Manufacturing wins through consistency: strong uptime, stable flow, predictable yield. Yet unplanned failures still drain 5-20% of productive capacity through sudden stoppages, inflated repair costs, and lost throughput.
The barrier is disconnected systems. Production, maintenance, quality, and supply teams see different slices of reality, creating blind spots that turn into downtime.
We build systems that close those gaps. Our digital twins and predictive models flag early risks and route the right actions, unifying data flows, decision workflows, and operational roadmaps into one learning architecture that stabilizes output and protects capacity.
Detect and prevent equipment failures before they happen using ML algorithms that analyze real-time sensor data, minimizing downtime and optimizing maintenance schedules.
Make intelligent, autonomous decisions on inventory, production planning, and logistics through AI algorithms that anticipate disruptions and optimize in real-time.
Monitor production processes continuously, detect anomalies instantly, and trigger automated actions to prevent faults before they impact output.
Our Art of Problem-Solving System decodes interconnectedness across sensor patterns and maintenance cycles, enabling AI to dynamically optimize machine settings, schedule maintenance, and eliminate hidden inefficiencies.
Deploy computer vision algorithms that inspect every product at speed, identifying defects and anomalies to ensure zero-defect production at scale.
The Manufacturing Excellence team at a leading Optical Fiber manufacturer was responsible for driving innovations by developing new products and optimizing resource utilization. One key metric in this process was After Draw Yield (ADY), which measures fiber production at each stage of the manufacturing process. However, the team lacked visibility into probable fiber ADY in real time and sought to understand how process variables impacted yield.
Increase in Fiber Yield
Potential Annual Revenue Gains
A leading computer manufacturer recently outsourced its factory operations. But with most processes being manual, there were significant variations in production across multiple product lines, resulting in excessive backlogs and long cycle times.
Validated model accuracy
Reduction in potential annual backlog
For three years, a Fortune 500 company’s manufacturing plant struggled to meet its order demand. This persistent shortfall resulted in a staggering $2M monthly loss.
boost in revenues
reduction in unused inventory
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Contact Mu Sigma today and discover how we can guide you to the forefront of the digital manufacturing revolution.
