Retail businesses face a multitude of challenges, including increasing customer satisfaction, reducing carbon footprint and balancing business objectives. A primary focus for most retailers is improving the in-store customer experience, which can be achieved by increasing employee presence or investing in the latest technology. Energy-savings is another key objective that can be achieved through retrofitting of lighting and heating, ventilating, and air conditioning (HVAC) equipment, which can be capital intensive projects. IoT-enabled analytic solutions, however, can offer an innovative and cost-effective approach that allows retailers to manage their facilities digitally and address the dual challenges of cost efficiency and customer satisfaction.
Conventional wisdom suggests retailers should collect a large volume of IoT data and use it to draw insights on energy consumption and equipment operations for operational savings. While data collection is a must, its analysis to draw insights is even more critical. Over a period of time, data analysis can help retailers figure out the not-so-obvious factors that impact energy efficiency and customer experience, for example, issues such as insulation problems with the building envelope and heat gain through the skylights. IoT solutions can assist retailers to increase operational efficiency, occupant comfort and safety.
The next level of intelligence and value arrives from combining process data from external systems with IoT data. For example, the performance data of equipment coupled with maintenance history is essential for achieving energy efficiencies. This data, together with the data from technician visits and costs of parts or types of parts used, can offer insights into improving maintenance operations and reducing costs. This leads to less equipment downtime and, therefore, improved occupant comfort.
Examining data from multiple systems is poised to play a pivotal role in enhancing the customer experience. Retailers collect and analyze immense volumes of information via web channels and devices and sensors at the store level to obtain more actionable data that can be used to ensure better customer experience. The data from in-store devices, video and wearable technology has the potential to improve sales effectiveness and improve workforce productivity.
We are seeing immediate benefits with equipment maintenance. By combining business and IoT data, retailers are enabled to plan maintenance activities proactively. We analyze equipment performance, failure and maintenance data, including preventive and reactive maintenance visit dates as well as the type of repairs and parts replaced, to help create the models that identify equipment health and performance deterioration to trigger condition-based maintenance. Remotely adjusting the operation of unhealthy equipment, collating and carrying out proactive dispatches, and monitoring every dispatch closely leads to a reduction in store visits. This approach significantly increases the likelihood a store will perform better for a longer duration, with a reduction in cost. For one retailer with over 1,600 stores, the approach resulted in a 29% reduction in store maintenance visits during 2019 when compared to the 2015 baseline. Further, the average time period between visits for maintenance repairs increased from the 2015 baseline of 45.2 days to 57.9 days in 2019 - a 28% improvement!
Another good example of successfully combining business and IoT data is in the area of retrofitting. Traditionally, roof top unit (RTU) retrofit decisions are based on business data such as RTU age, maintenance data, region of operations, available RTU technology, etc. However, when this data is combined with IoT data from the RTUs such as individual RTU performance, the number of run hours, efficiency and demand, it helps a retailer make informed decisions based on the performance, efficiency and capacity requirements of a store. The retailer is now able to prioritize capital expenditure and phase out retrofit planning based on current facility requirements and future business decisions such as expansion, expected change in footfall, etc.
When combined with business data, IoT data is a game-changer and can drastically transform the retail industry. Artificial Intelligence and Machine Learning tools can use the data to mine further insights on what’s driving the energy consumption pattern, establish efficiency blueprints and support maintenance efforts. The deeper insights obtained will help retailers achieve larger business goals. They will be able to deliver quality services, improve inventory control and reduce operational expenditures. Retailers then only need to focus on using the insights from the analysis to take actions in a timely manner to achieve their goals.
MAR - 15th