Smart catering serves up food trends

Whether they operate delivery services or fast-food chains, system caterers – including airline caterers – need to be able to satisfy customers with increasingly high expectations. To do so, they must harness the opportunities being provided by digital transformation in order to improve material requirements planning, production and product development.

From vegan diets to dinner delivery: A wide range of trends in the food industry are posing challenges for system catering. Whether they are eating in an airplane or at a fast-food restaurant, guests want to be able to comfortably enjoy food that fits in with their diet wherever they are. They celebrate their meals, which are now about more than just ingesting food.

The result: Catering companies have to find new solutions to optimize the entire value-added process – from precise material requirements planning to digital, HACCP-based manufacturing for preventing food hazards to customized product development based on customer feedback. This is the only way that they can maintain a competitive edge. The following three examples show how system caterers and caterers in general can utilize new technologies to become faster, smarter and more efficient.

1. Planning material requirements more efficiently using data analyses

From meat to vegetables to dairy products: Every system caterer tries to have just enough ingredients in stock, as precise material requirements planning is essential for sustainable production and punctual deliveries. On the one hand, it ensures that the items are available and, on the other, optimizes the costs of obtaining materials.

Customer wishes are becoming more diverse, which means an ever-increasing number of dishes for caterers to provide. This makes it more and more difficult for system caterers such as airline caterers and franchise companies to optimally stock their supplies. Predictive analytics provide one remedy. Data analyses can be used to work out patterns and contexts for different material requirements and make reliable predictions about upcoming requirements. This allows airline caterers to minimize not just waste, but also high prices for replacement stocks.

2. Transmitting recipe changes to production in real time

Recipes and stock lists are still frequently being changed centrally and manually in airline catering – despite production having to cope with a continuously growing number of special demands. This means that employees only find out about these changes later, which slows down production. Moreover, changes like these are often printed out on paper. But this sort of paper-based production is prone to error.

By digitalizing processes, caterers can reduce the manual effort required to adjust production data, thereby accelerating production. Specifically, this means that data is collected in a central system and changed there. Afterwards, this simply and clearly prepared information about food standards – whether organic, halal or vegan – is transmitted in real time to tablets installed in production facilities. This means that all employees have the most up-to-date information at hand – an important factor in ensuring quality.

3. Big data: Developing new products on the basis of customer feedback

Aesthetics, portion size, taste: All of these factors influence guests’ satisfaction, which makes customer feedback a key factor in improving products and increasing customer satisfaction. But it is often difficult to draw conclusions from individual customer opinions – for example, when air passengers provide flight attendants from different airlines with feedback about menus or catering concepts. It is very difficult for airline caterers to filter out information pertinent to product development from the comments provided in free-text formats.

By using big data analytics methods, airline caterers can collect and evaluate customer feedback from different airlines. The responses provided on social media channels can also be included in these evaluations. Exploratory data analysis systematically recognizes connections between data. The system can also be taught how to correctly interpret subjective comments. The result for product development is a statistical evaluation and detailed analysis of each meal, which companies can use to successfully develop new products.

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