How do you buy groceries today? If you are like me, you perhaps prepare a shopping list – things like fresh burrata, pasta, fruits, chocolates, cereals and milk – then go out and buy it. However, the shopping happens at different stores or even online, as much depends on brand, price, quality, availability, discounts and promotional offers. But can this task of everyday grocery shopping be simplified further? Of course, technology can make things better: You just ‘ask’ the mobile app for it and within a few seconds, the app returns a list of expected shopping items. Behind the scene, the app looked into my purchase history, mapped it against my brand preferences, chose from the best available offer for each and added the quantity I normally buy after taking into account my current stock in the refrigerator. Impressive, isn’t it? This is possible because a number of technologies and platforms, ranging from Artificial Intelligence (AI), Blockchain, Analytics and the Internet of Things (IoT), worked swiftly to bring together distributed information, dug out historic and real time data, processed it and finally presented it appropriately.
A scenario like this need not be restricted to the world of consumers. It will soon find its way into the enterprise. In fact, a growing number of CIO conversations are already shifting away from optimizing (reducing labor) to augmenting skills to scale up worker efficiency.
This demand has resulted in the need to stitch different technologies, systems and data sources together. Valuable as it is, no enterprise can afford to depend only on an ERP system to manage these complex and diverse requirements.
Moving from reactive to predictive
What modern real-time businesses actually need is the ability to mix internal and external data that result in an exponential improvement in intelligent, holistic decision-making, and the critical ability to move from reactive to predictive modes of operation.
A typical example of this would be the field operations required by an oil and gas (O&G) organization. Their pipelines snake across hundreds of kilometers, often through remote terrain where a fault could take days to identify, isolate and fix, resulting in unnecessary downtime and production loss. Instead, using pressure sensors, temperature gauges, cameras, weather forecasts, etc., that provide data in real time, the O&G company can predict expected failure even before something happens (for a real-life implementation of a similar system by Wipro, click here.) The system can automatically send out a drone to the exact location of the expected failure, examine the state of the asset, send back images, use computer vision and analytics to identify the part number of the component or asset that has to be fixed/ replaced, look it up in the inventory, order the part and make it available to the field engineer ahead of the inspection. Furthermore, it may so happen that the repairs need the participation of multiple parties-such as the OEM, the spares manufacturer, a logistics partner, etc. Blockchain ensures that the needed inventory/spare part detail is exposed to everyone in the maintenance and supply chain. It also determines the right vendors based on competitive quotes, and executes automated payments based on smart contracts.
The advantages of such a system are easy to see. The field engineer does not have to wait for the failure, then visit the location, identify the component that requires replacement, come back to place an order and return to the site at a later date. Instead, combining external information with internal ERP data, the system automatically takes a number of decisions. It can even, potentially, estimate the assigned engineer’s capability and ensure that Augmented Reality (AR) was made available to cover the gaps in the engineer’s knowledge and precisely guide the engineer through the repair process. All this contributes to an exponential increase in worker productivity and shorter process completion time.
The platform is the key
Admittedly, this has become far more complicated than just me going shopping, for some cheese and chocolates, that we began with. To bring together these complex technologies like Analytics, IoT, Machine Learning, Blockchain, Big Data, Data Intelligence, etc., businesses need a platform like the cloud-based SAP Leonardo.
A platform like Leonardo can bring diverse technologies together, scale them in-or-out at the touch of a fingertip and yet make them work in concert. It can be used across industries and line-of-businesses. At the core of the platform is its native connectivity to the backend enterprise system, ensuring every insight results in meaningful action.
Wipro is in a sweet spot in relation to SAP Leonardo which was launched at Sapphire 2017. It is perfectly aligned with the existing technology themes – analytics, IoT, Machine Learning, Blockchain and Data Intelligence – being pursued at Wipro. In addition, a strong Digital practice ensures a robust foothold in Design Thinking framework, which lays the foundation for such engagements. Currently, Wipro is in a unique power position owing to its ready assets and end-to-end services that come along with a strong partnership with SAP.
Continuous innovation is no longer a choice, it’s a constant. Innovations are not just driving new business models but finding ways to augment the efficiency of the new-age worker!!