Anupam Bandyopadhyay is a senior supply chain and technology leader with over 18 years of global experience in warehouse design, logistics, and automation. As a senior manager and design lead at a global supply chain software company, he has delivered transformative projects for Fortune 500 and multi-billion-dollar enterprises across retail, manufacturing, and entertainment. His expertise spans WMS, TMS, robotics, digital twins, and AI-driven optimization, driving measurable gains in efficiency, accuracy, and scalability.
In this interview, Anupam shares how modern assembly lines are evolving through the integration of robotics, sensors, and real-time analytics with human intelligence. He discusses how digital twins are redefining design and decision-making, and why the future of manufacturing depends on building systems that are not only automated but also adaptable, human-centric, and sustainable.
1. You’ve spent nearly two decades designing and optimizing global supply chains. How have digital twins transformed the way organizations approach warehouse and logistics design compared to traditional modeling methods?
Organizations running 24/7 fulfillment centers are approaching the implementation of digital twin solutions for their warehouses. Digital twins provide a robust platform for strategic decision-making by enabling in-depth scenario experimentation whereas traditional models rely on reporting/dashboard methodology, where operations look at the dashboard to analyze problems. Implementing this technology, organizations model various configurations of automation, labor distribution, and equipment utilization, gaining profound insights into how each scenario impacts operational efficiency and overall performance.
2. Many companies see digital twins as a visualization tool, but their real power lies in prediction and optimization. Could you walk us through how simulation modeling helps organizations anticipate disruptions and plan contingencies before they occur?
Digital twins enable virtual simulation and analysis of processes, information, and resource flow along various pathways, allowing for detailed examination of possible inefficiencies or optimization areas. Simulating different “what-if” scenarios with these features can help reduce unexpected outcomes and improve readiness for automation implementation. Digital twin technology provides data-driven support for operational decisions in warehouse automation, which are reviewed thoroughly before enactment. The simulation process facilitates a comprehensive analysis of the impact of changes on key performance indicators such as efficiency, throughput, and resource utilization. Through modeling and scenario testing, decision-makers can base their choices on empirical evidence rather than speculation. For example, a digital twin model deployed in a warehouse environment can anticipate workforce requirements during unexpected increases in order volume and generate corresponding task allocations for users.
3. Your work has involved integrating robotics, WMS, TMS, and AI-driven analytics. How do these technologies interact within a digital twin ecosystem to deliver a unified, real-time view of supply chain performance?
Digital Twin constitutes a virtual representation of a physical supply chain environment, including the warehouse activities, inventory movements, and material-handling equipment and robots managed by the WCS (warehouse control systems) that is continuously synchronized with real-time operational data. The model serves as a comprehensive data integration and decision support layer; it unifies WMS, TMS, and WCS systems and AI-driven analytics within a single control platform.
WMS transmits transactional data such as task progress, order updates, labor metrics, activities at pack stations and dock activity to the digital twin. Digital Twin performs predictive analytics and optimization suggestions to the WMS, such as slotting recommendations, picking route enhancements, or congestion predictions. AI anticipates possible congestion or delays and directs the WMS to adjust task sequencing or putaway priority.
TMS provides shipment orders, carrier information, estimated arrival times, and route planning services. The integrated models for dock scheduling, yard movement patterns, and load organization are capable of adjusting truck or shipment routes based on warehouse throughput data. If the twin anticipates delays in inbound deliveries, it notifies WMS to adjust outbound planning accordingly. The digital twin incorporates IoT data such as battery levels, location, cycle durations, and fault alerts from robotic systems. Upon identifying reduced speed in an ASRS aisle or AGV lane, the twin prompts the WMS to redirect picking assignments or reallocate robots to maintain throughput. The model tasks distribution and route selection for both robots and humans.
4. You’ve led major automation initiatives for Fortune 500 companies. What lessons have you learned about balancing technological innovation with human expertise to create supply chains that are both efficient and resilient?
In nearly two decades of leading digital transformation and automation initiatives within the retail, logistics, and manufacturing sectors, the human element remains the decisive factor. While technology drives supply chain processes, human expertise shapes the intelligence that guides each phase from initial concept through implementation. The main lessons learnt are that effective and resilient operations are founded on a thorough understanding of business goals, process dynamics, and operational limitations. During design, experts translate business goals into data flows, configuration logic, and automation for WMS, TMS, and OMS systems. Institutional knowledge, such as order movement, handling exceptions, and measuring service level, is embedded during this process. Even advanced models encounter real-world variability, such as unforeseen delays, data latency, or spatial limitations. In these instances, warehouse operators and planners play a critical role in aligning system logic with operational realities. By conducting scenario analyses and pilot implementations, human expertise validates that the design functions effectively under practical conditions. Resilient supply chains extend beyond digital capabilities; they are human-centered systems designed to think, learn, and recover.
5. Sustainability has become a core business objective. How are simulation-based designs helping enterprises reduce waste, optimize energy use, and make measurable progress toward environmental goals?
In traditional supply chains, modifications to processes or facility layouts are typically evaluated through physical implementation, which can result in inefficiencies due to trial-and-error. Simulation-based design addresses this by enabling virtual experimentation, thereby reducing unnecessary waste. In a high-throughput distribution center, simulation identified excessive idle conveyor usage. Re-sequencing the generation of work on the floor reduces energy consumption by 18% without impacting throughput. Simulation goes beyond the warehouse to include transportation and network planning, enabling planners to model routes, consolidate shipments, and select carriers to reduce carbon emissions. Energy modeling is an application of simulation-based design. By integrating IoT, equipment data, and process simulation, organizations can determine where energy inefficiencies occur and adjust workflows as needed. Simulation-based design, underpinned by digital twins, artificial intelligence, and predictive analytics, represents a transformative advancement. This approach enables organizations to virtually test, assess, and refine processes prior to implementing physical modifications, thereby aligning operational efficiency with environmental stewardship.
6. In your experience, what are the biggest barriers preventing companies from fully adopting digital twins at scale and what strategies have proven effective in overcoming them?
Digital twins depend on the efficient, real-time exchange of data across WMS, TMS, OMS, ERP, IoT devices, robotics, and planning platforms. However, many organizations continue to function within isolated systems, with data often confined to legacy infrastructure, presented in inconsistent formats, or integrated through manual processes. Sometimes upgrading the technology stacks for all the systems becomes very expensive and is not affordable at all times. The way to overcome the challenge is to build a central repository data lakehouse, which can integrate with different systems using an integration system. Use standardized APIs and event-driven architecture such as MQTT (Message Queuing Telemetry Transport). MQTT can be used for real-time data streaming in supply chain, warehouse, and manufacturing settings where multiple devices such as sensors, AGVs, robots, and conveyors continuously transmit status updates. Start with simple workflows, such as inventory movement, before scaling to full enterprises. Organizations pursuing technology upgrades are increasingly adopting digital twin modeling on a larger scale throughout various flows within their supply chain networks.
7. As a thought leader and mentor in supply chain innovation, how do you see the role of digital twins evolving in the next five years, especially with advances in AI, IoT, and edge computing?
Digital twins are evolving from digital replicas to systems capable of independent decision-making and learning. In the next several years, developments in AI, IoT, edge computing, and agentic systems are expected to shift digital twins from being primarily descriptive to more prescriptive and autonomous. Many organizations currently operate separate digital twins for different functions, such as factories, warehouses, and logistics. By 2030, digital twins will no longer work as individual software running independently; rather, the twin model will be treated as a nervous system and integrate well within the systems connecting manufacturing, logistics, transportation, sustainability, and customer demand models using shared data standards.
8. Finally, when you advise organizations on future-proofing their operations, what core mindset or design principle do you emphasize to ensure that resilience and adaptability remain at the heart of their supply chain strategy?
To ensure future resilience, systems must be designed for seamless integration, allowing operations to anticipate and effectively manage sudden changes or disruptions within the supply chain network. It is critical that these systems are engineered with safeguards against potential risks such as cyber-attacks, supply chain interruptions, network outages, or unexpected surges in volume. Involving the operations team throughout the design phase is imperative to provide clear guidance on required responses to major changes. The workforce should continually develop in line with technological progress, leveraging advancements in artificial intelligence to enhance skills and performance rather than opposing innovation. Furthermore, environmental performance must be established as a fundamental system metric, not simply regarded as an aspirational objective.
