NAVIGATION

Potential Applications of Big Data in Food Industry

Big data is one of the latest technological innovations that have already revolutionised how businesses are conducted. Out of all industries, food and beverage can benefit significantly using big data. It can be leveraged by the retailers, manufacturers, and even restaurant chains. By applying big data, it is possible to optimise all aspects of any food related business, including customer service, production, sales, marketing, and much more.

According to the unique needs and goals of your facility, it is possible to customise the data and analytics tools. Whether you want to install system-wide automation technology to improve your efficiency or simply resolve certain pain points of the business, big data has the solution for you. Mentioned below are some of the most important applications of big data in food industry.

Waste Minimisation:

Unused raw ingredients are a major concern for almost all food and beverage businesses. It is possible to reduce this waste and increase efficiency by adopting the made-to-order strategy instead of the traditional mindset of made to stock.

Food manufacturing units can anticipate consumer demand for the entire year by using predictive analysis. This will ensure that they will only manufacture what they are capable of selling. By sharing analytics with their most important suppliers, they can minimise waste as well as their requirement for extra storage. This type of collaboration with the suppliers can be accomplished by installing sensors capable of detecting raw ingredient level in storage.

On-time Delivery:   

Owing to the nature of products it deals with, timely delivery is extremely critical to all food related businesses. Today’s advanced big data analysis tools and techniques can be used to time and optimise food delivery. Big data is capable of collecting information from various sources such as temperature, weather, route, road traffic, etc to provide accurate estimates of the time required to deliver goods. The impact of these factors on food quality can also be predicted by big data.

Operational Efficiency:

Big data helps enhance operational efficiency by helping analyse the effect temperature on food quality, impact of market trends on stock consumption, customers’ behaviour, and much more.

 

Sentiment Analysis:

Sentiment analysis makes use of techniques like data analysis and natural language processing to monitor the emotion of customers. This big data analysis technique can be used by food companies to understand what their customers feel about them and initiate measures accordingly.

Quality Enhancement:

Maintaining consistency in terms of taste is a challenge for all food production facilities. This capability depends on season, storage, quality, and proper measurement of ingredients. Changes to these factors can be analysed using big data analysis. The impact of such changes can also be predicted by big data. These insights can be critical, particularly for maintaining the desired quality for packaged foods.

Customer Service:

These days, we have numerous customer touchpoints that collectively impact customer experience. These touchpoints include outlet, website, mobile app, review sites, social media, and more. Big data provides us meaningful insights by analysing inputs from all these factors.

Personalisation:

Many of the advanced big data projects have the goal of creating a customer-centric and personalised experience for all buyers. This essentially involves analysing what customers like, their social media activities, their preferred budget, their online reviews, etc. Tracking and analysis of all these information helps businesses deliver personalised experience for their customers.

If you have any other questions related to the application of big data for your food unit, please contact us at Lumix.