Top 5 Reasons why Digital Transformations can Fail !!

By Sairam Bollapragada & Rajesh Mohandas

Digital has been the heartbeat of the emerging technology forums and every second evangelist can talk for hours together on the benefits various industries, sectors, functions etc.… can derive out of Digital.

This note is an attempt to portray top 5 reasons why Digital Transformation(DT) journeys fail based on learning experiences of real time case studies and suggestions on measures that need to be proactively taken to be successful…

  1. Digital Definition : DT may mean different things to different people in senior management, depending on the role they play. It is evident from CXO studies that CXOs today take a very different approach towards DIGITAL; while the CIO and CTO are more technical to digitalization,  the CMO and CEO see it as business while the VP Sales may see it as new revenue channels. dt2The result is spectrum of variances in expectations and ambitions within the company. Some convergence is necessary to drive the scope and its applicability to an organization.

Suggestion : Establish a common DIGITAL DEFINITION across the organization and connect the definition with the objectives every unit in the organization can relate to and carry.

  1. Upskilling of workforce : It is no wonder then, that many traditional skills are now facing obsolescence. To put this in context, the latest report titled “The future of jobs” by the World Economic Forum cited that on average, by 2020, more than a dt3third of the desired core skill sets of most occupations will be comprised of skills that are not yet considered crucial to the job today.

 Suggestion : Workforce includes all, not just the IT folks. While IT teams can enable bring the technology solutions to the table, the SMEs should look at explosion of up-skilling in their skills distribution reflecting on the critical dynamic of  “Digital catch-up” and potentially improved economic inclusivity. Business leaders, educators, and policymakers observe that we now face a massive digital inclusion challenge  and everybody needs to transform and upskill/re-skill themselves. (

  1. Lack of ownership : Digital Transformation (DT) efforts should be all prevalent and not limited to just a few focus groups or techies. Even though the CxOs can call upon teams to help drive adoption, it does not mean in any way that the adoption is somebody else’s sole responsibility. dt4The maturity lies in each and every unit in the organization to realizing that the DT journey is mandatory to their very survival – nothing less. Most misses result from the fact that “this is not my responsibility”

 Suggestion: To succeed, every person and unit in the organization should agree and understand that each of the them need to evolve for survival and hence share equal responsibility for the overall DT journey.

  1. Shun Big Bang Approach : Every organization wants the digital spread to be quick and now! It does not mean you need a big bang approach of a centralized guideline or Digital playbook and then wait for it to pass through several internal champions and unit heads. dt5This approach of roll out has rarely worked fast. Organizations must get the independent business groups/units to understand that “Change starts with you”..

Suggestion : DT journey is more about bringing the fit-to-purpose ideas between Domain and technology. A careful consideration of divide-and-rule with sufficient degrees of freedom provisioned to each Independent Business Grups/Units following a continuous improvement framework will be the best way forward.

  1. Hyper efforts in creating lab with eternal PoC mode: We see that organizations have started being so blind-sighted making their labs that they seem to be stuck in time. Sometimes the solutions are found not to leave the lab forever – keeping stakeholders guessing. Culmination of innovative prototypes can be only called out ONLY if it hits the markets and finds a customer base. dt6An eternal POC mode, builds a perception that either organizations are not confident of their solution or are lost.

 Suggestion: Fail and fail fast! That mantra works. The labs cosmetics to impress potential to-be clientele is colossal waste of time/effort/energy. Clients refuse to be enchanted by the show and feel of real estate. Solutions speak for themselves and the organizations capability to deliver outcomes and most clients are aware. Help clients co-create solutions to their specific needs and the earlier you come to this state, the better is your hold on the potential client – even if it means fail fast a few times.

While there can be many more reasons to add to the above list, the basic insurance against these would lay a foundation to cover other “Don’ts” like some want to scamper onto the analysts quadrants to prove how digitized they are, some may still be wading through the haze of their digital objectives, some may try to copy from what has worked for others without proper fit-to-purpose analysis, some may lose sight of their current differentiators to create new ones to cling to the market, etc.

However, set of failure points will vary from organization to organization and so will the remedies. However, it is proven that Digital is an inevitable transformation journey for organizations to survive and sustain themselves in the market. The maze they can traverse will decide the destination and destiny!


Swarm Robotics: 2 Potential Use Cases

By Sairam Bollapragada & Rajesh Mohandas

A single ant or bee isn’t smart, but their colonies are. Inspired by Bacteria colonies, Fish Schools, Locusts, Bird crowds, Primates etc… the study of swarm intelligence is providing insights that can help humans manage complex systems. Intelligent Swarm robotics coupled with Cognitive Algorithms help improve the coordination of multirobot systems which consist of large numbers of mostly simple physical microrobots and nanobots. Swarm robotics systems are characterized by simplicity of individuals, local sensing and communication capabilities, parallelism in task execution, robustness, scalability, heterogeneous, flexibility and decentralized control. The Swarm intelligence market is valued at $447.2 million and is growing at 40% CAGR (Markets&Markets Research).

Every bot is powered with sensors, actuators, control logic, power source and software that essentially captures data, processes it and takes action, the self-learning faculty of such devices open up a paradigm of new perspectives improving efficiency and productivity while lowering cost and risk.

Data laced cognitive behavior of swarm robotics can form a solid case in ensuring great advantages to the farming sector and also in military services like surveillance/monitoring, hazards location (like gas/chemical/radioactive leaks,etc) and potential rescue missions, etc. Let us look at one or two use cases to understand the right applicability of the swarm robotics.

Precision Farming:

New precision farming practices carry the objective to be far more efficient and waste fewer resources than conventional techniques. The aim of precision agriculture is to apply agrochemicals (fertilisers, pesticides, herbicides) to the areas where they are most needed, at a given time, instead of the traditional approach to spray whole fields uniformly almost every day.

The Solution: to design a sustainable swarm robotics system with expected result to a reduced but more effective usage of agrochemicals to give a higher quality and quantity of product crop. These robot swarms would be distributed over a farm or orchard, collecting data like the number of plants and their fruits, so that the farmer can estimate total yields, optimizing the production chain.

Distribute a whole bunch of these little critters over an area, and you’ve got an efficient, robust autonomous system that can scale up — way up, for any size of farm, from small acreages to the factory-like mega-farm. If these precision farming robots are developed with affordability in mind, they may become a cheaper option for small farms in developing countries for whom large, expensive equipment would be unnecessary and out of reach. Of course, employability versus yield is a debatable point.

Usage of algorithms like aggregation, self-assembly, object clustering, cooperative stick pulling, etc are all ways of leveraging the swarm robotics to ensure we are fighting the regression of natural forces which were catalytic to our food needs by artificial human introduced ways to fulfill the gaps so that the ever increasing need for yields can be addressed.

As massive numbers of bees continue to mysteriously die off, the story of Swarm robotics can be visualized beyond the precision agriculture where the scientists are already doing prototypes of robotic pollinators for increasing the pollination and pesticide treatment activities for getting better positive advantages of micro-robotic application, ignoring the negatives of how the same can be applied.

Emergency Rescue Missions: Swarm Robotics to the Rescue of Mankind :

Limitations of humankind in terms of their physical and biological tolerances to the weather conditions has almost always been challenging. We have seen in movies where robots transform themselves into various shapes and adopt to suit the next desired action.

Robots are being created for typical rescue missions to move through an environment which contains large number of obstacles anywhere and of any kind: from fissures to deep vertical holes, from small pebbles to large rocks, from wires to walls, from long tubes to compact blocks, etc.; Sometimes robots need to be introduced into small holes, and once inside they need to overcome large gaps, to descend a vertical duct ending in a large void, and finally to pass in other narrow passageways (with grippers, mechatronics and super light-weight? – remember s-bots?)

Self-reconfigurable robots research is one area picking up where reshaping themselves to legged bots or snake-like shapes can help address reach constraints.

The swarm robotics re-summarized finally can raise a lots of hopes for the receding natural support ecosystem for many areas:

  1. Autonomous robots that are independent and can interact with each other and the environment for data collection for more predictive-productive algorithms.
  2. They are in large number so they can accomplish synergized outcomes through
  3. With scale and robustness you can add a new unit easily to the system so the system is easily scalable. More number of units improve the performance of the system. The system is quite robust to the loosing some units as there still exists some units left to perform – all however at an additional ask for management/monitoring effect.
  4. The robots communicate with each other and with environment to take the final decision with potential to converge to a decentralized coordination, not to forget the flexibility it shows with the ability to generate modular solutions to spectrum of tasks at hand.

Imagine if we created a whole lot of earthworms which would be a best thing to happen for a farmer. Imagine, a swarm of bees being used to address uniform distribution of pesticides in the given quantity to the crops to make them right-healthy for the desired yield… and many more!!  

How Secure is your API?

By Sairam Bollapragada & Sandeep Mehta

Technology will keep evolving and the existing platforms will keep transforming to make our solutioning richer with far more reaching impacts. API is the evolving technology glue which is promising strategic and much higher complex communications between the applications with many of the technologists vouching for them.

Just to strengthen the case and context here, Ovum Survey mentions that 30% of APIs are designed without the inputs from the infosec teams, 27% proceed with the development without security teams weighing the same, while 53% of IT/security professionals feel security teams should be responsible and 47% feel it should be developers. Of the API platforms used by companies (borrowed or in-house developer), only 22% had protections from 4 critical attack parameters – developer errors, web/mobile API hijack, automated scraping or malicious usage). 45% of the API Management platforms are infested by the rate limiting features. The list and arguments go on.

What are the threats to an API Platform

There are some jot-it-down parameters which should be hard-looked at whenever an API based solution design becomes a suspect.  Let us look at them and ponder on detections or workarounds.

  1. Unprotected APIs: API’s should have prioritized insulation built around them. REST, SOAP,  make access strictly controlled as back-end systems lack access control, management and monitoring. Exposed APIs must be dynamically scanned to ensure system exposures to unprotected assets via APIs are identified whenever there are requests made for access through the API layers.
  2. Hack-in Attempts: The attackers have high sustenance to break into the systems with spectrum of persisting techniques. Effective usage of rate-limiting services via API gateways to choke access requests and detect break-in patterns will help, upfront security testing and policy designs to block out users with patterns of malicious failed break-in attempts will be good strategy.
  3. Injections: High Impact attacking techniques like the SQL injections can become a most serious security failure with all your information compromised. One of them could be for output data written back to the API caller, is the source of data authentic and how is the encryption taking place? What is the extent of the data user control? Etc. This being a very vulnerable part, tools like SQP Map for testing the SQL injection, Burp Site for the same or even cross-site scripting are useful to prevent such threats.
  4. Strong Authentication!: APIs are designed to be exposed for external usage and hence every caller should be authenticated. The authentication cycle must be completely audited and checked from request initiation to termination using approved authentication standards. Application level testing to ascertain weakness of API approved authentication protocols would greatly assure validation of calling applications token.
  5. Session depravity: Not knowing who is the caller of API when the tokens are corrupted or cannot be authenticated will deem it impossible for API servers to differentiate between well-intended and ill-intended access. Tokens when tampered or replayed with altered privileges can create such a scenarios. Token-protection schemes like hashing and ensuring tokens are fresh using verified timestamp will help. A test suite developed to check token tampering is identified/tracked, or only accepted from authorized sources is mandated.
  6. TLS/SSL Protections: TLS/SSL Protection makes sure that data transferred between users/sites or between two systems becomes impossible to read. It uses  encryption algorithms to scramble data in transit.
  7. Trusted V/s Trustworthy: A trusted computer system is a system that is trusted to perform security- or safety-critical operations, a trustworthy system is a system which has already been trusted and secured using encryption methodologies.
  8. API Right Usage: An API should be used for what it is designed for. Many times the implementation on the API platform exceeds the functionality available on the platform. This exposes the whole platform to new set of risks. The limitations of an API platform have to be strongly kept into consideration whenever it is being evaluated for any solution. Also one fact to be kept in mind is that the API should peacefully Coexist
  9. Poor Code: Poor Code on an API exposes the platform to lot of vulnerability, examples of Poor Coding on API are not implementing certificate based authentication and not restricting IP addresses to filter only from known sources. This will expose the API platform to all external IP’s and anyone with basic skills can access the API and perform their operations.

Finally, whenever an API is being designed or being evaluated for usage, the base security parameters have to be sufficiently considered and evaluated. The evaluation or design of the product should always keep enough room to extend security features as and when required. There should also be enough facilities to implement remedial measures or alerts which will alert the users whenever there is any security threat or breach of the API.

Cognitive Blockchain?? An Agriculture sector perspective!

–          By Sairam Bollapragada and Rajesh Mohandas

In today’s hyper-connected world which is driven by hyper-dependent technology landscape, black swan effects are necessarily increasing as a result of Volatility, Uncertainty, Complexity and Ambiguity in the given globalization scenario. There is recognition that the demand (crisis?) in food security is only going up. Trust is becoming more a mandate than an option, especially for this sector that focus on “Farm to Fork” while already battling Drought, Emerging crop diseases, Pest resistance to chemicals and genetic traits, Phosphorous mines depleting, Salty soils, Fertilizer dependence and growing influence of anti-science forces.

A bigger threat faced by this $5 trillion sector representing 15% of global consumer spend, 40% employment and 30% greenhouse emission, is the “agro-terrorism” too. In the United States alone, crop and forest production losses from invasive insects and pathogens have been estimated at almost US$40 billion per year.

Hence the need for a “TRUST PROTOCOL” and a ledger of everything to establish transparency and traceability to the sources assisted by cognitive systems, to address the ever increasing concern of providing necessary quality and safe nutrition for a growing population. To accomplish the growing need, the food production should increase by 70% from the current levels (at near zero wastage) and one potential solution to the rescue could be the intelligence driven Blockchain (Cognitive Blockchain?)

This kind of Blockchain can create techniques for  upfront detection of malicious agents in the autonomous AI dominated supply chain. It brings in “Consensus” and “Persistent” algorithms rendering multi-agent Cognitive Connected Solutions into an evolving self-organized structure capable to overcome Data Oligopoly. Let us look at few sample applications:

  1. Transparency related issue resolution:
    1. Fund allocation by Government or private agencies to farmers in developing nations do not reach the intended farmers in time due to various reasons and leads to farmer’s bad debts. Usage of Blockchain based digital tokens as currency can come to the rescue of this scenario where bitcoin-enabled farmers can transact for fertilizers, seeds, and other necessities with least probabilities of being misused or imitated, ensuring the right usage of allocated funds creating maximum needed impact. Furthermore the AI Algorithms can add predictive features to enable the agencies to take proactive measures and plan for the future budgetary allocations.
    2. The above can also be used to stall the misuse of land documents by middleman or land-sharks standing in for the farmers. Blockchain can help create immutable land titles to prove the ownership and insure the farmers from the widespread corruption through the trust and transparency driven by Blockchain. The cognitive techniques can further aid in fighting fraud detection, money laundering, counterfeit currency, etc.
  1. Brokerage Avoidance:

High insurance premiums paid by farmers is a wide known issue, where majority of the play is by the middlemen. Cognitive Blockchain solutions, can help eliminate the middlemen and add the required intelligence where the CPQ algorithms will help farmers configure the insurance plans and opt the lowest premium for their crop produce, avoiding the brokerage/processing fee involved ensuring their premiums are actually paid in time.

In addition, such solutions will help insurance companies to better understand a farmer’s risk profile by predictive modeling, estimated profitability bettering their credit history and making them more creditworthy.

  1. Supply Chain to Demand Chain:

With AI, ML and analytics, the behavior of both the buyer and seller is seeing a change. Blockchain makes it even more interesting with Cognitive demand forecasting. As the appetite to handle more data goes up, the number of input sources and players participating in the demand forecasting will increase. With intelligent Blockchain solutions, one can not only keep track of the transactions and contractual obligations with suppliers and other stakeholders, but also gain visibility into the demand-supply scenarios. The farmers can run queuing algorithms and thus proactively route the supply to predictively meet the right demand in right time, making the shift to the supply chain as demand chain.

  1. The Food Security factor:

Forbes reports that a study conducted at WALLMART that took 6 days and 18+ hours to trace the source of a Mango carton, was completed in less than 3 seconds with Blockchain. Described by the Economist as “the trust machine”, blockchains can provide supply chain transparency and data integrity, allowing a visible assurance of authenticity and assist fighting food fraud, especially, the organic food fraudulent labeling that is becoming prevalent today.

If leveraged effectively, Cognitive Blockchain can play a very critical role in accomplishing the target of safe, quality nutrition needs of 9.6 billion population by 2050!!

Sustainability in the Digital World: Do’s and Don’ts

By Sairam Bollapragada

While the entire humankind is going ga-ga over the word Digital, there still seems to be much struggle around organizations in creating a Digital transformation blueprint/value and adopting the same quickly.

This note is an attempt to bring to table salient features of becoming Digital relevant in true spirit and deeds. Let us take the points, one after the other:

  1. Goals and Objectives: An Organization should ask itself if they have a dossier which explains to all its employees what Digital means to their business. It is not mandatory that all aspects of Digital should mean something to you. Pick the relevant ones which are critical to your business and get started on the transformational journey.


Organizations should get aligned to their clients (both current and potential) on how they can leverage your Digital capabilities to strike a chord with the digital needs of their clients as well. Hence the sales teams should understand the needs and current capability. In fact they should be the first agents of the change to bring to table the digital market needs and hence what we need to nourish as capability.


The upskilling is the next most important action. Since the entire demand is moving towards Digital, your upskilling plays a strategic role. The two cannot be misaligned considering even the short term requirements.


  1. Adaptability: Your strength to react to the changes in market demand is very critical if you need to be seen as the early adopters in the market . Understanding the market conditions and demand fast, acting to invest in a skilled workforce faster and be the first implementers is essence – which all sums up to reflect on how Agile you are as an organization. You may have to ruthlessly clear the clutter or legacy clingers who can become a challenge to the road to transformation. This will also help your perception in the market and make your sales teams to approach the market with that much more confidence. Unless you up your risk antennas, the conviction will be missing in your commitments. The challenges are greatest learning tools which prepare you to handle bigger commitments. Hence create a risk taking culture that thrives on innovation and experiments.


  1. Change Management needs to be carefully crafted out of a network of sources which should become your strongest source of drivers in enhancing your objectives of Digital transformation. Change. When inevitable has to bring in objectivity to avoid chaos. In the Digital space, it pays you richly through both internal and external partnerships. Co-creation is a critical component of this Change process. Please refer my earlier blog:


  1. Congenial Work Environ: The culture of clinging and hugging often seen as threat to change, is led by folks who don’t want things to change as it reflects their insecure mind-set. Millennials must be provided a platform to bring in fresh ideas through their out of box creative minds. They don’t carry any baggage and hence you can almost always expect a fresh bag of ideas. Once you encourage such an environment, ideas will flow automatically. Let the owners of execution incubate these ideas and convert them into compelling propositions for their clientele. The more fresh ideas you take to clients, the more your probability as being perceived as a leader in the space. Remember perception management is also very critical across the ecospace. Strategic initiatives cannot be allowed to be held ransom to the feudal mind sets of folks obsessed with large teams. The question then to them is – how would you embrace the upcoming digital twins in your workforce.


  1. Focussed Teamwork aligned to Objectives: In the services business of annuity, we seem to understand a lot about value creation. We try and demonstrate through our PIPs (productivity improvements), CoD (Cost Of Delivery), etc. However, with the advent of Digital and Automation, the client’s expectations have gone exponentially wild compelling all service providers to think radically different. Hence the above point 4 holds that much more water. Most persons facing the client needs to come across as your digital brand ambassadors (if not all). The approaches you position to the clients should prove your thought leadership. Often the rift between what is sold and what is delivered leaves a bad taste with the clients. This is a true reflection of what lack of orchestration within the teams. Hence your need to align all the teams to speak one language of offerings-capabilities-capacities to establish credibility.


Last but not the least, every Digital customer is looking for uniqueness in the solution being delivered. So please be very cautious before you replay a plethora of offerings while you showcase your might.

Real Time Supply Chain Analytics

Real Time Supply Chain Analytics

By Sairam Bollapragada & Rajesh Mohandas

Supply chain managers have seen increasing challenges to create, and keep, efficient and effective supply chain methods, Customer Service, Operating Cost Control, Planning and Risk Management, Inventory Control, GTM Speed, Supplier / Partner relationship management, Green Supply Chain and Talent (Resources) are a few burring issues that are on the top of the charts that burn the midnight oil. A survey by McKinsey says the companies who have already engaged in leveraging Digital Technologies have managed key trade-offs on barriers to better performance : raising risk, lack of collaboration and low CEO involvement.

The fall of this decade has seen major shift in the supply demand dynamics, the traditional supply chain solutions are not equipped to handle complex scenarios due to lack of visibility. There is a competitive demand for real-time responsiveness which can be addressed by the combination of Data Science and Emerging Technologies connecting customer service with Social and Mobile platforms that are cloud enabled on one hand and strengthening visibility into operations with IOT based real-time analytics. The icing on the cake is emergence of machine learning which is all set to address the tactical challenges and give signature ready recommendations for decision makers to gain maximum mileage.

Supply chain management will gradually be disrupted by the rising adoption of IoT and advanced analytics.  The challenge faced by the supply chain practitioners and players is inability to take advantage of technology to the fullest extent while they are trying to simultaneously integrate their supply chain systems across a much wider range of information sources due to lack of 360 degree view of both business and customer.

  1. Strategic Planning: The very first component of the supply chain, Strategic Planning, comprises of Strategic supply design and Strategic sourcing. Here real time analytics can be used for Supply Chain alert monitoring and the insights will flow into Long term planning, Bid Management, Contract management, Catalogue management and Source determination, real time information can be accessed by decision makers that are geographically dispersed to take collaborative decision on a cloud enabled mobile application. 
  2. Demand Planning: The Next component of the supply-chain value stream is Demand Planning the three crucial capabilities here are Forecasting, Promotions and Demand Consensus. Advanced real-time analytics help with Macro calculations and planning bill of materials leading to collaborative demand planning and characteristic based forecasting.
  3.  Supply Planning: The heartbeat for any supply chain is the supply planning which comprises of capabilities like Safety stock planning, supply network and outsourcing decisions, Distribution planning, Customer and Supplier collaboration. The best component in the value chain where Emerging technologies can be used with the fullest potential is here, the power of cloud enabled CRM systems and Communities connecting the enterprise giving a 360 degree view to the entire ecosystem including the suppliers, customers, partners and the enterprise following with the power of advanced analytics and big data enabled deep learning algorithms come handy. Some examples where one can leverage advanced real-time analytics are in the area of multi-level supply demand matching, whereas the artificial intelligence neural network algorithms can be plugged into supply network planning.

Procurement, Warehousing and Order Fulfilments are three equally crucial parts of the supply chain operations that are interlinked and often become bottleneck in the supply chain due to lack of visibility. In a connected supply chain environment the intelligent algorithms need to be plugged in here such that all three areas are interconnected and all repetitive processes can be automated through RPA, what with NLP loaded Analytics providing intelligence and insight into the process.

4. Transportation: The last component of the value chain is Transportation. Often the biggest complaint from the players of supply chain is “Visibility to the Tail”. This is already being accomplished by real time analytics, bringing in a predictive model that combines features like load consolidation, intelligent route optimization, carrier selection and shipment trending which are few elements of the Transport planning while the Transport execution can feed in data from the shipping interfaces capable of distance determination services. The last inputs come from the freight-costing component that strengthens the predictive model and the machine learning algorithm continuously churns the data and gives real-time insights for both strategists and operations.

Hence, Analytics working in tandem with Cloud, Mobility, and AI, can play a very critical role to bring in great value add to the Real Time Supply Chain scenarios for all those who embrace and leverage. This is even more critical with markets moving towards a more C2C value chain where the customer experience and expectations are fast changing. Retailers, Warehouses, Suppliers, Logistics, and all the agents in the Supply Chain have to work in an orchestrated mode need to be on their toes to remain competitive and relevant to the market expectations.

VUCA in Digital Manufacturing

By Sairam Bollapragada Sairam & Rajesh Mohandas

In our first part of this series Digital in a VUCA World we walked thru various facets of Digital being impacted, the first paper was domain agnostic and we will today focus on the impact of VUCA on DIGITAL MANUFACTURING!


Manufacturing roughly contributes to 1/3rd of the global GDP as per the world bank figures and approximately 10% of the global workforce is directly employed by manufacturing companies. The “Multiplier Effect” brings in nearly 37% of the entire global workforce is indirectly connected with manufacturing sector as per the Forbes. Compared to that of discrete manufacturing there is more technology penetration and today the emergence of Digital and Adaptive manufacturing has clearly redefined this prone-to-be disrupted sector, adding predictability, efficiency, effectiveness and above all cost optimization with improved productivity as challenges.


VUCA conflates four distinct types of challenges that demand four distinct types of responses; the need of the hour for companies during an economic downturn is business developers and not problem solvers or better a combination of the two.


Along with VUCA came the concept of working world 4.0. Derived from industry 4.0, the fourth industrial revolution, it names its immense and rapidly spreading impacts on many areas of work and life. It changes the way we communicate, get and read information and prepare decisions. The special feature of Industry 4.0 is networked manufacturing, i.e. the further development of digitisation through emerging technologies…


ART OF THE POSSIBLE in the VUCA world for manufacturing sectors leveraging Digital …


Volatility: The Manufacturers are increasingly becoming aware of the fact that to alter their manufacturing strategies face the raising volatility. One has to firstly understand the volatility exposure and assess how agile are internal business processes, the business operations and at least 75% knowledge about the customers customer in all three B2B, B2C and C2C markets.


Manufacturers are under constant pressure of continuously improving QPM, especially in the fluctuating market demand irrespective of the magnitude. One bad product and the digital reach being so large and quick, it can dent your credibility.


Big Data with Predictive analytics and bots leveraging machine-learning algorithms will bring in mechanisms to tackle volatility and hence automate a large chunk of the manufacturing process.


Uncertainty: The manufacturing sector has lived thru multiple uncertain eras and has indeed mastered the art of change management, in the digital world the same can be replicated with “USE-PREPARE-FOCUS-FIND” cycle


  • USE : use Data: Knowledge – Process – Technology, to arrive at strong data analytics platforms to predict and handle uncertainty. Data lakes can help drive multiple inferences and leverage on historical information. The shift to Virtual prototyping, IoT based surface modelling QAC, Sheet metal design, CAPP, AR based marketing, process simulation, are all areas that need to be understood well.


  • PREPARE: be well prepared to tackle situations raising out of events unknown, with digital technology like cognitive computing, neural networks, artificial intelligence algorithms etc. to speed up effective decision making capabilities with a “First – to – Market” objective. Prepare well to use tools like SAMCEF, NASTRAN, ABAQUS (to name a few), etc. for FEA, embedded M2M based information analytics, Connected Device Platforms (CDP), AEP, etc. You need to move fast and as much to Intelligent Manufacturing.


  • FOCUS: The market is shifting towards customer specific demand fulfilment, hence analytics, cognitive computing and plethora of such tools available can help you focus on very specialized “M2C – Manufacturer to Customer” markets – hence the agility and reach. Continuously focus to improve the PLM, from conception to service and disposal.
  • FIND: the digital marketing and media provides platforms for very fast feedback which can be leveraged catalytically to improve the products and build variants, thus maximizing footprint.


Complexity: Looking the way Digital Manufacturing is being challenged, the four influencing factors are:

  1. optimized resource usage,
  2. shortened lead times,
  3. personalized fit-to-purpose manufacturing,
  4. increased (squeezed?) productivity



The complexity in the Digital Manufacturing space is predominantly, due to the fact, that manufacturing is shifting focus from pure play product philosophy to Product & Services philosophy. Hence, the challenge shifts to balancing maintenance with production.



Haze in vision to the roadmap on how technologies can better your product or services can be a killer. You need to have a dynamic strategy which keeps refreshing its goals every 6 months to a year. 64% of the leadership time is being spent on articulating shared vision as per a CII-EY report.


Any organization unclear of the path it wants to tread to embrace technological advances to transform itself, will not be kindly treated by the market demands and especially in Manufacturing segment. In fact, the Industry 4.0 is exactly about that. 27% of the so-called $19 Trillion Digital economy is due to the manufacturing sector.  Hence in this ambiguity (though not a choice anymore), crafting out leadership opportunities can be indeed be an opportunity.



Manufacturers, with so much at stake, simply can chose either to run the race or to become legacy as they are challenged every day to the field by new and modern entrepreneurs who are coming up with some very interesting and disruptive innovations continuously shifting the co-ordinates to newer business parameters. The VUCA in DM is all of that – to be strategized and attacked in a truly multi-pronged approach.


VUCA in the Digital world!!

VUCA in the Digital world!!

By Sairam Bollapragada & Rajesh Mohandas

Across the globe, all are now connected in unprecedented ways. This is both a boon and a bane, where we live in an era that is transforming and setting stage for the next revolution. Times when we were disconnected and every country operated in silo the challenges were limited to the internal affairs and the near border conflicts only.

With technological advances where today we look at a bright and secured future on one hand, on the other hand the unrest continues and is growing bigger day by day, conflicts, civil unrest, terrorism, ransom ware, cyber crimes, etc… are now integrated into our daily life.

The digital reality is shaking up some of the beliefs and compelling us to move to a more knowledgeable IT economy what with automation and AI which were limited to books, have finally come to the open challenging how that can transform every space of the life. Soon all white-space is expected to be filled with cognitive behavior and techniques. Automation is forcing re-wiring of skills for many of the IT workforce (read : ) spelling end of the careers if not done.

Hence one can relate to the 4 key parameters of VUCA : Volatility, Uncertainty, Complexity and Ambiguity. Each of these factors are challenging the order of the day stuff and hence the need to cope with the same in the turbulent times.

The compounded problem statement with external influencing factors from market pressures, competition, shareholder expectations, stakeholders, are strong indicators, to the fact that the leaders will need to be hard wired to resilience.

The role of the leaders managing workforce, will be crucial and critical in shaping the digital future of any organization.  Most of the requirements to support a digital environment are not about the technology per se, but it is also about creating the environment to re-skill, create flexibility to be agile, adopt to changing demands, and groom the right talent for a safe digital future.

Let’s take each of the parameter at a time to see what it means in Digital world:

(V) Volatility: The nature and Dynamics of change that is blowing across the landscape mandates catalysts to adopt to these changes. The legacy of efficiency and productivity will no longer continued business anymore. Disruptive innovations are indeed unsettling dominant industries in today’s world. Hence the times call for compulsive innovation and a drift away from SOPs.

U (Uncertainity): This is a factor which reflects the lack of predictability and many surprises. Another indicator of this is the refusal of the current technology wave to move easily beyond the labs. The ever-experimenting mind-set is also reflecting that the solutions themselves are prone to obsolescence, from the very moment they are conceived with high degree of unpredictability.

( C)Complexity:  Multiple parameters built into the character of the issue spells complexity – be it chaos or confusion-led issues.

Complexity can also reflect multiple influencing factors which can unsettle easily. Complexity is good or bad depending on your strategy. Having a bullet proof strategy is impossible – nevertheless one should have a solid strategy to counter complexities and challenge the same.  Even if it comes with short expiry date (2 years) you should have one.

Digital space is getting more and more complex with each passing day rolling out a new platform, new innovations coming to light, new solutions offered, disruptive models coming to life, etc. Hence to deal with all these changes, a strategy for managing this change is mandatory and thus the

(A)Ambiguity : The fact that we only know 40% of how technology will fold into the lives and markets as an influencer, is a true reflection of haze in the Digital space. This then raises the question of business risk, which is quite a reality today.

At various levels of an organization, there are ambiguities relating to progression and growth, whether at organization level or career levels of professionals.  Except for the lexical meaning of the Strategic and Tactical approaches, the lines are thinning out.

Volatility, Uncertainty, Complexity and Ambiguity will continue to exist but what leaders today can do is to play a vital role and attempt to control the levers by moving in to a Hyperawareness zone of informed decision-making, and fast execution. Winning in the Digital Vortex is not just about algorithms, architectures or innovative business models; it requires organizational change and workforce transformation. And successful transformation is enabled by a company’s digital business agility, building on the fact that people are an organization’s most important asset. Hence, everybody is but compelled to think on the forward thinking strategies to adopt to the Digital VUCA scenarios….

Why existing estimation tools are not realistic for Digital Project Estimation

Why existing estimation tools are not realistic for Digital Project Estimation

by Bollapragada Sairam, Rajesh Mohandas, Dattatreya Rao, & Ravi Pandikunta

Digital for some executives is primarily about the technology. For others, digital is a new way of engaging with customers. And for others still, it represents an entirely new way of doing business. None of these definitions is necessarily incorrect. But, the variation results in piecemeal initiatives and misguided efforts.

Industry experts have started to believe that digital should be seen less as a thing and more a way of doing things … this creates complexities with respect to estimation, how can one estimate and cost a concept. In digital projects “basic concept” is a starting point for estimation, or at least an idea, but it’s loose and not particularly well defined. Sometimes that’s because there hasn’t been time to develop it or there simply isn’t the ‘appetite’ from the creative to think through the detail.

Unlike traditional development parameters, the Digital World carries many more and they are unique in nature, variety of products, applications, data bases, technologies, middle-wares, hosting types and the entire eco system. Few more elements such as, sensors/devices, platform/infrastructure, testing, integration, security, scalability, robustness, seamlessness are multi folded efforts in development.


Some cost estimation models used in software development today are

Cost Model Description Best Fit Environment Formula type
COCOMO Constructive Cost Model Large corporate and government software projects, including embedded firmware Logarithmic
COSYSMO Constructive Systems Engineering Cost Model Large corporate and government projects, including embedded firmware and hardware Logarithmic
FP Function Points Software projects of all sizes, mainly desktop OS based platforms Linear
WMFP Weighted Micro Function Points Commercial software projects of all sizes and environments, including embedded firmware Linear
REVIC REVised Intermediate COCOMO Large military software projects, including embedded firmware Logarithmic

Some typical challenges with traditional estimation techniques in software development:

Unlike other industries, here often the estimates are done with partial data and sometimes with incorrect data, too. Several techniques / tools have been introduced over the years to make the process systemic and not a gut-based guesstimate. However, lapses still occur and this is still one of the toughest to-dos for a project. Following are few more parameters

  1. Poor design: Poor design results in unnecessary code tweaking and heavy-duty maintenance applying pressure on schedules.
  2. Not splitting the tasks enough: Most common method is to split project tasks into a WBS, but sometimes they are not broken enough to be conceptualized with clarity.
  3. Top to bottom scheduling: This is a practical problem one needs to deal with. Instead of doing bottoms-up estimation, most projects start with – “I need this done in 6 months” and then a work breakdown is done where the task estimates are retrofitted inside these 6 months.
  4. Factoring the dependencies right: Often, an external dependency or a decision point is missed-out causing the project to suffer, this is “coordination neglect”.
  5. Factoring right buffer: This is a common challenge and there is no simple formula here.
  6. Analogous Estimation Risk: Often, project estimates are done based on an expert judgment or from past projects’ experience. While picking an analogy and mapping the estimate might seem like an intuitive thing to do, it’s often risky because of the numerous variables in a project and the unique elements and dependencies, the people involved and their skillset, diverse tools and technologies adopted and the infrastructure and resources in place.
  7. Ignoring Team Capacity: There is a lot of debate about what unit or estimates need to be factored – should we measure complexity, time or effort? Irrespective of what unit is followed, many Project Managers tend to ignore considering their team’s capacity. It seems obvious that different people would take different time to code, but when we draw estimates, we come up with a standard effort estimate.

One other challenge is not only the technology but also the periphery elements on the topology, network, security and emerging areas like Artificial Intelligence, Autonomous vehicles, Cloud Manufacturing and 3d Printing, IoT and Connected Devices, Robots, Drones, and social media platforms couple with decisions on the emerging approaches like DevOps, Dockers, Microservices etc… add further complications into the estimation cycle.

Changing expectations from the customer are forcing service providers and manufacturers into a hyper personalization spiral, thus adding cost pressures. In these cases, the technology needed to solve our problem is well established indeed; in fact, it’s possibly the most important technical innovation in the history of humanity ranging from B2B, B2C, B2M, C2C etc…

The players in the market too have made the situation complex, though each provider promotes “On Demand and Pay as you Go” models the terms and conditions are quite different, for example the pricing metric in case of AWS is number of messages, Cisco looks at Cellular, GE bills on number of instances, IBM looks at data ingested and bills accordingly, Microsoft costing is depended on Number of messages, devices and feature set while SAP looks at an annual fixed subscription model and there are many more such elements to consider in your costing model…

The exponential growth in the technology as well as diversification of the same, new solution components, hyper-personalization, and other needs, all mushrooming towards building of modern solutions; but one cannot ignore that there is a dire need for building standard cost estimation frame-works where the conventional methods cannot be afforded….

The Need for Intelligent Command Control Center for Robots (IC3R)

By Sairam Bollapragada & Rajesh Mohandas

It is predicted that the industry economy whether IT or non-IT, will go full throttle in the upcoming FY 2017-18 to create a financial realization called autonomics – unlocking the potentials  of robots that are being conceived. Over 2.5 billion people have at least one messaging app installed, within a couple of years that will reach 3.6 billion, about half of humanity. (Source: The Economist) However, the outcomes as suggested by many big market research houses have not been up to the desired expectations. With the things heating up around automation and artificial intelligence/RPA, we can foresee that we will be very soon seeing an increasing need to have some solid controls in place.

Today, the market is focused on Industrial Networks, Industrial Robots, Machine Vision, Control Valves/Devices, Field Instruments, Enclosures and Cables.  Each of these components have an IT and a Non-IT element with technology landscape consisting of SCADA, PLC (Programmable Logic Controllers), PAC (Programmable Automation Controllers), RTU(Remote Terminal Units), DCS (Distributed Control Systems), MES (Manufacturing Execution System), PLM (Product Lifecycle Management), HMI(Human Machine Interface), and above all Safety.

While creating new technical solutions every day and getting excited with it, we are probably too casual on the flip side of the consequences. Lets focus on the negatives for a moment – what if an unmanned vehicle had a bad bug? or what if the programming in the automated manufacturing plants were intercepted/hacked altering the desired behavior or leading to disturbing outcomes?, it can become a nightmare!! For example a recent crash involving Uber Technologies Inc. driverless car suggests autonomous software sometimes takes the same risks as the humans it may one day replace. While we are creating bots at an unprecedented speed and passion, we may also need to secure these advancements through a control mechanism, which will help us to have the desired outcomes, intact. The technological singularity will compel us to start thinking on automatic recovery with deep machine learning capacity.

Hence, are we talking about having a Command Control Mechanism to protect the desired outcomes of all the automated bots whether Soft or hard? The answer is yes. We need to soon develop and establish command control centres for a set of digital work force you want to monitor on a continuous basis to ensure they are aligned to the expected behavior patterns. In fact, there should be a proper set of guidelines issued by the state agencies before allowing any robot to go commercial in the market. The audit and strictures will help control the release of un-certified or Rogue robots. This would be especially true with the craze of smart cities catching up like around the globe. The creation of the digital twin space is also something that must be looked into seriously for potential disruption.

A command control center will help in creating a centralized monitoring service which will track monitor and report the behavior of these bots while positively looking at it, it could also lend performance improvements towards the desired outcomes. What with the introduction of aggressive mind-control technology and Drones we should have a proper access control on this technology based robots. A C3 with an end-to-end visibility across robots with real-time rolling view to help us have a central control of work schedules, job cards, execution, and support for various robotic activities

While the support for high availability/disaster recovery and network load balancing is the intent, the central control mechanism, will be mandated to have a cyber-cop kind of functionality. For example, while monitoring the bunch of UV Cars, suppose an unmanned vehicle on road was malfunctioning, one should have the ability(or create one) to monitor it in real time and stop the functioning of the engine remotely to avoid any major disaster.

A secure central monitoring system laced with analytics, could be enabled through the log base where robots pass on every information pertaining to each activity they are instructed to perform. With this much of an information being logged, one can get a deep insight into the business and the activity patterns being conducted by or through robots. With so much of information at our disposal one can really create a very good analytics use case to understand and comprehend the behavior of these robots as they are unleashed into the market.

The global industrial automation market is extremely fragmented due to the presence of several players in the global market. Some of the leading players operating in the global market are ABB Ltd., FANUC Corporation, Honeywell International Inc., Toshiba Machine Corporation Ltd., Yokogawa Electric Corporation, Emerson Electric Company, General Electric Company, Yaskawa Electric Corporation, Rockwell Automation, Inc., Mitsubishi Electric Corporation, and Voith GmbH.

However, while doing a cherry pick of the best of the lot or robots to make their organizations more productive and efficient, we hope that the focus will begin from creating a solid Intelligent Command Control Center upfront to monitor, maintain, track and continuously do course correction for these disparate bots – soft and hard alike. The industrial control and factory automation is projected to reach USD 153.30 Billion by 2022, at a CAGR of 4.88% during the forecast period and hence the emphasis. The state agencies must work towards evolving policy guidelines within and beyond for all entities looking to employ the automation-Digital bots effectively.