Reduce downtime

The company however was facing a severe problem with data collection in the manufacturing process and their manual method was giving rise to various inconsistencies.

A CII report prepared in support of the Department of Heavy Industry's Samarth Udyog programme. It surveys national smart manufacturing programmes from Germany, USA, France, China and Sweden, and proposes a five-step approach for India covering stakeholder mapping, definitions, sector prioritisation, an Industry 4.0 India Platform, and an enabling ecosystem.

The biggest challenge for this customer is timely distribution and accurate pumping of products 24 hours a day, seven days a week. Any disruption could increase refinery inventory and leave retail units without adequate supply.

The Coffee manufacturer had suffered huge production loss due to sudden stoppage or unplanned breakdowns. The end user had to maintain huge inventory of utility motors and manpower had to be kept on standby to cater to the sudden break down. Besides there was a pressure to raise the productivity owing to the rising market demand. Lab reports and other mission-critical details like temperature and pressure settings were all manual entries, indicating a strong need to digitize the same.

Due to continuous pressure of increasing production, reducing downtime had lately emerged as a big challenge for the customer.

Manufacturers have a wealth of operational data, but many find it challenging to use advanced analytics to turn their data into insights that improve operations. Symphony AzimaAI estimates that most industrial companies use only 5 percent of the data they collect. As a result, they’re more likely to suffer operational losses and downtime than if they took advantage of AI and machine learning solutions.