Mejorando la planificación de la demanda en el Perú: Un enfoque colaborativo entre un retail y una empresa importadora de productos de consumo masivo basado en el método CPFR, con el apoyo del modelo SARIMAX y la técnica FEFO
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Advisors
León Chavarri, Claudia Carolina Issue Date
2023-07-08
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Improving demand planning in Peru: A collaborative approach between a retailer and a company that imports mass consumption products based on the CPFR method, with the support of the SARIMAX model and the FEFO techniqueAbstract
El trabajo colaborativo, la aplicación de modelos estadísticos de series temporales y la gestión de inventarios son fundamentales para mantener un control óptimo en la cadena de suministro. Aunque hay investigaciones en estas áreas, su aplicación en sistemas de colaboración para una gestión eficiente de la cadena de suministro es escasa en el contexto peruano. Por ende, este estudio se enfoca en mejorar el nivel de servicio en el canal moderno entre una empresa importadora y su principal cliente, utilizando la metodología CPFR (Colaborative Planning, Forecasting, and Replenishment) con el apoyo del modeló estadístico SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Variables) y la técnica de gestión de inventario FEFO (First Expired, First Out). El estudio se centró en analizar los procesos de gestión de la demanda entre 2018 y 2023. Mediante la implementación del CPFR, se facilitó una comunicación más efectiva entre ambas empresas, una gestión diaria de la demanda, el intercambio de información y un seguimiento cercano de los resultados. Para respaldar esta metodología, se emplearon modelos de series temporales como SARIMAX que consideraron factores exógenos relevantes, como tendencias económicas, condiciones climáticas, promociones, entre otros. Además, la técnica FEFO contribuyó a una mejor gestión de inventarios, garantizando que los productos con fechas de vencimiento más cercanas fueran vendidos o distribuidos primero, reduciendo así las pérdidas por productos caducados almacenados durante largos periodos. Los resultados obtenidos respaldan la eficacia de la metodología colaborativa y técnicas de apoyo antes mencionados: el nivel de servicio se mantuvo por encima del 90% desde su implementación, las situaciones de falta de stock disminuyeron en un 10.9%, el error en los pronósticos se redujo en un 20% y los problemas de incumplimiento por vida útil se redujeron en un 1.4%. Además, las ventas de ambas compañías aumentaron en más del 20%. No obstante, es crucial considerar los costos asociados a la implementación, los cuales pueden variar, así como los riesgos, tales como la necesidad de personal adicional, restricciones técnicas y la implementación de nuevos procesos y procedimientos.Collaborative work, the application of time series statistical models and inventory management are essential to maintain optimal control in the supply chain. Although there is research in these areas, its application in collaboration systems for efficient supply chain management is scarce in the Peruvian context. Finally, this study focuses on improving the level of service in the modern channel between an importing company and its main client, using the CPFR (Collaborative Planning, Forecasting, and Replenishment) methodology with the support of the SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Variables) and the FEFO (First Expired, First Out) inventory management technique. The study focused on analyzing the demand management processes between 2018 and 2023. Through the implementation of the CPFR, more effective communication between both companies was facilitated, daily demand management, the exchange of information and close monitoring of the results. To support this methodology, time series models such as SARIMAX were used that considered relevant exogenous factors, such as economic trends, climatic conditions, promotions, among others. In addition, the FEFO technique contributed to better inventory management, ensuring that products with closer expiration dates were sold or distributed first, thus reducing losses due to expired products stored for long periods. To support this methodology, time series models and Machine Learning algorithms were used that considered relevant exogenous factors, such as economic trends, weather conditions, promotions, among others. In addition, the FEFO (First Expired, First Out) technique contributed to better inventory management, ensuring that products with closer expiration dates were sold or distributed first, thus reducing losses due to expired products stored for long periods. The results obtained support the effectiveness of the collaborative methodology and support techniques mentioned above: the service level remained above 90% since its implementation, out-of-stock situations decreased by 10.9%, the error in the forecasts was reduced. reduced by 20% and lifetime non-compliance issues were reduced by 1.4%. In addition, sales of both companies increased by more than 20%. However, it is crucial to consider the costs associated with implementation, which can vary, as well as risks, such as the need for additional staff, technical constraints, and the implementation of new processes and procedures. Keywords:
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Attribution-NonCommercial-ShareAlike 4.0 Internationalinfo:eu-repo/semantics/openAccess
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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International


