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Improving On-Time Delivery in a Metalworking SME Using Demand-Driven MRP and Simulation-Based Validation

Research output: Chapter in Book/ReportConference contributionpeer-review

Abstract

Peruvian metalworking SMEs struggle to meet the 87.5 % on‑time delivery (OTD) benchmark, largely because existing material requirements planning systems fail under intermittent demand. This study closed that gap by integrating Demand‑Driven Material Requirements Planning (DDMRP) with Croston‑based forecasting and validating the approach through discrete‑event simulation. After mapping the current value stream, strategic inventory buffers were dimensioned from a 31‑month demand series for chassis tanks and embedded in an Arena model of the line. Compared with the baseline, the proposed system raised simulated OTD from 41.6 % to 94.0 % and cut average production lead time from 55.4 to 18.3 days, delivering an estimated annual net profit of US $73,894 against an investment of US $4,533. The work provides the first quantitative evidence of DDMRP’s effectiveness in the Peruvian metal‑mechanic sector, demonstrates its transferability to settings with high demand variability, and contributes a replicable procedure for practitioners seeking sustainable, low‑capital performance gains.

Original languageAmerican English
Title of host publication2025 11th International Conference on Innovation and Trends in Engineering, CONIITI 2025 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Edition2025
ISBN (Electronic)9798331591328
DOIs
StateIndexed - 2025
Externally publishedYes
Event11th International Conference on Innovation and Trends in Engineering, CONIITI 2025 - Bogota, Colombia
Duration: 1 Oct 20253 Oct 2025

Conference

Conference11th International Conference on Innovation and Trends in Engineering, CONIITI 2025
Country/TerritoryColombia
CityBogota
Period1/10/253/10/25

Bibliographical note

Publisher Copyright:
©2025 IEEE.

Keywords

  • Demand-Driven MRP
  • Discrete-Event Simulation
  • Intermittent Demand
  • Inventory Buffer Optimization
  • Metalworking SME
  • On-Time Delivery

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