How Semicab is Supporting Coca-Cola's Freight in India

Algorhythm Holdings has announced that SemiCab has been selected for a pilot programme with Coca-Cola India, launching a transportation initiative on SemiCab’s collaborative freight platform.

Through this partnership, SemiCab will onboard Coca-Cola India onto its Collaborative Transportation Platform, marking a significant step in SemiCab’s expansion in India’s fast-moving consumer goods (FMCG) sector.

The pilot aims to test SemiCab’s ability to reduce empty miles, enhance delivery efficiency, and lower transportation costs. If successful – like several of the company’s previous pilot projects – this proof-of-concept could set the stage for a larger rollout of SemiCab’s solution.

SemiCab is developing a system of intelligence for freight, shifting from isolated shipment planning to orchestrating a coordinated logistics network. Its platform fosters collaboration among shippers, logistics providers, and carriers.

The company’s focus lies in tackling structural inefficiencies such as empty miles, while addressing broader network gaps in planning, routing, and asset utilisation.

As SemiCab grows across India, the US, and other markets, its goal is to build a continuously learning system that adapts to network behaviour – driving more efficient, resilient supply chains while cutting costs, emissions and waste across the ecosystem.

The programme will demonstrate how optimised delivery operations can not only save money but also reduce food waste through smarter route planning.

SemiCab’s CEO, Ajesh Kapoor, notes that if one out of every three flights were flown empty, we would notice it and make a bigger issue about the resulting waste of time and resources. However, because it involves trucks and isn't something that is seen visibly, there is a realisation around the significance of the problem.

Ajesh explores the partnership, the project’s ‘North Star’ and the unique challenges Coca-Cola faces in transforming its logistics operations.

How do you manage data privacy concerns and competitive sensitivities of rival brands while orchestrating “collaborative” round trips?

This is foundational to our multi-enterprise collaboration model. Collaboration only works if trust is engineered into the system. We operate on a privacy-first, network-level architecture with layered access controls.

At the organisational level, data isolation is enforced through row-level security, ensuring queries never cross organisational boundaries. Within each organisation, role- and privilege-based access determines what individual users can see and do.

As a result, participants never see each other’s operational data, including pricing, volumes, or customer information. They can track their own transactions, but do not see how these interact at a network level to create efficiency. Think of it as coordination with the same level of data privacy expected from an enterprise SaaS system. In addition, all usage is governed by strict contractual frameworks, clear consent-based participation, and predefined operational rules.

What we enable is not competitors sharing data, but participating in a neutral orchestration layer that identifies mutual efficiency opportunities without compromising competitive boundaries.

What specific ‘North Star’ metrics or percentage reduction in empty miles have you agreed upon with Coca-Cola India to trigger scale?

We start our relationships with large enterprises at a smaller scale to fine-tune operational processes and create cultural alignment. This first step has allowed us to scale more effectively and sustainably with a large number of customers now.

Customers like Coca-Cola have seen us reduce empty miles by up to 70% in the network and understand that efficiency improves as the network scales with the gains directly passed on to them through lower costs, more reliable service, and better capacity availability.

As more lanes and participants come into the network, we see consistent reductions in unproductive miles along with better asset utilisation for carriers. Decisions to scale are based on sustained network-level performance, where cost efficiency, service reliability and carrier economics improve together in a repeatable way.

How is SemiCab incentivising carrier loyalty to ensure capacity for large FMCG volumes?

Carrier participation is critical, and it must be economically compelling, not mandate-driven. Carriers, in all geographies, face challenges meeting their daily expenses and profitability.

We minimise their operational risk by addressing their fixed expenses and improve carrier economics at a structural level by increasing asset utilisation, enabling more predictable trip cycles, and reducing idle time and empty repositioning.

This translates directly into better earnings per truck, improved working capital cycles, and more stable demand visibility. As the network scales, we are also building a growing base of dedicated fleet capacity operating within this model, benefiting from consistent, repeatable lanes and predictable operations.

Importantly, we integrate into existing carrier workflows rather than disrupting them, which reduces operational friction. Loyalty in this system does not come from traditional incentives, it comes from making the network fundamentally more profitable for carriers to operate in.

How has your AI/ML engine been adapted for India, and what unique challenges does Coca-Cola face?

India is a highly fragmented and high-growth market, so the system is designed to handle significantly higher variability and coordination complexity. These structural challenges exist in all geographies but are accentuated in high-growth markets.

A core part of our approach is automating decision-making at the network level, from load allocation to network optimisation and real-time adjustments, instead of relying on static, lane-based planning. We account for variability through stronger risk assessment, exception handling, and by making information more accessible through intuitive interfaces.

For large FMCG networks like Coca-Cola, a key challenge is managing imbalance at scale, where demand and supply do not align cleanly across regions, leading to unproductive miles.

Our platform continuously identifies and acts on these network-level opportunities while adapting in real time.