Supply Chain Management is the management of the flow of goods from raw materials to the final products and ensuring the delivery of the demanded products at the right place and at the right time. There are many use cases of Data Science in Supply Chain Management. In this article, I will take you through how Data science is used for Supply Chain Management by businesses worldwide.
The Traditional way of Supply Chain Management
Before understanding how Data Science is used for Supply Chain Management, let’s first understand how businesses used to manage their supply chain when Data Science was not mainstream in the industry.
When Data Science was not mainstream in the industry, Supply Chain professionals relied heavily on Supply Chain software and their experience in managing the overall supply chain of the business. Although there were statistical approaches to forecast demand using the historical data of their business, the traditional methods were limited in providing real-time insights, risks, potential customers, faster delivery methods, and many more techniques that could help a business in its supply chain.
For example, traditionally, companies relied on manual data collection and analysis. Their supply chain operations used to centre around the network of distribution centres, which served as hubs for receiving, storing, and distributing goods to its stores. To manage their supply chain, companies used to use a combination of technology and operational excellence. Their focus on operational excellence involved minimizing inventory levels, reducing lead times, and optimizing transportation routes.
Now Here’s How Data Science is Used for Supply Chain Management
The use of Data Science techniques has helped every business in their supply chain management by enabling them to collect, analyze, and interpret large volumes of data in real time.
Here are some ways in which the use of Data science has helped businesses in supply chain management:
- Analyze data from multiple sources, such as RFID tags, sensors, and GPS trackers: It helps supply chain managers track products as they move through the supply chain, monitor inventory levels, and identify potential disruptions.
- Analyze real-time data on customer behaviour, market trends, and supply chain performance: It helps a company make quick changes to its supply chain operations, such as changing production schedules or reallocating inventory.
- Analyze data on historical sales patterns, customer behaviour and external factors such as weather conditions: It helps a business forecast demand more accurately and maintain optimal inventory levels.
- Analyze real-time data on vendor performance, transportation routes and market trends: It helps a business identify potential risks and develop proactive strategies to mitigate them.
So today, companies can optimize their supply chain operations even further by analyzing real-time data on customer behaviour, weather patterns, inventory levels, market trends, sales patterns, and transportation routes. Today, businesses also use Data Science techniques to optimize their transportation network, reducing transportation costs and delivery times for each shipment.
Summary
Supply Chain Management is the planning of the flow of goods from raw materials to the final products and ensuring the delivery of the demanded products at the right place and at the right time. When Data Science was not mainstream in the industry, Supply Chain professionals relied heavily on Supply Chain Management software and their experience in managing the overall supply chain of the business. Today companies can optimize their supply chain operations even further by analyzing real-time data on customer behaviour, weather patterns, inventory levels, market trends, sales patterns, and transportation routes.
