Supply Chain Optimization: What Leaders Need to Know in 2026

Supply chains in 2026 are more complex and volatile than ever. Geopolitical tensions, shifting trade policies, and unpredictable demand are putting increasing pressure on highly interconnected global networks. At the same time, leaders are expected to make faster, better decisions using ever-growing volumes of data, often with tighter constraints and less margin for error. 

Traditional approaches to supply chain optimization are struggling to keep up. As supply chain decisions—from routing to inventory to production—become more interdependent, many problems grow too complex to solve efficiently without simplifying assumptions or settling for suboptimal solutions. 

When I was at CSCMP Edge this month, these challenges were on top of everybody’s mind. Here are a few of the top questions I heard at the event, and my thoughts on how leaders are addressing these challenges with new technologies, including quantum computing.  

What is the supply chain polycrisis and what does it mean in practice today?

A supply chain polycrisis occurs when multiple simultaneous disruptions compound and amplify one another. Today, we see this at the intersection of geopolitical tensions, tariff volatility, drought restrictions in the Panama Canal, and inflationary pressures weighing down demand. Together, these forces make long-term supply chain planning increasingly difficult. 

Production plans were once built made with an expectation of relative stability. Supply chain managers could plan for the average and manage the exceptions. Since the COVID-19 pandemic, however, disruptions have become routine. Planning must now anticipate volatility while preserving enough flexibility to adapt to the unforeseen.  

But planning cycles that take weeks or months don’t provide the agility today’s supply chains demand. To stay ahead of volatility, organizations need a faster way to optimize the many interconnected variables across their global supply chains. The faster supply chain managers can adapt, the greater their competitive advantage.  
 
Agility is now a core requirement for every supply chain. New trends and situations emerge quickly, whether that’s a sudden shift in consumer demand, a new tariff, or a disruption at a key port or supplier. Organizations need supply chains that can sense these changes early and act on them decisively, adjusting sourcing, production, inventory, and distribution in days rather than months. Those that build this agility into their operations will be positioned to turn disruption into opportunity, while those that cannot will continue to react after the fact. 

Annealing quantum computing, which is well suited for addressing computationally complex optimization problems, offers a new path forward. The technology is being used today by organizations to solve complex optimization problems often faster, better and more efficiently than classical-only solutions. For supply chain leaders, this could open the door to more efficient, resilient, and adaptive operations.

Many companies optimize individual parts of the supply chain. What opportunity is missed without a holistic approach?

When each element within the supply chain is optimized individually, planning becomes siloed. Each step gets measured against a different—and sometimes competing—set of KPIs. As a result, organizations miss the opportunity to plan with an end-to-end perspective that optimizes the whole supply chain. What looks optimal for one part of the business can create inefficiencies elsewhere. 

To give an example, the purchasing department might find a great bulk deal on materials and order a large quantity from the supplier. As a result, excess inventory sits idle in a warehouse. Purchasing has met its objective of sourcing materials at the best price, but the broader supply chain absorbs the inventory cost. Manufacturing might then optimize for machine utilization, or overall equipment effectiveness, by running longer production batches, adding even more finished goods inventory. Distribution is then left trying to meet service levels with inventory that may not align with actual demand. 

It’s a classic example why a local optimum doesn’t equal global optimum. Very rarely are things looked at across the spectrum of the entire supply chain. The greater opportunity comes from optimizing the whole supply chain, bringing demand, manufacturing, distribution, and procurement under one shared set of objectives.

Why is true end-to-end optimization still so difficult?

There are three primary barriers to true end-to-end optimization: data, math, and organizational structure. 

The data challenge comes from the fact that different functions often operate in different systems, leaving critical information siloed across the supply chain. Purchasing may use one system, manufacturing another and forecasting yet another. Without a unified view of that data, it’s difficult to optimize the supply chain as a whole.  

The math challenge comes into play as organizations try to optimize more of the supply chain at once. The number of variables, constraints and possible decisions grows dramatically. These problems are mathematically complex, non-linear and non-convex. To solve these problems with classical solvers, organizations are often forced to use heuristics, simplify constraints, reduce the number of objectives, or limit scenario analysis to arrive at useful solutions within the available decision timeframe.   

Then there’s the organizational challenge, where each functional area has become comfortable optimizing their own piece of the supply chain to meet their own KPIs, even when those objectives may not produce the best outcome for the business overall.  

True end-to-end optimization requires overcoming all three barriers: connecting the data, solving the increasingly complex math and aligning the organization around shared objectives.  

What makes a supply chain problem a strong fit for quantum optimization?

There are several factors to consider and questions to ask. First and foremost, is the classical approach hitting its limits? Are runtimes extending beyond the decision window? Have you had to model things at a higher level of abstraction than you really want, simplifying problems in a way that ignores real-world constraints? Are you limiting the number of possible scenarios you can explore? Does the solution quality decline as the problem scales?  

These limitations are signs that your problem could be a good fit for quantum optimization.  Annealing quantum computing is particularly well suited to addressing non-linear, non-convex optimization problems with many interdependent variables and large search spaces. The key is identifying problems where complexity is limiting what classical approaches can achieve and where a better solution could deliver meaningful business impact.  

The strongest candidates sit at the intersection of three factors: classical approaches are falling short, the problem aligns with the strengths of quantum optimization and there is significant business value at stake. 

Where are supply chain leaders seeing success and ROI from quantum applications today?

D-Wave's customers are seeing real-world value from annealing quantum computing today in areas across the supply chain such as scheduling, sequencing, and allocation. 

Take BASF, for example. BASF, one of the world’s leading chemical companies, completed a joint proof-of-concept project with D-Wave that used a hybrid-quantum application to optimize manufacturing workflows in a BASF liquid-filling facility. The hybrid-quantum technology set a new benchmark for manufacturing efficiency, allowing reduction of production scheduling time from 10 hours to just seconds. The application was tested in a real-world production scenario to optimize task scheduling and liquid tank assignments while meeting business and quality requirements. The hybrid-quantum application outperformed an existing optimization solution across key operational metrics, reducing lateness by 14%, reducing setup times by 9%, and shortening tank unloading durations by up to 18%. 

Workforce scheduling is another area where quantum optimization pays off. Pattison Food Group built a hybrid-quantum auto-scheduler for its delivery drivers with D-Wave. The application went into production, taking weekly scheduling time from roughly 80 hours down to 15. Driver schedules are a deceptively hard problem—shift patterns, seniority rules, and policy requirements all have to line up simultaneously, and the inputs shift constantly. Automating the scheduling frees up senior planners’ time to focus on higher level tasks. 

To further expand quantum computing’s impact on complex supply chain problems, D-Wave recently partnered with the Supply Chain Management Research Center (SCMRC) in the Sam M. Walton College of Business at the University of Arkansas. The collaboration will explore foundational and applied research in quantum optimization for a variety of supply chain use cases, including transportation routing, logistics network design, inventory and resource allocation, scheduling and decision-making under disruption.  
 
We believe the next advantage in supply chain will come from making better decisions, faster, at a scale classical methods struggle to reach. The Sam M. Walton College of Business is well-positioned to prepare the industry for that shift. Through this initiative, we will give students, researchers and industry partners hands on experience applying quantum optimization to real challenges in retail, supply chain and contested logistics. Learn more about the partnership here.

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