Manufacturing Data Strategy Services: A Practical Guide for Microsoft-First Factories

Most manufacturers don’t have a “data problem.”

They have a “too much data, not enough direction” problem.

Over the last few years, plants have added sensors, new MES modules, upgraded ERP, and rolled out tools like Power BI. The result?

Tons of reports, dashboards, and exports… but the same old issues:

• Chronic downtime and firefighting
• Conflicting numbers between teams and plants
• Decisions still made on gut feel

That’s the gap manufacturing data strategy services are meant to close. In this guide, we’ll show you how it happens – with real examples from our own projects.

Why Manufacturers Need a Data Strategy (Not Just More Dashboards)

If this sounds familiar, you’re not alone:

• Each plant has its own way of tracking downtime and OEE
• Maintenance logs live in one system, quality data in another, and production schedules in a third
• Supervisors keep “shadow spreadsheets” because they don’t trust the official numbers
• Power BI reports exist… but no one is sure which version is correct

You technically “have data,” but:

• The SCADA team has their truth
• The MES team has their truth
• The ERP/finance team has their truth
• And Excel has everyone’s truth

When someone asks, “What’s our OEE across all lines?” or “How much did scrap cost us last month?” you get three different answers and a long debate about filters and definitions.

The hidden cost of “dashboard chaos”

More dashboards without a strategy create noise, not clarity.

That noise shows up as:

• Slow decisions – managers spend half a meeting arguing about whose numbers are “right”
• Manual work – analysts stitching together data exports late at night for management reports
• Local optimizations – each plant or line optimizes its own metrics, even if that hurts the bigger picture
• Hidden capacity – you suspect there’s more output available, but you can’t see the bottlenecks clearly

In the end, leaders revert to what they know:

“I know the dashboards say X, but my gut tells me Y.”

If the organization doesn’t trust the numbers, it doesn’t matter how beautiful the visuals are. That’s exactly why “just building more reports” isn’t the answer.

Why tools alone don’t fix the problem

A lot of manufacturers start with technology:

• “Let’s implement a data lake.”
• “Let’s move everything to the cloud.”
• “Let’s roll out Power BI to everyone.”

These are good moves only if they’re guided by clear answers to questions like:

• What business problems are we trying to solve first?
• Which KPIs actually matter, and how are they defined?
• Where will the data come from, and who owns its quality?
• How will plant teams and leaders use the data in daily, weekly, and monthly routines?

Without those answers, you get:

• A data lake full of poorly structured data no one uses
• Power BI reports connected to different sources, calculating KPIs differently
• A cloud environment that’s technically impressive but operationally underused

In other words: expensive chaos in a nicer package.

What a data strategy changes for manufacturers

A good strategy doesn’t start with “Which tool?” It starts with:

1. Outcomes:
   
   • Reduce unplanned downtime by X%
   • Cut scrap/rework costs by Y%
   • Improve on-time delivery, cycle times, or capacity utilization
     
2. Common language and KPIs:
   
   • Clear, agreed definitions for OEE, scrap, yield, performance, availability
   • Standardized calculations across lines and plants
     
3. Data foundations:
   
   • Which systems provide which data (ERP, MES, SCADA, historians, quality, maintenance)
   • How that data is modeled and integrated (e.g., into a Microsoft Fabric or Azure-based platform)
     
4. Governance and ownership:
   
   • Who is responsible for data quality in each area
   • Who approves KPI definitions and changes
   • How new reports are requested, built, and maintained
     
5. Usage and routines:
   
   • Which dashboards are used in daily tier meetings at the line/area/plant level
   • What leaders look at weekly and monthly
   • How data feeds continuous improvement projects

Instead of “yet another dashboard,” you get a coherent system where:

• Everyone sees the same numbers
• Everyone knows what those numbers mean
• Everyone understands how to act on them

Especially critical for mid-market manufacturers

Large global manufacturers might have entire internal data teams and big-budget programs.

Mid-market manufacturers typically don’t:

• IT teams are small and stretched thin
• Operations and engineering teams already have full plates
• Data and analytics get tackled in ad-hoc projects when someone has time

That’s why structured manufacturing data strategy services are so important in this segment:

• They prevent you from overbuying and underusing technology
• They focus limited resources on high-impact use cases instead of scattershot projects
• They give you a clear roadmap: what to do now, what to defer, and what to ignore

You don’t need a “big-bang” digital transformation.

You need a practical plan that ties your data to real business outcomes and uses the tools (like Power BI and Microsoft Fabric) you already have or are planning to adopt.

In short: strategy is the multiplier

Dashboards are the output.

A manufacturing data strategy is the operating system that makes those dashboards reliable, consistent, and genuinely useful.

Without strategy:

• Data stays fragmented
• KPIs stay inconsistent
• Decisions stay slow and political

With strategy:

• You know what you’re solving for
• You know which data and systems matter most
• You know how each role on the plant floor and in the boardroom will use data to make better decisions

From here, the next step is to unpack what manufacturing data strategy services actually include in practice—what manufacturers can expect to get out of a structured engagement, and how that looks step by step.

What “Manufacturing Data Strategy Services” Actually Include

In plain language:

Manufacturing data strategy services help your organization decide how to collect, standardize, govern, and use data across plants to improve performance, quality, and profitability.

That means aligning:

• Business goals – downtime reduction, scrap reduction, throughput, cost per unit
• Data & systems – ERP, MES, SCADA, historians, quality, maintenance, spreadsheets
• People & processes – who owns the data, who uses it, and how often

Instead of data being a byproduct of machines and systems, it becomes a deliberate asset with a plan.

Core building blocks of a manufacturing data strategy

Most structured services for manufacturers revolve around a few key components. The names may change, but the content is similar.

1. Data vision and business outcomes

This is the “why” of your strategy.

• What’s hurting you the most today? (e.g., unplanned downtime, quality issues, late orders)
• How will success be measured? (e.g., +3% OEE, –20% scrap, +10% throughput)
• Which plants, lines, or product families are in scope first?

The output is a short, clear statement that links data to outcomes, like:

“Use standardized, near-real-time production data to improve OEE by 3% across Plant A and B within 12 months.”

That becomes the north star for everything that follows.

2. Data architecture blueprint

This is the “how data flows” part.

You map:

• Source systems: ERP, MES, SCADA/PLC, historians, quality systems, maintenance CMMS, spreadsheets
• Integration & storage: typically a cloud platform (e.g., Azure, Microsoft Fabric) where data is collected and standardized
• Modeling layer: curated data models for production, quality, maintenance, supply chain
• Analytics & apps: Power BI reports, dashboards, and possibly Power Apps / workflows on top

The result is a high-level architecture diagram and a set of principles such as:

• “Single, governed dataset for OEE across all plants.”
• “All production data lands in the lakehouse before reporting—no direct-report connections to MES.”

It doesn’t have to be deeply technical, but it must be consistent and repeatable across plants.

3. Data governance and ownership

Governance answers the question: “Who is responsible for what?”

For manufacturers, that typically covers:

• Data owners:
  • Production data → Operations
  • Maintenance data → Maintenance/Reliability
  • Quality data → QA/QC
  • Financial data → Finance
    
• Data stewards: People who understand the data best and ensure its quality and definitions (often planners, engineers, or senior analysts).
  
• KPI definitions & approval process:
  • How is OEE calculated?
  • What counts as “scrap”?
  • How do we define “on-time delivery”?
    
• Change management:
  • How are new metrics added or changed?
  • How are sites aligned when standards change?

Manufacturing data strategy services typically deliver a lightweight governance model that fits your size—enough structure to stop chaos, not so much that it slows everything down.

4. Use case roadmap

This is where strategy becomes a plan of action.

Rather than “do everything,” you prioritize 8–15 potential use cases and decide which to tackle in what order. For example:

1. Standardized OEE & downtime reporting across 2 pilot lines
2. Scrap & rework analysis by product, shift, and supplier
3. Maintenance performance dashboard (MTBF, MTTR, planned vs unplanned)
4. Production planning vs actual performance view

Each use case is scored by:

• Business value (e.g., potential savings, risk reduction)
• Complexity (data availability, integration effort)
• Time to value (how quickly you can see results)

The outcome is a phased roadmap (e.g., 90 days, 6 months, 12 months) that tells you:

• What to build first
• What to defer
• Which enabling capabilities (data models, pipelines) you need in place

5. Adoption and analytics operating model

Even the best dashboards are useless if no one uses them.

This part of manufacturing data strategy services focuses on how data fits into daily work:

• Who uses what, when?
  • Operators and supervisors in daily tier meetings
  • Plant managers in weekly performance reviews
  • Leadership in monthly ops/financial reviews
    
• Standard report sets:
  • “These are the 5 official production dashboards”
  • “These are the executive KPI views”
    
• Training & enablement:
  • Power BI training for key users
  • Guides and playbooks on how to read and interpret KPIs
  • Champions in each plant who support others

The output often includes an analytics catalog (what reports exist and for whom) and clear guidelines to reduce “DIY chaos” and duplicate reports.

For instance, with MSA Safety, we combined standardized data capture on the shop floor with a central Dataverse model and Power BI dashboards to unify productivity and quality metrics across global plants—turning a high-level data strategy into something operators, plant managers, and executives use every day.

Tangible deliverables you should expect

A well-structured engagement won’t just leave you with nice conversations. You should walk away with things like:

• Data Strategy Summary
  • 5–10 pages summarizing business goals, scope, and guiding principles
• Architecture & Integration Blueprint
  • Diagrams showing how data flows from systems into your analytics platform
  • Key technologies and design standards
• KPI & Data Glossary
  • Definitions of core manufacturing metrics and shared data terms
• Governance & Operating Model
  • RACI-style view of who owns what
  • Processes for new reports, metric changes, and data quality checks
• Use Case & Implementation Roadmap
  • Phased plan for 3, 6, 12+ months
  • Prioritized list of analytics use cases with expected benefits
• Adoption Plan
  • Training topics, audiences, and cadence
  • Initial rollout plan for pilot plants or lines

These assets become the playbook for your IT, OT, operations, and finance teams to work from the same script.

How this differs from a one-off analytics project

A typical one-off project might sound like:

“We need a new OEE dashboard in Power BI for Plant A.”

You’ll probably get that dashboard—but you might also get:

• Different OEE logic than Plant B
• Manual workarounds because data models weren’t standardized
• Another siloed solution that’s hard to scale

With manufacturing data strategy services, that same request becomes:

“We need a standard OEE model and dashboard pattern we can roll out across all plants.”

The difference:

• You design once, then deploy many times
• New reports build on a shared foundation instead of starting from scratch
• You avoid rework, inconsistency, and fragmentation

In short, manufacturing data strategy services package all of this—vision, architecture, governance, use case roadmap, and adoption—into a coherent, actionable plan.

Next, we can look at the core pillars of a strong manufacturing data strategy and break down in more detail how foundations, governance, analytics, culture, and improvement all fit together.

The Core Pillars of a Strong Manufacturing Data Strategy

You can think of your manufacturing data strategy like building a plant:
if the foundations and utilities are wrong, it doesn’t matter how shiny the machines are.

Most successful strategies rest on five pillars:

1. Data Foundations
2. Data Governance & Quality
3. Analytics & Insight Delivery
4. Adoption & Culture
5. Continuous Improvement & Advanced Analytics

Get these right, and tools like Power BI, Microsoft Fabric, and Azure finally start delivering the value you hoped for.

Pillar 1: Data Foundations

This is the plumbing of your data strategy – invisible when done right, painful when done wrong.

For manufacturers, that means:

1. Integrating OT and IT data

• OT: PLCs, SCADA, historians, MES, machine sensors
• IT: ERP, WMS, quality systems, maintenance CMMS, spreadsheets

A strong foundation answers:

• Which systems are “systems of record” for which data?
• How often do we need data (near real-time for production vs daily for finance)?
• How do we link everything together (orders, batches, lines, machines, shifts)?

2. A consistent data model

If every plant has its own naming, codes, and structures, analytics will never scale.

You need shared definitions for things like:

• Equipment hierarchy (plant → area → line → machine)
• Product and material master data
• Downtime categories and reason codes
• Scrap/rework reasons
• Shift, crew, and calendar logic

These decisions usually become a standardized manufacturing data model that lives in a platform like Microsoft Fabric / Azure and feeds Power BI.

A good example of this in practice is a project with a premium appliance manufacturer like Sub-Zero. 

Instead of building reports directly on top of disparate operational systems, the team designed a Kimball-style dimensional model in a central warehouse (Snowflake). That model became the single source of truth for quality data: tests, defects, products, lines, and time were all modeled once, cleanly, and then exposed to Power BI.

The impact:

• Quality and operations teams stopped arguing about whose spreadsheet was right
• New reports were built by reusing the same conformed dimensions and facts
• Global and factory-level views came from one consistent model, not a patchwork of local extracts

That’s exactly what “data foundations” mean: not just storing data somewhere, but structuring it so you can reuse it across use cases and plants instead of reinventing the wheel every time.

3. Scalable, cloud-ready architecture

Whether you’re all-in on Microsoft or moving gradually, a modern strategy usually leans on:

• Centralized, secure storage (e.g., data lake or lakehouse)
• Reusable pipelines (not one-off integrations per report)
• Semantic models that can be reused across many dashboards

The goal: add a new line, plant, or product without reinventing your data every time.

Pillar 2: Data Governance & Quality

If Data Foundations are the plumbing, governance is the maintenance plan and quality is the water itself.

Without governance and quality, you end up with:

• Different OEE numbers in different reports
• Disagreements over what counts as “scrap”
• Gaps and errors in downtime classification
• General “we don’t trust the data” feeling across the board

A strong pillar here includes:

1. Clear roles and responsibilities

• Data Owners – accountable for data in their domain (e.g., Operations for production data, QA for quality data, Maintenance for asset data).
• Data Stewards – operational experts who understand the data, validate quality, and help define rules.
• Data Team / IT – responsible for the platforms, pipelines, and models.

Everyone knows: “When there’s a data issue here, this is the person we talk to.”

2. Standardized definitions and rules

• KPI glossary: OEE, scrap rate, yield, throughput, on-time delivery, etc.
• Business rules: What counts as planned vs unplanned downtime? What is rework vs scrap?
• Coding standards: How downtime, defects, and products are coded.

This gets documented and shared so debates about numbers turn into improvements to definitions, not politics.

3. Quality monitoring

• Automated checks (e.g., missing values, impossible values, inconsistent codes)
• Regular reviews of key fields like downtime code completeness or scrap reason accuracy
• A simple process for flagging and fixing data issues

The result is data people actually believe and are willing to act on.

Pillar 3: Analytics & Insight Delivery

This pillar is where most organizations start (“We need dashboards!”), but in a robust strategy, it’s the third step, not the first.

The key is to design analytics around decisions, not visuals.

1. Standard manufacturing KPIs

At a minimum, you’ll want consistency across:

• OEE and its components – Availability, Performance, Quality
• Downtime – duration, frequency, reasons, by line/shift/product
• Quality – scrap, rework, right-first-time, defects by cause
• Throughput & capacity – units per hour, bottlenecks, utilization
• Maintenance – MTBF, MTTR, planned vs unplanned, backlog
• Supply / delivery – on-time delivery, plan vs actual, lead times

Your data strategy defines how these are calculated and how they appear in analytics.

2. Role-appropriate dashboards and reports

Different roles need different views:

• Operators & Supervisors → simple, real-time-ish views: “How is my line performing right now? What’s my OEE today?”
• Plant Managers → daily/weekly views across lines and shifts with trends and Pareto charts.
• Operations Directors / COO → cross-plant comparison, capacity, and risk views.
• Finance / Leadership → rolled-up KPIs with cost and margin insights.

A strong strategy will define a core set of standard reports, then allow controlled flexibility on top (self-service for analysts, governed datasets).

3. Design principles that encourage action

Good analytics:

• Highlight exceptions and trends, not just raw numbers
• Make it easy to drill from global → plant → line → shift → order
• Show context (targets, baselines, last month/last year)
• Use simple, consistent layouts so people don’t have to “re-learn” each dashboard

This is the difference between “pretty charts” and “daily decision tools.”

Pillar 4: Adoption & Culture

You don’t change performance by changing software. You change performance by changing conversations.

This pillar is about embedding data into how the organization works:

1. Meeting cadences with data

• Line-level daily standups using production and downtime dashboards
• Weekly plant performance reviews based on standard KPIs
• Monthly leadership reviews with consistent cross-plant metrics

Each meeting has:

• A defined set of reports
• A standard set of questions (What happened? Why? What will we do?)
• Owners for follow-ups and actions

2. Training and enablement

You make it easy and safe for people to use data:

• Intro and advanced Power BI training for key roles
• Short guides or videos on “how to read this dashboard”
• Local champions in each plant who help their colleagues

Instead of “only the data guy can use this,” it becomes natural for supervisors and managers too.

3. Behavioral norms

• Decisions are expected to be backed by data
• People are encouraged to question numbers constructively
• Issues with data trigger improvement, not blame

Culture is the hardest pillar to build, but once it’s there, the rest compounds.

Pillar 5: Continuous Improvement & Advanced Analytics

Once the fundamentals are in place, you can safely move into the fun stuff: predictive, prescriptive, and AI-driven use cases.

1. Continuous improvement loop

• Track the impact of improvements (did the new changeover process actually improve performance?)
• Use data to prioritize CI projects (which line, which product, which type of downtime first?)
• Refine KPIs and reports based on feedback

Your data strategy becomes a living document, updated as your operations evolve.

2. Stepping stones to advanced analytics

With clean, governed data in place, you can explore:

• Predictive maintenance
  • Predicting failures based on sensor data and history
  • Optimizing maintenance windows
• Quality prediction
  • Linking process parameters to defect rates
  • Early warning for quality drifts
• Scheduling & optimization
  • Using data to improve production schedules and changeovers

The data strategy clarifies which advanced use cases make sense, in what order, and what data/skills you need to support them.

Bringing the pillars together

These pillars aren’t separate projects; they’re different angles on the same system:

• Data Foundations ensure you can get the right data
• Governance & Quality ensure you can trust it
• Analytics & Insight Delivery ensure you can see and understand it
• Adoption & Culture ensure people actually use it
• Continuous Improvement & Advanced Analytics ensure you keep getting better

In the next section, we’ll zoom in on how a Microsoft-first architecture supports these pillars in a way that fits mid-market manufacturers—using tools like Microsoft Fabric, Azure, and Power BI as the backbone of your manufacturing data strategy.

A Microsoft-First Architecture for Manufacturing Data

For most mid-market manufacturers, the smartest move is getting more value out of the Microsoft tools you already pay for—and building a data strategy on top of them.

A Microsoft-first architecture means using:

• Azure for data ingestion, storage, and processing
• Microsoft Fabric (or modern Azure analytics) as your analytics backbone
• Power BI as the main interface for insights
• Power Apps & Power Automate to close the loop with workflows and simple apps

All tied together with Azure AD security and governance.

Let’s walk through what that looks like in manufacturing terms.

Your typical manufacturing system landscape (and where Microsoft fits)

Most manufacturers already have a mix of:

• ERP – Dynamics, SAP, Infor, Epicor, or similar
• MES / MOM – production execution, orders, confirmations, some OEE
• SCADA / PLCs / Historians – machine data, tags, events
• Quality Systems – LIMS, QMS, lab data, inspections
• Maintenance / CMMS – work orders, failures, spare parts
• Spreadsheets & Access Databases – the “real” glue where people fix gaps manually

A Microsoft-first architecture doesn’t replace all of that. It sits on top and standardizes the way you collect and use the data.

1. Data ingestion: getting data out of silos

Goal: Bring key data from ERP, MES, SCADA, historians, and spreadsheets into a single, governed environment.

Typical Microsoft components:

• Azure Data Factory / Fabric Data Pipelines
  • Extract data from ERP (via APIs, OData, database connections)
  • Pull MES and quality data from SQL databases or APIs
  • Load CSV/Excel files from SharePoint/OneDrive
• Azure IoT Hub / Event Hubs / Streaming (where needed)
  • Stream machine or sensor data into the cloud
• Fabric Real-Time Analytics (or Azure Stream Analytics)
  • For near-real-time processing of events if you need “now-ish” dashboards

In a well-designed strategy, you define:

• Which data to ingest (and how often)
• Where it lands (raw zone vs curated)
• How to avoid point-to-point integrations for every new report

2. Central storage & modeling: Fabric / Azure as the “single source of truth”

Instead of every report connecting directly to operational databases, you centralize the heavy lifting.

Storage:

• Microsoft Fabric Lakehouse or Azure Data Lake
  • Stores raw and transformed data in an open format (often Delta/Parquet)
  • Separates “raw” from “modeled” data
• Optionally SQL-based models (e.g., Azure SQL / Fabric Warehouse)
  • For structured, relational reporting requirements

Modeling:

Here you build subject-area models tailored to manufacturing:

• Production model – orders, lines, machines, shifts, cycles, OEE components
• Downtime model – events, durations, reason codes, categories
• Quality model – tests, results, defects, scrap, rework
• Maintenance model – work orders, failures, assets, MTBF/MTTR
• Supply & planning model – plan vs actual, backlog, delivery

These become semantic models (in Fabric or directly in Power BI) that:

• Enforce shared business logic
• Reuse calculations and relationships
• Feed many reports without having to rebuild logic each time

A good illustration of this is work done with a large food manufacturer like Johnsonville. 

Their original Power BI model had grown into a tangle of complex relationships and unused tables, which made refreshes painfully slow and reports hard to maintain. 

By redesigning the model into a clean star schema – a single fact table for key production events, surrounded by well-structured dimension tables (time, plant, line, product, etc.) – they drastically improved refresh times and made the whole solution easier to extend.

That’s the essence of a Microsoft-first modeling layer: simple, well-designed semantic models that perform well and can support dozens of reports without collapsing under their own weight.

3. Power BI: the shop window for manufacturing data

Once the data is standardized, Power BI becomes the main interface for people across the business.

Typical design patterns:

• Standard report packs
  • OEE & downtime dashboards
  • Scrap and quality performance
  • Plant KPI overview (per shift, line, product)
  • Maintenance performance dashboards
• Role-specific views
  • Supervisor views for tier meetings (current shift performance, actions needed)
  • Plant manager views (yesterday, last week, trends, Pareto charts)
  • Executive views (cross-plant comparison, high-level KPIs, financial impact)

Key advantages of a Microsoft-first approach:

• Seamless integration with Office 365 – embed Power BI in Teams, SharePoint, or even PowerPoint
• Familiar security model with Azure Active Directory (Entra) – use existing groups and roles
• Strong self-service and governed datasets so power users can build on trusted data instead of exporting to Excel every time

4. Power Apps & Power Automate: closing the loop

A modern data strategy means acting on it, not just seeing it clearly.

That’s where Power Apps and Power Automate come in:

• Power Apps
  • Simple apps for operators to log downtime reasons on a tablet
  • Forms for quality incident registration or non-conformance management
  • Maintenance request apps tied to the same data model
• Power Automate
  • Alerts when specific KPIs cross thresholds (e.g., scrap above X%, downtime exceeding Y minutes)
  • Automatic notifications to maintenance or quality teams
  • Workflow to route approvals, escalate issues, or trigger tasks

Because everything runs in the Microsoft ecosystem, you can:

• Trigger flows from Power BI alerts
• Store app data back in Fabric/Azure
• Maintain one user and permission model across apps, reports, and data

5. Security, governance, and compliance baked in

Manufacturing data often includes sensitive operational, financial, and customer information. With a Microsoft-first architecture, you leverage:

• Azure AD-based security
  • Role-based access to datasets, reports, and apps
  • Row-level security in Power BI (e.g., a plant manager sees only their plant data)
• Fabric / Power BI governance
  • Workspaces mapped to domains (Operations, Finance, Executive, etc.)
  • Certified & promoted datasets for official reporting
  • Lineage views to track where data comes from and where it’s used
• Compliance & auditability
  • Logging of access and changes
  • Built-in features for retention, backup, and recovery

Your data strategy defines who can see what, where certified content lives, and how new content is published—so you avoid the “Wild West” of ad-hoc reports.

6. Why this architecture fits mid-market manufacturers especially well

For mid-market manufacturers, the Microsoft-first approach has some very specific advantages:

• You likely already own licenses for many components (Power BI, Azure, M365).
• IT and business users are already familiar with the ecosystem.
• You can start small (one plant, one use case) and scale across sites without changing platform.
• There’s a large pool of Microsoft-skilled talent (internal and external) to support your journey.

Compared to stitching together multiple niche tools, a Microsoft-first manufacturing data architecture:

• Reduces integration complexity
• Centralizes security and governance
• Lowers total cost of ownership over time

From architecture to action

A good manufacturing data strategy service doesn’t just draw you a pretty architecture diagram. It helps you:

• Decide which parts of the Microsoft stack to use now vs later
• Design a reusable manufacturing data model that supports multiple plants and use cases
• Implement a first wave of high-value analytics (like OEE, downtime, and scrap) on top of this architecture
• Set governance rules so the ecosystem stays clean and maintainable

In the next section, we’ll look at how all of this turns into a step-by-step engagement – from initial audit to a concrete roadmap and 90-day action plan for your manufacturing data strategy.

From Audit to Roadmap: How a Manufacturing Data Strategy Engagement Works

A “data strategy” sounds big and fuzzy. In reality, a good manufacturing data strategy engagement is very down-to-earth:

• Talk to the right people
• Map the current mess
• Decide what matters most
• Design a practical target state
• Build a 90-day plan and start executing

Here’s how that typically looks step by step — with a real-world example from a manufacturer we worked with (MSA Safety) to make it concrete.

Step 1: Discovery & Assessment – Understanding Where You Are

Every engagement starts with a structured audit of your current situation:

1. Stakeholder interviews

You talk to people across the value chain:

• Operations & Plant Managers – production targets, daily pain points, bottlenecks
• Maintenance / Reliability – failure patterns, asset criticality, CMMS usage
• Quality – scrap/rework drivers, inspections, complaint handling
• Supply Chain & Planning – plan vs actual, schedule adherence, constraints
• Finance – cost per unit, margin visibility, reporting pains
• IT / Data – existing systems, integrations, security, and constraints

Outputs:

• A map of what hurts, in the language of each role
• A list of existing reports and “shadow systems” (spreadsheets, Access, local tools)

2. Systems & data source mapping

You build a picture of:

• Which ERP/MES/SCADA/CMMS/quality systems exist
• Which plants use which systems (and which versions)
• Where data is stored (on-prem, cloud, file shares, local PCs)
• How data currently flows (or doesn’t flow) between systems

This becomes your system landscape diagram.

3. Analytics and reporting inventory

You gather:

• Existing Power BI reports, SSRS reports, Excel workbooks, etc.
• Who uses them and for what decisions
• Where the data behind them comes from

Here, you often discover:

• Multiple OEE reports with different results
• Reports no one uses anymore
• Critical reports maintained by just one individual

4. Maturity and gap assessment

Based on all of the above, you score your current state in areas like:

• Data integration and architecture
• Data quality and governance
• Standardization of KPIs
• Use of Power BI / self-service analytics
• Adoption and data-driven culture

Here, you’re identifying where you’re strong, average, and exposed.

In MSA’s case, the discovery phase revealed diverse manufacturing sites all capturing productivity and quality data in different ways. 

Each plant had its own methods, spreadsheets, and reports. KPIs like OEE and efficiency weren’t defined or calculated consistently, and leadership lacked a unified view across the global network. That mismatch between local data habits and global needs made it obvious: they needed a global standard.

Step 2: Data Strategy Blueprint – Designing Where You’re Going

Once you clearly see the current state, you design the target state.

This is the “blueprint” part of manufacturing data strategy services.

1. Business outcomes and scope

You define the focus:

• Which plants are in scope first?
• Which processes (e.g., filling, packaging, machining, assembly)?
• Which outcomes matter most in the next 12–18 months?

Examples:

• “Increase OEE by 3% at Plant A and Plant B”
• “Reduce scrap by 15% for Product Family X”
• “Improve on-time delivery by 5% through better plan vs actual visibility”

2. Target architecture (Microsoft-first)

You outline a high-level architecture:

• Data ingestion (Data Factory / Fabric pipelines, IoT/streaming where relevant)
• Central storage and modeling (Fabric lakehouse, semantic models)
• Analytics layer (standard Power BI datasets and reports)
• Optional apps & workflows (Power Apps, Power Automate)

The blueprint shows how data will flow from machines and systems all the way to dashboards and actions.

3. Data governance model

You define:

• Who owns which data domains (production, maintenance, quality, finance, etc.)
• Who approves changes to KPI definitions
• How sites will align when a new global standard is introduced

This often includes a simple RACI (Responsible, Accountable, Consulted, Informed) for key processes like:

• Introducing a new KPI
• Changing a data field structure
• Requesting a new report or dataset

4. Standardized KPI & data model

At this stage you also:

• Agree key KPI definitions for OEE, scrap, yield, etc.
• Sketch the logical data model for core subject areas (production, downtime, quality, maintenance)

The blueprint doesn’t have to go to table-column level but should be clear enough that IT and business understand the same picture.

For MSA, the blueprint phase meant defining a global data model for productivity and quality: one set of dimensions (plants, lines, products, shifts) and one set of standard metrics everyone could share. On top of that, the architecture leaned on the Microsoft stack:

• Power Apps on the shop floor to capture data in a consistent format
• Dataverse as the central, structured data store
• Power BI as the analytics layer for both plant teams and executives

That blueprint turned a patchwork of local solutions into a single global design everyone could build on.

Step 3: Use Case Prioritization & 90-Day Action Plan

A strategy only becomes real when you decide what to do first.

Rather than 50 ideas, you focus on a handful of high-impact use cases and lay them out over time.

1. Use case backlog

From discovery, you’ll have a list of potential use cases, such as:

• Standardized OEE dashboards for key lines
• Downtime Pareto by cause, line, shift, product
• Scrap analysis by product/supplier/shift
• Maintenance performance and asset reliability
• Plan vs actual production performance
• Order tracking and delivery performance

2. Prioritization criteria

Each potential use case is evaluated against:

• Business value (savings, revenue, risk reduction)
• Data readiness (how available and clean is the data?)
• Technical complexity (integrations, modeling effort)
• Time to value (how quickly we can deliver something usable)

The outcome is a ranked list of use cases.

3. 90-day plan

Then you carve out a concrete first 90 days, for example:

• Month 1
  • Finalize KPI definitions for OEE and downtime
  • Set up initial data pipelines from MES/ERP into Fabric
  • Build first version of production/downtime model
• Month 2
  • Build pilot OEE & downtime dashboards for one line/plant
  • Test with supervisors and plant managers
  • Refine based on feedback
• Month 3
  • Extend to additional lines / plants
  • Start work on second use case (e.g., scrap analysis)
  • Define adoption/training plan

This plan is very specific: owners, timelines, deliverables, and success criteria.

After agreeing on the global model, MSA’s roadmap focused on practical first wins:

• Phase 1 pilots used Power Apps to standardize data capture at selected sites.
• That data flowed into Dataverse and into pilot Power BI dashboards for productivity and quality.
• Feedback from those pilots then shaped the rollout plan to additional plants and use cases.

The 90-day plan wasn’t “do everything”—it was “prove the model works in a few sites, then scale.”

Step 4: Implementation & Adoption Support – Making It Real

A strategy becomes valuable only when it’s implemented and used.

Depending on your internal capabilities, an engagement may include:

1. Co-building the architecture and models

• Setting up Fabric / Azure environments according to the blueprint
• Creating data pipelines from source systems
• Building reusable semantic models for production, downtime, quality, etc.

The focus is on:

• Doing it once properly instead of hacking quick fixes
• Documenting so your team can maintain and extend it

2. Building core dashboards and reports

You develop:

• Standard OEE & downtime dashboards
• Scrap and quality performance dashboards
• Plant overview and management views

Each report is built on top of the shared models, so the logic stays consistent across plants and new reports.

3. Adoption-focused rollout

The rollout isn’t just “send a link to a Power BI report.”

It typically includes:

• Pilot with a small group (e.g., one plant, one line)
• Joint review sessions: what works, what’s confusing, what’s missing
• Refinements to visuals and logic
• Documentation and training sessions for supervisors, managers, and analysts
• Integration into daily/weekly meetings (so reports actually get used)

For MSA, implementation meant bringing the blueprint to life across multiple plants:

• Operators and team leaders used Power Apps to enter standardized data.
  
• That data fed a central Dataverse model, then Power BI dashboards that showed global, regional, and plant-level performance.
  
• Rollout included training, feedback loops, and adjustments so the dashboards fit real shop-floor conversations.

Over time, this led to tens of thousands of hours saved per year, hundreds of thousands of dollars in estimated annual savings, and a level of standardized reporting they simply couldn’t achieve with spreadsheets and disconnected reports.

Step 5: Managed Analytics & Continuous Improvement (Optional but Powerful)

After the initial 90 days and early wins, manufacturers often choose to keep the momentum with ongoing support.

This can take the form of:

1. Managed analytics services

• Monitoring data pipelines and model health
• Handling small changes and maintenance requests
• Regularly reviewing usage (which reports are used, by whom)
• Suggesting improvements and additional use cases

This is particularly useful for mid-market manufacturers who don’t have a big internal data team but want to keep evolving.

2. Strategy refresh cycles

Every 6–12 months, you:

• Revisit the original outcomes and KPIs – what has improved?
• Review the roadmap – what’s done, what still matters, what changed in the business?
• Adjust priorities – new lines, new products, acquisitions, market shifts

This keeps your manufacturing data strategy alive, not a one-time exercise.

The real value of the roadmap showed up over time: as standardized apps and dashboards rolled out to more plants, MSA could continuously refine metrics, add new views, and expand into additional improvement areas—without rebuilding everything each time. 

The quantified results (huge time savings, significant cost reductions, and consistent global reporting) are exactly what a well-executed audit-to-roadmap journey is designed to deliver.

What you end up with

By the end of a proper manufacturing data strategy engagement, you should have:

• A shared understanding of your data challenges and opportunities across functions
• A documented blueprint for architecture, governance, and KPIs
• A prioritized roadmap with a clear 90-day action plan
• Initial working solutions (e.g., OEE/downtime dashboards) running on a solid Microsoft-based foundation
• A plan for ongoing improvement—whether through your own team, a partner, or a mix of both

In the next section, we’ll dive into which high-impact manufacturing use cases your data strategy should prioritize first, so that value shows up quickly on the shop floor and in financial results.

High-Impact Use Cases Your Data Strategy Should Prioritize First

Oone of the most important parts of manufacturing data strategy services is deciding which use cases to tackle first.

You don’t need advanced AI to start seeing value. You need a shortlist of practical, high-impact scenarios that:

• Use data you already have (or can get easily)
• Matter to both operations and finance
  Build reusable data structures for future use cases

Let’s walk through some of the best “first wins” for most manufacturers.

1. Standardized OEE & Downtime Analytics

If your data strategy did only one thing well, it might be this.

Why it matters

• OEE (Overall Equipment Effectiveness) is a common language across production
• Downtime is often the largest visible source of lost capacity
• Most plants already collect some downtime data – just not consistently

Typical problems before strategy

• Each plant calculates OEE differently
• Downtime reasons are inconsistent (or logged as “OTHER” most of the time)
• Reports are delayed and hard to compare across lines/plants

What the use case looks like

• A standard OEE model with clear definitions for Availability, Performance, and Quality
• A unified downtime model with consistent reason codes and categories
• Power BI dashboards such as:
  • Real-time-ish view for supervisors (current shift/last shift OEE, downtime by cause)
  • Daily/weekly view for plant managers (trend by line, Pareto of top downtime reasons)
  • Cross-plant view for operations leaders (comparisons, best/worst performers)

Why it’s a great starter

• High visibility: everyone feels the pain of downtime
• Builds core structures (equipment hierarchy, time, shifts) that other use cases reuse
• Creates quick, tangible wins in daily tier meetings and CI projects

2. Scrap, Rework, and Quality Performance

Once you can see where you’re losing time, the next big lever is where you’re losing product.

Why it matters

• Scrap and rework hit both cost and customer satisfaction
• Quality issues often hide in spreadsheets and local systems
• Better visibility can drive root-cause analysis and preventive action

Typical problems before strategy

• Scrap is tracked differently between plants or even lines
• No unified view of scrap by product, customer, line, or shift
• Quality reports are backward-looking and prepared manually at month-end

What the use case looks like

• A standardized scrap and rework model, linking:
  • Product / SKU
  • Production line and machine
  • Shift, operator, or crew
  • Supplier (where relevant)
  • Defect or scrap reason
• Dashboards like:
  • Scrap by product and line, with trends and Pareto of reasons
  • Cost of scrap over time, with ties to material and labor cost
  • Quality performance vs targets, with drill-through to specific batches or lots

Why it’s a great starter

• Direct line to money: scrap reduction is easy to quantify in financial terms
• Encourages cross-functional collaboration (quality + operations + engineering)
• Reuses much of the same production data you already modeled for OEE

3. Maintenance & Reliability Insights

You’ve tackled “how well we’re running” and “how much we’re scrapping.”

Next: why we keep breaking down.

Why it matters

• Unplanned downtime is one of the most expensive problems in a plant
• Maintenance teams often have lots of data but little consolidated insight
• This use case lays groundwork for predictive maintenance later

Typical problems before strategy

• CMMS or maintenance data is isolated from production data
• KPIs like MTBF and MTTR exist, but only in reports that few people see
• No easy way to correlate breakdowns with products, shifts, or operating conditions

What the use case looks like

• A combined asset & maintenance model, linking:
  • Equipment hierarchy
  • Work orders and failure codes
  • Downtime events
  • PM schedules and completion
• Dashboards and reports such as:
  • Asset reliability: MTBF, MTTR, failure frequency by asset type
  • Planned vs unplanned maintenance, backlog, overdue PMs
  • Top “bad actors” equipment and their impact on production and scrap

Why it’s a great starter

• Builds a bridge between maintenance and production using shared data
• Highlights where targeted improvements and investments will pay off most
• Sets up the data structure needed later for machine learning and prediction

4. Throughput, Capacity, and Bottleneck Analysis

Once you’ve improved reliability and reduced scrap, the next question is: “Can we produce more with what we already have?”

Why it matters

• Capacity is a big lever for revenue and on-time delivery
• Many manufacturers underestimate or misjudge their true constraints
• Better throughput analytics can delay or refine capex decisions

Typical problems before strategy

• No single, clear view of capacity vs actual output across lines and plants
• Bottlenecks identified by anecdote rather than data
• Poor alignment between planning and what actually happens on the shop floor

What the use case looks like

• A throughput model that connects:
  • Production orders and schedules from ERP/MES
  • Actual output by line/product/shift
  • Downtime and performance losses
• Dashboards that show:
  • Actual vs theoretical capacity per line/plant
  • Bottleneck analysis (where time is really being lost)
  • Performance by product mix (which combinations hurt throughput most)

Why it’s a great starter

• Connects operational data to commercial outcomes (more throughput = more revenue)
• Helps both operations and leadership make better decisions on staffing, scheduling, and investments
• Reuses production and downtime data, extending the same models

5. Plan vs Actual Production & On-Time Delivery

You’ve made the line more stable and efficient. Now it’s about keeping promises.

Why it matters

• Customers judge you on whether you deliver on time and in full
• Planning and scheduling rely on realistic assumptions about plant performance
• Visibility into plan vs actual drives better commitments and fewer surprises

Typical problems before strategy

• Planning lives in ERP; reality lives in MES and spreadsheets
• No consistent, timely view of where orders stand
• On-time delivery metrics are calculated differently for different customers or plants

What the use case looks like

• A planning vs execution model combining:
  • Planned orders, sequences, and quantities from ERP/MES
  • Actual production from line and batch records
  • Logistics or shipping data for final delivery
• Dashboards such as:
  • Plan vs actual for the day/week, by line and product
  • On-time delivery performance by customer, product family, or region
  • Root-cause views (why were orders late? capacity, quality, material shortages?)

Why it’s a great starter

• Links operations directly to customer satisfaction and revenue
• Helps both planning and production talk the same language
• Makes your data strategy visible outside the plant, too (sales, customer service, leadership)

6. Why starting with a few use cases is smarter than doing everything

It’s tempting to try to solve all data problems at once: full-blown digital twin, predictive models everywhere, real-time everything.

But in practice, the best manufacturing data strategies:

• Start with 2–4 core use cases like the ones above
• Build clean, reusable data models around them
• Use early wins to build trust, culture, and momentum
• Expand from there into more advanced analytics and AI

Manufacturing data strategy services are there to:

• Help you pick the right first use cases for your situation
• Make sure each use case builds toward a coherent architecture, not one-off solutions
• Tie every dashboard and report back to clear business outcomes

In the next section, we’ll look at role-based benefits—how different stakeholders (from operators to CFOs) experience the value of a solid manufacturing data strategy in their day-to-day work.

Role-Based Benefits: How Different Stakeholders Win

A good manufacturing data strategy makes specific people’s lives easier.

One of the big mistakes in data projects is talking only about “the business” and forgetting the people who actually run it. 

Let’s break down what manufacturing data strategy services mean for each key role.

Plant Managers: One Version of the Truth for the Entire Plant

Before data strategy:

• You walk into a morning meeting and see three different OEE numbers
• Each line lead brings their own spreadsheet or local report
• You spend half the time debating whose data is “right,” instead of talking about action

With a solid manufacturing data strategy:

• There’s a single, trusted OEE and downtime dashboard for the whole plant
• KPIs are calculated the same way across lines and shifts
• You get a clear daily view: what happened yesterday, what’s happening now, and where the biggest losses are

Day-to-day benefits:

• Faster morning meetings with less debate, more decisions
• Ability to drill into issues: from plant → line → shift → batch → downtime reason
• Easier comparison between teams/lines to spot best practices and problem areas
• Data-backed arguments when talking to leadership about resources or investments

Your job shifts from mediating data arguments to leading performance improvements.

Operations Directors / COOs: Cross-Plant Visibility and Strategic Control

Before data strategy:

• Every plant reports differently, using local systems and definitions
• Consolidated performance views arrive late, usually in PowerPoint or Excel
• It’s hard to tell whether a problem is local or systemic

With a strong data strategy:

• You have standardized KPIs across all plants (OEE, scrap, throughput, delivery)
• A single, cross-plant Power BI view shows how sites compare
• You can quickly see which plants, products, and lines are driving performance up or down

Day-to-day benefits:

• Clear visibility of where to focus support, investment, and coaching
• Easier identification of top-performing lines/plants to replicate their practices
• Better alignment between operations and commercial commitments (can we reliably take on that volume?)
• Confidence when communicating with the board or investors: data is consistent, repeatable, and defensible

You move from reacting to surprises to steering the network proactively.

Maintenance & Reliability Leaders: Data-Driven Asset Strategy

Before data strategy:

• Maintenance KPIs live in the CMMS and rarely escape its UI
• Production blames breakdowns, maintenance blames “bad data” or poor scheduling
• It’s hard to connect maintenance actions to production and financial impact

With a strong data strategy:

• Maintenance data is integrated with production and downtime
• You have shared views of:
  • MTBF, MTTR, and failure counts by asset
  • Planned vs unplanned maintenance and backlog
  • Impact of key assets on OEE and throughput

Day-to-day benefits:

• Easier to justify maintenance windows and improvements with hard data
• Clear list of “bad actor” assets to prioritize in reliability efforts
• Ability to show leadership the return on maintenance investments
• A foundation for future predictive maintenance (clean history, clear patterns)

Instead of being seen as “the team that causes downtime,” maintenance becomes a strategic partner in protecting capacity.

Quality Managers: Traceability, Root Cause, and Fewer Surprises

Before data strategy:

• Quality data is scattered across lab systems, forms, and spreadsheets
• Tracing defects back to specific batches, machines, or conditions takes ages
• Scrap and rework show up as big numbers, but not clearly linked to causes

With a strong data strategy:

• Quality results, scrap, and rework are connected to:
  • Product and batch
  • Line and machine
  • Shift and operator
  • Supplier and material lot (where relevant)
• You have dashboards that show:
  • Scrap and rework trends by product/line/shift
  • Top defect types and their root causes
  • Quality hotspots before they explode into major issues

Day-to-day benefits:

• Faster investigations when issues occur (less “data hunting,” more “problem solving”)
• Better collaboration with operations, engineering, and suppliers using shared numbers
• Ability to prove the impact of quality improvements on scrap costs and customer complaints
• Stronger support for certifications, audits, and customer requirements

You go from being the “police” at the end of the process to being a proactive driver of process quality.

CFOs & Finance Leaders: Clear Cost Visibility and Smarter Investment Decisions

Before data strategy:

• Operational KPIs and financials live in different worlds
• Cost of downtime, scrap, and rework is estimated with rough assumptions
• Capex decisions are often based on slides and stories, not integrated data

With a strong data strategy:

• Operational metrics are tied directly to financial impact:
  • Cost of scrap by product/plant
  • Cost of unplanned downtime by line or asset
  • Margin impact of quality issues and performance losses
• You can see:
  • How improvements in OEE or scrap roll up to cost per unit
  • Which plants or lines are driving margin up or down
  • Where investments (people, capex, maintenance) will deliver the highest return

Day-to-day benefits:

• More confident business cases for improvement projects and capital investments
• Better alignment between finance and operations on priorities
• Fewer end-of-month surprises, because performance is visible daily/weekly
• Ability to challenge and support operational decisions with data both teams trust

You shift from backward-looking scorekeeper to forward-looking business partner.

IT & Data Teams: From Report Factory to Strategic Enabler

Before data strategy:

• IT and data teams are flooded with ad-hoc report requests
• Everyone wants “their own version” of the data
• The environment is fragile: point-to-point connections, manual refreshes, confusing report sprawl

With a strong data strategy:

• There’s a clear architecture (Microsoft-first) and standard way of moving data from systems to reports
• You build governed, reusable semantic models instead of one-off data pulls
• There is a governance process for new KPIs, reports, and access

Day-to-day benefits:

• Fewer emergencies and “I need this for tomorrow’s meeting” crises
• More time spent on high-value work (improving models, adding new data sources) instead of patching spreadsheets
• A cleaner, more maintainable Power BI/Fabric environment with clear ownership
• Stronger relationship with the business as a trusted enabler, not a bottleneck

IT moves away from being the “reporting helpdesk” and becomes the backbone of data-driven operations.

Why role-based value matters for your data strategy

When you design manufacturing data strategy services with these stakeholders in mind:

• It’s easier to get buy-in and budget
• Adoption skyrockets because people see “what’s in it for me”
• The strategy survives leadership changes and reorganizations, because it’s anchored in day-to-day work

How Simple BI’s Manufacturing Data Strategy Services Are Different

By now, you’ve seen what a good manufacturing data strategy should include. The natural next question is: “Why would we choose Simple BI to help us with this?”

Short answer:

Simple BI is built around Microsoft, manufacturing, and making things simple enough that people actually use them.

Let’s break that down.

1. Microsoft-First, Not Tool-Agnostic Buzzword Soup

A lot of strategy firms stay very high level and “platform neutral.” That sounds nice… until someone has to actually build something.

Simple BI takes a different approach:

• We lean into the Microsoft ecosystem you already own and understand:
  • Azure for data ingestion, storage, and processing
  • Microsoft Fabric and/or Azure-based analytics for modeling
  • Power BI as the main analytics front-end
  • Power Apps & Power Automate to close the loop with workflows and apps
• We design a strategy that is immediately implementable using this stack, not a theoretical “to-be architecture” that needs three more vendors and a huge budget.

That means:

When we say “data strategy,” we can also show you exactly where your data will live, how it will flow, and what it will look like in Power BI.

2. Deep Power BI & Fabric Governance, Not Just Slides About “Data Culture”

Many consultancies talk about data culture and self-service… and then leave you with a jungle of ungoverned reports.

Simple BI’s roots are in BI implementation and governance, so your data strategy is wired for reality:

• We help you define standard, certified datasets for production, downtime, quality, and maintenance
• We set up Power BI workspaces, roles, and access that match your organization (plants, functions, leadership)
• We address “dashboard chaos” head-on by designing:
  • A core catalog of official reports for each role
  • Guidelines for self-service that don’t break your environment
  • Processes for requesting, approving, and publishing new analytics

In other words, governance isn’t an afterthought—it’s built into the strategy from day one.

3. Manufacturing Reality Over Abstract Consulting

Simple BI doesn’t treat “manufacturing” as a generic vertical.

Your data strategy is shaped around real-world situations like:

• Different plants using different MES/ERP systems
• Inconsistent downtime and scrap reason codes (and lots of “OTHER”)
• Critical KPIs like OEE, throughput, and scrap calculated three different ways
• Shadow spreadsheets that “everyone secretly trusts more than the system”

So instead of generic frameworks, you get answers to questions like:

• How do we standardize OEE across plants that run different equipment and systems?
• What’s the simplest way to start getting reliable downtime data from operators?
• How do we phase the rollout so one pilot plant leads the way instead of overwhelming everyone at once?
• How do we design data models in Fabric that can handle multiple plants, multiple lines, multiple shifts without collapsing?

The result is a strategy that feels like it was written for your factories, not for a generic “manufacturing case study.”

4. “As Simple as 1–2–3”: A Practical, No-Jargon Way of Working

There’s a reason the company is called Simple BI.

We assume:

• Plant managers don’t have time for 80-page strategy documents
• Supervisors don’t want to guess which report to open
• IT doesn’t want another platform that’s impossible to maintain

So we structure manufacturing data strategy services in a way that’s:

• Clear and visual – architecture diagrams you can actually explain to your team
• Step-based – from assessment → blueprint → 90-day plan → execution
• Prioritized – starting with a few high-value use cases (OEE, downtime, scrap, plan vs actual) before chasing everything else
• Documented just enough – concise playbooks, KPI glossaries, and governance docs that people will actually read

If something is too complicated to explain to a plant manager in one meeting… we keep simplifying it until it isn’t.

5. Strategy That Connects Directly to Implementation & Managed Services

Simple BI doesn’t stop at “here’s your strategy, good luck.”

Because the team also delivers:

• Modern Data Analytics Solutions on Microsoft Fabric/Azure
• Power BI implementations and redesigns
• Managed analytics services (ongoing support, optimization, monitoring)
• Staffing/augmentation for Power BI, Fabric, and Power Platform specialists

…your data strategy is designed to flow naturally into execution:

• The same people who helped define your architecture can help build the pipelines and models
• The governance and workspace structure in the strategy is exactly what gets implemented in Power BI
• The roadmap is realistic because we know what it actually takes to get things live in your environment
• If your internal team is small, we can support with managed services or embedded specialists so the strategy doesn’t die halfway

You avoid the classic trap where a high-level strategy is handed to an implementation team that says, “This isn’t feasible.”

6. Obsession With Adoption and Real-World Use

Plenty of data strategies end with a stack of diagrams and a handful of pilot reports no one uses.

Simple BI measures success differently:

• Are morning tier meetings actually using the new production dashboards?
• Does the plant manager trust the OEE and downtime numbers?
• Does maintenance see value in the way asset and breakdown data are visualized?
• Can finance tie improvements in OEE and scrap to real financial outcomes?

That’s why the strategy:

• Specifies which roles use which dashboards in which meetings
• Includes training and coaching plans tailored to supervisors, managers, and analysts
• Tackles “dashboard chaos” by cleaning up existing content, not just adding new reports
• Encourages feedback loops so analytics evolve with the plant, not against it

The end goal isn’t “a strategy document delivered.” It’s a habit: people across your plants checking and using trustworthy data as a normal part of work.

7. Designed for Mid-Market Manufacturers, Not Only Global Giants

Many big-name consultancies build strategies aimed at enterprises with:

• Huge internal data teams
• Multi-million-dollar transformation budgets
• The capacity to run complex multi-vendor ecosystems

Simple BI is intentionally different:

• We design lean, effective strategies that match your team size and budget
• We prioritise quick wins and reusable building blocks over large, slow programs
• We work with your existing Microsoft investments instead of pushing you into unnecessary complexity

That makes manufacturing data strategy services from Simple BI a strong fit if you are:

• Running multiple plants, but don’t have a large central data function
• Already using (or planning to use) Microsoft 365, Azure, Power BI, or Dynamics
• Tired of one-off reports and want a scalable, governed approach that doesn’t require an army

Your Next Step

If you’re reading this and recognizing your own situation—multiple plants, lots of data, not enough clarity—a simple next step isn’t a huge project.

It can be as small as a short manufacturing data strategy conversation with the Simple BI team to:

• Review where you are today
• Identify 2–3 high-impact use cases to start with
• Sketch what a Microsoft-first architecture could look like in your environment

No big commitment, no hard sell—just a structured chat to see whether a focused Manufacturing Data Strategy Service engagement would move the needle for your plants.