The same count, keyed three times
Production counts go onto the traveler, into the MES, and into an end-of-shift spreadsheet — by different people, in different ways.
Use case · Manufacturing
One shift. Five systems. One number you can trust.
A line runs 1,200 units today. Scroll, and follow that count from the confirmation at the machine, through the five systems that hold your production today, to tomorrow morning's OEE dashboard — and watch what changes when it's only entered once.
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Why manufacturing
Manufacturing doesn’t have a data shortage. It has a distance problem.
Every confirmation, quality check, and machine signal is data. The gap is the distance between the line where numbers are born and the reports leadership steers by.
The floor is a shift ahead of the office
What happened on the line today reaches a report tomorrow, if at all. Scrap and downtime causes fade before anyone asks.
Variance surprises at month-end
Standard-cost variances are explained weeks after the fact — long after the lot that caused them left the floor.
Know-how lives in a few heads
Machine setups, changeover tricks, and quality dispositions belong to senior operators — and leave with them.
The journey
Seven steps. One number.
Plant performance lives in counts, scrap, and downtime — born at the line, steered by in the boardroom. The seven steps below walk one shift's production along that route, each removing a place where the number breaks today. Follow the line.
Data FoundationBusiness IntelligenceAI & Automation
ERP / MRPMES / shop floorQuality (QMS)Maintenance (CMMS)Warehouse / inventory
Where plant performance is scattered today
One central platformMicrosoft Fabric
This is where the number's journey changes. Instead of being re-keyed into the MES, the ERP, and an end-of-shift spreadsheet separately, it lands once — here — and flows everywhere it's needed. Five versions of the order become one.
Step 01 · Data Capture & Quality
01The number is born
A line runs 1,200 units. Today that count is logged on a paper traveler and re-keyed into the ERP hours later — whether leadership ever trusts it is decided right here, at the machine.
Our approach
We help define what each work order captures — counts, scrap codes, downtime reasons — and bring in digital confirmation at the line with quality checks built in, so the number is right at the source.
Who feels it
OperatorOne confirmation at the line — no end-of-shift paperwork to re-key.
Plant mgrYesterday’s output, scrap, and downtime by line, on screen by 7am.
FinanceActuals that reconcile to standards without scrubbing.
The payoff — Shop-floor reporting moves from end-of-shift-and-approximate to real-time-and-reliable — and every downstream number gets cleaner.
Step 02 · System Integration
02It stops being re-keyed
That count used to be re-entered between the MES, the ERP, and an end-of-shift spreadsheet — each holding its own version of the order.
Our approach
We help map how order and inventory data should flow and implement the integrations — so one confirmation updates inventory, costing, and the schedule on its own.
Who feels it
PlannerSchedules built on live inventory and machine status, not stale extracts.
QualityNCRs and holds tied to lots automatically — traceability without spreadsheets.
ITGoverned integrations on one platform — not a web of point-to-point scripts.
The payoff — Re-keying between systems disappears, and “which count is right?” stops being a daily conversation.
Step 03 · Business Intelligence Roadmap
03It reaches the report by itself
OEE, scrap, and variance are still compiled by hand for the 6am meeting — so cost problems surface at month-end, weeks after the lot left the floor.
Our approach
We help automate the reports you already run and stand up self-serve OEE and variance — refreshed overnight, so causes surface while the lot is still on the floor.
Who feels it
ExecutivePlants compared on the same definitions — margin visible mid-month.
Plant mgrSelf-serve OEE, scrap, and yield, by line, shift, and SKU.
FinanceVariance analysis from data, not archaeology; a shorter close.
The payoff — Scrap and downtime causes surface while the lot is still on the floor — answers in minutes instead of weeks.
Step 04 · Data as a Product
04Everyone agrees what it means
“OEE” is calculated three different ways across plants — and comparing sites means arguing about definitions first.
Our approach
We help the business settle on one definition per number and publish each KPI with an owner and a source — findable by anyone, from line to boardroom.
Who feels it
New hiresRamp on the numbers in days, not months.
AnalystsFewer “can you pull this for me” requests.
LeadershipPlant-to-plant comparisons on one agreed definition per number.
The payoff — Self-service that’s actually self-serve — the KPI library becomes the shared language across plants and shifts.
Step 05 · AI & Automation Governance
05It stays safe around AI
Engineers are already pasting specs and costing into AI tools — recipes, process parameters, and HR data need guardrails before that spreads, not after.
Our approach
We help classify data by sensitivity and set up a governed AI environment — a maintenance copilot can read manuals and fault history while recipes stay ring-fenced, with every use logged.
Who feels it
ExecutiveAI adoption without IP or compliance risk — policy and audit trail in place.
EngineeringFormulas and process parameters ring-fenced from external tools.
EveryoneA clear answer to “am I allowed to use AI for this?”
The payoff — AI gets adopted broadly and safely — on governed data, with an audit trail.
Step 06 · Data Culture & Engagement
06The floor shapes what’s built next
Operators know exactly what’s broken about the data they’re asked to produce — but improvement ideas die at the line.
Our approach
We help put a visible request channel in place, from idea to shipped — so the people producing the number keep shaping how it’s captured and used.
Who feels it
Shop floorTheir input visibly shapes the tools — so they keep giving it.
LeadershipPain points surface early, with context, instead of festering.
The orgContinuous improvement extends to the data itself — each shipped request builds trust in the next.
The payoff — The business keeps getting more data-driven after the engagement ends — improvement becomes routine, and visible.
Step 07 · Centralized Knowledge Base
07It becomes know-how
Machine setups, changeover tricks, and quality dispositions live in a few senior operators — and walk out the door with each retirement.
Our approach
We help stand up a central knowledge base with AI-assisted capture — so a temperamental press’s setup notes become the next shift’s fix, not a retirement loss.
Who feels it
MaintenanceFault history and known fixes at hand, machine by machine.
New staffOnboarding from a knowledge base, not from shadowing alone.
AI toolsThe context they need to be accurate about your plant.
The payoff — Onboarding and AI both get faster and more accurate — know-how stops walking out the door.
The same number, everywhereLine to boardroom
Plant dashboardsKPI portalGoverned AIKnowledge base
The count the operator confirmed at 4pm is the OEE on the morning dashboard at 7am. One entry, one truth — plant performance visible while the shift can still be steered.
Benefits realized
What changes when the strategy lands.
The shifts below are what the seven focus areas add up to in a manufacturing business — line to office, confirmation to boardroom.
BeforeCounts logged on paper, re-keyed after the shift
AfterCaptured once, at the line
BeforeMorning meeting off a 6am spreadsheet
AfterLive dashboards, agreed definitions
BeforeVariance surprises at month-end
AfterCost visible while the lot is on the floor
Before“Ask the analyst” for every report
AfterSelf-serve KPI portal with definitions
BeforeAI experiments near recipes and costing
AfterGoverned AI on one source of truth
BeforeSetups and fixes in a few senior heads
AfterA searchable, living knowledge base
How we engage
Strategy and execution, delivered together.
We don't spend months writing a roadmap before anything changes. Each phase combines discovery, design, and implementation, so your teams begin using new capabilities while the roadmap continues to evolve.
Discovery & Assessment
We learn how your plant actually runs — from the confirmation at the line to the variance report on the CFO’s desk.
Interviews across the floor and the office — operators to finance Inventory of ERP/MRP, MES, quality, maintenance, and warehouse systems Gap map: where counts, scrap, and downtime data break down Audit of existing reports and the manual effort behind them
Mostly conversations and a walk of the floor — no disruption to production. You end the phase with a map of every place the same number is re-keyed, and what that costs.
Analysis & Framework Design
We design the central platform and start building — capture, connections, and guardrails, as requirements firm up.
Central platform and customer/vehicle data model on Microsoft Fabric / Azure / Databricks Capture-at-the-line design: digital confirmations with built-in checks Integrations ranked by ROI — production confirmation to ERP first Sensitivity tiers and AI guardrails for recipes, costing, and HR
The first automated reports replace manual ones mid-phase. Pilot lines run the new capture on live production, and operator feedback shapes the design.
Roadmap & Validation
You leave with a phased roadmap and a substantially implemented platform — a plan and working capability, together.
Phased roadmap covering every data initiative Priority dashboards and system connections live KPI portal and knowledge base stood up Wish-list platform open to floor and office
The morning meeting already runs off live numbers. There’s one place to find every KPI — and a prioritized plan for what comes next.
Phases can be resequenced to fit your priorities. Engagement model and investment are tailored per organization — book a meeting and we'll scope it to you.
Start here
Bring us your systems and your plants.
We'll show you how your data should flow, identify the biggest opportunities for improvement, and define a phased engagement that fits your business.
30 minutes No obligation Microsoft Fabric partners