AI in the Chain

The supply chain world loves buzzwords. “Orchestration” has recently become one of the most overused terms, often implying seamless end-to-end coordination through AI. Yet as Lora Cecere points out in her blog post “The Orchestration Shuffle,” the term often masks vague strategies and poor governance. In many cases, companies try to orchestrate processes without clear definitions of outcomes, roles or data standards, leading to confusion and inefficiency. This article unpacks what orchestration should mean, highlights the role of digital twins, and shows how to align AI with real-world constraints. It ends with a practical example to demonstrate the concept.

Why Orchestration Is Misunderstood

In theory, orchestration implies the seamless coordination of multiple functions—procurement, manufacturing, logistics, sales—towards a common goal. Many vendors promise “one-click” orchestration through AI. But Cecere argues that the term has become a marketing buzzword with little substance. She warns that vendors often conflate transaction automation with orchestration and ignore the need for governance, context and human oversight. Without clearly defined roles and data standards, orchestration initiatives fail, leading to misaligned processes and poor decision making.

The Importance of Governance

True orchestration requires robust governance. This means establishing decision rights, data ownership, KPIs and guardrails before automating anything. Cecere emphasizes that organizations must avoid layering AI onto broken processes and instead redesign workflows from the ground up. Governance boards should include cross-functional leaders who define metrics, approve models and monitor outcomes. They should ensure that AI recommendations align with company policies, ethics and regulatory requirements.

Digital Twins: The Engine Behind Orchestration

Digital twins—virtual replicas of physical assets and processes—serve as the engine for orchestration. Gartner ranks intelligent simulation as a top trend for 2025. Digital twins allow companies to run “what-if” scenarios, evaluate trade-offs and identify bottlenecks before they occur. For example, Rolls-Royce uses a simulation twin to model engine operations and forecast the impact of maintenance on performance. Ports like Los Angeles have built digital twins to manage container flows and anticipate congestion. Simulation enables outside-in planning by considering external factors like weather, demand signals and geopolitical events.

AI Decision-Making Frameworks

GAINS Systems proposes a three-stage framework for AI-driven decision making: decision support, decision augmentation and decision automation. In the support stage, AI provides recommendations that humans review. In the augmentation stage, AI executes routine decisions with human supervision. In the automation stage, AI acts autonomously within defined boundaries. Many companies prematurely jump to automation without mastering support and augmentation. In orchestration, it is crucial to gradually move from support to augmentation while maintaining governance.

Limitations of Generative AI

Generative AI excels at creating content such as shipping documents, reports and code snippets. However, it struggles with demand forecasting and complex decision making due to unpredictable patterns and inconsistent data. Companies should use generative AI for content creation and explanation while relying on predictive models and simulations for decision making. Combining generative AI with agentic AI and digital twins yields better results.

Real-World Alignment: The Uber Freight Example

MIT Sloan’s article on AI in logistics shows how Uber Freight uses algorithmic pricing and machine learning to reduce empty miles and match loads to carriers. Their system continuously learns from market data and adjusts rates. However, they also emphasise the importance of operations research and human dispatchers. Orchestration doesn’t mean eliminating humans; it means giving them tools to make better decisions faster.

Practical Example: Building a Digital Twin for Warehouse Orchestration

To illustrate how digital twins enable orchestration, consider a warehouse serving an e-commerce retailer. Use the following steps to build a simple digital twin:

1. Map the physical processes: Document the layout of the warehouse, including receiving, storage, picking, packing and shipping areas. Identify equipment, rack locations, and conveyor paths.
2. Collect data: Gather historical data on orders, inventory levels, travel times, pick accuracy, and labour availability. Use sensors or manual logs.
3. Create a simulation model: Use a simulation tool (e.g., AnyLogic, Simio or Python’s SimPy) to model the flow of goods. Define variables like arrival rates, processing times, and capacity.
4. Test scenarios: Run scenarios such as promotions, equipment failures or labour shortages. Measure KPIs like throughput, order cycle time and labour utilisation.
5. Optimise: Use the simulation to test changes like rearranging pick locations, adjusting staffing, or implementing automated guided vehicles (AGVs). Identify which changes yield the best results.
6. Integrate with AI: Combine the simulation with AI agents. For example, use reinforcement learning to identify optimal pick paths or generative AI to suggest shift schedules.
7. Deploy and monitor: Implement the selected changes in the real warehouse. Use sensors to collect data and continuously update the twin, creating a feedback loop.

This practical exercise demonstrates that orchestration isn’t just about connecting software; it requires understanding physical processes, using simulations, and iteratively improving.

Conclusion

Orchestration should not be an empty buzzword. True orchestration aligns processes, data and decisions across the supply chain. It requires governance, digital twins, and a thoughtful approach to AI adoption. Companies must recognise the limitations of generative AI, follow a staged AI decision-making framework, and prioritise simulation to align plans with reality. By focusing on governance and digital twins, organisations can move beyond hype and achieve meaningful orchestration.

References

• Supply Chain Shaman – The Orchestration Shuffle: Lora Cecere critiques the misuse of “orchestration” and emphasises the need for governance and outside-in planning.
• Supply Chain Shaman – Mistakes and Opportunities: Urges companies to redesign work rather than layer AI onto old systems.
• GAINS Systems: Defines stages of AI decision-making—support, augmentation, automation—and lists key applications.
• GAINS Systems: Notes limitations of generative AI for demand forecasting.
• MIT Sloan Management Review: Discusses the complementarity of AI and operations research; highlights Uber Freight’s use of machine learning.
• Woolpert article: Provides examples of digital twins such as Rolls-Royce engines and port logistics.
• Gartner/Logistics Management: Highlights agentic AI and intelligent simulation as key trends for 2025.