In 2017, the chief strategic concerns for Global Product Manufacturers are manifold. These range from their ability drive growth in new markets by creating products that younger customers need, cut costs by efficient high volume manufacturing spanning global supply chains & effective distribution and service. While the traditional lifecycle has always been a huge management challenge the question now is how digital technology can help create new markets and drive higher margins in established areas. In this blogpost, we will consider how IIoT (Internet Of Things) technology can do all of the above and foster new business models -by driving customer value on top of the core product.
Global Manufacturing is evolving from an Asset based industry to an Information based Digital industry. (Image Credit – GE)
A Diverse Industry Caught in Digital Dilemmas..
The last decade has seen tectonic changes in leading manufacturing economies. Along with a severe recession, employment in the industry has moved along the technology curve to a more skilled workforce. The services component of the industry is also steadily increasing i.e manufacturing now consumes business services and also is presented as such in certain sectors. The point is well made that this industry is not monolithic and there are distinct sectors with their own specific drivers for business success[1].
The diverse sectors within Global Manufacturing (McKinsey [1])
Global manufacturing operations have evolved differently across industry segments. McKinsey identifies five diverse segments across the industry
- Global innovators for local markets – Industries such as Chemicals, Auto, Heavy Machinery etc.
- Regional processing – Rubber and Plastics products, Tobacco, Fabricated Metal and
- Energy intensive commodities – Industries supplying wood products, Petroleum and coke refining and Mineral based products
- Global technologies and innovators – Industries supplying Semiconductors, Computers and Office machinery
- Labor intensive tradables – These include textiles, apparel, leather, furniture, toys etc.
Each of the above five sectors has different geographical locations where production takes place, they have diverse supply chains, support models, efficiency requirements and technological focus areas. These industries all have varying competitive forces operating across each.
However the trend that is broadly applicable to all of them is the “Industrial Internet”.
Defining the Industrial Internet Of Things (IIoT)
The Industrial Internet of Things (IIoT) can be defined as a ecosystem of capabilities that interconnects machines, personnel and processes to optimize the industrial lifecycle. The foundational technologies that IIoT leverages are Smart Assets, Big Data, Realtime Analytics, Enterprise Automation and Cloud based services.
The primary industries impacted the most by the IIoT will include Industrial Manufacturing, the Utility industry, Energy, Automotive, Transportation, Telecom & Insurance.
Globally integrated manufacturers must constantly assess and fine-tune their strategy across these above eight stages. A key aspect is to be able to collect data throughout the process to derive real-time insights from the lifecycle, suppliers and customers. IoT technologies allied with Big Data techniques provide ways to store this data and to derive real-time & historical analytic insights. Thus the Manufacturing industry is moving to an entirely virtual world across its lifecycle, ranging from product development, customer demand monitoring to production to inventory management. This trend is being termed as Industry 4.0 or Connected Manufacturing. As devices & systems become more interactive and intelligent, the data they send out can be used to optimize the lifecycle across the value chain thus driving higher utilization of plant capacity and improved operational efficiencies.
Let us consider the impact of the IIoT across the lifecycle of Industrial Manufacturing.
IIOT moves the Manufacturing Industries from Asset Centric to Data Centric
The Industrial Internet of Things (IIoT) is a key enabler in digitizing the legacy manufacturing lifecycle. IIoT, Big Data and Predictive Analytics enable Manufacturers to reinvent their business models.
The Generic Product Manufacturing Lifecycle Overview as depicted in the above illustration covers the the most important activities that take place in the manufacturing process. Please note that this is a high level overview and in future posts we will expand upon each stage accordingly.
The overall lifecycle can be broken down into the following eight steps:
- Globally Integrated Product Design
- Prototyping and Pre-Production
- Mass production
- Sales and Marketing
- Product Distribution
- Activation and Support
- Value Added Services
- Resale and Retirement
Industry 4.0/ IIoT impacts Product Design and Innovation
IIoT technology can have a profound impact on the above traditional lifecycle in the following ways –
- The ability to connect the different aspects of the value chain that hitherto have been disconnected. This will fundamentally transform the asset lifecycle leading to higher manufacturing efficiencies, reduced wastage and more customer centric manufacturing (thus reducing recall rates)
- The ability to manage and integrate diverse data from sensors, machine data from operational systems, supplier channels & social media feedback drives real time insights
- The Connected asset lifecycle also leads to better inventory management and also drive optimal resupply decisions
- Create new business models that leverage data across the lifecycle to enable better product usage, pay for performance or outcome based services or even a subscription based usage model
- The ability track real time insights across the customer base thus leading to a more optimized asset lifecycle
- Reducing costs by allowing more operations ranging from product maintenance to product demos, customer experience sessions to occur remotely
Manufacturers have been connecting the value chain together for many years now. The M2M (mobile to mobile) implementations have already led to rounds of improvements in the so called ‘illities’ metrics– productivity, quality, reliability etc. The real opportunity with IIoT is being able to create new business models that result from the convergence of Operational Technology (OT) with Information Technology (IT). This journey primarily consists of taking a brick and mortar industry and slowly turning it into a data driven industry.
The benefits of adopting the IIOT range from improved quality owing to better aligned, efficient and data driven processes, higher operational efficiency overall, products better aligned with changing customer requirements, tighter communication across interconnected products and supplier networks.
Deloitte has an excellent take on the disruption ongoing in manufacturing ecosystems and holds all of the below terms as synonymous – [2]
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Industrial Internet
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Connected Enterprise
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SMART Manufacturing
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Smart Factory
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Manufacturing 4.0
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Internet of Everything
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Internet of Things for Manufacturing
Digital Applications are already being designed for specific device endpoints across thought leaders across manufacturing industries such as the Automakers. While the underlying mechanisms and business models differ across the above five manufacturing segments, all of the new age Digital applications leverage Big Data, Cloud Computing, Predictive analytics at a minimum. Predictive Analytics are largely based on a combination of real time data processing & data science algorithms. These techniques extract insights from streaming data to provide digital services on existing toolchains, provide value added customer service, predict device performance & failures, improve operational metrics etc.
Examples abound. For instance, an excellent example in manufacturing is the notion of a Digital Twin which Gartner called out last year in their disruptive trends for 2017. A Digital twin is a software personification of an Intelligent device or system. It forms a bridge between the real world and the digital world. In the manufacturing industry, digital twins can be setup to function as proxies of Things like sensors and gauges, coordinate measuring machines, vision systems, and white light scanning. This data is sent over a cloud based system where it is combined with historical data to better maintain the physical system.
The wealth of data being gathered on the shop floor will ensure that Digital twins will be used to reduce costs and increase innovation. Thus, in global manufacturing – Data science will soon make it’s way into the shop floor to enable the collection of insights from these software proxies.
What About the Technical Architecture..
For those readers inclined to follow the technology arc of this emerging trend, the below blogpost discusses an IIoT Reference Architecture to a great degree of technical depth –
A Digital Reference Architecture for the Industrial Internet Of Things (IIoT)..
References
- McKinsey & Company – Global Manufacturing Outlook 2017 – http://www.mckinsey.com/business-functions/operations/our-insights/the-future-of-manufacturing
- Deloitte Press on Manufacturing Ecosystems – https://dupress.deloitte.com/dup-us-en/focus/industry-4-0/manufacturing-ecosystems-exploring-world-connected-enterprises.html