The food service industry is a business like any other: rife with manual, back-office tasks that should be handled smoothly to allow kitchens to be in full swing. In this post, we’ll take a look at two case studies where RPA was successfully applied in the Finance operations of the restaurants.
Daily reports
RPA in reporting can automate financial reports generation by extracting the financial data from various ERP applications, consolidating them, and saving them in target folders.
Case study:
Primanti Bros., a sandwich chain restaurant in Pittsburgh, needed 8 regional managers to spend 45 minutes each day to pull the daily sales data of five restaurant locations. Sometimes they had to prepare reports for weekends and holidays, too. Primanti Bros. leveraged IBM RPA technology to program bots that produce daily sales and labor reports in under 3 minutes, not 45 minutes. Numerically, IBM RPA helped Primanti Bros. save 2,000 hours annually and $84K in savings.
Revenue reconciliation
Revenue reconciliation involves ensuring that the amounts of goods/services sold correspond to the equivalent amount of cash received. Revenue reconciliation is important in the food industry because of the large volume of (relatively) low-dominated transactions that happen both online and on-site. If revenue reconciliation is done manually and at scale, it can lead to missing sales revenues and unmatched invoices. A use case of RPA in the food service industry is its capability to automate reconciliations in real-time.
Case study:
Paradise, an Asian restaurant, works with four digital food platforms, such as Uber Eats and Zomato. Across these platforms and in-store, they have over 6,000 daily transactions. Prior to automation, it took them five days to reconcile them. Manual, lengthy reconciliation was a bottleneck that resulted in delays in revenue collection and stretching out the Daily Sales Outstanding (DSO) – and it didn’t help matters that the reconciliations took four FTEs to complete.
Paradise leveraged RPA to:
- Extract data from the ERP system (perhaps a POS) that housed all orders’ data in it – the food app through which the sale was made, including the customer’s name, their banking info, the order details, etc.
- Put the orders’ data, and the receivables’ data, in a single file to auto-match and reconcile them on a daily basis, and not at week’s end.
- Flag exceptions and inform a human in the loop about them.
- Reconcile transactions with 100% accuracy, in under 4 hours, instead of the previous 5 days.
- Improve the restaurant’s cash flow as a result, since reconciled/unreconciled transactions were identified instantly and could be followed up on.
