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Future-Proofing Distribution Systems with Quantum Computing

Quantum computing offers a significant opportunity to solve complex constrained optimization problems in near real-time, as demanded by modern distribution challenges, with more accuracy and better answers to drive better decisions.
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The past year reinforced the notion that we are dependent on our logistics and distribution channels and we need to reinforce and expand our abilities to optimize them dynamically when massive and unexpected shifts occur. Luckily, quantum computing is rapidly evolving, and with it, the ability to deliver insights that can help planners optimize logistics and distribution and rapidly adjust to unforeseen events. It won’t prevent catastrophes — but the technology will ensure more resilient and agile capabilities.

Classical computing limitations

Rising to modern distribution challenges and achieving the resilience and agility we need transcends the capabilities of even the most powerful classical computers.

The monumental shift in processing requirements due to the rapid rise of e-commerce creates an even larger challenge. In the past, a major retail logistics challenge was to ensure that the right products get to the right places for the lowest costs. As online purchases soar, customers expect unlimited product selection and availability at the lowest price. They also now expect immediate delivery of their purchases, even as they buy single items in multiple purchases, creating more and more shipments for vendors to manage logistically. The emerging differentiator for e-commerce businesses is quickly becoming delivery time.

The staggering 154% compound annual growth in same-day delivery points to the importance of distribution management. Ensuring next-day or a same-day delivery with zero delays and errors requires end-to-end optimization of the entire process, from raw materials all the way to the customers’ front door.

A field of mathematics called “constrained optimization” (CO) addresses these kinds of problems. It is about optimizing a function’s variables (e.g. trucks, SKUs, people), taking into account their constraints (e.g. cost, volume, time), for better business decision-making and efficiency.

CO has helped large organizations and their logistics and distribution professionals adjust to unforeseen events, balance myriad variables and optimize operations. Classical computers use this method to generate estimations and approximations. But as our data volumes increase geometrically, they hit a wall, taking too long to return insights, or simply failing at being able to run the larger problems.

This forces analysts to use all of their mathematical skills to find a way to get the best answers possible, including methods such as compressing info to run a computation or only computing part of the problem, which means the results are not as accurate as they would be if complete data sets were analyzed. Often, analysts are forced to manually determine which insights they will follow, without all the supporting data they require.

The path to quantum optimization

Quantum computing offers a significant opportunity to solve these complex CO problems in near real-time as demanded by modern distribution challenges, with more accuracy and better answers to drive better decisions. They can and will process these kinds of large, extremely complex problems.

However, the technology is not yet production-ready, i.e. able to solve the kinds of large problems described above. Even when they can scale, and are widely commercially available, pure quantum systems likely won’t be the answer for supply chain and logistics optimization.

They are fundamentally different from classical computers, which excel at running the search-based computations that find the best answers to these problems. Quantum computers offer the high impact value of being able to provide insights to the portions of these problems that are highly complex, the portions that stagger classical systems. They can offload these problem areas, while classical systems work to find the insights in other problem areas. Consequently, hybrid approaches that combine the power of classical and quantum computing are most likely to be the best approach to computational excellence. These types of classical/quantum hybrid systems however, are not commercially available just yet, although they are expected in the near-term.

However, there’s an opportunity to apply quantum-ready techniques to classical processing today, accelerating performance, increasing accuracy and providing a diversity of answers that chip away at these problems.

Given recent world events and the continuing growth of online business, it’s reasonable to conclude that the jobs of distribution planners will not get any easier. Forward-thinking managers can look to the field of quantum computing, specifically applying quantum techniques to classical computing, to get a jump on how to best adapt, adjust and optimize their operations.

Robert Liscouski is president and CEO of Quantum Computing Inc. Serving as CEO and chairman of QCI since February 2018, Liscouski has more than 35 years of executive experience at public and private companies, as well as federal agencies.  Reach him on LinkedIn and for more information about QCI, visit quantumcomputinginc.com.

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