Overview

A global furniture manufacturer with over a century of business and more than 800 dealer locations needed help developing a software product that would optimize room usage and create smart, connected spaces for their customers. They needed a partner that could provide experts in DevOps solutions in order to get this new product to market quickly to outpace their competitors. In addition, the client required IoT expertise to automatically extract and input sensor data in real time into a fully customized, user-friendly dashboard that could be used by both our client and their customers.
• Luxoft created a solution that pulls sensor data via Java and runs it through an analytics engine, displaying interactive visuals on an intuitive dashboard (developed using AngularJS and Node.js), which can be viewed anywhere – searching by timeframe, room location, room amenities and other attributes
• Incorporated a backend via Azure to collect data onto the cloud
• Created regular developer workflows with Scrum ideology by setting up a Scrum team and advising the client on how to operate
• Opened a DevOps pipeline to speed up software development and testing process
• Allows users to make data-driven decisions to optimize room use, such as what office furniture to buy and what conference rooms are necessary, cutting costs
• Created an agile development process, increasing team velocity from 8 points to 55 points per sprint, reducing build times from days to hours
• The application increases satisfaction of the client’s customers, helping facilities managers determine if they can buy more furniture and what types
• Application gives our client a niche spot in the market, being the first of its kind
• Eliminates slow, manual and anecdotal processes to find out if space use is optimized
• Keeps data accurate and easy to obtain by using an automated, unbiased approach, allowing users to act on the findings immediately
• Automatic data collection frees up workers for other tasks
• By storing on the cloud, the data collected from sensors is highly secure
Full case study (81 kB)