Nearly three quarters of supply chain leaders are looking to transform their operating models within the next few years, according to a KPMG survey. But what exactly is making supply chains so difficult to manage, and why are leaders so anxious to change how their organization approaches them?
A few things come to mind: trade policy, demand volatility, shipping disruptions, and post-COVID competition pressure. These compounding disruptions are increasingly upending months of careful planning overnight, making it hard for supply chains with rigid operating models to adapt at the same pace. In fast-moving supply chain environments, resilience to disruption depends on the ability to recalculate far-reaching decisions continuously. If the time required to adapt and return to normal operations is slower than the time it takes for the next disruption, supply chain operators will constantly be behind.
The goal of delivering to consumers at speed, within operational costs, while maintaining the flexibility to adapt to disruption, is an enormously complex task across a global supply chain. Supply chain managers have more data than ever at their disposal, which enables better forecasting. But the challenge remains in applying that data and forecasting to make better informed operational decisions, in a short time frame, given thousands of interconnected variables and constraints. Each decision ultimately compounds the impact on cost, service levels, and risk. It’s not a data challenge—it’s an optimization challenge.
Traditional optimization approaches can face challenges as problems grow in scale and complexity. In fact, in a Wakefield Research report commissioned by D-Wave, 81% of business leaders surveyed believe they’ve reached the limit of benefits they can achieve through optimization solutions running on classical computers alone. To deliver a solution on a production-relevant timeline, organizations may use heuristics, simplify constraints, reduce the number of objectives, or limit scenario analysis to arrive at useful solutions within the available decision window.
But it doesn’t have to be this way: quantum-powered optimization uses annealing quantum computing alongside classical resources to address complex optimization problems. Organizations are already using these approaches in production applications, with some reporting measurable operational improvements in areas such as production scheduling and workforce scheduling.
For supply chain managers, quantum optimization can help organizations shorten time to solution for some complex optimization problems such as inventory placement or workforce scheduling. Murray Thom, vice president of quantum technology evangelism at D-Wave, refers to the “cone of uncertainty,” wherein the further we look into the future, the less certainty we have about how events will play out. If optimization solvers take hours to run, then organizations may need to rely on older data that may become out of date by the time the solver arrives at its solution, limiting the ability to pivot if anything changes.
Take an inventory placement schedule, for example. As demand, inventory levels, transportation availability, and other conditions change, organizations may need to reconsider decisions across a large number of interconnected variables and constraints. The faster those decisions can be recalculated using current data, the more opportunity an organization has to respond to changing conditions. Complex inventory allocation and loading decisions are examples of optimization problems that organizations can evaluate for potential fit with quantum-powered approaches.
Organizations see results using quantum optimization
Ford Otosan, an automobile manufacturer in Turkey, deployed a hybrid-quantum application in production to streamline the manufacturing processes for its Ford Transit line of vehicles. Ford Otosan used D-Wave’s hybrid solvers to optimize the production sequencing, reducing scheduling time for 1,000 vehicles per run from 30 minutes down to less than five minutes. Moreover, the quantum optimization solution offered more flexibility for the manufacturer to adapt to changes in demand or auto part availability, maintaining productivity without disruption.
Workforce scheduling is another area where supply chain organizations can benefit from quantum optimization. Pattison Food Group worked with D-Wave to develop a hybrid-quantum auto-scheduler for its delivery drivers, reducing weekly scheduling effort from approximately 80 hours to 15 hours. Workforce scheduling requires complex, ever-moving parts like shift work, seniority, and policies. Now, the senior workforce is able to redirect their time and energy to other supply chain operation management issues.
What’s slowing down adoption for quantum optimization?
According to Andre Pharand, founder and CEO at Pharand Advisors, legacy IT systems, lack of general awareness, and financial restraints can lead to perceived difficulty in adopting quantum optimization solutions. Pharand notes that efficiency improvements can help strengthen the business case, which is one reason organizations may start with a proof of concept or pilot and measure results against their existing approach.
Thom explains how “small inefficiencies have huge potential repercussions in terms of cost.” For example, unnecessary parcel touch, extra loading steps, failed first deliveries, underused vehicles, or poorly aligned shift schedules can lead to employee dissatisfaction. Therefore, having predictable work schedules and smoother operations makes every employees’ day easier. This efficiency helps protect costs that might be otherwise lost if ultimately high frustration leads to high turnover.
For suitable optimization problems, reducing time to solution can give organizations more opportunities to incorporate changing conditions and evaluate alternative scenarios. The potential business impact will vary by application, making it important to define the operational outcome and evaluate performance against the organization's existing approach.
How to incorporate quantum optimization within a supply chain organization:
Rest assured, supply chain professionals don’t need to know everything about quantum computers. D-Wave's hybrid applications can integrate with existing business technology stacks, allowing organizations to explore quantum-powered optimization without necessarily replacing their existing systems.
Here are a few practical ways to get started:
- Start with a hard optimization problem. Look for decisions involving many variables, constraints, tradeoffs, or complex interactions where current approaches struggle.
- Understand what makes the problem difficult. Consider whether scale, nonlinear relationships, competing constraints, or reliance on heuristics or manual work are limiting performance.
- Define what better looks like. Identify the outcome you want to improve, such as reducing delays, improving utilization, shortening planning cycles, or producing better schedules.
From there, the next step is to evaluate whether the problem is a good fit for quantum or hybrid-quantum approaches using representative data and appropriate benchmarks.
To find out if quantum optimization would be a good fit for your business, start with D-Wave’s guide: https://www.dwavequantum.com/learn/resource-library/quantum-optimization-fit-ebook/
