The Organisation

Rohil

The ET Supply Chain roundtable was unusually clear on sustainability: the industry’s problem is not lack of sustainability language. It is the failure to translate that language into operating decisions that make commercial sense. Vinayaka Gangavathi put it most directly: sustainability will scale when it makes both environmental and business sense. His argument was simple. Companies may like EVs, solar warehouses, and green sourcing in principle, but adoption will remain slow until they can answer practical questions around cost, infrastructure, returns, and day-to-day productivity.

That grounding matters because it moves the conversation out of boardroom aspiration and into execution reality. Vinayaka argued that the real shift happens when sustainability moves from annual reports into purchase orders, supplier scorecards, and daily decisions. He used bigbasket’s own experience with EV last-mile deployment to show why this matters. The business did not evaluate EV adoption through virtue-signaling; it evaluated it through operational questions: Where will charging infrastructure come from? What about battery replacement economics? Will delivery productivity fall? His point was not anti-sustainability. It was that sustainability adoption becomes real only when it survives business scrutiny.

Swaminathan Ramachandran reinforced the same idea from a strategic angle. He argued that sustainability should not be sold merely as something “good to do.” It should be framed as a business imperative with clear P&L impact, especially because it can deliver both cost advantage and greater resilience to shocks. That is a critical reframing. Once sustainability is tied to resilience, operating risk, and performance impact, it becomes easier to justify as a supply-chain investment rather than an image exercise.

Pankaj Aggarwal added a more execution-led version of the argument. He said the industry is “missing the boat” in logistics sustainability, especially because smaller EV trucks are now viable for last-mile and intra-city distribution, which already account for a large share of logistics activity. He also pointed to practical wins from rooftop solar in warehousing and EV forklifts inside warehouses, framing sustainability as a win-win on both sustainability and operating cost.

Taken together, the case-study lesson is quite sharp: sustainability will not scale in supply chains because leaders repeat the right words. It will scale when companies can prove that greener choices improve one or more of the following: cost, resilience, productivity, asset performance, supplier discipline, or long-term risk management. The roundtable makes clear that the missing bridge is not awareness. It is business-case discipline.

Why it matters:
Supply-chain sustainability becomes real only when it moves from ESG language to commercial logic, when the green decision is also an operating decision the business can defend.

Rohil

India is not facing a pure supply-chain talent shortage. The sharper issue, as the ET Supply Chain roundtable suggests, is whether the industry is building the kind of leadership pipeline the next decade will require. Pankaj Aggarwal took the contrarian view that India is already producing enough talent through engineering colleges, MBA programs, and other institutions, and that supply chain fundamentally needs people with logical thinking and problem-solving ability. But the wider discussion shows that talent volume is not the same as leadership readiness.

Vinayaka Gangavathi pushed the debate in a more practical direction. His point was that building future supply-chain leaders requires more than hiring smart people. Younger professionals increasingly want exposure to technology, analytics, AI, and real business problems. When companies hire strong talent but trap them in low-value reporting work, they waste the very capability they say they need. His line was memorable for a reason: hiring talented people and then making them spend half their day creating PowerPoint slides is “like hiring a Formula 1 driver and asking him to wash the car.” He also cited bigbasket’s internal mentoring effort, Project Drona, and its dedicated in-house L&D setup as examples of what leadership development can look like when it is treated intentionally.

Samrat Sehgal added a second layer to the argument. He pointed out that India has already created quick-commerce companies that are essentially technology-enabled supply chains, and that Indian supply-chain professionals are increasingly being exported into global roles by multinational firms. That is a sign of strength, not weakness. But he also made an important caveat: supply chain is still not always the first-choice career magnet for top talent, even though the function is gaining a stronger voice in the boardroom and evolving from an execution role into a strategic business enabler.

Sunit Mukherji sharpened the capability challenge further. He argued that what India now needs is a stronger blend of techno-commercial knowledge, data analytics, digital fluency, leadership traits, and commercial acumen. In other words, the next generation of supply-chain leaders cannot be built only around functional execution. They need to be able to navigate ambiguity, lead through disruption, and make business trade-offs in increasingly volatile operating conditions.

That is the real case-study takeaway from this discussion. India appears to have enough raw talent entering the system. The unresolved question is whether companies are giving that talent the right mix of exposure, mentorship, digital capability-building, and strategic ownership to become future leaders. The risk is not that India cannot produce supply-chain talent. The risk is that the industry may underdevelop the talent it already has.

Why it matters:
The next supply-chain advantage will not come from hiring more people alone. It will come from building leaders who can combine operations, analytics, technology, and business judgment at the same time.

Rohil

The strongest consensus in the ET Supply Chain roundtable came on AI: the industry is moving faster on adoption intent than on operating readiness. The core issue, according to multiple leaders, is not lack of ambition. It is the fact that companies are trying to scale AI on top of fragmented, inconsistent, low-trust data environments. Vinayaka Gangavathi stated it most bluntly: “Before AI, we need to fix our data.” He described a familiar reality where supply-chain data still sits across ERPs, Excel files, emails, WhatsApp groups, and warehouse systems. His summary was simple and memorable: data readiness must come before AI readiness.

That argument was echoed across the panel. Samrat Sehgal said AI can help process vast amounts of data, detect risk earlier, improve forecasting, optimize inventory, and generate decision options faster than planners in some cases, but it cannot fix poor data quality. Sunit Mukherji made a similar point from a value-chain angle: without proper data management and transparency across the chain, AI implementation becomes an onerous task. Swaminathan Ramachandran added that organizations need both a clear AI strategy and a realistic assessment of data preparedness before they can build meaningful use cases.

What makes this discussion especially valuable is that it goes beyond the standard “garbage in, garbage out” warning. It points to a broader readiness problem. Pankaj Aggarwal argued that many companies are still missing the fundamentals of a robust, cloud-based, multi-source supply chain. In that environment, AI becomes an expensive overlay on top of weak operating basics. Swaminathan extended the point further by saying leadership teams also need a better understanding of AI’s capabilities and limitations. Vinayaka captured that in one sharp line: AI success starts with leadership literacy, not technology deployment.

There is also a practical proof point in Vinayaka’s example from bigbasket. He described an earlier stage where purchase orders were created in Excel or Word, converted to PDFs, emailed, and stored in Google Drive. Only after the underlying data across channels was cleaned up through a custom ERP did implementing an AI-driven procurement ERP become much easier. That is the real case-study lesson here: companies do not fail with AI because AI is weak. They fail because the data model, process foundation, and leadership understanding are too weak to support it.

This makes the real supply-chain AI question much sharper. It is not “Who is experimenting with AI?” It is “Who has built the data discipline, process clarity, and leadership literacy needed for AI to deliver repeatable value at scale?” The roundtable suggests that this is where the true competitive gap will open.

Why it matters:
In supply chain, AI will not become a durable advantage until companies fix the layer underneath it: clean data, clear use-case strategy, resilient processes, and leaders who understand what the technology can actually do.

Rohil

India reaching 7.97% of GDP in logistics cost is a meaningful milestone. But the ET Supply Chain roundtable makes one thing clear: this is not the end-state. It is the start of a harder conversation about what supply-chain transformation should actually optimize for next. Vinayaka Gangavathi argued that India should celebrate the achievement, but “not get carried away,” because the next benchmark is not cost alone, it is speed, visibility, reliability, and customer experience. He grounded that in a practical quick-commerce reality: customers do not ask what logistics costs; they ask, “Where is my order?” and “Why is it late?”

That shift in framing matters. For years, Indian supply-chain discussions were dominated by the idea that lower logistics cost would automatically make the system more competitive. But the panel points to a more mature truth: low cost without high reliability is an incomplete win. Swaminathan Ramachandran said India should benchmark cost “along with speed and reliability,” and added that the country still has distance to travel in digitization and multimodal maturity. Sunit Mukherji reinforced that view by pointing to unfinished work in multimodal transport, waterways, last-mile delivery, hinterland access, and farm-to-fork efficiency.

There was also an important challenge to the benchmark itself. Pankaj Aggarwal questioned whether India is even comparing logistics costs correctly against the West, arguing that raw percentage comparisons ignore purchasing power parity, labor economics, fuel structures, and infrastructure differences. His point was not that India has arrived, but that global benchmarking needs to be interpreted more carefully. Vinayaka agreed with that caveat, but pushed the discussion toward what matters more operationally: logistics productivity, how efficiently the system moves goods, manages inventory, improves asset utilization, and delivers predictable service levels.

That is the real case-study lesson from this discussion. India may have improved the cost number, but the next stage of supply-chain advantage will be decided by whether the country can build a network that is not only cheaper, but faster, more digitized, more multimodal, and more dependable under stress. In other words, the country has improved the economics of movement; now it has to improve the quality of movement.

Why it matters:
The next supply-chain advantage for India will not come from lowering logistics cost alone. It will come from converting that progress into better service, better predictability, and better productivity across the network.

Rohil

Global FMCG companies are increasingly treating India as a manufacturing and export base, not just a consumption market. Business Standard says this shift is visible across food and beverages in particular, with companies now investing in local factories, food-processing infrastructure, and export-oriented supply chains rather than relying only on distribution-led growth.

The strongest example in the article is PepsiCo, which plans to invest up to ₹5,700 crore by 2030 to expand its India foods manufacturing footprint, including a concentrates plant in Madhya Pradesh and snacks facilities in Assam and Tamil Nadu. The article also notes that Coca-Cola bottlers announced nearly ₹25,760 crore in greenfield and brownfield investments across nine states, while Reliance Consumer Products pledged about ₹40,000 crore toward integrated food manufacturing units and AI-enabled food parks in Maharashtra and Andhra Pradesh.

What makes this strategically important is why the economics now work better. According to experts quoted in the piece, India’s rising domestic demand, lower logistics friction after GST, abundant agricultural inputs, and global China+1 diversification are making local large-scale production more viable. The article argues that this is shifting the model from “manufacturing to save tax” toward “manufacturing to export,” especially as India’s scale lowers unit costs and supports better export economics.

The deeper signal is that India is moving into a different role in global FMCG supply chains. Analysts cited by Business Standard say India is becoming a regional manufacturing hub, especially in food, beverages, cosmetics, snacks, frozen foods, and ready-to-eat products. The article also points to investments in automation, quality systems, cold-chain infrastructure, and state-level support as critical enablers of that shift.

There is a broader ecosystem effect too. The report says this manufacturing push could create spillover benefits across jobs, agriculture, MSMEs, packaging, warehousing, logistics, and supplier networks, because organised food processing and export operations demand more structured sourcing and stronger local supply chains.

Why it matters:
India’s next FMCG opportunity may not be defined only by how much global brands can sell into the country, but by how much they can manufacture from India for the rest of the region and beyond.

Visit BusinessStandard

Rohil

India’s FMCG industry is moving into a more AI-led phase where the technology is no longer being treated as an experiment or a dashboard enhancer. In this ET Retail piece, AI is framed as a structural lever across demand sensing, quality assurance, and consumer engagement, three areas that directly affect how FMCG companies plan, produce, and grow. The article says 43% of FMCG companies in India have already adopted AI for forecasting, supply chain, and consumer analytics, signaling that adoption is becoming mainstream rather than niche.

The strongest operating signal is around hyperlocal demand sensing. The author argues that FMCG players have historically relied on lagging indicators such as monthly sales data, distributor feedback, and quarterly reviews for stocking and distribution decisions. AI changes that model by combining point-of-sale data with variables such as weather, agricultural cycles, and regional festive calendars to predict demand at a much more granular level. The implication is clear: better forecasting can reduce waste, cut stockouts, and improve consistency in how products reach market.

The second important shift is on the factory side. The article says brands are using AI for real-time anomaly detection on production lines, including impurities, inconsistent fill levels, and labelling errors, while predictive maintenance tools help identify equipment issues before they cause downtime or contamination risk. That matters because in FMCG, quality is no longer a brand differentiator alone, it is a trust baseline, and AI is increasingly being used to protect that baseline at scale.

The third shift is consumer-facing. As more FMCG brands build direct-to-consumer channels, they are accumulating richer data on purchase patterns, repeat cycles, and product preferences. The article argues that AI is helping convert that data into more personalized recommendations and communications, giving brands a more individual, data-informed way to engage consumers in a crowded market.

There is also a cautionary note worth keeping. The article explicitly warns that data integrity is critical, because skewed or incomplete data can cause algorithms to amplify bias as efficiently as they amplify insight. That makes this less a story of AI replacing FMCG judgment and more a story of AI becoming valuable when it is built on clean inputs and tied to real operating decisions.

Why it matters:
For FMCG companies, AI is beginning to shift from a back-end productivity tool to a system that can influence what gets stocked, what gets flagged, and what gets recommended across the value chain. The next winners may be the brands that embed AI closest to demand, quality, and decision-making. This final line is an inference grounded in the article’s three adoption pillars.

Visit EconomicTimes

Rohil

India’s FMCG sector is entering another cost-heavy phase as repeated fuel price increases in May intensify pressure on transport, packaging, and crude-linked inputs. BusinessWorld frames this as a “new cost crisis,” while Reuters reports India’s state-run fuel retailers raised petrol and diesel prices for the fourth time in May 2026, after crude surged amid the Iran war and Strait of Hormuz disruption.

What makes this strategically important is that fuel inflation does not stay confined to logistics. It spreads through the FMCG cost stack via freight, plastics, surfactants, packaging materials, and last-mile distribution. The Financial Express notes that surging crude and fuel prices are likely to raise input costs and squeeze margins, while The New Indian Express reports the industry is bracing for 4%–5% price increases over the next two to three months.

The bigger risk is on the demand side. The Economic Times recently reported that a crude-linked energy crisis and weaker monsoon outlook could slow FMCG volume growth this year, with adverse scenarios pulling growth down to 3%–4%. It also noted that consumers are already consolidating purchases and buying less frequently, which means fresh price hikes could hit consumption just as recovery was starting to stabilize.

That turns fuel inflation into more than a cost problem. It becomes an affordability-management problem. In this environment, FMCG companies are likely to respond through a familiar mix of selective price hikes, pack-size recalibration, and tighter margin discipline rather than broad-based price resets. This final point is an inference based on the recent sector reporting on price hikes, demand softness, and input pressure.

Why it matters:
For FMCG brands, the next competitive edge may come less from demand generation alone and more from how well they absorb fuel-led inflation without breaking price points, volume momentum, or consumer trust.

Visit BusinessWorld

Rohil

Quick commerce has now become the largest online sales channel for India’s top FMCG companies, with brands such as Britannia, Tata Consumer, Dabur, Parle, AWL, and ITC deriving a majority of their digital sales from 10-minute delivery platforms. According to Economic Times, q-commerce accounted for 60%–75% of total online sales in FY26 for several major FMCG firms, up sharply from less than half a year earlier.

What makes this strategically important is that the shift is not just digital-channel growth. It is a change in consumer buying behavior. Executives told ET that grocery shopping is increasingly being broken into frequent top-up purchases through the week, and that q-commerce is now cannibalising not only traditional e-commerce but also modern trade and kirana sales. That means FMCG demand is not merely moving online; it is being reorganized around immediacy and replenishment.

The second big signal is premiumization. Britannia said the move to q-commerce is helping it sell more indulgent and premium categories, rather than staying heavily skewed toward staples as on older online marketplaces. The company said this channel has already led to a threefold increase in sales of adjacency categories, and it expects q-commerce’s share of its online sales to rise to 85% from 70% currently.

The broader implication for FMCG players is operational, not just commercial. Marico said it is strengthening its q-commerce supply chain through digitisation, automation, and AI-based forecasting, which signals how seriously brands now view this channel. ET also reports that most FMCG companies posted 70%–100% year-on-year growth in q-commerce sales in FY26, making it the industry’s fastest-growing channel for the past two to three years.

Why it matters:
For FMCG in India, quick commerce is no longer an experimental online format. It is becoming the default digital shelf for frequent replenishment, premium discovery, and faster category expansion.

Visit EconomicTimes

Rohil

How India's second-largest pharmaceutical company automated order capture across 6,000+ stockists and delivered real-time order visibility to a 10,000-strong sales force.

6,000+ Stockists across India, each ordering on their own terms
10,000+ Sales team members with on-demand order visibility
250+ Stockist ERP formats parsed autonomously, without human intervention

The Organisation

A $2 billion pharmaceutical leader operating at global scale.

Dr. Reddy's Laboratories is one of India's most recognised pharmaceutical companies, with revenues exceeding USD 2 billion and a presence spanning multiple countries across Asia, Europe, and North America. Headquartered in Hyderabad, the company manufactures and markets a broad portfolio of generics, branded formulations, and active pharmaceutical ingredients.

Pharmaceutical - India & Global
USD 2 Bn+ Revenue
10,000+ Sales Representatives
6,000+ Stockists in India
SAP S/4HANA ERP

The Challenge

An informal market. A fragmented order channel. A thousand formats.

Dr. Reddy's top leadership set an ambitious benchmark: process depot orders faster than a consumer orders groceries on a quick-commerce platform. The gap between that ambition and operational reality was significant.

The deeper problem was structural. The Indian pharmaceutical distribution market operates informally, and asking 6,000 stockists to change how they place orders simply was not a viable path.

Challenge 1
No order visibility for the field

With no real-time order status, 10,000+ sales representatives were spending valuable time calling depots manually to chase updates, time that could be directed toward growing the business.

Challenge 2
Orders arrived in every format imaginable

Stockists sent orders written directly in email bodies, as informal Word or Excel attachments, and as PDFs generated from their own ERP systems. With 250+ ERP vendors in the market, no two PDF layouts were alike.

Challenge 3
Product matching across unstandardised descriptions

Each stockist described products in their own shorthand. Mapping informal product names to the correct product IDs in Dr. Reddy's master catalogue was a critical, error-prone, and time-consuming step.

The Solution

A fully AI-native order processing engine, built to meet the market where it is.

Dr. Reddy's partnered with JAVIS to design and deploy an end-to-end autonomous order processing system. Rather than forcing the market to adapt, the technology was built to absorb the complexity, handling every format, every ERP, every informal description — without manual intervention.

The rollout began as a single-depot pilot before scaling across the network, validating both the AI model's accuracy and the depth of ERP integration at each stage.

Layer 1: JAVIS Vision AI

Intelligent Document Parsing
JAVIS Vision AI ingests incoming orders regardless of format, plain email text, Word attachments, Excel files, or PDF outputs from any of 250+ stockist ERP systems. Every order channel is captured without requiring the sender to change their behaviour.

Layer 2: AI Product Matching

Real-Time Catalogue Reconciliation
A two-step AI model first reads and extracts the order content, then performs real-time semantic matching of stockist product descriptions, however informal, against Dr. Reddy's standard product master. The correct product ID is resolved automatically, ready for ERP order creation.

Layer 3: Deep ERP Integration

Hands-Free Order Execution
A deep integration between JAVIS and SAP S/4HANA automates order creation end-to-end. Sales order numbers sync back to JAVIS in real time. Inventory and product availability pipelines, including differential phase-in and phase-out positions across depots, ensure only serviceable orders are raised.

Layer 4: GENIE Agentic Platform

Order Truth for the Sales Force
GENIE, JAVIS's agentic AI platform, delivers order visibility directly to the field. Sales representatives ask natural language questions and receive instant, territory-specific answers on order status, fill rates, and inventory positions, without a single call to a depot.

Layer 5: Inventory Intelligence

Availability-Aware Processing
Live inventory data pipelines from SAP S/4HANA feed JAVIS with accurate stock positions across every depot. The system validates availability before creating orders, accounting for complex product transitions, making the process not just fast, but reliable.

Architecture

Built for Scale, Not a Workaround
The solution was architected as a competitive capability, not a tactical fix. The JAVIS platform integrates with the SAP S/4HANA integration suite, ensuring enterprise-grade data integrity, security, and operational resilience as volumes grow.

"We wanted to process orders faster than a consumer orders groceries. JAVIS made that a reality ,without asking a single stockist to change how they do business."

-Senior Leadership - Dr. Reddy's Laboratories

The Impact

Speed, clarity, and a structural competitive edge.

Zero Manual order entry steps for incoming stockist orders, regardless of format, ERP source, or product description style. A pharmaceutical distribution operation that was once reliant on manual intervention at every step now runs end-to-end on AI, from inbox to ERP, without a human in the loop.
Real‑time Order truth delivered to 10,000+ sales representatives via natural language queries, on demand, by territory
100% Of stockist ERP formats and email-based order channels handled autonomously, preserving the informal workflows the market depends on

Platform & Products

Built on JAVIS

JAVIS Vision AI
Multi-format document intelligence that parses orders from any source, email body, Word, Excel, and PDFs from 250+ stockist ERP systems, without templates or pre-configuration.

AI Product Matching Engine
Semantic matching layer that resolves informal stockist product descriptions to the correct SKU in the product master catalogue in real time, eliminating the most error-prone step in the order workflow.

SAP S/4HANA Integration Suite
Deep bidirectional integration with Dr. Reddy's ERP, automating order creation, syncing sales order numbers, and streaming live inventory and product availability data back to JAVIS.

GENIE: Agentic AI Platform
Conversational AI interface that gives field sales teams on-demand, natural language access to real-time order status, fill rates, and territory-specific business intelligence, replacing depot calls entirely.

Rohil

Dabur reported a Q4 FY26 net profit of ₹362 crore, up about 16% year on year, while full-year FY26 revenue rose roughly 5% to ₹13,792 crore, according to Business Standard’s summary of the company’s results.

The more important signal is that the quarter suggests profit recovery is back, but the operating environment is still not easy. Earlier commentary around Dabur had already pointed to growth headwinds from rural demand and rising costs, so this Q4 improvement should be read less as a clean breakout and more as evidence that the business is stabilizing despite pressure on consumption and margins.

That makes the result strategically interesting for FMCG watchers. Dabur appears to be showing the same pattern visible across much of the sector: demand is improving, but companies are still navigating a difficult mix of cost inflation, uneven category momentum, and cautious consumer behavior. This final framing is an inference based on the reported Q4 profit growth and the company’s previously flagged headwinds.

Why it matters:
For FMCG companies, the next phase is not only about returning to growth. It is about proving that recovery can hold even when input costs, rural demand quality, and margin pressure remain uncertain.

Visit IndianTelevision

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Posts

The Organisation

The Challenge

The Solution

Layer 1: JAVIS Vision AI

Layer 2: AI Product Matching

Layer 3: Deep ERP Integration

Layer 4: GENIE Agentic Platform

Layer 5: Inventory Intelligence

Architecture

"We wanted to process orders faster than a consumer orders groceries. JAVIS made that a reality ,without asking a single stockist to change how they do business."

The Impact

Platform & Products

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