The 3 Lessons Rail Maintenance Can Learn from the Lean Model of Auto-Manufacturers

Pioneered by Toyota after WWII and later refined by a greater group of auto-manufacturers including Daimler, GM, and Ford, the lean operating model underlies process improvements in many industries. From personal banking to aircraft maintenance, services have even adopted lean principles to drive organizational change. Here are the five fundamental lean principles:

1. Identify value in production that is aligned with customer demands for a final product.
2. List each step in the production process.
3. Integrate these processes.
4. Produce only what customers demand — both in quality and quantity.
5. Revise processes for continual process improvement and integration.

Easier said than done — but lean process improvement methods like Six Sigma and Kaizen are important reasons why the real output of manufacturing in the United States has grown by two-thirds in the last thirty years even as the sector employs a quarter fewer people. Despite the differences of production on the plant floor versus services of the repair shop, the importance of adapting capacity to meet operating demands is consistent.

For railways to get lean, the repair shop must rethink its own operations as serving internal customers and their demands. Those customers are rolling stock and their demands are failures in need of service. It’s an unlikely way to think about rail maintenance, but it’s driven greater labor productivity in a range of industries.

To reach a level of predictive and even prescriptive maintenance that will enable shop optimization, fleet managers and shop supervisors must understand three lessons about how the lean model restructures its internal processes so that capacity meets operating needs.

1. Make railcar failures your own internal customer demand

Insight into future demand enables auto-manufacturers and other lean operators to adjust capacity for future operating contexts. That way, auto-manufacturers can pivot as consumer tastes change. Before forecasting sets changes in operations in motion, process improvements make manufacturing plants adaptable.

While auto-manufacturers rely on data from dealerships and oil prices to forecast demand, condition monitoring analysts and craft workers have predictive analytics to anticipate specific maintenance demands before they warrant service. Rail maintenance teams have the added benefit of sensored customers. That is, railcars and wayside systems are equipped with sensors that make them behaviorally predictable in ways that consumer demand for cars isn’t — shop planners can know the required steps for repairs and financial outcomes of maintenance decisions with certainty ahead of time by taking analytics-driven steps toward process improvement.

2. Reduce variability to cut out surprises and establish routine

With the precise forecasting of consumer preferences, auto-manufacturers are able to eliminate volatility in their operating responses to changing consumer preferences. Toyota defined this core lean concept as Kanban, which means that materials are available “just in time” for incorporation in assembly processes. Over time, floor-level process improvements give regularity toward production so that adjustments in capacity — whether to produce a certain body style that is interchangeable with other finished cars, for example — result in automated, more efficient changes in how manufacturers tool their plants.

With predictive analytics and condition-based insight into railcar health, repair shops are also able to optimize maintenance. Advanced visibility into locomotive health provides precision: planned parts inventory, bundled repairs, reduced dwell time, extended lead time, and prioritized service. Many reliability leads know that their unplanned events have common root causes and are buried in data. Making sense of those patterns is challenging because many repair shops are simply overworked and scrambling to make repairs.

3. Create digital switches for operating excellence throughout the rail ecosystem

After following lean principles to eliminate waste on factory floors, Toyota discovered that process efficiencies confined to specific areas like the paint or press shops didn’t necessarily translate into system-wide improvement. What Toyota learned was that continual improvements spanning processes were important in integrating “just in time” supply with demand.

With AI-driven asset management, railcars have a leg-up on system-wide improvement. Since there are sensors on railcars at the component-level and expertise about them can be digitized, rolling stock reliability can be easily connected with how cost-effectively the shop performs maintenance. The challenges lie with the initial preparation of rail organizational processes, like cleansing work-order data across varied IT and OT systems, for making the prediction of failures possible. In the coming weeks, we’ll be talking more about the challenges rail maintenance teams must overcome to get lean.

Optimizing maintenance capacity represents a significant competitive advantage in the rail ecosystem. With standardized fuel costs industry-wide, locomotive and train car lifecycles, and infrastructure depreciation, about 40 percent of the total cost structure for rail is variable maintenance spend.

Through lean principles, auto-manufacturers have confronted the flexibility challenges of heavy capital expenses and made their production processes more interchangeable. By adopting a lean approach, railways can differentiate operating reliability and treat railcar failures as common sources of demand, taking digital steps to gain a comprehensive view of failures, and tying maintenance decisions in the shop to financial outcomes across the railway ecosystem.