Few leaders sit at the intersection of technology, operations, and strategy quite like Anupam Bandyopadhyay. With nearly two decades of global experience designing and optimizing complex warehouse and logistics networks, he has helped some of the world’s largest enterprises reimagine how automation, robotics, and artificial intelligence can work together to drive precision and scale. As a senior manager and design lead at a global supply chain software company, Anupam has led transformative projects spanning retail, manufacturing, and entertainment, applying AI and digital twin technology to make operations smarter, safer, and more sustainable.
In this conversation with AI Journal, he offers an inside view of how artificial intelligence is rewriting the fundamentals of industrial engineering. From the rise of adaptive, self-learning systems to the evolution of human–robot collaboration, Anupam discusses how data-driven design is redefining factory efficiency and reshaping the engineer’s role itself. He also shares lessons from real-world implementations and explores what it will take for organizations to bridge legacy systems, harness emerging technologies, and prepare for the next decade of intelligent supply chain transformation.
From your experience leading global automation and warehouse design projects, how do you see artificial intelligence reshaping the fundamentals of industrial engineering and factory efficiency today?
Based on my extensive experience managing large-scale automation and warehouse design projects across global organizations, I have observed that artificial intelligence is significantly transforming the core principles of industrial engineering. The field is progressing beyond conventional deterministic optimization towards adaptive and self-learning systems design. As this evolution continues, engineers will increasingly transition from developing models to managing advanced systems, overseeing intelligent networks that seamlessly incorporate human, robotic, and digital operations, facilitating effective collaboration in a “cobot” environment where robots and humans work together safely. Industrial engineers use AI to sift through operational data, from machine output to worker movements, to spot inefficiencies. AI can suggest better equipment use, task order, or labor allocation based on current trends and real-time needs. This leads to ongoing process improvement based on data, in place of scheduled time studies.
You’ve worked extensively with digital twins and AI-driven optimization. How are these technologies helping manufacturers predict bottlenecks, improve line balancing, and make faster, more informed decisions on the factory floor?
Digital twins, alongside AI-driven optimization models, play a critical role in advancing predictive modeling within manufacturing. These technologies facilitate a shift from reactive management strategies to proactive operational oversight, enabling manufacturers to anticipate constraints and allocate resources more efficiently. The digital twin has evolved from a static representation to a dynamic, interactive model of the production environment, continuously refreshed with data from IoT sensors, robotics, and other sources.
Artificial intelligence evaluates key metrics such as cycle times, machine utilization, and queue durations within these digital environments. When integrated, AI and digital twins form self-correcting systems capable of automatically optimizing facility layout and workflow. The system can forecast potential bottlenecks and simulate various operational scenarios, including fluctuations in orders, equipment failures, or staffing adjustments. This predictive capability empowers planners and engineers to modify workflows and schedules preemptively, thereby supporting consistent throughput levels.
Computer vision has become a key enabler of smart manufacturing. What are some of the most impactful ways you’ve seen it improve inspection accuracy, quality assurance, or workplace safety in real-world operations?
Computer vision has progressed from a specialized quality control tool to a pivotal driver of autonomous, data-informed operations. Its strength is in converting visual observations on the factory floor into actionable, real-time data for informed decision-making. Unlike traditional manual inspection, which may overlook subtle defects, AI-powered vision systems can identify issues such as scratches and misalignments with speed and accuracy, surpassing human capabilities. Computer vision also facilitates efficient product return processing and inventory actions, such as cycle counting from pick-face locations, thereby reducing false positives through image analysis.
For example, a major mobile service provider leverages computer vision in their returns process: cell phones and tablets undergo a comprehensive inspection using this methodology before being dispositioned, allowing for the detection of minor cracks and other imperfections. In a comparable scenario, a leading global apparel company implemented computer vision cameras on robotic picking arms to verify SKU color, label orientation, and stitching quality. This approach resulted in inspection accuracy exceeding 98%, a notable improvement over the 85% accuracy achieved with manual checks alone.
As factories evolve into connected ecosystems of people, robots, and algorithms, what new skill sets or mindsets will industrial engineers need to succeed in this AI-augmented environment?
As factories, warehouses, and supply networks become interconnected ecosystems, the responsibilities of industrial engineers are changing. Industrial engineers act as system designers and analysts, focusing on optimization and process design. They apply skills in software and hardware integration and use data analysis to improve operational efficiency. Besides mapping processes, industrial engineers are involved in designing adaptive ecosystems, developing networked robots with self-learning functions, and collaborating with IoT sensors.
Competencies such as algorithm training and interpretation, along with knowledge of machine learning, predictive analytics, and digital twins, are relevant in this sector. Industrial engineers who adapt to future demands will set up feedback mechanisms to support continuous learning, utilizing operational events for data analysis. Success in the field involves applying engineering principles and engaging with digital innovation to promote ongoing progress.
Many companies struggle to integrate AI into legacy systems or traditional workflows. Based on your experience, what are the most significant barriers to adoption, and how can organizations overcome them strategically?
Many organizations continue to rely on legacy ERP, WMS, or MES systems that, despite being extensively customized and developed decades ago, have proven to be highly stable. While these companies may be cautious about undertaking large-scale technology transformations, they remain interested in incorporating AI solutions into their current workflows. A primary challenge is that legacy systems often store data across numerous silos, even though they can interface with IoT devices and robotics via APIs.
The foundational step in implementing AI solutions involves unifying these disparate datasets. Once consolidated, artificial intelligence and machine learning applications can perform predictive analytics on the unified data, enabling real-time dashboards for insights such as line balancing, order prioritization, and predictive maintenance. Instead of opting for a complete system replacement, organizations may deploy an AI middleware or microservices layer that communicates with existing infrastructure via APIs or ETL pipelines. For environments where latency is a critical consideration, such as warehouses or manufacturing plants, edge computing facilitates AI processing closer to the data source. In summary, legacy systems do not always require replacement; rather, they can be modernized and enhanced by integrating advanced technologies.
You’ve designed and implemented large-scale logistics and automation systems across multiple industries. Can you share a real-world example where AI directly contributed to measurable improvements in efficiency, accuracy, or sustainability?
A global apparel distributor with annual revenue exceeding $3 billion implemented an AI-based optimization system across its distribution network. The objectives included increasing merchandise accuracy within the store network, improving e-commerce shipping accuracy, optimizing labor utilization, and reducing carbon footprint at multiple distribution centers. Previously, the company’s replenishment and picking processes followed rule-based logic that responded solely to static demand forecasts. Seasonal fluctuations often resulted in stock imbalances, suboptimal labor allocation, and higher energy usage due to inefficient material movement.
In response, an AI-driven machine learning engine was integrated into the merchandise management and distribution systems. This model predicted SKU-level demand by analyzing order histories and regional patterns, dynamically optimized slotting and task sequencing, and allocated work based on real-time congestion and travel-time data.
Continuous feedback loops supported decision-making by simulating various shift configurations and equipment loads for energy efficiency. These measures led to reduced overstocking, increased store sales, improved distribution system balance, and fewer customer returns. Through optimizing equipment usage and efficiently allocating labor via predictive analytics, the company achieved a reduction of approximately 400 metric tons in annual CO₂ emissions, thereby demonstrating a concrete connection between AI implementation and environmental responsibility.
AI often raises questions about its impact on the workforce. How do you see the balance evolving between automation and human expertise, and what steps can leaders take to ensure that technology enhances, rather than replaces, people?
The balance between AI and human capability is increasingly characterized by collaboration. Organizations that integrate AI as a tool to support human intelligence, rather than replace it, are noted for leveraging both strengths. The development of explainable and transparent AI systems aims to clarify decision-making processes for human users. As manufacturing plants and warehouses adopt smarter technologies, designs increasingly consider human experience as a measurable outcome. While AI automates repetitive analysis, human roles may shift toward areas such as creativity, coordination, and ethical decision-making. Successful implementation of AI positions the technology to enhance human contribution. This approach can support innovation, accountability, and trust within organizations adopting AI.
Looking ahead, what emerging technologies or AI capabilities do you believe will define the next decade of industrial engineering and supply chain transformation and how can companies start preparing for that future now?
Over the next decade, industrial engineering will be transformed through the development of intelligent ecosystems, where artificial intelligence, automation, and sustainability intersect to establish adaptive, self-optimizing networks. The focus will shift from traditional operational management to the orchestration of intelligence across systems, assets, and personnel. Advanced AI solutions will evolve beyond analytics to encompass agentic, self-learning systems capable of making autonomous decisions. It is essential to enhance data readiness and interoperability among WMS, TMS, and ERP platforms.
The use of digital twins will progress from modeling individual facilities to creating multi-layer enterprise twins that integrate warehousing, transportation, and manufacturing networks. Organizations are advised to begin by twinning a single process (such as slotting or putaway) and then scale their efforts. Architectures should be designed to facilitate bi-directional data flows between operational and simulation environments.
As AI models increase in complexity, edge computing will enable real-time inference at the factory floor, while quantum computing will offer unprecedented optimization capabilities for supply chain networks. Modernizing infrastructure to support edge computing is critical. The next generation of industrial engineers will need to demonstrate proficiency in AI, design thinking, and systems empathy.
