This prediction is based on the 2016 Global Manufacturing Competitiveness Index (GMCI) report published by Deloitte Touche Tohmatsu, and the US Council on Competitiveness. According the report, by 2020 MITI-V represents a potential “new China” in terms of low- cost labor, agile manufacturing capabilities, favorable demographic profiles, market and economic growth.
MITI-V nations also share a desire to fast track their Industrial Internet of Things (IIoT) or Industry 4.0 readiness through government, and private sector programs. Across ASEAN in fact, 4.0-focused spending is expected to grow in value by 35% from US$1.6bn in 2015 to reach US$7.35 by 2020.
Trends driving this, as well as innovative enterprise case studies, proof-of-concepts and R&D across Asia were showcased at the Internet of Things World Asia summit held in Singapore in early October.
Among the more than 2,500 tech professionals attending the event was Alvin Ng, the General Manager ASEAN for GE Digital. In addition to participating in a panel discussion – “Identifying Core Process Efficiencies Driven by IIoT” and a round table - “Developing Strategies for IIoT,” Alvin spoke with GE Reports to share his insights from the sessions.
Alvin, how IIoT ready is Asia and ASEAN?
In Asia, and especially ASEAN, the big positive is that every nation in the region has an IIoT plan in place – but some countries are moving faster than others.
Singapore is the most advanced in pushing the 4.0 agenda. Coming up close are Malaysia and Thailand, where there is not just industrial push, but also governmental support. In these markets, key stakeholders are coming together to find new ways to incorporate new technologies, and leverage data to boost productivity, and other benefits, to drive economic success.
Alvin from GE’s perspective, what are the key benefits of the IIoT?
We see data as central to how companies can advance or transform. For GE, it’s all about getting connected, and getting insights, to get optimized. This means getting connected across the entire manufacturing landscape, then closely analyzing the information produced from this to make informed insights to improve productivity, efficiencies, cost savings, and other important benchmarks.
Predictive maintenance is one the big benefits offered by IIot could you explain what this means?
When we talk about predictive maintenance at GE – we focus more on asset performance management – that is, how can we use digital solutions to optimize the performance of an asset.
We look at two important parts of the equation - the physics, and the digital side of the asset. Many plants for example, operate a furnace 24-hours a day, it’s one of the most vital and expensive pieces of equipment for plant owners.
If we want to maximize the performance of the furnace, it’s not a case of putting sensors in it and collecting, and mining the data. It’s also important to physically understand how the furnace functions, its various components, and chemistry, so we can marry that information to the right digital tools and solutions - and find the right data - to drive optimal predictive maintenance and asset performance.
You also spoke about the digital twin – what is that?
In simple terms, it’s a digital replica of an industrial asset such as a jet engine, gas turbine, and other pieces of equipment. But not only does it digitally represent the physics of an asset, it also includes a learning model. So fundamentally a digital twin combines physics, data, and a learning model to enable performance analysis over any period-of-time.
How are GE digital solutions applied - can you share any case studies?
Across the region, and globally, customers are leveraging GE Digital tool sets in their factories to automate, and become – what we call in GE – a ‘Brilliant Factory.’ The concept behind the Brilliant Factory is identifying the outcomes we want to achieve and combine our offerings including digital. robotics, analytics, and additive manufacturing, to reach those goals.
To do this, we have created a ubiquitous operating platform called Predix which is designed to remove data silos. In simple terms, it enables Operating Technology (OT) and Information Technology (IT) data to all come into view to create a “digital thread.”
This is useful, and important, because at the end of a manufacturing cycle, we can trace all the way back - digitally - from start, to finish, to continuously observe, and learn.