Despite the pandemic-driven shift from “just-in-time” to “resilient” supply chain management, companies still struggle with unexpected interruptions in the supply chain and volatility in consumer demand that can lead to inventory shortages and the inability to fulfill orders on time and in full (OTIF). Consequently, they’re at risk of frustrating their retail partners and losing consumers and market share. Promising inventory to the right partners requires processes and tools with more sophistication than the traditional solutions that were designed at a time of stable market demand. A first-come/first-serve process benefits those who come first, but may not be what is best for the business overall, and typical spreadsheet-based allocation tools are too time-consuming and inflexible to deliver timely order adjustments.

In order to succeed in today’s dynamic, shifting markets, consumer packaged goods (CPG) and consumer products (CP) companies need to maximize their market position by prioritizing, promising, and allocating their inventory across orders for all sales channels, including direct store delivery (DSD).

Intelligent Order Promising (IOP) - delivering the added precision of AI to ATP

IOP is the evolution in the machine learning world of what is known today as Available to Promise (ATP). It uses a combination of demand sensing, to anticipate the near-term demand from each retailer’s ship-to location, and AI/ML modeling, that incorporates customer attributes, including service level targets, profitability, volume growth, and potential OTIF fines.

These attributes define the significance of each retailer partner, providing the ability to optimize for two key outcomes:

1. Allocated Inventory – determining what is the ideal fixed quantity that should be apportioned to customers based on the segmentation strategy in times of a SKUs scarcity.

2. Unallocated Inventory – at those times when supply and demand vary to such a degree that it creates an inability to fulfill all quantities requested across the firm orders for that SKU.

IOP enables a streamlined handling of order fulfillment adjustments for complex supply chain situations. The solution also builds a direct bridge between strategic segments, business goals, and day-to-day execution, and can adapt immediately when those strategic goals change.

IOP leverages data and models to help you see through the noise and confusion of short-term demand volatility and react quickly by automating the prioritization of order fulfillment. The net result is better management of on-hand and inbound supply in a way that respects the customer segmentation strategy that best achieves your business KPIs.

Improve Available to Promise (ATP) and Mitigate OTIF Penalties

Strategic Order Placement – Segmentation of case fill is strategic, instead of first-come first-serve, with order execution driven by strategic priority (e.g., customer tier, due date, revenue, margin, service level, etc.) and enabled by a Dynamic Reserve. Optimal inventory assignment is achieved by considering both on-hand and in-transit inventory and planned production.

Time-saving Productivity – Order processing is fully automated, replacing a manual, high-touch process. Easy-to-manage promising strategies help achieve OTIF targets and complex business outcomes with simple configuration. Execution is streamlined thanks to up-front rules and a consistent process to manage exceptions, resulting in fewer expedites.

Consistency and Collaboration – Decision criteria are standardized and visible, replacing a sometimes siloed and inconsistent approach across the network. Customer Service strategy is considered upfront in the analysis, instead of as an afterthought.

Responsiveness – Fulfillment guidance is responsive to business dynamics, instead of reacting with a lag. An intuitive user-interface, running in the cloud, facilitates the steps to review and refine system generated order promises.

Replacing an antiquated ATP in order to ensure an optimized allocation of inventory

Economic impacts of inflation and threatening recession, as well as market volatility in the aftermath of the pandemic, have altered what used to be more stable consumer preferences for the food products they choose to afford and prepare at home. For one multibillion-dollar global leader in the food industry, this change in market dynamics caused the company to seek a solution that would help them react and readjust more quickly to inventory planning and order fulfillment. At the same time, the company’s major retail customers have raised their On-Time In-Full (OTIF) requirements from their consumer goods vendors in order to retain consumer trust and loyalty, adding financial penalties as a significant complication to the allocation decision process.

The company turned to the application of data and analytics to overhaul their Order Processing and Available-to-Promise (ATP) capabilities. Their older ERP-based ATP and Order Promising systems had limited input capability and typically required 3 to 4 months to recalibrate - which was no longer the timeframe in which they could afford to respond across multiple warehouse locations. Their warehouse managers needed a more strategically-derived plan for dealing with fulfillment when shelves, and the selection of products on those shelves, were emptying in far less predictable ways. Absent better direction, warehouse managers resorting to first-come first serve could put major customer relationships in jeopardy and increase the risk of OTIF penalties.

Following the implementation of antuit’s Intelligent Order Processing solution, the positive measurable results were immediate. The company achieved a 4-5% improvement in case fill rate for strategic customers and a 10X increase in ROI from revenue maximized and fines avoided. Implemented at the height of the pandemic, the company continued to grow operating profit despite continued volatility in demand.

The IOP solution is able to catch some shortages even I would have missed”

Outbound Planner

Our solutions are built upon’s world-class AI Demand Forecasting

Unified Demand Signal

Control for the differences between regions, stores, online, and even the fulfillment type, and serve as the connective tissue across financial, assortment, allocation, size, and pricing decisions.

Dynamic Aggregation

An analytic methodology to address data sparsity, avoid the impact of fringe sizes, handle new items, and protect unit minimums.

Omnichannel Profiling

Delivering demand profiles that consider store and online sales independently, but optimize for BOPIS and ship-from-store (SFS) aspects of inventory location.

Seamless Integration

Delivers pricing and forecasting results through API integration, feeding either’s application suite or existing ERP solutions.

Scalable Data

AI models capable of digesting data that accounts for every demand driver - including seasonality, price, product lifecycle, trends, and local events.

Cloud Native

Built natively in the cloud with scalable distributed processing.

Download Solution Sheet (PDF)