Success Stories – Manufacturing

Mid-Size Manufacturer Connects Operational Data to Reduce Downtime

Who Are They?

A 180-employee precision manufacturer operating three shifts across two facilities had a data problem — not a shortage of it, but an inability to act on it in real time. The cost of that gap is significant: Siemens’ True Cost of Downtime 2024 report estimates the average large
manufacturing plant loses approximately $253 million per year to unplanned downtime.[9]

The Situation

Operations managers were spending an estimated 8-10 hours per week compiling shift reports from multiple disconnected systems. Maintenance was primarily reactive. Supply chain exceptions were often not visible until they disrupted the production schedule. Deloitte
research indicates AI-driven predictive maintenance can reduce equipment downtime by 35-45% and eliminate 70-75% of unexpected breakdowns.[7]

The Engagement

A systems inventory across both facilities identified five automation and intelligence opportunities for the initial 90-day deployment:

Production KPI data lived in three separate systems; shift reports were assembled manually each morning
Equipment maintenance logs showed historical patterns preceding failures, but no automated analysis existed
Supplier delivery variance was tracked in a spreadsheet, reviewed weekly rather than monitored in real time
Quality inspection records were stored but not systematically analyzed for defect trends
Compliance and safety documentation was compiled manually for each audit cycle

Kyber Insight was deployed in three phases: unified production dashboards pulling live data from all systems; a predictive maintenance layer flagging equipment anomalies before failure; and a supply chain monitoring engine surfacing delivery risks and quality trends automatically each morning.

35-50%

Potential reduction in unplanned downtime

Deloitte/McKinsey [7,8]

10-40%

Potential reduction in maintenance costs

McKinsey [8]

70-75%

Potential reduction in unexpected
breakdowns

Deloitte [7]

~$253M

Avg. large plant annual cost of
unplanned downtime

Siemens, 2024 [9]

Published research benchmarks. Results vary by organization and scope.

Before Kyber Insight, our operations managers were historians — documenting what had already happened. Now they are managing operations in real time. They can see what is developing and respond before it becomes a problem. That is a fundamentally different job.
VP of Operations (representative scenario)

Key Outcome

Operations leaders who benefit most describe a fundamental change in how they spend their day. Instead of compiling reports on what happened, they are reading intelligence about what is developing. Instead of discovering equipment issues after a line goes down, they are acting on predictive alerts days in advance. McKinsey’s research suggests predictive maintenance can reduce downtime by up to 50% and lower maintenance costs by 10-40%.[8] The compounding value is in decision quality that comes from current, connected intelligence — rather than a manually compiled report from yesterday.

Sources

[7] Deloitte, Predictive Maintenance & the Smart Factory: 35-45% downtime reduction; 70-75% fewer unexpected breakdowns.
[8] McKinsey manufacturing research: Predictive maintenance reduces downtime up to 50%; lowers maintenance costs 10-40%.
[9] Siemens True Cost of Downtime 2024: Avg. large plant ~$253M/yr; Fortune 500 ~$1.4T/yr (11% of revenues).