Condition Monitoring Condition-based Maintenance Predictive Maintenance Reliability

Extend Legacy Asset Lifespans

EP Editorial Staff | May 1, 2022

Implementing Industry 4.0 technology on existing machinery contributes to overall operational sustainability.

By Alexander Hill, Senseye

Today, the most successful manufacturing organizations recognize that sustainability is not only good for business but is also integral to the way products are marketed, purchased, and operated. It should influence all decisions about engineering, operations, and maintenance, from machinery selection, maintenance scheduling, process control, materials, and component management, to investment planning, and health and safety management.

Improving the performance and optimizing the efficiency of legacy machinery not only makes financial sense, it’s also far more sustainable than buying new equipment. If the purchase of expensive capital assets can be deferred by extending the life of existing assets, it has a direct impact on cash flow. However, there comes a point when investing in new equipment is necessary to overcome inefficiencies in existing machinery or to remain competitive due to a step-change in technology. 

Working out which assets to replace and when is a complex process involving a thorough life-cycle cost analysis. This involves factors such as the purchase price, running costs, output, maintenance costs, energy consumption, and the labor and parts costs of a new machine, compared to an existing one. It’s rarely as simple as saying that older assets are inefficient and more energy intensive than more recent models.

To optimize the accuracy and effectiveness of analytics software, real-time data needs to be enriched with data on the maintenance and performance history of equipment. This helps machine-learning algorithms make more accurate predictions about how that equipment may perform in the future and how components degrade, helping to inform decisions on when to invest in new assets.

Benefits of Industry 4.0

In many factories, operators have worked with their machines for a long time and have become accustomed to making decisions about asset replacement based on gut feel. Real-time data analysis technologies overcome any subjectivity in the way machines are operated and enable asset replacements to be planned more effectively, based on real data rather than instinct.

This is where Industry 4.0 technology can help. By installing relatively low-cost sensors in the right places on existing machines, you can collect and analyze data on energy efficiency, productivity, and maintenance costs. 

This provides essential information for your cost analysis, helping to inform investment decisions. Using real-time analytics, machine learning, and AI plays an important role in maximizing the lifespan of legacy assets and informing the transition to new equipment at the right time,” stated Adam Lea-Bischinger of MCP, Chicago (, an asset-management consultant organization.

By combining and analyzing multiple data feeds, predictive-maintenance software helps to build a single version of the truth about each piece of equipment. It enables manufacturers to better understand and optimize performance, as well as accurately predict the remaining useful life of equipment so that its replacement can be planned and budgeted for in a controlled way. 

The operational efficiency and targeted maintenance benefits achieved by using predictive-maintenance technologies in the manufacturing environment have been found to extend asset lifespan by as much as 50%.

Use case

A global leader in bauxite, alumina, and aluminum products, Alcoa Corp., Pittsburgh, PA (, deployed predictive-maintenance technology at one of its zero-waste-to-landfill aluminum smelting plants. The predictive-maintenance software was connected to Alcoa’s existing machine and maintenance systems to monitor operational and critical machinery. By analyzing machine condition indicators against historical information, the machine-learning technology was able to automatically provide maintenance engineers with alerts and diagnostics before any functional failures. 

As a result, the company has seen a 20% reduction in unplanned downtime, a reduction in maintenance costs, and improved operational efficiencies that have helped extend the lifetime of its assets. The focus on sustainability from the machine level upward has allowed Alcoa to develop new lines of reduced-carbon products using their innovative Elysis carbon-neutral smelting process.

Predictive maintenance

The benefits of using data-driven manufacturing to improve environmental performance and sustainability are significant. The more effectively manufacturers maintain and operate their machines and facilities, the greater the carbon-emission reductions and associated sustainability benefits. 

By enabling plants to run optimally for extended periods without interruption and helping to ensure machines are operated and maintained efficiently and consistently, machine reliability and performance solutions help organizations deploy successful and sustainable maintenance programs. Predictive-maintenance capabilities, enabled by Industry 4.0 technology, deliver:

• 85% improvement in downtime forecasting accuracy
• 50% reduction in unplanned machine downtime
55% increase in maintenance staff productivity
50% increase in asset lifespan
40% reduction in maintenance costs
40% reduction in inventory and waste
30% improvement in operational efficiency
20% reduction in spares consumption
15% increase in machinery efficiency and energy consumption.

Replacing components before the end of their useful life is an unseen source of waste with significant sustainability implications. Using fully automated machine-health monitoring ensures assets can operate for their full working life while avoiding the unnecessary consumption of new parts. Industrial machines are a significant expense, representing billions in investment. Many machines may only be rated for 10 to 15 years. If you can extend that lifespan and stretch the initial capital investment safely, that can be a tremendous saving. EP

Alexander Hill is Chief Global Strategist and Co-Founder, Senseye (, Southampton, UK. U.S. offices are in Nashville, TN. Download a report on legacy asset sustainability at


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