Run Your Plant on Live Data, Not Last Month's Guess
The Operational Challenges Holding You Back
Scheduling by Whiteboard
Production plans live in spreadsheets, so when a machine goes down or parts arrive late, the floor re-plans in circles.
OEE Numbers You Can't Trust
Downtime, scrap, and speed losses are logged by hand and inconsistently, so nobody believes the utilization reports.
The Expedite Tax
Missing parts and slipping schedules force rush freight and weekend labor, a panic tax paid straight out of margin.
Inventory That Isn't Real
Counts don't match the floor, so you buy safety stock you don't need while jobs sit waiting for parts you can't find.
Quotes That Take Days
Pricing, capacity, and lead times live in different systems, so sales chases answers while competitors quote faster.
Reports That Describe Last Month
Analysts stitch ERP exports into decks by hand, so S&OP decisions run on stale numbers instead of live plant facts.
The Cost of Doing Nothing
11%
Of annual revenue lost to unplanned downtime
$125K
Median cost of one hour of unplanned downtime
62%
Rise in the cost of downtime since 2019
Hidden costs like idle wages, premium parts, and delivery penalties have driven a 62% rise in downtime costs since 2019, even as incidents fell.3
- Downtime share of revenue: Siemens / Senseye, The True Cost of Downtime 2024. https://assets.new.siemens.com/siemens/assets/api/uuid:1b43afb5-2d07-47f7-9eb7-893fe7d0bc59/TCOD-2024_original.pdf
- Median hourly downtime cost and frequency: ABB, Value of Reliability survey, 2023. Referenced at https://blog.siemens.com/2024/07/the-true-cost-of-an-hours-downtime-an-industry-analysis/
- Rise in downtime costs: Siemens / Senseye, The True Cost of Downtime 2024 (hidden-cost analysis).
Get Your Roadmap for Manufacturing Data & AI
Digital Transformation Strategy 101: Roadmap to Factory Growth
Step-by-step guide
- What transformation really means on the shop floor
- How to calculate the cost of the status quo
- The core pillars that make execution stick
- A 6-to-10-week pilot playbook
- How to scale a pilot and prove hard ROI
How Data Pilot Solves It
One Source of Operational Truth
We unify ERP, shop-floor, quality, and finance data into one governed warehouse, so sales, operations, and finance all see the same capacity, cost, and margin numbers instead of arguing over whose export is right.
Downtime You Can Predict
We connect machine data and maintenance logs to predictive models that flag failure risk early. Maintenance shifts from reactive firefighting to planned interventions, protecting throughput and delivery promises.
Schedules Tied to Reality
We replace static spreadsheet plans with connected scheduling that updates as materials arrive, labor changes, or a machine goes down. Sales sees real ship dates, customers get proactive updates, and OTIF improves.
Margins Visible by Job and Line
We replace scattered reports with unified dashboards showing OEE, scrap, expedite spend, and cost per unit by job, line, and site, so leadership runs the plant on live facts instead of last month's reconciliation.
High-Value Use Cases
Real-Time Production Tracking
Business Problem:
Job status lives in operators’ heads, so bottlenecks surface only after schedules slip.
Data/AI Solution:
Live tracking of jobs, machines, and WIP across the floor with automated status capture.
Projected Business Value:
10-12% average gains in output, factory utilization, and labor productivity.
Typical Tech Stack:
MES/SCADA, barcode scanners, BigQuery, Power BI
Predictive Maintenance
Business Problem:
Equipment failures strike without warning, causing costly stops and emergency repairs.
Data/AI Solution:
Sensor and maintenance-log data feeding models that flag failure risk before breakdown.
Projected Business Value:
30-50% less machine downtime and 20-40% longer machine life.
Typical Tech Stack:
IIoT sensors, CMMS, Python/scikit-learn, AWS
Computer Vision Quality Inspection
Business Problem:
Manual inspections miss defects, and quality data sits in silos across the line.
Data/AI Solution:
Unified defect data hub with computer vision detection and automated dashboards.
Projected Business Value:
Up to 90% better defect detection and up to 50% higher inspection productivity.
Typical Tech Stack:
Vision cameras, TensorFlow/OpenCV, unified data hub
Demand Forecasting for S&OP
Business Problem:
Production plans rely on historical guesses, causing overbuilds and missed demand.
Data/AI Solution:
Forecast models blending order history, pipeline, and seasonality into S&OP planning.
Projected Business Value:
20-50% lower forecast errors and up to 65% fewer lost sales.
Typical Tech Stack:
ERP, CRM, BigQuery, Prophet/ARIMA, Tableau
Inventory Accuracy & Visibility
Business Problem:
Counts don’t match the floor, forcing excess safety stock and stalled jobs.
Data/AI Solution:
Connected inventory data across ERP, warehouse, and floor with exception alerts.
Projected Business Value:
20-30% lower inventory levels while maintaining or improving fill rates.
Typical Tech Stack:
ERP/WMS, barcode scanning, CDC pipelines, Looker
Automated Quote Engines
Business Problem:
Quotes take days because pricing, capacity, and lead times live in separate systems.
Data/AI Solution:
One quoting view pulling live cost, capacity, and lead-time data from source systems.
Projected Business Value:
28% shorter sales cycles and 49% more proposals per rep.
Typical Tech Stack:
ERP, CRM, CPQ tools, integration pipelines
Expedite & Freight Spend Analytics
Business Problem:
Rush freight and weekend labor quietly erode margin as a routine panic tax.
Data/AI Solution:
Expedite spend tracked by root cause, customer, and part across orders and sites.
Projected Business Value:
3-6% savings from improved freight spend visibility alone.
Typical Tech Stack:
ERP, freight invoices, BigQuery, Power BI
OEE & Downtime Analytics
Business Problem:
Downtime and scrap are logged inconsistently, so improvement efforts aim blind.
Data/AI Solution:
Automated OEE capture with downtime reasons ranked by frequency and cost impact.
Projected Business Value:
About 30% fewer downtime hours as tracking and prediction mature.
Typical Tech Stack:
MES/SCADA, machine data, BigQuery, Tableau
Central Warehouse & Auto-Reporting
Business Problem:
Analysts spend weeks manually stitching reports across units and source systems.
Data/AI Solution:
Governed warehouse with CDC pipelines feeding live dashboards for every unit.
Projected Business Value:
25-40% lower planning and administration costs from automation.
Typical Tech Stack:
SAP/Sage, CDC pipelines, cloud warehouse, Tableau
Supplier Performance Analytics
Business Problem:
Late or variable suppliers disrupt schedules, and nobody quantifies the damage.
Data/AI Solution:
Supplier scorecards tracking on-time delivery, quality, and cost variance by part.
Projected Business Value:
Roughly 20% savings potential reported from procurement analytics.
Typical Tech Stack:
ERP, procurement data, BigQuery, Looker
- Smart factory output gains: Deloitte & Manufacturers Alliance (MAPI), Smart Factory Study: companies report average 10-12% gains in manufacturing output, factory utilization, and labor productivity. https://www.manufacturersalliance.org/research-insights/capturing-value-through-digital-journey
- Predictive maintenance impact: McKinsey, “Manufacturing: Analytics unleashes productivity and profitability”: predictive maintenance typically reduces machine downtime 30-50% and increases machine life 20-40%. https://www.mckinsey.com/capabilities/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability
- AI visual inspection: McKinsey, “Smartening Up with Artificial Intelligence”: AI-based visual inspection can raise defect detection rates by up to 90% versus human inspection, with productivity gains of up to 50%.
- Forecasting & inventory benchmarks: McKinsey, AI-driven forecasting research: forecast error reductions of 20-50%, lost sales down up to 65%, and inventory reductions of 20-30% while maintaining fill rates. https://www.mckinsey.com/capabilities/operations/our-insights/ai-driven-operations-forecasting-in-data-light-environments
- CPQ sales-cycle gains: Aberdeen Group CPQ research: average sales cycle of 3.42 vs 4.68 months (28% shorter) and 20.9 vs 14 proposals per rep per month for CPQ users.
- Freight spend visibility: Deloitte analysis: companies can save 3-6% by improving visibility into freight spend.
- Downtime reduction: Siemens / Senseye, The True Cost of Downtime 2024: the average large plant’s unplanned downtime fell from 39 to 27 hours per month (about 30%) as tracking and predictive maintenance matured.
- Automation admin savings: McKinsey, AI-driven forecasting research: administration costs fall 25-40% with AI-driven forecasting and automation. https://www.mckinsey.com/capabilities/operations/our-insights/ai-driven-operations-forecasting-in-data-light-environments
- Procurement analytics savings: McKinsey CPO research (2025): organizations report roughly 20% savings potential from deploying procurement analytics tools. https://www.mckinsey.com/capabilities/operations/our-insights/transforming-procurement-functions-for-an-ai-driven-world
Proven Results
Manufacturing & FMCG Group: From Five Silos to One Source of Truth
Challenge: Five disconnected systems, including SAP, Sage, marketing, and HR, with no central repository or single version of truth across seven business units. Reporting was fully manual, tying up analysts in every team, week after week.
Solution: Built one central warehouse consolidating all five source systems into a governed model, with CDC pipelines streaming near real-time updates and seven Tableau dashboards, one per business unit.
Key Outcomes:
- 85% faster reporting turnaround, from ~3 days to under 1 hour
- 2,000+ analyst hours per year reclaimed from manual work
- 50+ recurring reports automated end to end across all units
- Data latency under 15 minutes via change data capture
Manufacturer: Computer Vision Quality Inspection
Unified siloed defect data and deployed computer vision inspection, delivering 100% defect detection, 70% faster audits, and a 20% cost reduction.
How to Get Started
Diagnostic
A bottleneck and data maturity assessment, a ranked use-case backlog, identified quick wins, and a 90-day pilot roadmap.
Visibility
Real-time production tracking and unified reporting for plants feeling immediate pain from blind spots.
Live tracking across jobs and machines, automated OEE and downtime capture, and dashboards your whole team can trust.
You’re booking: Visibility
No spam. No obligation.
Step 3 of 3
Intelligence
For plants ready for predictive maintenance, demand forecasting, and AI-assisted quality control.
Predictive models built on your visibility foundation, from failure-risk alerts to forecasts and vision inspection.
No spam. No obligation.
Frequently Asked Questions
How long does a manufacturing data project take?
It depends on your systems, data quality, and scope. We usually start with a diagnostic, then run a 6-to-10-week pilot.
Do we need to replace our ERP, MES, or SCADA systems?
Usually not. We keep, wrap, or connect the systems you already run, and replace only what blocks growth.
What determines the cost of a project?
Cost depends on project scope, data sources, sites, integration complexity, and business needs.
What kind of data do you typically work with?
We work with ERP, shop-floor and machine data, quality, maintenance, inventory, supplier, and finance data.
What if our data is messy or lives in silos?
That is common. We define good-enough data rules, assign owners, and fix the top drivers first.
How do you decide which use cases to start with?
We rank a backlog by value, data readiness, effort, and risk, then pilot where flow improves end to end.
Will operators feel like the system is surveillance?
No. We co-design workflows with frontline leads and frame automation as capacity for growth, not job cuts.
How do you measure whether the work is successful?
We baseline metrics like OTD and cycle time before the pilot, then track them against agreed targets.
Can you support both strategy and implementation?
Yes. We support diagnostics, roadmap, integrations, warehousing, dashboards, AI models, and adoption.
What happens after the initial project is complete?
Most plants scale the pilot across sites, then add the next value stream. We also offer managed analytics support.