How the Internet of Things (IIoT) Digitizes Industrial Manufacturing..

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

  1. Global innovators for local markets – Industries such as Chemicals, Auto, Heavy Machinery etc.
  2. Regional processingRubber and Plastics products, Tobacco, Fabricated Metal and
  3. Energy intensive commodities – Industries supplying wood products, Petroleum and coke refining and Mineral based products
  4. Global technologies and innovators – Industries supplying Semiconductors, Computers and Office machinery
  5. 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:

  1. Globally Integrated Product Design
  2. Prototyping and Pre-Production
  3. Mass production
  4. Sales and Marketing
  5. Product Distribution
  6. Activation and Support
  7. Value Added Services
  8. 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 –

  1. 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)
  1. The ability to manage and integrate diverse data from sensors, machine data from operational systems, supplier channels & social media feedback drives real time insights
  2. The Connected asset lifecycle also leads to better inventory management and also drive optimal resupply decisions
  3. 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
  4. The ability track real time insights across the customer base thus leading to a more optimized asset lifecycle
  5. 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]

  • Industrial Internet

  • Connected Enterprise

  • SMART Manufacturing

  • Smart Factory

  • Manufacturing 4.0

  • Internet of Everything

  • 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

  1. McKinsey & Company  – Global Manufacturing Outlook 2017 – http://www.mckinsey.com/business-functions/operations/our-insights/the-future-of-manufacturing
  2. Deloitte Press on Manufacturing Ecosystems – https://dupress.deloitte.com/dup-us-en/focus/industry-4-0/manufacturing-ecosystems-exploring-world-connected-enterprises.html

The Three Core Competencies of Digital – Cloud, Big Data & Intelligent Middleware

Ultimately, the cloud is the latest example of Schumpeterian creative destruction: creating wealth for those who exploit it; and leading to the demise of those that don’t.” – Joe Weiman author of Cloudonomics: The Business Value of Cloud Computing

trifacta_digital

The  Cloud As a Venue for Digital Workloads…

As 2016 draws to a close, it can safely be said that no industry leader questions the existence of the new Digital Economy and the fact that every firm out there needs to create a digital strategy. Myriad organizations are taking serious business steps to making their platforms highly customer-centric via a renewed operational metrics focus. They are also working on creating new business models using their Analytics investments. Examples of these verticals include Banking, Insurance, Telecom, Healthcare, Energy etc.

As a general trend, the Digital Economy brings immense opportunities while exposing firms to risks as well. Customers now demanding highly contextual products, services and experiences – all accessible via an easy API (Application Programming Interfaces).

Big Data Analytics (BDA) software revenues will grow from nearly $122B in 2015 to more than $187B in 2019 – according to Forbes [1].  At the same time, it is clear that exploding data generation across the global economy has become a clear & present business phenomenon. Data volumes are rapidly expanding across industries. However, while the production of data itself that has increased but it is also driving the need for organizations to derive business value from it. As IT leaders know well, digital capabilities need low cost yet massively scalable & agile information delivery platforms – which only Cloud Computing can provide.

For a more detailed technical overview- please visit below link.

http://www.vamsitalkstech.com/?p=1833

Big Data & Big Data Analytics drive consumer interactions.. 

The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just provide engaging visualization but also to personalize services clients care about across multiple channels of interaction. The only way to attain digital success is to understand your customers at a micro level while constantly making strategic decisions on your offerings to the market. Big Data has become the catalyst in this massive disruption as it can help business in any vertical solve their need to understand their customers better & perceive trends before the competition does. Big Data thus provides the foundational  platform for successful business platforms.

The three key areas where Big Data & Cloud Computing intersect are – 

  • Data Science and Exploration
  • ETL, Data Backups and Data Preparation
  • Analytics and Reporting

Big Data drives business usecases in Digital in myriad ways – key examples include  –  

  1. Obtaining a realtime Single View of an entity (typically a customer across multiple channels, product silos & geographies)
  2. Customer Segmentation by helping businesses understand their customers down to the individual micro level as well as at a segment level
  3. Customer sentiment analysis by combining internal organizational data, clickstream data, sentiment analysis with structured sales history to provide a clear view into consumer behavior.
  4. Product Recommendation engines which provide compelling personal product recommendations by mining realtime consumer sentiment, product affinity information with historical data.
  5. Market Basket Analysis, observing consumer purchase history and enriching this data with social media, web activity, and community sentiment regarding past purchase and future buying trends.

Further, Digital implies the need for sophisticated, multifactor business analytics that need to be performed in near real time on gigantic data volumes. The only deployment paradigm capable of handling such needs is Cloud Computing – whether public or private. Cloud was initially touted as a platform to rapidly provision compute resources. Now with the advent of Digital technologies, the Cloud & Big Data will combine to process & store all this information.  According to the IDC , by 2020 spending on Cloud based Big Data Analytics will outpace on-premise by a factor of 4.5. [2]

Intelligent Middleware provides Digital Agility.. 

Digital Applications are applications modular, flexible and responsive to a variety of access methods – mobile & non mobile. These applications are also highly process driven and support the highest degree of automation. The need of the hour is to provide enterprise architecture capabilities around designing flexible digital platforms that are built around efficient use of data, speed, agility and a service oriented architecture. The choice of open source is key as it allows for a modular and flexible architecture that can be modified and adopted in a phased manner – as you will shortly see.

The intention in adopting a SOA (or even a microservices) architecture for Digital capabilities is to allow lines of business an ability to incrementally plug in lightweight business services like customer on-boarding, electronic patient records, performance measurement, trade surveillance, risk analytics, claims management etc.

Intelligent Middleware adds significant value in six specific areas –

  1. Supports a high degree of Process Automation & Orchestration thus enabling the rapid conversion of paper based business processes to a true digital form in a manner that lends itself to continuous improvement & optimization
  2. Business Rules help by adding a high degree of business flexibility & responsiveness
  3. Native Mobile Applications  enables platforms to support a range of devices & consumer behavior across those front ends
  4. Platforms As a Service engines which enable rapid application & business capability development across a range of runtimes and container paradigms
  5. Business Process Integration engines which enable rapid application & business capability development
  6. Middleware brings the notion of DevOps into the equation. Digital projects bring several technology & culture challenges which can be solved by a greater degree of collaboration, continuous development cycles & new toolchains without giving up proven integration with existing (or legacy)systems.

Intelligent Middleware not only enables Automation & Orchestration but also provides an assembly environment to string different (micro)services together. Finally, it also enables less technical analysts to drive application lifecycle as much as possible.

Further, Digital business projects call out for mobile native applications – which a forward looking middleware stack will support.Middleware is a key component for driving innovation and improving operational efficiency.

Five Key Business Drivers for combining Big Data, Intelligent Middleware & the Cloud…

The key benefits of combining the above paradigms to create new Digital Applications are –

  • Enable Elastic Scalability Across the Digital Stack
    Cloud computing can handle the storage and processing of any amount of data & any kind of data.This calls for the collection & curation of data from dynamic and highly distributed sources such as consumer transactions, B2B interactions, machines such as ATM’s & geo location devices, click streams, social media feeds, server & application log files and multimedia content such as videos etc. It needs to be noted that data volumes here consist of multi-varied formats, differing schemas, transport protocols and velocities. Cloud computing provides the underlying elastic foundation to analyze these datasets.
  • Support Polyglot Development, Data Science & Visualization
    Cloud technologies are polyglot in nature. Developers can choose from a range of programming languages (Java, Python, R, Scala and C# etc) and development frameworks (such as Spark and Storm). Cloud offerings also enable data visualization using a range of tools from Excel to BI Platforms.
  • Reduce Time to Market for Digital Business Capabilities
    Enterprises can avoid time consuming installation, setup & other upfront procedures. consuming  can deploy Hadoop in the cloud without buying new hardware or incurring other up-front costs. On the same vein, even big data analytics should be able to support self service across the lifecycle – from data acquisition, preparation, analysis & visualization.
  • Support a multitude of Deployment Options – Private/Public/Hybrid Cloud 
    A range of scenarios for product development, testing, deployment, backup or cloudbursting are efficiently supported in pursuit of cost & flexibility goals.
  • Fill the Talent Gap
    Open Source technology is the common thread across Cloud, Big Data and Middleware. The hope is that the ubiquity of open source will serve as a critical level in enabling the filling up of the IT-Business skills scarcity gap.

As opposed to building standalone or one-off business applications, a ‘Digital Platform Mindset’ is a more holistic approach capable of producing higher rates of adoption & thus revenues. Platforms abound in the web-scale world at shops like Apple, Facebook & Google etc. Digital Applications are constructed like lego blocks  and they reuse customer & interaction data to drive cross sell and up sell among different product lines. The key components here are to ensure that one starts off with products with high customer attachment & retention. While increasing brand value, it is key to ensure that customers & partners can also collaborate in the improvements in the various applications hosted on top of the platform.

References

[1] Forbes Roundup of Big Data Analytics (BDA) Report

http://www.forbes.com/sites/louiscolumbus/2016/08/20/roundup-of-analytics-big-data-bi-forecasts-and-market-estimates-2016/#b49033b49c5f

[2] IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions

Can Your CIO Do Digital?

Business model innovation is the new contribution of IT”  — Werner Boeing, CIO, Roche Diagnostics

Digital Is Changing the Role of the Industry CIO…

A Motley crew of some what interrelated technologies – Cloud Computing, Big Data Platforms, Predictive Analytics & Mobile Applications are changing the enterprise IT landscape. The common paradigm that captures all of them is Digital. The immense business value of Digital technology no longer in question both from a customer as well as an enterprise standpoint. However, the Digital space calls for strong and visionary leadership both from a business & IT standpoint.

Business Boards and CXOs are now concerned about their organization’s overall level and maturity of digital investments. And the tangible business value in existing business operations– (e.g increasing sales & customer satisfaction, detecting fraud, driving down business & IT costs etc)-but also in helping finetune or create new business models by leveraging Digital paradigms. It is thus an increasingly accurate argument that smart applications & ecosystems built around Digitization will dictate enterprise success.

The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous micro level interactions with global consumers/customers/clients/stockholders or patients depending on the vertical you operate in. Initially enterprises viewed Digital as a bolt-on or a fresh color of paint on an existing IT operation.

How did that change over the last five years?

Mobile applications first begun forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. We have seen how how exploding data generation across the global economy has become a clear & present business & IT phenomenon. Data volumes are rapidly expanding across industries. However, while the production of data by Mobile Applications that has increased but it is also driving the need for organizations to derive business value from it, using advanced techniques such as Data Science and Machine Learning. As a first step, this calls for the collection & curation of data from dynamic,  and highly distributed sources such as consumer transactions, B2B interactions, machines such as ATM’s & geo location devices, click streams, social media feeds, server & application log files and multimedia content such as videos etc – using Big Data. Often these workloads are run on servers hosted on an agile infrastructure such as a Public or Private Cloud.

As one can understand from the above paragraph, the Digital Age calls for a diverse set of fresh skills – both from IT leadership and the rank & file. The role of the Chief Information Officer (CIO) is thus metamorphosing from being an infrastructure service provider to being the overall organizational thought leader in the Digital Age.

The question is – Can Industry CIOs adapt?

The Classic CIO is a provider of IT Infrastructure services.. 

what_cios_think                                                Illustration: The Concerns of a CIO..

So what do CIOs typically think about nowadays?

  1. Keep the core stable and running so IT delivers minimal services to the business and disarm external competition
  2. Are parts of my business really startups and should they be treated as such and should they be kept away from the shackles of inflexible legacy IT? Do I need a digital strategy?
  3. What does the emergence of the 3rd platform (Cloud, Mobility,Social and Big Data) imply?
  4. Where can I show the value of expertise and IT to the money making lines of business?
  5. How can one do all the above while keeping track of Corporate and IT security?

 CIO’s who do not adapt are on the road to Irrelevance…

Where CIOs are being perceived as managing complex legacy systems, the new role of Chief Digital Officer (CDO) has gained currency. The idea that a parallel & more agile IT organization can be created and run to create an ecosystem of innovation & that the office of the CDO is the right place to drive these innovative applications.

Why is that?

  1. CIOs that cannot or that seem dis-engaged with creating innovation through IT are headed the way of the dodo. At the enterprise officer – CIO/CTO level, it becomes very obvious that more than ever “IT is not just a complementary function or a supplementary service but IT is the Business”. If that was merely something that we all paid lip-service to in the past, it is hard reality now. So it is not a case of which company can make the best widgets or has the fastest trading platforms or efficient electronic health records. It is whose enterprise IT can provide the best possible results within a given cost that will win. Its up to the CIOs to deliver and deliver in such a way that large established organizations can compete with upstarts who do not have the same kind of enterprise constraints & shackles.
  2. Innovation & information now follow an “outside in” model. As opposed to data and value being generated by internal functions (sales,engineering, customer fulfillment, core business processes etc) . Enterprise customers are beginning to now operate in what I like to think of as the new normal: entropy.  It’s these conditions that make it imperative for IT Leadership to reconsider their core business applications at the enterprise level. Does internal IT infrastructure need to look more like those of the internet giants?
  3. As a result of the above trends, CIOs are clearly now business level stakeholders more than ever. This means that they need to engage & understand their business at a deep level from an ecosystem and competitive standpoint. Those that cannot do it are neither very effective nor in those positions for long.
  4. Also,it is not merely enough to be a passive stakeholder, CIOs have to deliver on two very broad fronts. The first is to deliver core services (aka standardized functions) on time and at a reasonable cost. These are things like core banking systems, email, data backups etc. Ensuring smooth operation running transactional systems like ERP/business processing systems in manufacturing, decision support systems, classic IT infrastructure, claims management systems in Insurance and Billing systems in Healthcare. The systems that need to run to keep the business operations.The focus here is to deliver on these on time and within SLAs to increasingly demanding internal customers. Like running the NYC subway – no one praises you for keeping things humming day in and out but all hell breaks loose when the trains are nonoperational for any reason. A thankless task but one essentially needed to win the credibility with lines of business.
  5. The advent of public cloud means that internal IT no longer has a monopoly and a captive internal customer base even with core services. If one cannot compete with the likes of Amazon AWS or any of the SaaS based clouds that are mushrooming on a quarterly basis, you will find that soon enough you have to co-exist with Not-So-Shadow IT. The industry has seen enough back-office CIOs who are not perceived by their organizations as having a largely irrelevant role in the evolution of the larger enterprise.
  6. Despite the continued focus on running a strong core as the price of CIO admission to the internal strategic dances, transformation is starting to emerge as a key business driver and is making its way into the larger industry. It is no longer the province of Wall St trading shops or a Google or a Facebook. Innovation as in “adopt this strategy and reinvent your IT and change the business”. The operative word here is incremental rather than disruptive innovation. More on this key point later.
  7. Most rank and file IT personnel in general cannot really keep up with all the nomenclature of technology. For instance, a majority do not really understand umbrella concepts like Cloud, Mobility and Big Data. They know what these mean at a high level but the complex technology underpinnings, various projects & the finer nuances are largely lost on enterprise customers. There are two stark choices from a time perspective that face overworked IT personnel – a) Do you want to increase your value to your corporation by learning to speak the lingua franca of your business and by investing in those skills away from a traditional IT employee mindset? b) do you want to increase your IT depth in your area of expertise.The first makes one a valued collaborator and paves your way up within the chain, the second may definitely increase your marketability in the industry but it is not that easy to keep up. We find that an increasing number of employees choose the first path which creates interesting openings and arbitrage opportunities for other groups in the organization. The CIO needs to step up and be the internal change agent.

CONCLUSION…

Enterprise wide business innovation will continue to be designed around the four key technologies  (Big Data, Cloud Computing, Technology & Platforms). Business Platforms created leveraging these technologies will create immense operational efficiency, better business models, increased relevance to customers and ultimately drive revenues. Such platforms will separate the visionaries, leaders from the laggards in the years to come. As often noticed, the keyword accompanying transformation is often digital. This means a renewed focus on making IT services appealing to millennial or the self service generation – be they customers or employees or partners. This really touches all areas of enterprise IT while leaving behind a significant impact on organizational culture.

This is the age of IT with no boundaries – the question is whether the role of the CIO will largely remain unscathed in the years to come.