The food and beverages (F&B) industry has been transformed digitally, resulting from new technology, including GenAI. In short, GenAI is a type of artificial intelligence that is capable of creating content and offering predictions that have transformed the operations of a business in this industry.
In this blog, we will look at some of the approaches GenAI has advanced in food and beverage,
supported by relevant research statistics as well as real-life experiences and case studies in detail.
1. Supply Chain and Inventory Optimization
Management of the Supply chain is one of the more resource-draining processes in the operation of the food and beverages F&B sector. A new study by McKinsey shows that companies that incorporate AI in their supply chain management were able to realize up to 15% cost reduction and 30% improved time for feedback.
For example, during a warehouse network design with the aid of GenAI, a food company was able to reduce supply chain costs by optimizing freight storage and handling expenses and realized significant cost saving.
2. Enhancing Customer Experience
In this age of increased competition, none of the companies can afford to ignore the personalization of their clients. The blending company F&B utilizes GenAI to figure out what products and services customers want by studying their reviews and purchasing history.
In a report released by PwC, it was noted that 80 percent of clients have a greater chance of purchasing once given a personalized experience.
3. Cost Reduction and Operational Efficiency
GenAI, in many instances, cuts down operational expenses due to the mechanization of repetitive activities and the streamlining of processes.
Deloitte’s report says that organizations deploying such methods of AI applications to automate operations can reduce costs by as much as 40%.
4. Food Safety and Quality Control
F&B companies have to think about food safety as well as the high quality of the products that they manufacture as one of their top priorities. GenAI improves these in relation to these critical areas by embedding predictive analysis and automatic quality checks.
For example, Kraft Heinz implements monitoring AI devices to their production lines for QC processes to predict safety risks even before they occur.
5. Recipe Development and Ingredient Sourcing
Another area where AI is advancing with great impact is food product development. Guided by AI algorithms, companies are able to create new dishes that take into account different flavour combinations, nutritional value, customer tastes, and other trends at the present time.
For instance, Impossible Foods company, which tried to produce fiction meat from plants, applied AI in plant-based meat production. AI examined massive amounts of food block structures and was able to define the required structures to create meat as an appealing product and a new popular food category.
6. Demand Forecasting and Menu Planning
This significant role of demand reliance and menu planning is effectively executed with the help of GenAI. Advanced predictive models are able to include seasonal influence, past behaviour, and trends of customers to target those that convert to sales and optimize orders.
In the case of Chipotle, for example, their AI is able to provide prediction statistics for demand for specific items studied when constructing their menu. This leads to the improvement in the management of inventory and enhances food wastage.
7. Overcoming Challenges with AI Adoption
In spite of all the advantages that GenAI possesses, there are some difficulties concerning its application. For example, one of the challenges is dealing with a manual decision-making process, which is often cumbersome.
Industry compliance is another problem that remains. To reinforce rule compliance, GenAI solutions integrate food standards compliance with automated monitoring systems and data analytics, thereby minimizing risks of non-compliance.
Case Study: Reducing warehousing cost using GenAI
One of our prestigious clients wanted help with their supply chain. The client had a large network of warehouses and they were primarily struggling to make an optimal storage plans for their products that would reduce their product storage cost.
Challenges:
There were a couple of challenges with the entire warehousing set up of the client:
- The manual method which they were using to determine the optimal warehousing plan was both inefficient and costly.
- They were unable to take several factors into account such as storage costs, freight costs, etc. while deciding the optimal number of warehouses.
- There was no real-time monitoring of the supply chain operations which often resulted into incurring higher costs.
Solution
To address these challenges and come up with a more linear approach, we adopted a Generative-AI based solution to help our client. The factors which the client was unable to accommodate earlier into their equation of determining optimal number of warehouses was now approached using a linear programming model.
This model effectively captured all the different varying factors on which the optimal number of warehouses could depend. The AI based solution was strategic in the sense that it provided 360-degree view of their supply chain model thereby helping their decision-makers to optimize warehouse locations and reduce cost at the same time.
Results
By using our data driven approach, the client was able to:
- Observe reduction in their supply chain cost by 20-30%
- Observe reduction in their operational cost by 12-15%
- Perform real-time monitoring of their entire supply chain network
- Perform strategic and data driven decision-making
- Enhance operational efficiency through real-time monitoring and better scheduling
This case study enlightens the cogent application of Generative-AI on modernizing the supply chain realm of food and beverage operations. It is evident that the introduction of data-driven approaches to more state-of-the-art problems can bring revolutionary impact on the traditional solutions.
Final Thoughts
The compliance of Generative AI to various food and beverage channels of distribution is facilitating rapid change in current business practices, which are more efficient, less costly, and focused on the customer. Everything from supply chain operations to customer engagement, the assets of AI are plenty, if not simply numerous. As the business progresses, AI will serve a greater purpose in regard to food production, food service delivery, and satisfaction levels of the entire culinary experience.
In the coming years, we will witness AI-focused technologies taking precedence in areas such as inventory control, order fulfilment, and customer relationships. The integration of data intelligence through cognitive computing enables firms to stay one step ahead in terms of market trends.