How intelligent order management strengthens consumer goods supply chains

AI-driven intelligent order management helps consumer goods companies improve availability, reduce costs and optimize working capital.

In brief

• Ineffective order allocation can drive execution losses of up to 30%, resulting in stock outs and lost sales across channels.
• AI-enabled OMS improves range availability by 10% to 15%, OTIF by 5% to 10% and same-day delivery by 30% to 40%.
• Better order decisions reduce delivered costs by 2% to 3% while lowering working capital needs and operational effort.

While India’s consumer goods industry is growing steadily on a large base, its underlying supply chain is becoming increasingly complex. This is on account of both unpredictability of demand and its bifurcation across traditional and digital channels on the one hand, and an unreliable supplier, vendor and logistics network on the other. This results in weak demand supply alignment and the unavailability of finished goods at the right location, time and quality (freshness).

Given this mismatch between demand and product availability, organizations typically resort to sub-optimal decisions while fulfilling distributor orders, causing delayed delivery and stock outs at the point of sale. This directly contributes to order execution losses in supply chains.

In such a complex and volatile environment, an AI/ML-based intelligent order management in consumer goods has evolved from a technology upgrade into a strategic capability—one that directly drives growth, profitability, supply chain efficiency and working capital optimization.

Underlying order management challenges in consumer goods supply chains

Traditionally, organizations have relied on basic principles (such as stock segregation by channel) and rules (such as first-in, first-out of pending orders) to allocate orders in constrained scenarios. However, several challenges make this approach ineffective and limit order execution optimization.

First, in a bid to expand coverage across India, most consumer goods companies have built vast and varied distributor networks. These distributors typically have other businesses competing for the same capital, leading to situations where orders are not backed by adequate working capital at the right time. As a result, constrained stocks either get blocked while awaiting working capital from distributors or get dropped altogether, leading to order execution losses and stock outs in the market. Some large organizations report order execution losses of as high as 30% or more on this account—underscoring the need for AI-based solutions for distributor order optimization.

Secondly, distributor networks often have inadequate data and system controls, resulting in poor real time order visibility for consumer goods companies. Moreover, given their working capital challenges, distributors tend to order only fast-moving SKUs. Organizations therefore end up relying on distributor orders as a proxy for actual demand, leading to poor SKU availability and ineffective inventory optimization.

Thirdly, maintaining high-quality master data is critical to ensure timely and accurate allocation outputs within an order management system. Many organizations still rely on fragmented processes to manage promotions, schemes and fixed allocations. This often leads to repeated manual interventions, frequent CFA requests to amend outbound deliveries and delays that compromise the effectiveness of the allocation engine.

Fourthly, there is significant reliance on third-party logistics providers and market vehicle availability to execute orders. Given last-minute order confirmations and a sharp drop between planned and confirmed orders, transporters are often unable to provide vehicles of the required size or configuration for specific routes on the same day. This results in poor same-day delivery of orders (SDD%). This metric is often overlooked and not tracked by most organizations. However, improving it is critical for improving same day delivery rates in consumer goods, as it can reduce distributor working capital by a full day (a 5% to 10% reduction) while also improving product availability.

Lastly, given real-time changes in orders driven by partial working capital availability, vehicle availability and product availability, organizations often compromise on creating full truck loads. This leads to inefficient logistics movements due to poor vehicle utilization, smaller vehicles or partial truckload movements, limiting logistics optimization and increasing delivered costs.

All these factors contribute to execution losses. While many planning solutions are available in the market, their OMS platform capabilities for real-time decision-making remain limited, forcing organizations to take linear and manual approaches to address these challenges.

For instance, organizations typically engage third-party agencies with large manual teams to capture orders across channels, clean these orders and process them based on predefined static rules on a daily basis. Not being integrated with real-time data or enabled by AI in supply chain execution, these manual processes are sequential and reactive, resulting in inefficient order allocation and execution.

Intelligent OMS: From back-office activity to strategic capability

EY has developed a robust intelligent order management system that supports operations across several large and mid-sized consumer goods companies in India. The key benefits of intelligent order management in consumer goods observed across these organizations can be grouped into three broad areas.

Firstly, it improves market presence by prioritizing high-demand SKUs for key distributors and fast-growing channels, protecting shelf space and strengthening competitiveness in critical micro-markets. This has resulted in improved range and SKU availability of 10% to 15% with lower working capital requirements, driven by a reduction in stock outs of 4% to 5%, OTIF improvement of 5% to 10% and improvement in same-day order execution or delivery of 30% to 40%.

Secondly, improved availability is achieved with lower total delivered costs of 2% to 3%, driven by better vehicle utilization of 4% to 7% and improved vehicle right-sizing. These outcomes clearly demonstrate the impact of real time data on order management performance.

Lastly, efficient order processing leads not only to lower per-order costs of 30%–40% but also to reduced firefighting within the organization and the release of sales team bandwidth, while also improving order accuracy improvement outcomes.

To summarize, an intelligent OMS process effectively captures existing demand despite supply chain constraints by leveraging AI/ML capabilities through a robust, modular platform. This becomes a competitive differentiator for consumer goods organizations by unlocking growth, lowering delivered costs, reducing working capital needs and improving customer loyalty.

Summary

India’s consumer goods sector faces growing supply chain challenges, including fragmented distributor networks, unpredictable demand and logistical constraints. This leads to stock outs and delayed deliveries. Traditional order management methods often rely on manual processes and static rules, which are inefficient in this dynamic environment. By applying AI and machine learning, intelligent order management transforms order allocation from a back-office task into a strategic capability. It improves product availability, optimizes vehicle utilization, reduces costs and frees operational bandwidth. This approach enables companies to better meet demand, protect shelf space, and enhance competitiveness while driving growth and efficiency across the supply chain.