Robotic Process Automation 2.0​

Case studies and Automation approaches
from​
iCuboid Private Limited​

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RPA Strategy

PROCESS OPTIMIZATION​

Basic and fundamental problems are identified within the process​

DEPLOY ML & AI CAPABLE BOTS​

Bots, with AI and ML power, are deployed for unattended RPA

SETTING TUNING DATA PIPELINE​

Tuning the data pipeline is important in optimizing the output

POWERFUL RULE ENGINES​​

Rules are guidelines for bots to process the data stream. The powerful rule engines are built for RPA

KEY TOOLS & ​INTEGRATION​

Tools such as RORO, AutomationAnywhere, along with custom API based soft bots

CASE STUDY 1

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CASE STUDY ONE


63 % MORE ORDERS REALIZED​

Woodland Power Products - designs, manufactures and markets, engine-powered outdoor equipment, sold factory-direct to homeowners and contractors throughout North America.​


Woodland Power Products​

Problem Statement

  • Orders are received via multiple channels such as phone calls, emails, web orders etc. Maintaining the customer details, order details, supplier details in the Salesforce has become a huge manual effort.
  • The tracking of order till shipment was done by a team of 8 people and was prone to human errors.
  • The sales support team had tough time in answering the customer calls, due to increased number of calls and un-indexed knowledge base.
  • Accumulated customer queries on various products during company holidays and non-working hours were kept unattended.

How did we solve?

  • 01 Process Optimization

    The overall ordering process was analyzed and knocked of redundant stages. Optimized the workflow, with a minimal learning curve for the support team.​

  • AI bots are deployed for taking orders, and extracting the information from the row data came from multiple channels. Also, query bots are deployed to reply to customer queries automatically.​

  • The customized RORO tool is used to update the salesforce database with customer and order details, with extreme accuracy and integrity.​

  • The knowledge base is indexed and categorized, and built as a quick search engine. The database was made self upgrading and auto indexable.​

  • Rule engine with pre trained data sets are used to predict and alert any delays that could be occurring, based on the data accumulated in all the stages of RPA.​




CASE STUDY 2

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CASE STUDY TWO


30 % PRODUCTIVITY IMPROVED​

Cogent automation is a small-medium segment, hardware automation designing company located in Coimbatore, India. ​


Cogent Automation Pvt. Ltd.​​

Problem Statement

  • Raw material management and refilling was having issues and delay, as many steps were manually done by shop floor managers. Updating in SAP was error prone and unreliable.
  • The shop floor wastages, rejections and breakdowns were unaccounted and was making the decision making on order delivery unpredictable.
  • The service requests from the customers were received via phone, email and web based ticket system. There was no ticket management system and many tickets were left unattended for many days.
  • The parts/order completion was updated manually in a ledger book and later fed to the SAP and other software tools. The live/recent data was not available in software.

How did we solve?

  • 01 Process Optimization

    The parts manufacturing process was made parallel in nature rather than the sequential operating mode with zero process change for the shop floor operator.​

  • Intelligent chat bots are added in the ticketing workflow and integrated with a ticket management software. Streamlined the ticket addressing system by auto assigning to engineers based on ticket content.​

  • The self learning and AI based industrial automation platform- sense wire was employed to collect the shop floor data and generate live status on shop floor displays, measure OEE, generate daily/hourly reports on production status, etc. ​

  • Stream analytics rule engine is running to analyze the shop floor data received via various channels by aggregating them and generating predictions on breakdowns, providing alert on issues, etc..​