Application of the BPM-Kraljic model to improve supply management in an agroindustrial MSE

• Autores: Rivera Gómez, Camila Thais, Kovac Alvarado, Ljubica
• Formato: tesis de grado
• Fecha de Publicación: 2023
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/18576
• Enlace del recurso: https://hdl.handle.net/20.500.12724/18576
• Nivel de acceso: acceso abierto
• Materia: Administración de la producción; Cadena de suministro; Pequeñas y medianas empresas; Industria agrícola; Production management; Supply chain; Small business; Agricultural industries; https://purl.org/pe-repo/ocde/ford#2.11.04

Studies present insufficient information on working models within the purchasing area using the BPM tool for agro-industrial companies, especially in Latin America, which aim to improve performance and optimize the processes of an organization. For this reason, the need for this research arises. This scientific article presents a model to improve supply management. The BPM methodology was proposed to standardize internal processes and the Kraljic matrix, a tool used in purchasing management to classify materials and optimize purchase planning. The level of compliance within the purchasing area did not satisfy the established policies, reaching only 55.96% and generating a gap in the savings goal of approximately S/.500,000. This causes an imbalance at the administrative and operational level due to a lack of general standardization of the processes. This triggered problems at the macro level, such as lack of communication between areas, inefficient operations, and economic losses. The proposal that was carried out covers different areas, such as a harmonization within the administrative processes to reduce time variability and logistics focused on the efficient management of purchases and classification of materials. It was possible to prove the hypothesis; it is feasible to reach 85% of the target scope by restructuring the areas and generating strategies. The simulation could prove the optimization of resources by up to 60% and reduce processing times.