DOI:

https://doi.org/10.14483/23448393.19423

Published:

2024-01-13

Issue:

Vol. 29 No. 1 (2024): January-April

Section:

Computational Intelligence

Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming

Metodología para la gestión de inventario en tiendas de barrio utilizando aprendizaje de máquina y programación lineal entera

Authors

Keywords:

Machine learning, inventory, constrained optimization, demand estimation (en).

Keywords:

Aprendizaje de máquina, inventario, optimización restringida, estimación de la demanda (es).

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Abstract (en)

Context: Nowadays, inventory management poses a challenge given the constant demands related to temporality, geographic location, price variability, and budget availability, among others. In neighborhood shops, this process is manually done based on experience (the data generated are ignored), which is sometimes not enough to respond to changes. This shows the need to develop new strategies and tools that use data analysis techniques.

Method: Our methodology predicts the weekly demand for 14 common products in neighborhood stores, which is later refined based on investment capital. The method is validated using a database built with synthetic information extracted from statistical sampling. For the prediction model, three supervised learning models are used: support vector machines (SVM), AutoRegressive models (Arx), and Gaussian processes (GP). This work proposes a restricted linear model given an inversion and the predicted quantity of products; the aim is to refine the prediction while maximizing the shopkeeper's profit. Finally, the problem is solved by applying an integer linear programming paradigm.

Results: Tests regarding the prediction and inventory adjustment stages are conducted, showing that the methodology can predict the temporal dynamics of the data by inferring the statistical moments of the distributions used. It is shown that it is possible to obtain a maximum profit with a lower investment.

Conclusions: Our method allows predicting and refining inventory management in a neighborhood store model where quantities are managed to maximize the shopkeeper's profits. This opens the way to explore this approach in a real scenario or to introduce new techniques that can improve its performance.

Abstract (es)

Contexto: En la actualidad, la administración del inventario representa un reto dadas las constantes exigencias de temporalidad, ubicación geográfica, variabilidad en los precios, disponibilidad presupuestal, entre otros. En las tiendas de barrio, este proceso se realiza de forma manual y con base en la experiencia (se ignoran los datos generados), lo que en ocasiones no es suficiente para responder a los cambios. Esto muestra la necesidad de desarrollar nuevas estrategias y herramientas que utilicen técnicas de análisis de datos.

Método: Nuestra metodología predice la demanda semanal para 14 productos comunes en tiendas de barrio, la cual se refina posteriormente en función del capital de inversión para optimizar la ganancia. El método se valida a través de una base de datos construida con información sintética extraída a partir de muestreo estadístico. Para la predicción, se utilizan tres modelos de aprendizaje supervisado: máquinas de soporte vectorial (SVM), modelos AutoRegresivos (ARx) y procesos Gaussianos (GP). Luego, se plantea un modelo lineal restringido dada una inversión y las cantidades pronosticadas; el propósito es refinar la predicción maximizando la ganancia del tendero. Finalmente, el problema se soluciona aplicando un paradigma de programación lineal entera.

Resultados: Se realizan pruebas para las etapas de predicción y ajustes del inventario, donde se demuestra que la metodología logra predecir la dinámica temporal de los datos infiriendo los momentos estadísticos de las distribuciones utilizadas. Se muestra que es posible obtener una máxima ganancia con un monto menor de inversión.

Conclusiones: Nuestra metodología que permite predecir y refinar la gestión de inventario en un modelo de tienda de barrio en el que las cantidades se administran para maximizar las ganancias del tendero. Lo anterior abre el camino para explorar este enfoque en un escenario real o introducir nuevas técnicas que puedan mejorar su desempeño.

Author Biographies

Carlos Alberto Henao-Baena, Technological University of Pereira

Professor at the Electronics Engineering Program of Universidad Tecnológica de Pereira

Bibiana Zuluaga-Zuluaga, National Training Service

Instructor linked to research processes at SENA Risaralda, Mercator group (Pereira, Colombia).

Julian Galeano-Castro, National Training Service

Instructor linked to research processes at SENA Risaralda, Mercator group (Pereira, Colombia)

Edward Jhohan Marín-García, University of Valle

Professor at Universidad del Valle, Cartago Campus (Cartago, Colombia)

Andrés Felipe Calvo-Salcedo, Technological University of Pereira

Full professor at Universidad Tecnológica de Pereira (Pereira, Colombia)

References

F. E. Castro and J. A. Oviedo, "Tiendas de barrio a la vanguardia de la competitividad," in XIX Congreso Internacional AECA, 2017, pp. 72.

E. J. Albarracín and S. C. Erazo, “Influencia de las tecnologías de la información y comunicación en el rendimiento de las micro, pequeñas y medianas empresas Colombianas,” Estudios Gerenciales, vol. 30, no. 133, pp. 355-364, 2014. https://doi.org/10.1016/j.estger.2014.06.006 DOI: https://doi.org/10.1016/j.estger.2014.06.006

J. M. Caicedo and J. M. Quiceno, “Situación actual de la tienda de barrio frente a la aparición de la nuevas superficies ARA y D1 en la ciudad de Manizales,” Undergraduate thesis, Univ. Manizales. Manizales, Caldas, Colombia. [Online]. Available: https://ridum.umanizales.edu.co/xmlui/handle/20.500.12746/2080

Semana, “El fenómeno D1: la revolución de las tiendas de descuento,” 2016. [Online]. Available: https://www.semana.com/edicion-impresa/caratula/articulo/como-funcionan-las-tiendas-d1/218767/

Portafolio, “Los tenderos no conocen a un competidor directo: la cadena D1,” 2016. [Online]. Available: https://www.portafolio.co/negocios/tenderos-conocen-competidor-directo-cadena-d1-495847

J.M. Paúcar, E. Vargas, “Propuesta de mejora de la gestión de inventarios en una empresa del sector retail,” Undergraduate thesis, Univ. Peruana Ciencias Aplicadas, Fac. Ing., Lima, Perú. [Online]. Available: http://hdl.handle.net/10757/623832

M. Ulrich and H. Jahnke, "Classification-based model selection in retail demand forecasting," Int. J. Forecast., vol. 30, no. 1, pp. 209-223, 2022. https://doi.org/10.1016/j.ijforecast.2021.05.010 DOI: https://doi.org/10.1016/j.ijforecast.2021.05.010

M. Doszyń, "Expert systems in intermittent demand forecasting," Procedia Comp. Sci., vol. 192, pp. 3598-3606, 2021. https://doi.org/10.1016/j.procs.2021.09.133 DOI: https://doi.org/10.1016/j.procs.2021.09.133

F. Tao and Q. Qi, "Data-driven smart manufacturing," J. Manuf. Syst., vol. 48, part C, pp. 157-169, 2018. https://doi.org/10.1016/j.jmsy.2018.01.006 DOI: https://doi.org/10.1016/j.jmsy.2018.01.006

J. A. Arango and J. A. Giraldo, “Gestión de compras e inventarios a partir de pronósticos Holt-Winters y diferenciación de nivel de servicio por clasificación ABC,” Scientia et Technica, vol. 18, no. 4, pp. 743-747, Dec. 2013. https://doi.org/10.22517/23447214.7171

J. E. Montemayor, Métodos de pronósticos para negocios, Monterrey, Nuevo León, México: Tecnológico de Monterrey. [Online]. Available: http://hdl.handle.net/11285/621230

T. Tanizaki and T. Hoshino, "Demand forecasting in restaurants using machine learning and statistical análisis," Procedia CIRP, vol. 79, pp. 679-683, 2019. https://doi.org/10.1016/j.procir.2019.02.042 DOI: https://doi.org/10.1016/j.procir.2019.02.042

J. P. Usuga and S. Lamouri, "Trends in machine learning applied to demand & sales forecasting: A review," presented at International Conference on Information Systems, Logistics and Supply Chain, Jul. 2018, Lyon, France. [Online]. Available: https://hal.science/hal-01881362

D. A. Collier and J. R. Evans, Administración de operaciones, Boston, MA, USA: Cengage, 2019.

T. Boone and R. Ganeshan, "Forecasting sales in the supply chain: Consumer analytics in the Big Data era," Int. J. Forecast., vol. 35, no. 1, pp. 170-180, 2019. https://doi.org/10.1016/j.ijforecast.2018.09.003 DOI: https://doi.org/10.1016/j.ijforecast.2018.09.003

A. Kumar and R. Shankar, "A Big Data driven framework for demand-driven forecasting with effects of marketing-mix variables," Ind. Marketing Manag., vol. 90, pp. 493-507, 2020. https://doi.org/10.1016/j.indmarman.2019.05.003 DOI: https://doi.org/10.1016/j.indmarman.2019.05.003

S. Chopra, "The evolution of omni-channel retailing and its impact on supply chains," Transport. Res. Procedia, vol. 30, pp. 4-13, 2018. https://doi.org/10.1016/j.trpro.2018.09.002 DOI: https://doi.org/10.1016/j.trpro.2018.09.002

S. Mou and D.J. Robb, "Retail store operations: Literature review and research directions," European J. Oper. Res., vol. 265, no. 2, pp. 399-422, 2018. https://doi.org/10.1016/j.ejor.2017.07.003 DOI: https://doi.org/10.1016/j.ejor.2017.07.003

E. Casado and M. La Civita, "Estimated time of arrival sensitivity to aircraft intent uncertainty," IFAC-PapersOnLine, vol. 51, no. 9, pp. 162-167, 2018. https://doi.org/10.1016/j.ifacol.2018.07.027 DOI: https://doi.org/10.1016/j.ifacol.2018.07.027

M. A. Sellitto and E. Balugani, "Spare parts replacement policy based on chaotic models", IFAC-PapersOnLine, vol. 51, no. 11, pp. 945-950, 2018. https://doi.org/10.1016/j.ifacol.2018.08.486 DOI: https://doi.org/10.1016/j.ifacol.2018.08.486

F. Hamilton and A. Lloyd, "Hybrid modeling and prediction of dynamical systems", PLoS Comp. Biology, vol. 13, no. 7, pp. 1-20, 2017. https://doi.org/10.1371/journal.pcbi.1005655 DOI: https://doi.org/10.1371/journal.pcbi.1005655

J. C. Sanclemente, “La reputación del tendero de barrio ante su mercado y sus consecuencias,” PhD thesis, Univ. EAFIT, Medellín, Colombia . [Online]. Available:

Servinformación, “Estudio revela que los tenderos barranquilleros madrugan más que los bogotanos”, 2019. [Online]. Available: https://www.colombia.com/actualidad/nacionales/barranquilleros-madrugan-mas-que-los-bogotanos-241787

eSemanal, “Estudio global de la cadena de suministro de retail”, 2020. [Online]. Available: https://esemanal.mx/2020/07/estudio-global-de-la-cadena-de-suministro-de-retail/

El Espectador, “Un estudio comparó las tiendas de barrio de bogotá con las de barranquilla”, 2019. [Online]. Available: https://www.elespectador.com/bogota/un-estudio-comparo-las-tiendas-de-barrio-de-bogota-con-las-de-barranquilla-article-879317/

R. Gaku, "Demand forecasting procedure for short life-cycle products with an actual food processing enterprise," Int. J. Comp. Intel. Syst., vol. 7, no. 2, pp. 85-92, 2014. https://doi.org/10.1080/18756891.2014.947121 DOI: https://doi.org/10.1080/18756891.2014.947121

J. E. Hanke and D. W. Wichern, Pronósticos en los negocios, 9th ed., Mexico DF, Mexico: Pearson Educación, 2010.

P. L. Meyer and C. P. Campos, Probabilidad y aplicaciones estadísticas, Mexico DF, Mexico: Fondo Educativo Interamericano, 1973.

M.T. Farrell, A. Correa, "Gaussian process regression models for predicting stock trends,” Relation, vol. 10, pp. 3414–3423,. https://api.semanticscholar.org/CorpusID:16646484

B. Schölkopfa and A. J. Smola, Learning with kernels: Support vector machines, regularization, optimization, and beyond, Cambridge, MA, USA: MIT press, 2002.

C. M. Bishop, Pattern recognition and machine learning (information science and statistics), 1st ed., New York, NY, USA: Springer, 2006.

K. S. Shanmugan and A. M. Breipohl, Random signals: Detection, estimation and data analysis, Hoboken, NJ, USA: Wiley, 1988.

C. E. Rasmussen, "Gaussian processes in machine learning,” in Advanced Lectures on Machine Learning, O. Bousquet, U. von Luxburg, and G. Rätsch, Eds., Berlin, Heidelberg, Germany: Springer, 2003, pp. 63-71.

C. Krzysztof and C. K. Williams, "Empirical evaluation of Gaussian Process approximation algorithms," Master's thesis, S. Informatics, Univ. Edinburgh, 2011. [Online]. Available: https://homepages.inf.ed.ac.uk/ckiw/postscript/Chalupka2011diss.pdf

How to Cite

APA

Henao-Baena, C. A., Zuluaga-Zuluaga, B., Galeano-Castro, J., Marín-García, E. J., and Calvo-Salcedo, A. F. (2024). Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming. Ingeniería, 29(1), e19423. https://doi.org/10.14483/23448393.19423

ACM

[1]
Henao-Baena, C.A. et al. 2024. Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming. Ingeniería. 29, 1 (Jan. 2024), e19423. DOI:https://doi.org/10.14483/23448393.19423.

ACS

(1)
Henao-Baena, C. A.; Zuluaga-Zuluaga, B.; Galeano-Castro, J.; Marín-García, E. J.; Calvo-Salcedo, A. F. Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming. Ing. 2024, 29, e19423.

ABNT

HENAO-BAENA, Carlos Alberto; ZULUAGA-ZULUAGA, Bibiana; GALEANO-CASTRO, Julian; MARÍN-GARCÍA, Edward Jhohan; CALVO-SALCEDO, Andrés Felipe. Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming. Ingeniería, [S. l.], v. 29, n. 1, p. e19423, 2024. DOI: 10.14483/23448393.19423. Disponível em: https://revistas.udistrital.edu.co/index.php/reving/article/view/19423. Acesso em: 14 jun. 2024.

Chicago

Henao-Baena, Carlos Alberto, Bibiana Zuluaga-Zuluaga, Julian Galeano-Castro, Edward Jhohan Marín-García, and Andrés Felipe Calvo-Salcedo. 2024. “Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming”. Ingeniería 29 (1):e19423. https://doi.org/10.14483/23448393.19423.

Harvard

Henao-Baena, C. A. (2024) “Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming”, Ingeniería, 29(1), p. e19423. doi: 10.14483/23448393.19423.

IEEE

[1]
C. A. Henao-Baena, B. Zuluaga-Zuluaga, J. Galeano-Castro, E. J. Marín-García, and A. F. Calvo-Salcedo, “Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming”, Ing., vol. 29, no. 1, p. e19423, Jan. 2024.

MLA

Henao-Baena, Carlos Alberto, et al. “Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming”. Ingeniería, vol. 29, no. 1, Jan. 2024, p. e19423, doi:10.14483/23448393.19423.

Turabian

Henao-Baena, Carlos Alberto, Bibiana Zuluaga-Zuluaga, Julian Galeano-Castro, Edward Jhohan Marín-García, and Andrés Felipe Calvo-Salcedo. “Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming”. Ingeniería 29, no. 1 (January 13, 2024): e19423. Accessed June 14, 2024. https://revistas.udistrital.edu.co/index.php/reving/article/view/19423.

Vancouver

1.
Henao-Baena CA, Zuluaga-Zuluaga B, Galeano-Castro J, Marín-García EJ, Calvo-Salcedo AF. Methodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming. Ing. [Internet]. 2024 Jan. 13 [cited 2024 Jun. 14];29(1):e19423. Available from: https://revistas.udistrital.edu.co/index.php/reving/article/view/19423

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