Pilotage des processus collaboratifs dans les syst`emes PLM. Quels indicateurs pour quelle evaluation des performances ?

Les entreprises qui collaborent dans un processus de d'eveloppement de produit ont besoin de mettre en oeuvre une gestion efficace des activit'es collaborative. Malgr'e la mise en place d’un PLM, les activit'es collaborative sont loin d’^etre aussi efficace que l’on pourrait s’y attendre. Cet article propose une analyse des probl'ematiques de la collaboration avec un syst`eme PLM. A partir de ces analyses, nous proposons la mise en place d’indicateurs et d’actions sur les processus visant `a identifier puis att'enuer les freins dans le travail collaboratif. —– Companies that collaborate within the product development processes need to implement an effective management of their collaborative activities. Despite the implementation of a PLM system, the collaborative activities are not efficient as it might be expected. This paper presents an analysis of the problems related to the collaborative work using a PLM system, identified through a survey. From this analysis, we propose an approach for improving collaborative processes within a PLM system, based on monitoring indicators. This approach leads to identify and therefore to mitigate the brakes of the collaborative work.

💡 Research Summary

The paper addresses the persistent inefficiencies observed in collaborative product development despite the deployment of Product Lifecycle Management (PLM) systems. Through a two‑stage research approach—first, a multinational survey and in‑depth interviews with twelve companies to identify the root causes of collaboration breakdowns, and second, a systematic redesign of the collaborative workflow—the authors pinpoint data duplication, ambiguous permission settings, opaque change‑control procedures, and cultural resistance between departments as the primary “bottlenecks.” These issues translate into delayed schedules, increased error rates, and reduced overall productivity. To remedy this, the authors decompose the collaborative process into four logical phases: requirement definition, design review, change approval, and production hand‑over. For each phase they propose concrete, measurable Key Performance Indicators (KPIs). In the requirement phase, metrics such as “number of change requests,” “average approval time,” and “conflict incidence rate” monitor requirement volatility. The design review phase employs “review meeting‑to‑decision ratio,” “design error recurrence,” and “reviewer comment count” to gauge design quality and communication effectiveness. The change approval phase tracks “approval waiting time,” “rejection reason analysis,” and “version‑control accuracy,” while the production hand‑over phase monitors “BOM consistency errors,” “shipment delay causes,” and “quality issue recurrence.” All KPIs are integrated into a real‑time PLM dashboard that visualizes trends, triggers alerts when thresholds are breached, and automatically launches root‑cause analysis workflows. The authors embed KPI outcomes into a continuous improvement loop based on the PDCA (Plan‑Do‑Check‑Act) methodology, ensuring that performance data feed regular process‑review meetings and drive corrective actions. A pilot implementation of this indicator framework demonstrated tangible benefits: average approval time in the requirement phase fell by 35 %, design error recurrence dropped by 22 %, and overall project duration shortened by 12 %. The study concludes that a disciplined, indicator‑driven approach to PLM‑supported collaboration not only uncovers technical and organizational impediments early but also empowers data‑driven decision‑making, thereby enhancing product development efficiency and strengthening competitive advantage.