Supporting Dataset for 'Optimizing Procurement Process in Building Material Retail Supply Chain: A Business Process Improvement Approach'

This dataset supports the research on optimizing the procurement process within a medium-sized building material retail company using Business Process Improvement (BPI) methodology. The research hypothesizes that the integration of activity classification, Failure Mode and Effects Analysis (FMEA), and process simulation can effectively identify inefficiencies and lead to measurable improvements in procurement operations. The dataset includes transcribed interview data from key stakeholders (Warehouse Crew and Head of Merchandise), detailed business process models (As-Is and To-Be), activity classification based on value contribution (RVA, BVA, NVA), FMEA assessments highlighting high-risk tasks, and simulation results comparing time efficiency between the current and improved process. The data shows that by eliminating non-value-added tasks and automating critical steps, the average procurement cycle time was reduced by 48.26%. All data was gathered through interviews, observation, documentation review, and process modeling using BPMN. This dataset can be interpreted as a case-based framework for improving procurement efficiency in retail settings and can be used by practitioners and researchers to understand, replicate, or benchmark BPI-driven process improvement initiatives.