Track Maintenance North America 2017
Taking The North American Rail Industry Forward
Increased ridership and reliance on American and Canadian rail has driven wear and tear on railways across the continent and has increased maintenance costs and strained operator's resources.
Infrastructure operators are looking for new, cost-effective processes that will streamline their operations. Every organisation faces hurdles and Track Maintenance Modernization & Asset Utilization 2017 will bring together key industry experts and infrastructure operators together to provide the roadmap towards a continuous life cycle of predictive maintenance.
Taking place in Washington D.C., the conference will tackle every angle from life cycle engineering and change management to multi-generational training and track component-specific maintenance improvement through presenting attendees process-orientated case-studies that will effectively maximize an infrastructure operator's assets.
Move To Predictive Maintenance
Led by infrastructure operators across North America and Europe, the conference will provide the ideal baseline to set the foundation for your predictive maintenance programme. All attendees will witness the roadmap to predictive maintenance through dissecting which cost-effective technologies, data compilation techniques and cultural requirements are needed for an organization to succeed.
Identify And Integrate New Technology
Access key studies that will allow you to harness the latest technologies that will minimize inspection times. Through identifying user-friendly software suited at all levels of an organisation to remote condition monitoring technology, all case-studies are tailored to enhance decision-making and maximize resource efficiency.
Putting Life-Cycle Engineering In Focus
Renewal costs of drastically affect an organization's bottom line, reducing the opportunity to invest in new technologies. Through component-level detail, attendees will access the best practices and approaches towards extending the life cycle of assets while minimizing unnecessary resource allocations.