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Supply chain case study



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The Product Movement and
Planning Problem:

An enterprise client didn’t have an Automatic Incident Planning system in place based on actual product movement throughout the downstream supply chain involving their existing procurement, production and warehouse distribution systems. This lack of functional integration was seriously deterring them from reaching higher business velocity.

Salient Features of the solution

Permissioned blockchain based distributed ledger for role based supply chain management

  • IoT integration for improved traceability
  • Low-barrier Web/App enabled entry for the participants in the supply chain
  • Smart contract based instant payment to the intermediaries
  • Tracking of conditions at the production facility
  • Tracking of food volume in the supply chain
  • Smart contract based tracking of conditions of food items in transit in the supply chain
  • Fraud and counterfeit detection
  • Establishing provenance of food items
  • Cloud hosted solution for reliability, scalability and availability

Ono-Components Used:

Track and Trace Manager, Message Bus, Predictive Analytics Engine. An enterprise client didn’t have an Automatic Incident Planning system in place based on actual product movement throughout the downstream supply chain involving their existing procurement, production and warehouse distribution systems. This lack of functional integration was seriously deterring them from reaching higher business velocity.

Onometra Impact:

Track and Trace Manager was employed in the customized solution delivered to RST for product movement information collection over the Message Bus. The acquired information was then used for incident planning with Predictive Analytics Engine. Onometra solution helped RST to have a better incident planning system.

System & Architecture:

A microservices based implementation of the business process management platform with Node.js framework along with integrated Web and mobile app based service delivery channels. Hybrid database architecture for heterogeneous data management. Use of deep learning technologies and conversational agents for business process optimization and automation. The solution was deployed and delivered from AWS platform with Docker support following DevOps practices.

Software technology stack

Server Side      
Client side           
Database
API