The increasing sophistication of today’s advanced industrial equipment often exceeds the capabilities of on-site, general-purpose technicians to diagnose and repair. Increasingly, industrial equipment incorporates computer controls with elaborate interactions among software, electronics, sensors, actuators and other sub-systems. Today’s equipment manufacturers also often incorporate advanced science into their R&D efforts to optimize the performance of their equipment, and IIoT with PDM can address this complexity.
One solution is to outsource some maintenance activities for sophisticated assets to the OEM using Industrial IoT (IIoT) to remotely monitor the health of the equipment. The combination of remote monitoring and analytics with predictive maintenance (PdM) by experts who know the equipment best can provide significant business benefits.
Growing Equipment Complexity
Equipment complexity is increasing across two major dimensions:
- Automation: Mechanical controls have given way to common use of computer controls with software, electronics, electronic sensors and electro-mechanical actuators with convoluted interaction among these sub-systems. The technician’s eyes and ears alone are no longer sufficient to identify and resolve impending problems. Debugging and problem isolation requires specialized training and tools (like the OBD2 code reader and analyzer for your car).
- Science: The folks in R&D have used their advanced degrees along with computer aided design (CAD) software to optimize the performance of their company’s equipment. With advanced science incorporated in the design, adjustments made by a general-purpose technician can be counterproductive (long gone are the days when you could tune your car by turning the carburetor on its shaft until it sounded right).
Legacy Maintenance Management Unsustainable
Keeping up with changes in technology and automation is a challenge. New equipment for replacements or plant upgrades often have complex systems with mechanical, electrical, electronics, and software components. This technical intricacy has been building for a decade or more. In most plants today, a significant portion of the critical assets reflect a high degree of technical sophistication, making problem isolation and identification increasingly difficult. This change has been gradual over an extended period of time and many have not recognized that the old approach to managing maintenance has become unsustainable.
Craft Skills vs. Root Cause Identification
Each craft focuses on their skill set which is their strength and the basis for their employment. When isolating a problem, a mechanic tends to focus on the mechanical aspects and will often make a “repair” in one or more mechanical components that is really a symptom rather than the root cause. Similarly, an electrician focuses on electrical components. While several people with different crafts could be assigned to a single work order, this approach significantly reduces productivity.
With today’s sophisticated systems, the traditional dedicated craft approach to maintenance is becoming unsustainable since it is often not very effective at identifying the root cause of an issue, much less rectifying it.
Systems Approach vs. Deep Technical Understanding
A systems approach - where the technician has skills across multiple crafts - provides an improvement for supporting more modern equipment. The goal has the technician accessing mechanical, electronics, and software to be able to isolate and identify problems. For complex equipment, this multi-talent approach provides an improvement over specific crafts. Unfortunately, the viability this systems approach is impacted by these issues:
- The science embedded in equipment goes beyond the capabilities of those without deep training and an understanding of the design
- A skills shortage makes it difficult to hire people with the needed talent and willing to work in a demanding industrial environment
- Cascading systems-of-systems designs for which extensive knowledge and experience is required to understand the interrelationship among subsystems
For a particular type of equipment, deep and expensive training ages quickly and has a short shelf life – usually a few months at best. Unless the technician engages those skills on a daily basis, the knowledge decays. Also, the good technicians who can absorb the training are the ones most likely to move onto another job through promotion or otherwise. The pace of technology change continues to increase, and this equipment complexity issue will only get worse.
Outsourcing Maintenance Enabled with IIoT
IIoT and analytics open the door to outsourcing maintenance of complex equipment – even critical equipment – to the OEM that typically has deep knowledge and understanding of the equipment it designed and built. Many suppliers of complex equipment have started to offer aftermarket services for asset health monitoring, PdM and maintenance. In many cases, these services prevent failures before they can occur.
Maintenance Workflow and Delivery with IIoT
When a customer has an equipment problem, the workflow for service and repair significantly improves with IIoT and PdM. The old method involved a complaint call with high urgency due to unplanned downtime causing production interruptions. The repair process needed two passes – one to determine the source of the problem and another to execute the repair. Getting the machine back online could take several days, which is typically unacceptable to the customer. Avoiding production interruptions and associated revenue losses provide the key reasons for having an internal maintenance staff.
PdM using IIoT and analytics via the web provides advanced warning of a pending failure, and repair is planned ahead to minimize production interruptions. An added benefit is that, often, the repair occurs via the web. At the ARC Industry Forum in February 2015, an OEM stated, “80 percent of breakdowns are resolved online.” Another OEM reported a 35% remote fix rate. This results in a far smoother and more client-friendly process for scheduling maintenance and repair. With IIoT and PdM, outsourcing maintenance to the OEM can involve less risk and improve first-time repair. Response to a pending issue can take the form of an email notice, prescriptive information, or scheduling a service technician from a local distributor.
Business Benefits of Predictive Maintenance
With predictive maintenance, technicians can perform work at the optimum time. Compared to preventive maintenance, a study by Shell shows that PdM reduces maintenance costs by half. The Plant Engineer’s Handbook mentions the following benefits for PdM:
- Maintenance costs - down by 50 percent
- Unexpected failures - reduced by 55 percent
- Repair and overhaul time - down by 60 percent
- Spare parts inventory - reduced by 30 percent
- 30 percent increase in machinery mean time between failures (MTBF)
- 30 percent increase in uptime
The business impact of lower unplanned downtime has immediate benefits like increased capacity and revenue. Secondary benefits include lower inventory (less safety stock for unplanned events) and improved customer satisfaction (with higher on-time shipments).
Predictive maintenance services provided by the OEM using IIoT and analytics provides a means to mitigate the challenge of supporting new, technically complex equipment while also reducing costs. ARC has the following recommendations:
- Owner-operators should consider changing their purchasing criteria for new equipment to include post-sale support with an IIoT offering. Make IIoT, analytics, and predictive maintenance a major selection criteria for equipment purchases.
- OEM suppliers should rapidly adopt IIoT to be able to provide outsourced PdM services for their more sophisticated equipment. Where resources are limited, they should consider partnering with global service providers with the appropriate resources and experience with IIoT, cloud, and analytics. Infosys, L&T Technology Services, and Tata Consultancy Services (TCS), are three GSPs that come to mind.
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Keywords: Maintenance Management, IIoT, Predictive Maintenance, PdM, ARC Advisory Group.