Saviant

Many industry leaders that offer Smart Energy Management products & solutions face challenges including:How to build a scalable platform that can automatically scale-up to on-board ‘n’ number of Smart home devicesData security, solution availability, and reliability are the other critical factors to deal withHow to create a robust common IoT platform that handles any kind of smart devicesHow to enable data management capabilities that would help in intelligent decision-making

The big problem that Utilities face includes:(i) How to make meters “smart” and ingest the data from homes to the companies and;(ii) how to derive intelligent actions from huge amounts of meter data, in an efficient and cost-effective way.Solving these problems would help Utilities track & monitor water consumption data, identify leakage, theft & various anomalies. This demands the need of a scalable system with remote asset management & proactive maintenance capabilities. This would also help end-consumers understand their water usage for better decision-making to conserve water.

Many instrumentation leaders face challenges including how to:Track & monitor their instrument data such as Temperature, Humidity, Dew point or PressureAchieve real-time visibility of data to take necessary measures/actions.Enable Remote Asset management capabilities to keep a watch on the end-devices at pre-scheduled intervalsIntegrate notification engine to alert the end-customers in case of asset failures or abnormal conditions

Food service delivery companies generally manage large volumes of food or non-food products, with very low profit margins. And, one of their major challenges involves optimizing operational efficiency during food delivery, for accelerated business growth and competitive advantage.However, to address this challenge, they had no means to know what exactly happens during the delivery hours. Was the food inventory ready to deliver is kept under optimum temperature conditions? Were the drivers taking the right routes? Was the customer order being delivered on time? Their business required visibility of its supply chain operations in real-time, so they could track & monitor what’s happening every minute. This would help optimize operations and save money.

Sensors capture physical data from equipment at a high frequency which is then managed and analysed locally on desktop based systems. Their end users then process the data locally on their desktops, and manually create visibility into equipment conditions & their failure modes. Using the existing solution resulted in Manual, inaccurate and time-consuming fault detection. They decided to automate data collection, enable cloud-based data processing as well as AI-based fault detection while making it more data driven instead of age-old rules based. There were a few challenges to this – Traditional predictive tools are hard to scale and deploy Need predictive analytics to be embedded within their application Data preparation, cleansing, choosing the right algorithm, training it and validating needs expertise with modern data platforms The platform and application need to easily integrate with all hardware products This required a well thought data engineering approach to build robust Industrial IoT analytics solution, for capturing, managing and analyzing real-time data.

The client had multiple data acquisition platforms for their different products, leading to a disconnected sales process and lack of standard functionality.