How do I choose between different OEE dashboards for comparing software platforms during a proof of concept?

To choose between OEE dashboards during a proof of concept, focus less on “which dashboard looks best” and more on “which one helps us make the right operational decision fastest and most reliably.”

Here’s a practical way to compare them.

1) Start with the decision you need to make

Before comparing tools, define what success looks like for your PoC:

• Are you trying to compare data accuracy?
• Ease of configuration?
• Operator usability?
• Ability to support multiple plants/lines?
• Integration effort with PLCs, MES, historians, or ERP?
• Reporting and root-cause analysis?

If you don’t define this first, every dashboard will seem “good” in different ways.

2) Use the same use cases for every platform

Create 5–10 standard scenarios and test each dashboard on the exact same ones, for example:

• Show OEE for one line over a shift
• Break down losses by availability, performance, quality
• Drill into a top downtime reason
• Compare two lines or two sites
• Filter by date, shift, product, or machine
• Identify where OEE changed after a changeover
• Export data or share a report

This makes the comparison fair and measurable.

3) Compare the dashboards on key criteria

A. Data trustworthiness

This is usually the most important.

Ask:

• Does the dashboard calculate OEE the same way your team expects?
• Can you trace values back to source events?
• Are downtime, micro-stops, scrap, and planned stops handled correctly?
• Can it explain discrepancies clearly?

If the numbers can’t be trusted, the dashboard won’t be adopted.

B. Flexibility of OEE definitions

Different companies define OEE differently.

Check:

• Can you customize shift calendars, planned production time, ideal cycle time, and loss categories?
• Can it handle multiple product recipes or machine states?
• Can it support your local process rules?

A “fixed” OEE model may be fine for simple cases, but limiting for real operations.

C. Usability for the intended audience

A dashboard for operators should be different from one for executives or engineers.

Evaluate:

• Can operators understand it quickly on the shop floor?
• Can supervisors drill down without training?
• Is the visual hierarchy clear?
• Does it avoid clutter while still showing useful detail?

If users need a manual to interpret it, adoption may suffer.

D. Root-cause analysis and drill-down

A good OEE dashboard doesn’t just show a KPI—it helps answer “why.”

Look for:

• Pareto charts
• Downtime event timelines
• Alarm/event correlation
• Shift comparisons
• Product/asset/loss breakdowns

This is often where platforms differ most.

E. Configuration and deployment effort

Consider how much effort it takes to get meaningful results.

Ask:

• How long to set up a line?
• Can business users configure it, or does it require a vendor?
• How much scripting or custom development is needed?
• How easy is it to duplicate setups across lines/sites?

A dashboard that looks great but takes weeks to configure may not be the best PoC winner.

F. Integration and data handling

Evaluate:

• PLC/SCADA connectivity
• Support for historians, MES, databases, APIs
• Data refresh latency
• Handling of missing or noisy data
• Ability to normalize data from different machines

G. Performance and scalability

Test with realistic data volume:

• Can it load quickly with a month of data?
• What happens with multiple lines or users?
• Does it slow down on drill-downs or filters?

H. Reporting and sharing

Check:

• Export to PDF/Excel/CSV
• Scheduled reports
• Email/notification options
• Embedded dashboards or mobile access

I. Security and governance

Especially important if comparing enterprise platforms:

• Role-based access
• Audit trail
• Multi-site separation
• Cloud/on-prem options
• Data ownership and compliance

4) Use a weighted scorecard

Create a simple scoring matrix with weights based on your priorities.

Example:

Criterion	Weight	Platform A	Platform B	Platform C
Data accuracy/trust	30%	4	5	3
Ease of setup	15%	3	4	5
Usability	15%	4	3	4
Drill-down/root cause	20%	5	3	4
Integration effort	10%	4	4	2
Scalability	10%	3	5	4

Multiply score × weight, then compare totals.
This reduces “demo bias” and keeps the team aligned.

5) Include real users in the PoC

Don’t let only IT or procurement judge the dashboards.

Get feedback from:

• Operators
• Supervisors
• Process engineers
• Maintenance
• Plant leadership

Ask each group:

• What information do you need first?
• How easy is it to find the cause of a loss?
• Would you use this daily?
• What would stop adoption?

6) Watch out for common PoC traps

Avoid judging based only on:

• Pretty charts
• Vendor demo data
• One “golden” machine that’s easy to integrate
• Features that are impressive but irrelevant to your use case
• A tool that needs heavy customization just to look good

7) Final selection rule

Choose the dashboard that best balances:

1. Trusted OEE calculations
2. Fit to your operational model
3. Ease of adoption by end users
4. Effort to deploy and maintain
5. Ability to scale beyond the PoC

If two platforms are close, pick the one that:

• fits your workflow better,
• requires less customization,
• and is easier for plant users to adopt.

If you want, I can also help you build a PoC evaluation scorecard template for OEE dashboards, with categories and weights you can use directly.