Explore our sample Power BI dashboard for shop-floor downtime, root-cause analysis, and cost impact.

What You’ll See

• Top‐row KPIs: Total downtime, downtime %, events count, avg DT/event, deviation vs goal
• Trend Analysis: Daily downtime vs a 7-day rolling average
• Root-Cause Pareto: Identify the 2–3 causes driving 40–50% of all downtime
• Heatmap: Machine-by-shift performance highlights trouble spots
• Cost Gauge: Translate minutes into dollars lost
• Planned vs. Unplanned: See how much is scheduled maintenance vs breakdowns
• Asset Health: Scatter of machine age vs downtime %

Why It Matters

Every minute of unplanned downtime costs money, delays production, and eats into margins. This demo shows you not just how much you’re losing, but where to focus your resources for the biggest gains—helping you slash downtime by up to 40% in weeks, not months.

How to Use It

1. Filter the last 30 days or explore custom date ranges.
2. Hover over any bar or map cell for detailed tooltips (events, severity).
3. Click a root cause in the Pareto to drill through to event‐level details.
4. Download the PDF one-pager for a share-ready summary.

Production Downtime Summary – Last 30 Days
Over the past 30 days, the plant logged 42,000 minutes of downtime—10.9% of available shift time—costing roughly $2.1 million in lost production. A Pareto analysis reveals that Lack of Manpower and Mechanical Failures alone account for 42% of all downtime, so focusing on cross-training and preventive maintenance will deliver the fastest gains. Rolling 7-day trends are ticking upward, underscoring the urgency of proactive staffing adjustments and equipment upkeep to reverse this trajectory.

Challenge

42,000 minutes of unplanned downtime in one month (10.9% of available shift time)

• No unified view of which machines, shifts, or causes drove the losses
• Hard to translate minutes into cost impact for finance approvals

Solution

We built a star-schema Power BI model fed from simulated ERP event logs, plus:

• Top-row KPIs: Total downtime, downtime %, event count, avg DT/event, deviation vs 5% goal
• 7-day rolling average trend to spot emerging spikes
• Pareto analysis that ranks root causes and shows cumulative impact
• Heatmap pinpointing machine × shift hotspots
• Cost gauge translating downtime minutes into $50/min cost
• Planned vs. Unplanned split for proactive vs reactive maintenance
• Asset health scatter correlating machine age vs downtime %

Impact

By focusing on the top two Pareto causes—Lack of Manpower and Mechanical Failures—we can eliminate 42% of downtime, recovering over 17,500 minutes (≈ 12 days) per month. At $50/min, that’s $875,000 in immediate savings.

Next Steps

1. View the live demo at: https://hallpoint.co/live-demo-operations-insight-maintenance-dashboard/
2. Book a free walkthrough to see this live on your own data.
3. Contact us to scope your custom Operations Insight engagement.

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