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Asset efficiency – a leap towards the industrial IoT

The evolution of smart, connected and autonomous products is reshaping the manufacturing industry and its ecosystem. The convergence of operational technology and information technology is opening a completely new axis, and a large one at that, for creating path breaking innovations and opportunities in the manufacturing ecosystem.

On this wide canvas, the first brushstrokes that will pull organisations away from others will be the ones that will monitor and utilise their assets best. Function, process and components of every machine, followed by a family of machines in one company, which will be followed by a family of plants in the complete supply chain, can be monitored and then optimised.

We have barely started scratching the surface of the immense potential that exists within asset efficiency, in what is the infancy stage of the Industrial IoT.

And while asset efficiency is ripe for the picking, there are some critical questions out there: has the commitment towards IoT overall and asset efficiency in particular filtered down from the board room to the shop floor? Have organisations relying on a tremendous legacy infrastructure begun to think about the future where their legacy infrastructure is part of this journey, and has the technology infrastructure matured enough to allow for this seamless transfer of data in real time? Regarding security, how will organisations secure this critical data while it is being transmitted? And do we have the necessary standards to support this data flow across these diverse legacy and new assets?

Study: global manufacturing struggles with asset efficiency

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. Recently, a global study between Infosys and the Institute for Industrial Management (FIR) at RWTH Aachen, one of the most reputed technical universities in Germany, uncovered interesting findings on the topic. The objective of the study was to find out how well today’s industrial organisations are taking advantage of technologies to leverage the value from their assets and how they plan to undertake this in the future leading up to 2020. More than 400 industrial manufacturing and process industry executives from the US, China, France, Germany and the United Kingdom were polled, and the industries spanned several sectors, including aerospace, automotive and electronics along with supply chain-related companies.

It is interesting to deep dive into the findings. While more than 85% of manufacturing companies said they are aware of the potential of technologies to institute asset efficiency, only 15% said they’ve implemented dedicated strategies to increasing asset efficiency. And this gap between awareness and actual execution runs right through the various subcomponents that make up asset efficiency.

Further, 89% reported knowing that information efficiency could help their businesses, but only 11 percent have systematically implemented it. Similarly, 81 percent of respondents said they know what machine condition surveillance could do for enhancing maintenance, but only 17% have put those principles into practice.

Among the greatest potential values of the IoT is the ability to adapt in real-time. The study also found that while 57% of companies measure the operational efficiency of production machinery and production systems with indicators, only 13% do this in real-time — which is critical for just-in-time delivery and maintenance.

The largest improvements over the next five years, according to the study, will take place in information interoperability, data standardisation and advanced analytics. Since manufacturing is energy intensive, 88% of surveyed companies say energy management is a critical factor for achieving asset efficiency. Despite the desire for better energy management, only 15% of surveyed companies have implemented a systematic energy efficiency for the lifecycle of assets.

The future with asset efficiency

With technology improvements comes opportunity for broad-based optimisation and efficiency. Industries now can be more integrated and gain a complete and holistic view of their suppliers and their supply chains. This integration is possible only if all the individual links adopt technology. Supply chain efficiencies are found not only within individual factories, but at the ecosystem level. It is imperative for the ecosystems to be efficient to remain relevant to all the stakeholders like investors, customers, and employees.

Asset availability is one of the critical parameters for computing the operational equipment effectiveness (OEE), and monitoring these parameters of machines in real time can also help in assessing the performance of the machine. This will help in improving the operational efficiency while reducing the maintenance cost for the equipment.

The data from the study shows that industries can tap huge potential by improving asset efficiency. But since this is new and next generation, enterprises and industries need proof of concept, blueprints, and testbeds to gain confidence before exploring adoption. The testbeds serve to showcase not only the technology integration but frameworks for adoption.

In the case of flight, one of the greatest feats in human engineering, a natural initial implementation is the installation of sensors and monitors on the landing gear of aircraft that sustains 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.

Organisations need to adopt condition-based maintenance for their critical assets such as landing gear, which is possible only with an effective health management system. This asset monitoring could provide automatic detection that alerts airlines of any real-time issues in the landing gear — giving mechanics and engineers the time and resources they need to make successful repairs before any component failures take place. In addition to diagnosis, this data can also be used to predict wear and replacement cycles, leading to more efficient maintenance that prevent significant issues with landing gear before they arise.

Drawing parallel from the landing gear example, this proves that monitoring the assets with right set of data can help enterprises a better control and visibility of the asset performance but also reduce unplanned downtime to improve operational efficiency while reducing the maintenance cost. The asset can be an expensive machine on the shop floor, a certain tool, a locomotive, or nearly any other critical element. This also proves that there can be efficiencies in inventory and spare parts if the organization can plan for an eventuality that is going to happen, which is proactive rather than reactive.

Making asset efficiency come to fruition

This can only happen with the right foundation of tools and solutions necessary for real time monitoring the assets. It is a challenge today in our legacy environment where lack of the necessary sensors and instrumentation of the assets leads to missing real-time data analytics. This does not allow for the necessary context to be developed on the given asset or a family of assets.

A practical way to move forward is to develop real proof points that are able to demonstrate the value of such monitoring – thereby providing a business case for making changes on the assets. Significant technology companies such as GE, PTC, Bosch, Intel, Infosys and IBM are partnering to develop multiple testbeds that demonstrate the value of asset efficiency. The goal is to make clear how organizations can benefit by investing in asset efficiency because it leads to increased ROI by reducing downtime of valuable assets, maximising production, and helping ensure predictable service delivery.

The intention across these testbeds is to make it practical and real – ultimately, it can appeal to everyone who understand the domain and the context in manufacturing. Testbeds consider operational, energy, maintenance and service components through real-time information on assets, and they can predict which critical assets are in need for maintenance or replacement as a prioirity — avoiding costly downtime.

With equipment and system processes becoming intelligent, virtually every process and activity in the manufacturing enterprise involves data. Solutions such as the asset efficiency testbed can help with transforming machine data into meaningful insights, and providing maintenance engineers with powerful 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, maximizes performance, consumes less energy, produces less waste and enhances overall quality of products.

The author, Sudip Singh, is senior vice president and global business unit head of engineering services at Infosys.