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  August 18th, 2017 | Written by

The Urgency to Transform Global Logistics

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  • The technology and tools in place today to handle data inhibit innovation.
  • Logistics data processes remain outdated and manual.
  • The logistics industry remains stuck in neutral due to the data challenge.

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 Data processes and the tasks that surround them remain outdated and manual.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.