Quantum Transformation in Retail:

How Pattison Food Group & D-Wave Are Rewriting the Rules of Workforce Optimization

ABSTRACT

Digital transformation is reshaping industries globally, yet finding the right tool that sticks remains the central challenge. Quantum annealing — a specific application of quantum computing — may be one of those rare technologies that fundamentally changes business operations the way the paperclip changed office work. This paper examines how Pattison Food Group (PFG), a multi-billion dollar Canadian retail conglomerate, partnered with D-Wave Systems to solve complex workforce scheduling challenges across 300 locations and 30,000+ employees — and what the results mean for the broader retail and logistics industry.

Introduction

Digital transformation is no longer optional. Companies that have failed to adapt have gone bankrupt or been rendered obsolete, while those that embraced it have maintained competitive advantage. The technologies enabling this transformation range from widely adopted cloud systems to cutting-edge emerging technologies — and few are more promising than quantum computing.

Quantum computers differ fundamentally from classical systems. Rather than binary bits (0 or 1), they use qubits that leverage superposition — existing as 0, 1, or both simultaneously — enabling parallel computation across exponential solution spaces. More importantly, quantum systems grow in power exponentially as qubits are added, making them uniquely suited for optimization problems that overwhelm classical hardware.

Among the early adopters of this technology is Pattison Food Group (PFG), a major Western Canadian grocery chain. PFG partnered with D-Wave Systems — a pioneer in commercial quantum annealing — to tackle one of the most complex logistical problems in large-scale retail: workforce scheduling for over 30,000 employees across 300+ locations.

Literature Review & Technical Context

Quantum annealing is a distinct paradigm within quantum computing. Unlike gate-based quantum systems, an annealer solves optimization problems by seeking the ‘ground state’ — the lowest energy configuration of a system — which corresponds to the most optimal solution. This approach has shown particular effectiveness in combinatorial optimization problems that exist across finance, logistics, traffic management, and drug discovery.

Research by Arute et al. (2019) demonstrated that quantum systems can process vast solution spaces more efficiently than classical systems for specific problem types. Meanwhile, Boixo et al. (2013) documented evidence of quantum advantage in systems with 100+ qubits, suggesting commercial viability was approaching rapidly. D-Wave Systems, based in British Columbia, has been commercializing quantum annealers for over a decade and counts Ford Otosan, Mastercard, and PFG among its customers.

KEY CONCEPT: What is Quantum Annealing?

A quantum annealer finds optimal solutions by simulating a physical process where a system evolves from a disordered, high-energy state toward its lowest-energy configuration. In business terms: the ‘lowest energy state’ IS the optimal solution — whether that’s the most efficient delivery route, optimal loan approval process, or best-fit employee schedule.

Company Overview: Pattison Food Group

To understand why PFG makes an ideal quantum case study, one must start with its founder. Jim Pattison began his career selling doughnuts and washing cars before pivoting to automotive sales. In 1961, he secured an unconventional bank loan and opened his first car dealership. By his 40s, he was a millionaire with holdings spanning radio stations, outdoor advertising, and grocery chains.

Today, The Jim Pattison Group (TJPG) encompasses food and beverage, media, entertainment, agricultural equipment, and retail — including the Guinness World Records. PFG, as a subdivision of TJPG, operates as an umbrella for multiple food-related brands including Save-On-Foods and Western Farms.

What makes PFG uniquely suited for this case study is scale: over 30,000 employees, 300+ retail locations, and a logistics operation that — prior to quantum implementation — required 80 person-hours per week just to schedule its 2,000+ e-commerce drivers.

Pre-Implementation Challenges

Before quantum scheduling, PFG faced a set of compounding operational pain points that worsened significantly during the COVID-19 pandemic:

• Administrative Overhead: Scheduling a team of three to four schedulers dedicated 80 person-hours weekly to manually managing driver assignments.

• Coverage Gaps: Frequent mismatches between driver availability and operational demands due to cognitive limitations of manual management.

• Low Agility: Difficulty rapidly adapting to driver cancellations or surges in e-commerce demand.

• Employee Dissatisfaction: Perceived scheduling bias among drivers led to morale issues and elevated turnover.

• Customer Impact: Downstream scheduling errors led to delayed deliveries — including prescriptions and groceries — impacting vulnerable customers.

The pandemic acted as a forcing function. Surge in online orders combined with diminished capacity exposed the limits of Excel-based macros and linear optimization models that had previously been sufficient. PFG needed a fundamentally different solution.

D-Wave Implementation: Process & Architecture

PFG first piloted quantum computing during the early pandemic period, initially automating store-level shift scheduling. The success of this pilot prompted expansion to the more complex challenge of fleet-wide driver scheduling.

The implementation followed a structured four-stage process:

• Stage 1 — Data Modeling: Structured modeling of shift constraints, driver availability, geographic coverage, and minimum rest periods between shifts.

• Stage 2 — Quantum Formulation: Conversion of the scheduling problem into Quadratic Unconstrained Binary Optimization (QUBO) format for D-Wave processing.

• Stage 3 — Hybrid Solving: Deployment of D-Wave’s hybrid solver — combining classical pre-processing with quantum optimization — to generate optimal weekly schedules.

• Stage 4 — Legacy Integration: Connection of the quantum scheduling output to PFG’s existing HR and operations systems.

DIRECT QUOTE — Benny Wai, Manager of Analytics Development, PFG

“There’s a team of three to four e-commerce driver schedulers that creates schedules for all drivers, all across the provinces. This accounts for 80 person-hours of work to build out a schedule for each week.” — D-Wave Systems Case Study, 2024

Post-Implementation Results & ROI

The outcomes following D-Wave implementation were measurable, significant, and strategically important:

• Time Savings: Weekly scheduling time dropped from 80 hours to 15 hours — an 81% reduction in administrative labor.

• Demand Fulfillment: PFG now consistently meets 95% of e-commerce fulfillment demand.

• Labor Cost Reduction: Optimized scheduling reduced overtime occurrences and labor misallocation.

• Improved Accuracy: Better staffing-to-demand alignment reduced delivery errors and rescheduling.

• Employee Satisfaction: Automated, transparent scheduling eliminated perceived bias and reduced driver disputes.

While exact financial disclosures have not been made public, conservative estimates based on national average wages suggest that reducing 65 administrative hours per week translates to $50,000–$75,000 in annual direct savings — excluding indirect benefits from improved customer satisfaction and reduced employee turnover.

Risks, Challenges & Ethical Considerations

• Labor Law Implications: Schedulers whose roles have been significantly automated may face employment uncertainty, raising concerns under provincial and federal labor law.

• Union Considerations: If affected employees are unionized, layoffs or role reassignments could trigger collective agreement provisions.

• Cost Trajectory: Ongoing licensing and compute costs from D-Wave could grow with scale and economic fluctuations.

• Consumer Data Ethics: Third-party processing of driver and customer data introduces privacy obligations under PIPEDA.

Strategic Lessons & Broader Implications

PFG’s quantum journey offers a replicable playbook for organizations considering emerging technology adoption:

• Start Small, Scale Fast: Begin with one high-friction use case before committing to enterprise-wide quantum integration.

• Target High-Friction Problems: Prioritize problems with combinatorial complexity where classical methods are demonstrably insufficient.

• Embrace Hybrid Systems: Leverage hybrid classical-quantum solutions for near-term viability — pure quantum is not yet necessary.

• Build Internal Literacy: Invest in internal capability to interpret quantum outputs and integrate them into decision-making.

The implications extend beyond retail. Any industry managing complex resource allocation — healthcare staffing, construction logistics, financial portfolio optimization — can derive value from the PFG model.

Conclusion

Quantum computing is no longer theoretical. PFG’s partnership with D-Wave is generating measurable ROI today — reducing scheduling effort by over 80%, improving demand fulfillment to 95%, and setting the stage for expansion across the broader Jim Pattison Group.

For business leaders, the message is clear: the window for quantum advantage is opening. Organizations that identify the right optimization problem, partner with proven quantum vendors, and pilot carefully before scaling will gain the competitive edge that defines the next era of digital transformation.

References

Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510. https://doi.org/10.1038/s41586-019-1666-5

Boixo, S., et al. (2014). Evidence for quantum annealing with more than one hundred qubits. Nature Physics, 10(3), 218–224. https://doi.org/10.1038/nphys2900

Ciocoiu, A., & Turner, L. E. (2025). Introduction to Quantum Annealing. Quantum Algorithms Institute. https://www.qai.ca/resource-library/introduction-to-quantum-annealing

D-Wave Systems. (2024). Pattison Food Group case study. D-Wave Systems Inc.

Investors Business Daily. (2025). Quantum computing stocks: Does ‘annealing’ pioneer D-Wave get enough respect? https://www.investors.com/news/technology/quantum-computing-stocks-dwave-annealing-commercialization

TechTarget. (2023). Quantum computing in business applications is coming. https://www.techtarget.com/searchcio/feature/Quantum-computing-in-business-applications-is-coming

The Jim Pattison Group. (2025). Our story. https://www.jimpattison.com/about/our-story/