By Prashant Gupta, Partner, and Kartik Pal,Manager, Kearney India
Over the last decade, supply chain control towers have become a familiar fixture across Indian enterprises. Retailers, automotive manufacturers, and FMCG companies invested heavily in dashboards promising end-to-end visibility across inventory, logistics, and fulfillment. Yet, despite this surge in transparency, many supply chains remain trapped in reactive firefighting. The issue is not a lack of data. It is a limited enablement of faster decision making.
As India’s economy scales toward a USD 5 trillion milestone, supply chains are under pressure to operate faster, leaner, and more resilient than ever. Demand volatility, fragmented supplier ecosystems, omnichannel complexity, and rising service expectations have rendered traditional planning-led models insufficient. What organizations now require is not better visibility, but AI-powered orchestration embedded within control towers.
When Visibility Is Not Enough
Supply chain control towers, in their current form, are largely rudimentary – functioning primarily as transportation visibility platforms rather than true orchestration engines. They were originally designed to monitor performance, not to make decisions. Even today, most of these platforms are working as a track and trace based transportation monitoring platforms, with limited integration with WMS and OMS platforms.
Even if the platforms aggregate data from OMS, ERP, WMS, TMS, POS, and supplier systems, they are rarely able to provide an order-to-warehouse-to-transportation flow in a way that enables coordinated action. As a result, decision-making remains fragmented across functional silos—planning, procurement, logistics, manufacturing, and store operations—rather than being synchronized across the end-to-end supply chain (Figure 1).
In retail, this often results in inventory piling up in slow-moving stores while fast-selling locations face stockouts. Industry estimates suggest that 10%-15% of inventory inefficiency in Indian retail stems from poor allocation and delayed replenishment decisions. Despite real-time dashboards, store managers continue to rely on intuition, while planners replan weekly as disruptions unfold daily.
Automotive supply chains face a different but equally acute challenge. Component shortages or logistics delays can shut down production lines within hours. Several OEMs have now discovered that while control towers could flag supplier delays early, manual coordination across procurement, production, and logistics delayed corrective action, resulting in costly line stoppages running into crores per day. Visibility alone highlights problems—it does not resolve them.
Control Towers as Orchestration Engines
AI-powered orchestration fundamentally redefines the role of the control tower. Instead of acting as a passive observer, it becomes an active decisioning layer that continuously senses changes, recommends actions, and triggers execution across the network
In an orchestrated environment
* Inventory is dynamically reallocated across nodes based on real-time demand velocity, reducing both stockouts and markdowns.
*Order promises are adjusted automatically based on picking capacity, transport availability, and service priorities.
* Manufacturers proactively reroute inbound components or resequence production when supplier delays are predicted, preventing line stoppages before they occur.
* Logistics routes are recalculated intra-day based on traffic, congestion, and capacity constraints.
Organizations that embed orchestration into their control towers can potentially see measurable impact via stockout reductions (15%-30%), fulfillment cost reduction (2%-3%), OTIF improvement (10%-15%) along with a massive reduction in response time from days to few hours. Importantly, these outcomes are achieved without ripping out existing ERP or execution systems. Orchestration layers sit above current infrastructure, connecting decisions across functions rather than replacing systems.
The Often-Overlooked Requirement: Operating Model Change
Technology alone does not deliver orchestration. The most successful transformations are accompanied by operating model changes such as:
* Transition of planners and dispatchers from manual execution to exception management, with greater focus on high-impact deviations rather than routine tasks
* Clear definition of decision rights, outlining what AI can fully automate, which decisions require human review, and which scenarios must be escalated
* Establishing a small central “decision operations” team responsible for governing decision rules, thresholds, and performance standards.
Without these shifts, even the most advanced control tower risks becoming an expensive reporting tool rather than a true orchestration engine. Kearney views supply chain control tower as an AI-enabled orchestration layer that unifies data, decisions, and execution across the end-to-end value chain.
Predict: Anticipate service-level risks, inventory imbalances, and logistics bottlenecks through real-time, multi-source data integration.
Diagnose: Identify root causes of OTIF failures, stock-outs, and productivity deviations with embedded decision intelligence.
Optimize: Dynamically rebalanced inventory, warehouse operations and transport flows to protect service and cost.
By closing the loop between planning and execution, the control tower becomes a strategic nerve center that drives sustained performance advantage.
The Road Ahead
As supply chains navigate an era of persistent uncertainty, competitive advantage will increasingly hinge on one capability: the ability to sense change and act decisively in real time. Control towers must therefore evolve—from tools that show what happened, to orchestration engines that determine what should happen next. In a world where speed defines success, visibility is table stakes; orchestration is the differentiator.