Track & Measure operational metrics and KPIs in real-time across all plants and workstations to identify areas for improvement and the consistent leaders.
Optimized Order Scheduling led to a 45% reduction in setup time and a 7% increase in run time.
Optimized Production & Minimizing Downtime helped to increase machine utilization by over 20%.
Eliminate Data Silos & Gaps to provide a shared single source of truth across all plants and connect assets.
Bridge IT / OT Gap by combining machine telemetry with operator actions at their workstations.
Enable Data-Driven Decisions to boost output and minimize downtime patterns.
Eliminate Manual Tracking so operators may log downtime and production status directly from the shop floor via their mobile devices.
Standardize Results to normalize disparate data formats from multiple machine vendors, ensuring consistent and reliable metrics.
Overview
A leading Manufacturer faced a critical challenge—its production lines ran without an automated and consistent way to track downtime, operator efficiency, and production output. Operations managers struggled to understand why some production lines performed better than others and were unable to identify the root cause of bottlenecks and inefficiencies.
Before partnering with Kinetech, the Manufacturer relied on disparate data sources from multiple machine vendors, manual tracking via paper and Excel, and siloed data visualization tools to piece together performance metrics. The result was inconsistent data with no reliable way to identify trends or improve throughput across their network of plants.
Challenges
The Manufacturer struggled with limited visibility into key production metrics, such as machine uptime / downtime, run rates, and scrap. Without a single source of truth, management couldn’t confidently make data-driven decisions to reduce bottlenecks or optimize production processes.
Tracking methods were also fragmented and inconsistent. Each production line used machines from different manufacturers, and each kiosk reported data in a different format that made data aggregation difficult and unreliable. Operators manually logged downtime and status at scattered kiosks across the floor, introducing delays, data inaccuracies, and gaps in reporting.
Because there was no real-time insight into machine performance, the Manufacturer couldn’t pinpoint recurring patterns that resulted in unexpected machine downtime, accurately track waste reduction efforts, or test process improvements across its plants. This lack of visibility made implementing a culture of continuous improvement to production difficult.
The Solution
Kinetech developed an ecosystem of custom-built solutions to aggregate machine data real-time to improve visibility into plant operations and ultimately provide the data needed to adapt operations to increase output across production lines. The solution integrates IoT sensor data from machines with operators' input on the shopfloor, providing a single source of truth for machine and operator performance.
Previously, operators had to walk to a kiosk to manually log downtime or job status. Now, they can log activity instantly from their work stations on a mobile tablet, enabling the merging of telemetry data from machines with human action.
The solution also normalizes data from various sources into a centralized dashboard that management can use to monitor long-term trends of their key production KPIs and identify where to focus their improvement efforts.
Outcome
The Manufacturer has transformed its ability to capture and analyze key operational data. Management now has the insights needed to identify areas for improvement and then to monitor the effect of their improvement initiatives.
With reliable data at their fingertips, the Manufacturer has shifted operations to realize improvements in machine downtime, output, scrap and ultimately improved financial performance of their operations.
The Manufacturer also realized significant advantages in adopting a low-code approach. The solution was designed and built to their exact specification to ensure the typical failures of commercial-off-the-shelf SaaS solutions were avoided. The total cost of delivery was also a fraction of the expected cost of implementing a comparable MES solution.