How Data Science is evolving the Food Industry?

Roles/Applications of Data Science in the Food Industry

6 min readJun 22, 2021

Introduction

It comes as no surprise that the food industry is the most vital industry segment in the world. As customers, we need our food fresh, clean, and healthy while as stakeholders, we need to find efficient outputs for food manufacturing, food discovery, customers’ problems, customer preferences, supply chain management, etc. Along with this, it is no wonder that Data Science and Big Data are booming in various domains with their tremendous applications. As a result, many FoodTech industries are solving problems with the help of Machine Learning and Artificial Intelligence (AI).

In this contemporary era, Food Industry is evolving dynamically as a result of new food discovery and food delivery options. Also, food manufacturing companies are getting more into knowing customers’ preferences, improvising quality standards, and satisfying customers’ demands and needs using Big Data Analytics and Machine Learning algorithms.

How Data Science/ Big Data Analytics in the Food Industry?

Quality Control — Health management
Enhanced Efficiency
Improvised Insights
Marketing
Customers’ Sentiment Analysis
Predicting Life-Span of Products
Supply Chain Management — on-time deliveries
Demand Forecasting

Health Management — Quality Control

Various vegetables and fruits, dairy products, etc are temperature-sensitive items that require monitoring of temperature. Thus, Big Data Analytics can be used to monitor these items while considering the full supply chain cycle in the picture. These give full access to replace or return these items and to take preventive measures when quality is compromised.
Big-data-powered solutions can also be used to check the quality of the materials during product production.
Also, the conduction of a feedback survey regarding supply change management and the quality of products from consumers can help the brand to improvise customer service management and insufficient food quality management by Data Analysis and Machine Learning.

Enhanced Efficiency

By applying data analytics and various ML algorithms to the acquired data, the efficiency of the products can be improved. For an instance, weather prediction reports can help farmers with cropping, shippers for transportation, restaurants for customers’ availability and pricing, etc.
Moreover, the information of temperature, humidity, nutrients in the soil, etc of farm areas can guide in knowing the severe effects that can be used by crop production in the specific farm area.
The application of predictive algorithms can save tons of products from damage. Thus, knowing the weather conditions will guide shippers to transport the products efficiently.
Data science can be used as a powerful tool for restaurant owners to make their business strategy to build or to maintain their brand.

Improvised Insights

Data Analytics and Data Science can be applied to restaurants or any food segment industry to know customers’ satisfaction, pricing, brand value or popularity, quality of products, product popularity, market situation, etc. Thus, innovative solutions can be used to effectively analyze acquired data to make business strategies.

Marketing

Improvised insights can help the company with marketing to spread awareness and acquire potential customers.
Data Science can help to understand various customers and their requirements based on their order value, demographics, product purchasing patterns, customers’ feedback, etc. This eventually helps in strategizing marketing campaigns.
For example, Zomato (online food ordering app) gives recommendations based on previous purchases and location recognition. Moreover, for specific restaurants, offers and discounts are given based on past orders. Thus, data science can help in attracting potential customers.

Customers’ Sentiment Analysis

Customers’ sentiment analysis is the process of detection of emotions when customers interact with certain products, services, or brands.
Knowing what customers want and how that can be kept on supplying help food industries to keep old customers and acquire new customers.
This can be achieved through categorizing customers’ feedback or reviews into positive, negative, or neutral sections using Natural Language Processing (NLP) and improving the decisions based on the sections and respective sectional requirements.

Predicting Lifespan of Products

Everything comes with a certain timeline, so are the food products. It may change or expire after a certain period.
Managing food and drink with different shelf lives is a huge challenge for the industry because there are different procedures for each category.
Data Science and Data Analytics can be used to predict the lifespan of various products such as dairy items, bakery products, wine, sugar-made drinks, etc.
This can help in saving the products from wastage (saves money and time) and guide users’ to consume before expiration to be saved from side effects (saves health).

Supply Chain Management

Consumers expect to know how the food is produced, which kind of material is used, how the product is stored, what chemicals are used, etc.
Data science helps build transparency within supply chains, so they can be more honest with their customers.
Transparency also helps in solving problems and increasing efficiency in supply and logistics. For example, it will be easier to track contaminated food supplies to their storage location, reducing the chances of food-borne diseases.
Data Science also helps for on-time delivery of products. It helps in comprehending factors that can affect delivery such as, traffic, route, climatic conditions, etc. Then, the model can be created to estimate the delivery time.

Demand Forecasting

For maximizing the revenue of the organization, it is crucial to maintain effective demand forecasting for production planning reducing bottom-line expenses, and precise resource allocation.
A restaurant chain, for example, can track the time and recurrence of their customers’ visits and estimate if (and when) they are going to return).
They can also use sentiment analysis to learn which recipes their customers enjoy most and plan product delivery and the work of their chefs accordingly.

Real-time applications

A bunch of companies is implementing Data Science and Analytics for food products innovation and solutions, as they realize the change in the business and their streamlined profit.

The Cheesecake Factory utilizes Big Data-driven software to process and analyze enormous data sets from 175 locations in the U.S.
FreshDirect uses sensors, processing, and analyzing data to monitor product status and environmental conditions during transportation.
Connecterra has designed a predictive analytics-based tool to aid farmers in determining cattle health problems.
The Yield company has developed the solution to monitor planting beds and the whole agricultural ecosystem and foresee any arising issues.
The all-known restaurant KFC uses Big Data to analyze customer feedback and food preferences, which comes up with better customer experience and sales.
Bright seed uses AI, predictive analysis, big data to identify beneficial plant compounds. Data is used to create bio-actives that can be added to food to make it healthy.
Quantzig focuses on the business end of the food industry. Its products help companies for strategic planning to make better decisions related to marketing, sales, and pricing.

Final Thoughts

With all that said, Data Science and Analytics solutions have various use cases in any food industry sector. Such technology makes the most effective strategies available to stick to. Especially when it comes to predictive analytics capabilities, thanks to AI-driven power.

There are a lot of companies offering such food tech solutions, but typically they cannot meet all of your specific requirements, which makes it quite hard to find exactly what you need.