5 Best Uses of Data Science in Supply Chain Management

In our previous article, you’ve read the best use cases of Data Science in Banking industry. Besides the banking & finance industry, almost all industries can benefit from data science applications. In this article, we’ll talk about the uses of data science in supply chain management & logistics.

In logistics & supply chain management, companies can use data science for better process optimization and improve their operations across locations. This process includes everything starting from demand & supply analysis, delivery management, inventory management, managing transportation, optimizing routes, saving fuel, and much more.

A company in the supply chain industry has various departments to manage its operations. Every department has its own processes and operational values. Companies opt for data science applications to make this process profitable to generate more revenue and increase their market value.

Before discussing data science’s uses in supply chain management, let us first briefly describe the supply chain & logistics industry and how it works.

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What is Supply Chain Management?

Almost all of you must have heard the names like DHL, DTDC, First Flight, India Post, etc. These are some of the most popular logistics & supply chain companies that you know. In other words, any company that stores, transports, imports, exports, or delivers any kind of goods are a logistics & supply chain company.

In the language of the supply chain, there are 5 primary drivers for this industry: Production, Inventory, Location, Transportation, and Information.

Dependent on these factors, a supply chain company holds a large amount of data on customers, clients, partners, locations, and companies.

Managing this kind of data is the trickiest part, and sometimes it results in the loss of millions of dollars when any type of error occurs while working with such data manually.

Why Data Science in Supply Chain Management?

Data science is one of the most trending and valuable technology in the 21st century. It is the study of data and finding useful insights that can help in the growth of a company and revenue generation.

Data Science in supply chain can be a proven growth driver. Since most of the data in this industry can result in a profit of millions of dollars, companies are going with the data science technologies to eliminate the pain points that can cause them a significant amount of money.

You’re reading the article, Uses of Data Science in Supply Chain Management.

Here are some of the primary processes in any supply chain management company:

Production of Raw Materials
Procurement of Raw Materials
Inventory Management
Inbound Logistics
Outbound Logistics

Understanding the process and optimizing it can benefit the supply chain industry. And this can be done with the help of data science.

How Can We Apply Data Science in Supply Chain Optimization?

Data Science in the supply chain industry can help in the managing & optimization of the following processes:

Demand & Supply Analytics

Inventory Optimization

Network Planning

Procurement Analytics

Logistics & Transportation Analytics

With the help of the analytics mentioned above & optimization, we can get valuable insights into the company and the industry. Using these analytics, companies can improve their performance, do the cost-cutting, do accurate forecasts and predict future patterns, understand the peak profitable time of the industry, target more clients, enhance their production and logistics, and launch newer products or services for their customers.

Hope you liked reading the Uses of Data Science in Supply Chain Management article. Please share your thoughts in the comments section below.

Data science technologies are constantly evolving and will be the backbone of all industries shortly. Seeing this as a profitable bet, companies invest more into data science applications and hire more data professionals.

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