The IoT and AI Is Changing Enterprise Asset Management

Enterprise Asset Management (EAM) is the core concept for maintaining quality and utilization of the assets and equipment. There are several asset management software options which are helping customers to better visualise, plan and maintain their organisation’s asset. 

IBM Maximo is a product which is helping clients all over the world. 

IBM Maximo and other EAM software does guide customers into better Asset maintenance, yet it still is dependent on human inputs and cognizance.  Maintenance scheduler and planner is a major role to avoid emergency maintenance and outage. 

Along with corrective and emergency maintenance, customers schedule several inspections to avoid outages and downtime of the asset. Even after all the precautionary measures of better preventive maintenance plans (which sometimes is done more than required) and frequent inspection, Asset downtime happens and most importantly the mean time to repair (MTTR) impacts organisation’s quality and utilisation of the assets. 

IOT and AI in Asset Management brings the flexibility and agility in the way organisations run asset maintenance. 

IoT is all about connecting IT systems to on-field equipment in real-time. It enables experienced maintenance planner/supervisor to prevent an asset outage by providing a platform to visualise asset condition. IoT is a concept which enables customers to capture readings and conditions from assets and transit the data into IT system.  This can display the same data in the format which maintenance planners understand and schedule maintenance activity better. IoT data can also be used to pin-point root cause without the technician going on-site, helping decrease MTTR and improve Asset utilization. 

AI and machine learning is much wider than IoT. AI requires data from IoT devices and legacy maintenance system as a base to help customers in better futuristic asset maintenance. AI or Machine Learning is all about engineering IT system to create self-learning models. These models use a lot of statistics and software programming as a core. There are ‘N’ number of use cases for AI and one such example would be a predictive maintenance. Customers who have been using a robust asset maintenance system have been storing their Asset’s maintenance data. This data is used by AI models to build a product which will predict an asset’s downtime or maintenance schedule. Another use case would be a scenario where a customer’s Asset is not an equipment which logs data, for example, drainage grates or a bridge. AI enables customers to better maintain such assets by using edge devices (mobile or CCTV cameras or drones) for a 24/7 inspection. 

Results of real-life use of IoT and AI in day-to-day Asset maintenance is futuristic, saves dollar, improves maintenance and utilization of assets. IBM Maximo 8.0 aka IBM Maximo Application Suite (MAS) is one such product suite. It brings traditional Asset maintenance system, Next gen Mobility Solution, Data and AI based predictive maintenance and AI based remote inspection into one platform or suite. EAM is moving towards predictive and data centric technologies. Applications enabled with IoT and AI capabilities is the future of Asset maintenance systems. 

Some details about the product stack in IBM Maximo Application Suite: 

1. Manage: Manage in MAS is the core of customer’s maintenance system. If a customer is upgrading from current Maximo 7.6 system, then manage is the new Maximo for customers. For new customers, think of it as your core application where you setup all your master data related to your assets. All the core concepts of Asset Management, Procurement, Maintenance planning and scheduling, Contracts, General ledger, Work Management and Service management is maintained in this product. All the other product in the suite depends on manage for master data and asset management business process. 
   
   
2. Mobile EAM (aka Maximo Mobile): Mobile EAM is the mobility solution under the MAS. It connects to Maximo/Manage and other MAS solutions for enabling field technicians, users, and supervisors to run their day-to-day maintenance activity using traditional work management business process or use MAS’s Machine learning and AI capabilities for futuristic asset maintenance. For example, Maximo Mobile enables technicians to connect to experienced technicians to troubleshoot during a complex maintenance activity. Click this link to learn more about the product. 
   
   
3. Maximo Visual Inspection: This is one my favourite products in the Maximo Application suite. IBM has given pre-built machine learning models which can help customers with quicker deployment to production timelines. This product, with the help from edge devices, can be used for 24/7 inspections of the assets. Customers must train the models given by the product. It then uses feed from edge devices to identify an issue or incident. It also comes with a mobile app which can be used by technicians with less experience to run inspections.
   
   There are a lot of use case for the mobile app but the most common one is where client’s in-experienced technician is on-site for an inspection. If the Visual inspection model has been trained and deployed, then the same technicians can take a picture of the asset.   This picture will then be analysed by the AI model to inform the technician about its condition and store it securely for future analysis. The application is also very user friendly for data setup and PoC. This is an independent application and can be used by customers who are not using Maximo as their core Maintenance system. 
   
   
4. Maximo Monitor: Monitor is another impactful product in the Application suite which aggregates data from IoT and other systems, displays them on a human readable dashboard and uses AI models for fault detection.  
   
   
5. Predict and Health: Predict and Health are two products that use machine learnings to predict asset failure and asset health. Predict can be integrated with Health for better visualisation and condition monitoring. These 2 products have capabilities which can reduce maintenance costs, quality, and improve utilisation. Both the products are a perfect example of how IoT and AI concepts are used together for changing the EAM.  

There are ‘N’ number of products and services which are built to help customers in their journey towards a pro-active and predictive Asset management strategy. Some clients have built their own services and solutions to suit their needs and business logic. Finally, I must acknowledge that the journey towards IoT and AI based Asset Management strategy will be rugged and challenging.  Nevertheless, it is the path which will lead clients to better asset maintenance and utilisation of Assets and equipment.