As we all know Machine Learning and Python go hand in hand. Big food industrial cooperations use data science technology to improve their products over time. There are numerous benefits of using machine learning in the food industry.
The industry has seen fantastic innovations in the range of products due to the benefits of machine learning
. Intelligent technology makes the products more feasible and meets the demands of the customer base of food companies. The delivery of these fantastic products has been made possible due to huge robotic innovations based on python and machine learning.
Reasons Why Python is So Trustworthy
Python has seen its demand increase over time due to its high reliability and resourcebility. There are many programming languages available these days, but the reason for choosing python over all of them is that it is extremely easy and simple for both coding and implementing.
Ways Python is Used in Food Industries
1) Analysis of the food market
The food industry
uses machine learning to reach out to complex problems. For example the issue of treating waste food. Trillions of tons of food are wasted every single day across the globe.
Hence, analysis of this wastage and its treatment has become big responsibility for the whole industry as well as the governments where these wastages are frequent. Since this problem has come to light, many NGOs have come forwards to offer a helping hand to deal with it. The leftover food is packed and delivered to the needy so their daily food requirements can be satisfied.
2) Optimization of the food that is being produced
Food optimization is one of the primary concerns of the food industry in recent years. The goal is to avoid problems of under-production as well as over-production that can lead to shortage or wastage accordingly. The level of tolerance of the people in such a sensitive matter is very less, and strict actions are taken to avoid wastage.
Python and Machine Learning Algos help to optimize the production of food at various levels of the supply chain cycle like production, selling, etc.
3) Sharing of Knowledge
The sharing of knowledge is an important factor in the growth of any industry. The takeaway and dining type of food service has experienced a whole shift in its popularity due to the introduction of Machine Learning.
The customer base of all companies has seen a hike since they adopted the method of online advertising of the services they offer. And because of this, it is fair to say that AI Machine Learning plays an important role in the growth of this sector.
The food industry uses machine learning to understand the behaviors and needs of the customer, it also helps to forecast the profits of the business and helps make improvements in the quality and production quantity of the products that the food company offers to its customers.
Almost all major food cooperations McDonald's Subway, Dominios, KFC, Pizza Hut, Burger King, Starbucks, and Tim Hortons, and beverage companies like Coke, Pepsi, and Sprite use these methods to increase the demand and sales of their products.
Benefits of Using Python and Machine Learning in the Food Industry
Now that we have studied the ways that python is being implemented, let us move further and take a look at the advantages of using Python data science in the food industry.
The list of benefits is given below:
Improved quality of food products
Less risk due to demand forecasting
Improved marketing skills. Now many small businesses have received a platform where they can gain a wider customer base rather than just being dependent on their local market. The engagement rate of these businesses has increased. For example, Zomato has become an advantage for all food companies to make their products reach out to target audiences.
The working space has improved efficiency as there are less wastage and most work is being handled by Artificial Intelligence.
The increase in efficiency of supply chain management. The customer now has a full record of who produced, manufactured, and delivered the product that they are purchasing instead of just blindly trusting the vendor.
Easily being able to predict the need and demand for products
Quick determination of the shelf life of all easily perishable goods such as milk and dairy products and in turn avoid wastage
Health Standards are maintained
An increase in the profits of the company as loss can be avoided due to numerous factors like transporting of raw material and finished goods during bad weather
Reduction of the wastage of food due to quantity optimization
Keeping quality checks becomes easier due to the implementation of the benefits of machine learning in the food industry.