The modern-day notion of a digital supply chain primarily relates to tracking the lifecycle of goods from end to end. But the technology can do a lot more than that: It uses tools such as algorithms, artificial intelligence, machine learning and big data sets to view historical and real-time data, which can inform predictive modeling.

The goal of digitization is to create full transparency into every stage of the supply chain. It provides a real-time feed of progress, risks and inefficiencies; allows for reporting on every stakeholder in the supply chain; streamlines business processes; brings clarity and visibility to the finance function, and enables shared visualization of high-level supply chain processes.

In 2016, analysts at McKinsey articulated the idea of a next-generation digital supply chain, calling it “Supply Chain 4.0.” They envisioned the internet of things, robotics and advanced analytics of big data as transformational for the industry. They were right — today, most major companies are improving performance through enhanced networking, ongoing analysis and workflow automation.

Used tactically, a digital supply chain might simply mean creating a visualization of the physical supply chain, or digital twin. The concept isn’t exclusive to the supply chain; researchers at MIT have identified digital twins at scale as the best way to create predictive virtual models for aeronautics and astronautics.

The digital twin provides unique diagnostic, analytical and feedback capabilities. MIT illustrates one application of the model in this way: If a delivery drone suffers damage in flight, should it carry on or reroute? A “virtual” drone traveling the same flight path can generate the data needed to make the right decision.

Applying the technology to a broader view of the supply chain, the complexity increases, but the fundamental principles remain the same. In a virtual environment, businesses can send data signals that can used to pivot, reroute or reallocate finances. Multiple scenarios don’t have to be tried in the real world, where the stakes are immeasurably higher. They can be tested in the virtual world, with the winning move made on the ground.

The ingredient that makes all of this possible is data —clean, accurate, and reliable. The need for data quality cannot be overstated. Nor can the fact that data needs to be cross-checked, compared and aggregated from a variety of reliable sources to present the fullest possible picture.

Data from one sector of the supply chain can have a serious impact on all others. A weather pattern here, or political unrest there: Any one event can trigger issues that impact the entire supply chain. The answer to the old question “What does that have to do with the price of tea in China?” is: Plenty. And these days, it takes more than a human to draw meaning from the interrelation of huge volumes of data, both structural and dynamic.

Advanced analytics interprets intelligence from all types of data sets, including fluctuating market conditions, weather, travel patterns, government policy changes, international regulatory environments, and much more. The sum of all of these findings may yield insights that create new possibilities for proactive course-correction or problem avoidance.

The real and present benefits of these modern-day technological tools can be summed up in a word: resilience. Businesses need to be able to maintain operations, even grow, in the face of adversity and changing market conditions. Far from being “nice to have,” these tech solutions are becoming essential for survival and growth. Technology is available to accelerate progress, and adopting it is the intelligent thing to do.

Josh Bouk is president of Trax Technologies.