Dashboard

Core production inputs

Shared model: Demand + time + count + defects + costs -> takt, OEE, sigma, COPQ

Dashboard notes

Takt compares customer demand to available time, while OEE shows how much of that time actually converted into good production.

Sigma and DPMO translate defect performance into a quality capability view. COPQ converts that same quality picture into business impact.

The most useful signal is often the linkage between metrics: poor OEE can drive takt gaps, while poor sigma can drive COPQ even when flow looks acceptable.

Flow and Utilization

Takt and OEE detail

Net available time
397 min
Required hourly output
52.8
Good units per hour
58.0
Runtime
397 min
Operating time
359 min
Constraint signal
Performance loss

Quality and Cost

Sigma and COPQ detail

DPU
0.035
DPO
0.0071
DPMO
7,089
Yield
96.6%
COPQ % revenue
4.4%
Largest cost driver
Internal failure

Connected Insights

What the metrics mean together

If actual cycle stays above takt, downstream quality gains alone will not close the customer-demand gap.

If sigma improves without reducing downtime, COPQ may improve faster than OEE.

If performance and quality both improve together, this dashboard will show compounding gains across takt, OEE, and COPQ simultaneously.

Instructions

How to use this app

Enter one shift or one time-bounded production period using the same core operating data your team already tracks. The dashboard calculates takt, actual cycle, OEE, sigma, DPMO, yield, and COPQ together from that single input set.

Use this when you want one fast daily readout rather than opening multiple calculators. It is especially useful for morning reviews, shift handoff, production meetings, or weekly operational summaries.

The most valuable output is the interaction between metrics. If takt is being missed because of performance loss, attack cycle and micro-stoppage issues first. If sigma is weak and COPQ is high, defect reduction will usually return faster business value.

What This Manufacturing Dashboard Calculator Combines

This dashboard calculator pulls takt, OEE, sigma, and COPQ into one connected view so the team can see how production, quality, and cost metrics interact. It is built for managers who do not want to jump across four separate worksheets just to understand the health of a manufacturing process.

Use it for production reviews, leadership updates, improvement prioritization, and cross-functional discussions where one metric alone can hide the real problem.

Core Metrics Included

Metric Question It Answers Why It Matters
Takt How fast must the line run? Connects customer demand to required pace.
OEE How effectively is equipment producing? Combines time, speed, and quality losses.
Sigma / DPMO How capable is the process at the defect level? Normalizes quality performance into a common scale.
COPQ What is the financial cost of failure? Connects process weakness to margin and business impact.

Worked Example

A line may appear healthy because output meets the schedule, but the dashboard can still reveal that OEE is weak, sigma performance is mediocre, and COPQ remains high because the line is hitting demand only by absorbing rework and downtime. That is the value of one integrated view.

Instead of debating which metric matters most, the dashboard shows how they reinforce or contradict each other.

How to Interpret the Results

Manufacturing Dashboard Frequently Asked Questions

Why combine multiple metrics in one dashboard?

Because single metrics can hide tradeoffs. A combined dashboard helps leadership see whether the process is fast, capable, efficient, and financially healthy at the same time.

What is the biggest dashboard design mistake?

Tracking too many disconnected numbers without showing how they relate to demand, process stability, and financial impact.

Should this replace the specialist calculators?

No. The dashboard is a synthesis view. Teams should still use the dedicated tools when a deeper diagnosis is required.

Who should use this tool most often?

Operations leaders, plant managers, improvement leads, and quality managers who need one operating view across production and quality performance.

How often should the dashboard be refreshed?

That depends on the process cadence, but it should be refreshed often enough to support real decisions rather than become a historical decoration.

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