Manufacturing Metrics & Quality Control: A Practical Guide

Together, they help answer questions such as:

• Are we producing what we planned?
• Are machines available when needed?
• Are processes running at expected rates?
• Are products being made correctly the first time?
• Where are defects occurring?
• How much are quality problems costing us?
• Are processes stable?
• Can we trace what happened to a particular part or lot?
• Are corrective actions actually preventing recurrence?
• Which problems deserve attention first?

Metrics are valuable only when they support decisions.

A manufacturing dashboard containing 50 indicators is not necessarily better than one containing 8 carefully selected measures tied to operational objectives.

Key Takeaways

• Manufacturing metrics should help people identify losses and make decisions.
• Overall Equipment Effectiveness, or OEE, combines availability, performance, and quality into one equipment effectiveness measure.
• OEE can be useful in high-mix, low-volume manufacturing, but the calculation becomes more difficult when products have different cycle times and routings.
• First Pass Yield measures how often work passes through a process correctly without rework.
• Scrap rate measures material or units that cannot be recovered as acceptable product.
• Cost of Poor Quality converts defects, rework, failures, returns, and related losses into financial terms.
• Statistical Process Control, or SPC, helps distinguish normal process variation from signals that something has changed.
• Inspection detects problems. Process control attempts to prevent them.
• A Nonconformance Report records a specific failure to meet a requirement. Corrective Action addresses the cause of a problem so it does not recur.
• Traceability depends on linking materials, processes, people, equipment, inspections, revisions, and production events.
• The best KPI set varies by manufacturing environment.

Start With the Question, Not the Metric

Manufacturing organizations often begin measurement programs by asking:

What metrics should we track?

A better starting point is:

What decisions do we need to make?

Consider a plant that reports:

OEE = 67.4%

That number alone does not tell a production manager what to do.

The useful questions are:

• Is availability low?
• Is the equipment running below its expected rate?
• Is quality reducing productive output?
• Did the product mix change?
• Did setup time increase?
• Is the equipment actually the constraint?
• Does improving this machine increase plant throughput?

The purpose of measurement is to expose something that can be acted upon.

The Main Categories of Manufacturing Metrics

Manufacturing metrics generally fall into several categories.

Category	Examples
Equipment	OEE, availability, downtime
Production	Output, attainment, schedule adherence
Flow	WIP, lead time, queue time, throughput
Quality	FPY, scrap, defects, rework
Cost	COPQ, cost per unit, overtime
Maintenance	MTBF, MTTR, planned maintenance compliance
Delivery	On-time delivery, past-due orders
Capacity	Utilization, constraint loading
Improvement	Defect reduction, setup reduction, productivity

These metrics influence each other.

For example, increasing machine utilization may increase WIP.

Reducing inspection may improve cycle time while creating quality risk.

Running larger batches may improve equipment efficiency while increasing lead time.

No individual KPI should be optimized without considering the system around it.

Overall Equipment Effectiveness

OEE combines three factors:

Availability × Performance × Quality = OEE

Each component represents a different type of production loss.

OEE Component	Question
Availability	Was the equipment running when it was scheduled to run?
Performance	Did it run at the expected rate?
Quality	Did it produce acceptable product?

OEE is useful because a single poor production result can be separated into three fundamentally different causes.

Availability

Availability measures how much of planned production time the process actually ran.

Availability = Run Time / Planned Production Time

If a machine was scheduled for 480 minutes but experienced 80 minutes of tracked stops:

Run Time = 400 minutes

Availability = 400 / 480 = 83.3%

Availability losses can include:

• equipment failures
• setups and changeovers
• tooling problems
• material shortages
• waiting for operators
• planned production interruptions

The exact definition of planned production time should be established consistently across the organization.

Performance

Performance measures whether the process operated at its expected production rate while running.

One common calculation is:

Performance = (Ideal Cycle Time × Total Count) / Run Time

Performance losses can include:

• reduced machine speed
• minor stops
• short interruptions
• inefficient cycles
• operator delays
• degraded tooling

An incorrect ideal cycle time can make the performance calculation misleading.

If calculated performance routinely exceeds 100 percent, the expected cycle time probably needs to be reviewed.

Quality

The quality component of OEE measures the percentage of production that was acceptable without requiring rework.

Quality = Good Count / Total Count

For example:

Total units produced = 1,000
Good units = 970

Quality = 970 / 1,000 = 97%

OEE quality is concerned with good production during the measured process.

A broader quality system usually tracks much more than this one number.

OEE Example

Assume:

• Availability = 85%
• Performance = 90%
• Quality = 98%

OEE is:

0.85 × 0.90 × 0.98 = 74.97%

That result means approximately 75 percent of planned production time became fully productive time at the defined ideal rate and acceptable quality level.

The important information is not simply 74.97%.

The components tell us where to investigate.

Why OEE Can Be Difficult in HMLV Manufacturing

These environments produce many different products, usually in relatively small quantities.

Examples can include:

• aerospace
• defense
• medical equipment
• industrial equipment
• custom machining
• electronics manufacturing
• contract manufacturing
• engineered-to-order products

Traditional OEE measurement is easiest when a machine repeatedly produces the same product at a known ideal cycle time.

HMLV operations may have:

• dozens or hundreds of part numbers
• different cycle times
• different setups
• different routings
• short production runs
• frequent changeovers
• engineering changes
• variable labor content

This makes the performance component of OEE more difficult to calculate correctly.

If Machine 17 runs five different parts during a shift, there may be five different ideal cycle times.

The calculation must account for the mix rather than treating every unit as equivalent.

When OEE Can Mislead a Job Shop

OEE should not automatically become the primary plant KPI.

Consider a machine producing noncritical inventory because management wants to improve its utilization.

OEE may rise.

Meanwhile, a critical order is waiting at another operation.

The plant-level business result may become worse even though equipment efficiency improved.

This exposes an important distinction:

Equipment efficiency is not the same as manufacturing flow.

For a job shop, measures such as these may sometimes provide greater operational value:

• order lead time
• schedule adherence
• queue time
• WIP age
• bottleneck utilization
• throughput
• past-due operations
• on-time delivery
• setup time
• first pass yield

OEE is one tool.

It should be applied where it helps explain the performance of the manufacturing system.

Go deeper: Manufacturing Performance Metrics: The Practical Guide to OEE and Beyond

Related articles:

• How to Calculate OEE in High-Mix Low-Volume Manufacturing
• Why Traditional OEE Can Mislead a Job Shop
• The Manufacturing KPI Dashboard: Which Metrics Actually Matter?
• How to Measure Schedule Adherence

First Pass Yield

A basic calculation is:

FPY = Units Passing First Time / Total Units Entering the Process

If 100 units enter a process and 94 pass without rework:

FPY = 94 / 100 = 94%

The other six units may eventually become acceptable.

That does not change first pass yield.

This is important because a process can achieve excellent final shipment quality while hiding significant internal rework.

For example:

Metric	Result
Units started	100
Passed first time	90
Successfully reworked	9
Scrapped	1
Final acceptable units	99
FPY	90%
Final yield	99%

Looking only at final yield could make the process appear healthy.

FPY exposes the hidden factory required to repair work that was not produced correctly the first time.

Rolled Throughput Yield

When a product passes through multiple operations, the probability of getting through all of them without a defect can be significantly lower than the yield of any individual operation.

Suppose three sequential processes have first pass yields of:

• Operation 1: 98%
• Operation 2: 95%
• Operation 3: 97%

Rolled Throughput Yield is approximately:

0.98 × 0.95 × 0.97 = 90.3%

Each operation looks reasonably good individually.

Only about 90 percent of units would be expected to pass all three operations without a defect if those yields apply independently.

This is why seemingly small quality losses can accumulate across a complex routing.

Scrap Rate

A simple unit-based formula is:

Scrap Rate = Scrap Quantity / Total Quantity Produced

Scrap can also be measured by:

• weight
• material value
• production cost
• labor cost
• dollars per work order
• dollars per sales value

The correct measurement depends on the process.

For expensive materials or components, the financial value of scrap may be more meaningful than the number of units.

Rework Is Not the Same as Scrap

Scrap cannot normally become acceptable product without replacement or disposition.

Rework involves performing additional work to make a nonconforming item meet requirements.

Rework may include:

• additional machining
• repair
• disassembly
• retesting
• refinishing
• additional inspection

Rework consumes capacity.

That makes it both a quality problem and a production problem.

A plant with low scrap but extensive rework may still have a significant quality cost.

Cost of Poor Quality

Typical internal failure costs include:

• scrap
• rework
• retesting
• reinspection
• sorting
• production disruption
• additional handling
• downtime related to defects

External failure costs can include:

• returns
• warranty
• field service
• customer complaints
• recalls
• expedited replacement
• penalties
• lost customer confidence

Cost of quality is broader than failure cost.

A common framework separates quality costs into four categories:

Category	Examples
Prevention	Training, process design, quality planning
Appraisal	Inspection, testing, audits
Internal Failure	Scrap, rework, sorting
External Failure	Returns, warranty, field failures

The cheapest defect is generally the one prevented before it occurs.

The farther a defect travels through the process, the more expensive it tends to become.

Quality Management

Quality management involves more than inspection.

A manufacturing quality system may include:

• incoming inspection
• in-process inspection
• final inspection
• statistical process control
• nonconformance management
• corrective actions
• supplier quality
• calibration
• document control
• training
• audits
• customer complaints
• traceability
• quality planning
• continuous improvement

The objective is to create processes that consistently produce conforming output and provide evidence that requirements were met.

Go deeper: Manufacturing Quality Management: SPC, NCR, CAPA, Scrap and FPY

Related articles:

• First Pass Yield: Formula, Examples and How to Improve It
• How to Calculate Manufacturing Scrap Rate
• Cost of Poor Quality: What Should You Include?
• SPC vs. Final Inspection: When Should Each Be Used?
• NCR vs. CAPA: What’s the Difference?

Statistical Process Control

One of its most important tools is the control chart.

A control chart typically contains:

• measured process values
• a center line
• an upper control limit
• a lower control limit

The purpose is not simply to determine whether a measurement is within engineering specification.

SPC asks a different question:

Is the process behaving consistently?

Common Cause vs. Special Cause Variation

A fundamental SPC concept is the distinction between two types of variation.

Common Cause Variation

Common cause variation is inherent in the current process.

Examples may include normal variation in:

• material
• equipment
• environment
• measurement
• process methods

Reducing common cause variation usually requires improving the overall process.

Special Cause Variation

Special cause variation results from something unusual or identifiable.

Examples could include:

• damaged tooling
• incorrect setup
• failed sensor
• wrong material
• programming error
• unusual temperature condition

The appropriate response differs depending on which type of variation is present.

Reacting to every normal fluctuation can actually make a stable process worse.

Control Limits vs. Specification Limits

This distinction is fundamental.

Specification limits describe what the product or process is required to meet.

Control limits describe the observed behavior of the process.

A process can be:

• stable but incapable of meeting specification
• unstable while still producing measurements inside specification
• stable and capable
• unstable and incapable

Control limits should not simply be copied from engineering tolerances.

They answer different questions.

SPC vs. Final Inspection

Final inspection asks:

Did this unit meet requirements?

SPC asks:

Is the process behaving in a way that is likely to continue producing acceptable units?

Inspection detects defects.

SPC can help detect changes in the process before they result in large quantities of defects.

Both may be appropriate.

The right approach depends on:

• product risk
• process capability
• measurement capability
• regulatory requirements
• customer requirements
• process stability
• cost of failure

Read: SPC vs. Final Inspection: When Should Each Be Used?

Nonconformance Reports

An NCR should usually identify:

• what was found
• the affected material or product
• the requirement that was not met
• quantity affected
• where the issue was discovered
• who discovered it
• relevant lot or serial numbers
• immediate containment
• disposition

Possible dispositions may include:

• use as-is when properly authorized
• rework
• repair
• return to supplier
• scrap

The exact process depends on the product, customer, regulatory environment, and quality system.

An NCR records the nonconformance.

It does not automatically determine why it happened.

Corrective Action

A corrective action process can include:

1. problem definition
2. containment
3. data collection
4. root cause analysis
5. corrective action selection
6. implementation
7. effectiveness verification
8. closure

The important step is effectiveness verification.

Changing a procedure does not prove that the problem has been corrected.

The organization needs evidence that the action actually reduced or eliminated recurrence.

NCR vs. CAPA

CAPA means Corrective and Preventive Action.

The terms NCR and CAPA are sometimes confused.

A practical distinction is:

NCR	Corrective and Preventive Action
Documents a nonconformance	Addresses its cause
Product or event focused	System or cause focused
May require simple disposition	May require investigation
Does not always require CAPA	Often triggered by significant or recurring problems

Not every NCR should generate a major corrective action.

If every minor defect launches a full investigation, the quality system can become overwhelmed.

Risk, recurrence, severity, and systemic significance should influence escalation.

Root Cause Analysis

Common root cause tools include:

• 5 Whys
• fishbone diagrams
• Pareto analysis
• fault tree analysis
• process mapping
• 8D
• failure mode analysis

A root cause should explain why the problem occurred and provide a path toward preventing recurrence.

Statements such as:

Operator error

are rarely sufficient by themselves.

The next questions should include:

• Why was the error possible?
• Was the instruction clear?
• Was training adequate?
• Could the process detect the error?
• Could tooling prevent it?
• Was the correct material available?
• Was the process designed to make the correct action obvious?

Good corrective action moves beyond assigning blame and examines the process that allowed the failure.

Manufacturing Traceability

Depending on the product and industry, traceability may include:

• supplier
• purchase order
• raw material lot
• component lot
• serial number
• work order
• routing
• operation
• machine
• operator
• tooling
• process parameters
• engineering revision
• inspection results
• calibration status
• nonconformances
• rework
• shipment
• customer

Traceability is fundamentally about relationships.

The system needs to answer questions such as:

Which products used this material lot?

and:

Which material lots, processes, and inspections were associated with this serial number?

Lot, Batch and Serial Traceability

Different manufacturing environments need different levels of identification.

Lot Traceability

A group of units shares an identified material or production lot.

Common in:

• raw materials
• components
• food
• pharmaceuticals
• electronics

Batch Traceability

A defined quantity is produced together under common process conditions.

Common in:

• chemicals
• pharmaceuticals
• food processing
• coatings
• process manufacturing

Serial Traceability

Each individual unit receives a unique identifier.

Common in:

• aerospace
• medical devices
• industrial equipment
• electronics
• high-value assemblies

The required traceability granularity should be driven by risk and business requirements.

More granular traceability provides more information but also requires more transactions and infrastructure.

Forward and Backward Traceability

A strong system supports both directions.

Backward Traceability

Starting with a finished product:

What materials, components, processes, equipment, people, and inspections created this item?

Forward Traceability

Starting with a material lot or component:

Which finished products contain this material?

Forward traceability becomes especially important during containment or recalls.

Traceability and Digital Thread

As manufacturing information becomes more integrated, organizations increasingly connect records across the product lifecycle.

A product record might link:

Engineering definition → material → work order → production process → inspection → shipment → service history

This broader connection is often described as part of a digital thread.

The value comes from maintaining relationships across systems rather than merely accumulating records.

ISO 9001, AS9100 and Quality Records

Quality management standards require organizations to control processes, maintain appropriate documented information, address nonconforming outputs, evaluate performance, and improve their quality management systems.

ISO 9001 provides a broadly applicable quality management framework.

AS9100 builds on quality management requirements for aviation, space, and defense organizations.

The specific records required by a manufacturer depend on:

• applicable standards
• customer contracts
• regulatory requirements
• product risk
• internal quality procedures

Digital systems can make these records easier to control and retrieve, but software itself does not create compliance.

The process still has to meet the requirement.

Go deeper: Digital Traceability and Quality Compliance for ISO 9001 / AS9100 Manufacturers

Related articles:

• Manufacturing Traceability: Lot, Batch, Serial and Process Traceability Explained
• How to Handle Engineering Revision Changes on the Shop Floor
• Manufacturing Traceability Without Implementing a Full MES

Manufacturing KPI Dashboards

For a plant manager, useful measures might include:

• safety
• on-time delivery
• production attainment
• past-due orders
• first pass yield
• scrap cost
• downtime
• bottleneck status
• schedule adherence

A production supervisor may need different information:

• jobs currently running
• jobs waiting
• downtime reason
• staffing
• current output
• quality holds
• shortages
• priority orders

An operator needs something different again.

The same dashboard should not necessarily be used by everyone.

Leading vs. Lagging Indicators

A useful KPI system includes both.

Lagging Indicators

These tell you what already happened.

Examples:

• monthly scrap
• warranty claims
• on-time delivery
• final yield

Leading Indicators

These can provide earlier warning.

Examples:

• process drift
• overdue preventive maintenance
• WIP aging
• open corrective actions
• increasing setup time
• supplier defect trends

Lagging indicators measure results.

Leading indicators may provide an opportunity to change those results before the reporting period ends.

Avoid KPI Overload

Every metric has a cost.

Someone must:

• collect it
• validate it
• maintain it
• explain it
• respond to it

A metric that nobody uses should be questioned.

For every KPI, define:

• what it measures
• why it matters
• how it is calculated
• who owns it
• how frequently it is updated
• where the data originates
• what action should result when it moves outside expectations

If two departments calculate the same KPI differently, the definition needs to be standardized.

Quality Data Architecture

Quality information often spans several systems.

For example:

Information	Possible System
Engineering specification	PLM
Work order	ERP
Production result	MES
Inspection result	QMS or MES
Machine measurement	PLC or edge system
Calibration record	QMS
NCR	QMS
Corrective action	QMS
Customer complaint	QMS or CRM
Supplier lot	ERP or QMS

The architectural goal is to connect these records without forcing employees to reenter the same information in several applications.

A useful quality record should preserve enough context to reconstruct what happened.

Quality and Measurement Planning for a New Manufacturing Facility

Quality infrastructure should be considered during facility design, not after production equipment has been installed.

Important questions include:

• Where will incoming inspection occur?
• Where will in-process inspection occur?
• Is a dedicated quality laboratory required?
• What environmental controls does the metrology area require?
• Which measurements must be made near production?
• Which equipment requires vibration isolation?
• What electrical and compressed air services are needed?
• How will calibrated equipment be stored?
• How will material move into and out of inspection?
• Where will nonconforming material be physically controlled?
• How will quarantine areas be secured?
• How will serialized or lot-controlled material be scanned?
• What network connectivity will inspection equipment require?
• Will inspection equipment automatically transfer results?
• How will quality data connect to MES, ERP, or QMS?
• What expansion space should be reserved?

Metrology equipment can have requirements that ordinary office or manufacturing equipment does not.

Depending on the measurement system, these may include:

• temperature control
• humidity control
• clean air
• vibration control
• stable foundations
• controlled lighting
• electrical conditioning
• compressed air quality
• isolation from production contamination

These requirements should be incorporated into facility planning before construction decisions become difficult to change.

Common Manufacturing Measurement Mistakes

Measuring What Is Easy Instead of What Matters

Modern equipment can generate thousands of measurements.

That does not mean all of them deserve management attention.

Comparing OEE Without Standard Definitions

Two plants can report different OEE values simply because they define planned production time, downtime, ideal cycle time, or quality differently.

Standardize the calculation before benchmarking.

Setting Arbitrary OEE Targets

A universal OEE target does not account for manufacturing environment, equipment function, product mix, or economic role.

The important comparison is often improvement against the process’s own historical performance and business requirement.

Optimizing Equipment Instead of Flow

Keeping every machine busy can create excessive WIP and longer lead times.

The objective is plant performance, not maximum local utilization.

Measuring Final Quality Only

Final inspection can hide rework and repeated process failures.

Track first pass performance where useful.

Counting Defects Without Measuring Cost

Ten minor cosmetic defects may matter less financially than one expensive assembly failure.

Frequency and economic impact should both be considered.

Treating Every Nonconformance the Same

Quality resources should be applied according to risk and significance.

Confusing Control Limits With Specifications

Specification limits define requirements.

Control limits characterize process behavior.

They serve different purposes.

Collecting Traceability Data That Cannot Be Reconstructed

Recording thousands of transactions is useless if the system cannot answer basic genealogy questions later.

Test traceability by attempting to reconstruct real production history.

A Practical Manufacturing Measurement Process

Step 1: Define Business Objectives

Determine what matters.

Examples:

• improve delivery
• reduce scrap
• increase throughput
• reduce rework
• improve machine availability
• reduce customer escapes

Step 2: Identify the Process

Define the manufacturing process that influences the objective.

Step 3: Identify the Losses

Determine what prevents the desired result.

Step 4: Select Metrics

Choose measurements capable of revealing those losses.

Step 5: Define Each Metric

Document:

• formula
• data source
• owner
• frequency
• exclusions
• units
• target

Step 6: Validate the Data

Do not build management decisions on unreliable source data.

Step 7: Establish Response Rules

Determine what happens when performance changes.

Step 8: Improve the Process

Measurement should lead to action.

Step 9: Review the Metric

Once a problem has been solved, another measurement may become more important.

Metrics should evolve with operational priorities.

Frequently Asked Questions

What is OEE?

Overall Equipment Effectiveness measures how effectively planned production time becomes good output at the expected production rate.

It combines availability, performance, and quality.

Is 85% OEE always good?

No.

The usefulness of an OEE value depends on the manufacturing process, product mix, definitions used, equipment role, and business objective.

Comparisons are meaningful only when the underlying calculations are consistent.

Can OEE be used in high-mix low-volume manufacturing?

Yes, but care is required.

Different products may have different ideal cycle times, setups, routings, and production quantities.

The calculation needs to reflect the actual product mix.

In some HMLV environments, flow, delivery, bottleneck, and schedule metrics may be more useful than plant-wide OEE.

What is First Pass Yield?

First Pass Yield measures the percentage of units that complete a process successfully the first time without rework, repair, or retesting.

What is the difference between yield and FPY?

Final yield measures how many acceptable units ultimately result from the process.

FPY measures how many passed correctly the first time.

A process can have excellent final yield while still consuming significant resources on rework.

What is the difference between NCR and CAPA?

An NCR documents a specific nonconformance.

Corrective action investigates and addresses the cause of a problem to prevent recurrence.

A nonconformance may trigger corrective action depending on its severity, recurrence, risk, and systemic significance.

What is SPC?

Statistical Process Control uses statistical methods to monitor process behavior and distinguish expected variation from signals that indicate a process may have changed.

Does SPC replace inspection?

No.

SPC and inspection answer different questions.

SPC evaluates process behavior.

Inspection determines whether product meets requirements.

The appropriate combination depends on process capability, risk, customer requirements, and the manufacturing environment.

What is manufacturing traceability?

Manufacturing traceability provides the ability to reconstruct the history and relationships of products, materials, lots, serial numbers, processes, equipment, people, inspections, and other production records.

Do you need QMS software to achieve traceability?

No.

Traceability can be implemented across ERP, MES, QMS, databases, and focused workflow applications.

The architecture must reliably maintain the required relationships and records.

When should a manufacturer move beyond spreadsheets for quality management?

Spreadsheets become problematic when quality processes require:

• multiple concurrent users
• controlled workflows
• approvals
• audit trails
• revision control
• automated notifications
• linked records
• traceability
• permissions
• integration with other systems

At that point, a database-backed QMS or manufacturing workflow application is usually more appropriate.

Practical Tools and Templates

Custom Industrial Solutions will provide practical resources including:

• OEE Calculator
• HMLV OEE Worksheet
• First Pass Yield Calculator
• Scrap Cost Calculator
• Cost of Poor Quality Worksheet
• NCR Template
• Corrective Action Template
• Manufacturing Traceability Matrix
• KPI Definition Worksheet
• Manufacturing KPI Dashboard Template
• Quality Data Architecture Worksheet
• New Facility Quality Infrastructure Checklist

Authoritative Frameworks and References

ISO 9001

ISO 9001 establishes requirements for quality management systems and provides a broadly applicable framework for consistent processes, customer requirements, performance evaluation, and improvement.

AS9100

AS9100 extends quality management requirements for organizations involved in aviation, space, and defense.

Statistical Process Control

SPC provides methods for understanding process variation and identifying changes in process behavior.

ISA-95

ISA-95 can help define how quality and production information moves between enterprise, manufacturing operations, and control systems.

Standards provide structure and common terminology.

The manufacturing system still has to be designed around the actual product, process, risk, customer, and regulatory environment.

The Bottom Line

Manufacturing measurement should tell people where performance is being lost and help them decide what to do about it.

OEE can reveal equipment losses.

First Pass Yield exposes hidden rework.

Scrap measures unrecoverable loss.

Cost of Poor Quality converts quality problems into financial terms.

SPC reveals changes in process behavior.

NCRs document failures to meet requirements.

Corrective action addresses their causes.

Traceability reconstructs the history of what was produced.

No single metric describes manufacturing performance.

A useful measurement system combines the right metrics for the process, connects them to reliable source data, and presents them to the people who can act on them.

Explore Manufacturing Metrics & Quality Control

Manufacturing Performance Metrics: The Practical Guide to OEE and Beyond

Availability, performance, quality, FPY, throughput, schedule performance, and how to choose useful KPIs for different manufacturing environments.

Manufacturing Quality Management: SPC, NCR, CAPA, Scrap and FPY

A practical guide to process control, inspection, nonconformance management, corrective action, scrap, rework, yield, and quality improvement.

Digital Traceability and Quality Compliance for ISO 9001 / AS9100 Manufacturers

Lot, batch, serial and process traceability, production genealogy, revision history, quality records, and digital compliance architecture.

Need Help With Manufacturing Performance or Quality Systems?

Custom Industrial Solutions helps manufacturers define meaningful operational metrics, map quality workflows, design traceability systems, connect quality and production data, and replace fragile spreadsheet processes with practical manufacturing applications.

Start with the decision that needs to be made.

Then determine what information is required to make it.