Big Data Analytics in the Agribusiness Supply Chain Management

Marisol Valencia-Cárdenas; Jorge Anibal Restrepo-Morales; Francisco Javier Día-Serna

doi:10.15649/2346030X.2583

Autores/as

Marisol Valencia-Cárdenas Tecnológico de Antioquia - Medellín, Colombia https://orcid.org/0000-0003-3135-3012
Jorge Anibal Restrepo-Morales Tecnológico de Antioquia - Medellín, Colombia https://orcid.org/0000-0001-9764-6622
Francisco Javier Día-Serna Universidad Nacional de Colombia - Bogotá, Colombia https://orcid.org/0000-0003-1057-1862

DOI:

https://doi.org/10.15649/2346030X.2583

Palabras clave:

Structural Equation Modelling, Poisson Regression Model, Big Data Analytics, Agroindustrial Business, perishable products

Resumen

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness.

Referencias

A. Montoya, I. Montoya, and O. Castellanos, Situación de la competitividad de las Pyme en Colombia : elementos actuales y retos, Agron. Colomb., vol. 28, no. 1, pp. 107–117, 2010.

M. D., Flores, R. M. Acosta, and R. G. Acolt, Estrategias y Retos en la incorporación de la Pequeña y Mediana Empresa al Comercio Internacional: un enfoque conceptual. RAN-Revista Academia & Negocios, Vol. 6, No., 1, 31-40. 2020.

R. A. Goldberg, A concept of a global food system and its use by private and public manager,. Agribusiness, vol. 1, no. 1, pp. 5–23, 1985.

A. Machado, De la Estructura Agraria al Sistema Agroindustrial. Universidad Nacional de Colombia, 2002.

G. Behzadi, M. J. O’Sullivan, T. L. Olsen, and A. Zhang. Agribusiness supply chain risk management: A review of quantitative decision models. Omega (United Kingdom), vol. 79, pp. 21–42, 2018.

D. F. Riedl, L. Kaufmann, C. Zimmermann, and J. L. Perols, Reducing uncertainty in supplier selection decisions: Antecedents and outcomes of procedural rationality. J. Oper. Manag., vol. 31, no. 1–2, pp. 24–36, Jan. 2013.

H. Sarimveis, P. Patrinos, C. D. Tarantilis, and C. T. Kiranoudis, Dynamic modeling and control of supply chain systems: A review. Comput. Oper. Res., vol. 35, no. 11, pp. 3530–3561, Nov. 2008.

F. Chahkoutahi and M. Khashei. A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting. Energy, vol. 140, pp. 988–1004, 2017.

D. K. Barrow and N. Kourentzes, Distributions of forecasting errors of forecast combinations: Implications for inventory management. Int. J. Prod. Econ., vol. 177, pp. 24–33, 2016.

M. D. Arango-Serna, W. Adarme Jaimes, and J. a. Zapata Cortés, Inventarios Colaborativos En La Optimización De La Cadena De Suministros, Dyna, vol. 80, no. 181, pp. 71–80, 2013.

R Core-Team, R: A language and enviroment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria., 2017.

D. Simchi-Levi, P. Kaminski, and E. Simchi-Levi, Designing and Managing the Supply Chain, 3rd ed. New York: McGraw-Hill, 2008.

M. Valencia, F. J. Díaz, and J. C. Correa, Planeación de inventarios con demanda dinámica . Una revisión del estado del arte, Revista DYNA, vol. 82, no. 190, pp. 182–191, 2015.

M. M. Simanca, L. A. Montoya, and C. A. Bernal, Gestión del Conocimiento en Cadenas Productivas: El Caso de la Cadena Láctea en Colombia, Inf. tecnológica, vol. 27, no. 3, pp. 93–106, 2016.

I. B. Suryaningrat, Raw Material Procurement on Agroindustrial Supply Chain Management: A Case Survey of Fruit Processing Industries in Indonesia, Agric. Agric. Sci. Procedia, vol. 9, pp. 253–257, 2016.

S. Ahmedova, Factors for Increasing the Competitiveness of Small and Medium- Sized Enterprises (SMEs) in Bulgaria, Procedia - Soc. Behav. Sci., vol. 195, pp. 1104–1112, 2015.

M. Grazia-Speranza, Trends in transportation and logistics, Eur. J. Oper. Res., vol. 264, no. 3, pp. 830–836, Feb. 2018.

G. Büyüközkan and F. Göçer, Digital Supply Chain: Literature review and a proposed framework for future research, Comput. Ind., vol. 97, pp. 157–177, 2018.

D. Simchi-Levi, X. Chen, and J. Bramel, “The logic of logistics, Theory, Algorithms Appl. Logist. Supply Chain Manag. Second Ed. Springer, 2005.

M., Papadimitrakis, et al. Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications. Renewable and Sustainable Energy Reviews . Vol 145, 111072. 2021.

S. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, Big Data in Smart Farming – A review,Agric. Syst., vol. 153, pp. 69–80, 2017.

H. Arslan, S. C. Graves, and T. A. Roemer, A single-Product Multiple Inventory Model Demand Classes,vol. 53, no. 9, pp. 1486–1500, 2007.

D. P. Morton and E. Popova, A Bayesian stochastic programming approach to an employee scheduling problem, IIE Trans., vol. 36, no. 2, pp. 155–167, Feb. 2004.

A. Correa and R. Gómez, Tecnologías de la Información en la Cadena de Suministro, DYNA, vol. 76, no. 157, pp. 37–48, 2009.

X. Lu, J. Song, and A. Regan, Inventory Approximate Updates : Planning with Forecast and Cost Error Bounds Solutions, vol. 54, no. 6, pp. 1079–1097, 2014.

G. M. Kopanos, L. Puigjaner, and M. C. Georgiadis, Simultaneous production and logistics operations planning in semicontinuous food industries, Omega, vol. 40, no. 5, pp. 634–650, 2012.

W. Puchalsky, G. Trierweiler, C. Pereira, R. Zanetti, and S. Coelho, Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: an analysis of the soybean sack price and perishable products demand, Int. J. Prod. Econ., vol. 203, no. June, pp. 174–189, 2018.

M. Valencia-Cárdenas, F.. Díaz-Serna, and J. Correa-Morales, Multi-product inventory modeling with demand forecasting and Bayesian optimization, DYNA, vol. 83, no. 198, pp. 236–244, 2016.

V. Gutiérrez and C. J. Vidal, Modelos de gestión de inventarios en cadenas de abastecimiento: Revisión de la literatura, Rev. Fac. Ing., vol. 43, pp. 134–149, 2008.

T.-M. Choi, D. Li, and H. Yan, Optimal two-stage ordering policy with Bayesian information updating, J. Oper. Res. Soc., vol. 54, no. 8, pp. 846–859, Aug. 2003.

U. W. Thonemann, Improving supply-chain performance by sharing advance demand information, Eur. J. Oper. Res., vol. 142, no. 1, pp. 81–107, Oct. 2002.

L. Gao, Collaborative forecasting, inventory hedging and contract coordination in dynamic supply risk management, Eur. J. Oper. Res., vol. 245, no. 1, pp. 133–145, 2015.

S. Huang, B. Ming, Q. Huang, G. Leng, and B. Hou. A case study on a combination NDVI forecasting model based on the entropy weight method. Water Resources Management, 31(11), 3667-3681. 2017.

J. D. Velásquez, I. Dyner R, and R. C. Souza, Tendencies In The Prediction And Estimation Of The Confidence Intervals Using Models Of Neuronal Networks Applied To Temporary Series, DYNA - Fac. Minas la UNAL Medellín, vol. 73, no. 149, pp. 141–147, 2006.

W. Gao, V. Sarlak, M. R. Parsaei, and M. Ferdosi, Combination of fuzzy based on a meta-heuristic algorithm to predict electricity price in an electricity markets, Chem. Eng. Res. Des., vol. 131, pp. 333–345, Mar. 2018.

F. L. Chu, Using a logistic growth regression model to forecast the demand for tourism in Las Vegas, Tour. Manag. Perspect., vol. 12, pp. 62–67, 2014.

F. Chahkoutahi and M. Khashei, A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting, Energy, vol. 140, pp. 988–1004, 2017.

S. Wang, Exponential Smoothing for Forecasting and Bayesian Validation of Computer Models, no. December. 2006.

B. Bowerman, A. Koehler, and R. O’Connell, Pronósticos, series de tiempo y regresión: un enfoque aplicado. México, DF:. CENCAGE Learning, 2007.

F. Zhang, C. Deb, S. E. Lee, J. Yang, and K. W. Shah, Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique, Energy Build., vol. 126, pp. 94–103, 2016.

B. West, K. Welch, and A. Galecki, Linear mixed models A practical guide using Statistical Software, Taylor and. 2007.

W. Gao, V. Sarlak, M. R. Parsaei, and M. Ferdosi, Combination of fuzzy based on a meta-heuristic algorithm to predict electricity price in an electricity markets, Chem. Eng. Res. Des., vol. 131, pp. 333–345, 2018.

V., Dorie, and M. V., Dorie, Package ‘blme’. Bayesian Linear Mixed-Effects Models. URL https://CRAN. R-project. org/package= blme. pp. 1–8, 2015.

G. Petris, S. Petrone, and P. Campagnoli, Dynamic Linear Models with R. 2009.

G. Petris, An R Package for Dynamic Linear Models, J. Stat. Softw., vol. 36, no. 12, pp. 1–16, 2010.

S. S. Sana, A collaborating inventory model in a supply chain, Econ. Model., vol. 29, no. 5, pp. 2016–2023, 2012.

V. Gružauskas, S. Baskutis, and V. Navickas, Minimizing the trade-off between sustainability and cost effective performance by using autonomous vehicles, J. Clean. Prod., vol. 184, pp. 709–717, 2018.

L. Camanzi, E. Arba, C. Rota, C. Zanasi, and G. Malorgio, A structural equation modeling analysis of relational governance and economic performance in agri-food supply chains: evidence from the dairy sheep industry in Sardinia (Italy), Agric. Food Econ., vol. 6, no. 1, 2018.

O. Palacio and W. Adarme, Coordinación de inventarios : Un caso de estudio para la logística de ciudad, DYNA - Fac. Minas la UNAL Medellín, vol. 81, no. 186, pp. 295–303, 2014.

M. Arango-Serna, W. Adarme-Jaimes, and J. Zapata-Cortes, Collaborative inventory in supply chain optimization, no. Vmi, pp. 71–80, 2013.

T. Willems and A. Marrewijk, Building Collaboration? Co-Location and ‘Dis-Location’ in a Railway Control Post, Rev. Adm. Empres., vol. 57, no. 6, pp. 542–554, 2017.

C. W. S. Chen and S. Lee, Generalized Poisson autoregressive models for time series of counts, Comput. Stat. Data Anal., vol. 99, pp. 51–67, 2016.

Y. Rosseel, lavaan: An R package for structural equation modeling, J. Stat. Softw., vol. 48, no. 2, pp. 1–36, 2012.

M. T. Escobedo Portillo, J. A. Hernández Gómez, V. Estebané Ortega, and G. Martínez Moreno, Modelos de ecuaciones estructurales: Características, fases, construcción, aplicación y resultados, Cienc. Trab., vol. 18, no. 55, pp. 16–22, 2016.

M. Nakano, Collaborative forecasting and planning in supply chains: The impact on performance in Japanese manufacturers, Int. J. Phys. Distrib. Logist. Manag., 2009.

Y. Nuñez and C. Rodriguez, Gestión de recursos intangibles en instituciones de educación superior, Rev. Adm. Empres., vol. 55, no. 1, pp. 65–77, 2015.

S. R. Cabana, F. H. Cortés, D. L. Vega, and R. A. Cortés, Análisis de la fidelización del estudiante de ingeniería con su centro de educación superior: Desafíos de gestión educacional, Form. Univ., vol. 9, no. 6, pp. 93–104, 2016.

T. Raykov and G. a. Marcoulides, A First Course in Structural Equation Modeling, vol. 13, no. 1. 2006.

Y. Rosseel, lavaan: An R Package for Structural Equation Modeling, J. Stat. Softw., vol. 48, no. 2, pp. 1–36, 2012.

T. Ojha, S. Misra, and N. S. Raghuwanshi, Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges, Comput. Electron. Agric., vol. 118, pp. 66–84, 2015.

J. Fox. Structural Equation Modeling With the sem Package in R. Structural Equation Modeling, Vol 13, No 3, 465–486. http://doi.org/10.1207/s15328007sem1303_7. 2006.