Logistics Tech Outlook

Oii.ai
Building a Decision Intelligence Layer for Modern Supply Chains

What challenges arise when supply chain planners manage interconnected operational tradeoffs at scale?

Supply chains rarely struggle because data is missing. Challenges arise when too many variables interact for planners to evaluate outcomes with confidence. Inventory, sourcing, lead times and service commitments create constant tradeoffs across cost, cash and service, a tension Oii.ai helps enterprises navigate through patented probabilistic AI and digital twins modeling.
Positioned above planning platforms such as SAP, Kinaxis and o9, the platform functions as a decision intelligence turning operational data into scenario-based simulations and prescriptive Recommendation RTAs. A digital twin of the end-to-end supply chain allows organizations to test thousands of possible outcomes before committing to sourcing, inventory or network decisions. Decisions are made based on what is likely to happen, not assumptions.

“We take all the data from existing systems and turn it into an intelligence layer that allows companies to make decisions based on what will happen, not guesswork,” says CEO Uzair Bawany.

From Planning Data into Decision Intelligence

How does scenario-based supply chain modeling improve sourcing and inventory decision-making processes today?

Uncertainty is where the platform proves its value. Tariff changes, supplier shifts, distribution network changes and lead-time disruptions can all be modeled before decisions are made. A sourcing move from one geography to another can be assessed for impacts on inventory, lead-time variability and cost before execution. Similarly, distribution center consolidation decisions can be evaluated before physical changes are made. Scenario testing replaces assumptions with measurable tradeoffs, giving planners stronger control over service, working capital and operating cost.

  • We take all the data from existing systems and turn it into an intelligence layer that allows companies to make decisions based on what will happen, not guesswork.


Running through that approach is a simple principle captured in the Goldilocks philosophy, not too much inventory, not too little inventory, but just the right balance across cost, cash and service. This balance ensures improvements in service do not come at the expense of cost or working capital. Recommendations remain visible, reviewable and adjustable, ensuring the system is not a black box and that decision logic can be validated before execution.

Managing Complexity at Scale

Why is probabilistic modeling important for managing large-scale supply chain planning complexity effectively?

Complexity at scale makes that transparency critical. One retail use case involved more than 240,000 adjustable planning parameters, far beyond practical human management.

Probabilistic modeling identifies which levers matter, where parameter changes unlock value and how supply chains can be tuned more intelligently. Instead of managing every variable, planners can focus on the actions that drive the highest impact.

Proving Value before Transformation

In what way does proof-of-value testing support supply chain transformation and optimization initiatives?

The commercial model follows the same logic. A four-week proof-of-value exercise quantifies opportunities before broader deployment begins. Speed-to-value is central, with insights delivered within weeks rather than months. Clients receive a digital twin of their network, visibility into gains across cost, cash and service, and in some engagements identified opportunities to reduce inventory by as much as 30 percent while improving service levels.

Execution results have reinforced the model. One UK manufacturer operating seven ERP systems and five planning platforms used Oii.ai across 18,500 SKUs to expose excess buffers, refine reorder settings and surface sourcing tradeoffs that improved working capital and decision consistency. Hidden inefficiencies in reorder cadences and inventory policies became visible, allowing leadership to make better margin and service decisions.

“We are solving a very complex mathematical equation, but customers can see the workings,” says Bawany.

Scaling Optimization through Explainable AI

Patented probabilistic modeling remains the foundation, while agentic workflows, expanded optimization modules and API-driven automation continue broadening the platform’s reach. Monthly optimization cycles move planning closer to a continuously improving decision model. Automation allows recommendations to be applied directly into operational systems while still enabling user control and overrides. Supplier performance modules and expanded scenario capabilities continue to evolve, ensuring decisions remain responsive to changing supply chain conditions.

Greater value lies in helping organizations improve decisions before disruption forces reaction. Digital twins, probabilistic AI and transparent recommendations turn supply chain complexity into measurable planning action, positioning Oii.ai at the forefront of AI-powered supply chain planning.

Deep Dive

Decision Intelligence in Supply Chains: Moving Beyond Static Planning

Supply chain planning has entered a phase where static models and deterministic forecasts struggle to keep pace with volatility in demand, sourcing and distribution. Enterprises operating across manufacturing, retail or pharmaceuticals face a persistent imbalance between inventory investment, service levels and cost control. Traditional planning systems capture historical patterns but fall short when leaders need to evaluate forward-looking decisions under uncertainty. The gap is not access to data but the ability to translate that data into decision-ready intelligence before capital is committed. What increasingly separates effective planning environments from constrained ones is the ability to construct a full, end-to-end representation of the supply chain that reflects how it behaves in reality rather than how it is configured in systems. Organizations that gain this visibility are able to align finance and supply chain functions more closely, grounding decisions in a shared understanding of trade-offs across stock, service and working capital. Without this unified view, planning remains fragmented, with excess buffers in one area compensating for risk in another. A second shift lies in how uncertainty is treated. Deterministic models assume a single outcome, while modern planning requires the evaluation of thousands of potential scenarios. Leaders must be able to ask practical questions—how a supplier change affects lead times, how tariffs alter cost structures or how demand surges impact fulfillment—and receive quantified answers before acting. This capability transforms planning from reactive adjustment to informed decision-making, where choices are tested against a range of plausible futures rather than a fixed forecast. The third dimension is execution confidence. Planning outputs must translate into clear, actionable changes within existing systems, not remain theoretical recommendations. Organizations increasingly expect platforms to prescribe parameter adjustments across stock levels, reorder points and sourcing decisions, while also allowing controlled overrides. Transparency in how recommendations are generated builds trust, particularly when changes challenge established practices such as reducing safety stock or consolidating distribution networks. The ability to simulate alternatives and validate outcomes in advance reduces resistance and accelerates adoption. Equally important is the speed at which value can be demonstrated. Lengthy transformation cycles delay impact and increase risk. A staged approach that surfaces measurable outcomes early allows leadership teams to assess potential gains before committing fully. Rapid visibility into cost reduction, service improvement or working capital release provides a clearer basis for investment decisions, especially in environments where margins are sensitive to even small inefficiencies. Within this evolving landscape, Oii.ai presents a planning platform that centers on probabilistic modeling and digital twin technology to address these challenges. It builds a comprehensive representation of the supply chain, enabling organizations to evaluate decisions across cost, cash and service simultaneously. Its approach focuses on generating prescriptive recommendations derived from large-scale scenario analysis, allowing users to test changes such as supplier shifts, network redesign or inventory adjustments before implementation. The platform integrates with existing enterprise systems, layering intelligence on top of current data rather than replacing core infrastructure. It introduces a staged engagement model that begins with a rapid proof of value, where measurable improvements in inventory, service and cost are quantified within weeks. This is followed by a pilot phase that validates recommendations in execution, giving organizations direct visibility into realized gains. The platform’s transparency and ability to simulate alternative scenarios support informed decision-making while maintaining user control. In an environment where supply chain complexity continues to expand, it stands out as a considered choice for organizations aiming to move from reactive planning to evidence-based decision intelligence. ...Read more
Top AI-Powered Supply Chain Planning Platform 2026

Company
Oii.ai

Management
Uzair Bawany, CEO

Description
Oii.ai is a supply chain decision intelligence platform that uses digital twin technology and probabilistic AI to simulate outcomes before execution. It integrates data from existing systems to provide scenario-based insights, helping organizations optimize cost, service, and working capital while enabling faster, more informed, and scalable operational decision-making.