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Publication
Procurement inteligente na indústria: Mapeamento de processos e propostas de melhoria
| Name: | Description: | Size: | Format: | |
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| 5.08 MB | Adobe PDF |
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Abstract(s)
Esta dissertação tem como principal objetivo a análise e proposta de melhorias ao processo de procurement da EFACEC, com um foco específico no Departamento de Procurement e Compras (DPC). A investigação partiu da identificação de dificuldades na resposta às previsões de consumo recebidas de outros departamentos. Foi adotada uma abordagem metodológica mista que combinou entrevistas aos compradores com técnicas de process mining através da ferramenta Apromore com dados extraídos diretamente da base de dados da empresa. Esta combinação permitiu mapear o processo de compras “as-is”, identificar gargalos e variantes de execução e compreender a lógica de decisão interna do DPC. Com base nos dados analisados, foram propostas métricas para a avaliação do alinhamento
entre o forecast e as compras reais, nomeadamente o volume monetário comprado sem previsão, o MAPE e o forecast bias. Estas métricas foram adotadas através de dashboards desenvolvidos no Power BI, proporcionando uma ferramenta de monitorização contínua. Os resultados da análise evidenciaram a existência de falhas no processo de previsão do consumo e na gestão das prioridades de compra, resultando na sobrecarga dos compradores,
necessidade de realizar compras urgentes e perda de poder negocial. Como resposta, foram recomendadas medidas como a melhoria da análise das previsões, a padronização dos ficheiros, o reforço do controlo contratual e a adoção de critérios objetivos de priorização. Este estudo contribui, assim, para uma maior eficiência do processo de procurement,
promovendo o melhor alinhamento entre o planeamento e a execução.
This dissertation aims to analyse and propose improvements to the procurement process at EFACEC, with a special focus on the procurement and purchasing department (DPC). The investigation began with the identification of challenges in the response to the consumption forecasts received from other departments. A mixed methodological approach was adopted, combining interviews with buyers and process mining techniques using the Apromore tool, supported by data extracted directly from the company’s database. This combination allowed for the mapping of the “as-is” procurement process, the identification of bottlenecks and execution variants and a better understanding of the internal decision-making logic within the DPC. Based on the data analysed, metrics were proposed to assess the alignment between the forecast and the actual purchases, specifically the monetary volume purchased without forecast, the Mean Absolute Percentage Error (MAPE) and the forecast bias. These metrics were implemented through dashboards developed in Power BI, providing to the team a continuous process monitoring tool. The results of the analysis highlighted the existence of shortcomings in the forecasting process and in the management of purchasing priorities, leading to the buyer’s work overload, the need for urgent purchases and a loss of bargaining power. In response, measures were recommended, including the automation of the forecast analysis, standardization of forecast files, reinforcement of contract control and the adoption of objective prioritization criteria. This study contributes to greater efficiency in the procurement process by promoting a better alignment between planning and execution.
This dissertation aims to analyse and propose improvements to the procurement process at EFACEC, with a special focus on the procurement and purchasing department (DPC). The investigation began with the identification of challenges in the response to the consumption forecasts received from other departments. A mixed methodological approach was adopted, combining interviews with buyers and process mining techniques using the Apromore tool, supported by data extracted directly from the company’s database. This combination allowed for the mapping of the “as-is” procurement process, the identification of bottlenecks and execution variants and a better understanding of the internal decision-making logic within the DPC. Based on the data analysed, metrics were proposed to assess the alignment between the forecast and the actual purchases, specifically the monetary volume purchased without forecast, the Mean Absolute Percentage Error (MAPE) and the forecast bias. These metrics were implemented through dashboards developed in Power BI, providing to the team a continuous process monitoring tool. The results of the analysis highlighted the existence of shortcomings in the forecasting process and in the management of purchasing priorities, leading to the buyer’s work overload, the need for urgent purchases and a loss of bargaining power. In response, measures were recommended, including the automation of the forecast analysis, standardization of forecast files, reinforcement of contract control and the adoption of objective prioritization criteria. This study contributes to greater efficiency in the procurement process by promoting a better alignment between planning and execution.
Description
Keywords
Procurement Forecast Accuracy Process Mining BPM Power BI SQL Data Integration Compras Precisão da previsão