Development of a cloud-based cost factor database for strategic procurement decisions : a design science approach integrating data quality index (DQI) and cloud platform evaluation

Abstract

In the manufacturing sector, strategic sourcing and supplier negotiations depend on reliable procurement cost data. However, in the digitalised era of information systems, global manufacturing firms still face concerns related to poor cost data transparency that affects economically sustainable decisions and social responsibility to reinforce data traceability and ethical sourcing. The study assists in identifying the absolute challenges and categorises various cost data sources to provide a centralised artefact. The artefact involves design attributes and validation of a cost factor database in cloud. The unified cost factor database ingests procurement cost data from heterogeneous cost data sources along with metadata. The metadata extends to introduce a practically applicable quantitative Data Quality Index (DQI) framework to assess procurement data trustworthiness. The evaluation extends to cloud platform selection applicable for any priority-based requirement situations. The artefact development in the study applies Design Science Research Methodology (DSRM). The method progresses from problem identification to communication with the data consumers. The DQI evaluation employs four data quality metrics. The DQI and cloud platform assessment are weighted with Analytic Hierarchy Process (AHP) and consolidated using Multi-Attribute Utility Theory (MAUT). The evaluation has been meticulously calculated for the academic and practical aspects. The findings present a structured and unified cost factor database with heterogeneous procurement data ingestion, applying DQI scores that are practically applicable with empirical justification. Microsoft Azure has been chosen as the preferred cloud platform compatible for a full-scale cost factor database supporting dashboard analytics leveraging AI in the longer lifecycle.