Capturing knowledge from lessons learned at the work package level in project engineering teams
Notice bibliographique
Résumé
Purpose The purpose of this paper is to describe the knowledge management (KM) loop process in a work package (WP)-based project engineering management method. The purpose of the KM loop is the routine capture of learnings to improve work practices in both the project and the firm. Design/methodology/approach A conceptual model for a project KM loop is developed by researching various KM theories found in the literature and incorporating the most applicable concepts and bridging any gaps in an attempt to overcome the reported impediments to learning in projects. A specific WP-based project engineering method (the STBQ method) is chosen as the framework for illustrating the workings and advantages of the KM loop. The author’s experiential judgement is used in applying selected academic concepts to create a KM process particularly useful for consulting engineering firms engaged in the detailed design phase of heavy industrial projects notwithstanding the fact that it may be beneficial in other project environments. Findings Completion of a WP can be used as a natural point in time for the collection of lessons learned (LL). At post-WP debriefing meetings, intuitive learnings can be contributed by individuals and interpreted in the context of the recently completed WP. When seen to be applicable, the project engineer integrates this newly gained experiential knowledge into the project’s job instructions for immediate implementation on other WPs remaining in the project scope. Through the project manager, these new or revised job instructions are proposed as candidates for new or revised standard practices to the senior managers of the engineering firm who can institutionalize them by approval for use in other in-progress or future projects. Research limitations/implications The KM loop described here is specifically intended to be used with the STBQ method where the 100 per cent rule is applied and where each WP sub-team is tasked with the delivery of their WP safely, on-time, on-budget and with no quality deficiencies as the criteria for success of their WP. A research limitation is that capturing learnings throughout the project does not solve the problem of capturing post-project learnings from design errors surfacing during construction, in commissioning, or after start-up during on-going operations and maintenance. Nonetheless, innovative ideas and improvements can be found during the detailed engineering phase and the KM loop captures these for intra-project and inter-project use. Practical implications The extra effort of decomposing requirements into WPs not only helps control project costs, schedule, quality and safety but also provides an effective way to capture knowledge from project learnings for intra-project and inter-project use. Social implications The lessons-learned sessions held at the completion of each WP provides an opportunity to provide motivation and morale boosting to the WP sub-team members. Originality/value This paper contributes what is believed to be the first WP-based KM loop in project engineering management using a specific application of the 4I framework of organizational learning. In addition, when applied in the STBQ method or any other method that uses interim WPs for both planning and reporting, the LL sessions can be pre-scheduled and budgeted separately from the subject WP. This helps to overcome the problem widely reported in projects that not enough calendar time or person-hours can be spared to attend the LL sessions.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».