Ujjwal Basu is a supply chain management professional with over 24 years of global experience across the India Subcontinent, Asia Pacific, the Middle East, and the United States territories. He has led transformation initiatives across procurement, logistics, inventory management, and production planning for leading multinational organizations. Through his consulting and corporate training work, he focuses on helping professionals understand the practical applications of emerging technologies. His book, The Thinking Supply Chain, explores how Artificial Intelligence can transform supply chain operations by enabling smarter decision-making, greater efficiency, and sustainable competitive advantage.
The Literature Times: You bring over 24 years of global experience in supply chain management. What inspired you to write The Thinking Supply Chain, and why did you feel this book was needed now?
Ujjwal Basu: My inspiration for writing The Thinking Supply Chain came from the intersection of my industry experience and my subsequent journey into consulting and corporate training. After more than 24 years of working in complex global supply chains, I transitioned into a role where I could help organisations navigate transformation and prepare professionals for the future of supply chain management.
Over the last six years, my interactions with more than 5,000 participants across 300+ organisations have revealed a significant gap between the reality of Artificial Intelligence implementation and the expectations, assumptions, and sometimes myths surrounding it. While AI is increasingly being positioned as a transformative force, many industry professionals and business leaders are still uncertain about where it can create genuine value, what it takes to implement it successfully, how much investment it requires, and how its benefits can be measured.
I felt there was a need for a book that bridges this gap—not from a purely technological perspective, but through the lens of business realities and managerial decision-making. The Thinking Supply Chain is written in a practical, accessible, and non-technical manner to help supply chain professionals, business leaders, and decision-makers understand AI beyond the hype.
The book explores the questions that matter most to industry: Where should we begin? What capabilities and data are required? What are the costs and implementation challenges? Which benefits can be quantified? And, perhaps most importantly, when does adopting AI make business sense—and when does it not?
I believe the time for this book is now because organisations can no longer afford to approach AI either with blind enthusiasm or with apprehension. They need informed judgement, a clear understanding of business objectives, and the ability to distinguish technological possibilities from practical outcomes. My intention is to help readers move beyond simply understanding AI to thinking critically about how, where, and why it should be applied to build smarter, more resilient, and future-ready supply chains
The Literature Times: Through your consulting and corporate training, you identified a gap between AI awareness and its practical understanding. What are the most common misconceptions about AI among supply chain professionals?
Ujjwal Basu: From my experience in supply chain leadership, consulting, and corporate training, I believe the biggest misconception about Artificial Intelligence is that AI adoption automatically translates into business transformation. The reality is far more nuanced. AI can create significant value, but only when it is aligned with business priorities, supported by the right data and processes, and implemented with a clear understanding of its limitations.
Through my interactions with more than participants across organisations, I have observed a recurring gap between AI awareness and the ability to translate that awareness into practical business decisions.
One common misconception is that AI is primarily a technology initiative. In reality, it is fundamentally a business transformation opportunity. Whether we are addressing demand forecasting, inventory optimisation, strategic sourcing, supplier risk, or logistics efficiency, the starting point should be a clearly defined business problem—not the technology itself.
The second misconception is that AI can compensate for poor data and inefficient processes. In supply chain management, where decisions depend on the quality, timeliness, and reliability of information, weak data foundations can undermine even sophisticated AI solutions. Technology can enhance a well-designed process, but it cannot automatically resolve every underlying operational deficiency.
The third misconception concerns the expectation of immediate returns. Organisations often focus on the capabilities of AI tools without adequately evaluating the total cost of implementation, system integration, capability development, change management, and governance. The more important question is not what AI can do, but whether it can deliver a measurable business outcome at an economically justifiable cost.
There is also an understandable concern that AI will replace human expertise. My view is that the greater opportunity lies in augmenting managerial intelligence rather than eliminating managerial judgement. AI can process information at scale, identify patterns, generate insights, and support recommendations. However, contextual understanding, ethical considerations, stakeholder alignment, and accountability remain critical to sound decision-making. As AI becomes more autonomous, the need for appropriate human oversight and governance becomes even more important.
Finally, many organisations mistake digitalisation for transformation. Introducing an AI platform does not, by itself, create a smarter supply chain. Transformation requires rethinking workflows, decision rights, performance measures, and organisational capabilities so that intelligence is embedded in everyday business decisions.
These are precisely the issues that inspired me to write The Thinking Supply Chain. My intention was to move the conversation beyond the hype surrounding AI and bring it back to business fundamentals. The book helps professionals evaluate where AI is relevant, what prerequisites are necessary, what investment and implementation challenges to anticipate, and how to assess tangible business benefits.
Ultimately, the future will not belong simply to organisations that adopt AI first, but to those that understand it well enough to apply it intelligently. In supply chain management, the real differentiator will be the ability to combine artificial intelligence with human experience, critical thinking, and disciplined execution to build more responsive, resilient, and competitive operations
The Literature Times: Your book focuses on making AI accessible to business leaders rather than presenting it as a purely technical subject. Why was this approach important to you?
Ujjwal Basu: My approach was guided by a simple conviction: Artificial Intelligence creates business value not merely through technological sophistication, but through the quality of decisions it enables.
With over 24 years of experience in global supply chain management, followed by six years in consulting and corporate training, I have witnessed the challenges organisations face when translating new technologies into meaningful business outcomes. My interactions with organisations have reinforced one important observation: while awareness of AI is increasing rapidly, its practical application and business implications remain insufficiently understood by many professionals and decision-makers.
I believe this gap exists partly because AI is often presented through a technical lens, emphasising algorithms, models, and technological capabilities. While these aspects are important, business leaders need a different perspective. They must understand how AI addresses specific business challenges, what organisational readiness it requires, what investment it entails, what risks need to be managed, and—most importantly—whether it can deliver measurable and sustainable value.
Consider demand forecasting, inventory optimisation, or strategic procurement. A supply chain leader does not necessarily need to understand the mathematical architecture of an AI model. What matters is whether it can improve forecast accuracy, reduce working capital, minimise stock-outs, strengthen supplier performance, or enhance operational resilience. Equally important is understanding the quality of data required, the cost of implementation, the limitations of the solution, and the extent to which human judgement remains essential.
This distinction shaped the philosophy behind The Thinking Supply Chain. I wanted to move the conversation beyond the excitement surrounding AI and focus on the realities of implementation. AI transformation is not simply a technology deployment; it requires business alignment, process maturity, data readiness, change management, governance, and a clear framework for measuring returns.
The book therefore adopts a practical, non-technical, and business-oriented approach. It is designed to help professionals evaluate AI opportunities with greater clarity, ask the right questions before investing, distinguish genuine business potential from technological hype, and make informed decisions about where AI can create value—and where conventional solutions may be more appropriate.
My intention is not to make every business leader an AI expert, but to enable them to become informed decision-makers who can bridge the gap between technological capability and business performance.
Ultimately, I believe the competitive advantage will not come simply from adopting AI, but from applying it with purpose, sound judgement, and a clear understanding of business priorities. That is the central message of The Thinking Supply Chain: making AI understandable, actionable, and relevant to the people responsible for transforming businesses.
The Literature Times: How can Artificial Intelligence help supply chain leaders improve demand forecasting, inventory management, and overall operational efficiency?
Ujjwal Basu: Artificial Intelligence has the potential to fundamentally improve supply chain performance by enabling organisations to move from reactive decision-making towards more predictive, responsive, and data-driven operations. However, its real value lies not in the technology itself, but in how effectively it addresses specific business challenges and translates insights into measurable outcomes.
From my perspective, there are three areas where AI can make a significant difference: demand forecasting, inventory management, and overall operational efficiency.
First, demand forecasting. Traditional forecasting methods often rely heavily on historical sales data and established assumptions. While these methods remain relevant, AI can analyse a much broader range of variables, including market trends, seasonality, promotional activities, customer behaviour, weather patterns, and external market signals. This enables organisations to identify complex demand patterns, improve forecast accuracy, and respond more quickly to changing market conditions. For supply chain leaders, the objective is not simply to produce a more accurate forecast, but to make better decisions on production planning, capacity utilisation, procurement, and customer service.
Second, inventory management. Inventory represents a critical balance between customer availability and working capital. Excess inventory ties up funds and increases storage, obsolescence, and carrying costs, while insufficient inventory can result in stock-outs, lost sales, and production disruptions. AI can support more dynamic inventory policies by analysing demand variability, supplier lead times, service-level requirements, and supply risks. It can help identify potential shortages, recommend replenishment quantities, and prioritise inventory across locations and product categories. When integrated with sound inventory policies and reliable operational data, these capabilities can help organisations improve product availability while reducing unnecessary working capital.
Third, operational efficiency across the supply chain. AI’s application extends well beyond forecasting and inventory. In procurement, it can support spend analysis, supplier risk assessment, contract intelligence, and identification of sourcing opportunities. In manufacturing, predictive maintenance can help anticipate equipment failures and reduce unplanned downtime. In logistics, AI can improve route planning, shipment visibility, capacity utilisation, and delivery scheduling. At an enterprise level, AI-enabled analytics can help leaders identify bottlenecks, anticipate disruptions, evaluate alternative scenarios, and make faster, better-informed decisions across the end-to-end supply chain.
An equally important development is the emergence of AI agents capable of performing defined tasks, coordinating workflows, and supporting decisions across business applications. These capabilities offer considerable potential, but their deployment must be supported by appropriate controls, human oversight, data security, and clear accountability.
Having said that, I would caution against the assumption that AI automatically guarantees better performance. The results depend on data quality, process maturity, system integration, employee capabilities, and the organisation’s ability to manage change. Businesses must also evaluate the total cost of implementation against measurable benefits rather than treating AI adoption as an objective in itself.
My recommendation to supply chain leaders is to begin with clearly defined business problems, establish baseline performance, identify suitable AI applications, and implement focused pilots before scaling successful solutions. Metrics such as forecast error, inventory turns, stock-out rates, procurement savings, order fulfilment, and logistics costs can help establish whether the investment is delivering tangible value.
This approach reflects the philosophy behind my book, The Thinking Supply Chain: the objective is not to make every supply chain process AI-driven, but to make every technology investment business-driven. By combining AI’s analytical capabilities with human experience, operational discipline, and sound managerial judgement, organisations can build supply chains that are more efficient, resilient, responsive, and competitive.
The Literature Times: In your experience, what are some of the biggest challenges organizations face when integrating AI into their existing supply chain systems?
Ujjwal Basu: The biggest challenges are data quality, legacy system integration, process maturity, and resistance to change. Many organisations expect AI to deliver immediate results without adequately assessing these foundational requirements.
AI solutions must work with existing ERP platforms, procurement systems, warehouse management systems, and logistics applications. Poor data quality or fragmented systems can limit their effectiveness. Organisations must also consider implementation costs, cybersecurity, employee capabilities, and governance.
My recommendation is to begin with a clearly defined business problem, assess data and process readiness, and run a focused pilot with measurable KPIs. AI integration should be treated as a business transformation initiative, not merely a technology upgrade. This is a central principle of The Thinking Supply Chain: technology creates value when it addresses real operational challenges.
The Literature Times: You have worked across diverse global markets. How do regional differences, business cultures, and operational challenges influence the adoption of AI in supply chains?
Ujjwal Basu: AI adoption varies significantly across regions because of differences in digital infrastructure, data availability, regulatory environments, workforce capabilities, and business maturity.
In developed markets, organisations may have more established digital systems, while emerging markets may face challenges involving fragmented supplier networks, inconsistent data, and infrastructure limitations. Business culture also matters. Organisations that encourage collaboration, experimentation, and data-driven decisions are generally better positioned to implement AI successfully.
Global supply chains must also account for local regulations, geopolitical risks, supplier capabilities, and regional customer expectations. Therefore, a standard AI strategy may not work equally well everywhere.
My approach is to maintain a common strategic vision while adapting implementation to local realities. Successful adoption depends not only on technology, but also on organisational readiness, cultural alignment, and the ability to translate AI insights into practical business decisions.
The Literature Times: Many organizations invest in digital transformation but struggle to achieve meaningful results. How can leaders identify the right AI applications to generate measurable business value?
Ujjwal Basu: Leaders should begin with business priorities rather than available technologies. The first question should be: Which operational problem has the greatest impact on cost, service, working capital, or risk?
For example, recurring stock-outs may justify AI-enabled demand forecasting, while excessive inventory may require better replenishment policies and inventory analytics. Supplier disruptions may call for risk analytics rather than a complex AI platform.
I recommend five steps: define the business problem, establish baseline performance, assess data readiness, evaluate implementation costs and expected benefits, and pilot the solution against measurable KPIs.
Metrics such as forecast accuracy, inventory turnover, stock-out rates, procurement savings, and on-time delivery help establish whether the investment is worthwhile. Leaders should also compare AI with simpler alternatives, as conventional analytics or process improvements may sometimes deliver better returns.
The objective is not to maximise AI adoption, but to maximise measurable business value.
The Literature Times: As AI becomes increasingly integrated into business operations, what role do human judgment, leadership, and decision-making continue to play in building an effective supply chain?
Ujjwal Basu: AI can analyse vast amounts of data, identify patterns, predict potential disruptions, and recommend actions. However, effective supply chain management requires more than analytical capability.
Human judgment remains essential when decisions involve competing business priorities, supplier relationships, ethical considerations, geopolitical uncertainty, or exceptional circumstances that historical data cannot adequately represent.
Leadership is equally important in defining strategic priorities, building trust in AI-enabled decisions, developing employee capabilities, and establishing accountability. As AI agents take on more operational tasks, appropriate human oversight and governance become even more critical.
I see AI as an enabler of managerial intelligence, not a substitute for it. The strongest supply chains will combine machine intelligence with human experience, critical thinking, and disciplined execution.
The Literature Times: For small and medium-sized businesses with limited resources, what practical steps would you recommend for beginning their AI adoption journey?
Ujjwal Basu: Small and medium-sized businesses should adopt a practical, incremental approach rather than investing immediately in expensive AI platforms.
First, identify one recurring business problem, such as inaccurate demand forecasts, excess inventory, delayed supplier deliveries, or inefficient procurement processes. Second, organise the relevant data and improve basic process discipline. Third, explore affordable, existing software capabilities or targeted AI tools before considering major investments.
Start with a small pilot, establish baseline KPIs, train employees, and evaluate the results against implementation and operating costs. Scale the solution only when the business benefits are demonstrated.
SMEs should also pay attention to data security, access controls, and employee training. They do not need to build sophisticated AI systems from scratch; they need to identify where accessible technology can solve a genuine business problem.
The guiding principle is simple: start small, measure results, learn quickly, and scale selectively
The Literature Times: Looking ahead, how do you envision AI reshaping global supply chains over the next five to ten years, and what should professionals do today to prepare for this transformation?
Ujjwal Basu: Over the next five to ten years, I expect AI to make supply chains increasingly predictive, connected, and responsive. Advances in forecasting, digital twins, risk analytics, and AI agents will support faster decisions, more adaptive planning, and greater visibility across global networks.
AI agents may increasingly coordinate routine workflows across procurement, inventory, logistics, and supplier management. However, the pace of adoption will depend on data readiness, system integration, governance, and demonstrable business value.
For professionals, the priority is to combine supply chain fundamentals with digital and analytical capabilities. They should develop an understanding of AI applications, data interpretation, business-case evaluation, risk management, and change leadership. Equally important is the ability to question AI-generated recommendations and apply sound managerial judgment.
I believe the future belongs to professionals who can bridge business knowledge and technological capability. As I emphasise in The Thinking Supply Chain, success will not depend simply on who adopts AI first, but on who applies it intelligently, responsibly, and with a clear focus on business outcomes.
