The $2.6 trillion logistics industry is primed for digital disruption. The shift is being driven by numerous factors, including overwhelming complexity in the supply chain, understanding the importance of data intelligence over economies of scale, and acknowledgement that existing technologies are insufficient.

In recent years, the C-suite has increasingly come to recognize the strategic value of supply chain and logistics processes. This has translated to an elevated focus on supply chain within the business and, in turn, more pressure to innovate through technology.

Just as machine learning and artificial intelligence (AI) have been the source of innovation in other industries, these technologies are set to have a transformative impact in supply chain through the multi-billion dollar category of “predictive logistics.” Already, AI built for the logistics industry has been proven to increase accuracy of behavioral predictions– such as shipment cancellations– up to 90 percent and produce millions of dollars in profitability gains.

Predictive logistics processes integrate and mine myriad forms of data and overlay AI and machine learning to create clear visibility of the global supply chain. The result: extremely accurate predictions of what will happen in the near-to-mid-term future. This insight is critical because nearly every decision in the supply chain is already based on a prediction of some kind. And by predicting more accurately, carriers, forwarders, terminals, shippers, 3PLs, and 4PLs are able to plan far more effectively than ever before and realize new levels of efficiency and profitability.

Predictive Logistics is poised to be the next frontier for global supply chains. Here’s why.

The Ceiling Has Been Reached with Current Approaches and Tools
Today’s global supply chain participants rely largely on approaches and technology that haven’t changed a great deal in many years. Traditional tools are exhausted and outdated. Data is dirty, siloed, and inaccessible. Few utilize their data thoroughly and even fewer employ data science to help assist decision making processes. The result is suboptimal forecasting, thus, under-informed decision-making and, ultimately, reduced efficiency and profits.

Current forecasting solutions were not built specifically for the supply chain. Off the shelf solutions that utilize forecasting models supported by even the top operations researchers are not dynamic enough to keep up with the industry’s staggering number of variables and “what-if” scenarios.

With over 500 million booking revisions per year, according to the IHS Global Insight World Trade Services, for example, supply chain complexity has moved beyond human capacity to accurately predict the movement of goods. True efficiency will only be attained if we can predict with increased accuracy every event and contingency on the horizon.

Growing Interest in AI and Machine Learning Will Supercharge Prediction Accuracy
AI and machine learning are poised to solve supply chain uncertainties that are too complex for the human brain. Machine learning uses computing power to identify patterns in data and draw insights that humans could never realize on their own.

Harnessing these technologies can yield a level of granularity and specificity that can make a material difference in supply chain.

However, logistics professionals should be aware that machine learning is not a box you can buy, plug in, and receive instant results. Technology platforms need to be custom built and tailored to the nuanced logistics industry. Companies should seek custom solutions that gather, cleanly structure, and integrate siloed data into machine intelligence-ready data supersets.

Complexity and Interdependency are Only Expanding
The global supply chain has become far too complex to accurately predict the behavior of customers and the movement of goods. As a result, suboptimal asset allocation and trade management decisions have a significant negative impact on profitability for participants throughout the supply chain.

The industry’s recent efforts to drive efficiency through scale have not only been bringing diminishing returns (think: mega ships) but have, in fact, become detrimental due to oversupply. To drive profitability, we must operate smarter, not bigger. Unprecedented efficiencies and profitability can be reached by digging into internal data and the broader data sets that exist within the industry, and overlaying that with AI and machine learning.

Organizations have an opportunity to reevaluate their current digital supply chain strategies and focus on generating better predictions – because it’s prediction that drives our industry.

Adam Compain is the CEO of ClearMetal, a predictive logistics company that uses data science and machine learning to unlock new efficiencies for global trade. Compain co-founded ClearMetal after working in Hong Kong at one of the world’s largest container-shipping companies and for five years prior, Adam deployed the newest geo-commerce technologies at Google.