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August 18, 2017

The Intelligence in Using Renewable Energy

Posted by Ashiss K Dash (View Profile | View All Posts) at 5:40 AM

The Intelligence in Using Renewable Energy

While it is hard to ascertain by when, it is widely acknowledged that fossil fuels are not the future sources to fulfill our energy needs. Renewable energy is emerging as the most reliable alternative for our future, however, despite significant progress in pockets, there are quite a few daunting challenges. Among the leading sources of renewables, solar and wind are dependent on the weather, are unpredictable, and we still need to smoothen the flow of energy from generation to consumption. The advancement in storage technology is promising yet far from where it needs to be. From time to time, generation of renewable energy will fall short of demand and hence it cannot be the reliable source of base load for consumption. The base load continues to shift to fossil fuel-based generation which defeats the purpose to move to renewables in the first place.

One solution is to use demand-side flexibility cleverly, cutting demand for renewable energy when supply is low and bumping it up in times of plenty. This is possible by advanced load control at an equipment and appliance level, such as large air-conditioning units or industrial furnaces to switch off when power generation is low and consume more energy when there is excess supply. Additionally, the owners of this equipment could also be contracted to make their stored energy and battery packs available to the grid when required.

While the concept is sound, there are a few problems when it comes to implementation. Before the grid can decide whom to tie up with and what tariff to pay, it must know the number of devices in play and the extent to which they will participate. It also needs to safeguard the energy consumption data it will collect from those devices from being misused and misinterpreted.

My view, deployment of Artificial Intelligence (AI) and machine learning technologies can resolve most of these issues. By applying machine learning to the data generated by advanced sensors, smart meters and intelligent devices beyond-the-meters, grid operators can estimate how individual appliances behave. They can also use algorithms to predict the storage life and accordingly determine the payouts to be made.

In the case of large Commercial and Industrial consumers like supermarkets, office buildings, factories, railways, grid operators can use AI to analyze relevant operational data from all the relevant equipment, such as solar panels and cooling systems, to make informed real-time decisions to maximize demand flexibility. Germany, for instance, is using a machine learning-based early warning system that takes real-time data from wind turbines and solar panels around the country to predict the energy that will be generated over the next two days.

Other ways in which AI can facilitate demand flexibility is by employing game theory algorithms to devise incentives to improve overall participation, and leveraging blockchain or other distributed ledger technologies to protect data. It is possible to create a market place for consumers to participate in demand side management initiative in their local market.

While managing intermittency of renewable energy is the biggest goal, AI can also help the industry improve safety, reliability and efficiency. It can also provide visibility into energy leakage, consumption patterns and equipment health. For instance, predictive analytics can take sensor data from a wind turbine to monitor wear and tear, and predict with a high degree of accuracy when it would need maintenance.

Artificial Intelligence technologies can also help renewable energy suppliers launch new service models and expand the market place for higher participation. By applying AI to data pertaining to the energy collected, the industry can gather granular consumption insights that it can use to introduce new service, the industry can also locate upstream/ downstream products operating in dynamic pricing models. This will also create an opportunity for retail suppliers to tap into the consumer market.

And perhaps best of all, AI can also facilitate the development of strategy, policy, and planning, around current use and future demands. I can't help but think, that can only mean one thing - more power to human potential!

August 9, 2017

Lightning Speed E-commerce

Posted by Arish Ali (View Profile | View All Posts) at 12:25 PM

Lightning Speed E-commerce

Think about the last time you needed to access a service. Perhaps it was to pay a utility bill, pick an internet and phone package, choose an insurance plan, or place an order with your favorite retailer. It's very likely that on each of these occasions you simply reached for that do-it-in-a-minute app on your smartphone. When your employees, partners and customers need to access services or assets within the enterprise ecosystem, would you be surprised if they too instinctively reached for their smartphones? Like you, they too are accustomed to the convenience, speed, addictive ease, and ubiquity of mobile. And that's not all they're counting on.

As customers switch channels, they expect to receive the same quality of user experience they've always associated with your brand. Anything short of that frustrates them - even leading them to bail on a transaction or intended purchase. That's why, beyond the mobile-first approach, it's important to make sure that the experience your brand delivers is uniquely compelling and yet unfailingly consistent throughout a multi-event, multi-channel journey. And why not, if that's what it takes to keep your customers coming back for more, and the competition at bay? The challenge then is around executing a strategy that delivers on this vision.

It begins with adopting an approach of continuous innovation that supports rapid and flexible response to frequent change and feedback from customers and users. Embracing agile application development methods, promoting adaptive planning, making evolutionary advances, rapid prototyping, and continuous improvement are all integral to the game plan. Things that can happen only when enterprises get past traditional monolithic deployments. Costly, time consuming and inflexible to customize as they are, monolithic deployments hinder innovation. And worse, they lead businesses to overinvest in superfluous digital capabilities while underinvesting in features that can potentially differentiate the user experience.

A mobile-first, cloud-native, microservice-based modular e-commerce solution is the essential alternative.

Microservices allow you to pick and choose the exact capabilities you need, and then to deploy these independently. With microservices, you can replace one with another, easily customize them, and even plug in a third party service through a simple microservices adapter layer. Because changes are made to individual capabilities, the scope of regression testing is minimal with fewer resources needed to execute it. With a full set of out-of-the-box e-commerce microservices at your disposal, you can build the ideal digital platform for all your unique needs. Each microservice can scale independently and the whole platform can scale based on traffic and demand. This ensures your sites and apps always stay up and running even at peak Black Friday load. On the other hand, the computing capacity powers down during low traffic periods to save costs. What's even better is that microservices can be easily integrated into an existing solution - monolithic or loosely coupled. So for instance, if you are using a monolithic e-commerce platform, you do not have to completely re-platform to fold in a new capability for 'rating and review'. This microservice can be quickly integrated into your existing solution stack, and deployed just as quickly.

Today, you have the flexibility to deploy the microservice-based modular e-commerce solution to serve as a full, end-to-end e-commerce platform, augment your existing commerce stack or simply be your licensed source code in whole or part. Apart from the ease with which this fits into your tech landscape, it offers great commercial advantage. At a time when advantages are scarce and competition is abundant, your enterprise is looking for the flexibility to do more with less. It can no longer afford the luxury of excess, be it in consumption, investment or capacity creation. A cloud-native microservices based e-commerce platform enables you to build and consume only what your enterprise needs, when it needs it. And also stay ahead of your customers and competition.

August 4, 2017

Automate, Innovate, Learn: Way Ahead for IT Services Providers and Buyers

Posted by Ravi Kumar S. (View Profile | View All Posts) at 6:35 AM

Automate, Innovate, Learn: Way Ahead for IT Services Providers and Buyers

Digital technologies continue to disrupt every business as we know it. In response, IT service providers must first help clients navigate the disruption to future-proof their business, innovate, and grow. Then, they must do the same for themselves. From a conventional services and applications provider to a problem-finder, strategic advisor and innovator of next-generation services, the role of the IT service provider has irrevocably changed.

Technologies for smart automation and Artificial Intelligence (AI) are fuelling this change in the way IT services are delivered. The implementation of these technologies is resulting in pervasive automation, moving the industry away from the conventional 'people only' IT services delivery model to a much more powerful 'people plus software' model to deliver these essential services.

The foundation of the 'people plus software' model is premised on the principle that if a task (as part of a service) needs to be done more than once, in the same manner as it was first done, and if the need for the task to be performed can be predicted, it can be easily automated by software. So, the 'people plus software' model solves the same problems that traditional IT services do, but with much greater efficiency and productivity. In fact, several traditional services lend themselves readily to the application of automation, robotics, and AI. For example, Business Process Outsourcing (BPO), application verification and infrastructure services comprise functions that involve several well-defined predictable, repeatable tasks and therefore can be software-driven with relative ease. Automation in IT services, however, can be significantly more intelligent and disruptive than just mechanizing simple routine tasks. Consider the case of software-led automation that goes well beyond voice-based simple first line service support, to provide last line support in IT operations - that often entails specialised tasks such as fixing software code, automating test-frameworks, curating knowledge and even intelligently automating process management.

But the point of this automation, lies well beyond mere automation itself. It creates productivity gains - both for the service provider as well as the enterprise contracting these services - that can then be channelled to power innovation. As automated systems and software increasingly solve well-defined problems for IT service providers, it creates for people the time and bandwidth to devote to more creative, value-generating activities that no machine can deliver. Be that finding problems that don't yet have solutions or discovering breakthrough innovations. However, for the technology worker with a skill set honed solely to work with the traditional IT services model, transitioning to roles requiring fundamental innovation and problem finding will take some doing. That's why, IT companies have a responsibility to help their employees re-train and right-skill so they can continue to be relevant in these times of deep and disruptive change.

To help employee's upskill, design thinking can play an essential role in unshackling practical creativity and inspiring valuable innovations for customers. The design thinking approach is a great way to teach people, at scale, how to problem-find using repeatable and practical methods as well as explore new avenues of innovation. It means ideas will no longer be rare events that happen within innovation departments and labs, but an integral part of business as usual. With the most junior engineers working alongside senior leaders to imagine and implement new ideas and concepts, breakthrough innovation might happen intermittently, but bite-sized, on-the-job, grassroots innovations-at-scale will be an almost every day occurrence. For example, when we trained our employees in design thinking, ideas began to go viral. One idea about how best to optimise railway maintenance was then repurposed in a post office, in a retail setting, and in a distribution chain: a single great thought transformed multiple industries. These ideas impressed clients so much that they too are now investigating how design thinking can transform their organisation.

Whilst we are seeing good outcomes, finding enough people trained in all the new technologies that are rapidly emerging, remains a challenge. It is no easy task for the workforce to keep their skills up-to-date with these emerging technologies. One way to address this is to ensure that people with related or adjoining skills are continuously upgrading their skills, and reskilling in closely related areas to meet changing demands. And most importantly, learning must continue throughout our lives, well beyond the traditional classroom, with enterprises and employers investing in and nurturing their talent pools.

To automate is to make space and bandwidth to innovate. And this duality of automation and learning can only be sustainably pursued in a culture of lifelong education and learning, where finding and framing problems is as valued as thinking critically and building creative solutions on the job. This is the future of the business of next-generation IT services. Driven by people ready to adapt and learn with the next wave of disruptive technologies and the new roles that come with it, not once, but constantly throughout their careers.

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