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RPA in Food Industry: Top 11 Use Cases in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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When we think of restaurants or the food industry in general, this is what normally comes into mind: 

On top of the manual kitchen work, the food industry is rife with manual, back-office tasks that should be handled smoothly to allow kitchens to be in full swing.

In this article, we will go over the top 11 use cases of RPA (robotic process automation), a technology that allows for the automation of mundane and repetitive tasks in the food industry. 

Our 11 use cases of RPA in food industry are grouped under three broad, back-office categories: 

  1. Finance 
  2. Administrative 
  3. Inventory management

Finance 

1. 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 eight 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’s RPA technology to program bots that produce daily sales and labor reports in under 3 minutes, not 45 minutes. Numerically, IBM’s RPA helped Primanti Bros. save: 

  • 2,000 hours annually 
  • $84K in savings

The benefit of using RPA is that the daily reports are free of manual errors, are done within the specified time-range, and they allow managers to tend to more strategic tasks such as quality assurance of employee/customer satisfaction.

2. 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 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 channeled into 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: 

  1. 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. 
  2. Put the orders’ data, and the receivables data, in a single file to auto-match and reconcile them on a daily basis, and not on week’s end. 
  3. Flag exceptions and inform a human in the loop about them.  
  4. Reconcile transactions with 100% accuracy, in under 4 hours, instead of the previous 5 days. 
  5. Improve the restaurant’s cash flow as a result since reconciled/unreconciled transactions were identified instantly and could be followed up on.

3. Procure-to-pay

Restaurants, like any other business, should pay their vendors on-time to get the goods they want. 

Additionally, because restaurants typically deal with perishable items, they must pay their suppliers promptly to ensure that their products are received quickly and used immediately in the cooking process.

RPA bots can be programmed, via screen recording, to follow a series of steps to: 

  • Open a browser
  • Navigate to the banking website and log in 
  • Make a payment to a vendor – by extracting their banking information from the vendors’ sheet, and the invoice amount from the purchase orders. 

Procure to pay automation ensures that money transfers are done as scheduled. This minimizes delivery delays and allows kitchen managers or purchasing managers to focus on their core responsibilities rather than time-wasting, repetitive tasks.

Administrative

4. Price updates 

RPA can help eliminate menu costs. Restaurants today are leveraging digital QR menus that patrons can access via their mobile phones. 

Through API, they can tie the digital prices to real-time price breakdowns of the dishes’ ingredients. This means that any time an ingredient’s price changes, RPA bots can automatically reflect that item’s change in the overall meal price instantly, accurately, and without human intervention.

For instance, recently, the price of cooking oil has been volatile. Restaurant managers can create a digital “bill of material (BOM)” for each dish, specifying the amount of oil that is used, its price per litre, and its contribution to the overall dish price.

They can then program RPA bots to change the meal price with respect to incremental, approved increases in the price of oil. 

Apicbase (see Figure 1) is a software that does that. Kitchen supervisors, sous-chefs, and chefs can enter the ingredients’ names, costs, and amounts that go into a certain dish. RPA bots will then adjust the final meal price whenever an adjustment is made to one of its ingredients’ prices. 

The benefit of this use case of RPA in the food industry is that managers do not need to spend time manually calculating the percentage points by which each dish’s price will be affected by changes in its ingredients.

A use case of RPA in the food industry is bots automatically reflecting each ingredient's price into the overall dish cost. This is Apicbase's visible dashboard UI that brings those functionalities into light.
Figure 1: Apicbase’s visible dashboard UI. Source: Apicbase

5. Floor management   

Restaurants can leverage restaurant POS systems that: 

  • Give waiters/waitresses the option of recording a customer’s order via touch pads
  • Allow the staff to monitor multiple tables’ orders simultaneously
  • Leverage the system’s communication capabilities to communicate with the kitchen about incoming and outgoing orders
  • Divide a table’s tab amongst the diners if needed
  • Apply discounts if applicable
  • Keep data-driven records of the orders, receipts, number of diners, number of diners at each table, etc. on each night (see Figure 2)
A use case of RPA in the food industry is their application in POS systems that automatically calculate each table's total amount tab, allows the division of tab, and more. This is a real-life UI of a POS app.
Figure 2: The UI of a POS application in real-life. Source: Touchbistro

The benefit is data-driven floor management. For instance, by digitizing orders and storing them on the cloud, managers can calculate how many times a night a certain dish was ordered to identify underperforming/popular dishes. 

This is important because it gives chefs the chance to use their ingredients efficiently to cook up meals that are popular with diners instead of conjuring up new ones that do not seem to be well received.

Another benefit is that RPA bots can be complemented with OCR capabilities to read invoices and extract their data. So managers can, for instance, program the bot to extract the table numbers from all invoices to see which tables have the most/least number of diners. 

Or the internal communication capabilities mean that when the cooks signal on the system that an order is ready to be served, the RPA bot can send an automatic notification to the staff to pick it up quickly while it’s hot, and serve it to a specific table number. 

6. Food hygiene certificates

To meet governmentally-mandated health regulations, restaurants should meet some basic requirements. One of those requirements is that the staff that handles the food and is involved in its sale, should all have food hygiene certificates. 

In the UK, for instance, it is claimed that these certificates need updating every three years. A use case of RPA in the food industry is that restaurant managers can program the RPA bots to send them a notification anytime the food hygiene license of a staff member is close to expiry. 

This gives them time to schedule retraining workshops to be undertaken and completed before the staff’s certificate expires and renders them unauthorized to work in the restaurant. 

It is challenging for managers and employees alike to personally keep track of such deadlines, particularly in kitchens with a high number of people. RPA bots can easily automate alerts, which can save the restaurant money by preventing fines and/or seeing employees’ licenses revoked.

7. Workforce scheduling 

Offices use absence management technology to give employees a chance to input their absences all in one location, so employers can know when certain team members will not be available. Automating absence management also means that payroll calculations are accurate and do not over/under-deduct vacation days. 

Moreover, via integration with employee scheduling software that schedules a business’ activities and workflows with respect to availability and absences, an office runs more smoothly and efficiently. 

Restaurants, like corporate offices, can use software that leverages RPA to automate the workflows with respect to the absence/presence of kitchen cooks, waitresses, and waiters, cleaning staff, purchasing managers, maitre d’, etc.

Especially in the food industry which is highly seasonal, it is important to have a transparent view of your workforce in order to avoid operational disruptions due to staff shortages in rush times. 

RPA bots can follow rules-based frameworks to automatically reshuffle shifts and reassign tasks as absence requests are made in real-time. 

Inventory management 

8. Shelf-inventory management

IoT devices in retail, such as digital shelf tags, are used to monitor the real-time quantity of products on the shelves. Each time a product is taken off the shelf, it is recorded and via API, the new quantity number is reflected on the product inventory database. 

Moreover, once the total quantity of a certain product, such as the cans of tuna, falls below a certain threshold, lets say 8, RPA bots send an automatic notification to the staff to remind them that inventory has fallen below the threshold and the reordering of the goods should be restocked.

9. Automated reordering

Automated shelf-inventory monitoring could power one-click reordering. Restaurants can also automate the process further by consulting with RPA developers to develop a customized, unattended RPA bot that sends out order refills of “normal” staple products (i.e., anything that’s not seasonal, is not a “luxury” nor a Giffen good) automatically and in the same amount every time.

Upserve, for instance, is a vendor that offers digital inventory management solutions (see Figure 3). Through real-time tracking of the goods that are used in the food preparation process, it allows managers to see their quantity in real-time, and to restock ingredients quickly. So this use case of RPA in the food industry means reorders are made before an item is completely out of stock, in addition to eliminating the need to manually conduct inventory control. 

UI of Upserve's application that allows reordering of food ingredients with one tap.
Figure 3: Kitchen managers can place reorders with one tap. Source: Upserve

10. Produce supply management

RPA-IoT integration is already used in fleet management to send real-time cargo locations to the manufacturers in real-time. That use case can be extended to the food industry – kitchens can monitor their food delivery shipments in real time to adjust their dishes, or their specials, with respect to that.

Supply chain management solutions leverage RPA bots. Those bots can be programmed to send an email or a push notification to the inventory manager when a certain delivery gets on the road, whenever it’s expecting any delays, and/or its latest ETA.

Especially in restaurants, where the quality of ingredients plays a crucial role in the meals’ taste, quality, and safety, RPA bots allow chefs to have an efficient and accurate “First Expired, First Out” strategy. An up-to-date supply chain management system allows chefs to manage and plan their logistics down to the second. 

11. Monitoring of equipment 

Periodic maintenance of equipment in the manufacturing sector, for instance, helps companies get ahead of the downtime that breakdowns cause by preventing them in the first place. 

For instance, IoT devices can monitor the gas levels in the grills, the air filters in the kitchen hoods, or the temperature levels in the walk-in refrigerators and use RPA bots to automatically transfer the data onto a singular dashboard that chefs and kitchen managers can monitor access from anywhere. 

This way, temperature fluctuations can be reversed as soon as they happen. Or if the ventilators’ air filters need changing, the sensors would alert the staff ahead of time before it gets completely clogged up. Similarly, if the grill’s gas needs to be replenished, it can be done before the grill stops working in the middle of a busy night. 

For more on RPA

To learn more about RPA and its use cases in different industries, read:

Download our RPA whitepaper to learn more about the topic:

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And if you believe your business would benefit from adopting an RPA solution, head to our RPA vendors’ list to find a data-driven assortment of RPA providers.

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
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Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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