Search results

As quality becomes increasingly prioritized in supply chain management, understanding how supply chain quality risk management (SCQRM) practices impact quality performance (QP) is essential. This study investigates the effects of two SCQRM practices – risk prevention (RP) and proactive product recall (PPR) – on QP, with a particular focus on the mediating role of supply chain quality integration (SCQI).

A structured survey was administered to gather data from 400 Chinese manufacturing firms. Structural equation modeling was employed to evaluate the proposed relationships among SCQRM practices (RP and PPR), SCQI and QP.

The findings reveal that both RP and PPR significantly and positively influence QP. Specifically, in the structural model, RP exerts a positive effect on SCQI, while PPR also positively impacts SCQI. Additionally, SCQI serves as a mediator between RP and QP, as well as between PPR and QP.

This study contributes to the supply chain management literature by elucidating the beneficial effects of RP and PPR on QP and identifying SCQI as a key mediating factor in these relationships. Leveraging information processing theory (IPT), the study provides new theoretical insights into the mechanisms through which SCQRM enhances QP via SCQI.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.