Your dashboard project just went live. The CFO opens it once, scrolls through seventeen charts, and goes back to asking her analyst for the same Excel pivot table she’s used for three years.
Most dashboards are not decision-first BI. Teams start with “what data do we have?” instead of “what decisions do we need to make?”
They optimize for visual appeal instead of decision speed. They build comprehensive reports when they need focused diagnostic tools.
The manufacturing plant manager who decides on overtime allocation doesn’t need a trend line. He needs to see which specific line and shift is burning through labor hours.
The procurement director facing a supplier crisis doesn’t need a balanced scorecard. He needs to identify which parts are at risk and the backup options’ cost in Microsoft Fabric/Power BI.
Here’s how to build dashboards around decisions instead of information:
Tip 1: Start with decisions, not data
Before opening Power BI or talking to IT, spend an hour mapping your recurring decisions.
Map not metrics, but decisions—the specific choices someone in your organization makes weekly or daily that could benefit from data.
Walk through your last month of operations. What decisions kept surfacing? What questions sent someone scrambling for data?
What calls would benefit from consistent signals?
For manufacturing operations, this might be deciding whether to run weekend overtime when production is behind, choosing between suppliers due to quality issues, or determining if equipment maintenance should be moved up due to vibration readings that are cause for concern.
For each decision, build a decision profile:
Trigger: What signal or threshold initiates this decision? Instead of vague efficiency percentages, consider real operational signals, like when the third-shift supervisor reports unusual noise from the main conveyor, or when two customers in the same week complain about the same defect type.
Owner: Identify who makes this call in practice, not according to the org chart. Often it’s the shift supervisor, not the plant manager.
Frequency: Some decisions occur daily during busy periods but monthly during normal operations. Capture both patterns.
Define the operating bands where no action is needed, and the thresholds that trigger investigation to show what “good” looks like.
Avoid false precision—no exact percentages, just clear boundaries between normal variation and actionable signals.
Next step: Map out the actual response, not the theoretical one. If the next step is “call Jim in maintenance,” document that, not “start work order process.”
Key insight: Each decision profile becomes a dashboard page. It is not a collection of metrics, but a comprehensive decision-support tool.
When you hand this to your BI team, they can design each page so a user can go from signal to context to action in under a minute.
The plant manager opens the dashboard, sees which line needs attention, clicks to understand why, and knows who to call—all without scrolling through irrelevant charts or applying filters.
Tip 2: Define users and moments of use
Real dashboard usage doesn’t happen at desks during quiet moments. It happens in hallways between meetings, on shop floors with safety glasses on, during conference calls when someone asks, “What do the numbers show?”
Map out who opens your dashboard and when. The plant manager is checking overnight performance while walking to his car.
The quality supervisor pulls up defect rates during the morning huddle with operators around a tablet.
The CFO is looking at margin trends on her phone between investor calls.
Each context demands different design choices. The hallway check needs big, obvious visual cues—green/red indicators readable from arm’s length.
The shop floor review needs visuals that work under fluorescent lighting and handle fingerprints.
The mobile executive needs information that loads quickly on cellular data and answers the core question promptly.
Document the physical reality: Where are they when they open this? What prompted them to look? How long do they have? What decision are they trying to make?
The worst dashboards try to serve every context with the same layout.
The best ones recognize that the same person needs different information in the production area versus their office planning next month’s schedule.
Tip 3: Define your data foundation
Before design starts, lock down three essential elements:
KPI dictionary: Create a KPI card for each metric with name, business purpose, formula, data source, owner, and edge cases. Get cross-functional sign-off—disagreements over definitions undermine trust later.
The finance team counts revenue when invoices are sent, operations counts it when products ship, and sales counts it when deals close.
Without alignment, your dashboard becomes a battleground of competing truths. Spend time up front to get stakeholders in a room and define these terms.
Document the formula and the business rationale. When someone questions the numbers six months later, you’ll have the evidence.
Data Sources: List the systems/files feeding these KPIs. For each, capture location, access owner, update frequency, and caveats. Document the real-world flow (exports every Tue 5pm from Payroll to SharePoint), not just system names.
Manufacturing environments have data scattered across multiple systems—ERP for financials, MES for production, CMMS for maintenance, spreadsheets for everything else.
Map the actual data journey, including undocumented manual steps. That weekly export from the quality system that Jim from QA runs every Friday? That’s part of your data architecture now.
Know the key people and what happens when they’re on vacation.
Grain & Breakdowns: Pick the lowest useful grain (shift, line, job requisition) and rank your must-have vs. nice-to-have dimensions. Document expected drill paths (company → plant → line → shift) and reorganization handling.
Getting the grain wrong breaks everything. If the grain is too high, you can’t diagnose problems—knowing plant efficiency doesn’t help when Line 2 is the bottleneck.
If the grain is too low, the dashboard becomes slow. Think about your typical root-cause analysis.
When efficiency drops, do you first look at individual operators or shift patterns? When costs spike, do you examine individual purchase orders or supplier categories?
That flow determines your dimensional hierarchy.
Tip 4: Design the user experience
First-Screen Layout: Agree on a single screen that fits without needing to scroll:
- Headline KPIs (with targets/thresholds),
- Context trend (last 13 periods),
- One diagnostic visual that points to the reason.
Sketch it on paper. Decide the number of visuals (usually 6–8 max), label them in business terms, and include prior period/target context so numbers have significance.
The first screen is your elevator pitch to busy executives. They’ll spend thirty seconds deciding if this dashboard is worth their time.
Lead with the headline number that matters most, show enough trend to indicate direction, and include one diagnostic hinting at the story behind the numbers.
Resist the temptation to cram everything important onto the first page. That leads to dashboard wallpaper—lots of colorful charts that nobody reads.
It’s better to have three meaningful visuals that tell a story than eight metrics competing for attention.
Filters & Navigation: Include filters that map to your critical questions.
Set business-friendly defaults (e.g., “Last full week; BU = Appliances; Entity = US”). Mark some filters mandatory (e.g., entity) and define Reset to Default behavior.
Rename technical fields (“Dim_Product_Name”) to plain labels (“Product”).
Every added filter increases cognitive load. Users see a dozen filter options and either get overwhelmed by choices or pick the wrong combination and draw false conclusions.
Start with the view that answers the most common question, then add filters only when you can’t answer a critical question without them.
Tip 5: Set thresholds, alerts, and narratives
For each KPI, define targets, SLA thresholds, and seasonality rules.
Decide who gets alerts, on what channel (email/Teams/mobile), and when to suppress alert fatigue (e.g., only the first breach per day).
Draft short insight → action narratives for the dashboard (e.g., “Attrition rose 1.8pp MoM, driven by 0–6 month tenure in Plant A; action: review onboarding coverage”).
Thresholds turn dashboards from reporting tools into management systems. Without them, users stare at numbers, wondering, “Is this good or bad?”
With them, the dashboard does the thinking—green means normal, yellow means watch, red means act.
But static thresholds often fail in manufacturing. Equipment efficiency varies by product mix. Quality rates change with raw material suppliers.
Build in seasonal patterns and contextual rules. The threshold for overtime hours should differ between peak and slow periods.
Alert fatigue kills dashboard credibility. If the manufacturing manager gets fifteen “urgent” notifications every morning, they’ll ignore them.
Design alerts for decisions that require immediate attention. The rest should be visual indicators on the dashboard, not interruptions to workflow.
Tip 6: Plan for actual usage
Access & Roles: Create a simple role-data matrix showing visibility and access levels.
Mark sensitive fields and define row-level security (RLS) rules (plant, territory, hierarchy). Clarify viewer, explorer, and editor roles.
In manufacturing, access controls often follow the organizational hierarchy, but data needs don’t always match reporting relationships.
The maintenance supervisor might need to see cost data for repair decisions, even though they don’t manage budgets.
The quality engineer might need access to supplier performance across plants, even though they’re only responsible for one facility.
Document who can see what and who should see it. There’s a difference between technical access and practical need.
Just because someone can drill down to individual operator performance doesn’t mean they should in their daily role.
Refresh cadence & timing: Specify freshness metrics (real-time, hourly, daily, monthly). Capture tolerance windows, blackout periods, and late data rules. Show “as-of” timestamps.
Real-time isn’t always better. While production metrics might need hourly updates, financial data updates with incomplete actuals create more confusion than value.
Match refresh frequency to decision frequency. If someone reviews supplier performance weekly, daily updates just create distractions.
Manufacturing operations have natural blackout periods, such as month-end close, shift changes, and maintenance.
Instead of fighting them, plan for these in your refresh schedule. Users understand stale data at 6 AM during shift handover, but lose trust when it’s randomly outdated without explanation.
Mobile Access: Choose the subset of KPIs for mobile. Use larger fonts, fewer visuals, and touch-friendly targets.
Set alert thresholds for on-the-go decisions. Mobile isn’t just a shrunk desktop.
The plant manager doesn’t need detailed trend analysis—they need to know which line is underperforming.
Design mobile views for triage, not deep analysis. Big numbers, clear colors, straightforward next steps.
Tip 7: Validate with actual scenarios
Prepare 5–7 decision scenarios and then measure time-to-answer and clicks.
- “Can I isolate the attrition spike in Plant A by tenure and shift ≤ 3 clicks?”
- “Margin dip: can I see driver waterfall and top 10 SKUs?”
Capture feedback as issues, enhancements, or future requests to avoid scope creep.
Sign off when scenarios can be answered quickly and consistently.
Don’t test with hypothetical scenarios. Use real problems from the last quarter.
Pull up the actual emails where someone asked for analysis, or the meeting notes where people struggled to answer a key question.
Build those scenarios into your UAT script.
Time the interactions. If it takes over two minutes from question to answer, most users will give up and ask their analyst instead.
The dashboard becomes a nice-to-have rather than a go-to tool.
Measure clicks and comprehension. Can someone look at the result and know what action to take?
Before testing, set clear acceptance criteria.
“This looks good” isn’t a sign-off. “I can identify last month’s efficiency problem’s root cause in under ninety seconds” is.
Tip 8: Implementation & training
Define audiences, a simple “How to read this” page, and a champions network in each function.
Hold short post-launch office hours, measure adoption & trust (logins, time on page, filter use, survey confidence), and adjust monthly.
Dashboard training isn’t about showing where to click—it’s about changing their decision-making approach.
During training, use actual scenarios. Show them how to use the dashboard to answer the questions from last month.
Make it relevant to their current challenges.
Champions aren’t just enthusiastic users—they’re the go-to people for data questions.
Identify the informal data experts in each area and get them comfortable with the dashboard.
When the plant supervisor has a question about yesterday’s numbers, they’ll ask their trusted colleague, not refer to the training manual.
Change Management: Define how KPI changes are requested, reviewed, and approved.
Keep a visible changelog and owner for each metric. Set a release schedule and criteria for discontinuing unused tiles.
Dashboards evolve or die. Business priorities shift, new regulations appear, and organizational structures change.
Build a lightweight process for requested changes that balances stability with adaptability.
Monthly review cycles work better than random changes that break user habits.
Track usage closely. Suppose nobody clicked the “Supplier Diversity” tile in three months; it’s either unimportant or undiscoverable.
Either way, it’s clutter. Clean house regularly to keep the dashboard focused on what drives decisions.
Getting Started
Most dashboard projects fail in the requirements phase, not the technical build.
You can have the most elegant Power BI implementation, but if it doesn’t answer the questions that concern your operations leaders, it becomes expensive digital wallpaper.
The framework works because it starts with decisions instead of data, focuses on real usage contexts instead of theoretical requirements, and acknowledges that manufacturing environments have unique constraints that generic BI approaches overlook.
Your next step isn’t opening Power BI. It’s spending two hours mapping your organization’s repeated decisions.
Get the plant managers, supervisors, and analysts together. Ask them about last month’s fires and how they figured out what was burning.
Those diagnostic paths become your dashboard navigation.
If you’re ready to build daily-used dashboards instead of monthly ones, SimpleBI can help you implement this framework in your manufacturing environment. We’ve guided numerous operations teams through this process and know the common pitfalls.
