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Article
Publication date: 12 September 2016

Petros Pistofidis, Christos Emmanouilidis, Aggelos Papadopoulos and Pantelis N. Botsaris

Field expertise in industry is often poorly recorded and unexploited. The purpose of this paper is to introduce a methodology and tool that incorporates a knowledge validation…

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Abstract

Purpose

Field expertise in industry is often poorly recorded and unexploited. The purpose of this paper is to introduce a methodology and tool that incorporates a knowledge validation loop to leverage upon human-contributed field observations in industrial maintenance management. Starting from a failure mode, effects and criticality analysis (FMECA) model, it defines a collaborative process that links FMECA knowledge with field maintenance practice.

Design/methodology/approach

A metadata management system is designed to encourage staff involvement in enriching knowledge with field observations. The process supports easy feedback and collaborative annotation and is pilot tested via an industrial case study.

Findings

Streamlining FMECA validation is welcomed by maintenance staff, empowering them to exert more control over the management, usage and versioning of reference knowledge.

Research limitations/implications

The methodology for metadata management in industrial maintenance enables staff participation in a collaborative knowledge enrichment process. Metadata management is a pre-cursor and therefore an important step to drive future analytics.

Practical implications

Industry personnel are more inclined to contribute to organisational knowledge if the process is based on reference knowledge and requires minimal interaction.

Social implications

Facilitating individual contribution to collective knowledge strengthens the sense that each staff member can have organisational impact.

Originality/value

The paper introduces a methodology and tool to stimulate human-contributed knowledge in industrial maintenance, strengthening collaborative organisation knowledge flows.

Details

Industrial Management & Data Systems, vol. 116 no. 8
Type: Research Article
ISSN: 0263-5577

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