Harnessing Real-Time Visibility to Redefine Supply Chain Strategy

As global supply chains become more interconnected and increasingly intricate, the ability to maintain clear, real-time visibility across every node and nuance has become a defining factor in business resilience and responsiveness. No longer confined to logistics, visibility now spans sourcing, production, distribution, and risk management—turning Data into Decisions and Uncertainty into Opportunity. In this special report, leading experts share their perspectives on how organizations can harness real-time insights to drive agility, resilience, and end-to-end transparency. Through candid Q&A sessions, they reveal the technologies, mindsets, and frameworks that are reshaping the future of supply chain management—one visible link at a time. The expert perspectives shared here converge on a critical truth: in an era defined by disruption, visibility isn’t just an operational advantage—it’s the foundation for Future-Ready, Competitive Supply Chains.

We often hear about transformative technologies such as AI, IoT, and blockchain. How do you perceive their role in enhancing visibility? Additionally, could you share any successful implementations that you have witnessed?

Deepika Arora, Global Logistics Transformation Lead, TE Connectivity: The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain technology is fundamentally redefining operational visibility across industries. These technologies not only facilitate real time data acquisition but also ensure data integrity, security, and contextual relevance, enabling businesses to make faster, smarter, and more trustworthy decisions.

Traditionally, organizations faced considerable challenges in maintaining data security, especially across complex, multi-tier supply chains. With the advent of blockchain, however, those limitations are being addressed with an immutable, distributed ledger that ensures tamperproof data transmission and enhances trust across stakeholders—suppliers, distributors, partners, and customers alike.

IoT has dramatically evolved data collection practices by embedding high precision sensors at critical operational junctures—from factory floors and warehouses to transport vehicles and retail shelves. These sensors enable real time visibility into physical processes, capturing granular data points such as temperature, location, humidity, and shock levels. This rich data stream is continuously fed into AI-powered systems.

AI, in turn, transforms this raw data into actionable intelligence, providing deep analytics that go beyond conventional descriptive metrics. Through predictive and prescriptive analytics, AI helps organizations forecast demand fluctuations, identify operational bottlenecks, preempt equipment failures, and optimize resource allocation. This analytical transformation turns what would otherwise be an overwhelming flood of information into strategically aligned insights.

A powerful use case demonstrating this synergy lies in supply chain and logistics, particularly within FMCG sectors. For instance, through IoT-enabled packaging, organizations can monitor product movement and environmental conditions in real time. This is particularly vital for temperature-sensitive goods, such as food or pharmaceuticals. In my experience, this approach has resulted in fewer claims, reduced losses, and increased confidence in product integrity. When conditions deviate from acceptable thresholds, stakeholders are alerted instantly, enabling proactive interventions before spoilage or degradation occurs.

Additionally, automation—especially in regions with high labor costs like Europe—has become a central lever for operational efficiency. With AI-driven robotics, automated picking systems, and real-time integration platforms, supply chain operations are experiencing significant improvements in speed, cost control, and accuracy. The combination of AI and IoT drives efficiencies in inventory management, last-mile delivery, and resource utilization, unlocking measurable improvements in agility and customer satisfaction.

Beyond operational efficiency, the strategic integration of blockchain brings another critical dimension: end-to-end traceability. This is particularly important in industries such as pharmaceuticals, food safety, and luxury goods, where authentication, provenance verification, and compliance are essential. Blockchain’s decentralized ledger ensures that every transaction and movement of goods is securely recorded and accessible, minimizing fraud risks and ensuring full transparency. When combined with IoT sensor data, blockchain can create a trusted digital thread that traces a product from its origin to its final destination—an invaluable capability in today’s globally dispersed supply chains.

In essence, blockchain closes the data intelligence loop, ensuring that the insights provided by IoT and AI are secure, verifiable, and immune to tampering. This integrated framework strengthens enterprise resilience, risk mitigation, and transparency, making it not just a competitive differentiator but a strategic necessity in today’s data-driven business landscape.

As industries continue to grapple with increasing complexity and customer expectations, the fusion of AI, IoT, and blockchain represents the foundation of next-generation operational intelligence—one that is predictive, real-time, and trusted.

Mandar Shirsavakar, Founder & CEO, Translytics Business Services: At Translytics, we view these technologies as enablers—not silver bullets. AI, for example, plays a central role in our platform by predicting lead times dynamically and flagging inventory risks in advance. Rather than relying on static buffers, our clients use AI-driven signals to make real-time replenishment decisions. IoT becomes truly powerful when integrated with predictive analytics. A sensor on its own tells you current stock or temperature, but when that data flows into a decision layer—like our inventory digital twin—it enables proactive interventions. While blockchain is still maturing, we believe its future lies in multi-party visibility—especially for traceability and compliance in regulated industries.

One successful implementation we’re proud of is with a leading company in Adhesives business in India, where we built a dynamic Inventory Prebuild engine. Instead of static inventory norms, our solution adjusts prebuilds based on seasonality, promotions, and plant-specific capacity—resulting in better fill rates without overstocking. This is the kind of visibility transformation that creates real bottom-line impact.

Can you provide an example of how visibility initiatives transform supply chain operations?

Prashant Patel, Global Sourcing Leader, GE Vernova: A great example is in large-scale industrial operations where thousands of machines are deployed globally. Without real-time visibility, predicting failures or maintenance needs becomes a significant challenge. To address this, engineers work closely with customers and on-site teams to collect machine performance data, generating intelligent insights that drive predictive maintenance strategies.

This visibility extends beyond internal operations—it enables better coordination with suppliers. With access to accurate predictive data, organizations can share long-term and short-term demand forecasts with component manufacturers and raw material suppliers. This results in a well-integrated supply chain ecosystem where every stakeholder, from the end customer to the supplier, operates with increased efficiency and reduced risk. Ultimately, leveraging data-driven visibility creates a more resilient, responsive, and optimized supply chain, yielding a significant competitive advantage in the industry.

How does real-time visibility help e-commerce fulfillment and D2C operations, especially in the context of the Indian supply chain landscape?

Vaibhav Dhawan, CTO, Prozo: Real-time visibility is critical for both e-commerce and D2C supply chains, but the level of control required in each model is vastly different. In a typical e-commerce setup—like selling on marketplaces such as Amazon—a brand’s responsibility often ends at order dispatch. The marketplace takes over the customer experience, and the brand mainly ensures smooth warehouse handovers within SLA timelines.

In contrast, D2C brands own the entire customer journey—from order placement to doorstep delivery. This demands real-time visibility across the full chain: packing, transit, delays, delivery, and exceptions. It’s not just about internal efficiency—it’s about delivering a seamless, transparent experience to the end consumer.

At Prozo, we built our tech stack in-house to address India’s unique logistics challenges—cost pressures, infrastructure gaps, and data fragmentation. Central to this is our proprietary Control Tower, a real-time performance platform covering 2.2 million+ sq. ft. of warehousing and nearly half a million in-transit shipments. Within minutes, it can surface critical insights on order status, SLA risks, and fulfillment bottlenecks.

While D2C was the initial driver, even B2B stakeholders in modern and general trade are now demanding the same level of visibility. However, poor integrations and reluctance to share data remain major roadblocks. True visibility isn’t a dashboard—it’s a deeply engineered capability that requires aligned systems, scalable tech, and a culture of collaboration.

Why do digital transformation initiatives often struggle to deliver results? What typically goes wrong, and what do organizations need for future transformations to succeed?

Kaushik Mitra, Vice President - India Business, Celonis: Digital transformation initiatives often begin with ambition and investment, yet many fall short of delivering meaningful outcomes. Having worked for large enterprise technology firms like Oracle, Microsoft, and now Celonis for over two decades, I can comfortably say the challenge lies not in the lack of advanced tools, but in how organizations approach change.

Most enterprises implement systems of record and analytics platforms successfully. However, what they frequently overlook is the creation of a true system of intelligence—one that provides contextual, real-time insights to drive decisions. There is a tendency to focus on technology upgrades in isolation, without an integrated view of how processes run and interact across departments and functions. This siloed execution leads to disconnected improvements that fail to deliver systemic impact.

In the context of supply chains and large-scale business operations, transformation is more than adopting new software or changing strategies. It requires a complete understanding of end-to-end processes. Without that visibility, teams may optimize individual parts while remaining blind to the broader improvement opportunities that exist across the organization.

Process Intelligence is vital in this landscape. It enables organizations to gain real-time operational transparency, ensuring that decisions are grounded in data that reflects the full scope of their workflows. And as AI becomes more embedded in enterprise systems, Process Intelligence gives AI the input it needs to be effective and relevant for the enterprise. It lets people make faster, more informed decisions, and take decisive action to optimize business performance.

My recent move to Celonis, a company recognized as a leader in process mining and process intelligence, was driven by this exact need in the market. What stood out was not only the technology but its impact—bringing measurable value to customers by illuminating hidden value opportunities and enabling targeted, impactful improvements. This shift from reactive reporting to proactive intelligent action is what separates successful transformation from superficial change.

As industries continue to evolve rapidly, particularly with the rise of direct-to-consumer models, quick commerce, and precision fulfilment, the demand for intelligent, agile operations will only grow. Transformation must evolve from being a technology-centric endeavour to a process-centric one. Organizations that embrace this shift will be better equipped to adapt, innovate, and consistently meet rising customer expectations in a competitive landscape.

Mandar Shirsavakar: We’ve observed that digital transformation often fails not because of technology, but because of alignment—or the lack of it. Here are three common reasons we see projects under-deliver:

• Initiatives are led by tech, not by business priorities. Without a clear supply chain objective—like improving service levels or reducing working capital—tools become shiny dashboards, not decisions.
• Data readiness is overestimated. Companies often attempt advanced analytics without first fixing data quality and governance issues.
• Change management is sidelined. Adoption falters when users don't trust the insights or when workflows aren't designed around their daily decisions.

What’s needed is a ‘Crawl-Walk-Run’ approach, where value is demonstrated early, teams are trained incrementally, and technology supports—not replaces— business judgement. At Translytics, we pair our AI/ML capabilities with deep supply chain consulting, ensuring every implementation aligns with KPIs and business logic.

How do you prioritize visibility initiatives by balancing cost and impact?

Deepika Arora: This prioritization fundamentally begins with an organization’s long-term vision. Alignment at the leadership level is crucial before embarking on any visibility initiative. Once the strategic direction is established, the next step is to break it down into smaller, manageable phases. Conducting pilot projects and proof-of-concept (PoC) initiatives is an effective way to build confidence among stakeholders, ensuring feasibility before committing to full-scale implementation.

An open dialogue with vendors and service providers is also essential. Organizations need to communicate their expectations clearly, especially regarding scalability. In today’s rapidly evolving landscape, businesses are increasingly concerned about whether a solution can scale with their growth. A PoC serves as a vital checkpoint, not just in assessing impact but also in verifying if a solution meets the organization's future needs. 

How should organizations navigate the balance between long-term strategic gains and immediate return on investment (ROI) expectations?

Kaushik Mitra: From a process optimization standpoint, this is a critical challenge. In today’s fast-moving environment, particularly among newer generations of decision-makers, prolonged implementation cycles are increasingly unviable. Any initiative exceeding two to three months risks losing momentum and stakeholder engagement. However, supply chain leadership cannot afford a shortsighted, siloed approach in an effort to work quickly. Focusing narrowly on immediate gains without considering the broader operational ecosystem can lead to inefficiencies and missed strategic opportunities. The key is to develop a comprehensive, long-term strategy while executing in well-defined, incremental phases. By securing measurable wins at each stage, organizations can ensure sustained engagement while maintaining a holistic vision.

Given the complexity of supply chains—comprising countless interdependent processes—it is unrealistic to embark on large-scale transformations spanning multiple years. Instead, the optimal approach is to establish a robust strategic framework upfront, as Mandar rightly pointed out, ensuring end-to-end visibility while structuring implementation in a modular and agile manner. This method ensures the successful integration of long-term strategic priorities with short term execution, creating a sustainable roadmap for supply chain excellence.

How does real-time data enhance supplier collaboration? What measures can mitigate disruptions, and how receptive are suppliers to this approach?

Prashant Patel: Real-time data plays a critical role in ensuring supply chain efficiency. Every organization, whether in procurement, manufacturing, or logistics, seeks greater visibility into the movement of materials. The ability to track shipments, anticipate delays, and proactively resolve bottlenecks is no longer a competitive advantage but a necessity. However, having access to data alone is not sufficient—it needs to be structured, analyzed, and shared in a way that creates a fully integrated supply chain. The true challenge lies in ensuring that data transparency extends beyond a single organization and encompasses the entire supplier network, including Tier-1, Tier-2, and even Tier-3 suppliers.

Beyond technological integration, the key aspect of supply chain resilience is trust and reliability in supplier relationships. This is where organizations must shift their perspective from treating suppliers as mere vendors to viewing them as strategic partners. When suppliers are engaged as long-term partners, they become more invested in the shared goal of delivering value to the end customer. This approach fosters collaboration, accountability, and mutual problem-solving, leading to a more robust and transparent supply chain.

How can organizations establish trust with suppliers and encourage them to share data more openly?

Prashant Patel: Establishing trust is not a one-time effort but an ongoing process that requires consistency, transparency, and shared value creation. Suppliers are more likely to share critical insights when they see tangible benefits in doing so. Organizations must demonstrate that data sharing is not just for oversight but for collective problem-solving and efficiency gains.

One of the most effective ways to build trust is through direct engagement with suppliers. Rather than relying solely on reports and dashboards, organizations should actively engage with suppliers in their operational environments. Visiting supplier facilities, understanding their constraints, and collaborating on process improvements create a sense of partnership. When suppliers see that their concerns are acknowledged and addressed, they become more willing to engage in open discussions and share real-time data that can improve overall supply chain performance.

Continuous improvement initiatives also play a vital role in fostering trust. Organizations that work alongside suppliers to implement lean methodologies, optimize production processes, and enhance efficiency create a win-win scenario. This approach, rooted in mutual benefit, ensures that suppliers do not perceive data sharing as a compliance requirement but as a strategic advantage.

Another key factor is encouraging proactive issue resolution. Instead of waiting for disruptions to escalate into crises, suppliers should feel empowered to flag potential risks early. This requires a fundamental shift in mindset—from punitive responses to a collaborative approach where suppliers and manufacturers work together to resolve challenges before they impact operations. When suppliers trust that they will not face penalties for highlighting a potential bottleneck but will instead receive support in resolving it, they are more likely to engage in transparent communication.

Ultimately, real-time data sharing and supply chain transparency are not just about technology but about relationships. When suppliers feel valued and supported, they are far more likely to actively contribute to predictive analytics, risk mitigation, and supply chain resilience. By embedding these principles into supplier engagement strategies, organizations can transform their supply networks into agile, adaptive ecosystems capable of navigating complex global challenges.

How should organizations strike this balance when engaging with suppliers?

Prashant Patel: Let’s first look at this from a strategic standpoint before narrowing it down to the operational level. Any long-term supply chain initiative must align with an organization’s broader business goals, vision, and mission. The procurement and supply chain functions serve as fundamental pillars, ensuring business continuity and long-term competitiveness.

The challenge, however, lies in structuring supplier relationships in a way that balances immediate operational needs with long-term value creation. Consider a scenario where a company must scale a supplier’s production capacity to meet rising demand. This often necessitates direct investment— whether through capital expenditure or process optimization initiatives. The financial returns on such investments may not materialize for two to three years, but the long-term benefits, such as enhanced supply stability and agility, are critical for sustained growth.

However, market conditions are fluid. If an organization faces a sudden shift— such as a declining order backlog or a shift in financial priorities—the supply chain must be adaptable enough to recalibrate its investment strategy. This highlights the need for a supply chain that is not only strategically robust but also operationally flexible, ensuring optimal resource allocation while mitigating long-term risks. Despite technological advancements, including AI-driven forecasting and automation, the fundamental challenge remains achieving this delicate balance in real world supply chain operations. It is the responsibility of supply chain leaders— both within organizations and across supplier networks—to navigate these complexities, making informed decisions that create sustainable, long-term value.

How do you help organizations calculate or quantify the return on investment (ROI) for visibility solutions? What KPIs should companies track to measure their impact?

Deepak Jain, Director India, Argon & Co: Visibility solutions, in and of themselves, do not generate ROI. The value they create is contingent upon how effectively organizations leverage the insights they provide. Simply having a world-class visibility system does not translate into financial returns unless it drives informed decision-making and operational improvements.

One of the most common pitfalls organizations faces is evaluating visibility solutions solely through the lens of cost savings. While cost reduction is a key consideration, a more comprehensive approach is required—one that accounts for improved decision-making speed, enhanced customer satisfaction, and overall business impact. Our approach begins with defining the strategic intent behind the investment. When engaging with stakeholders, we first ask:

• What business objective is this solution intended to serve?
• Is the primary focus cost efficiency, sustainability, customer experience, or another factor?

Clarity on these objectives is essential before implementing any solution, whether it’s IoT, digital twins, or RFID technology. Without this, organizations risk implementing solutions that fail to deliver meaningful business impact. Beyond defining objectives, it is equally important to align expectations with measurable financial and operational outcomes. Take RFID technology as an example. Despite being available for years, widespread adoption remains limited. This is largely because many organizations evaluate its viability based solely on direct labor cost savings, such as reduced time spent on inventory counts.

However, the real value of RFID extends beyond labor efficiency. When implemented effectively, it enhances inventory accuracy, directly improving stock availability and customer satisfaction. Studies indicate that a 4-5% improvement in inventory accuracy can lead to a 2-3% increase in revenue—a metric far more significant than labor cost savings alone. This highlights the need for a more holistic approach to ROI assessment. 

The KPIs should be aligned with the organization’s strategic objectives. Generally, they fall into three primary categories:

• Operational Efficiency Metrics: These measure improvements in internal processes, such as reduction in lead times, increase in inventory accuracy, and reduction in process errors.
• Customer Experience Metrics: These assess the impact on service levels, including on-time delivery rates, stock availability, and order fulfillment accuracy.
• Financial Performance Metrics: These quantify the direct business impact, including revenue growth, cost savings, and return on assets (ROA).

For instance, in an RFID implementation, organizations that focus solely on direct cost savings from reduced manual inventory counts may miss the broader financial impact. A more effective approach would be to track the correlation between increased inventory accuracy and sales growth, ensuring that ROI calculations encompass both efficiency gains and top-line revenue improvements.

Beyond defining objectives and tracking KPIs, what additional steps should organizations take to validate the ROI of visibility solutions?

Deepak Jain: Once the objectives are established and the appropriate KPIs identified, the next step is to test the solution in a controlled environment. Rather than implementing it across the organization, we recommend a phased approach—starting with a pilot in a specific store, warehouse, or operational segment.

Piloting allows organizations to capture real-world data on performance improvements before making large-scale investments. For example, by introducing RFID in a single retail store, a company can measure the impact on inventory accuracy, customer experience, and sales performance. If the data supports the business case, scaling the solution becomes a more informed and justifiable decision.

Short-term ROI expectations must be balanced with long-term strategic gains. Not all visibility solutions deliver immediate cost savings; in many cases, the benefits materialize over time. A useful analogy is the transition from CRT televisions to LED/LCD models. Despite the higher initial cost, consumers

made the switch because the long-term benefits—such as superior picture quality, lower energy consumption, and enhanced durability—justified the investment.

The same principle applies to visibility solutions. While initial costs may seem high, the cumulative benefits—improved operational efficiency, better decision making, and increased revenue—create substantial long-term value. Organizations should adopt a holistic ROI framework that accounts for both immediate cost savings and long-term business growth.

How do we strike the right balance between achieving quick wins and building toward long-term transformation?

Deepak Jain: A phased strategy is essential. Breaking large initiatives into smaller, manageable parts allows for both immediate impact and sustainable growth. Both short-term and long-term goals are critical. Focusing only on quick wins risks missing out on lasting benefits, while aiming solely for long-term outcomes — such as implementing a full-scale digital twin or building a world-class supply chain — can make it difficult to secure organizational buy-in. Without early success and effective change management, projects often lose momentum and fail.

Consider the example of digital twins. While the ultimate goal might be a comprehensive digital twin solution, the journey should start small. A practical first step could be container tracking. This provides immediate, measurable benefits like reducing detention charges, demurrage costs, and expedited freight expenses. Early wins like these build momentum and confidence while setting the foundation for more advanced capabilities, such as smart tracking systems, AI/ML integration, weather analysis, and congestion forecasting.

Since every business is fundamentally driven by financial outcomes, it's important to consistently deliver tangible results. Capturing low-hanging fruit along the way keeps teams motivated and committed. Especially in large-scale transformations, where achieving full maturity often takes three to four years, early and continuous value generation is critical for long-term success.

Mandar Shirsavakar: We believe the key is to start small but design for scale. For example, when we begin with a client, we often start with a focused use case—like improving inventory allocation across a few plants or channels. Once the value is proven and trust is established, we layer on additional modules, such as multi-echelon optimization or expiry risk analysis. Quick wins come from solving pain points with measurable ROI, while the long-term journey requires an architecture that supports integration and flexibility.

In one case, we worked with a retail brand that had fragmented visibility across warehouses. We first built a unified stock view, enabling faster rebalancing decisions. Over time, this led to broader capabilities like predictive procurement and automatic reorder recommendations, all integrated into their ERP. In essence, the right balance comes from treating digital transformation not as a one-time project, but as a continuous journey—with business goals guiding every step.

What trends and supply chain visibility shifts should businesses prepare for?

Kaushik Mitra: If I may extend that thought, while the technological capabilities are indeed extraordinary, the real constraint will not be the tech itself but the maturity of internal processes and data quality. There’s a phrase I like to use… ‘There is no AI without PI — Process Intelligence’.

Organizations are sometimes too eager to implement AI without acknowledging that if their foundational processes are broken or inconsistent, AI will simply amplify the chaos. The biggest risk isn't that AI won't work; it’s that it will produce unreliable or misleading outputs if fed poor inputs. Before scaling predictive analytics or AI-based decision support, companies must harmonize, standardize, and rationalize their supply chain processes. They must focus on process integrity to ensure that data flows are clean, consistent, and reliable. Only then can AI models generate truly trustworthy and actionable insights. Thus, while it’s tempting to talk about advanced technologies, I would argue that the next three years must involve a heavy focus on process reengineering and data governance, because without that, predictive supply chains will remain aspirational.

In the future, competitive advantage in supply chains will not stem solely from internal optimization. It will be defined by an organization’s ability to orchestrate, influence, and thrive within a connected, intelligent, and open value network. Companies must shift their mindset from focusing purely on isolated enterprise efficiency to building resilient, collaborative ecosystems. Trust-building, establishing shared standards, and co-innovating with partners will no longer be optional; they will be business-critical imperatives for long-term survival and growth. Those who succeed will be the ones who recognize that ecosystem resilience — not just internal excellence — is the true foundation of future-ready supply chains.

Deepika Arora: Another critical layer is data integration across systems. A significant reason many organizations delay their digital transformation initiatives is the fear that their legacy systems will not integrate easily with newer, cloud-native solutions. This concern is valid. However, over the next few years, we will see a significant maturation of plug-and-play integration technologies. Interoperability frameworks, cloud-native data fabrics, and no-code/low-code integration platforms will allow even highly heterogeneous IT landscapes to operate more cohesively. In essence, the barriers that currently exist between enterprise resource planning (ERP) systems, warehouse management systems (WMS), transportation management systems (TMS), and external data sources will diminish.

This seamless flow of data across the enterprise will unlock real-time digital twins of supply chains, allowing organizations not only to predict outcomes but to simulate scenarios and optimize decisions in a dynamic environment. While technology promises significant advances, we must not lose sight of the human dimension in this transformation. Investments in AI, machine learning, and predictive analytics are vital, but it is ultimately the skills and adaptability of people that will determine success.

Technology provides tools, but human capital powers outcomes. A generational shift is unfolding — younger employees are digitally native and comfortable with AI. Yet, we cannot afford to sideline experienced employees whose domain expertise and operational insights remain critical. Thus, reskilling, continuous learning, and change management must be prioritized alongside technology investments. Organizations must deliberately build capabilities that bridge digital gaps and empower all employees to adapt. Without a structured focus on human capital, sophisticated tools risk becoming underutilized assets, and much of the potential value of transformation will remain unrealized. Sustainable success lies not just in deploying the right technologies, but in equipping people to use them effectively.

Prashant Patel: As organizations open up their data architectures and increase real-time connectivity, cybersecurity becomes an existential priority. With vast amounts of sensitive operational, financial, and strategic data moving across internal and external networks, attack surfaces expand exponentially. Thus, supply chain leaders must embed ethical governance frameworks from the outset — not as an afterthought.

This includes ensuring that AI models are explainable, bias is systematically monitored and mitigated, and data privacy regulations such as GDPR and evolving global standards are adhered to rigorously. We are not just talking about technical security anymore. Model governance, ethical AI design, and trust frameworks will be critical to maintaining license-to-operate in increasingly digital, interconnected supply ecosystems.

Another critical point is that visibility must extend beyond the enterprise’s internal operations. True resilience and agility demand that organizations connect and share data across the entire value chain — with suppliers, distributors, logistics providers, and even customers. Building multi-enterprise platforms that enable real-time, secure, and trusted data exchange will be essential.

Deepak Jain: One of the biggest shifts that is already beginning to reshape supply chain operations — and which will continue to accelerate — is the transition from reactive to predictive decision-making. Traditionally, supply chain visibility has been a backward looking process. Organizations would monitor shipments, detect delays, and react after the fact. But what we are now seeing — and what will become a dominant expectation — is that visibility solutions will become increasingly forward-looking.

Leveraging real-time data sources such as weather patterns, geopolitical risks, financial markets, and social media signals, organizations will start predicting disruptions even before they occur. It won’t just be about knowing where your shipment is; it will be about anticipating that a disruption is likely and having the tools to make decisions proactively. Predictive insights, rather than retrospective data, will become the currency of competitive supply chains.

Furthermore, the idea of multimodal data fusion — where structured enterprise data combines seamlessly with unstructured external data — will become mainstream. This means that supply chains will be informed by a much richer and more dynamic set of signals than has historically been the case.

Mandar Shirsavakar: Before advancing into the terrain of sophisticated analytics, it is vital to understand where most organizations currently stand. Today, the majority of companies continue to operate within a descriptive analytics paradigm. Their data strategies primarily answer "what happened?" — typically in retrospect.

While descriptive insights have historically supported stable operations, they are increasingly inadequate in an environment marked by rapid change and complexity. Moving beyond this into predictive, prescriptive, and cognitive analytics is not simply a matter of adopting better tools. It requires a fundamental transformation in organizational culture, data literacy, and business processes. Organizations must cultivate a culture where data-driven decision-making is second nature, raise data fluency across all levels, and restructure processes to leverage real-time insights.

A compelling example comes from Manyavar, the leading ethnic wear brand. Without immediately deploying advanced AI or cognitive solutions, Manyavar focused relentlessly on data quality, governance, and analytic discipline. As a result, it achieved less than 3% excess inventory across its network — a remarkable outcome in a sector notorious for supply chain challenges. This illustrates a critical point: Companies that excel in the basics of data management and operational analytics can achieve outsized business performance, even before fully embracing AI-driven systems. As organizations build toward greater maturity, the next logical step is cognitive analytics.

Unlike predictive analytics, cognitive systems learn, reason, and adapt autonomously, drawing on techniques such as machine learning, natural language processing, and pattern recognition to simulate human thought processes at scale. Yet cognitive analytics is not a shortcut.

It demands that data foundations, organizational readiness, and process maturity are already firmly in place. Only businesses that invest methodically — starting with descriptive analytics and progressively advancing through predictive stages — will be poised to harness the full strategic power of cognitive systems. Thus, the journey from descriptive to cognitive analytics is a gradual evolution, requiring consistent leadership, disciplined capability-building, and a long-term view. Organizations that internalize this progression stand to create lasting competitive advantages in an increasingly data-driven world.

Vaibhav Dhawan: The supply chain landscape is undergoing rapid transformation, bringing with it increasing complexity. Multiple factors are converging—each marketplace operates with different SOPs, newer delivery formats like Same-Day (SDD) and Next-Day Delivery (NDD) are gaining traction, and the explosive rise of quick-commerce is pushing the envelope further by promising deliveries in under 20 minutes.

In response, businesses are no longer looking at tech as a mere reporting or automation layer. They are seeking predictive analytics, intelligent automation, and hyper-flexible networks that can adapt to shifting consumer expectations and operational challenges in real-time. As these complexities scale, brands and enterprises are increasingly turning to professional, full-stack supply chain partners—ones that offer deep tech integration and operational agility—to manage this orchestration effectively and stay competitive.

One game-changing initiative that worked for you — could you share that with us?

Kaushik Mitra: By combining real-time process Intelligence, AI-powered root-cause insights, and prescriptive automation into one platform, we moved from fragmented dashboards to end-to-end operational orchestration. That integration cut time-to-value dramatically, slashed process inefficiencies, and fostered seamless collaboration across finance, supply chain, and order-to-cash teams. This holistic platform approach has truly been a game-changer for Celonis and our customers.

Deepika Arora: For me, what has worked is starting with a proof of concept — showing the capability first and then going full scale.

Prashant Patel: For me, it has to be ‘Invest in Supplier Partnerships’. Supply chains are deeply connected. A supplier’s struggle today is our bottleneck tomorrow. I was talking to a logistics solutions provider recently — we don't even use their platform directly in India, but many of our suppliers rely on it. If they hit a roadblock, guess who feels it next? Us. That’s why we believe in stepping in early — collaborating, problem-solving, making their processes stronger. If we help them win, we win too. It's not just partnership; it’s proactive ownership.

Deepak Jain: When I was at Coca-Cola, one of the key initiatives I led was around IoT and predictive maintenance. We had a huge number equipment i.e. fridges and fountain machines out in the market, and maintaining them efficiently was a major challenge. To tackle this, we started embedding IoT sensors into the equipment to monitor their health remotely. On top of that, we leveraged AI and machine learning to predict failures before they actually happened. It wasn’t a quick win — the entire process took close to 18 months. There was a lot of work involved: building the right data models, cleaning the data, getting teams aligned on new processes. But with consistent effort, we were able to achieve about 70% predictive maintenance. That meant most issues were being identified and fixed before a machine could break down. In the long run, it drastically improved operational efficiency and reduced downtime. The big lesson for me was that if you stay patient, stay clear about the goal, and give technology initiatives the time they need to mature, the results can be transformational.

Vaibhav Dhawan: One of the most impactful decisions we made at Prozo was to build everything in-house and maintain end-to-end control over the entire supply chain experience—both for our clients and their end customers. This approach has allowed us to own every aspect of the service journey, ensuring consistency, transparency, and responsiveness at every step.

Our journey over the past two and a half to three years has been both intense and fulfilling. We've scaled significantly during this time, and achieving that while maintaining service excellence is no small feat. What sets us apart is not just real-time visibility but the ability of our tech stack to surface critical insights about every order and shipment being processed. Whether it's a brand, a warehouse manager, or a customer success executive—every stakeholder has access to the information that matters most.

We’re proud to be one of the most SLA-compliant supply chain service providers in the country today, and that’s largely due to the power and precision of our tech. And we don’t stop there—we push out meaningful feature updates regularly, with major enhancements deployed nearly every month.

Some standout examples include:

• Our CCTV-enabled Control Tower that delivers packaging videos on demand to your email inbox within 15 minutes.
• A Marketplace Courier Reconciliation module that allows users to map orders and submit claims in under 10 minutes.
• An ever-evolving Prozo WMS that’s integrated with all leading marketplaces, enabling clients to operate with true end-to-end visibility without needing to juggle disconnected systems.

This continuous innovation and full-stack integration are what allow us to deliver future-ready, client-centric supply chain solutions every day.

(Disclaimer: The views and opinions expressed are solely experts' own and do not represent the official policy, position, or views of their employers or any organization with which they are affiliated.)