EroNkan Technologies: Enhancing Operational Efficiencies through IIoT

Manufacturing plants, quite often, are known to be less efficient in terms of the output produced against their theoretical capacities. “With reference to inefficiencies occurring at the production floor or manufacturing plant, operators and management do not have enough visibility of what is happening at the operations front,” says Vish Desai, Co-founder and CTO of EroNkan Technologies. Although the utilization of equipment and optimization of operations primarily relies on industrial processes or effectiveness of manual labor, automation prevails as one of the proven methodologies to obtain better output. EroNkan—a connoisseur of industrial automation—adopts the principles of Industrial Internet of things (IIoT) to streamline the production line by employing smart sensors to monitor, report, and obtain actionable data to derive greater operational efficiencies.

The company’s IIoT platform is based on a combination of hardware, software, and Amazon Web Services (AWS) based cloud systems and offers a wide array of functionalities and features such as asset management, real-time streaming and complex analysis, customizable UI with dashboards, enterprise integration, and real-time notifications and reporting. By aggregating data from machines, stations, ERP, and PLM systems, EroNkan’s IIoT platform computes idle time of equipment, time wasted by manual labor, task schedules, and operational parameters to provide real-time analytics that enhance the productivity of the industrial floor. “There is a lot of lag in the traditional production line, and due to the lesser visibility, it takes two to three days for the management to understand the cause for such a lag.

The idea behind EroNkan’s portfolio is to push actionable data in front of the management team, in near-real-time and cut-short any inefficiencies within the production line,” adds Desai.

The IIoT platform also offers the flexibility of on-premise deployment with a variety of operational requirements such as equipment monitoring, data aggregation, and real-time operational analysis. These offerings cater to the distinct requirements of customers using the Pure Play IIoT platform, along with a combination of customizable user interface and professional services addressing various plant specifications. As maintenance and quality control stand out to be some of the highly essential prerequisites, following the maintenance and quality procedures recommended by the manufacturers guarantees a 20-25 percent increase in yield. EroNkan’s real-time monitoring systems allow operators to keep a constant check on equipment and the processes followed while abiding by quality standards of industries.

Similar solutions allowed a client of EroNkan to attain a 20 percent increase in efficiency and a 10 percent increase in yield. Prior to its IIoT overhaul, the company’s client—a large food manufacturer in India—wasted a significant amount of their produce due to inefficient operations. The food manufacturer had to churn out 20 grams of potato wafers for each retail unit per packet. As most of the workers were not mindful of the periodic cleaning required for one of the machinery, packing and weighing calibrations were not accurate. This, in turn, led to the production of 22.7 grams of wafers instead of 20 grams, resulting in wastage of 2.7 grams per unit. These problems were noticed only after several days due to the lack of real-time visibility. By implementing EroNkan’s monitoring systems and IIoT platform, the food manufacturer realized an immediate increase in their production capacities with fewer losses.

“We have noticed that, at least in India, a lot of the industries do not even a have an ERP system or a PLM system. They are running a $100 million business on Excel sheets,” highlights Desai. EroNkan leverages such opportunities to develop inventory management, process management, and digitization of logbooks onto its IIoT platform in the near future to drive greater productivity and higher efficiencies.