Asset Efficiency: Taking a Leap Toward the Industrial IoT
The evolution of smart, connected, and autonomous products is reshaping the manufacturing industry and its ecosystem. The convergence of operational and information technologies is opening a completely new axis – a large one at that – for creating path-breaking innovations and opportunities in the manufacturing ecosystem.
On this wide canvas, the first brushstrokes to separate leaders from laggards will be those that monitor and use their assets best. Function, process, and components of every machine, followed by a family of machines in one company, followed then by a family of plants in the complete supply chain, can be monitored and then optimized.
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 boardroom to the shop floor? Have those organizations that rely on a tremendous legacy infrastructure begun thinking about the future where their legacy infrastructure is part of this journey? Has the technology infrastructure matured enough to allow for this seamless transfer of data in real time? How will organizations 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?
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 organizations globally look at asset efficiency. Recently, a worldwide study between Infosys and the Institute for Industrial Management (FIR) at German technical university RWTH Aachen uncovered interesting findings on the topic. The study's objective was to discern how well today’s industrial organizations use technologies to leverage value from their assets and how they plan to undertake this leading up to 2020.
While more than 85 percent of manufacturing companies are aware of the potential of technologies to institute asset efficiency, only 15 percent said they’ve implemented dedicated strategies to increasing asset efficiency. This gap between awareness and actual execution runs right through the various subcomponents that make up asset efficiency.
Further, 89 percent 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 percent have put those principles into practice.
Among the greatest potential values of IoT is the ability to adapt in real-time. The study found that while 57 percent of companies measure the operational efficiency of production machinery and production systems with indicators, only 13 percent 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 standardization, and advanced analytics. Since manufacturing is energy intensive, 88 percent of surveyed companies say energy management is a critical factor for achieving asset efficiency. Despite this desire, only 15 percent 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 optimization and efficiency. Industries now can be more integrated and gain a complete and holistic view of their suppliers and supply chains – but only if all individual links adopt technology. Supply chain efficiencies are found within individual factories and at the ecosystem level. It is imperative for ecosystems to be efficient to remain relevant to all stakeholders, such as 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 assess the performance of the machine. This improves operational efficiency while reducing the equipment's maintenance cost.
Industries can tap huge potential by improving asset efficiency, the study shows. But since this is new and next generation, enterprises need proof of concept, blueprints, and testbeds to gain confidence before exploring adoption. Testbeds showcase not only the technology integration, but also frameworks for adoption.
In the case of flight, a natural initial implementation is the installation of sensors and monitors on the landing gear, which sustain a great deal of wear over its 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 steeply increases the cost of maintenance, especially in the case of an aircraft operating beyond its designed service life.
Organizations should adopt condition-based maintenance for 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 — 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 predict wear and replacement cycles, leading to more efficient maintenance that prevents significant issues with landing gear before they arise.
Drawing parallel from the landing gear example, this proves monitoring assets with right set of data gives enterprises better control and visibility of asset performance and reduces unplanned downtime to improve operational efficiency while curtailing maintenance costs. The asset can be an expensive machine on the shop floor, a certain tool, a locomotive, or nearly any other critical element. This also demonstrates there can be efficiencies in inventory and spare parts if an organization plans for an eventuality that is going to happen, which is proactive rather than reactive.
Making Asset Efficiency Come to Fruition
This proactive practice can only happen if enterprises have the right foundations of tools and solutions necessary for real-time monitoring of assets. That's a challenge today, in our legacy environment where lack of necessary sensors and instrumentation leads to missing real-time data analytics.
A practical way to move forward is to develop real proof points that demonstrate the value of such monitoring – thereby providing a business case for making changes on the assets. 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 clearly show how organizations can benefit by investing in asset efficiency for increased ROI by reducing downtime of valuable assets, maximizing production, and helping ensure predictable service delivery.
The intention across these testbeds is to make it practical and real; ultimately, it appeals to everyone who understands the domain and context in manufacturing. Testbeds consider operational, energy, maintenance and service components through real-time information on assets, and they predict which critical assets need maintenance or replacement — avoiding costly downtime.
As equipment and system processes become intelligent, virtually every process and activity in the manufacturing enterprise involves data. Solutions such as an asset efficiency testbed help transform machine data into meaningful insights, and give maintenance engineers 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.