AI-Driven Work Order Classification in IBM MAS 9

The latest IBM Maximo Application Suite (MAS) 9 includes advanced AI capabilities integrated through IBM's Watsonx framework, aiming to streamline asset management tasks with intelligent automation and actionable insights. MAS 9 leverages AI models to automatically analyze work order descriptions and suggest relevant problem codes, helping teams prioritize and reduce unplanned downtime. These AI recommendations can be accepted or modified by technicians, which reduces repetitive tasks and improves consistency in data input.

A foundational component of MAS 9's AI setup is the AI broker, which manages data flow between Maximo and AI-powered functionalities, enabling seamless training, inference, and model updates. Additionally, the AI broker supports integration with multi-tenant systems, allowing various data sets to inform model performance for improved decision-making across different maintenance contexts.

In MAS 9, Work Order Problem Code classification is a key feature enhanced by artificial intelligence (AI) that allows organizations to effectively categorize and manage work orders based on specific issues encountered with assets. Here’s a breakdown of how the classification process works:

1. Data-Driven Classification

AI uses Work Order description (training data) to classify problem codes. By analyzing patterns in work orders that have been previously categorized, the system learns which codes are most relevant for particular types of issues. This data-driven approach enables the AI to make informed recommendations when new work orders are created​.

2. Natural Language Processing (NLP)

The integration of NLP allows the system to interpret the language used in work order descriptions. The AI can extract keywords and phrases that are indicative of specific problems, making it easier to match these descriptions to the appropriate problem codes. For example, terms like “leak,” “failure,” or “overheating” can trigger the classification system to suggest corresponding problem codes​.

3. Real-Time Problem Code Suggestions

As technicians enter work order details, the AI can provide real-time suggestions for problem codes based on the entered descriptions. This feature enhances efficiency by allowing users to quickly select the correct classification, reducing the potential for errors and ensuring that work orders are documented consistently​.

4. Feedback Loop for Continuous Improvement

The system incorporates a feedback mechanism where user selections of problem codes are tracked. This feedback helps the AI refine its classification algorithms over time, ensuring that the recommendations become more accurate as more data is collected. If a specific problem code is often assigned to similar descriptions, the AI adjusts its learning model to recognize this correlation more effectively​.

This feature offers several benefits across various industries by improving maintenance management and operational efficiency. Here are some key industry benefits:

1. Standardization of Maintenance Practices

   By implementing standardized problem codes for work orders, organizations can ensure consistent categorization of issues across different teams and departments. This standardization helps in establishing best practices for maintenance and troubleshooting, enabling technicians to respond more effectively to recurring problems​.

2. Improved Data Analysis and Reporting

   The ability to assign problem codes allows for better data aggregation and analysis. Industries can track the frequency and types of problems encountered, which aids in identifying trends and recurring issues. This insight can inform preventive maintenance strategies and help allocate resources more efficiently. For instance, a manufacturing facility can use this data to determine whether certain machinery requires more frequent maintenance, ultimately leading to reduced downtime and improved productivity​.

3. Enhanced Communication and Collaboration

   Using problem codes enhances communication among maintenance staff, management, and stakeholders. When everyone uses the same codes to describe issues, it minimizes misunderstandings and ensures that all parties have a clear understanding of the work being performed. This is particularly beneficial in industries like utilities and construction, where teams often work collaboratively on complex projects​.

4. Streamlined Workflows

   The problem code feature can lead to more efficient workflows by allowing for quick identification of work order types. This means that technicians can spend less time diagnosing issues and more time performing repairs. In sectors such as transportation and logistics, where timely maintenance is critical, this efficiency can significantly enhance operational performance​.

5. Better Resource Allocation

   Organisations can use data derived from problem codes to analyze resource allocation effectively. By understanding which problems are most prevalent, companies can adjust their maintenance strategies and workforce deployment accordingly. For example, if a specific piece of equipment frequently requires repairs related to a particular problem code, additional training or resources can be directed to address those issues more effectively​.

6. Regulatory Compliance and Audit Readiness

   Many industries face strict regulatory requirements that necessitate detailed reporting and documentation of maintenance activities. By utilizing problem codes, organizations can maintain comprehensive records of work performed, making it easier to demonstrate compliance during audits and inspections​.

Conclusion:

IBM MAS 9's AI-driven work order classification transforms asset management by enhancing data accuracy, streamlining workflows, and enabling proactive maintenance decisions across industries. By leveraging Watsonx and AI-driven insights, organizations can optimize their maintenance processes, reducing downtime and ensuring regulatory compliance. For companies looking to implement or upgrade MAS 9 effectively, Sedin's Maximo Center of Excellence (COE) offers deep expertise and tailored support. From setup to customization, Sedin's Maximo COE ensures your organization fully harnesses MAS 9's AI capabilities to drive operational excellence.

Author: 

Ashwini Chauhan

Lead Consultant - Maximo