Artificial intelligence in manufacturing is most commonly associated with robotics, smart machines, and predictive maintenance. However, AI can also streamline everyday business processes beyond the production floor, from invoice processing and issue reporting to procurement management and work documentation.
In many manufacturing facilities, certain tasks still involve manual data entry, email exchanges, or working with Excel spreadsheets. How can manufacturers use AI and automation to reduce manual work without replacing their existing systems? We explore this in the article below.
Key takeaways
- AI in manufacturing can improve not only shop floor operations but also administrative, procurement, and operational processes.
- Practical AI applications include automated invoice data extraction, voice-based issue reporting, and form completion.
- AI and workflow automation can work together. AI processes information, while automation manages document workflows, approvals, and task tracking.
- Implementing AI does not necessarily require replacing an existing ERP system. No-code platforms such as Qalcwise enable companies to streamline specific processes and integrate them with their existing systems.
- The best approach is to start with a single process where time savings and reductions in manual work can be easily measured.
Where can AI and automation be used in manufacturing?
Not every process requires artificial intelligence. AI is well suited to processing documents and extracting information, while workflow automation manages task routing, approvals, and status tracking. Combining both technologies can streamline the entire process.What does this look like in practice? Here are five examples of how AI and automation can be used in manufacturing.
1. Automated invoice data extraction using AI/OCR
Manually entering invoice data is time-consuming and increases the risk of errors. AI-powered OCR technology enables automatic extraction of information from documents, including supplier details, amounts, and payment due dates. The documents can then be routed through a digital workflow for verification and approval.
What does this look like in practice? At MCPP Poland, a manufacturing company, an AI-powered OCR module was implemented in Qalcwise in just two days. The solution now supports the processing of approximately 500 invoices per month, reducing the need for manual data entry. Read the MCPP Poland case study.
2. Voice-based issue reporting with Voice AI
Reporting equipment failures, non-conformities, or completed work often requires manually filling out a form. With Voice AI, employees can instead record a short voice message, which the system converts into text, identifies the relevant information, and uses to populate the appropriate fields.
For example, an employee can describe an issue with a material delivery, and the application can use this information to prepare a report for review and approval.
This type of solution can be built in Qalcwise using its Voice AI functionality, helping reduce manual data entry and speed up issue reporting.
3. Managing complaints and non-conformities
Handling complaints and non-conformity reports often requires collaboration between multiple departments, with information exchanged through emails. This makes it difficult to track progress and increases processing times.
Automation helps streamline the entire process, from registering a report and assigning responsibilities to tracking its status and the history of actions taken.
One example is a Qalcwise implementation at a frozen bakery products manufacturer, where the platform supports processes such as non-conformity management and equipment failure reporting.
4. Automating procurement and purchase requests
Purchase requests, approvals, and data transfers to ERP systems are areas where manual handling can slow down operations.
Automation helps streamline the entire process, from submitting a purchase request and obtaining approval to forwarding the information to the procurement department. This makes it easier to track statuses, reduce manual data transfers, and improve collaboration between departments.
5. Reporting and work documentation
Reports on completed work, service requests, and status updates are often recorded across multiple spreadsheets and email threads. This makes it difficult to consolidate information and prepare up-to-date reports.
Digitalizing these processes enables companies to centralize data and use it for ongoing task monitoring and reporting. As a result, employees spend less time manually gathering information, while managers gain easier access to up-to-date data.
7 signs your manufacturing company needs AI and automation
See if any of these situations sound familiar in your organization:
- Do employees manually enter data from invoices and other documents?
- Does reporting issues and equipment failures require filling out multiple form fields?
- Are complaints handled primarily through email or Excel?
- Do purchase requests require information to be passed manually between employees?
- Is it difficult to check the current status of a document or reported issue?
- Does reporting involve collecting data from multiple sources?
- Are some processes still managed outside the ERP system?
If you answered “yes” to several of these questions, it may be time to explore automation opportunities. AI is not always necessary. Sometimes, a digital form, an automated document workflow, or ERP integration is all that is needed.
How to implement AI in manufacturing without replacing your ERP system?
Implementing AI does not have to mean overhauling existing systems. The best approach is to start with a single, time-consuming process, such as invoice processing or issue reporting.
The next step is to identify which tasks can be automated, where AI can add value, and what data needs to be exchanged with the ERP system. No-code applications can help by complementing existing solutions and enabling companies to gradually expand automation.
Qalcwise supports this approach by combining workflow automation, AI capabilities, and integrations with external systems.
For more insights into digitalization, read our article Automating processes in manufacturing – which processes should be digitalized outside your ERP system?.
AI in manufacturing – where should you start?
Using AI in manufacturing does not necessarily require major investments or a complete overhaul of existing processes. A good starting point is to identify areas where employees spend the most time on repetitive tasks.
Artificial intelligence is not always the right solution. Sometimes, automating a simple process is enough, while in other cases, combining AI with traditional automation delivers the best results. The key is to start with a real business challenge, not the technology itself.
