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Record 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 on OpenAlexaff
Andrew Stewart Herbst

Bibliographic record

VenueJournal of Knowledge Management · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExperiential learningComputer scienceProject managementKnowledge managementProcess (computing)Context (archaeology)Scope (computer science)Work breakdown structureProject planningEngineering managementProcess managementOPM3EngineeringSystems engineering

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.226
GPT teacher head0.414
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations14
Published2017
Admission routes1
Has abstractyes

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