Industrial Internet of Things (IIoT) – Bridge to Monetization

March 31, 2018

We are in digital age, smart phones started the revolution, social networks followed it and Internet of Things, (IoT) is ramping up.  In consumer space, purchasing thresholds - emotional & financial, are elevated every year; a $1,000 cell phone is poised to be the best seller this holiday season.  Social networks have become ubiquitous and transformed into a powerful mass communications tool.  Cloud services have redefined online purchasing and B2B transactions.

In Industrial Internet of Things (IIoT), data generation devices and data acquiring IT infrastructure is commercially viable, there are multiple service providers for data storage, security and mining.  Regression, machine learning and AI algorithms are available for integration with applications. The technology stack and infrastructure is understood and ready, but IIoT is still in early adoption stage. The missing piece of the puzzle in IIoT is monetization or business model for digital services. Consultants and thought leaders are actively researching trends and publishing white papers to guide specific segments of the industry on robust SaaS models. But, business case for monetization is an elusive factor, majority of business leaders are deferring investments in IIoT


Third parties can provide hardware and software services from step 1 through 4, but every business has to complete its own steps 5 & 6 to close the loop and create a business model. Most of the businesses are hesitating taking the first step because they do not have a line of sight to step 6, it’s a chicken and egg situation.  A paradigm shift will help to break this dilemma: instead of seeking revenue from product service, SaaS, explore improving operational efficiencies through IIoT. Access to product (systems) response from the field can close the perennial gap between assumed product applications to actual. Even the best of pre launch studies are unable to capture ultimate application(s) of a product.  This gap has always been an Achilles heel of a new product development team.  Over design of a product leads to higher cost and lower profitability, while, cutting corners results in failures, customer dissatisfaction and loss of market share. If product application is understood satisfactorily there is as much as 10% - 15% margin improvement possible.  Other internal benefits of product digitization are: insight into customer application and an opportunity to innovate based on voice of the device, which in the real sense is the voice of the customer.  Based on component level signals, match component functionally to application. Streaming data analytics complemented with interpretation can improve service delivery significantly, reduce device down time and cut labor and inventory expenses. 


Product Factors

Historical Practice

Current Practice

Connected Device

Analytics & Interpretation

Design: Sub System response to loads

Rules of Thumb

CAE:  FEA, CFD, Reliability

Monitor application response

Design Optimization

Operating Conditions

Customer Specifications

Customer Specifications

Device feedback

Sustainability: Energy Savings & Reliability

Product response to operating conditions

Controlled test environment

Pilot Studies

Install base input

Voice of the Customer



Proto types

Segmentation insight

Machine Learning, Knowledge Curves

Cost Reduction

Supplier Negotiations

Competitive assessment and market study

Devise performance at sub system & system level

Match Functionality to Application


Periodic Maintenance

Reliability methods

Predictive, Prescriptive

Savings of Labor and Inventory


Step 5 is the bridge to monetization and has to be addressed internally by the product (device) OEM.  In strategic planning, develop digital/smart products team and initiatives.  The team should include product design experts who are passionate to grow, smart product managers who are hungry to launch killer products.  Train the team in data analytics and invest in IT infrastructure, steps 1-4.

 As the analytics and related interpretations roll in, expect validation and surprises at all levels and functions responsible for product life cycle: product management, product engineering, operations, supply chain, services and sales. There will be opportunities to revisit long held commercial policies, branding practices and sacred design rules.  Careful and deliberate planning, robust validation of hypothesis should be completed before gains are derived from newly acquired knowledge.  Product knowledge is a powerful and significant competitive advantage achieved through a complete digital strategy.  IT infrastructure, internal skills, data analytics and interpretation readiness will ultimately lead to SaaS models with monetization roadmap on the front end.  Meanwhile product differentiation, margin improvements and customer satisfaction would have already provided boost to the bottom line and separated leaders from followers.


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About Me

Ajay Garg has 28 years of engineering, operations, and innovation leadership experience which includes 7 years as an ex-pat in Asia Pacific. Mr. Garg is skilled in disruptive innovation and new product development in the industrial and consumer products space in emerging and global markets. He has a successful record of leading large technology teams and delivering market differentiating multi-million dollar growth platforms with speed. He has extensive experience in leading functional teams across engineering, product management, supply chain, operations and services. Ajay has authored eight technology papers and holds four US patents.


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