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Enregistrement W2623725241 · doi:10.1108/jkm-07-2016-0273

Capturing knowledge from lessons learned at the work package level in project engineering teams

2017· article· en· W2623725241 sur OpenAlex

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Notice bibliographique

RevueJournal of Knowledge Management · 2017
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueConstruction Project Management and Performance
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésExperiential learningComputer scienceProject managementKnowledge managementProcess (computing)Context (archaeology)Scope (computer science)Work breakdown structureProject planningEngineering managementProcess managementOPM3EngineeringSystems engineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,884
Score d'incertitude au seuil0,774

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0020,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,226
Tête enseignante GPT0,414
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle