Your production AI is saving money.
Now prove exactly how much.
Uprovd connects to your MES and sensor platforms, locks a pre-AI production baseline, and delivers a confidence-scored ROI report - by line, by shift, by dollar.
Your AI tools are running.
But can you prove they're working?
Predictive maintenance ROI is invisible
Your AI system flagged 14 potential failures last quarter. Maintenance acted on them. But nobody calculated the downtime cost that was actually avoided.
Scrap reduction claims need a baseline
Vision AI reduced your scrap rate - but scrap varies with material batches, machine wear, and operator skill. Without a controlled baseline, the improvement isn't attributable.
Management wants dollars, not percentages
'Scrap reduced 18%' doesn't pass a board review. '$13,000 in monthly material waste avoided' does. Uprovd converts your operational metrics into financial proof.
Built for every type of manufacturing operation
Automotive & Auto Components
OEMs, tier-1/tier-2 suppliers, stamping, casting, assembly lines
Pharmaceuticals & Chemicals
API manufacturing, formulation, batch process, QC inspection
Electronics & Semiconductors
PCB assembly, SMT lines, AOI inspection, yield optimisation
Food & Beverage
FMCG, packaging lines, cold chain, quality grading, waste reduction
Textiles & Apparel
Spinning, weaving, defect detection, cut-and-sew efficiency
Heavy Industry & Metals
Steel, aluminium, forging, casting, CNC machining, OEE improvement
Industry-specific KPIs.
Your cost inputs. No benchmarks.
From your data to a CFO-ready report
Connect your MES and sensors
Integrate with your Manufacturing Execution System, SCADA, or IoT sensor platform via API or CSV. Works with SAP PM, Siemens, Honeywell, and custom systems.
Establish the production baseline
60–90 days of pre-AI production data captured across downtime, scrap, OEE, and energy consumption - by line, by shift, by product type.
Apply industry-specific formulas
Downtime cost = machine rate × hours lost. Scrap cost = material value × scrap units. Every formula uses your actual machine rates and material costs.
Deliver confidence-scored ROI
A CFO-ready report that your plant manager and finance director can both stand behind - typically 60–90 days after baseline lock, depending on MES/CMMS integration.
How we calculate predictive maintenance ROI
Every number in your Uprovd report is traceable to a formula. Click any metric and see exactly how it was calculated - using your own cost inputs, not industry benchmarks.
See It LiveIllustrative example using realistic industry baselines. Real engagements use your actual numbers.
What an Uprovd engagement looks like
Illustrative example using realistic industry baselines. Not a verified customer engagement - actual numbers vary by company.
An auto-components plant running vision AI on a critical inspection line can expect to quantify scrap reduction and yield improvement in dollars, not percentages. The methodology applies regardless of plant size.
Apply to be the real case study →Go deeper on the data
Uprovd works across your entire operation
Frequently asked questions
How do you measure manufacturing AI ROI?
Uprovd locks a 60–90 day pre-AI production baseline (by line, by shift, by product type), then measures vision AI and predictive maintenance against it on downtime hours, scrap rate, OEE, and maintenance cost - converting each into dollars using your actual machine and material rates. Average ROI across tracked manufacturing deployments is 3.8×.
How is predictive maintenance ROI calculated?
Uprovd computes downtime hours avoided × your fully-loaded rate (machine + labour + lost output). Worked example: 24 hrs/month avoided × $540/hr = $13,000 gross, minus $2,650 AI tool cost = $10,350 net monthly saving (391% ROI, 0.79 confidence).
What is OEE Improvement and how is it valued?
OEE (Overall Equipment Effectiveness) is Availability × Performance × Quality. Uprovd measures the pre- vs post-AI delta and translates it into dollars using your actual machine rates, so an OEE gain reads as recovered output, not just a percentage.
How do you attribute scrap reduction to AI and not to material batches?
Uprovd measures scrap rate against a controlled baseline that accounts for material batch, machine wear, and operator skill, so only the AI-attributable reduction is credited - then priced at your material cost per unit for a defensible dollar figure.
Which production AI can Uprovd measure?
Uprovd measures vision defect detection, predictive maintenance, AI quality scoring, anomaly detection, and scheduling AI - any system that produces logs.
What data do you need and how do you connect?
Uprovd needs 2–3 months of pre-AI baseline - units per day, defect %, rework %, downtime hours, scrap cost per unit, downtime cost per hour, and maintenance cost - from your MES, CMMS, SCADA, or IoT platform (SAP PM, Siemens, Honeywell, or custom) via REST API or CSV, plus vision-AI logs.
Why does management get dollars instead of percentages?
'Scrap reduced 18%' does not pass a board review; '$13,000 in monthly material waste avoided' does. Uprovd converts operational metrics into financial proof - by line and by shift - so plant and finance leaders stand behind the same number.
How long until a CFO-ready result?
Uprovd delivers a confidence-scored report 60–90 days after baseline lock, depending on MES/CMMS integration complexity and data availability.
Ready to prove your AI ROI?
Confidence-scored results. No consultant required.