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.

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 THREE “R”s as outcomes of Automation!!

The THREE “R”s as outcomes of Automation!!

By Sairam Bollapragada

IT has been predominant for its people and associated costs. People have been the epicenter of all the transformation/automation and the benefits measured have always hovered around the people, the efforts, their packages and associated costs.

These are the days of automation, machine learning, artificial intelligence and introduction of robotics. We are creating digital workforce, in a big way to transform the way we deliver solutions and services today.  Due to cost pressures, many times, evidently the quantitative savings take advantage over the qualitative ones. The more demanding clients do not budge on either.

The bi-modal approach on what you can do better with our existing work in your scope as well as what else you can do with our other work with other vendors is becoming a natural ask by clients. This then creates the platform to compete and who brings what to the table matters. While everybody seems to be selling the concepts and ideas, the rollouts from adoption is slow as indicated by a recent report. Hence, the benefits slowly reflected in the books.

Many a times, the teams are not able to articulate the savings and calculate on how do we arrive at the magical savings number and translate that to dollars. The efforts thus required to deliver the same service with the productivity improvements should lead to benefits that can needs to be captured and reflected.

All the benefits can be thus, categorized into THREE R’s that relate to the people aspect as follows:

  1. R1: Resize: when transformation/automation saves engineering effort and hence the cost of solution/delivery drops, you can release few team members. This resized team can deliver the same volume of work or keeping the same team size can take up more work. In typical annuity projects, one can re-plough the saved effort to create additional work in terms of additional tickets or CRs, either with no drop in revenue or additional revenue.


  1. R2: Restructure: while betting big on outcomes of automation, one can expect the productivity of the team as a whole to gain upward momentum. This should lend the capability of the higher end of the pyramid to delegate the some of, if not all their tasks to the lower band teammates. This is a true indicator of productivity improvement.


  1. R3: Resite : In all engagements, many times we come across mandatory set of tasks that should be done onsite or at client’s site. Transformations/Automations can also bring in the capability to move those tasks offshore bringing down the cost of solution or engagement. This may add to your bottom lines or you may choose to pass on the benefits to the clients. Whichever way, more presence of tasks at offshore has always been a strong indicator of confidence levels of delivery as well as capability of the team.

However, when it comes to benefits @ R1 or R2, there is strong feeling that it only leads to job loss. Positively put, it can aso mean the higher band resources can be released (and if they are very capable) where they can be deployed for account mining or/and other transformational consultant roles to demonstrate technical prowess or thought leadership in different areas – both  focused at increasing the footprint from growth standpoint.

If we don’t embrace automation/transformation, somebody else may move your cheese. Till the outcomes hit the financial books, the last mile is not accomplished….so, we must compel ourselves to drive these market-mandated changes, as long as the choice is still with us….

The Railway Fleet Planning – Digital way!

By Sairam Bollapragada & Rajesh Mohandas

In our last blog on the topic (, we had a perspective about the railways fleet management.  In this sequel, we wish to bring on the aspects of Fleet Planning through levers from digital perspective and highlight a few important KPIs today as eyed by the railway planning commission. Fleet planning for any logistics entity is a continuous activity.

Fleet planning basically need to answer two most important questions, which locomotive is needed where and when – and when to acquire (buy / hire) one.

The most critical KPI the fleet planners need to be equipped with is a 360-degree view of the enterprise connecting the decision makers with information regarding its Vendors, Customers, Fleet Assets, the market intelligence to meet expectations. The very reason for such an infrastructure lack exists today due to disparate and disintegrated systems where in most of the countries data collection is still manual in many ways.

The rail network is increasingly busy – the number of passenger journeys made has risen by 70% across the globe over the last decade while US alone operated 1,471,736 freight cars and 31,875 locomotives and originated 39.53 million carloads (averaging 63 tons each) generating $81.7 billion in freight revenue for FY ‘16. So it’s important to make best use of the capacity through effective timetabling, and the right decisions about where to invest in developing the network, Planning and operating the network as seamlessly as possible in line with the existing and future demand. A few of the global KPIs measured and monitored by all railway operators are

  1. Asset Utilization (Train Km per Track Km)
  2. Efficiency (Planned track possession KM hour per track possession km hour)
  3. Service Quality and Reliability (Trains delayed due to Infrastructure)
  4. Innovation and Growth (Average relative age of fleet assets)
  5. Accessibility (Service coverage) and
  6. Safety (Accidental equivalent facilities per train km).


  • Asset Utilization

Logistics have a single goal even in asset planning, targeted at effective error-free stock management. Knowing item location, quantities on-hand, stock-outs, re-order triggers, space and scheduling, and how to minimize movement and manage assets in a harsh, high pressure environment, demanding growth patterns are only some of the challenges faced. Planning commission of Railways is constantly worried about the aging vehicles and tracks that need constant attention beyond the environmental and safety threats. Another critical area is to ensure consistency in operations while maximizing utilization. The statistics International Rail Journal show freight traffic is at a seven-year high with revenues reaching over $70 billion and customers are continuously demanding faster cycle times.

Focus on investments in real-time data analytics for smart DSS and IoT based sensor analytics solutions is mandated to provide critical continuous inputs to the fleet and asset planning exercise with constituent factors addressing the mobile workforce, data mobility and data quality for the fleet planning and management.

  • Efficiency

Railways use real-time telematics data and all the data that can be collated from the sensors and actuators to monitor, improve, optimize, the fleet plans with a singular goal of peaking at the efficiency of the operational process excellence. Apart from efficiency, the continuous need to reduce energy costs and minimize human intervention, reduce maintenance costs through real-time diagnostics and predictive analytics, eliminate waste in fleet scheduling through fleet instrumentation. Efficiency and productivity can be increased multifold leveraging intelligent technology, like digital tagging that can be automatically read without the need for a direct line of sight; towards a more real-time inventory intelligence, replacing all other manual efforts required otherwise.

  • Service Quality and Reliability

Railway infrastructure is under consistent heavy stress to do more with limitations at physical expansion. In countries like India and China where the fast-demands-up is a way, the need for better reliability, safety and QoS without much physical elbow space for infrastructure capacity increase is a continuous challenge.

Clubbed with this is to operate with the optimized OpEx with increasing pressure on the price point which needs to keep low as alternative completion threatens. Even passenger services are facing such a price competition from roadways and low cost Airlines. Customer satisfaction is what guarantees the future of railways. Predictive pricing through predictive analytics is increasingly finding space in the planning exercise to ensure all such pressures are catered to. The same holds for fleet procurement and phasing out plans for the obsolete fleet elements, etc.

Also it is an era of two-way communication between the passengers’/business houses and authorities leveraging mobility solutions as a constant feedback on improving the above continuously.

Some areas technology is being leveraged today is addition of intelligence with sensors for cold storage, capacity utilization and mobile-based condition monitoring, improving quality of the systems that trigger warnings, alarms and alerts generated after an event, incident or action by advanced measuring and modeling methods to eliminate the need for maintenance intervention.

  • Innovation and Growth

With Digital levers opening up doors to innovation and many impossibilities like real-time planning, open traffic data, social customer service, should ring in few disruptive trends around. User experience will increase manifold if the Digital fleet planning includes disruptive innovative features for better customer satisfaction and experience.  The solutions would need changes in the approach to planning based on user priorities, data flows, and dynamic response to disruption.

Another factor to fleet planning is the increasing need for an integrated and intelligent network leading to sense demand, measure performance, and monitor physical asset health.  Intelligent systems to respond in real-time to manage capacity and predict /avoid disruption.

Automation and safety will be another area of prime focus where exponential potential of the cognitive techniques will be relied on to save millions of lives, assets, pressing insurance industry as well to innovate.

Collaborative platforms for public and private will be created to meet the mobility challenges while fleet planning exercise would need to take innovative strides to take advantage of creating “low costs to scale” with high levels of participation globally.

The disruptive need to get the Innovation into the DNA of the Fleet Planning is seen as with the advent of autonomous vehicles and improved freight management. Legacy infrastructure is gradually being replaced by train management systems in which trains become interconnected communication hubs, transmitting data among themselves and to network control centers, and receiving instructions from control centers. M2M communication, with a dash from cloud, enabling operators to utilize equipment, tracks and stations more efficiently, while dramatically reducing safety risks. The IoT can further improve the system’s level of automation and its integration with the signaling system.

But all this needs meticulous planning. The yearly railway budget, coupled with a plan laced with the deadly combination of mobile-cloud-analytics as technological levers can bring in drastic improvements in fleet planning.

  • Safety

Though mentioned last, the railway safety is not merely an area of utmost criticality, but also poses challenges to the R&D to continuously create safety access mechanisms deterring crimes in railways infrastructure. As we incline more towards better digital equipped railways, an equal emphasis becomes mandatory for tighter data security and more on physical safety of the railway assets. While planning for the fleet through the year, a Q-o-Q Security is recommended to ensure all aspects of safety are addressed.

Today globally, we have 48,000 locomotives, more than 1.4 million rail cars and enough rail to circle the earth more than 13 times, railroads are relied on heavily by civilizations that be. To keep the vast 140,000 freight rail network and the equipment moving across it SAFE is definitely not an easy task for all the railway boards. The rail safety standards across the globe is getting addressed through the ISO/TS standards.

Managing high density areas challenges traffic regulation. T2T (Train to train communication) communication will allow drivers to understand the speed best adopted to ensure smooth flow of traffic. A real-time dashboard with surveillance video providing access information, where on board and ground staff are all on the same page for the traffic information, all powered by intelligent data gathering-dissemination systems, all alluding to predictive actions in the ecosystem providing safety as well as better functioning.

Digital features like sensors for diagnosing the condition of a motor, infrared sensors for counting the number of passengers, high quality on board internet connection, a suitable information system, onboard information kiosks, create rolling stock in order leveraging technologies, etc. Sometimes cost of such facilities on larger scale can be prohibitive and hence should be factored cautiously into fleet asset planning in a systematic phased manner.

However, the data points across rail locomotive logistics across the globe show that there is a drop of 14% on insurance costs, a drop of 15% on fuel costs, 21% drop in labor costs, 30% drop in operating costs and 21% Upward trend on safety and a similar 19% increase in profitability – all indicators to the fact that the railways across the globe are drastically improving. However, when all this can be factored into the fleet planning across railway authorities  it would be a perfect recipe for an outcome based fleet.

The KM role in “Staying Relevant” in the Digital Age

By Sairam Bollapragada & Bhuvaneswari Valluri


A recent Google Trends chart shows a spurt of interest in digital transformation from May 2015 to taking precedence over mission critical activity that has been the trend in the last couple of years. India tops the charts with a 100% interest followed by Australia at 75% and United Kingdom with 51% interest rate. Given this focus, and considering that technology once “belonged” and has/is become(ing) “open”, organizations worldwide are finding it difficult to deal with the abundance of information and knowledge assets being churned out.

While much of the above is freely available on the internet, knowledge workers and subject matter experts within the organization are largely unable to capture their expertise and experiences quickly enough to proliferate across – a natural transmission loss hence.

Businesses and customers alike are constantly demanding change and challenging the status quo.

The desire is to leverage emerging and disruptive technologies providing a competitive edge and building uniqueness into products & services, and ensure faster growth. The technology space is evolving faster and quicker than our imagination (refer:

Organizational speed and agility remains, key! Improved productivity, streamlined delivery, and higher levels of customer satisfaction are a few demands. In the process, organizations are generating that much more knowledge to present the world with alternate solutions to a multitude of problems and needs. But, organizations cannot afford to be data rich with poor insight. Availability of right information and knowledge at the right time continues to be the need of the hour. Organizational memory refresh needs to be that much quicker.

Knowledge acquisition and its conversion to explicit knowledge still remains a challenge. We need to be get more structured around how we want to manage information. The new smart knowledge management system (SKMS) is supposedly a hybrid knowledge-based decision support system that takes information and sends it through four macro-processes: diagnosis(base or integration layer), prognosis(analysis layer), solution(solutioning layer), and knowledge(finds solutions to issues and presents alternatives based on past experiences), in order to build the Decisional DNA of an organization. The SKMS implements a model for transforming information into knowledge by using sets of experience knowledge structures by leveraging Communities of Practice.

Heavy focus on Centralized KM repositories is essential and must be kept current with an inflow of latest information while ensuring redundant and outdated information is weaned out regularly and with shorter lifecycles. KM processes for the capture, storage, sharing and archival of knowledge assets have to be that much more efficient, quick and effective. Organizations that have invested in KM practices are making headway by focusing on smarter knowledge management frameworks and adopting tools and mechanisms, SEO, improved usability, tagging content to ensure relevant and faster search results, mobile interfaces to ensure availability of knowledge while on the move, etc. are trending this year.

Employee  learning and unlearning curve becomes that much shorter and challenges managers to keep pace, stay relevant, make decisions based on critical factors that can also include — availability of training by experts (external and internal), and individual employee attention and memory span.

Microsoft’s Satya Nadella says, “We are moving from a world where computing power was scarce to a place where it now is almost limitless, and where the true scarce commodity is increasingly human attention”. Interestingly, Microsoft recently conducted a study on “what impact technology and today’s digital lives are having on attention spans.” Not very heartening to see that while the average human attention span was around 12 seconds in 2000 it has dwindled to 8 seconds in 2013 and this apparently is less than that of a goldfish’s attention span! Alarming in a way, considering that customer expectations are volatile and employees need to ensure they efficiently deliver services and products well ahead of time, keeping in mind competitive pricing and high quality.

Businesses worldwide are figuring out ways of ensuing a higher frequency of knowledge asset updation. Current research from HBR suggests that machine learning and computational linguistics are making a difference to organizations worldwide. Interesting examples of how an organization has used natural language processing to perform and learn time intensive data entry and documentation tasks; use computer vision to scan and analyse images; perform predictive maintenance etc. have been shared. This is good for organizations that have made conscious investment choices to stay current. But, is the writing on the wall clear enough for those who are still dealing with such issues?

Simpler ways to address this need have to be adopted. Exchange of tacit knowledge through communities, discussion boards, wikis and micro blogging must increase. Digital transformation Project and Delivery stories need to be shared by making this the KRA of each project manager.  Cross pollination of expertise knowledge via webinar, podcast and other modes needs to be mandated. Usability is the essence here and information architecture is prime. Organizations must invest on periodically revamping their taxonomies and metadata structures to ensure employees are equipped with right information at the right time to make them that much more capable.  Incentivization in non-monetary forms must be encouraged as this may address the WIFM (what’s in it for me!) for the employees. Periodic promotion of existing knowledge to increase KA usage should also be considered.

However, all this is not possible without proper governance. Following can help:

  • Knowledge assets’ (KA) review mechanism must be established through  domain knowledge experts teams
  • Customer confidentiality and non-disclosure agreements must be made more stringent…
  • Knowledge assets’ usage reports have to be automated.
  • Managers and decision makers must be able to access these reports and dashboards as required.
  • KA retention period and archival mechanisms must be established through a structured KM Strategy Plan.
  • Measures to ensure knowledge is constantly being made shareable should be mandated.
  • Demarcation on what is mandatory and bolt-on for teams should be established (how about a team knowledge strategy?)

In essence, what is required is a coherent and concerted effort by organizations to ensure they have the wherewithal in terms of the right set of knowledge assets enabled by effective KM processes that allows their employees to maintain high knowledge levels while challenging them consistently with improving and sharpening  learning curves and hopefully better than the goldfish’s attention span!