Tablet With SwanLeap Analytics Dashboard

The digitization boom and changing global economy have created a data-driven push for businesses to fine tune their cost centers in new ways. Many businesses can agree their supply chain is a significant cost center, yet struggle to leverage technology in making informed decisions about the makeup of their supply chain process. According to a recent study, 20% of supply chain leaders believe the digital supply chain is already the primary model and 80% expect it to become predominant within five years. Having access to the data is one thing, but how can businesses use big data to drive supply chain management process improvements?


First, your team needs to answer a fundamental question; what are supply chain analytics? The answer to this question is going to be different for every organization and every department depending on what KPIs you look to in measuring performance. Aligning on common metrics and what those data points indicate for your team will set up organizational best practices around your supply chain. In short, these strategies and techniques help make sense of the data created from across several areas in the supply chain as a product moves from one location to another. As various elements feed into the data landscape, they present as graphs and charts to equip your business with insight into tough areas for more informed decision making.

Most businesses commit to supply chain improvement and then falter on execution. The key to driving meaningful change in response to data is to start both from the bottom up and the top down. You need to know the high level data that informs the success of your business, but you also need to know what metrics will show the effectiveness in every role and process within your supply chain. Knowing what you want to measure and what your goals are becomes a cyclical relationship with one constantly informing the other.


A foundation of strategic goals is essential to determine the approach that’s the best fit for your business’ needs. But how do you know where to optimize?

You could go down the time-consuming path of the supply chain “treasure hunt”, sifting through mountains of data from multiple sources, or adopt a quick and efficient supply chain analytics tool powered by artificial intelligence that extracts actionable insights from your supply chains to leverage the best strategies quickly.

Descriptive analytics act as a single source of truth by offering clarity across the entire supply chain, not just fragments, in real time. A unified data center offers hard data to support more informed business decisions by revealing hidden inefficiencies and broken processes. Using a transportation management system housing this historical data will help connect the dots for deeper insights on how to prepare for similar situations in the future, such as seasonal demand planning.

Predictive analytics have the potential to be a resource of disruption and competitive advantage. This type of strategy provides supply chain managers with a clearer picture of possibilities from the data they collect. As forecasting climbs the list of top challenges for companies, becoming an early adopter of this approach can strengthen the resiliency of your supply chain and stay ahead of changing supplier and commodity challenges.


As the transportation and logistics industry continues to play technological catch-up with most other sectors of business, the possibilities for streamlining data into actionable insights grows almost daily. Future-focused supply chain leaders won’t wait to incorporate supply chain analytics and artificial intelligence to elevate their supply chain, and ultimately the customer experience. It is this process of sweeping digitization that will deliver supply chain transformation. The ability to anticipate and quickly prepare for the future is a growing requirement for businesses to stay ahead of disruptions.

Why wait to uncover more paths to success?