Challenge
A small manufacturing company in the UK faced several hurdles:
- Frequent, unpredictable equipment failures causing costly production stoppages.
- Limited in-house data science expertise and outdated data collection systems.
- Fragmented data across different machines and manual logs, making it hard to get a clear picture of equipment health.
- Budget constraints that made large-scale technology investments risky.
Our Approach
For a client in this situation, CogrAI Consulting would design a phased, collaborative solution:
1. Discovery & Digital Readiness Assessment
We would conduct on-site workshops with operations and maintenance teams to map out existing processes, pain points, and data sources. A digital readiness audit would help identify gaps in sensor coverage and data infrastructure.
2. Data Unification & Quick Wins
We would help the client implement low-cost IoT sensors on critical machines and build a simple, cloud-based dashboard to centralize data from both new sensors and legacy manual logs. This would provide immediate visibility into machine status and failure patterns.
3. Pilot AI Model & Human-in-the-Loop Validation
Instead of a full-scale rollout, we would start with a pilot on one production line. Our team would develop a predictive model using the new data, while keeping maintenance staff “in the loop” to validate AI predictions and provide feedback, ensuring trust and accuracy.
4. Training, Change Management & Gradual Scale-Up
We would run hands-on training sessions for operators and maintenance leads, focusing on interpreting AI alerts and integrating them into daily routines. As confidence grows, we would expand the system to more machines and lines, continuously refining the model with real-world feedback.
5. Ongoing Partnership & ROI Tracking
We would set up monthly check-ins to review system performance, track ROI, and identify further automation opportunities—ensuring the solution continues to deliver value as the company grows.
Potential Results
Within six months, a company could expect to achieve:
- 40% reduction in unplanned downtime
- 25% decrease in maintenance costs
- Improved equipment lifespan and production efficiency
