Here are five challenges rail maintenance is facing that it must overcome to adopt lean operating principles:
1. Unplanned Service and the Reactive Maintenance Cycle
Repair shops have long depended on a combination of scheduled maintenance and reactive, unplanned maintenance. To get to a leaner shop model that foresees future operating activity as a locomotive experiences power assembly degradation, for example, the shop must move away from preventive routines to procedures that provide an optimized strategy for servicing power assembly degradation. Initiating that process improvement without adding more overhead is even more challenging when unplanned service events don’t allow for maintenance process refinement.
2. Imprecise Alerts from Sensor Data
After fault-codes fire, work orders saddle repair shops with diagnostics, troubleshooting, or repairs. The problem with these alerts is that they are often imprecise and nonspecific. Even when sensor data indicate a specific component-level failure, those data sit in different collection systems and makes condition-based monitoring unfeasible. As a result, many operators cannot provide sufficient lead time to enable shops to anticipate future operating contexts. In one recent engagement, Uptake found that OEM sensor data mirrored actual component-level conditions only 3 percent of the time.
3. Unstandardized Data Management Practices
Making the rail shop lean will require standardizing varied data collection practices, including supervisory control and data acquisition (SCADA), inspection documentation, problem reporting, dispatching, and work order management systems. These collection and cleansing practices complicate how operators structure data internally to deliver effective analytics. Rail executives admit as much: according to a 2016 survey, 75 percent of rail executives believe that IT and OT systems are poorly connected and that this lacking convergence impedes performance management.
4. Conflicting Claims of Data Ownership
In addition, as rolling stock OEMs attempt to provide analytics to guarantee the reliability of their rolling stock, they are finding that access to historic, peer, and design model data within an operating context are necessary — and that they are lacking it. They are also finding, however, that rail operators are not as willing to part with proprietary information which often represents a source of competitive advantage. Which party owns the data, whether OEM or operator, is set to determine how incentives in rolling stock performance inform the development and precision of railcar analytics and reliability.
5. Tying Maintenance Decisions to Financial Outcomes across the Railway
Relevant and precise insights that are easy to take action on are only so powerful as they impact the bottom-line. Right now, many rail operators have a limited view of how their maintenance strategies are impacting financial performance. A combined predictive and prescriptive view of maintenance can lessen the impact of unforeseen repairs on regular maintenance and operations, cutting dwell time, increasing mission readiness, preventing railcar failures and network delays, bundling service events on individual trains, and tying maintenance strategies to a cost-effective regime of service that optimizes service across an entire fleet.
