Manufacturing

OEE numbers trapped in MES silos don't help the executive team make faster production decisions.

We connect your MES, ERP, historian, and quality systems so plant managers and CFOs can see throughput, downtime, and quality trends in real time, from the shop floor up.

31%
less unplanned downtime

Monitor OEE, scrap rate, and line throughput in real time, with predictive alerts before equipment failure or schedule slippage hits the P&L.

The Challenge

The operational reality of manufacturing

01

OEE calculated weekly, useless for shift decisions

A weekly OEE number tells you what happened. It does not help you fix the shift that is happening right now. Shift managers need real-time visibility, not a Monday morning report.

02

Unplanned downtime surfaces days later

Equipment fails, production stops, and the cost analysis arrives 3 days later in a spreadsheet. The window to prevent it or minimize impact has long closed.

03

Quality issues reach customers before root cause is found

Defects make it through inspection and reach customers. By the time root cause analysis is complete, the production run is over and the damage is done.

04

Supply chain disruptions ripple through undetected

Material shortages and supplier delays hit the production schedule before anyone in the plant has been told. Production planning runs on assumptions that are already wrong.

Key Metrics

The KPIs we surface for manufacturers

OEE (Overall Equipment Effectiveness)
Throughput per line
Machine uptime
Scrap and rework rate
First-pass yield
Cycle time
Schedule adherence
Inventory turns
Cost per unit
Mean time between failures
Capabilities

How we help manufacturers

01

Real-time OEE and production dashboards

Shift-by-shift OEE, throughput, and downtime tracking by line and machine. Available on the plant floor and in the executive view.

02

Predictive maintenance models

Equipment failure prediction built on vibration, temperature, cycle count, and maintenance history data. Shift from reactive to condition-based maintenance.

03

Quality root cause analytics

Connect defect rates to specific machines, operators, shifts, materials, and conditions. Find root cause before it repeats.

04

Production scheduling optimization

Scheduling models that minimize changeover time, maximize throughput, and account for material availability and machine constraints.

05

Computer vision for defect detection

Camera-based defect detection deployed on production lines. Catch defects at the point of production, not at final inspection.

Integrations

Some of the systems we connect with

These are some of the platforms we connect. We integrate with almost any system. If you run it, we can likely work with it.

SAP S/4 HANA
Oracle ERP Cloud
Plex
Epicor
IQMS
Wonderware
OSIsoft PI
Ignition
Microsoft Dynamics 365 SCM
Rockwell FactoryTalk
SAP S/4 HANA
Oracle ERP Cloud
Plex
Epicor
IQMS
Wonderware
OSIsoft PI
Ignition
Microsoft Dynamics 365 SCM
Rockwell FactoryTalk
Use Cases

Where this applies

01

Food manufacturer: shift-by-shift OEE live

A food processing plant calculated OEE weekly from manual line logs. Shift managers had no visibility into current performance and could not intervene on low-output shifts.

Outcome

We connected the MES and historian data to a real-time dashboard. Shift managers now see OEE, throughput, and downtime codes every 15 minutes. OEE improved by 11 points in 90 days.

02

Discrete manufacturer: predictive maintenance on CNC fleet

A precision parts manufacturer experienced recurring unplanned downtime on its CNC machining center fleet. Each incident took 6 to 14 hours to resolve and cost an average of $28K.

Outcome

We built a predictive maintenance model on spindle vibration and temperature data from 24 machines. The model now flags high-risk machines 10 to 14 days before likely failure with 84% accuracy.

03

Consumer goods: quality root cause by shift and material lot

A consumer goods manufacturer had a persistent defect issue affecting 3 to 5% of production. Multiple root cause investigations over 18 months produced no resolution.

Outcome

We connected defect data to shift, machine, operator, and material lot records. The analysis identified a specific material supplier and two operator practices as root causes. Defect rate dropped to 0.8% within 60 days of corrective action.

Results

Outcomes you can expect

31%
Reduction in unplanned downtime
12%
Improvement in OEE
25%
Reduction in scrap rate
Shift-by-shift
Production visibility instead of weekly reports
FAQ

Frequently asked questions

Get Started

Real-time visibility from shop floor to executive suite.

Book a free call and we will assess your manufacturing data maturity in 30 minutes.