Top Quantum Techniques to Optimize that Complex Last Mile

Top Quantum Techniques to Optimize that Complex Last Mile

Agility and speed to optimization are critical to meet customers’ growing expectations for instant availability and near-immediate delivery. Yet, the last mile has always been the most expensive, long-bemoaned challenge of logistics.

With the “new normal” of changing consumption habits and channels creating unpredictable demand, forecasts have become meaningless. The last mile grows even more complex.

A fixed logistics model is not designed to be flexible or fast. Capgemini Research Institute’s Supply Chain Survey 2020 found that 70% of companies are prioritizing inbound and outbound logistics as part of their supply chain sustainability efforts post Covid.

Yet less than half of organizations asked by Accenture agree they’re currently meeting customer expectations for order fulfillment.

This may be because classical computers struggle to give accurate, high-quality answers to large scale, complex optimization requests. For today’s increasing data sets and complex optimizations, performance (aka time-to-results) slows.

Additionally, they give only a single optimal answer, which may not be the true best solution in today’s dynamic, change by the minute, market.

While the full capabilities of quantum computers are years away, emerging software solutions aim to bridge classical and quantum computing worlds using quantum-ready techniques.

This produces better results for constrained optimization, successfully solving expanding data sets and complexity on classical computers and eventually for quantum systems.

Hybrid approaches that combine the power of classical and quantum computing are most likely to be the best approach to computational excellence.

Until these types ofclassical/quantum hybrid solution are commercially available, get a jump on logistics optimization by applying quantum techniques while using classical…today.

Last Mile Improvements with Quantum

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.

The global pandemic has demonstrated how vulnerable our supply chain and logistics operations are in today’s dynamic global markets. The requirements for last mile delivery for retail customers has changed dramatically, outdating traditional optimization approaches.

Instead of serving brick and mortar customers with large in-store carts, we are now serving ecommerce shopping carts with a few items who may order multiple times a day and expect delivery within 24 hours. The optimization requirements are overwhelming.

There’s an opportunity to apply quantum-ready techniques to classical processing today, accelerating performance, increasing accuracy, and providing a diversity of answers. This chips away at logistics problems with constrained optimization to address the complexity of the last mile.

Quantum computers process complex computations to return a diversity of answers,. Every answer that meets the optimized state you need is delivered to you. When you apply quantum techniques to classical systems, you get the same diversity of results.

You get exposure to more viable options than with classical processors and can select the one that best matches your specific situation right now.

This is a much better way to make decisions vs traditional approaches that provide a single answer as your only option.

Quantum computing techniques can apply constrained optimization and empower supply chain and logistics-oriented companies with the in-depth insights needed to optimize the last-mile.

You don’t need to wait for pure quantum to simplify last mile complexity your classical computers struggle to solve.

Our Qatalyst application accelerator is ready-to-run software that applies quantum-ready techniques to classical today to solve constrained optimization problems—and serve as a bridge to quantum systems when the time comes.

Use Constrained Optimization to Address Retail and Channel Logistics

Constrained optimization is an important method to address retail logistics challenges.

The pandemic-driven boom in volumes and complexity of e-commerce drive additional use of constrained optimization beyond product lifecycle management to focus on effective, low cost and fast delivery.

For example:

  • Network design factors: The location, number and characteristics of distribution facilities must be optimized for rapid last-mile delivery.

  • Online order profiles: Facilities and processes must support a much larger volume of orders with a smaller number of items because each order increases the challenge of optimization.

  • Inventory segmentation: Many Internet retailers must provide a wide range of diverse products; inventory segmentation within internal and third-party distribution networks must be optimized.

  • Distribution logistics: Transportation and shipping must be optimized for fast, on-time delivery of high volumes of orders to end-user customers across geographies, down to the last mile.

You must control costs to support profit margins while doing all the above.

The Bottom Line

Quantum computing is one of the most promising technological innovations likely to shape, streamline and optimize the future of the supply chain. It offers better insights to make better decisions. That’s why there’s so much excitement about it.

Understanding and implementing quantum computing techniques today can help put supply chain and logistics-reliant companies (e.g., those in e-commerce, manufacturing, transportation, distribution, etc.) ahead of the competition.

IDC research concludes, “The ability to ingest broad and deep data sets to inform better decision making will be the single largest differentiator of supply chain performance in the future.

Quantum computing techniques empower constrained optimization to a new level of accuracy and performance.