AWS for Industries

How manufacturers are leveraging Amazon Q Business to increase productivity

Manufacturers are constantly seeking ways to streamline operations and maximize productivity. With the advent of generative AI, manufacturing companies now have powerful capabilities to boost efficiency across various aspects of their business. From accelerating product development and assembly to extending equipment lifetimes, minimizing downtime and improving operational equipment effectiveness, generative AI has potential to revolutionize the manufacturing industry. Recently, AWS introduced Amazon Q for Business, a generative AI-powered assistant that can answer questions, provide summaries, generate content, and complete tasks based on data and information from enterprise systems. In most use cases, customers have leveraged the tool to boost workforce productivity with generative AI. For example, consider a scenario where a plant floor worker notices a potential safety hazard on the production floor. Instead of sifting through volumes of documentation scattered across multiple systems, they can simply ask Amazon Q, “Who should I notify if I see a potential safety hazard on the production floor?” The assistant can then provide a quick and accurate response, enabling the worker to promptly address the issue and maintain a safe working environment.

In this blog, we’ll show various examples of how manufacturers can leverage Amazon Q Business to enable field technicians to quickly access institutional knowledge in disparate systems, including repair and diagnostics service guides, procedures, historical repair tickets, and service bulletins.

Leveraging Amazon Q Business for Maintenance

A field technician can leverage Amazon Q Business for wide range of maintenance use cases like – generating step-by-step maintenance procedures tailored to specific equipment, facilitating root cause analysis for complex issues and streamlining spare parts inventory management. Furthermore, Amazon Q Business can serve as a powerful knowledge management and training tool, enabling technicians to quickly access companies collective expertise to continually learn and develop skills.

For each use case, you start by creating your Amazon Q Business application, and then you can connect specific data sources applicable to your business or machines such as machine manuals, internal maintenance documents, and historical repair tickets.

Amazon Q Business maintenance example

To illustrate the maintenance procedure generation capabilities, let’s examine the sample application shown in Figure 1. In this example, a drill machine manual has been integrated as a data source, enabling the field technician to rapidly retrieve relevant information for performing routine maintenance tasks on that specific equipment.

Figure 1: Amazon Q Application for Manufacturing

With the Generative AI Manufacturing Q&A application, the field technician will prompt the following:

Sample prompt: Provide a bulleted list of the daily maintenance required for the drill machine.

As shown in Figure 2, Amazon Q leverages the integrated drill machine manual to generate a bulleted list outlining the daily maintenance requirements for the equipment. It transparently cites the source of the information, indicating that it obtained the data from “GenAI_Drill_Machine_Manual.pdf” file.

Figure 2: Amazon Q Application response

Now, let’s consider a scenario where the field technician has a question regarding the drill machine’s warranty while performing maintenance tasks. In such a case, they could enter the following prompt in Amazon Q application:

Sample Prompt: How many years is the warranty on the drill machine? Provide the exclusions of warranty in table format.

Figure 3: Amazon Q Application response

As shown in Figure 3, Amazon Q retrieves relevant warranty information for this drill machine. The response clearly indicates the 3-year warranty period along with exclusions in a tabular format.

These examples illustrate how manufacturers can leverage Amazon Q Business to expedite access to critical information, whether on a job site or for general inquiries related to equipment or manufacturing processes.

Leveraging Amazon Q business for troubleshooting issues

Engineers can leverage Amazon Q Business for troubleshooting issues, especially with complex machinery on factory sites. It can guide them through diagnostic processes by surfacing relevant information from various data sources including internal knowledge bases. Moreover, it modernizes customer experiences through real-time updates, tailored advice and dynamically generated instructions. Additionally, it provides live feedback from/between connected systems and enhances operational visibility and risk management.

You start by creating your Amazon Q Business application, and then you can connect specific data sources applicable to your business/machines such as operating manuals, maintenance manuals, troubleshooting guides, and historical maintenance records.

Amazon Q Business Application troubleshooting example

To enhance the troubleshooting capabilities of our existing Generative AI Manufacturing Q&A application, we’ll integrate additional data sources comprising troubleshooting guides and historical maintenance records.

For this example, a drill machine troubleshooting guide has been connected as a data source, enabling field technicians to quickly pinpoint the root cause of issues affecting the drill machine.

Figure 4: Amazon Q Application for Manufacturing

Now, with Generative AI Manufacturing Q&A application, the field technician will prompt the following:

Sample Prompt: List the reasons for an undersized hole.

As shown in Figure 5 below, Amazon Q application retrieves the relevant information from the integrated troubleshooting guide. It then presents the potential causes in a bulleted list format, accompanied by a citation indicating the source file from which the data was obtained.

Figure 5: Amazon Q Application response

Let’s consider another example: a field technician is trying to look for a possible solution for Excessive Cutting-Edge Wear by entering the following prompt:

Sample Prompt: List down the solution for Excessive Cutting-Edge Wear

As shown in Figure 6, Amazon Q listed the possible reasons for Excessive Cutting-Edge Wear along with the solution to correct each one of them.

Figure 6: Amazon Q Application response

By utilizing Amazon Q Business, engineers can expedite the troubleshooting process by rapidly retrieving relevant steps and guidance from various data sources, eliminating the need to sift through complex manuals manually and thereby minimizing the associated downtime.

Leveraging Amazon Q business to streamline workflows

Amazon Q Business can function as a powerful virtual assistant for manufacturing field technicians by integrating with various tools and services used in your daily operations. This frees up valuable minutes (or even hours) technicians can spend on core tasks like troubleshooting, repairs, and ensuring smooth operations. Here are some ways Amazon Q Business can streamline workflows for technicians.

  • Reduced administrative burden: Imagine a technician on-site who needs to report an issue and schedule a meeting with a maintenance team. Using Amazon Q, they can report the problem by requesting the creation of a service ticket in a project management system like Jira. Amazon Q can connect to Jira and automatically create the ticket, saving the technician time and effort compared to manual data entry. But that’s not all. With Amazon Q, technicians can summarize common failure points across different machine types or generate text with recommended preventative maintenance procedures based on historical reports. This empowers data-driven decision-making for maintenance prioritization.
  • Streamlined communication: After diagnosing an issue, a technician might need to draft an email to their supervisor with details and recommendations. Amazon Q can generate a draft email based on the technician’s instructions, including relevant information from the reported issue. The technician can then review and send the email quickly, improving communication efficiency.
  • Simplified scheduling: Coordinating schedules with supervisors or other technicians across different shifts can be a hassle. Amazon Q can integrate with calendar applications and schedule meetings based on technician availability and supervisor preferences. This eliminates the need for back-and-forth emails or phone calls to find a mutually convenient time.
  • Planned maintenance: After supervisor approval, a technician might start planning maintenance for a complex machine. A technician can ask Amazon Q to analyze the estimated repair time and calculate the potential downtime cost based on historical production data. This helps to prioritize repairs and minimize production losses with a clear understanding of the financial impact.

By reducing administrative tasks and streamlining communication, Amazon Q Business empowers technicians to focus on their core competencies, ultimately contributing to a more efficient and productive manufacturing environment.


Amazon Q Business provides a powerful generative AI assistant that can significantly boost productivity and knowledge accessibility for manufacturers. By connecting enterprise data sources like manuals, repair logs, and service bulletins, field technicians and plant workers can quickly get answers to their questions without having to manually search through disparate systems and documentation.

For a hands-on workshop on how to do this, check out the Generative AI for Manufacturing Workshop.

Brendan Jenkins

Brendan Jenkins

Brendan Jenkins is a solutions architect working with enterprise AWS customers, providing them with technical guidance and helping them achieve their business goals. He specializes in DevOps and machine learning technology.

Sushmitha Sekuboyina

Sushmitha Sekuboyina

Sushmitha Sekuboyina is an AWS Solutions Architect based in Austin. She specializes in AWS Security and Networking services. She is passionate about helping customers build secure, reliable and cost-effective solutions on the AWS Cloud.

Varun Saxena

Varun Saxena

Varun is a Solutions Architect at AWS working with enterprise customers at AWS.