We live in a world where everything is connected. We have moved far beyond conventional communication, information sharing, and collaboration, and this amplified state of connectedness is even more consequential in industrial settings. Machines exchange data seamlessly across vast distances; real-time information is available at your fingertips. However, manufacturers need massive amounts of computing power to make sense of all this information, and as a result they have had to adapt their approach to maintenance.
Smart maintenance builds upon the foundations of conventional maintenance practices, aiming to increase asset reliability by prolonging the equipment’s useful life. Modern methods of maintenance use different tools to achieve their objectives, but what they have in common is a much more targeted approach to maintenance.
The primary objective of smart maintenance is to maximize asset reliability while optimizing inputs. To get the maximum benefit from your smart maintenance strategy, you must ensure that it follows these three pillars of successful smart maintenance.
1: Resource allocation
A common misconception about digitalization is that its end goal is to eliminate the human component of a manufacturing operation. The role of the workforce in a digital factory is changing, and recognizing this reality is one step toward success.
The shift in workers’ roles can take different forms. For instance, you might be running predictive maintenance and have a good portion of the maintenance workflow automated. You still need maintenance technicians around to perform repairs and execute specific measurements that will feed equipment condition data to your predictive algorithms.
While sensors are capable of general conditional monitoring, diagnostic tools such as vibration analysis and oil analysis still require human input. Depending on the current level of digitalization, your workforce must learn how to use your plant’s tools and programs, such as Computerized Maintenance Management Software (CMMS) or Enterprise Asset Management (EAM.)
Although CMMS and EAM share some functionalities there are certain key differences between them. CMMS performs work-order management, asset and inventory management, and preventive maintenance. It also provides multisite support, project management, and parts procurement.
On the other hand, EAM provides unified management for the entire life cycle of an organization’s physical assets. It includes maintenance management features but considers the Total Cost of Ownership of all the assets; it also tracks, manages, and analyzes asset performance and costs.
Metal manufacturers improve their manufacturing processes through data analytics and machine learning. CMMS and EAM identify trends and patterns in equipment performance, enabling proactive maintenance tasks to be scheduled before equipment failure.
The upside of utilizing CMMS and EAM is that, while more specialized work is required, optimizing activities to the actual needs of an asset is achievable. Administrative tasks such as generating work orders and adding progress updates no longer require a physical paper trail. Additionally, since there is a comprehensive (automated) decision process for triggering maintenance tasks in CMMS and EAM software, scheduling activities automatically does not waste valuable management time.
2: Workflow capacity management
In metal manufacturing, smart maintenance relies on both human and digital components to optimize maintenance tasks and reduce downtime. When it comes to balancing technology and your workforce, the connected worker is the winning combination.
Connected workers use digital tools and data management techniques to improve and integrate their interactions with both their physical and virtual surroundings. By doing so, they improve decision accuracy, capture important data, and lessen variability.
The concept of ‘connectedness’ is changing how manufacturing workers access and share information for their jobs, as the main goal of connected worker solutions is to improve performance standards continuously. Moving away from manual processes brings some significant benefits:
- Reduction in the manufacturer's mistakes
- Improvement in time to execute tasks
- Reduction in the time frame to revise standard work.
If metal manufacturers combine artificial intelligence (AI), machine learning (ML) and interconnected devices, they can analyze massive volumes of real-time data from hundreds of machines, transport vehicles, and other equipment in their asset register.
By identifying and prioritizing maintenance tasks based on their impact on production, safety and equipment reliability, manufacturers can strike a balance between performing too much maintenance (incurring unnecessary expenses), and not performing enough maintenance (risking untimely deterioration and unplanned downtime.)
Refining your maintenance approach is possible by applying the CMMS and EAM systems, which also can help allocate resources like labor, materials, and tools more efficiently and effectively. By using predictive maintenance techniques, metal manufacturers can identify maintenance needs before they become critical and schedule maintenance activities while demand is low, to minimize the impact on production.
3: Team alignment
Aside from optimizing plant maintenance, smart technology can drive further efficiencies by expanding its integration capabilities in other areas.
Team alignment is critical in manufacturing, but it can be challenging to achieve with a limited workforce. Manufacturers may face obstacles like a lack of communication, siloed departments, and insufficient training and development programs for their maintenance teams. They use KPIs to track maintenance performance and identify areas for improvement, and smart maintenance technology helps them. With KPIs as their base, manufacturers can identify areas for improvement and track progress over time.
Additionally, manufacturers can invest in training and development programs to ensure that maintenance teams have the skills and knowledge they need to perform their work effectively.
Hyundai’s plant in the Czech Republic, for example, successfully implemented cross-functional teams in its manufacturing process. These teams consisted of members from various sections — such as stamping, welding, paint, interior, moving parts, chassis, and electrical sections — who work together in alignment with the goals of production efficiency and quality.
Cross-functional teams were formed to verify the car during its development stage. They collaborated with designers from Germany and Korea to identify issues and devise solutions. However, the designer had to approve all the design changes. Their collaboration has helped Hyundai successfully implement new lines since 2015.
Manufacturers increasingly rely on smart maintenance methodologies as an advanced approach to maintenance management. Smart maintenance combines technology, data analytics, and process optimization to enhance equipment efficiency, reduce downtime, and extend equipment lifespan.
And, smart maintenance has become increasingly important in the machining and fabricating operations, where equipment downtime and inefficiencies can result in significant losses. Many manufacturers also use predictive maintenance as part of their smart maintenance strategies.
By using real-time data, advanced algorithms, and automation, manufacturers can optimize their production processes, reduce operational costs, and improve overall equipment effectiveness. If you are making decisions for machining, fabricating, or other manufacturing operation, then adopting smart maintenance is not simply an option for you — it is a must-have to maintain a competitive edge in the industry.
Eric Whitley is Director of Smart Manufacturing with L2L, a Cloud-based software for use solving factory floor problems in real-time, reducing downtime, improving response time to production issues, and reducing overall maintenance costs.
