Factory Data: Track Decisions, Events and Exceptions

Factory Data: Track Decisions, Events and Exceptions helps you choose factory data that supports a named operational decision. Use one real order, one normal path and one exception in the evaluation.

For a late order, the useful record shows planned and actual operation times, shortage status, downtime reason, output, rejection and the owner of the next action.

Make the operational decision explicit

Start from order, material, operation, machine and quality decisions; record timestamps, owners and correction reasons.

Our AI agents work on connected sales, production, purchasing, inventory and task records, with permissions controlling the records and actions available to each user.

ISA-95 is the external process or control reference for this guide: https://www.isa.org/standards-and-publications/isa-standards/isa-95-standard. It does not prove an Optiwise feature or outcome; our product statements come from the current product record.

Implementation worksheet

#	Input	Evidence
1	Decision and owner	Owner and definition
2	Source event and timestamp	Authoritative source
3	Order or asset context	Acceptance rule
4	Exception and correction	Exception owner

Run the workflow with accountable roles

1. 1. Decision and owner. Name the person who owns the record and decision.
2. 2. Source event and timestamp. Use a current value from the representative order.
3. 3. Order or asset context. Write the pass condition before the demo or rollout.
4. 4. Exception and correction. Assign correction and escalation responsibility.

Reconcile the test by hand. When the system result differs from the source record, classify the gap as master data, timing, transaction ownership, permissions or configuration before changing the workflow.

Test exceptions before rollout

• Manual event arrives late
• Machine signal lacks job
• Definition changes by team

For each exception, record the expected status, permitted action, approver, correction path and audit evidence. A fast normal path does not compensate for an uncontrolled exception.

Connect the page to the next buyer task

review the product workflow, check the supporting module, prepare implementation inputs, read the related guide, open the detailed workflow guide, book a connected demo. Ask us to run the scenario with your roles and representative records, including the exception and correction path.

Questions to settle in the demo

What should we prepare?

Prepare one representative order, the four inputs above and a complication your team encounters today. Remove confidential values if needed while keeping the record relationships intact.

What is a passing result?

Accept the workflow when the normal path and exceptions leave a complete, role-appropriate record that another person can reconcile. Keep capability validation separate from any promised business result.

A factory should track data that helps managers make better decisions, not data that only fills reports. The most useful factory data shows what is being produced, what is delayed, where material is stuck, why machines stop, how quality is performing, and whether customer commitments are at risk.

Many manufacturers either track too little or track too much. Too little data means decisions depend on memory and follow-up. Too much data creates reports nobody uses. The right approach is to track the numbers that explain factory performance and support daily action.

Factory floor visibility starts with choosing the right data.

Start With Production Output

Production output is the most basic factory metric, but it should be tracked with enough detail to be useful.

Track:

• Planned quantity
• Actual quantity
• Good quantity
• Rejected quantity
• Rework quantity
• Output by line
• Output by machine
• Output by shift
• Output by work order
• Output by product

Total output alone is not enough. Management needs to know whether output matched the plan, which lines performed well, and which orders are still pending.

Track Planned vs Actual Performance

Planned versus actual data shows whether the factory is executing the plan.

Important measures include:

• Planned output versus actual output
• Planned start versus actual start
• Planned completion versus actual completion
• Planned machine time versus actual machine time
• Planned labor hours versus actual labor hours
• Planned dispatch versus actual dispatch

This helps teams understand whether plans are realistic and where execution gaps occur.

Track Downtime and Reasons

Downtime is one of the biggest hidden costs in manufacturing. Track downtime by machine, line, reason, shift, and work order impact.

Useful downtime data includes:

• Downtime duration
• Downtime reason
• Machine affected
• Job affected
• Maintenance response time
• Repair time
• Repeat issue count
• Production loss

Reason-wise downtime is more useful than total downtime. It shows whether the factory is losing time due to breakdowns, material shortages, changeover, quality holds, or waiting for approvals.

Track Work-in-Progress

WIP shows where work is sitting inside the factory. Without WIP visibility, managers may know that orders are inside production but not where they are stuck.

Track:

• WIP by stage
• WIP by order
• WIP age
• WIP value
• Jobs waiting longest
• WIP linked to urgent dispatch
• WIP on quality hold

Stage-wise WIP helps identify bottlenecks and delayed orders.

Track Quality Data

Quality data should be connected with production, not handled separately.

Track:

• Inspected quantity
• Accepted quantity
• Rejected quantity
• Rework quantity
• Defect type
• Defect severity
• Quality hold status
• Rejection by machine
• Rejection by product
• Rejection by supplier lot

This helps teams reduce repeated defects and understand the true cost of poor quality.

Track Inventory and Material Readiness

Production depends on material availability. Inventory data should support production planning, not only accounting.

Track:

• Raw material stock
• Component stock
• Reserved stock
• Material issued to production
• Shortage items
• Pending purchase
• Supplier lead time
• Material under quality inspection
• Slow-moving stock
• Critical spares

Material readiness helps planners schedule jobs that can actually run.

Track Labor and Shift Data

Labor data helps explain productivity and cost.

Track:

• Attendance
• Shift allocation
• Operator assignment
• Job-wise labor time
• Output per labor hour
• Overtime
• Idle time
• Skill availability
• Supervisor responsibility

This should be used carefully and fairly. Labor data is most useful when connected to machine status, material availability, and job progress.

Track Dispatch and Customer Commitment

A factory exists to fulfill customer orders. Dispatch visibility should connect with production progress.

Track:

• Orders due today
• Orders at risk
• Completed but not dispatched quantity
• Quality-cleared quantity
• Packing status
• Dispatch delay reason
• Customer priority
• Expected completion date

This helps sales, production, and dispatch work from the same facts.

Track Cost Drivers

Factories should also track data that affects cost.

Important cost signals include:

• Labor hours by job
• Machine hours by job
• Material consumption
• Rejection and rework cost
• Overtime
• Downtime impact
• Scrap
• Energy or utility usage where relevant

These numbers help improve pricing, quoting, profitability, and process improvement.

Avoid Vanity Metrics

Not every metric helps. A dashboard with 50 numbers may look advanced but still fail to guide action.

A good KPI should answer one of these questions:

• Are we on plan?
• What is delayed?
• Why are we delayed?
• What is costing us money?
• What affects dispatch?
• What needs action today?

If a metric does not support a decision, it may not deserve daily attention.

How Optiwise Supports the Workflow

AICAN Optiwise helps manufacturers bring production, inventory, purchase, quality, dispatch, and finance data into one connected system. This makes factory data easier to trust and easier to use.

With Optiwise, teams can track work orders, output, downtime, material readiness, WIP, quality, and reporting in a structured way. The goal is practical visibility: fewer assumptions, faster decisions, and stronger control over daily operations.

AICAN builds ERP for manufacturers who want useful factory data, not just software screens. You can learn more about the company on the About AICAN page.

FAQ

What are the most important factory KPIs?

Important KPIs include planned versus actual production, downtime, WIP, rejection rate, on-time delivery, material shortages, labor productivity, machine utilization, and dispatch risk.

Should small manufacturers track factory data?

Yes. Small manufacturers benefit from tracking practical data because it improves planning, costing, delivery, and control. The system can start simple and become more detailed as the business grows.

Is production output enough to track performance?

No. Output alone does not explain delays, quality problems, downtime, material shortages, or cost. It should be tracked along with supporting operational data.

How often should factory data be updated?

Critical operational data should be updated during the shift. Daily reports are useful, but live or near-live updates are better for production control.

Can ERP create factory dashboards?

Yes. ERP can create dashboards when production, inventory, quality, dispatch, and maintenance data are captured in the system.

What is factory floor visibility?

Factory floor visibility means having a clear, timely view of production status, machines, workers, materials, quality, WIP, and delays so managers can make better decisions.

Founder’s Note

Factories do not need more data for decoration. They need data that helps them act. A good dashboard should make the owner calmer, not more confused. It should show what is healthy, what is stuck, and what needs attention today.

At AICAN, we believe the best manufacturing software respects the practical rhythm of the factory. Data should come from real work, not from duplicate reporting. When the right data is visible, teams spend less time chasing updates and more time improving operations.

Final Thought

The best factory data is decision data. It tells you whether production is on plan, where work is stuck, why time is being lost, what quality issues are appearing, and which customer commitments are at risk.

Track what helps you run the factory better. Then review it consistently. That is how visibility turns into control.