Many of today’s supply chain processes bear little resemblance to those of decades past. The same can be said of enterprise asset management (EAM) processes. What fueled their rapid transformation is the availability and penetration of digital technologies and digitized data.
The tipping point came when the cloud and industrial internet of things (IIoT) transitioned from an opportunity to a reality. Connected smart devices, smart sensors, smart analytics, and complementary innovations started yielding remarkable new levels of intelligence and automation in factories, warehouses, storerooms, repair depots, and in the field.
EAM is both driving and being driven by supply chain digitization. Efficiencies gained in parts procurement, receiving, storage, delivery, and disposition reflect positively in maintenance efficiency, asset uptime, and performance. Similarly, improving asset management drives more lead time for procurement, lower inventory carrying costs, and fewer returns.
Unique EAM requirements compel refinements in the supply chain. For example, it is not uncommon for equipment that is 50-70 years old to still be in operation and require parts during maintenance. Support is also needed for well-coordinated overhauls, depot repairs, equipment rentals, warranty claims, and service supply chains, including seamless movement and availability of the necessary parts, components, or equipment.
In many ways, EAM is driving digital transformation across industrial organizations. In addition to scheduling and tracking work, labor, materials, and work completed, modern EAM solutions increasingly require integration, interoperability, and analytics to be able to predict future work, says Ed O’Brien, director of research at ARC Advisory Group.
“I think it’s an exciting time. Digital capabilities offer expanded capabilities, and memory and computing power are so powerful and inexpensive that organizations can use more sensors and edge devices and display information via dashboards to know exactly what’s going on,” adds O’Brien. “The industry is at an inflection point as far as information sharing.”
Ongoing opportunities for improvement
EAM supply chains continue to face challenges that digitalization has the potential to solve. Some prime opportunities include:
Data quality: One of the biggest impediments is the quality of the source data, says Marne Martin, President of IFS Service Management. “Data that is on paper, unstructured, or inaccurate enables ‘garbage in, garbage out.’ Companies with long-lived assets or that are slower to change have much to gain from digitizing their inventory records,” she observes.
Parts and repair planning: Machine sensors and condition monitoring tools provide rich data that can be converted into insights, helping to accelerate the transition to predictive and prescriptive analytics and maintenance. “It not only answers when something is going to fail, but what needs to happen to keep it from failing,” explains O’Brien.
Inventory tracking and auditing: Understanding exactly where parts are located – in the warehouse, truck, repair depot, or in the field – is particularly challenging when paper-based processes or older inventory are involved. Digitization of inventory data improves location accuracy and supply chain efficiency.
Leakage/shrinkage control: Inventory leakage, especially of spare parts in the field, is typically due to lax inventory tracking. Digitizing parts tracking eliminates paperwork errors, reduces errant deliveries, and helps to deter hoarding and theft.
Data intelligence: Tracking spare parts longevity, how often they’re replaced, their performance, and supplier issues – and layering that information into the asset’s “as maintained” record – improves planning for parts replacements and inventory replenishment while avoiding unplanned downtime.
Reverse logistics: Parts and equipment returns, depot repairs and replacement, and warranty claims processes require sophisticated inventory tracking and management capabilities.
Forecast analytics: Ordering parts and materials based on historical data will rarely match actual demand. Demand forecasting analytics must be tightly integrated with supply chain management to improve inventory planning and optimization.
Supply chain transparency: Transparency reporting is increasingly regulated to minimize risks and trace origins when issues arise. Ingredients for food processing and components for aircraft repairs are examples of where transparency is essential.
Digital transformation enablers
The variety of digital, connected, and cloud-based technologies modernizing the supply chain and advancing EAM outcomes continues to emerge and evolve.
Mobility: Using connected mobile devices in the plant, warehouse, or field allows data to be analyzed on the edge, at the point of use, enabling real-time or near-real-time insights.
Machine learning (ML): ML observes activity as it occurs and updates its own model to improve with experience, benefitting processes such as demand and supply planning and asset management.
Artificial intelligence (AI): Applying AI to predictive and prescriptive analytics, autonomous vehicles, and robots helps to optimize and automate activity normally performed by humans.
Digital twins: Digital twins allow companies to model the lives of their critical equipment and facilities, simulate changes to assets and systems before they are made, and stay on top of their parts. “Once a digital twin is complete and there’s good stewardship and control of its updates, I think it’ll improve the quality of the digital data to a huge degree,” says Martin.
Wearables: Sending real-time information directly to wearable devices such as smartglasses, smartwatches, and bar code scanners streamlines work processes and keeps hands free for other tasks.
Virtual reality (VR): Real-time, immersive simulation and 3D data visualization are examples of how VR can be applied to system and process design, virtual collaboration, and experienced-based learning.
Augmented reality (AR): AR-enabled smart devices help to streamline logistics processes and tracking, barcode scanning, object recognition, mobile data capture, visualization and simulation of changes, regulatory compliance, and provide navigation assistance.
Warehouse automation: Automated storage and retrieval systems (ASRS) and robots with AI and vision systems deftly perform pick, place, sort, pack, and other tasks automatically and safely among humans.
Additive manufacturing: Some OEMs are experimenting with 3D printing, even though the price point is currently relatively high. “Looking ahead, processes need to be established for identifying and tracking printed items in the EAM system, which would presumably be able to be based on a part or asset number,” suggests O’Brien.
GIS/GPS tracking: The locations of parts, vehicles, and personnel are tracked and managed with greater precision using geographic positioning systems (GPS) and geographic information systems (GIS).
Autonomous vehicles: Navigation and logistics support for autonomous vehicles are powered by AI, ML, machine vision, and GIS/GPS.
Drones: Wide-ranging drone applications automating tasks such as inventory scanning, counting, parts delivery, mapping, modeling, and inspections.
Blockchain: Blockchain simplifies the tracking and traceability of parts, assets, and components as well as tracking, settlement, and taxation associated with global transfer prices.
This is the new look of the EAM supply chain. Digitized supply chains bolstered by digital technologies such as the above are the key to agile, scalable, distributed supply networks – a fundamental enabler of EAM optimization.