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Automating Order Modifications Across Multiple Platforms

SphereGen Case Study
For Large Grocery Distributor

Learn how SphereGen was able to automate an order modification process which integrates data across multiple platforms. This automation saves over 10 hours per day of processing time while also providing automated volume scaling and time for increased focus on order exceptions.

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Automating
Order Modifications
Across Multiple Platforms

SphereGen Case Study
For Large Grocery Distributor

Learn how SphereGen was able to automate an order modification process which integrates data across multiple platforms. This automation saves over 10 hours per day of processing time while also providing automated volume scaling and time for increased focus on order exceptions.

uipath silver partner badge

O V E R V I E W

Order modifications for a large grocery distributor requires that data flows through multiple systems. Cascading the changes from the client stores to the main order processing system required a large manual effort within a tight timeframe. The process was automated to save time and manpower, increasing the number of order modifications processed within a given time period. Error logging and reporting now identify process points which are failing due to insufficient data flowing through the system. Overall, the automation changes have led to a reduction in order errors, thereby increasing customer satisfaction between the distributor and their client stores.

Challenges

When evaluating the solution requirements for the client, several challenges emerged related to existing processes:

  • Order modifications are entered through Salesforce, but the data was not integrated to the main Order Processing system on a Mainframe, requiring human overview, manual corrections and data entry
  • Client entered modifications sometimes contain bad data, which results in errors that can halt processing and affect the validity of the order
  • Time to respond to and apply multiple order modifications can sometimes be as little as 3 hours. When volume is high, staff is stressed to complete the work
  • Due to the amount of human involvement in the process, the only scaling available during high volume cycles was to add more people to the process

SOLUTION

  • Order modifications from Salesforce are checked for data accuracy. If the data is not clean, the order is routed to an exception report and sent to a specific error handler queue based on the order type. This means there is no disruption in the overall process flow even if data is erroneous
  • Order modifications that are clean are transferred to the main Order Processing system on the Mainframe so that the order can be updated
  • If the total volume of order modifications increases, extra bots are automatically spun up to handle the order overflow until the order level falls back down to a standard pace
  • Audit reporting is in place to report the number of exception orders, why the order was in error and how many order modifications were processed overall

RESULTS

Order Modification Processing time reduced from 15 to 5 hours/day

0 %

Complete Automation of order volume scaling

0 %

No. of Order Exceptions resolved per day doubled

0 X

THE DETAILS​

Identifying and Processing Order Exceptions

SphereGen worked closely with Client SMEs to determine all types of possible errors which contributed to an order exception. The order processing path was then based on which errors could be fixed and processed automatically, and which orders still required a manual effort to be corrected.

Clean orders, or orders which can be auto corrected, are processed and sent to the Mainframe to update the final order. The data in Salesforce is subsequently updated to register that the modification has been applied successfully.

All other order modifications fall into an exception category. Exception orders are classified by order type. The type is used to route the order to the appropriate exception queue which is reviewed by an SME. The exception report allows the SME to quickly determine the reason for error, which can be corrected and then manually updated to the Mainframe and Salesforce.

Data Integration

The existing legacy Order system residing on a Mainframe is a stable environment for maintaining orders. The Salesforce application is a dynamic client interface which allows grocery clients to enter order modifications as needed. Unfortunately, there was no integration for updating Mainframe orders with Salesforce modifications. All order modifications were handled manually. SphereGen designed the automation to use Dispatcher and Performer bots to successfully transfer data from Salesforce to the Mainframe. As a result, order modifications which contain clean data can be processed in less than a minute. Manual order modifications could take 3 or more minutes for processing. When processing roughly 300 cases per day, this reduced order modification processing time by 10 hours per day.

Scalability

When the modification process was completely manual, the only way to scale was to add more people to the task. This required training and scheduling of manpower with no precise ability to estimate exactly when the scaling would be required.

With the order modification automation, bots continually check Salesforce for modification entries. If the number of entries exceeds the target volume limit, bots are automatically added to the process so that modification entries can be acted upon quickly. The extra bots continue to process entries until the number falls below the target limit. Then the extra bots discontinue and regular level processing resumes.

This approach ensures immediate scalability on a “just in time” basis and requires no scheduling or management input.

Order Exception Analysis

Part of the automation process involves a high level of error handling and logging. Error logging can be used to produce exception reports targeting exactly where the error happened and why it happened. This type of reporting not only helps SMEs to a quicker resolution of an exception, it also allows the business to better study the source of the errors. This analysis is being used to determine how to fix the data problem at the source, so data can flow more cleanly through the system increasing the volume of savings already being realized.

CONCLUSION

The order modification automation has proven to be hugely successful in saving time and money for the grocery distributor.

Time Savings

  • The majority of order modifications flow through the system automatically instead of requiring manual attention
  • Less time is required to handle each exception because the error is routed to the appropriate SME and that SME is equipped with a report identifying the source of the error, resulting in quicker resolution

Cost Savings

  • More modifications can be processed successfully within the application time frame which means more orders ship correctly
  • Order volume fluctuations are handled automatically as they occur, therefore there is no downtime in order processing

Happier Customers and Employees

  • Order modifications are processed on time, meaning customers get expected results when they need them
  • Employees can now focus on exception orders instead of being overwhelmed with processing all orders and dealing with volume fluctuations, allowing them more time with each order

TECHNOLOGY USED

  • UiPath RPA Studio and Orchestrator
  • SQL
  • Visual Basic – VB.net

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SMALL + MEDIUM
SIZED BUSINESSES

As a privately-held small business, we understand the goals, needs, and challenges of organizations your size. SphereGen leverages 10+ years of experience to find the ‘best technology fit’ for clients whether you build, buy or customize software and services.