Asset Optimization: Predictive Maintenance has been saved
Perspectives
Asset Optimization: Predictive Maintenance
Connecting machines to reliability professionals
Traditionally, maintenance professionals combine quantitative and qualitative techniques to anticipate impending failures and mitigate downtime for critical assets that impact revenue. Predictive maintenance (PdM) can help optimize operations in real time and provide advance insights into failures, maximizing asset life while avoiding disruption.
Introduction to predictive maintenance (PdM)
One important objective of any maintenance organization is to maximize asset availability. In this article, we focus on the applicability of PdM to fixed and mobile assets within various industries and sectors. This could apply to many types of assets, including equipment in a manufacturing shop floor, vehicle subsystems in automotive fleets, or assets used in mining or medical environments.
Poor maintenance strategies can reduce an asset’s overall productive capacity by 5% to 20%1. Recent studies also show that unplanned downtime is costing industries an estimated $50 billion each year2. This begs the question, “How often should an asset be taken offline to be serviced?” Traditionally, this dilemma forced most maintenance organizations to choose between two options: Maximizing the run time of an asset at the risk of asset downtime (run-to-failure) or maximize reliability of an asset through early replacement of potentially good components (time-based preventive maintenance).
The run-to-failure approach of maintenance can lead to catastrophic asset damage as parts begin to vibrate, overheat, and break, reinforcing the adage “pay me now or pay me later.” Additionally, while run-to-failure may be an acceptable approach for some assets, the unplanned downtime when the assets ultimately fail is almost always more expensive and time consuming. Conversely, a preventive maintenance strategy to frequently replace parts and service equipment can increase replacement costs over time and increase planned downtime and disruption to operations.
Predictive maintenance promises to provide the best of both worlds by aiming to reduce unnecessary preventive maintenance while ensuring that assets don’t face catastrophic failure.
Leveraging the power of smart technologies
Predictive maintenance aims to empower companies to maximize the useful life of their assets while avoiding unplanned downtime and minimizing planned downtime across their operations. With the advent of Internet of Things (IoT), companies can leverage technologies to monitor and gain deeper insight into their operations in real time to increase efficiency and reduce costs. Simply put, a smart asset is one equipped with technology that enables asset-to-asset (i.e., machine to machine, or M2M) and asset-to-human (i.e., machine to human, or M2H) communication in tandem with analytical and cognitive technologies so that decisions related to the asset are data-driven and on time.
PdM as a concept has been around for years and is based on the utilization of data from various sources, such as critical equipment sensors, programmable logic controllers (PLCs), smart electronic devices, enterprise resource planning (ERP) systems, computerized maintenance management systems (CMMS), manufacturing execution systems (MES), GPS tracking, vehicle telematics systems, and onboard diagnostics (OBD) systems. Smart asset management3, systems couple this data with advanced prediction models and analytical tools to predict failures and proactively address them. By contrast, traditional maintenance strategies to balance between run-to-failure or preventive maintenance often require time-consuming, manual data crunching and analysis to gain insights from the data being collected. While many organizations have had some success with these strategies, they typically rely heavily on “tribal knowledge” estimates or require in-depth knowledge of the assets on an ongoing basis to stay accurate.
Moving towards predictive maintenance
Across industries, maintenance organizations are, by design or default, at different stages of maturity. Some may be running scheduled maintenance checks based on estimates or original equipment manufacturer (OEM) recommendations, while others may use statistics-based programs tailored to each asset. Still others are employing continuous monitoring technologies but are monitoring signals in isolation (univariant analysis), rather than leveraging predictive models.
There are steps to take on the journey towards reliability optimization, beginning with some of the basics of preventive maintenance and reliability-centered maintenance, while piloting PdM with one or two appropriate assets or facilities. Assets chosen for at least one of these pilots should be integral to operations and have enough run time to create baseline predictive algorithms.
Many of the technologies that make up a smart asset are not new but have become much more affordable, robust, and easy to integrate with big data platforms. Computing, storage, and network bandwidth are now available at fractions of the cost compared to 20 years ago4, making piloting and scaling financially feasible. These are some of the technologies applicable to intelligent assets that make PdM possible.
The Internet of Things (IoT)
IoT devices use internet infrastructure to stream continuous data, typically created in a continuous stream, from assets to private enterprise servers. IoT translates physical actions from assets into digital signals using sensors such as temperature, vibration, ultrasound, or conductivity. Data can also be streamed from other sources, such as a machine’s PLC, MES terminals, CMMS, or even an ERP system, GPS tracking, telematics systems, and onboard diagnostics (OBD) systems. IoT completes the first half of the physical-digital-physical (PDP) loop. (figure 2). With more bandwidth and storage, large amounts of data can be transmitted to give a full picture of assets in the network, reducing downtime and maximizing productivity.
Analytics and visualization
The second step in the PDP loop is to analyze and visualize digital signals using advanced analytics, predictive algorithms, and business intelligence (BI) tools. Many analytics platforms can incorporate unstructured data, cognitive technologies, machine learning, and visualization. Operations analysts, who are involved in maximizing working life of assets, can create dashboards using modern application program interfaces (APIs) created specifically for the everyday user.
Another trend is data moving back to the edge. Like the lean technique of storing tooling at the point-of-use, data computation is done at the “edge,” meaning it’s processed at the asset where it’s generated. This applies to both fixed and mobile assets. For mobile assets, edge computing can happen on the vehicle itself, allowing real-time insights to be pushed directly to operators and maintenance technicians. In post-edge processing, this data can be pushed to outer nodes or a data warehouse on cloud to alleviate the burden on edge and core network traffic to improve application performance5.
Closing the loop
After the signals have been processed, analyzed, and visualized, turn those insights into physical action. The digital conclusions may instruct assets to alter their functions, or maintenance alerts will spur a technician into action. Consider how predictive algorithms could trigger the maintenance request in the company’s CMMS, check the ERP system for spares on hand, and automatically create a purchase request for any additional parts required. For fixed assets, the maintenance manager only needs to approve the items in the workflow and dispatch the appropriate technician—all prior to machine failure. For mobile assets, this could mean scheduling the asset for maintenance at the nearest service center or dispatching a mobile technician to the asset's location.
A well-designed PdM solution requires orchestrated integration across an ecosystem of technologies as depicted in figure 3.
Potential benefits of implementing predictive maintenance
At first glance, undertaking a PdM program might seem overwhelming. However, the benefits of digital transformation far outweigh the efforts and investments. Potential benefits include:
- Improved safety, health and environment (SHE) compliance
- Reduced time spent on brute-force information extraction and validation; more time spent on data-driven problem-solving
- Optimal availability of skilled maintenance technicians and operators during labor shortages
- Better parts management and predictability
- More control over fleet operations planning
- Enhanced customer service and adherence to Service Level Agreements
- Clear linkages to initiatives, performance, and accountability
- Improved confidence in data and information leading to ownership of
- Enhanced “Track & Trace” capability to improve delivery estimation accuracy
Key adoption challenges
With the development of IoT technologies, reduced cost of data storage/computing, and advancements in artificial intelligence/machine learning (AI/ML) capabilities, industrial automation is growing rapidly. Yet, based on our experience, maintenance organizations have not been able to harness the power of these technologies beyond pilots for reasons described in figure 4.
A workable approach
Moving away from the pilot approach to a scalable and transformational approach requires strategic integration. With the help of an integrator, organizations can better leverage expertise to strategize, plan, and implement a holistic PdM program.
For example, Deloitte’s proven approach to adoption shown in figure 5 starts with vision and alignment of PdM goals with key stakeholders and focuses on identifying and prioritizing relevant use cases. Other best practices include selecting the right technology stack and ecosystem, a robust benefits case to drive action, and defining change management—all supported by a robust business case (cost vs. benefit) and a value tracking mechanism.
Deloitte’s approach to predictive maintenance
Smart asset management using PdM is the future, but it can be difficult to determine the right investment path to value. Download our point of views to learn more about Deloitte’s approach and offerings. We can meet you wherever you are on your digital transformation journey.
Endnotes
1 Article-Deloitte Insights, Making maintenance smarter Predictive maintenance and the digital supply network.
2 IndustryWeek, “Unlocking performance,” Emerson, accessed September 26, 2022.
3 “Smart Asset Management” refers to the use of advanced technologies, data-driven strategies, and intelligent tools to optimize the performance, efficiency, and value of physical or digital assets throughout their lifecycle. It’s commonly applied in industries such as finance, real estate, manufacturing, and utilities, as well as in IT for managing software and hardware resources.
4 Brenna Sniderman, Monika Mahto, and Mark Cotteleer, Industry 4.0 and manufacturing ecosystems: Exploring the world of connected enterprises, Deloitte University Press, February 23, 2016.
5 Adam Mussomeli, Doug Gish, and Stephen Laaper, The rise of the digital supply network: Industry 4.0 enables the digital transformation of supply chains, Deloitte University Press, 2016.
6 Geotab, 10 ways predictive maintenance with telematics data can boost fuel efficiency (fleetequipmentmag.com).
7 Internal Deloitte analysis derived from work with clients.
8 Marc Meyer, “Benefits of Predictive Maintenance for the Logistics Industry,” Supply & Demand Chain Executive, accessed September 14, 2022.
9 Position Paper - Deloitte Analytics Institute, Predictive Maintenance Taking pro-active measures based on advanced data analytics to predict and avoid machine failure.
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Deloitte Transactions and Business Analytics LLP
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