Analysis of Factors Causing Delays in Goods Delivery Using Multiple Linear Regression and RCA
Analisis Faktor Penyebab Keterlambatan Pengiriman Barang Menggunakan Regresi Linear Berganda dan RCA
DOI:
https://doi.org/10.21070/pels.v10i1.3134Keywords:
Logistics, Delivery Delays, Multiple Linear Regression, Root Cause Analysis, Preventative MaintenanceAbstract
General Background The continuous expansion of e-commerce and manufacturing industries heavily relies on logistics companies to execute punctual distribution. Specific Background Delays during factory-to-warehouse transit negatively impact supply chain effectiveness, requiring quantitative assessments to isolate variables like vehicle condition, driver performance, scheduling issues, and traffic conditions. Knowledge Gap While current studies utilize either regression models or root cause analyses independently, deploying a sequential integration of both methodologies to quantitatively isolate dominant variables before investigating their operational origins remains underexplored. Aims This study investigates the factors influencing delivery delays by quantifying their impacts using Multiple Linear Regression and tracing the underlying causes of the dominant variables via a 5 Whys Root Cause Analysis. Results Regression analysis determined that vehicle condition and traffic congestion significantly influence delivery delays, evidenced by t-values of 3.496 and 3.498, with significance levels of 0.002. In contrast, driver performance and scheduling discrepancies did not exhibit significant isolated effects. Furthermore, the 5 Whys analysis identified the absence of preventative maintenance scheduling and the failure to utilize real-time traffic monitoring systems as the fundamental operational root causes. Novelty This research integrates quantitative statistical regression directly with qualitative root cause tracking to formulate specific, evidence-based logistical interventions. Implications Establishing mandatory preventative maintenance protocols and implementing real-time traffic monitoring systems will mitigate technical vehicle failures and navigational delays, significantly enhancing delivery punctuality.
Highlights:
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Multiple Linear Regression identified vehicle condition and traffic congestion as the primary variables significantly influencing delivery delays.
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A 5 Whys analysis traced technical vehicle failures to the complete absence of a structured preventative maintenance schedule.
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Implementing real-time traffic monitoring protocols and mandatory maintenance tracking mitigates operational disruptions and enhances logistical punctuality.
Keywords: Logistics, Delivery Delaysl Multiple Linear Regression, Root Cause Analysis, Preventative Maintenance
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