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Top 7 Use Cases of Intelligent Automation in Manufacturing in '24

The use of artificial intelligence and automation technologies is not new for manufacturers. It’s already being observed that: 

By combining automation solutions such as RPA, with AI techniques such as machine learning, NLP, computer vision, and conversational AI, manufacturers can move from automating narrow tasks to end-to-end processes. This combination is known as intelligent automation, cognitive automation, or hyperautomation.

In that regard, we list 7 use cases of intelligent automation in the manufacturing industry:

1. Predictive maintenance

Using vibration, temperature, or pressure data, among others, from sensors and IoT devices connected to machines, intelligent bots can: 

  • Predict upcoming failures, 
  • Alert staff when checkups and maintenance are required,
  • Set and update scheduled tasks.

Real-time monitoring can help manufacturers avoid costly breakdowns, by tending to the machines before the damage has intensified. This helps reduce outages, lengthy downtimes, and is more economically efficient. 

Feel free to check our article on predictive maintenance for a more comprehensive account.

2. Supply chain management

Managing supply chains and maintaining their visibility is a complex task because it involves multiple suppliers operating in different regions and under different regulations.

Manually handling documents such as delivery orders, bills of lading, dock receipts, etc., and tracking orders, inventories, and shipment status would hinder a manufacturer’s operational agility. Intelligent automation can:

  • Standardize data across the supply chain by updating multiple ERP systems accurately,
  • Monitor shipping schedules and provide regular updates to relevant parties,
  • Enable end-to-end document automation by extracting data from different types of documents with an understanding of the context, updating internal systems, and notifying relevant staff.

You can also check our article on supply chain automation for more.

3. Order fulfillment

Manually processing orders from the moment you receive them until when it’s delivered to a customer is a time-consuming and error-prone task. Intelligent bots can automate the order fulfillment process by:

  • Identifying order emails
  • Downloading attached files
  • Extracting relevant data from the files
  • Creating order in the ERP system with extracted data
  • Communicating with customers about product availability and shipping options

We have a dedicated article on the O2C (order-to-cash) process that discusses the end-to-end logistical and financial steps of order fulfillment in more detail. 

4. Vendor and customer relationships

Intelligent bots with conversational AI capabilities can:

  • Assist support teams during their communications with vendors and customers by consolidating data from disparate systems and applications when they provide answers.
  • Send alerts to customer support staff when a customer raises a complaint.
  • Provide insights about customer and vendor relationships with advanced analytics.

This can help manufacturers provide seamless customer and vendor support and reduce response and problem resolution times.

5. Inventory management

Ensuring the optimum amount of inventory is crucial for manufacturers to maintain a smooth operation. Using ML models and historical inventory data, intelligent automation can help manufacturers prevent stockouts and reduce waste by: 

  • Alerting staff when inventory levels are low,
  • Predicting the right amount of inventory,
  • Reordering items automatically.

If you are interested in learning more about how smart inventory management works, we have a separate section that discusses it, and its use case in the retail industry, in more detail.  

6. Bill of materials (BOM)

A bill of materials (BOM) is an important document for production that lists materials, parts, and assembly required for manufacturing a product. Human error in the creation of BOM can cause product recalls, delay production, and reputational damage. By combining RPA and OCR, intelligent automation can help manufacturers:

  • Eliminate working with spreadsheets and papers to generate BOMs
  • Standardize BOM request templates
  • Extract data from different types of documents related to BOM creation
  • Alerts the staff if there’s missing information in a BOM

7. Invoice processing

By combining RPA bots with OCR and NLP capabilities, intelligent automation can significantly improve invoice processing. Specifically, it can:

  • Compare invoices against purchase orders and alert staff in case of a discrepancy,
  • Extract relevant data from invoices,
  • Enter the extracted invoice data into the various ERP systems.

Invoice automation can help manufacturers reduce invoice processing time and invoice errors. This can lead to faster and more accurate payments to suppliers and smoother production operations by enabling timely delivery of intermediate products.

For more, feel free to check our article on invoice automation.

For more on intelligent automation

Feel free to check our article on intelligent automation use cases & examples and our article on intelligent automation case studies to identify use cases for your business.

You can also check our data-driven list of intelligent automation solutions. If you need an expert opinion, we can help:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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