Restaurant Group Uses RPA
for Inventory Auditing and Sales Discounts

The Client

Our client is a company that owns, operates, and franchises two well-known restaurant brands. The brands specialize in the operation of fast-casual/quick-service restaurants that offer distinct and unique flavors with broad appeal at a compelling value. The brands specialize in fresh-made cooking, drive-thru service, and catering.

INDUSTRY: Food Service

Primary Issues and Objectives

Like many restaurant organizations, our client relied on a physical inventory count method to regularly manage large amounts of stock as well as daily sales discount information for their respective inventory. This resulted in multiple organizational pain points:

  • Intensive manual labor involved a weekly reconciliation that required 1 dedicated resource per brand. This resulted in at least 3-hours spent reconciling the quantities for each store in their reporting platforms

  • Impossibility to validate all critical and high-price tag items per store due to the high volume of records generated

  • Lack of effectiveness in communicating quantities to each store on time, before the upcoming weekly inventory cycle count needed to be completed

  • Accurate information about applicable sales discounts for the day on their variety of products

Smartbridge has extensive experience in food service automation

The business wanted to reduce the time and manual labor spent on the weekly inventory reconciliation process as well as automate daily sales discounts. They required a solution that would:

  • Verify the inventory count for each store (from the prior week to the current week) automatically

  • Reconcile discrepancies between inventory quantities and absolute cost deviation from an indicated metric value

  • Send an email outlining the details of items that needed to be recounted

  • Push daily emails to the managers with the discount information for specific inventory

Key Challenges

The volume of records and logic behind the inventory metrics needed to classify and filter data presented the biggest challenge to Smartbridge. However, there were various other milestones to overcome in implementing a solution:

  • Business logic definition from the client side – they needed a proper way to classify and segment data that needed to be validated during each inventory cycle

  • Statistics and metrics definition

  • The volume of data being processed and alternatives to manage this inside of the automation

Smartbridge Methodology

Smartbridge implemented an agile development approach with short sprints to present POC results in less than 3 days. Support from UiPath was also critical. Regularly reaching out to platform experts enhanced our deliverable skills.

An attended bot was required in the solution implemented. Through the interaction of key users, we input parameters to manipulate the execution according to the metrics needed, as well as cross check the data on each stage of the automation to ensure accuracy. Automatic email notifications ensured communication to each store that needed to recount inventory was done immediately after the validations were completed.

Here’s a look at the steps the bot takes to execute the weekly inventory audit process:

rpa inventory auditing

Here’s a look at the steps the bot takes to execute the daily sales discount review process:

rpa inventory audit

The technology stack required to accomplish this project included:

rpa uipath
rpa workday
rpa crunchtime


As expected, the POC was a success. The bot was able to read the input data from the inventory audit report and classify the records that needed validation using the metrics defined by the business (weekly cost, deviation of inventory, and standard cost deviation).

The data being filtered was easy to read and properly categorized by item and store. Once the planner completed or adjusted the output report, the bot was able to process that output, and send individual emails to each store detailing items in need or recount. The company received a time savings of 87% per process.


Time saved per process

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