Introduction

The new generative AI capabilities Maximo Application Suite (MAS) 9.0 are the next steps in the Maximo AI roadmap.

The 2024 release of MAS 9.0 is putting the focus on optimizing work order efficiency using generative AI powered by IBM watsonx™. The AI design is delivering on an AI strategy to compel business users to start consuming AI in small steps that deliver business value and progressively leads to increased usage at scale.

  • We are focusing on the business user and the business value.
  • We are providing value out-of-the-box
  • We are providing AI infused into adopted workflows and data
  • We are providing lifecycle support for models with watsonx

Work Order data quality and process automation AI helps automate the work management workflows and better support for decision making in Manage.

Objective

We will deliver improved efficiency, at the scale of thousands of hours, in everyday work order management by providing more accurate failure code recommendations as compared to current achieved by the organization.

User Research

Research objectives

Multiple qualitative user research projects were conducted as part of the asset management AI strategy and product usage studies, involving sponsor user groups and stakeholder interviews to identify business needs and validate proposed designs.

We sought to understand which personas would benefit most from AI-infused data and how generative AI could enhance their efficiency. Additionally, we explored how users perceive and trust AI recommendations, and we defined how trust in AI systems impacts adoption and usage.

Research targeted both internal and external stakeholders, including Maintenance Managers, Asset Managers, Operation Managers, Reliability Engineers, Technicians, and Business Partners.

Conclusions

“Even small gains in accuracy can have an enormous impact to an organization when it can be applied at scale.”

Organizations today are early in their adoption of Gen AI. Their organization lacks the AI governance and structures needed. Operations are focused on achieving short-term benefits and lack of high quality data is a challenge.

Organizations expect significant value outcomes from generative AI:

  • 10% increase in productivity: Access to recommended problem codes and related job plans will enhance fix rates, leading to cost savings and improved work productivity.
  • 10% increase in compliance: AI recommendations should utilize internal guidelines, regulations, rulings, and data.
  • 20% decrease in asset failures: As reliability engineers leave the workforce, generative AI models are needed to bridge skill gaps by leveraging learnings from historical data.

IBM AI Design

The Work Order Intelligence design is based on ‘Carbon for AI’ in the IBM Carbon design system.

Carbon for AI is an extension of the IBM Carbon design system, designed to give AI instances in IBM products a visually and behaviorally distinct identity. Generative AI introduces a range of new responsibilities when designing experiences, particularly regarding trust, transparency and explainability. Carbon for AI is the common design framework for identifying AI-generated content and delivering explainability in IBM products.

Carbon for AI is mandatory when introducing AI capabilities in IBM products. As IBM Software design leaders we are meeting weekly to share scenarios, review UI and unify usage of Carbon for AI.

AI label and styling

The AI label is a component used as an indicator of AI in the user experience. It is intended to be used in any scenario to identify context generated by AI. It enforces transparency, explainability and feedback. It also becomes the focal point for actions on AI-generated data..

Work Order Intelligence
The AI label components.

Explainability

The AI label contains the AI Explainability Popover, which provides a layer of explainability to the user. It provides a consistent summary of relevant model information and offers the option to dig into more details if needed. This may include an explanation of how the AI capability works, the model confidence, the model used, and its version.

Work Order Intelligence
The AI Explainability Popover.

Scenario

The end-to-end Work Order Intelligence scenario crosses over multiple maintenance roles and workflows. The main personas are Maintenance Managers, Maintenance Supervisors, Reliability Engineers, Domain Engineers, and Technicians.

First, new work orders are created by technicians from inspections and maintenance tasks. The technician describes the impacted asset, its location, a short and optionally long description of the problem. If known by the technician, the class of failure is provided, and a specific problem code is recorded. 

After the work order has been saved, Gen AI makes an inference and predicts the problem codes that best match the work order description. Additional information is also used in the prediction. The predicted problem codes with the highest confidence are captured. 

Maintenance Managers and Maintenance Supervisors use the operational dashboard and work queues to drive the work order review workflows at scale. With Work Order Intelligence, the work orders requiring attention may be identified using the ‘WOs with problem code recommendations’ work queue. The number of WOs in the queue is directly visible on the operational dashboard. 

When the Maintenance Manager or Maintenance Supervisor reviews the work order, the problem codes recommended by Gen AI are presented in the work order editor using the AI label. The Maintenance Manager views and chooses the best recommendation. Also, before closing a work order, the Maintenance Manager ensures the problem code is updated and matches any additional information or conditions discovered during the maintenance tasks. This ensures that the quality of historical asset data is continuously improved.

A Reliability Engineer may later review problem code recommendations and provide feedback to the model by identifying and tagging good examples of work orders. Retraining the model with such selected work orders improves its accuracy..

Design

Operational dashboard

The operational dashboard is the one-stop destination for the most critical KPIs and access points to maintenance workflows. Work Order Intelligence design identified the need to create a workflow using a work queue that selects all work orders requiring attention. A work queue, ‘WOs with problem code recommendations,’ should be added to the dashboard for that purpose.
Work Order Intelligence
Operational dashboard with Work Order Intelligence work queue.
Work Order Intelligence
Work Queue selecting all Work Order with problem code recommendation.

Work Order application

The work order application in Maximo Manage is the user experience for most work order related workflows. The application consists of two experiences; a list view page and a details page. The list page displays all, or a filtered set of, work orders. Work Order Intelligence design suggests a predefined view that filters all work orders with recommendations. The work order editor, or details page, is extends the standard editor by infusing AI generated data. New problem code recommendations are highlighted.
Work Order Intelligence
Work Order Intelligence
WO application and  and editor w/ problem code recommendation.

Problem codes and AI recommendations

Problem code recommendations can be inspected with details on the AI model confidence score. The best-fitting code can be selected for the work order. A user can also override any recommendations as decided.
 
The Gen AI capability generates new recommendations if the work order information is refined or updated. The recommended code may remain unchanged, or new information may generate an updated code with higher confidence.
Work Order Intelligence
Problem code recommendations for a work order.

Model management

Work Order Intelligence uses two separate AI models in its implementation.
 
  • Problem code predictions are performed by a classification model trained on high-quality work orders spanning all problem codes. The model uses the work order descriptions to classify the problem codes.
  • To achieve a large training and testing dataset of work orders, a second large language model uses a selected set of work orders to synthetically generate a larger dataset of work order variants. Improved model accuracy is achieved using the generated synthetic dataset.
Selecting work orders for model training is performed in the Work Order application, typically by a Reliability Engineer. The model configuration, models, and model training actions are managed by the system administrator in the AI configuration application.
Work Order Intelligence
Add work order to model training set to improve classification model accuracy.

Business Outcome

On June 25, 2024, IBM announced the availability of the 9.0 release of the Maximo Application Suite. Maximo Manage 9.0 introduces AI intelligence to the Maximo work management process with the new problem code classification capability. Maximo Manage 9.0 builds on a long history of AI capabilities in Maximo Monitor and Maximo Visual Inspection.

Work Order Intelligence AI in Maximo was presented at Maximo World 2024.

 


IBM.com
Maximo Work Order Intelligence: Where AI meets maintenance excellence.

 


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Learn more about Carbon for AI.