The practical applications for data analytics in the world of supply chain management are both numerous and remarkable. If you are interested in supply chain optimization, you should be interested in supply chain data analytics.

Read on for a targeted examination of data analytics in supply chain management, as well as advice on the best analytics practices and digital tools to promote supply chain optimization.

Understanding the Role of Data Analytics in Supply Chain Optimization

Data analytics plays a key role in the professional lives of countless individuals who want to optimize supply chain operations and oversight. From supply chain and purchasing managers to logistics analysts and warehouse operations team leads, nearly all supply chain management careers are shaped by data analytics.

The Evolution of Supply Chain Management Through Data Analytics

Efficient and effective supply chain management demands the seamless and simultaneous coordination of many different procurement, distribution, and general logistics operations. To accomplish this complicated feat, supply chain managers are increasingly turning to data analytics, which, in the words of business research and development authority Proventa International “involves the systematic analysis of vast amounts of data to derive valuable insights and support informed decision-making.” In terms of supply chain management, this means allowing companies “to make strategic choices based on accurate and real-time data, minimizing risks and maximizing opportunities.”

Key Benefits of Integrating Data Analytics

Supply chain organizations and professionals who embrace data analytics enjoy numerous key advantages. Proventa lists efficiency-related cost reduction, increased industry-wide transparency, and the mitigation of potentially disruptive risks among the most compelling supply chain benefits that data analytics offer.

Essential Data Analytics Tools for Supply Chain Management

A myriad of different analytics tools contribute to modern supply chain management. The most essential of these tools include the following.

Real-Time Tracking Systems

This category of data analytical tool is designed to help organizations and individuals track the movement of items as they pass from link to link in the supply chain. They can be programmed to alert users as items pass particular milestones (dates and times when certain activities occur) during their supply chain journey. Forbes Advisor considers Anvyl the best of all digital analytics tools for milestone tracking.

Predictive Analytics Software

Predictive analytics help supply chain managers determine probable outcomes and identify disruptive problems before they occur. In terms of predictive analytics capabilities, one of the leading supply chain management platforms today is Deloitte Supply Chain Solutions.

Inventory Management Software

Oracle NetSuite is a leader in the world of inventory management software. These automated digital systems can be programmed to maintain optimum inventory levels automatically while eliminating logistical inefficiencies and reducing handling expenses.

Transportation Management Systems

While many supply chain data analytics systems focus on packages, transportation management systems focus on the methods of conveying these packages from place to place. The enterprise transportation management system FreightPOP was declared “best for shipping” by Forbes Advisor.

Demand Forecasting Tools

To ensure that important goods are on hand when both businesses and individual consumers require them, demand forecasting tools that use digital analytics are an absolute must. When it comes to expert supply planning, Forbes Advisor names Oracle NetSuite best in class.

Strategies for Implementing Data Analytics in Your Supply Chain

While it is crucial to choose the right data analytics tools as a supply chain manager, the best tools in the world will accomplish nothing if they aren’t properly implemented and used. What follows are just a few broad strategies to guide that implementation and use.

Developing a Data-Driven Culture

You can greatly streamline the implementation of data analytics and other complex technologies by establishing a data-driven culture within your organization. A company culture that embraces data and its practical uses is better poised to discover hidden opportunities and drive effective decision-making. To foster a data-driven culture, the Harvard Business Review recommends starting at the very top by securing the partnership of the organization’s senior-most executives. You can then convince others of the value of data by demonstrating concrete proofs of concept beforehand and addressing data analysis-related issues immediately as they occur.

Integrating Cross-Functional Data Sources

In a Supply Chain Brain article, Connor Stewart and Adam Robbins recommend open collaboration with players and partners in both vertical and horizontal lines of integration. “Organizations will need to manage their supply chain network less like a cost center and more like a continuous value-generation engine,” they write. “People, process and technology all play a key role in continuously improving the supply chain and reducing disruptions to establishing cross-functional alignment and supply chain resiliency.”

Staying Updated on Industry Trends and Best Practices

Driven by the rapid pace of technological advancement as well as ongoing refinements in process strategy, the field of data analytics is in constant flux. Therefore, it is important to remain up to date with the latest changes in the field as it relates to the platforms that you use as a supply chain manager. This will not only pay dividends in terms of technology integration but ongoing technology employment.

Collaboration with Other Departments

Much like cross-functional player and partner collaboration across supply chain links, practical information-sharing among internal departments is essential to effective and efficient data analytics implementation. By ensuring that all company departments are on the same page with implementation processes, the professional leading these processes can better ensure the smooth delivery of the many benefits that data analytics has to offer.