Manufacturing Data Integration on Azure & Microsoft Fabric: A Practical Blueprint

What Is Manufacturing Data Integration (In Plain English)?

If you run a factory today, your data is probably scattered everywhere: ERP for orders and inventory, MES for production, maintenance in another system, quality in its own tool, plus a forest of Excel files on shared drives.

“Manufacturing data integration” is simply the work of getting all of that data to talk to each other in a consistent way so people can actually trust and use it.

Instead of dozens of point-to-point connections and manual exports, integrated manufacturers bring their data into a single analytics platform where it’s cleaned, organized, and modeled around the business – not around individual systems. For Simple BI clients, that platform is typically built on Microsoft Fabric and Azure Data Solutions, which are designed to pull together data from many different sources into one place for analysis.

It’s important to stress: integration is more than just “connecting systems”. You can have APIs and ETL jobs everywhere and still not be integrated in any meaningful sense. Real manufacturing data integration means:

• A common language for the business – plants, lines, products, customers, shifts are defined the same way everywhere.
• Shared metrics and calculations – OEE, scrap rate, on-time delivery, margin are calculated once and reused, not re-invented in every spreadsheet.
• One version of the truth – finance, operations, quality, and maintenance are all looking at the same numbers.

In manufacturing, this also means bridging the traditional gap between IT data (ERP, CRM, finance) and OT data (machines, sensors, PLCs, historians). When your production lines, quality checks, and orders all land in the same Azure-based platform, you can finally answer questions like, “Why is scrap up on Line 3 this week?” instead of just seeing that it happened.

All of this matters more than ever. Supply chains are volatile, margins are tighter, and most manufacturers are trying to do more with the same (or fewer) people. At the same time, plants are generating more data than ever from sensors, machines, and systems—but without integration, that data turns into noise instead of insight.

So when we talk about manufacturing data integration in this article, we’re really talking about building a Microsoft-based data foundation where your ERP, MES, quality, maintenance, and shop-floor data come together in a way that supports the decisions plant managers, engineers, and executives need to make every day. From there, tools like Power BI can sit on top and deliver dashboards and reports people actually use.

The Data Sources Manufacturers Actually Need to Integrate

When people hear “data integration,” they often think of a couple of systems talking to each other. In reality, most manufacturers are juggling seven or eight critical data sources that all matter for performance, cost, and customer service.

Let’s break down the main ones and why they’re important:

1. ERP (Enterprise Resource Planning)

What’s in it:
Orders, customers, inventory, purchasing, production orders, costs, finance.

Who cares:
Executives, finance, supply chain, customer service, plant management.

What goes wrong without integration:

• Operations is looking at “today’s reality” in MES, while ERP is a day behind.
• Finance reports profit by product or customer, but can’t see the real cost of downtime, scrap, or changeovers.
• Sales promises dates based on rough capacity assumptions, not what’s actually happening on the lines.

2. MES / Production Systems

What’s in it:
Production runs, line states, scrap, rework, changeovers, shift performance, often some downtime data.

Who cares:
Plant managers, production supervisors, process engineers, continuous improvement teams.

What goes wrong without integration:

• You know which lines are struggling, but can’t tie it back to orders, customers, or margin.
• Improvement projects are chosen based on gut feel instead of integrated data (cost + throughput + scrap).
• Different plants may measure things differently, so you can’t compare performance fairly.

3. SCADA / PLCs / Historians (OT Data)

What’s in it:
High-frequency sensor data: temperatures, pressures, speeds, vibration, states, alarms, etc.

Who cares:
Maintenance, reliability, controls engineers, process engineers.

What goes wrong without integration:

• Great detail about how a machine behaved, but no easy connection to which order, which product, or which operator.
• Predictive maintenance and advanced quality analytics stay stuck in pilots because they’re not joined up with ERP/MES data.
• Only a few specialists can access or interpret the data.

4. Quality Systems, LIMS, and Lab Data

What’s in it:
Test results, inspections, COAs, defects, nonconformances, customer complaints.

Who cares:
Quality, regulatory, customers, plant managers, engineering.

What goes wrong without integration:

• You see defect trends—but can’t easily connect them to specific suppliers, lots, lines, or process conditions.
• Root cause analysis becomes manual detective work in spreadsheets.
• You can’t reliably calculate true scrap cost or the impact on margin.

That’s exactly what we fixed for a premium appliance manufacturer like Sub-Zero and for MSA Safety: pulling lab/quality data into a central warehouse/Datamart and tying it back to products, lines and plants so quality issues weren’t just charts in a siloed tool, but part of the full production picture.

5. Maintenance / CMMS

What’s in it:
Work orders, planned vs unplanned maintenance, spare parts, downtime codes, asset hierarchies.

Who cares:
Maintenance, reliability, plant management, finance.

What goes wrong without integration:

• Maintenance is blamed for downtime they didn’t cause, or vice versa.
• No clear link between maintenance spend and line performance / OEE / scrap.
• Predictive maintenance opportunities are missed because maintenance data isn’t tied to sensor and production data.

6. Supply Chain Systems (WMS, TMS, Supplier Portals)

What’s in it:
Warehouse movements, shipments, freight, supplier performance, lead times.

Who cares:
Supply chain, logistics, customer service, finance.

What goes wrong without integration:

• You can’t see the full order-to-ship picture: where delays really happen and what they cost.
• Plants blame supply chain; supply chain blames plants; nobody has a unified view.
• Inventory decisions are made in isolation from production realities.

7. Shadow IT: Excel, Access, One-Off Databases

What’s in it:
Local metrics, manually maintained KPIs, one-off exports from systems, side databases built by power users.

Who cares:
Almost everyone on the shop floor and in middle management.

What goes wrong without integration:

• “Secret” logic and calculations live in individual files and people’s heads.
• Teams argue over whose spreadsheet is right.
• A lot of valuable local knowledge never makes it into a central, governed model.

The role of a modern platform like Azure Data Platform and Microsoft Fabric is to sit on top of all of these, pull the data together into a single lake/warehouse, and reshape it around how your business actually runs: plants, lines, products, customers, shifts, and assets.

Once these sources are integrated into a common model, tools like Power BI, Power Apps, and Power Automate can finally deliver something that feels simple on the surface—even though it’s powered by a very rich, very connected data foundation underneath.

What You Unlock When Manufacturing Data Is Integrated

Once your data is actually integrated, the conversation in your plants changes completely. Instead of arguing about whose report is right, people start asking better questions: “Why did OEE drop on Line 2?” and “What changed before scrap spiked yesterday?”

Here’s what that looks like in practice:

Clear, Trusted OEE and Downtime Visibility

With ERP, MES, and machine data pulled into a single model, you can see OEE and downtime in one place, by:

• Plant
• Line
• Product
• Shift
• Customer

You’re no longer stitching together exports from MES, a downtime spreadsheet, and an ERP report. Everyone sees the same OEE calculation, the same downtime codes, the same facts.

Daily huddles stop being “data wrangling meetings” and become problem-solving sessions: the dashboard is on the screen, the numbers are trusted, and the team focuses on what to fix next.

Scrap, Yield, and Quality Improvements

When quality systems, lab data, suppliers, and production are integrated, you can finally answer questions like:

• “Is this defect tied to a specific supplier lot or production line?”
  
• “Does scrap spike when we run at higher speeds?”
  
• “Are certain shifts or changeovers consistently causing more rework?”

For Sub-Zero and MSA Safety, integrating quality and production data into a single model turned root-cause hunts from spreadsheet detective work into a few clicks: drill from plant → line → product → batch and see the defect patterns immediately.

Instead of a quality engineer spending hours chasing data across three systems and Excel, you get:

• Scrap and FPY trends with drill-down from plant → line → product → batch.
  
• Automated views of defects by supplier, raw material, or process setting.
  
• Faster, more objective root-cause analysis.

Better Scheduling, Throughput, and On-Time Delivery

Integrated data lets you see the full picture from order to shipment:

• ERP shows demand, promise dates, and margins.
  
• MES shows actual capacity, changeovers, and bottlenecks.
  
• Supply chain systems show material availability and shipping constraints.

With that combined view, planners can:

• Build schedules based on real capacity, not averages.
  
• Prioritize orders by profitability and risk, not just due date.
  
• Spot bottlenecks early and adjust before customers feel it.

True Cost and Margin Visibility

When finance data, production data, scrap, and downtime are integrated, margin stops being a single number on a P&L and becomes something you can slice and act on:

• Cost and margin by plant, line, product, or customer.
  
• The real cost of scrap, rework, and changeovers.
  
• The impact of unplanned downtime on profitability.

This is where executives start using the same Power BI dashboards as plant managers—because they’re all looking at the same integrated truth.

A Platform for Predictive and Real-Time Decisions

Once your core data is integrated and modeled, you’re ready for the next step:

• Use time-series and maintenance data to spot patterns before equipment fails.
  
• Add real-time or near real-time views of line performance for supervisors.
  
• Trigger alerts or workflows (maintenance tickets, hold orders, QA checks) when the data crosses certain thresholds.
  

The key is that none of this is possible sustainably with siloed systems and ad-hoc Excel work. Integrated manufacturing data turns analytics from a side project into part of how the business actually runs, every shift of every day.

The Manufacturing Data Integration Blueprint on Azure & Microsoft Fabric

So what does “manufacturing data integration” actually look like when you build it on Azure and Microsoft Fabric, instead of with a tangle of scripts and one-off interfaces?

Think of it as five layers, stacked from raw data at the bottom to decisions and actions at the top.

1. Ingestion: Getting Data Out of Your Systems (Without Breaking Them)

First, you need reliable ways to bring data from all your systems into the Microsoft ecosystem:

• ERP, MES, quality, maintenance, supply chain systems
  • Use APIs, direct database connections, or file-based feeds.
  • Azure Data Factory / Fabric Data Pipelines handle scheduled and incremental loads.
  • The goal: repeatable, monitored pipelines, not ad-hoc exports.
• OT data: historians, PLCs, SCADA, sensors
  • Use OPC connectors, gateway services, or vendor APIs to land data in Azure.
  • For high-frequency or event-driven data (states, alarms, sensor readings), use Azure IoT Hub or Event Hubs.
  • The goal: get time-series and events into the platform in a way that can scale.

At this stage, you’re not trying to make the data perfect; you’re focused on getting it flowing consistently into Azure / Fabric.

2. Central Lake / Lakehouse: One Place for All That Data to Live

Once the data is landing in the cloud, you need a home for it:

• Use OneLake / Azure Data Lake as the central repository.
• Store data in layers, for example:
  • Raw: as close to source as possible for traceability.
  • Cleaned / standardized: with consistent formats, units, and key fields.
  • Curated: business-ready tables that analytics tools consume.

Because lakehouses in Fabric can handle structured ERP tables, semi-structured logs, and time-series data together, you don’t need a separate stack for every type of data. This is where you start to de-couple analytics from your operational systems.

3. Data Warehouse & Semantic Model: Reshaping Data Around the Business

This is where “integration” becomes real. You build models that reflect how your manufacturing business actually works:

• Fact tables for:
  • Production runs and quantities
  • Downtime events
  • Scrap and defects
  • Maintenance events
  • Inventory movements and shipments
  • Sales orders / demand
• Dimensions for:
  • Plant, line, machine, cell
  • Product / SKU / family
  • Customer and market
  • Shift, calendar, crew
  • Supplier and material

You then define shared metrics – OEE, FPY, scrap rate, on-time delivery, cost per unit – inside a semantic model that tools like Power BI use directly.

The result: every dashboard, report, or analysis uses the same calculations and definitions. No more re-creating OEE logic in every workbook or report.

With Sub-Zero (our client), that meant designing a Kimball-style warehouse and semantic model for quality on Snowflake + Power BI so every report pulled from the same facts and dimensions. At MSA, we did the same concept in Dataverse/Datamart for productivity and quality.

4. Real-Time & Near Real-Time Analytics: Seeing Issues as They Happen

Not everything needs to be real-time, but some things absolutely benefit from it:

• Streaming / near real-time data (seconds to minutes):
  • Line states, throughput, alarms, WIP, short stoppages.
  • Use Fabric Real-Time Analytics (or Azure streaming tools) to process events as they arrive.
  • Push results into tables and dashboards that refresh frequently.
• Batch data (hours to days):
  • Financials, month-end, standard KPIs.
  • Use scheduled pipelines and warehouse loads.

On the front end, your users don’t need to know what’s streaming vs batch. They just see dashboards with the right freshness: operators see the last few minutes; executives see daily or weekly summaries.

5. BI & Apps Layer: Turning Integrated Data into Decisions and Actions

Once the integrated data model is in place, you can put simple, focused tools in front of people:

• Power BI dashboards and reports for:
  • Plant managers and supervisors (OEE, scrap, throughput, downtime).
  • Maintenance and reliability (MTBF, repeat failures, maintenance backlog).
  • Quality (FPY, defects by supplier, line, product).
  • Finance and executives (margin by product/plant, cost of downtime, profitability).
• Power Apps for data capture and workflows:
  • Digital checklists, operator input, downtime reason codes, quality holds.
  • Maintenance or quality apps that write back into your systems or into Dataverse.
• Power Automate to close the loop:
  • Trigger maintenance tickets when conditions are met (e.g., repeated micro-stops).
  • Notify supervisors when scrap or downtime exceeds thresholds.
  • Route approvals or escalations based on data.

At this stage, integrated data stops being an IT project and becomes part of how the plant runs. People on the shop floor interact with the same data foundation that executives see, just through different views and tools.

Two Common Patterns We See

To make this more concrete, you can frame the blueprint with two patterns:

• Pattern A: Single-Site Manufacturer Starting Small
  • Ingest ERP + MES into Fabric.
  • Build a basic lakehouse and semantic model for production and OEE.
  • Stand up a small set of Power BI dashboards for one plant.
  • Add quality and maintenance later.
• Pattern B: Multi-Plant, Multi-ERP Manufacturer
  • Ingest from several ERPs/MESs into the same lake.
  • Standardize a common data model (plants, lines, products, customers, KPIs).
  • Compare performance across plants fairly and consistently.
  • Gradually add OT data and real-time views where they matter most.

In both cases, the Azure + Fabric blueprint is the same. You’re building a Microsoft-native integration and analytics layer that can start small, prove value quickly, and then scale across plants, systems, and use cases without having to reinvent the architecture every time.

A Step-by-Step Roadmap: From Siloed Plants to an Integrated Data Platform

You don’t go from spreadsheets and siloed plants to a fully integrated Azure/Fabric platform in one leap. The manufacturers who succeed treat it as a step-by-step journey, with clear wins at each phase.

Here’s a practical roadmap you can follow:

Phase 0 – Discover & Align (Don’t Skip This)

Before touching any pipelines, get clear on:

• Systems: What ERPs, MES, historians, quality, maintenance, and supply chain tools are in play? By plant?
  
• Reports: Which reports and spreadsheets do people actually use today?
  
• KPIs: How are OEE, scrap, on-time delivery, etc. calculated today (in reality, not in theory)?
  
• Priorities: What’s the one problem that, if solved, would make everyone say “this is worth it”?

Bring IT, OT, and operations into the same room and agree on a small set of priority use cases. Most manufacturers start with something like “trusted OEE and downtime visibility for Plant X.”

Output of this phase: a simple use case backlog, a first-plant/first-line focus, and agreement on what “success” looks like for phase one.

Phase 1 – Quick-Win Integration (ERP + MES)

Start by integrating just enough to deliver a meaningful, visible win:

• Ingest key ERP tables (orders, production orders, inventory, customers).
  
• Ingest MES data (production runs, scrap, downtime, line states).
  
• Land both in Azure / Fabric, and build a basic lakehouse.
  
• Create an initial semantic model:
  
  • Facts: production, scrap, downtime.
  • Dimensions: plant, line, product, shift, order.

Then build a small, focused set of Power BI dashboards for one plant:

• OEE by line and shift
• Top downtime reasons
• Scrap by product and line

Don’t try to boil the ocean. The goal here is to prove that an integrated model can replace manual reports and messy spreadsheets for a real team, in a real plant.

Phase 2 – Scale Out Across Plants and Data Domains

Once the first plant is using and trusting the integrated dashboards:

• Add more plants to the same model, even if they have slightly different systems or codes.
  
• Standardize common dimensions (plant, line, product, shift) and align KPI definitions across sites.
  
• Integrate additional domains:
  
  • Quality / lab data → for scrap and FPY analysis.
    
  • Maintenance / CMMS → for downtime and reliability views.
    
  • Supply chain → for order-to-ship visibility.

The focus in this phase is standardization and comparability:

• Can you fairly compare OEE across plants?
  
• Can you see scrap and defects by supplier across the network?
  
• Are plant managers still arguing about definitions, or are they solving problems?

Phase 3 – Add Real-Time & Predictive Capabilities

With a solid integrated model in place, you can start layering in more advanced capabilities without chaos:

• Streaming or near real-time feeds from machines and historians into Fabric.
  
• Dashboards that update within minutes for supervisors watching line performance.
  
• Alerts when key thresholds are crossed (e.g., scrap > X% for Y minutes, repeated micro-stops, abnormal temperature trends).

At this point, you can also start lightweight predictive work:

• Early-warning models for unplanned downtime based on sensor + maintenance history.
  
• Quality models that flag high-risk runs based on process parameters and past defects.

The key is that these models sit on top of an integrated foundation—not a one-off data science project with its own private dataset.

Phase 4 – Close the Loop with Workflows and Apps

Finally, integration becomes part of how people work, not just how they look at data:

• Use Power Apps for operators, quality, and maintenance to capture contextual data (reason codes, checks, notes) directly against the integrated model.
  
• Use Power Automate to trigger workflows from data events: maintenance tickets, quality holds, escalation emails, Teams notifications.
  
• Embed Power BI visuals into these apps and everyday tools so people don’t have to “go to the dashboard” to see the impact of their actions.

By this phase, you’ve moved from “We have better reports” to “Our processes are driven by integrated data.”

Across all phases, the manufacturers who win are the ones who:

• Start with one plant and one or two high-impact metrics.
  
• Build on a repeatable Azure/Fabric blueprint instead of a series of one-off fixes.
  
• Treat adoption and change management as seriously as pipelines and models.

Designing Data Models & KPIs for Integrated Manufacturing Analytics

You can integrate systems all day long, but if your data model and KPIs are a mess, you’ll still have arguments in every meeting. The real leverage comes when everyone agrees on how things are structured and how things are measured.

That’s what a good manufacturing data model gives you:

Start with the Right Fact Tables

In an integrated platform, you don’t model systems; you model events and business processes. For most manufacturers, that means a handful of core fact tables:

• Production fact: Each row = a run / shift / time slice on a line. Holds quantities produced, good vs scrap, runtime, planned vs unplanned stops.
• Downtime fact: Each row = a downtime event with start/end, duration, reason code, machine, line, shift.
• Scrap / defects fact: Each row = a scrap or defect event or lot, with quantities, defect type, cause codes, inspection results.
• Maintenance fact: Each row = a work order or maintenance event, with asset, type (planned/unplanned), duration, cost, parts used.
• Inventory and movement fact: Each row = a material movement (goods receipt, issue to production, transfer, shipment).
• Sales / demand fact: Each row = an order line or forecast line, with quantities, requested dates, prices, and customer.

You don’t need to get this perfect on day one, but you do need to separate these processes instead of stuffing everything into one wide, unmanageable table.

Add Shared Dimensions So Everything Joins Up

Dimensions are the “things” that facts relate to. They’re where standardization happens:

• Plant / site
• Line / machine / work center
• Product / SKU / family / brand
• Customer / channel / region
• Calendar / shift / fiscal period
• Supplier / material
• Optionally: crew / operator, if you track it

Once these dimensions are shared across fact tables, you can answer questions like:

• “What’s OEE by product family and customer?”
• “Which suppliers drive the most scrap and downtime?”
• “Which plants are best-in-class for this product line?”

Lock In KPI Definitions (and Stop the “Excel Wars”)

With facts and dimensions in place, you can define KPIs once in a semantic model instead of in dozens of spreadsheets:

• OEE
  • Availability × Performance × Quality, using agreed definitions of planned time, runtime, and good quantity.
• Scrap rate / FPY
  • Scrap rate = scrap quantity ÷ total produced.
  • First-pass yield = units passing all checks on first attempt ÷ units produced.
• OTIF / service level
  • On-time in-full = orders shipped on or before confirmed date, with full quantity, ÷ total orders.
• Cost per unit / per line
  • Allocating labor, overhead, scrap, and downtime cost onto units produced, by line/plant.

The important part isn’t the formulas themselves; it’s that everyone uses the same formulas, centrally maintained, and surfaced through Power BI or similar tools.

Turn the Model into a Reusable Semantic Layer

In the Microsoft world, this model lives as a Power BI semantic model (or Fabric warehouse/lakehouse + model) that:

• Feeds multiple reports and dashboards.
• Is owned by a clear data/analytics team.
• Has governed measures (KPIs) that are certified and reused.

That’s how you move from “Bob’s spreadsheet vs Maria’s spreadsheet” to one trusted source of metrics that can support everything from operator boards on the shop floor to board-level performance reviews.

Real-Time vs Batch: When Manufacturers Actually Need Streaming Data

“Real-time data” gets thrown around a lot, but not every problem needs sub-second streaming. In fact, trying to make everything real-time is one of the fastest ways to make your data platform expensive and fragile.

It’s more useful to think in three speeds:

1. True Real-Time (Seconds or Less)

This is the world of machine protection and safety:

• Stopping equipment when a critical limit is exceeded
  
• Interlocks, emergency stops, safety systems
  
• Millisecond/second-level control loops

These are usually handled by control systems, not your analytics platform. Azure and Fabric can receive this data for visualization and history, but you don’t want your plant safety depending on a dashboard refresh.

Rule of thumb: if human reaction time is too slow to matter, it probably belongs in the control system, not Power BI.

2. Operational “Near Real-Time” (Minutes)

This is where streaming into Azure/Fabric really pays off:

• Shift and hourly OEE / throughput
  
• Short stoppages and micro-downtime trends
  
• WIP visibility across lines or work centers
  
• Live scrap monitoring during a run

Here, updating every 1–5 minutes is usually enough. Supervisors and line leads need to see what’s happening this shift, not last week. Streaming or micro-batch pipelines can push data into Fabric, and Power BI dashboards can refresh frequently for these specific use cases.

Rule of thumb: if someone on the shop floor can change their behavior based on the data in the next hour, near real-time is worth considering.

3. Batch (Hours to Days)

Most of your analytics can live happily here:

• Daily production summaries
  
• Month-end financials and cost reports
  
• Trend analysis of scrap, downtime, and maintenance
  
• Executive views and board reports

Nightly or hourly batch loads are simpler, cheaper, and more than enough for strategic and tactical decisions.

Rule of thumb: if the decision happens in a meeting or planning cycle, batch is almost always fine.

The sweet spot for manufacturers is usually a mix:

• A small number of near real-time views for operations and maintenance
  
• A robust batch model for everything else

Design the platform so both speeds share the same data model, rather than building a separate “real-time” science project on the side that no one maintains.

Governance, Security & Change Management Across IT, OT, and the Shop Floor

Once you integrate data across ERP, MES, historians, quality, and maintenance, you’re not just moving numbers around—you’re changing how people work. Without governance and change management, the platform will either lock up (too controlled) or turn into a wild west (too loose).

You need just enough structure to keep things safe and trustworthy, without killing speed.

Clarify Who Owns What

Start by separating platform ownership from data and KPI ownership:

• IT / data team
  
  • Own the Azure/Fabric platform, security, pipelines, and performance.
    
  • Maintain the core data model and semantic layer.
    
• Business owners (operations, quality, maintenance, finance)
  
  • Own definitions of KPIs (OEE, scrap rate, OTIF, margin).
    
  • Sign off on changes to metric logic and dimension structures.
    
  • Decide what “good” looks like in terms of reports and usage.
    
• Local champions in plants
  
  • Provide feedback on whether dashboards and apps actually work in the real world.
    
  • Help roll out changes and training on the shop floor.
    

The key is that no one team can do this alone. Governance = shared responsibility, not a committee that meets once a quarter.

Put Guardrails Around Access and Security

With everything in one place, security matters more than ever:

• Role-based access:
  
  • Separate roles for admins, data engineers, report creators, and consumers.
    
  • Keep tight control over who can publish new datasets and reports to shared spaces.
    
• Plant- and region-based scoping:
  
  • Use row-level security patterns so a plant manager only sees their own site, while corporate can see everything.
    
  • Make “who sees what” predictable and documented.
    
• Sensitive data controls:
  
  • Be intentional with HR, pricing, cost, or customer details.
    
  • Where needed, split sensitive measures into separate models or tightly controlled workspaces.

Good security is mostly invisible to end users. They open a report and see what they should see, and nothing more.

Light-Weight Governance for Models and Reports

You don’t need a bureaucracy, but you do need a few non-negotiables:

• A central, certified model for core manufacturing KPIs (OEE, scrap, downtime, etc.).
  
• A simple change process for metric logic: propose → review → test → communicate → deploy.
  
• Naming standards for datasets, workspaces, and reports so people can find what they need.
  
• A basic catalog of “golden” dashboards vs exploratory or ad-hoc analysis.

This lets you support self-service (people can build their own views) without ending up back in spreadsheet chaos.

Treat Change Management as a Workstream, Not an Afterthought

Adoption doesn’t happen automatically just because the dashboards are good:

• Train supervisors, planners, and plant managers on how to use the new views in daily routines (huddles, weekly reviews, performance meetings).
  
• Make early users visible “champions” and capture their wins as short stories.
  
• Retire old reports and spreadsheets deliberately, with a clear handover to new ones.
  
• Measure usage: who’s actually logging in, which reports matter, where people get stuck.

The goal is simple: integrated data should make life easier for people, not give them “one more system” to fight with. Governance, security, and change management are how you get there.

When to Ask for Help (And How to Choose a Manufacturing Analytics Partner)

There’s a point where “we’ll fix it with another spreadsheet” stops working. Most manufacturers reach for outside help when at least one of these is true:

• You have multiple plants and/or multiple ERPs/MESs, and no one can get a clean, comparable view.
  
• IT is swamped with tickets for new reports and integrations and can’t keep up.
  
• You’ve already built a few dashboards, but no one trusts the numbers or uses them consistently.
  
• Every new analytics request turns into a mini-project because there’s no reusable data model underneath.

If that sounds familiar, it’s probably time to at least talk to a partner.

What to Look for in a Manufacturing Analytics Partner

When you evaluate partners, focus less on slideware and more on repeatable experience in environments like yours.

Look for:

• Deep Microsoft expertise
  
  • Real project experience with Azure Data Platform, Microsoft Fabric, and Power BI.
    
  • Ability to design both the back-end (pipelines, lakehouse, models) and the front-end (dashboards, apps).
    
• Manufacturing-first thinking
  
  • Familiar with ERP + MES + historian + quality + maintenance, not just “CRM and web data.”
    
  • Comfortable talking about OEE, scrap, changeovers, line performance, not just generic KPIs.
    
• Proven adoption and governance work
  
  • Can show how they’ve driven actual usage on the shop floor, not just delivered a technical solution.
    
  • Have opinions on governance, ownership, and how to avoid “dashboard graveyards.”
    
• Ability to stay after go-live
  
  • Offer managed analytics or ongoing support so your platform doesn’t decay after the first project.
    
  • Can help you grow from one plant and a few KPIs to a multi-plant, multi-use-case platform.
    

Where a Boutique Partner Like Simple BI Fits

A boutique, Microsoft-focused team is often a good fit for mid-market and upper mid-market manufacturers because:

• You get hands-on senior people who’ve seen similar environments before.
  
• They can move quickly on Azure, Fabric, and Power BI without pushing a massive, multi-year program.
  
• They can act as an extension of your team—helping you define the roadmap, build the initial blueprint, and then support it as new plants, systems, and use cases come online.

We’ve done this for global manufacturers like MSA Safety (integrated productivity and quality models), Sub-Zero (quality integration into a modern warehouse + Power BI), Tempur Sealy (near real-time schedule and throughput views), and even non-manufacturing clients like the Wisconsin DMA (integrated workflows and data for tuition grants).

If you’re already feeling the pain of siloed plants, inconsistent KPIs, and “Excel holding the business together,” that’s usually the signal: it’s time to get a second set of eyes on your architecture and start shaping a proper manufacturing data integration strategy.