How AI and Copilot Can Improve Manufacturing Quality with Business Central

Manufacturing businesses are constantly looking for ways to improve product quality, reduce waste and keep production running efficiently.

Even a small number of recurring defects can create significant costs when products need to be inspected, repaired, remanufactured or discarded.

For manufacturers, reducing rework is not simply about fixing defective products faster. It is about understanding why quality issues occur and using better information to prevent them from happening again.

This is where artificial intelligence (AI) and Microsoft Copilot can provide additional value.

When AI capabilities are combined with Microsoft Dynamics 365 Business Central and structured quality processes, manufacturers can explore new ways to analyse production information, identify patterns, support decision-making and reduce repetitive administrative work.

Business Central can provide the operational information needed to manage manufacturing, inventory, purchasing and production processes, while AI and Copilot can help employees work with that information more efficiently.

The opportunity is not to replace quality teams.

It is to give them better access to information and intelligent assistance when investigating quality problems and making production decisions.

What Is Manufacturing Rework?

Manufacturing rework occurs when a product, component or production batch does not meet the required specifications and needs to be corrected before it can be completed or delivered.

Rework can occur because of:

• Incorrect measurements or specifications
• Material defects
• Production process errors
• Equipment or tooling problems
• Inconsistent quality checks
• Incorrect assembly
• Supplier quality issues
• Operator or process errors

While occasional rework may be unavoidable, repeated quality failures can have a major impact on manufacturing costs and productivity.

Every reworked product can consume additional labour, materials, machine time and production capacity.

It can also delay customer orders and create additional pressure on production teams.

The key question for manufacturers is therefore not simply:

How quickly can we fix a defect?

It is:

How can we identify the cause of the defect and prevent it from happening again?

AI and Copilot can help businesses move towards this more proactive approach by making quality and production information easier to analyse and use.

The Problem with Manual Quality Processes

Many manufacturers still manage some quality activities using paper forms, spreadsheets, emails or disconnected applications.

These approaches can make it difficult to maintain consistent inspection procedures and gain visibility into quality problems.

For example, if a production team records an inspection result separately while inventory and production information remains in Business Central, employees may need to manually bring information together before they can investigate an issue.

This can create delays and increase the risk of missing important information.

It can also make it harder for management to answer questions such as:

• Which products are generating the most quality failures?
• Are certain production operations associated with recurring defects?
• Are particular materials causing problems?
• Which suppliers are associated with repeated quality issues?
• How much rework is being generated?
• Are defects increasing or decreasing?
• Which production areas require further investigation?
• What corrective actions have already been taken?

AI can potentially help teams analyse larger volumes of information and identify patterns that may otherwise require significant manual investigation.

What Can Copilot Do for Manufacturing Teams?

Microsoft Copilot is designed to provide AI-assisted support within business workflows.

For manufacturing organisations, the value of Copilot and other AI solutions can come from helping employees work with business information more efficiently.

Depending on the technology, configuration and integrations involved, AI-assisted tools can help with activities such as:

• Summarising business information
• Analysing data
• Identifying patterns
• Supporting research and investigation
• Generating useful content
• Helping employees find information
• Automating suitable repetitive tasks
• Supporting business decisions

For quality teams, this can create opportunities to spend less time manually searching through information and more time investigating the causes of quality problems.

The exact capabilities available depend on the Business Central environment and the AI technologies implemented.

Bringing AI Into Business Central Quality Management

Business Central is used by manufacturers to manage areas such as:

• Production
• Inventory
• Purchasing
• Warehousing
• Sales
• Finance
• Supply chain operations

Quality management can connect quality activities with these operational processes.

AI and Copilot can then provide an additional layer of intelligence on top of the information generated through these processes.

A simplified approach could look like this:

Production → Quality Inspection → Quality Data → AI Analysis → Insight → Corrective Action

For example, if a manufacturer records repeated inspection failures, AI-assisted analysis could help identify patterns across products, suppliers, materials, production operations or time periods.

The quality team can then investigate those findings and determine the appropriate action.

Using AI to Identify Recurring Quality Problems

One of the biggest opportunities for AI in manufacturing quality is identifying recurring patterns.

Imagine a manufacturer discovers that a particular component repeatedly fails a dimensional inspection.

Without structured quality information, the business may simply correct each affected product.

With consistent quality data, the business can investigate whether the failures are associated with:

• A particular supplier
• A specific material batch
• A production machine
• A work centre
• A routing operation
• A particular product
• A particular production period
• A recurring production condition

AI can help analyse these relationships and highlight patterns for further investigation.

This does not mean AI automatically determines the root cause.

Instead, it can help quality and production teams identify where they should look more closely.

Using Copilot to Analyse Quality Information

Quality teams often need to gather information from multiple areas before investigating a problem.

For example, a quality issue may require information about:

• The product
• Production order
• Materials
• Supplier
• Lot or serial information
• Inspection results
• Production operation
• Previous quality failures

Copilot and other AI-assisted tools can potentially make it easier for employees to work with this information.

Instead of manually reviewing large amounts of information, employees may be able to use natural-language interactions and AI-assisted analysis to help locate or summarise relevant information, depending on the solution implemented.

This can make investigation workflows more efficient.

AI and Manufacturing Defect Analysis

Manufacturing businesses can generate large volumes of quality information over time.

This historical information can be valuable.

For example, a business may want to understand:

Are defects increasing?

Which products have the highest failure rates?

Are quality problems concentrated around a particular operation?

Are supplier-related issues becoming more common?

Are particular materials associated with repeated failures?

AI can help businesses analyse patterns across larger datasets.

This can support a more data-driven approach to quality management.

Instead of looking at individual defects in isolation, manufacturers can begin looking at the wider pattern.

Managing Non-Compliant Inventory

A quality failure does not necessarily mean inventory should immediately enter normal stock or continue through the supply chain.

Business Central quality processes can help businesses manage non-compliant items and affected inventory according to their configured processes.

AI can complement this process by helping teams analyse the information surrounding quality failures.

For example, AI-assisted analysis could help identify:

• How frequently particular items fail inspection
• Whether failures are concentrated around certain suppliers
• Whether specific products require additional attention
• Whether particular production processes generate recurring issues

The actual inventory control action should remain governed by the manufacturer’s approved quality procedures.

AI should support the process rather than make uncontrolled decisions about product release.

Improving Quality Traceability

Traceability is particularly important when manufacturers need to understand where a quality problem originated.

Manufacturing and quality information can contain details relating to:

• Production orders
• Operations
• Items
• Lots
• Serial numbers
• Materials
• Suppliers

When this information is properly connected, manufacturers can investigate quality issues more effectively.

AI can add value by helping teams analyse and connect relevant information during an investigation.

For example, if a finished product fails inspection, a quality team may need to investigate the relevant production order, operation, materials and item-tracking information.

AI-assisted analysis can help employees work through this information more efficiently.

AI-Assisted Quality Inspections

Quality inspections remain an important part of manufacturing quality control.

Inspections can capture information such as:

• Measurements
• Test results
• Pass/fail outcomes
• Product information
• Production information
• Lot information
• Other quality-related observations

AI can potentially assist with analysing inspection information after it has been captured.

For example, businesses could use AI to identify trends across historical inspection results or highlight areas that warrant further investigation.

The inspection criteria themselves should continue to be based on the manufacturer’s technical, regulatory and quality requirements.

AI should complement established quality procedures rather than replace them.

Detecting Quality Anomalies

Anomaly detection is another potential use of AI in manufacturing.

An anomaly is information that differs from an expected or established pattern.

For example:

• A product suddenly has a higher failure rate
• A particular production operation shows an unusual increase in defects
• A supplier’s materials are associated with more quality issues
• A product’s inspection results change significantly
• Rework levels increase unexpectedly

AI can help analyse historical information and highlight unusual patterns.

This can allow quality teams to investigate potential problems earlier.

AI and Root Cause Analysis

Root cause analysis is one of the most important parts of quality management.

Finding and fixing the immediate defect is only part of the solution.

Manufacturers also need to understand why the defect occurred.

AI can support this investigation by helping teams organise and analyse relevant information.

For example:

Quality Failure → Analyse Historical Data → Identify Patterns → Investigate Potential Causes → Take Corrective Action → Monitor Results

AI does not replace engineering expertise or quality professionals.

Instead, it can provide another tool for examining complex information and identifying relationships that may deserve attention.

Reducing Manufacturing Rework

Rework can affect several areas of a manufacturing business at the same time.

Labour Costs

Employees need to spend additional time identifying, repairing or remanufacturing defective products.

Material Waste

Products that require rework may consume additional raw materials, components and packaging.

Production Capacity

Machines and production lines may spend time correcting products instead of producing new output.

Delivery Delays

Additional production work can push customer orders beyond their expected completion dates.

Customer Returns

If defects are not identified before products leave the business, they can result in returns, complaints and additional service costs.

AI can help manufacturers identify recurring patterns behind these problems.

By combining structured quality processes with better analysis, businesses can work towards reducing avoidable rework.

Supporting Continuous Improvement With AI

Quality management should not end when an inspection is completed.

The information generated by inspections can support broader continuous improvement initiatives.

Manufacturers can use quality information to identify trends, investigate recurring problems and determine where changes may be needed.

For example, a recurring defect could lead to:

• A production process review
• Supplier evaluation
• Equipment maintenance
• Employee training
• Updated inspection criteria
• Changes to production routing
• Material specification changes

AI can help teams analyse historical information and monitor whether quality trends are improving after changes are introduced.

This creates a continuous improvement cycle:

Inspect → Analyse → Identify → Investigate → Correct → Monitor → Improve

The goal is to make each stage of the manufacturing process more reliable over time.

Business Central, Copilot and Manufacturing Efficiency

Quality management is closely connected to manufacturing efficiency.

When quality processes are disconnected from production and inventory systems, employees may need to spend additional time searching for information or manually transferring records.

An integrated approach can bring quality information closer to the operational information manufacturing teams already use.

Business Central can provide the foundation for managing manufacturing, inventory and operational information.

AI and Copilot can then provide additional capabilities for working with that information, depending on the technologies and configuration used.

This combination can help manufacturers move towards a more connected and data-driven approach to quality management.

AI Does Not Replace Quality Professionals

It is important to understand the role of AI in manufacturing quality.

AI should not be viewed as a replacement for:

• Quality engineers
• Production managers
• Manufacturing specialists
• Compliance teams
• Engineering expertise
• Human judgement

Quality decisions can have significant operational, safety and regulatory consequences.

AI should therefore be implemented with appropriate controls, governance and human oversight.

The most valuable role for AI is often to help people find information, analyse patterns and complete appropriate tasks more efficiently.

The Importance of Data Quality

AI is only as useful as the information available to it.

Manufacturers should consider the quality and consistency of their:

• Production records
• Inspection results
• Product information
• Supplier information
• Inventory data
• Lot and serial information
• Production order data
• Rework records
• Defect information

If information is incomplete or inconsistent, AI-generated analysis may be less reliable.

Before introducing AI, businesses should therefore review their data foundations and determine whether the information required for the intended use cases is available and trustworthy.

Is AI Right for Your Manufacturing Quality Requirements?

Every manufacturer has different quality requirements.

Some businesses may need straightforward finished-goods inspections, while others may require more detailed in-process testing, lot controls, nonconformance processes and quality reporting.

AI can provide additional value where businesses have sufficient data and meaningful problems that AI can help address.

Potential use cases may include:

• Analysing quality trends
• Identifying recurring defects
• Supporting root cause investigations
• Analysing rework patterns
• Summarising quality information
• Identifying unusual production patterns
• Supporting management reporting
• Automating suitable administrative activities

The right approach depends on the organisation’s products, production processes, data, quality requirements and technology environment.

How Austral Dynamics Can Help

Introducing AI into manufacturing is not simply a matter of switching on an AI tool.

Businesses first need to understand where AI can provide practical value.

Austral Dynamics can help businesses assess their existing technology environment, business processes and data and identify opportunities for AI, automation and intelligent decision-making.

This can include reviewing:

• Business Central configuration
• Manufacturing processes
• Quality workflows
• Data availability and quality
• Reporting requirements
• Automation opportunities
• AI use cases
• Business intelligence requirements
• Integration requirements
• Security and governance considerations

Austral Dynamics works with Microsoft technologies including Business Central and Microsoft Copilot, as well as AI technologies and solutions designed to support business transformation.

The appropriate approach depends on each organisation’s requirements and existing technology environment.

Frequently Asked Questions

1. Can AI and Copilot be used with Business Central?

AI and Microsoft Copilot can be used alongside Business Central in appropriate scenarios to help users work with business information, support productivity and analyse data. The exact capabilities depend on the Business Central version, configuration, Microsoft technologies and other solutions being used.

2. How can AI improve manufacturing quality?

AI can help manufacturers analyse quality information, identify patterns, highlight unusual results and support investigations into recurring defects.

3. Can AI help reduce manufacturing rework?

Yes. AI can help identify recurring patterns in defects and rework, allowing manufacturers to investigate potential causes and take corrective action.

4. Can Copilot help quality teams?

Copilot can provide AI-assisted support for working with information and completing suitable tasks. Its usefulness for quality teams depends on the specific Copilot capabilities available in the organisation’s technology environment.

5. Can AI identify recurring manufacturing defects?

AI can analyse historical quality information and identify patterns that may indicate recurring defects or unusual changes in quality performance.

6. Can AI perform root cause analysis?

AI can support root cause investigations by helping teams analyse relevant information and identify patterns. However, quality and engineering professionals should evaluate potential causes and determine the appropriate corrective action.

7. Can AI manage non-compliant inventory?

AI can support the analysis of information relating to non-compliant inventory, but inventory release, quarantine, disposal and other quality decisions should remain controlled by the manufacturer’s established processes.

8. Does AI replace manufacturing quality teams?

No. AI is best viewed as a tool that can assist quality and manufacturing professionals with information analysis, investigation and appropriate administrative tasks.

9. Does a manufacturer need clean data before implementing AI?

Good-quality data is important for reliable AI analysis. Businesses should review the accuracy, consistency and availability of relevant production and quality information before implementing AI use cases.

10. Can AI improve quality reporting?

AI can help analyse and summarise business information and identify trends, depending on the reporting and AI technologies implemented.

11. Can AI work with manufacturing and production data?

AI solutions can potentially analyse manufacturing and production information when the relevant data is accessible through appropriate systems and integrations.

12. How can Austral Dynamics help with AI and Business Central?

Austral Dynamics can help businesses assess their Business Central environment, manufacturing processes, data and AI opportunities and identify practical ways AI, Copilot and automation can support their business objectives.

Improve Manufacturing Quality with AI and Copilot

Reducing manufacturing rework is not simply about fixing defective products faster.

The bigger opportunity is understanding why quality failures occur and using better information to prevent recurring problems.

Business Central can provide an important foundation for connecting manufacturing, inventory and operational information.

AI and Microsoft Copilot can add another layer of intelligent assistance, helping businesses analyse information, identify patterns and support employees in making better-informed decisions.

For manufacturers, the opportunity is to start with practical problems where AI can provide measurable value.

Ready to explore AI and Copilot for your manufacturing business?

Talk to Austral Dynamics to discuss your Business Central, manufacturing, quality management and AI requirements.

Contact Austral Dynamics to explore how Business Central, Microsoft Copilot, AI and intelligent automation can support your manufacturing operations.