem Using Genetic Algorithm with Nearest-Node Pairing Crossover Operator. In Journal of Phys-ics: Conference Series (Vol. 1366, No. 1, p. 012073). IOP Publishing.

Fitriana, R., Moengin, P., & Kusumaningrum, U. (2019, May). Improvement Route for Distribution Solutions MDVRP (Multi Depot Vehicle Routing Problem) using Genetic Algorithm. In IOP Conference Series: Materials Science and Engineering (Vol. 528, No. 1, p. 012042). IOP Publishing.

Frazzon, E. M., Albrecht, A., Pires, M., Israel, E., Kück, M., & Freitag, M. (2018). Hybrid approach for the integrated scheduling of production and transport processes along supply chains. International Journal of Production Research, 56(5), 2019-2035.

Zhou, L., Wang, X., Ni, L., & Lin, Y. (2016). Location-routing problem with simultaneous home de-livery and customer’s pickup for city distribution of online shopping purchases. Sustainability, 8(8), 828.

Gooran, A., Rafiei, H., & Rabani, M. (2018). Modeling risk and uncertainty in designing reverse lo-gistics problem. Decision Science Letters, 7(1), 13-24.

Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.

Zhou, Y., Chan, C. K., Wong, K. H., & Lee, Y. C. E. (2015). Intelligent optimization algorithms: a stochastic closed-loop supply chain network problem involving oligopolistic competition for multi-products and their product flow routings. Mathematical Problems in Engineering, 2015.

Afrouzy, Z. A., Paydar, M. M., Nasseri, S. H., & Mahdavi, I. (2018). A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product de-velopment. Journal of Industrial Engineering International, 14(1), 95-109.

Yun, Y., Chuluunsukh, A., & Gen, M. (2020). Sustainable Closed-Loop Supply Chain Design Prob-lem: A Hybrid Genetic Algorithm Approach. Mathematics, 8(1), 84.

Huang, R. H., Yu, T. H., & Lee, C. Y. (2018). Rolling Supply Chain Scheduling considering Suppli-ers, Production, and Delivery Lot-Size. Mathematical Problems in Engineering, 2018.

Huang, M., Yi, P., Guo, L., & Shi, T. (2016). A modal interval based genetic algorithm for closed-loop supply chain network design under uncertainty. IFAC-PapersOnLine, 49(12), 616-621.

Mohammadi, M., Tavakkoli-Moghaddam, R., Siadat, A., & Dantan, J. Y. (2016). Design of a relia-ble logistics network with hub disruption under uncertainty. Applied Mathematical Modelling, 40(9-10), 5621-5642.

Kumar, R. S., Choudhary, A., Babu, S. A. I., Kumar, S. K., Goswami, A., & Tiwari, M. K. (2017). Designing multi-period supply chain network considering risk and emission: A multi-objective ap-proach. Annals of Operations Research, 250(2), 427-461.

Xu, Y. P., & Liu, X. (2015, December). A New Genetic Type Method with Integrated Gradient Based Algorithm Method for Storage Optimization of Supply Chain. In 2015 International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 724-726). IEEE.

Dabibi, M., Moghaddam, B., & Kazemi, M. (2016). Locating distribution/service centers based on multi objective decision making using set covering and proximity to stock market. International Journal of Industrial Engineering Computations, 7(4), 635-648.

Wang, Y., Yuan, Y., Assogba, K., Gong, K., Wang, H., Xu, M., & Wang, Y. (2018). Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization. Journal of Advanced Transportation, 2018.

Saif-Eddine, A. S., El-Beheiry, M. M., & El-Kharbotly, A. K. (2019). An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Engineering Journal, 10(1), 63-76.

Wang, Y., Geng, X., Zhang, F., & Ruan, J. (2018). An immune genetic algorithm for multi-echelon inventory cost control of IOT based supply chains. IEEE Access, 6, 8547-8555.

Jahani, H., Alavifard, F., Ivanov, D., & Ghasemishabankareh, B. (2019). Managing the risk of sup-ply chain bankruptcy in supply chain network redesign. IFAC-PapersOnLine, 52(13), 2431-2436.

Shen, L., Li, F., Li, C., Wang, Y., Qian, X., Feng, T., & Wang, C. (2020). Inventory optimization of fresh agricultural products supply chain based on agricultural superdocking. Journal of Advanced Transportation, 2020.

Simić, D., Svirčević, V., & Simić, S. (2015). A hybrid evolutionary model for supplier assessment and selection in inbound logistics. Journal of Applied Logic, 13(2), 138-147.

Wang, H. S., Tu, C. H., & Chen, K. H. (2015). Supplier selection and production planning by using guided genetic algorithm and dynamic nondominated sorting genetic algorithm II.

Mahmud, S., Rahman, M., Hasan, M., & Hossain, M. (2016). Minimizing the bullwhip effect in a single product multistage supply chain using genetic algorithm. Uncertain Supply Chain Management, 4(2), 137-146.

Nakhjirkan, S., & Mokhatab Rafiei, F. (2017). An integrated multi-echelon supply chain network design considering stochastic demand: a genetic algorithm based solution. Promet-Traffic&Transportation, 29(4), 391-400.

Jing, Y., & Li, W. (2018). Integrated recycling-integrated production-distribution planning for de-centralized closed-loop supply chain. Journal of Industrial & Management Optimization, 14(2), 511-539.

Agrawal, A. K., & Yadav, S. (2020). Price and profit structuring for single manufacturer multi-buyer integrated inventory supply chain under price-sensitive demand condition. Computers & Industrial En-gineering, 139.

Gamasaee, R., Zarandi, M. F., & Turksen, I. B. (2015, August). A type-2 fuzzy intelligent agent based on sparse kernel machines for reducing bullwhip effect in supply chain. In 2015 Annual Confer-ence of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC) (pp. 1-7). IEEE.

Gonçalo, T. E. E., & Morais, D. C. (2015, October). Agent-based negotiation protocol for select-ing transportation providers in a retail company. In 2015 IEEE International Conference on Systems, Man, and Cybernetics (pp. 263-267). IEEE.

Hsieh, F. S. (2015, November). Scheduling sustainable supply chains based on multi-agent sys-tems and workflow models. In 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 252-259). IEEE.

Saoud, A., & Bellabdaoui, A. (2017, April). Model of distributed hierarchical framework for carrier collaboration. In 2017 International Colloquium on Logistics and Supply Chain Management (LOGISTI-QUA) (pp. 160-165). IEEE.

Fu, D., Zhang, H. T., Dutta, A., & Chen, G. (2019). A Cooperative Distributed Model Predictive Control Approach to Supply Chain Management. IEEE Transactions on Systems, Man, and Cybernet-ics: Systems

Fang, D., & Puqing, W. (2015, August). Simulating the Structural Evolution in Agri-food Supply Chain: An Agent-Based Model. In 2015 7th International Conference on Intelligent Human-Machine Sys-tems and Cybernetics (Vol. 1, pp. 214-219). IEEE.

Du, J., Sugumaran, V., & Gao, B. (2017). RFID and multi-agent based architecture for information sharing in prefabricated component supply chain. IEEE Access, 5, 4132-4139

Slimani, I., El Farissi, I., & Achchab, S. (2015, December). Artificial neural networks for demand forecasting: Application using Moroccan supermarket data. In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 266-271). IEEE.

Bousqaoui, H., Achchab, S., & Tikito, K. (2017, October). Machine learning applications in supply chains: An emphasis on neural network applications. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) (pp. 1-7). IEEE.

Li, Z., Li, G., Zhang, Y., Chen, J., & Dai, Y. (2019, October). Risk Early Warning Model for Distri-bution Network Material Supply Chain of Electric Power Enterprises. In 2019 12th International Confer-ence on Intelligent Computation Technology and Automation (ICICTA) (pp. 700-707). IEEE.

Yangyang, Z., Zhonghua, C., Quanyue, M., & Qian, W. (2019, October). Research on Supplier Se-lection Method Based on BP Neural Network. In 2019 IEEE 1st International Conference on Civil Avia-tion Safety and Information Technology (ICCASIT) (pp. 344-347). IEEE.

Lin, T. Y., Chuang, H. H. C., & Yu, F. (2018, August). Tracking Supply Chain Process Variability with Unsupervised Cluster Traversal. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Se-cure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data In-telligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 966-973). IEEE.

Xuefeng, H., Chi, Z., Yuewu, J., & Xingzheng, A. (2019, July). Risk Evaluation of Agricultural Product Supply Chain Based on BP Neural Network. In 2019 16th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-8). IEEE.

Cheng, Y., Peng, J., Gu, X., Zhang, X., Liu, W., Zhou, Z., ... & Huang, Z. (2020). An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain. Computers & Industrial Engineering, 139, 105834.

Liu, P., & Yi, S. (2016). New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment. Sustainability, 8(10), 960.

Pereira, M. M., & Frazzon, E. M. (2019). Towards a Predictive Approach for Omni-channel Retail-ing Supply Chains. IFAC-PapersOnLine, 52(13), 844-850.

Kara, M. E., Fırat, S. Ü. O., & Ghadge, A. (2018). A data mining-based framework for supply chain risk management. Computers & Industrial Engineering, 105570.

Besheli, S. F., Keshteli, R. N., Emami, S., & Rasouli, S. M. (2017). A fuzzy dynamic multi-objective multi-item model by considering customer satisfaction in a supply chain. Scientia Iranica. Transaction E, Industrial Engineering, 24(5), 2623-2639.

Ammar, O. B., Marian, H., & Dolgui, A. (2015, May). Supply planning for multi-levels assembly system under random lead times. In 15th IFAC Symposium on Information Control Problems in Manu-facturing—INCOM 2015 (Vol. 48, No. 3, pp. Pages-254). Elsevier Science, IFACPapersOnline. net.

Yahia, W. B., Ayadi, O., & Masmoudi, F. (2017). A fuzzy-based negotiation approach for collabo-rative planning in manufacturing supply chains. Journal of Intelligent Manufacturing, 28(8), 1987-2006.

Govindan, K., Mina, H., Esmaeili, A., & Gholami-Zanjani, S. M. (2020). An integrated hybrid ap-proach for circular supplier selection and closed loop supply chain network design under uncertainty. Journal of Cleaner Production, 242, 118317.

Publicado
2021-05-21
Cómo citar
Arango-Palacio, I. C. (2021). Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial. REVISTA POLITÉCNICA, 17(33), 47-63. https://doi.org/10.33571/rpolitec.v17n33a4
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