retail logistics

How to Optimize Retail Logistics in an E-Commerce World

Rebel Brown

Like most industries, a major retail logistics challenge has traditionally been ensuring that the right products get to the right places for the lowest costs. In retailing environments, providing on-shelf availability of products has been a critical success factor.

The shift to e-commerce was already happening as companies like Amazon challenged the traditional brick and mortar retailers like WalMart and more.

The days of Covid have accelerated the transformation from brick and mortar to e-commerce shopping, driving even more retail logistics challenges.

Comparing Growth: US Retail Sales vs eCommerce

As online purchases soar, customers expect unlimited product selection and availability at the lowest price. The emerging differentiator for e-commerce businesses is quickly becoming delivery time. Consider the following:

  • As many as 96% of customers consider faster delivery synonymous with same-day delivery.
  • A  154% compound annual growth in same-day delivery demonstrates the critical nature of supply chain logistics. Ensuring next-day or a same-day delivery with zero delays and errors requires end-to-end optimization all the way to the customers’ front door.
  • A study by McKinsey & Company showed that customer service represents the primary factor that brands and retailers can use to differentiate themselves and “delight” their customers. The primary aspect of customer service mentioned by customers? Delivery time.

These challenges can and will be solved with the help of constrained optimization engines.

These computational solutions analyze your complex logistics information to recommend the best optimization methods. That means you can assure fast, effective delivery and the best possible customer service.

When Retail Logistics Focuses on E-Commerce Efficiency

Constrained optimization has been an important method to address retail logistics challenges.

Today’s expansions 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, including the location, number, and characteristics of distribution facilities must be optimized for rapid last-mile delivery.
  • Facilities and processes must support online order profiles; a much larger volume of orders with a smaller number of items for each order is a new and different optimization.
  • Since many Internet retailers must provide a wide range of diverse products, inventory segmentation within internal and third-party distribution networks needs to be optimized.
  • Finally, distribution logistics must be optimized for fast, on-time delivery of high volumes of orders to end-user customers across geographies.
  • All of the above must be done while controlling costs to support profit margins.

Constrained optimization computations solve these problems. The challenge is that our dramatically growing data volumes are overwhelming classical computing architectures. They can’t effectively process all the data.

Tactics that compress, shrink or sample the data to be able to process a smaller computation only serve to reduce the accuracy of results.

That’s where quantum computers can bring an extraordinary advantage.

The Future is Quantum

Quantum computers analyze data and run simulations in a 3-dimensional space that matches how the real world works. As a result, they will have the ability to run more large-scale, accurate simulations.

Currently, most simulations are held back by the vast amounts of processing power required to run them, as well as a lack of accuracy.

Applying quantum computing to logistics optimization means that the number of variables (data) they can analyze simultaneously grows dramatically, allowing computations that would take classical computers years or even decades to run, if they could run them at all.

Even better, quantum computers also return a diversity of results, offering more and better opportunities to find the best possible solution in different situations, with much higher accuracy.

The Bottom Line

You may be thinking, “But quantum computers aren’t ready for prime time yet.” You’re right, they aren’t.

But that doesn’t mean quantum isn’t valuable right now. It is.

Because of the novel way quantum computers operate, they hold the promise, as previously discussed, to analyze vastly more data than conventional computers can today.

Consequently, the scientists and engineers at QCI have applied quantum methods to create a powerful new way to conduct constrained optimization–even with today’s classical computers.

You no longer have to wait for quantum computing to become mainstream to take advantage of the technologies that have been developed to support it.

Even on classical computers, the constrained optimization engine in Qatalyst can deliver even greater performance, accuracy, and diversity of results than traditional solvers. And it’s all available in the cloud.