How does data science affect logistics?

Applying data science to supply chain management helps businesses forecast future trends and make more accurate decisions.

Investing in data science is one of the most important strides of the past decade. Data science is defined as the mining, management and analysis of data to predict future trends and make decisions and action strategies.

Scientific data and systems help businesses make more accurate decisions. At the same time, data science and analytics have a positive impact on many areas such as marketing, sales, and human resources.

The application of data science helps to improve efficiency and enhance competitiveness in logistics operations.

For logistics, transportation and supply chain management operations, applying data science improves efficiency and enhances competitiveness. According to research by the Council of Supply Chain Management Professionals, data science is becoming more important to the industry.

Logistics

In the field of logistics, the application of data science helps companies optimize operations. Data science helps plan delivery routes, how to manage fuel, time of day travel, and more accurate forecasting of supply and demand.

Applying data science to logistics allows companies to use insights quickly to make adjustments as needed. In the event of fluctuations such as changes in customer demand or increased gasoline prices, businesses can make decisions more quickly. DHL's intelligent trucking system uses data science to calculate the best routes to save efficiency, cost and time.

Shipping Management

Without data science, it's impossible for businesses to know the impact of shipping and other costs (packaging, location, discounts, rates) on profits. By analyzing the transportation process from negotiation to packaging, businesses can optimize their operations and find ways to cut costs while ensuring service quality and speed.

Recently, e-commerce company Nisolo used data analytics to identify fees that negatively impact the company and make adjustments to save.

Supply Chain

The supply chain itself has become a strategic element of a company's business. Businesses have applied data science to automate demand forecasting, optimize replenishment and delivery times, and make inventories more accurately reflect market demand. In addition, this technology also helps to improve production and on-time delivery. These operations enable the supply chain to be more efficient and predictable.

Supply chain insights help businesses make quick adjustments in real-time and tackle crises around the globe. In fact, PepsiCo used analytics and learning to predict stock outs and warn retailers to reorder.

Make

By applying data science to manufacturing, companies can get closer to their goal of delivering the right product in the right quantity at the right time. This allows to lower costs and help more customers have access to the product

Data science can be applied to manufacturing systems from monitoring processes, modeling maintenance scenarios to reviewing safety progress, then building and improving operations based on Information has been collected

Data science can reduce risk, reduce costs, and improve productivity. The automaker Ford is a prime example of using data science to analyze equipment wear and tear and identify potential mechanical problems before they occur.

Applying data science brings many benefits so technology should be applied in every important area of ​​the business. The application of data science needs to be maintained continuously and constantly updated to help businesses capture and maintain a competitive advantage.

Phi Hung (Source: Forbes)