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Presents a dynamic programming model for studying the effects of automation acquisition on the value, cost, and quality control processes in an aggregate single product environment. The model provides the optimal automation acquisition policy, that is the optimal amount of automation to be acquired and the optimal timing for acquiring it, so that the accumulated net product value can be maximized. The model can be used with different sets of learning rates and cost data. It can also be used with non‐uniform learning rates among the different processes, and non‐uniform automation effects on the value, cost, and quality control learning curves. The cases both of unbounded and bounded learning curves are examined. Selective results demonstrate that the early acquisition of the optimal amount of automation enhances the accumulated net product value.

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.

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.

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

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.

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

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

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