Products-People-Digital Equilibrium

With Prof. Michael Porter

With Dr. Michael Porter

I am writing this blog post to bring out the essence of our discussions that occurred during the”FT-PTC Future of Industrial Innovation Global Series” organized in New York yesterday. Manufacturing industry thinktank and senior leadership personas have come together to exchange ideas on how manufacturers are adopting new-age technologies to compete.

A joint keynote address from Dr. Michael Porter and James Heppelmann (Jim), CEO of PTC, was an excellent “confluence of thought” that brought together strategic mindset and technology acumen.

While the siloed productivity of human and machine/product has been evolving over decades, the Digital technologies are offering capabilities that can enable progress to the global optima and excellence creating Human-Machine/Products-Digital Advantage. Machine and Products are interchangeably used from now on in the context of manufacturing.  The connection between Products/Machine and Digital (Cloud, Digital Twin, etc.) has been established for some time. This connection enables sensing of a product’s data by digital technologies (edge/embedded) or digital controlling through the optimization of products/machines. But there is a lag between the human-machine and the human-digital connection compared to the digital-machine connection. This lag is causing the “discontinuity” of humans in human-machine-digital ecosystems.

Prof. Porter elaborated on the manufacturing evolution to date as shown below. His vision of the next phase in the evolution is “Smart Connected People”. He emphasized that this phenomenon is happening now with progress from connected products (IIoT) to Smart Connected People with the advent of Augmented Reality (AR) on occasions combined with Virtual Reality (VR) and Xtreme Reality (XR).


Today’s interfaces separate the physical and digital worlds. A prime example being the GPS system in the car. The 2D display on the GPS shows directions, but human cognizance has to take that input, process it, and finally execute it. This 2D to the 3D gap is what Dr. Porter referred to as “Cognitive Distance” which results in “Cognitive Load”. Imagine a “Heads Up” display leveraging AR that minimizes and eliminates the cognitive distance and cognitive load. AR narrows the cognitive distance by integrating the Digital world into the Physical world, seamlessly.

Digital transformation is leapfrogging the industrial and manufacturing progress continuum from Monitor -> Control -> Optimize to “Autonomy”. AR technology is uplifting the human connection by enabling visualization and collecting the instruction to pass on to the machine. Technologies like computer vision are promoting the human-machine interaction such that the embedded software & systems are allowing humans to diagnose the inner workings of products which were an earlier limitation. In scenarios where AR gets dangerous, VR can fill the gap with simulations and move forward. Thus, the Human-Machine-Digital equilibrium is being established to drive the next-level of industrial innovation.


Prof. Porter’s strategic foresight was well complemented by Jim’s real-world technology development and use cases. New-age digital technologies are expanding industry boundaries through precision agriculture and smart city solutions. In the past, products progressed to smart products and then became connected smart products but the present and future of industrial evolution revolve around “product System” and “System of Systems”. All-in-all it was great mindshare on today’s manufacturing excellence. I am parking the detailed description of use cases to my next blog post.

P4 I summarize this post with two important closing thoughts from Dr. Porter and Jim.

  1. AR enables People as IoT enables Assets/Products
    • Enabling more effective training and guidance to address the shortage of skilled front-line workers
    • Enhancing worker productivity through better collaboration with machines
    • Counterbalancing the shift to automation by empowering human workers
  2. Both IoT and AR combined to change the competitive environment, requiring new strategic choices and organizational models. For example,
    • Technology development: internal or outsource?
    • Disintermediate distribution or service channels?
    • change the business model?

In the end, I interacted with Prof. Porter to reflect on the discussions of the day and sought his expert comments on the man-machine inflection point. Here is the gist of my discussion. Over the past decades, the industry experienced a gradual reduction in annual work hours, resulting in the gradual improvement of productivity and output. One key attribute of productivity is man-machine collaboration. With digital technologies, the man-machine inflection further uplifted the productivity to 2X, 4X and in the panel discussion yesterday, one company executive was mentioning about 9X productivity gains. In view of this, my questions were,

  • Where does the productivity multiplication (constant uplift of human-machine combined productivity/inflection point) lead next?
  • In the near future, is it going to be survival of the fittest between a human vs machine as the trend line of annual working hours continue to decline?
  • In the long term, would machine constantly chase & replace the humans or the cognitive distance prevail in the foreseen future?

I will follow up with Dr. Porter on this and share further learnings. Stay tuned!!

Monetizing IoT Data with Blockchain

IoT Data

“Data is the most important asset class of current generation”. In Internet of Things (IoT) era, with increasing device proliferation in hyper-connected world, humongous collection of sensor data can facilitate the conversion of incredible ideas into value-adding services. In creating value with data explosion, Blockchain Technologies can play a critical role creating a peer-to-peer marketplace providing IoT sensor owners an opportunity to monetize data and simultaneously enable data consumers with a decentralized market to buy IoT sensor data.

According to Allied Market Research (AMR), the global market of sensors is poised to grow with a compound annual growth rate (CAGR) of 11.3 percent until 2022 when the market would reach $241 billion. The data resulting from such vast reach of IoT sensors is for the primary usage of the sensor owner or it is enhanced with value-added insights and reselling. In both the scenarios of either for primary usage or for enrichment and re-sale, the data remains unacceptably under-utilized and the utility if hindered away in organizational silos. Blockchain can provide a marketplace for IoT sensor data connecting data owners with 3rd party data consumers directly by externalizing the data outside primary silos.

The upside potential arrives from expected growth of todays 10+ billion sensors deployed globally to reach 40+ billion by 2020. Blockchain technology can help monetizing data by creating a marketplace offering a fully built financial ecosystem with a very minimal fees compared to a traditional fiat payment processors who typically charge between 1 and 3% for transactions. Also with creation of data utility tokens offers possibility to use small fractions of the token combined with very low fees making micro-transactions feasible. As well decentralization with blochchain backbone enables a very large numbers of participants in a trustless environment transacting with each other.

As shown in the picture above, a perfect ecosystems can be built for monetizing IoT data with Blockchain technology backbone. The players include sensor owners, data lakes gets created, network providers, blockchian data broker framework, data processers/enrichers, and data consumers / buyers. Sensor owners get an opportunity to monetize their data recovering some of their investments in IoT sensors. Network operators can win-back their enterprise accounts gaining scale and speed in the adoption of their network. This creates new types of buyers offering ease of access to data. Alongside data processors gain an eco-system to sell their services to the right people.

The use cases for such monetization of IoT data can be numerous covering multiple industries. A few examples of described below.

Use case

Pragmatic solution for data-age


300 hours of video are uploaded to YouTube every minute! Almost 5 billion videos are watched on YouTube every single day. This is one example of the pace of data growth and overall size of data is piling into zetta-bytes faster. Where am I going with this is relevance of rapid analysis of data measured in hours or days rather than the stereotypical months of traditional data mining. The result is an opportunity to derive meaningful insights to business with more emphasis on predictive analytics.

Predictive analytics is nothing new. Predictive analytics is a way to identify the probability of future outcomes based upon historical data. For example, from customer perspective, companies can predict a likely lifetime customer value or the probability of either loyalty or churn. Let us look at few use cases to examine the relevance and importance of predictive analytics in the world of enormous data.

A fashion retailer story: Predictive analytics helped in analyzing the campaign spends and predicting incremental campaign impact of spend. This helped the retailer in understanding where not to spend: Having a close look predictive analytics helps in understanding the relationship between customer segments and the marketing campaigns being interacted with. This retailer predicted the probability of a particular channel influencing online or offline purchases within specific customer segments, and while this was enormously useful in understanding how to spend budgets targeting more personalized digital campaigns, it was equally insightful into identifying spend that wasn’t contributing to incremental value. The power of predictive analytics came in determining, should the retailer pay for this ad, or will a sale happen organically through another channel or communication that might cost nothing or next to nothing? This allowed the marketing team to choose the right channels to most effectively and efficiently reach different groups of prospects and customers, and second, it provided the information required to personalize by sending the right content and message to the right segments at a very granular level.

Preventing hospital readmissions: Hospitals are turning to predictive analytics as they began to feel the financial pinch of high 30-day readmission rates. Real-time EHR data analytics helps hospitals cut readmissions by five to seven percent. This demonstrates how predictive analytics in real-time can analyze EMRs data to automatically identify and target patients at the highest risk of readmission early in their initial hospitalization when there is a lot that can be done to improve and coordinate their care, so they will do well when they leave the hospital. Notably, Kaiser Permanente has been working to refine its readmissions algorithms in order to better understand which returns to the hospital are preventable and which are not, a crucial distinction for value-based reimbursements.

Lifetime value analysis of a subscribers of communication service providers: CSPs are lately realizing that not all customers are the same. It is important for CSPs to assign a quantifiable dollar value to each customer, in order to prioritize various sets of customers. The Lifetime Value of a subscriber provides the predicted yield from each customer over the customer life. This helps CSP in offering high priority customers loyalty bonuses, preferential treatment through personalized service, better credit norms etc.
We are living in a world of data overloading. Predictive analytics can be a true human partner from elections to sporting events to the stock market on what the future will bring. Predictive analytics elevate human kind from an educated guess to data backed decision.

Trends in Edge computing – Ease & secure access of Industrial data


In IIoT ecosystem, IT and OT meet at the edge. The edge is where data is sampled and collected from the environment by instrumented things or devices. Edge-Centric Architectures extend elastic compute, networking and storage across the cloud through to the edge of the network. The following are few of the trends for maintaining security and uptime with edge computing while streaming data securely.

  • Cloud offloading to address communication latency: Handling more processing at the network’s edge reduces latency from the device’s actions. Use cases that are highly time-sensitive and require immediate analysis of, or response to, the collected sensor data are, in general, unfeasible under cloud- centric IoT architectures, especially if the data are sent over long distances.
  • Encryption on endpoint to safeguard data security: By and large, sensitive and business-critical operational data are safer when encrypted adequately on the endpoint level. Unintelligent devices transmitting frequent and badly secured payloads to the cloud are generally more vulnerable to hacking and interception by unauthorized parties. Additionally, many enterprises may need to secure and control their machine data on the edge level for compliance reasons.
  • Sensor fusion: Combining data from different sources can improve accuracy. Data from two sensors is better than data from one. Data from lots of sensors is even better.
  • Sensor hubs: Developers increasingly experimenting with sensor hubs for industrial internet devices, which will be used to offload tasks from the application processor, cutting down on power consumption and improving battery life in the devices.
  • Analytics on the Edge leading to low cost-of-ownership and secured data: Reduce potentially huge cloud-computing costs (because of the sheer volume of 24/7 sensor data) by allowing “fog computing,” where the processing would be done right at the collection process combining real time analytics, with only the small amount of really relevant data being passed on to a central location.
  • Gateway-mediated edge connectivity and management architecture pattern: As the widespread acceptance of modern, open-field protocol standards has reduced the need for traditional gateways in the field, the IIoT has created a need for a new breed of intelligent gateways that unlock the full potential of interoperability among diverse real-world devices and industrial Internet systems

How global economies embracing Industrial Internet (IIoT)?

Industrial Internet (IIoT) incorporates machine learning and big data technology, harnessing the sensor data, machine-to-machine (M2M) communication and automation technologies that have existed in industrial settings for years. The driving philosophy behind the IIoT is that smart machines are better than humans at accurately, consistently capturing and communicating data. This data can enable companies to pick up on inefficiencies and problems sooner, saving time and money and supporting business intelligence efforts. In manufacturing specifically, IIoT holds great potential for quality control, sustainable and green practices, supply chain traceability and overall supply chain efficiency.

A major concern surrounding the Industrial IoT is interoperability between devices and machines that use different protocols and have different architectures. The nonprofit Industrial Internet Consortium, founded in 2014, focuses on creating standards that promote open interoperability and the development of common architectures.

How Manufacturing embracing IIoT:

The high level of interest and hype surrounding the Internet of Things is driven by the proliferation of everyday objects with an Internet connection — everything from kitchen appliances and household electronics to clothing, vehicles and retail goods. This transition is truly amazing, and developing faster than anyone could have imagined. But in the world of manufacturing, our own version of IoT, the Industrial Internet of Things (IIoT), is a logical extension of automation and connectivity that has been a part of the plant environment for decades, primarily in the area known as machine-to-machine (M2M) communication.

The IIoT movement is, of course, growing and expanding at least as fast as the Internet of Things (IoT) in the outside world because smart devices and connected sensors are proliferating in the plant as well. But the transition from M2M and plant networking to full IIoT presents interesting challenges that manufacturers must address before the technology gets out of hand and threatens, rather than enhances, the benefits that it promises to provide.

The connected factory as it exists today is a relatively closed environment, designed to communicate within the plant network and not necessarily with the outside world, with or through the Internet. Most companies are quite comfortable with that; the Internet can be a scary and threatening place. So one of the first decisions companies face when considering IIoT is whether the benefits are sufficient to overcome the risk of making all that detailed company information accessible through the Internet and leaving internal systems vulnerable to hacking, viruses and destructive malware. Despite the fact that Stuxnet was developed and deployed with the best intentions, its very existence is a wake-up call for any company thinking of opening internal systems up to the Internet.

It’s conceivable that you can obtain many of the benefits of IIoT without that outside connection. Upgrading or changing the internal network to Internet Protocol (if not already TCP/IP compatible) should allow a company to install and use the new devices and sensors in an internal IIoT and that may be perfectly adequate.

 Taking advantage of IIoT:

Why would a company want to enable the Internet connection? A connection to the Internet allows access to IIoT data and supporting applications from virtually any device, any time, from any place in the world. Functional managers can check on specific machines, schedules, inventories, etc. at any time, in full detail, no matter where they are. Executives can drill down to study situations and analyze performance and results when at home or on the go. More importantly, perhaps, IIoT with Internet connectivity can provide hands-on visibility and control capabilities for remote locations, subcontracted manufacturing plants or suppliers’ factories.

Although much intra-factory communications takes place over an Ethernet network, many existing devices use proprietary protocols and many are not Internet enabled. Can these existing devices play a role in a connected IIoT enterprise? The short answer is maybe — with limitations. The bigger question is whether they all have to be replaced by fully Internet-enabled IIoT devices. Again, the answer is not a simple yes or no.

Strategy and tactics for implementing IIoT must align with a company’s goals and concerns. An all-in commitment means the eventual replacement of non-compliant controllers and devices so that all detailed data is available to the network and authorized remote users. This strategy requires the most attention to security and access control and the most vigilance on a continuing basis. It’s possible that some of the existing equipment can be upgraded or modified to fit in with an IIoT implementation and not have to be replaced.

Taking a part-way approach to IIoT:

A part-way strategy leaves more options for continued use of some or all the incumbent equipment and also may alleviate some security concerns. The existing internal network can be preserved and even enhanced with the addition of more sensors and devices while remaining a closed system not attached to the Internet. This is arguably a corruption of the IIoT ideal but can be a practical approach to additional tracking and visibility. IIoT-like data management and analytics can be a part of the enhanced internal network structure so many of the IIoT benefits become available, just not from remote access. This approach provides what technologists call an “air gap” that separates internal data and controls from direct connection to the Internet, and is commonly done for security reasons. In this case, the motivation goes beyond security to include practical and monetary considerations as well.

This part-way strategy doesn’t fall under the definition of IIoT and certainly doesn’t deliver the “anytime from anywhere” access benefits. A possible workaround would be to pass the data to a database accessible from the Internet. Although this approach doesn’t provide real-time data, it does restore some remote availability benefits with reduced security concerns — hackers could access the data but not the controls themselves and any damage would be done to the copy in the accessible database, not the source data that remains in the internal network.

Whatever the approach or extent of commitment to IIoT, the availability of more connected sensors, controls and devices undoubtedly provides the opportunity for a level of visibility and control unimaginable just a few years ago. Companies have the opportunity to keep closer track of everything going on in the plant, at subsidiary and remote plant locations, at subcontractor and suppliers plants, at remote warehouses, and on goods in transit anywhere in the world. New and evolving analytics and data visualization tools are making the IIoT’s “big data” usable and beneficial as we continually strive for more efficiency, responsiveness and agility.

Any technological evolution will cause disruption, cost and strategic realignment but it’s always done with the promise of improved operational performance. IIoT may just be the current step in that evolution but it is a significant one. We can already see glimpses of what tomorrow’s factory will look like and IIoT will clearly be its eyes and ears.

Industrial IoT riding M2M advantage to next stage

Industrial IoT nonetheless needs better connectivity, business models.Proponents of the much-hyped Internet of Things (IoT) are entering the practical phase of figuring out exactly how to turn ideas into reality.

To be continued…

Making sure manufacturing teams up for challenge and the reward with IIoT

While the Industrial Internet of Things (IIoT) has the power to significantly improve reliability, production and customer satisfaction, taking full advantage of its capabilities requires that companies re-think the people, processes and technologies involved in all
aspects of operations.

I was part of a panel of industrial manufacturing leaders in the field of IIoT to discuss the necessary requirements for this transformation, and the best practice approaches for realizing maximum value.

We produced a report that provides a clear roadmap, important examples, and actionable insights for any manufacturing and operations team in the manufacturing sector. It also offers fresh perspectives for IIoT leaders. You can learn more from the report here:

Unleashing the Potential of Consumer IoT and Industrial IoT (IIoT) with Insights & Analytics


As Internet revolution redefined the way businesses run over last couple of decades, the future will be revolutionized by Internet of Things (IoT). The global enterprises are experiencing the accelerated momentum of Consumer IoT which centers on consumer applications, such as smart homes, connected cars and consumer wearables like wristband activity trackers. However, it is Industrial Internet of Things(IIoT) or Industrial Internet that will transform many industries, including manufacturing, oil and gas, agriculture, mining, transportation and healthcare. Collectively, these account for nearly two-thirds of the world economy.

“Data & Below” advancements placing an exciting challenge for “Data & Above” actions:

In this post, I am bifurcating fundamental layers of IoT/IIoT – one “Data & Below” and the second “Data & Above”. Data & Below starts with “The Things” in I of T, the Physical Devices with sensors and controllers. During the past three years, the number of sensors shipped has increased more than five times from 4.2 billion in 2012 to 23.6 billion in 2014 jump starting on ability to sense/communicate with the Things that are remote. On top of this layer sits the Edge/Fog Computing, which in 21st century layman terms pushes applications, data and computing power (services) away from centralized points to the logical extremes of a network. Majority of the IoT/IIoT processing will be machine to machine (M2M), without slow end-user response times that present in most client/server systems. Thus one of the vital role of Fog computing is enabling M2M interaction with near real-time response times. Next is Network & Connectivity layer with appropriate protocols helps in Data Abstraction from field Things/Machines leveraging Edge computing. M2M communication and automation is not new, but the recent technological advancements bringing a paradigm shift further easing the access and availability of data

Data & Above which will be the focus in rest of the post, starts with Data Storage of abstracted data . With >20B+ consumer Things (IoT) and billions of industrial Things getting connected in next 3 to 5 years, pushing the available data to 44 zettabytes by 2020 (i.e. 6 to 7 stacks from Earth to Moon). Food for thought is how to balance between first focusing on better usage of available data and prepare simultaneously to handle further data volumes being generated from connected devices. The layers above Data Storage are Advanced Analytics and Collaborated Business Processes & Eco-Systems making enterprises future ready with decision making capabilities. Control Towers et al. falls in between. Cloud plays a key role in data storage and processing offering efficient & effective computing power. Not to alleviate on Collaboration layer, in my view Analytics places light in tunnel of IoT/IIoT benefits realization.

Insights and Analytics in Context of IoT and IIoT:

The massive volume of IoT/IIoT data generated is not the transaction-oriented business data that is being used today. Coming from millions of sensors and sensor-enabled devices, IoT data is more dynamic, heterogeneous, imperfect, unprocessed, unstructured, and real-time than typical business data. It demands more sophisticated analytics to make it meaningful. Making sense of the Industrial Internet data, particularly in the context of a large enterprise, can be challenging.

Along with data complexities, real-time responses are often critical in manufacturing, energy, transportation, and healthcare. Real time for today’s Internet usually means a few seconds, whereas real time for industrial machines is often sub-millisecond. The engineering rule of thumb dictates that a 10x change in performance requires an entirely new approach, not to mention the 100x change that the Industrial Internet will likely need. With the above diversity the treatment and analytics of IoT and IIoT data differs. Without getting into further nuances, let us look at best of Insights & Analytics power in IoT/IIoT journey.

Leapfrogging with Insights & Analytics:

“Data & Above” value chain starts with big data synthesis, processing of big data with intelligent & advanced analytics, enable faster and effective business decision making, and finally act on decisions to realize outcomes. The traditional “Data-Analytics-Decision-Action” lifecycle with human intervention in between has to evolve to serve beyond the real-time needs of industrial internet and time sensitive IoT scenarios. Insights & Analytics focus has to elevate from decision support to decision automation.

At the core “Data & Above” offers businesses with wide range of analytic and programmatic capabilities necessary to create the business models, including:

Descriptive Analytics: the “what happened” analytics that glean insights about what happened. Descriptive analytics analyzes past performance data and look for the reasons behind past success or failure (e.g., trending, moving averages, comparisons with previous periods). Most management reporting uses this type of retrospective analysis.

Behavioral Analytics: the “what is happening” analytics to uncover (codify) device, product and network usage characteristics, tendencies, propensities, patterns and trends. This area of analytics would include graphic analytics to understand device and node behavioral characteristics in light of the overall network relationships and interactions (e.g., understanding relationship direction, strength, frequency, and regency).

Predictive Analytics: the “why did it happen” analytics that provide the reasons why something happened and form the foundation of predicting future performance and behaviors. Predictive analytics answers the question about what will likely happen (with some degree of confidence). Predictive analytics uses rules and algorithms to determine the probable future outcome of an event or the likelihood of a situation occurring.

Prescriptive Analytics: the “what should I do” analytics (e.g., recommendation engines, next best action) that deliver prescriptive, actionable recommendations that lead to direct action. Prescriptive analytics leverages multiple disciplines of mathematical sciences, computational sciences, and business rules, to suggest decision options to take advantage of the predictions (recommendations). Prescriptive analytics can continually take in new data to re-predict and re-prescribe. This automatically improves prediction accuracy and makes for better decision options (recommendations).Boundaries are further getting pushed with cognitive thinking and predictive analytics.

But the real benefits of the Industrial Internal and the Internet of Things won’t be realized until output of analytics engines are translated into business use cases. Potential of IoT and Industrial Internet (IIoT) will be unleashed with business applications as listed below that are enabled by Insights & Analytics.