TY - GEN
T1 - Data governance reference model to streamline the supply chain process in SMEs
AU - Barrenechea, Oscar
AU - Mendieta, Aaron
AU - Armas, Jimmy
AU - Madrid, Juan Manuel
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In this paper, we propose a Data Governance reference model to streamline supply chain processes in SMEs through Data Integration and Business Analytics techniques, using the DAMADMBOK reference framework which involves a set of processes and areas of knowledge that are accepted as good practices. We defined a data governance model, measuring the quality of data, the Purchasing, Sales and Inventory processes can make decisions based on more reliable and accurate data, that management time and costs are reduced customer service is improved and staff performance is measurable. In this way, the expected results can be achieved through a Data Warehouse of Key Performance Indicators (KPIs) and Dashboards implemented in the Pentaho Open Source platform. The proposed model is defined in 5 stages: 1. Data Management, 2. Data Architecture, 3. Data Integration, 4. Data Warehousing & Business Intelligence, 5. Data Quality. A real scenario was defined in a textile company located in Lima, Perú to validate the proposal; the supply chain processes were chosen as they are critical for the business. The monthly results showed that, for the Sales process, the issuance time for sales quotations was reduced by 50% equivalent to a total of 60 man hours which means that it takes less time to produce the sales quotations. The delivery cycle for sold products was reduced by 25% equivalent to a total of 30 man hours, for the inventory process the compliance cycle of satisfactory purchase orders was reduced by 37.5% equivalent to a total of 90 man hours, which means that it takes less time to manage purchase orders; finally, for the Purchasing process the withdrawal of complaints about purchased products was reduced by 25% equivalent to a total of 30 man-hours, and the delivery time of the supplier per order of purchase was reduced in a 41.67% equivalent to a total of 150 man hours.
AB - In this paper, we propose a Data Governance reference model to streamline supply chain processes in SMEs through Data Integration and Business Analytics techniques, using the DAMADMBOK reference framework which involves a set of processes and areas of knowledge that are accepted as good practices. We defined a data governance model, measuring the quality of data, the Purchasing, Sales and Inventory processes can make decisions based on more reliable and accurate data, that management time and costs are reduced customer service is improved and staff performance is measurable. In this way, the expected results can be achieved through a Data Warehouse of Key Performance Indicators (KPIs) and Dashboards implemented in the Pentaho Open Source platform. The proposed model is defined in 5 stages: 1. Data Management, 2. Data Architecture, 3. Data Integration, 4. Data Warehousing & Business Intelligence, 5. Data Quality. A real scenario was defined in a textile company located in Lima, Perú to validate the proposal; the supply chain processes were chosen as they are critical for the business. The monthly results showed that, for the Sales process, the issuance time for sales quotations was reduced by 50% equivalent to a total of 60 man hours which means that it takes less time to produce the sales quotations. The delivery cycle for sold products was reduced by 25% equivalent to a total of 30 man hours, for the inventory process the compliance cycle of satisfactory purchase orders was reduced by 37.5% equivalent to a total of 90 man hours, which means that it takes less time to manage purchase orders; finally, for the Purchasing process the withdrawal of complaints about purchased products was reduced by 25% equivalent to a total of 30 man-hours, and the delivery time of the supplier per order of purchase was reduced in a 41.67% equivalent to a total of 150 man hours.
KW - Analytical models
KW - Data Analytics Supply chain
KW - Data Architecture
KW - Data Governance
KW - Data Model
KW - Data Quality
KW - Data Visualization
KW - Data Warehousing
KW - Information Management
UR - https://www.scopus.com/pages/publications/85073556958
U2 - 10.1109/INTERCON.2019.8853634
DO - 10.1109/INTERCON.2019.8853634
M3 - Contribución a la conferencia
AN - SCOPUS:85073556958
T3 - Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
BT - Proceedings of the 2019 IEEE 26th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2019
Y2 - 12 August 2019 through 14 August 2019
ER -