Lakshmi Venkat Vemuganti, Co-Founder & CTO, Real Variable

With over two decades of experience in supply chain optimization for enterprises, Lakshmi Venkat, an Electronics Engineer, has been instrumental in developing innovative solutions. He has contributed extensively to GSMI 4.0, publishing impact use cases on cross-border shipments. At Real Variable, he leads multi-tier data acquisition and aggregation for supply chain risk identification, mitigation, and compliance. He is currently pursuing an Executive Program in Corporate Sustainability at IIM Kolkata.

In a recent interaction with M R Yuvatha, Senior Correspondent at siliconindia, Lakshmi Venkat shared his insights on the role of AI, IoT, and blockchain in driving predictive, efficient, and sustainable supply chains.

In today’s complex global markets, predictive supply chains are revolutionizing operations by shifting from reactive problem-solving to proactive, data-driven decision-making. This approach allows businesses to anticipate disruptions, optimize resources, and gain a strategic competitive advantage.

Redefining Supply Chain Resilience

While geopolitical risk is one factor affecting supply chains, other elements such as trade policy changes, port congestion, adverse weather, and commodity price volatility also have a significant impact on operations. Artificial intelligence has emerged as a vital tool in this landscape, capable of forecasting potential disruptions by analyzing weather patterns, vessel manifests, and regional commodity prices to generate probabilistic risk scenarios in advance.

For example, if a cyclone is predicted two weeks ahead to affect ports like Chennai or Visakhapatnam, organizations can identify the types of goods transiting through these ports. Supply chain teams can then assess the impact on outbound and inbound shipments, taking preventive measures or making course corrections. This AI-driven foresight empowers supply chains to be predictive, resilient, and operationally efficient.

Internet of Things (IoT) and blockchain technologies play a critical role in enhancing supplier collaboration, increasing transparency, and addressing challenges such as data privacy in the supply chain environment. IoT, which has been in use for some time, is vital not only for tracking the location of vehicles or vessels but also for monitoring parameters such as temperature, humidity, and shock.

These metrics help companies improve overall supply chain efficiency. In sectors heavily reliant on cold chain logistics, it is essential to ensure shipments maintain required conditions throughout the journey or chain of custody. The chain of custody often involves multiple parties not directly controlled by the shipper or consignee due to complex contractual restrictions and regulations, making reliable data transmission crucial.

While IoT generates this data, blockchain ensures its integrity by registering it immutably and verifying device signatures. Together, IoT and blockchain enable supply chains to be transparent, collaborative, and reliable.

AI-Powered Supply Chains

In the current supply chain landscape, transitioning to a proactive model can significantly transform business economics. Many organizations have a tendency to overstock. For example, when anticipating fuel shortages, companies often queue to fill tanks, and when commodity prices are expected to rise, businesses purchase and stock as much as possible. This behavior is also prevalent across B2B operations.

Overstocking has direct consequences on working capital, as higher purchases increase expenditure, occupy additional storage space, and require more manpower to manage and move goods efficiently. These operational spikes, however, can be mitigated through predictive technologies. While AI cannot entirely eliminate uncertainty, it can provide reasonably accurate forecasts up to 95% reducing overstocking from, for instance, 10% to 3-4%. This alleviates pressure on working capital, optimizes inventory management, and enables better planning of labor requirements.

AI’s ability to distinguish between assumptions and knowledge regarding potential shortages addresses business anxiety effectively. Economically, this leads to optimized working capital, reduced unnecessary expenditure, and avoidance of peak manpower surges. However, inaccurate AI predictions could result in tighter inventory and potential business loss, highlighting the need for careful oversight.

From a workforce perspective, the industry is shifting from traditional statistical supply chain planners to data modelers, emphasizing advanced data analysis skills over conventional statistical approaches. This evolution underscores the importance of equipping teams with analytical expertise to fully leverage predictive supply chain technologies.

AI-driven foresight empowers supply chains to be predictive, resilient, and operationally efficient, transforming risk management from reaction to preparation.

Future-Ready Sustainable Supply Chains

In modern supply chain operations, efficient load and inventory planning have a direct impact on sustainability and carbon optimization.

For instance, consider a scenario where 200 tons of materials need to be transported from one location to another over three days. Without proper load planning, this material might be split into 14 containers, assuming each container carries 20 tons. However, with optimized packaging and load arrangements, the same shipment could potentially fit into 12 or even 11 containers.

Reducing even a single container has a significant effect on carbon emissions, particularly when factoring in distance and shipment frequency. Similarly, AI-driven forecasts, with up to 95% accuracy, help optimize inventory and production levels, reducing overproduction, unnecessary resource consumption, and associated carbon emissions. Proper inventory management, production planning, logistics routing, and demand forecasting ensure resources are used efficiently while minimizing environmental impact.

Machine learning further enhances efficiency by analyzing material and component mixes, sourcing alternatives, and identifying production pathways with lower carbon footprints. Advanced AI models, previously applied in life sciences for molecule discovery and genealogy analysis, are now leveraged in supply chains to optimize operations and reduce emissions simultaneously, driving both operational efficiency and sustainability.

Also Read: Real Variable: Redefining Supply Chain Optimization with Advanced Technology

Democratizing AI in Supply Chains

The growth of proactive supply chains is increasingly influenced by challenges such as algorithmic bias and unequal access to predictive tools, particularly when comparing developing and mature markets. This issue fundamentally revolves around the democratization of artificial intelligence. Much like the early adoption of the internet, developed economies with robust infrastructure and skilled workforces were able to harness AI’s benefits long before emerging markets could fully leverage them.

While AI is technically accessible to anyone with internet connectivity, its effective business application requires substantial initial investments in model development, data analysis, and predictive capabilities. For equitable growth, emerging markets must actively participate in the broader technological ecosystem, contributing to and benefiting from innovations rather than passively adopting solutions created by developed economies and incurring perpetual licensing costs.

Ethical AI deployment in supply chains depends on strong data governance, ensuring models learn responsibly with clearly defined mechanisms for data usage. True accessibility involves not only adopting technology but also democratizing insights, allowing all participants to benefit without compromising proprietary information. Building AI-driven supply chains that are ethical, equitable, and accessible requires collaboration between developed and emerging economies, emphasizing both capacity building and fair distribution of technological benefits.

Closing it Up!

Businesses are increasingly shifting from managing risk to proactively preparing for it, driven by artificial intelligence and machine learning in supply chains. While this approach is already established in developed economies, SMEs in emerging markets must recognize its importance. Adopting a forward-looking mindset enables preparation over reaction, enhancing resilience, operational efficiency, and long-term competitiveness globally.