Toshiba and Toyota Tsusho Launch Quantum Supply Chain Forecasting Trial in Japan

In a bold attempt to solve some of the automotive industry's most persistent supply chain problems, Toshiba Digital Solutions and Toyota Tsusho Corporation—a key member of the Toyota Group—announced on March 15, 2023, the launch of a joint trial using Toshiba’s Simulated Bifurcation Machine (SBM). The initiative focuses on high-speed forecasting in volatile supply environments.

Japan’s manufacturing backbone has long relied on just-in-time (JIT) logistics and lean inventory practices. However, recent disruptions—including the global chip shortage, geopolitical tension, and material bottlenecks—have exposed the fragility of these systems. This joint trial leverages SBM, a quantum-inspired classical computing platform, to forecast and optimize supply chain decisions across thousands of variables in milliseconds.

The SBM is not a full quantum computer. Instead, it mimics certain quantum principles using classical computing hardware. Developed by Toshiba, it solves combinatorial optimization problems at lightning speed by simulating the evolution of a quantum system undergoing bifurcation—a form of dynamic instability.

In this project, SBM is applied to:

• Demand forecasting based on historical and real-time dealer data

• Supplier capacity matching across hundreds of components

• Chip allocation under high-volatility conditions

• Production rerouting during bottlenecks or labor shortages

The system processes high-dimensional constraint models involving supplier tiers, shipping schedules, and probabilistic part availability.

Toyota Tsusho has positioned itself as a leader in digital logistics 

transformation within the Toyota Group. This partnership with Toshiba is the latest in a string of initiatives aimed at predictive manufacturing resiliency.

In the March pilot, the companies tested the SBM in forecasting part availability under four simulated scenarios:

• Earthquake disruption to semiconductor facilities in Taiwan

• Tier-2 supplier bankruptcy in Indonesia

• Sudden EV demand surge in Europe

• Cyberattack on a major logistics provider

In all cases, the SBM outperformed conventional forecasting tools, generating optimized responses in less than 300 milliseconds and suggesting dynamic procurement reroutes and inventory reallocation.

The stakes are high. The Japanese automotive sector depends on ultra-efficient logistics. Even a delay of a few hours in chip delivery can halt entire assembly lines.

By deploying quantum-inspired solutions:

• Inventory buffers can be minimized without sacrificing risk preparedness

• Dynamic rerouting can occur within seconds of receiving threat signals

• Production simulations can be executed in real-time with market shifts

"We are reimagining what 'lean' means in the post-pandemic world. SBM provides a computational edge that aligns perfectly with Toyota’s logistics DNA," said a Toyota Tsusho innovation lead.

The Toshiba SBM system is built on FPGA-based accelerators and optimized for speed. During the March test window:

• The model processed over 5,000 constraints per run

• Average solve time was under 0.5 seconds

• Accuracy in chip shortage detection scenarios exceeded 93%

The trial ran parallel simulations on both cloud-based SBM nodes and on-premise FPGA arrays, allowing Toyota Tsusho to compare scalability and latency.

Beyond forecasting, Toshiba plans to connect SBM outputs to automated decision-making systems within Toyota Tsusho’s logistics networks. This would allow:

• Autonomous inventory ordering based on quantum forecasts

• AI-driven supplier negotiations initiated pre-emptively

• Production scheduling alerts that trigger robot-led rerouting in factories

In the next phase, SBM will interface with Toyota’s proprietary Logistics AI Orchestrator, enabling full-stack quantum-AI logistics coordination.

Toshiba and Toyota Tsusho aren’t alone. Around the world:

• Volkswagen has partnered with D-Wave and Google to forecast traffic and parts delivery

• BMW is testing quantum algorithms for assembly line flow optimization

• Hyundai is exploring quantum chemistry simulations for EV battery logistics

The Japan trial is unique for its emphasis on forecasting under constraint volatility—a field where SBM shows significant promise.

Still, hurdles remain:

• Hardware limitations: SBM is powerful but not a full quantum computer. Certain problems may require gate-based quantum systems.

• Model tuning: Constraint models must be continually refined to match evolving supply chain parameters

• Cybersecurity: Quantum-inspired platforms still operate within classical networks and must be protected from data tampering

Nevertheless, Toshiba’s roadmap includes cloud-accessible SBM-as-a-Service by late 2023 and expanded partnerships beyond automotive.

In deploying SBM for real-time forecasting across its supply chains, Toyota Tsusho is pioneering a new frontier: one where predictive analytics meet quantum-inspired speed.

For an industry built on precision and minimal margins for error, this could become the new standard. Quantum-inspired platforms like SBM bridge the present with the quantum future—proving that scalable impact doesn’t always require full quantum hardware.

The March 15 trial may well be remembered as a turning point when Japan’s manufacturing nerve center embraced quantum agility.