Adam Compain, Author at Global Trade Magazine

Nearly every industry on the planet is undergoing transformation. Disruptive pressures and heightened customer expectations – mostly driven by technology innovation – have pushed businesses everywhere to re-examine their strategies. The world of global logistics has long been insulated from these pressures and disruptive measures. But this is no longer the case. For a long time, building bigger ships and ports passed as industry innovation and adding buffer stock was the common method to combat supply chain uncertainty.

But today, the industry must get smarter, not bigger. This starts with digital transformation that unlocks the value of data in the global supply chain.

The Data Challenge that Plagues the Supply Chain

Gartner research predicts that 80 percent of supply chain software applications will include conversational artificial intelligence by 2020. But as Gartner’s Noha Tohamy points out, there is a number of challenges to implementing AI in global supply chains. The first and most pressing is the data problem.

Data today is siloed and remains vastly underutilized for multiple reasons. Often it is dirty, incomplete or unsequenced. Before it can be made actionable or fed into AI or machine learning engines to improve predictions and execution, a prerequisite step must occur: cleaning, sequencing, de-duplicating, and filling out sets of data. But this remains an elusive task.

Ironically, the technology and tools in place today to handle data are the primary inhibitors to innovation. Data processes and the tasks that surround them remain outdated and manual. Today, the industry remains stuck in neutral due to the data challenge and lack of tools and strategies for delivering actionable insights.

Shippers, terminal operators, carriers, forwarders and 3PLs are all feeling the pressure to adapt – to become smarter and more data-driven. But this path starts with more questions than answers.

Equipping their teams with tools and resources to utilize innovative technologies like artificial intelligence will empower shipping industry companies to better handle challenges that continue to cost the industry billions of dollars each year. Here are two steps that shippers, terminal operators, carriers, freight forwarders and 3PLs can take to become more data-centric.

Unlock the Power of Existing Data

Big data and analytics are buzzwords continually thrown around in the industry. The truth is, most data is not optimized and in many cases, its value is limited. Lack of tools and infrastructure leaves data inaccessible and unusable by more powerful technology engines.

The shipping community widely recognizes the urgency to innovate using AI. But the data challenge stands in their way. However, these are not two exclusive problems—AI and the data problem. In fact, companies can, and should, deploy AI to solve the data quality challenge. AI can be used to organize and cleanly structure data, making it machine readable. This is an essential first step that many companies have failed to execute.

Identify Initiatives that can Move the Needle

Every segment of the supply chain has pressing needs. Procurement, transportation, planning—each team has its own burning challenges driving an urgency to innovate. One common denominator exists: each team needs to operate smarter. They have to be data-driven. They’re all searching for ways to deploy AI and machine learning to improve decisions and operations. But few pull the trigger for multiple reasons. For one, the “safe” thing to do is to continue conducting research. Many wait until best practices are well-established. Others sit until an innovative leader moves first and showcases success.

Another reason for delay: pursuit of perfection. Teams struggle with analysis paralysis when examining AI strategies. With so many different directions to move in, it can be overwhelming to determine where to start. The third reason for delayed action is the misconception that AI and machine learning are futuristic technologies that are still three or four years out.

These delays are simply barriers to innovation, and they loudly call out the need for a shift in mindset. AI is here today, and it can deliver immediate value. But businesses today have to operate as technology companies. They must make innovation pilots a priority. Look no further than Amazon’s latest acquisition to be reminded of the disruption sweeping across industries such as retail. The pace of change in global commerce will only accelerate in the coming years. The gap between leaders and laggards will be significant. And as competition and macroeconomic pressures intensify, nobody can afford to fall behind. It’s critical to pick an AI project that can move the needle and jump start innovation.

But this is easier said than done. There’s a lack of talent and resources around AI and data science. This is not an area of business that can be bolted on to an organization. The logistics industry requires data science and solutions custom built for the industry. And as a result, there’s been a surge in tech startups servicing the industry.

Moving Ahead

As these worlds continue to converge, logistics startups should keep these tips in mind:

Know your industry and segment. Build customized solutions to solve real-world problems. Partner with industry pioneers.

Surround yourself and team with expertise. Build a bench of team members who know the pains and challenges of the industry.

Think long term; think short term. Set yourself up to deliver immediate value today to your target market, but have a long term strategic value roadmap that delivers customers a path to innovation.

Help customers act like startups. 100-year-old companies today strive to think and execute like a startup. Empower them to do so. In some cases, you may become their innovation incubator.

There’s mounting pressure for rapid business innovation. There’s growing demand for data science skills and know-how. And C-level executives are mandating that businesses act more like technology companies. As this continues to unfold, Silicon Valley will play an increasingly important role in the global logistics 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.

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.

The Urgency to Transform Global Logistics

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