In this guest blog Sudip Singh highlights the challenges of integrating multiple monitoring and control systems, and makes a case for test beds that can demonstrate the improvements in asset utilisation efficiency that can be gained from doing so.
The evolution of smart, connected and autonomous products is reshaping the manufacturing industry. The convergence of operational technology and information technology is opening a completely new axis, and a large one at that.
The organisations that pull away from others will be those that best monitor and utilise their assets. The functions, processes and components of every machine — and by extension the complete supply chain — can be monitored and optimised.
We have barely started to tap the immense potential that exists by increasing asset utilisation efficiency. We are still at the infancy stage of the Industrial Internet of Things (IoT).
However, there are some critical questions that need to be answered: has the commitment towards IoT filtered down from the boardroom to the shop floor? Have organisations that rely on legacy infrastructure begun to think about a future where that infrastructure is part of the IoT journey? Has the technology infrastructure matured enough to allow for the seamless transfer of data in real-time? And do we have the necessary standards to support this data flow across these diverse legacy and new assets?
Since data is at the heart of IoT, it makes sense to also lean on data to get a snapshot of how organisations globally are looking at asset efficiency. A recent global study of more than 400 manufacturing and process industry executives, organised by Infosys and the Institute for Industrial Management (FIR) at RWTH Aachen in Germany investigated how they plan to use technology to gain value from their assets.
It found more than 85 percent of manufacturing companies were aware of the potential of asset efficiency technologies, but only 15 percent had implemented dedicated strategies. This gap between awareness and execution runs right through the various subcomponents that make up asset efficiency.
According to the study the greatest improvements in asset efficiency over the next five years will be in information interoperability, data standardisation and advanced analytics.
Eighty one percent of respondents said they knew what machine condition surveillance could do to enhance maintenance, but only 17 percent had put those principles into practice. The study also found that while 57 percent of companies measure operational efficiency of production machinery and production systems with indicators, only 13 percent do this in real-time — a critical factor for just-in-time delivery and maintenance. Eighty eight percent of surveyed companies said energy management was a critical factor for achieving asset efficiency.
An industrial IoT future
An early IoT asset efficiency example from the aviation sector is the installation of sensors and monitors on the landing gear of aircraft, which sustain a great deal of wear over their lifecycles. Landing gear also happens to be one of the more intricate systems on an aircraft. The current practice of scheduled maintenance after a predefined number of flights increases the cost of maintenance steeply, especially in the case of an aircraft operating beyond its designed service life.
Condition-based maintenance for critical assets, such as landing gear, can provide automatic detection that alerts airlines of any real-time issues, giving mechanics and engineers the time and resources they need to make successful repairs before any component failures. In addition to diagnosis, this data can also be used to predict wear and replacement cycles, leading to more efficient maintenance.
In industrial IoT, the asset can be an expensive machine on the shop floor, a specific tool, an engine, or nearly any other critical element. Better control and visibility of asset performance can reduce unplanned downtime, improve operational efficiency and reduce maintenance costs. There can also be efficiencies in inventory and spare parts if the organisation can plan for an eventuality rather than reacting after the event.
With technology improvements come opportunities for broad-based optimisation and efficiency increases by integration different systems, but such integration is possible only if all the individual links in the chain adopt IoT in a standardised way and if the necessary sensors and instrumentation are in place to provide all the information needed for data analytics
A practical way to move forward is to develop real proof points that demonstrate the value of monitoring and make a business case for change. Technology companies, including GE, PTC, Bosch, Intel, IBM and Infosys are partnering to develop multiple testbeds that can demonstrate the value of asset efficiency.
These cloud-based testbeds, available to Australian businesses, consider operational, energy, maintenance and service components and can predict which critical assets are in need of maintenance or replacement to avoid costly downtime.
With equipment and system processes becoming intelligent, virtually every process and activity in the manufacturing enterprise produces data. Solutions such as the asset efficiency testbed can help transform these machine data into meaningful insights and provide maintenance engineers with the tools to accurately predict failures and make better-informed decisions.
Enterprises implementing technology-enabled data analytics approaches driven by a condition-based maintenance philosophy can optimally manage their assets and improve overall efficiency. This, in turn, improves availability, maximises performance, reduces energy consumption and waste and enhances the overall quality of products.
Sudip Singh is Senior Vice President and Global Business Unit Head of Engineering Services at Infosys.