Our Celerity Under-30 Supply Chain Super Stars 2025 have proven their mettle in one of the most demanding and dynamic fields. With every achievement, they raise the bar higher, setting new benchmarks of performance and excellence. Here’s to the champions who are powering the future of supply chains! Here are the Under-30 Winners with a synopsis of their winning case study.
Ankush Kumar Singhal, Fulfillment Planner, Signify Innovation India Ltd.
To improve the efficiency of direct-to-distributor deliveries—a channel representing a significant share of business—we set out to optimize vehicle fill rates right at the order entry stage. One of the main challenges was the highly dynamic sizing of open-body trucks in the Indian market. Additionally, implementing these improvements required updates to the mobile application, which meant coordinating IT infrastructure changes and ensuring user adaptability. To address these challenges, we conducted on-ground visits to plants, measured over 100 trucks of different tonnages, and identified the median dimensions for length, width, and height. Using this data and loading optimization software, we determined the optimal way of loading vehicles. We then presented these findings, along with the projected savings and usability benefits, to the leadership team. With IT support, we embedded fill rate calculations by weight and volume into the mobile app so that it could display real-time fill rates as quantities were added, and suggest adjustments to approach full capacity.
After conducting in-depth user acceptance testing across multiple scenarios, we rolled out the solution nationwide, supporting it with demos and training sessions for the sales team to ensure smooth adoption. As a result, vehicle fill rate improved from average 92% to 95%, which in turn generated freight savings. Additionally, the solution reduced manual workload for the sales support team by enabling optimization directly at the order entry stage.
Anukritika Jha, GM – Supply Chain Business Partner, Orkla India
Sachets make up nearly 65% of HUL’s Hair Care business, and leakage at the horizontal and vertical sealing points had long been the top contributor to consumer complaints and high defects per million units (DPMU). As the Manufacturing Manager, I led a project to eliminate sachet leakage through a data-driven, scientific approach. We identified sealing temperature, pressure, and dwell time as critical parameters, aiming to define ideal sealing conditions using machine learning. A key challenge was data availability, as most machines lacked built-in sensors. To address this, we collaborated with OEMs to enable temperature data capture and derived pressure and dwell time metrics by correlating servo movements and torque. After collecting extensive line data, we applied statistical modelling using Minitab, removed outliers, and derived the optimal sealing equation.
Controlled trials on a model machine validated the predicted parameters over 1–2 months, after which we scaled the solution across all lines by locking in the optimal sealing settings. This initiative successfully achieved zero leakage (0 DPMU) across sachet lines, significantly reduced consumer complaints, improved product quality perception, and boosted team morale and operator ownership. Beyond solving a long-standing quality issue, it also demonstrated a breakthrough application of machine learning in packaging and established a replicable digital problem-solving framework for manufacturing.
Ershad Jahagirdar, Supply Chain Consultant, Simwell Inc.
Our client was facing significant paper roll waste and production inefficiencies due to trim size misalignment across the supply chain. Traditional trim optimization methods were limited to individual sites, overlooking the broader network-level impacts on transportation and inventory costs. Key challenges included aligning trim decisions with total supply chain costs, balancing production efficiency against customer demand and logistics constraints, and managing large-scale data for real-time decision-making.
To address this, we designed and implemented an industry-first digital twin model using AnyLogistix that integrated trim optimization across the entire network. The model simulated real-world production, transportation, and inventory dynamics, enabling real-time adjustments and more holistic decision-making. We also developed an algorithm to minimize trim waste while optimizing supply chain costs, factoring in production schedules, customer demand, and transportation limitations.
The solution also incorporated scenario-planning tools to navigate tariff changes and other market shifts. Developed and validated with historical and live data, the model was refined through close collaboration with the client’s production and logistics teams to ensure alignment with operational goals and customer needs.
As a result, our client reduced paper roll waste from 10% to 3.7%, achieved USD 30 million in annual cost savings on USD 1 billion in revenue, improved production efficiency, and lowered transportation and inventory costs. This solution established a scalable, repeatable framework that set a new industry standard for network-level trim optimization.
Lakshmi Sowmya M, Supply Planning Manager, Nestlé India
Faced with coffee demand growing almost twice the forecasted rate and the season fast approaching, we confronted a critical challenge: existing filling line capacities were stretched, and built-up inventory was tight to meet demand, and planned CAPEX investments had been delayed due to geopolitical issues. This posed a risk to two of our most iconic brands’ opportunity sales.
Determined to deliver the growth regardless, I began by mapping spare capacity across Nestlé factories and co-packers and worked closely with technical experts to evaluate machinability of our key formats at these alternative sites. Collaborating with Quality teams, we quickly assessed feasibility, addressing changeovers and compliance requirements. Partnering with the Manufacturing Excellence team, I ran detailed capacity feasibility checks to validate these options.
Once the locations were finalized, I accelerated the process by fast-tracking trial laminates, creating visuals for Business Unit alignment, and securing swift buy-ins for new formats across key geographies and pack configurations. To ensure flawless execution, I developed a comprehensive Gantt chart and orchestrated the launches just ahead of the seasonal peak.
The results were transformative: both the flagship brands shattered previous records, delivering unprecedented growth in a single year. This project reinforced for me that true innovation isn’t always about new technology; it can come from reimagining what already exists and mobilizing teams with agility, speed, and cross-functional precision under pressure.
Pranav Aadithyan, Deputy Manager, Dabur India Ltd.
As Dabur’s portfolio grew across multiple categories, channels, and geographies, our supply chain needed to evolve from being efficient to becoming intelligent. The existing deployment planning process was largely manual and reactive, leaving us unable to adapt quickly to demand volatility—particularly in fast-moving channels like Modern Trade and E-commerce.
To address this, I led the end-to-end implementations of design customisations, process improvements, and new functionalities within SAP Integrated Business Planning (IBP) for supply and deployment, along with supporting tools developed outside IBP. I approached the initiative not as routine system updates but as a strategic transformation tailored to Dabur’s unique complexities. Key complexities included managing diverse product behaviors (seasonal, constant, intermittent), reducing heavy manual dependencies in stock transfer planning, and creating an opportunity to better integrate real-time sales signals into planning.
My approach involved designing differentiated deployment logic for Modern Trade and E-commerce, integrating open sales orders to improve responsiveness, aligning forecasting algorithms to product behavior using error-based tuning, and automating master data updates to make stock transfer order (STO) creation touchless for many SKUs. Additionally, I built ad-hoc demand capture workflows to manage sudden surges in demand. Leveraging Kotter’s change management model, I focused on building a sense of urgency, forming a cross-functional coalition, securing early wins, and sustaining adoption through ongoing support.
As a result, we achieved over 80% deployment automation, improved forecast accuracy and planning agility, and secured rapid, sustained user adoption. This transformation earned Dabur a “Best in Class Supply Chain Visibility” recognition and has become the backbone of our agile, digitally enabled supply chain—built to scale, sense, and respond effectively to market dynamics.
Rohan Shandilya, Associate Manager – Procurement Transformation, Haleon
I’m a procurement professional driven to create meaningful change through sustainability, intelligence, and financial efficiency. I build strategies that reduce emissions, strengthen supplier selection, and improve cash flow, while leading digital initiatives that enhance speed, transparency, and compliance.
One of my most impactful transformations began when I encountered a manual material follow-up process that was slow, opaque, and prone to delays. I mapped the workflow end-to-end, engaging Procurement, IT, and Operations to uncover inefficiencies and align on a shared digital vision. I then designed and implemented an automated process powered by real-time dashboards, proactive alerts, and centralized communication—eliminating silos and enabling instant visibility into deliveries, vendor responses, and pending actions.
Recognizing that technology alone wouldn’t guarantee success, I embedded change management and targeted training into the rollout, ensuring quick adoption and consistent engagement. The result was more than just faster follow-ups—it was a transparent, accountable, and scalable system that’s now replicated across multiple departments. This has freed up significant hours each month, strengthened vendor relationships, and instilled a proactive, data-driven culture that continues to drive operational excellence.
R J Shivali, Sr. Manex Executive, Hindustan Unilever Ltd.
As part of our strategic drive toward operational excellence, a key focus area is the optimization of manpower productivity. In the current manufacturing setup, several manual, repetitive, and non-value-adding tasks continue to consume significant operator time, leading to inefficiencies, fatigue, and inconsistent output.
The key challenges included high dependency on manual intervention for routine operations, Variability in output due to human error and fatigue and Limited visibility into real-time performance and productivity metrics.
To address these challenges, we are initiating a series of automation projects aimed at streamlining operations, reducing manual workload, and enabling our workforce to focus on higher-value tasks. These projects included Installation of Casepacker to automate secondary packaging, reduce manual intervention, and ensure consistent packing quality and speed and Deployment of Overhead Conveyors to streamline material flow between production and packaging zones, minimizing manual handling and improving space utilization.
This strategic shift is expected to deliver a 3% improvement in OEE 15–20% improvement in manpower productivity within 6–9 months of implementation, Reduction in manual errors by 30%, improving product quality and consistency, Reallocation of 20% of operator time from repetitive tasks to value-adding activities and Improved morale and engagement through upskilling and reduced physical strain.
As a Manufacturing Excellence Executive, my role is to lead this transformation by identifying automation opportunities, aligning cross-functional teams, and ensuring smooth implementation. These changes will not only improve productivity but also build a future-ready workforce capable of supporting smart manufacturing goals.
Sonik Sourabh, Senior Manager, Tata1MG
Large enterprises in the FMCG and pharma sectors, including ours, often face persistent supply disruptions driven by unreliable suppliers, fragmented data, and a lack of early warning systems. This reactive approach resulted in frequent stockouts, bloated safety stocks, and revenue leakage. A key problem was that supplier-related risk data remained siloed across systems, buried in spreadsheets and static dashboards, making proactive risk management near impossible. To address this, I led the end-to-end design and development of an internal Predictive Supply Assurance Engine, a real-time risk intelligence platform that transformed how our supply chain teams identify, assess, and act on emerging supplier risks.
We integrated live data feeds from ERP, logistics systems, and external signals such as regulatory alerts, ESG scores, and financial distress markers. With machine learning-based risk scoring and a dynamic heatmap dashboard, the tool proactively flagged at-risk suppliers and recommended actionable steps like alternate sourcing or PO shifts, enabling teams to prevent disruptions before they occurred. My responsibilities included scoping the MVP, building the data architecture, aligning with supply chain and IT leaders, and piloting across high-priority categories. The platform was tightly embedded into daily decision-making, ensuring adoption and ownership.
Impact: Within six months, we saw a 40% drop in unplanned supply disruptions and unlocked ?25–30 crore in working capital by right-sizing safety stocks. More importantly, we shifted from firefighting to foresight, embedding resilience into our operations by design. This experience reaffirmed my conviction in technology as a core lever for operational agility and sharpened my capability to drive cross-functional innovation at scale.
Vanshaj Srivastava, Sr Manager (PMO Lead) – SCM Strategy New Energy, Reliance Industries Ltd.
The project began with a fragmented inbound logistics landscape, where operations were dispersed across multiple unconnected platforms — from ERP and transport management systems to port community tools, customs portals, and manual trackers. This created a situation where visibility was partial at best, schedule adherence was inconsistent, and avoidable cost leakages from delays, demurrage, and damages were frequent. The complexity was magnified by a highly varied cargo mix, ranging from heavy-lift transformers to hazardous battery modules, each governed by its own compliance milestones and handling protocols.
The strategic response was to design a digital “nerve centre” that would unify visibility without forcing a disruptive overhaul of existing IT systems. The guiding principles were clear: integrate rather than replace, use data as the common language, deliver in modular sprints for rapid adoption, and shift the operating model from reactive firefighting to predictive control. A rapid proof-of-concept was built within weeks, demonstrating live milestone capture from customs and internal logistics systems with near real-time latency. This tangible demonstration secured leadership buy-in and the investment required for full-scale deployment.
Execution was structured around three agile workstreams. The Data stream focused on building API connectors to ERP, WMS scanners, GPS tracking, and port systems, ensuring a seamless data pipeline. The Process stream standardised milestones, implemented dwell-time SLAs, embedded exception protocols, and linked operational events to financial triggers for cost optimisation. The Vendor stream onboarded dozens of logistics partners onto the unified platform, ensuring that the control tower became the single source of truth for all stakeholders. An intelligence layer powered by machine learning enabled ETA reforecasting and early-warning alerts, allowing teams to intervene before delays breached SLAs. Governance was embedded through agile sprint reviews, keeping cross-functional squads aligned and adaptive.
Within months, the transformation delivered a step-change in supply chain execution. The organisation gained end-to-end visibility. Cycle times fell, delivery reliability improved, and cost leakages were curtailed. Most importantly, the initiative created a repeatable playbook for capital projects.
Vivek Joshi, Sr. Manager – Planning, Crompton Greaves Consumer Electrical Ltd.
As part of our continuous improvement efforts, we achieved savings of over ?1.5 crore by remodelling the air cooler supply network for the 2025 season. The existing network relied heavily on union-provided vehicles, leading to inefficiencies and higher costs. To address this, network was strategically redesigned placing a consolidation hub closer to suppliers, which minimized the use of routes dominated by union-operated vehicles and resulted in savings of approximately ?14,000 per truck. This initiative was recognized as the best Kaizen in 2025.
Additionally, we piloted a shift from a traditional S&OP-based approach to a replenishment-driven supply model. By establishing turnaround times (TATs), defining minimum stock levels (MSLs), and aligning both suppliers and operations teams, we improved availability for high-running SKUs—though challenges remained for items with constrained supply, variable lead times, and B and C class inventory management.
Alongside these larger initiatives, several smaller but impactful innovations and Kaizens were implemented: creating an Inter Branch Transfer Template dashboard that recommends ideal stock movements based on physical distance, saving supply planners around 3–4 hours each month; designing a Demand Consolidation Template to standardize S&OP inputs and streamline monthly consolidation efforts, saving about 4–5 hours; and developing a Forecasting Template to break down category-level forecasts into branch-SKU level details. Together, these initiatives significantly improved supply chain responsiveness, reduced manual workload, and contributed to a more agile, data-driven planning process.
Yogesh Lohiya, Manager/Assistant Director, Ernst and Young LLP
Accurate order confirmation is the backbone of a reliable supply chain but for one global client, it had long been a pain point. Their planners relied on manual workarounds to compensate for static Available-to-Promise (ATP) logic that overlooked key inventory signals such as stock in yard or in-transit stock. The result- frequent service failures, low customer satisfaction, and high planner workload.
To solve this, I led the design and implementation of a dynamic ATP engine integrated with SAP ECC and embedded within a Decision Intelligence platform. The solution reconciled real-time inventory across multiple supply types including Stock on Hand, Quality Stock, In Yard, In Transit, and Transfer Orders (requested, planned, and confirmed). We built cognitive decision trees to prioritize plant substitutions, forecast fill rate success, and automatically trigger fallback logic all seamlessly connected with SAP through automated writebacks.
The project was a true cross-functional effort. We collaborated closely with IT, business, and customer service teams, running iterative simulations across thousands of SKUs to validate the logic. A pilot-to-scale rollout followed, integrated with the client’s e-commerce platform to deliver real-time availability visibility to customers at the point of sale.
The impact was true game changer:
- 95% order confirmation rate
- 98% accuracy in first Material Availability Dates
- 20% improvement in OTIF (on-time in-full) performance
Beyond metrics, this ATP logic has become a cornerstone of the client’s digital supply chain roadmap shaping how they promise, fulfill, and delight their customers in a digitally connected world.
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