Big data y cadenas de suministros un binomio complejo para américa latina
DOI:
https://doi.org/10.15649/2346030X.825Palabras clave:
big data, redes neuronales, cadenas de suministro, modelos logitResumen
Este documento evalúa algunas medidas de rendimiento que afectan el uso de Big Data (BD) en la Cadena Oleaginosas, Aceite y Grasas en América Latina (CSM) el trabajo tiene el respaldo teórico de Big Data (BD), redes neuronales (RN) y cadenas de suministro (CSM), el articulo responde cuatro interrogantes ¿en qué actividades y operaciones productivas se está aplicando Big Data? ¿Determinar algunas variables de desempeño de las cadenas de suministro que afectan variabilidad del Big Data a partir de la teoría de las CSM? ¿Qué beneficios agregan las redes neuronales a las cadenas de suministro?, estimar la variabilidad de los procesos de BD por efectos de (CSM), para estos se utiliza la regresión logística, los datos provienen de distintos eslabones de CSM, los hallazgos sugieren que para la muestra estudiada las principales medidas de variabilidad para el BD en las CSM son, el tipo de formación del personal, la subcontratación en BD, aumento en las ventas, los tipos de pagos de pago por el servicio e-comerce y el uso de modelos predictivos incorporados en la cadena para prevenir fallas y proyectarlas.
Referencias
A. Bhimani, and L. Willcocks, Big Data’ and the transformation of accounting information. Account. Bus. Res. 44 (4), 469–490. 2014.
A. McAfee and E. Brynjolfsson, Big data: the management revolution. Harv. Bus. Rev. 90 (10), 60–68. 2012.
J. Desjardins, 2019. How much data is generated each day? April 15, Available at. Visual2019.
IBM. Big data analytics. At. 2019.
V. A. Grover , R. H. Chiang, T. P. Liang and D. Zhang, Creating strategic business value frombig data analytics: a research framework. J. Manag. Inf. Syst. 35 (2), 388–423. 2018.
O. Müller, M. Fay, J. Vom-Brocke, The effect of big data and analytics on firm performance: an econometric analysis considering industry characteristics. J. Manag. Inf. Syst. 35 (2), 488–509.2018.
A. Gunasekaran, T. Papadopoulos, R. Dubey, S. Wamba, S. Childe, B. Hazen, Akter, Big data and predictive analytics for supply chain and organizational performance.J. Bus. Res. 70, 308–317. 2017.
E. Hofmnn, Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. Int. J. Prod. Res. 55 (17),5108–5126, 2017.
C. Wilkin, A. Ferreira, Big data prioritization in SCM decision-making: Its role and performance implications, International Journal of Accounting Information Systems, 2020.
H. R. Varia, “Big Data: New Tricks for Econometrics.” The Journal of Economic Perspectives, vol. 28, no. 2, pp. 3–27. JSTOR 2014.
I. Porche, B. Wilson, E. Johnson, S. Tierney y E. Saltzman, Big Data: Challenges and Opportunities. In Data Flood: Helping the Navy Address the Rising Tide of Sensor Information (pp. 1-6). RAND Corporation. Retrieved August 6, 2020.
H. Chen“Business Intelligence and Analytics: From Big Data to Big Impact.” MIS Quarterly, vol. 36, no. 4, pp. 1165–1188. JSTOR 2012.
M. A. Landon y A. B. Michael, “Big Data and Intelligence: Applications, Human Capital, and Education.” Journal of Strategic Security, vol. 9, no. 2, pp. 92–121. 2016.
D. Chiang, M. Lin y C. Chen, The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres EnterpriseInf. Syst., 5 (2) (2011), págs. 219 – 234,2011.
T. H. Addo, Big data applications in operations/supply-chain management: A literature review Comput Ind. Eng., 101, pags. 528 — 543, 2016.
Chen y Zhang, Data-intensive applications, challenges, techniques, and technologies: Asurvey on Big pp. 314 — 347.2014
L. Alfalla, I. Mann, C. Garcia y L. Medina An analysis of the direct and mediated effects of employee commitment and supply chain integration on organisational performance Internacional de Economia de la Produccion, 1 62, pags. 242 – 257,2014.
A. Artigues, F. Cucchietti, C. Montes, D. Vicente, H. Calmet y G. Marin Scientific Big Data Visualization: a coupled tools approach Supercomputing Frontiers and ovations, pags. 4 — 18 DOI: 10.14529 / jsfi 140301,2015.
D. Angus, S. Rintel, J. Making sense of big text: a visual-first approach for analysing text data using Leximancer and Discursis International Journal of Social Research Methodology Volume 1 6, Issue 3, Pages 26 I -267, 2013.
R. Assun§iio, N. Calheiros., S. Bianchi, M. Netto y R. Buyya, Computacion debig data y nu bes: tendencias y direcciones futuras J. Paralelo Distrib. Comput, 79—80 (2015), pags. 3 — 15 Elsevier Inc 2015.
H. Tenkorang, Big data applications in operations/supply-chain management: A literature review Comput Ind. Eng., 101, pags. 528 — 543, 2016.
A. Ahiaga, dealing with construction cost overruns using data mining. Construction Management and Economics Volume 32, Issue 7-5, 2014, Pages 652-694,2014.
L. HazenData quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications lnternational Journal of Production Economics Volume 154, (2014) Pages 72-80,2014.
D. Chiang, M. Lin y C. Chen, The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres Enterprisef. Syst., 5 (2) (2011), pags. 219 — 234,2011.
D. Pietro, “Technological Change, Labor Markets, and Low — Skill, Low — Technology Try”, en: Technological Forecasting & Social Change. No. 69, pp. 885-95,2002.
C. Dobre y F. Xhafa, Intelligent services for Big Data sci Futuro Gener.pags.267,2014.
T. Hann y E. Steurer, Much ado about nothing’? Exchange rate forecasting: Neural networks vs. linear models using monthly and weekly data NeurocomputingV olume 10, Issue 4, 1996.
Y. Jagielska, Neural network for predicting the performance of credit Ord accounts Computational Economics Volume 9, Issue 1, Pages 77-82,1996.
W. Z. Pin, Bigdata logs analysis based on seq2seq networks for cognitive Internet of Things future Generation Computer Systems, Volu 90, 2019, Pages 477-488, ISSN 0 I 67-739X, 2019.
G. Zhang y V. Berardi, Time series forecasting with neural network ensembles: An application for exchange rate prediction Journ f the Operational Research Society V olume Issue 6, Pages 652-664, 2001.
D. Pietro y D. Giorgio, “Technological C hange, Labor Markets, and Low — Skill, Low — Technology Try”, en: Technological Forecasting & Social Change. No. 69, pp. 885-95.2002.
F. Ortiz y A. Arango, Pronostico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales, Contaduríay Administracion, Vol.58, Issue 3 Pages 203-225, ISSN 0 I 86- 1042, 2013.
A. Bhiman y L. Willcocks, Digitalización, 'Big Data' y transformación de la información contable págs. 469 – 490,2014.
D. Pietro, “Technological Change, Labor Markets, and ‘Low – Skill, Low – Technology Traps’”, en: Technological Forecasting & Social Change. No. 69, pp. 885-95.2002.
D. Chiang, M. Lin y C. Chen, The adaptive approach f or storage assignment by mining data of warehouse management system for distribution centres Enterprise Inf. Syst., 5 (2) (2011), págs. 219 – 234, 2011.
L. Alfall, J. Marín, C. García y L. Medina, An analysis of the direct and mediated effects of employee commitment and supply chain integration on organisational performance Revista Internacional de Economía de la Producción, 162, págs. 242 – 257, 2014.
D. Pietro, “Technological Change, Labor Markets, and ‘Low – Skill, Low – Technology Traps’”, en: Technological Forecasting & Social Change. No. 69, pp. 885-95.2002.
L. Hazen, Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications International Journal of Production Economics Volume 154, August 2014, Pages 72-80.2014
C. Nitzl, J. Roldan y G. Cepeda, Mediation analysis in partial least squares path modeling. Ind. Manag. Data Syst. 116 (9), 1849–1864. 2016.
C. Wilkin, Big data prioritization in SCM decision-making: Its role andperformance implications, International Journal of Accounting Information Systems 2020.
M. Kubina, M. Varmus y I. Kubinova, Use of big data for competitive advantage of company. Procedia Economics and Finance 26, 561–565. 2015.
M. Brinch, Understanding the value of big data in supply chain management and its business processes. Int. J. Oper. Prod. Manag. 38 (7), 1589–161.2018.
D. Arunachalam, N. Kumar y J. P. Kawalek, Understanding big data analytics capabilities in supply chainmanagement: unravelling the issues, challenges and implicationsfor practice. Transportation Research Part E: Logistics and Transportation Review 114, 416–436. 2018.
B. Hazen, C. Boone, J. Ezell y L. Jones-Farmer, Data quality for data science, predictive analytics, and big data in supply chainmanagement: an introduction to the problem and suggestions for research and applications. Int. J. Prod. Econ. 154 (C), 72–80. 2014.
C. Nitzl, J. Roldan y G. Cepeda, Mediation analysis in partial least squares path modeling. Ind. Manag. Data Syst. 116 (9), 1849–1864.Oxford Living Dictionary, 2019. 2016
M. Brinch, Understanding the value of big data in supply chain management and its business processes. Int. J. Oper. Prod. Manag. 38 (7), 1589–1614. 2018.
R. Zhong, S. Newman, G. Huang y S. Lan, Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. 572–591.2016.
M. Waller y S. Fawcett, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logist. 34(2), 77–84.2013.
Publicado
Cómo citar
Número
Sección
Altmetrics
Descargas
Licencia
La revista ofrece acceso abierto bajo una Licencia Creative Commons Attibution License
Esta obra está bajo una licencia Creative Commons Attribution (CC BY 4.0).
