A point-of-view paper for plant and operations leaders on why Industrial AI pilots stall before production: data that doesn't scale, no clear owner in the plant, tools that don't fit shift life, and no plan for upkeep.
It ends with six decisions leaders should make before a pilot starts, so the pilot is built to scale from day one.
Sample written to show my writing, based on general experience. No client data. In client work, the paper is published under your executive's name.
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A customer case study in the classic format: the problem before, what changed, and the results. Written the way an Industrial AI vendor would publish it.
It tells how a snack food plant moved from fixing machines after they broke to catching motor and gearbox faults days early, and why getting technicians to trust the alerts mattered as much as the technology.
Sample written to show my writing. The company, vendor, people and figures are fictional and clearly labelled. In client work, every number is checked against your customer's data, and every quote is approved.
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Designed and built an Industrial AI pilot to find and reduce minor stops on a high-speed packaging line, the short stoppages that quietly eat into OEE.
The system combines vision AI, which watches the line, with sensor AI, which reads machine signals to spot early drift. Together they show where and why the line loses time, so maintenance and operations can act on causes, not symptoms.
My role: architecture, data pipeline from PLC and sensors, model building and rollout.
Project details are kept general to protect client confidentiality. The project is at pilot stage.
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A short white paper on why predictive maintenance models miss faults on high speed machines when data is logged too slowly, and three practical fixes.
Written for maintenance and reliability engineers. It explains sampling, averaging and timing jitter in plain language, using everyday analogies, and ends with a checklist to run before building any model.
Sample written to show my writing. It uses general engineering principles and contains no client data.
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