Product life cycle assessment: Optimizing air, rail and freight costs through data insights

Business and Technology Writer

In the aviation industry, every flight hour contains a significant data stream. For rails and freight, every mile produces a wealth of travel data to be analyzed. And when it comes to manufacturing, deploying and maintaining the parts and vehicles critical to these industries, even more data is created. Data, data, data.

Data analytics, reporting, dashboards and insights are valuable for product life cycle assessment, as they can be leveraged for a deeper and more profitable understanding of rolling stock, vehicles and operations. The data also opens new opportunities for improved efficiency and best practice insights.

The following are some of the ways data analytics are working to help operations work smarter, leaner and more cost-effective while reaping greater returns.

Finding efficiencies at airports

Travel data is providing airlines with a better understanding of the cycles at work in airports. Network World recently reported that information and analysis is changing airline operations in the U.K., for example. At Heathrow, data analytics have fueled a 50 percent reduction in transfer times after analysts looked at the life cycle of gate-to-gate transfers. That means more on-time departures and fewer resources spent on rerouting passengers. Similarly, in Dubai, big data is allowing analysts to tap into the Internet of Things (IoT), pulling location-based information on the processes of luggage systems, security infrastructure, and the components and crew within airplanes.

According to a recent SITA survey, 86 percent of carriers expect IoT efficiencies to positively impact cost-control and revenue over the next three years. Thirty-seven percent of the responding airlines said they are investing in IoT to reap even greater rewards. operations: Key opportunities

Consider the impact that travel analytics could have on the following situation: In a 30-day period in 2014, according to 24/7 Wall St., some 9,000 canceled flights on major U.S. carriers accounted for $52 million in costs to the airlines. Historical data can inform airlines about weather, crew absences, maintenance and other factors behind such cancellations, and this could mean proactive and cost-preventive decision making.

For example, when plane sensors deliver temperature, pressure or other data readings to algorithms, this data can be correlated with historical asset problems. A maintenance crew can then be assigned to replace a component before it fails and causes delays or cancellations.

Monitoring train tracks, signals and rail-car operations

When rail companies apply historical data to real-time operational data on individual train cars, tracks and signals, the measured life cycle of critical components becomes a baseline against which to evaluate day-to-day readings. As analysts preparing a course on the convergence of IoT and rail systems recently noted on Data Science Central, gathering time-series data on rail cars, signal specifications, track types, schedule and historical incident records is crucial. With this data in hand, analysts can determine propensities and probable points of concern regarding breakdowns and illuminate patterns that emerge from each factor, highlighting potential trouble spots when it comes to safety.

Potential revenue from product life cycle insights

Many of these product life cycle assessments for aircraft and trains can also apply to trucks and vehicles that haul freight on roadways. Frost & Sullivan report that original equipment manufacturers, suppliers and delivery stakeholders see the potential for data analytics to generate $1.6 billion of revenue by 2022, taking into account new efficiencies, innovations and improved best practices.

One innovative way this occurs is through industry understandings of engine life cycle patterns. For example, FleetOwner reports a trucking-industry trend toward downsized engines, meaning drivers have to shift more often. However, this results in higher maintenance and fuel costs if a fleet doesn't compensate with new driver training or a switch to automatic engines to cut down on fuel use and future maintenance loads. Addressing cycles such as these is a key way that product life cycle assessment can highlight the interconnected, cost-influencing nature of a freight system.

Data allows airline, rail and freight providers to better understand the deeply interrelated nature of products, life cycles and best-use cases. The present is the best time to tap product life cycle assessment, and the stakes are nothing less than millions or billions of dollars when it comes to potential costs and revenue.

To learn more about how data analytics can optimize your business, visit IBM's Travel and Transportation page.