Five AI strategies for retail supply chains

How to leverage AI-powered insights to move supply chains from reactive to proactive.

Supply chain missteps can hit sales and brand loyalty hard. According to McKinsey, just 13% of consumers wait for out-of-stock items to be replenished, while 70% say they readily switch retailers or brands when they can’t find what they are looking for. Amid shifting trends, tariffs, supply chain disruptions, and more, CPG supply chain teams are constantly navigating uncertainty to keep products moving. Modern challenges require modern solutions – ones that are not only effective today but adaptable for the future. 

In a Deloitte survey of retail professionals, 60% reported that AI tools improved their ability to forecast demand and manage inventory in 2024. The key advantage AI brings to supply chain management lies in its ability to process and compute exponentially more information than human teams alone. Multinational food ingredient manufacturer Kerry, for example, demonstrates this power through its Trendspotter AI platform, which analyzes millions of data points from global media content to identify emerging food and beverage trends and develop products in as little as 2 months  – compared to traditional timelines of 6-9 months. Retail and consumer goods supply chain leaders can use AI to gain a similar competitive edge in the race to meet customer demand.

This level of computational power in the hands of supply chain professionals enables previously unattainable levels of responsiveness and efficiency. With clean, structured supply chain data as the cornerstone, AI-powered strategies enable supply chains to not only address manifold issues head-on but also capitalize on emerging opportunities that would otherwise remain hidden in the complexity of supply networks, empowering proactive market leadership.

1. Build a POS-powered data foundation

Effectively utilized, point-of-sale (POS) data from in-store retail accounts and e-commerce platforms offers a comprehensive view for supply chain teams to inform and optimize forecasting, inventory allocation, seasonal planning, and more. However, the challenge many CPGs and retailers face is getting clean, real-time data in the first place. Before AI can process and automate insights, organizations must overcome the hurdle of disparate, inconsistent, and often delayed data sources that prevent timely decision-making. 

To unlock the full power of POS data in an AI strategy, teams can begin by streamlining disparate sales and inventory insights across sources, unifying in-store and online data for a complete omnichannel performance picture. A reliable automation and integration solution to clean and structure the data, for enterprise-wide access and usability, ensures further success. 

AI-powered analysis of POS data enhances visibility into granular real-time trends, enabling supply chain teams to stay ahead of market shifts, proactively adjust inventory distribution, and optimize stock levels based on omnichannel demand. When these insights are accessible to AI in real-time, organizations’ supply chains can become as agile as needed in order to keep pace in today’s technologically evolving, competitive, and notably disruption-prone retail sphere.

A reliable automation and integration solution to clean and structure retail data for enterprise-wide access and usability, ensures supply chain success. 

2. Mobilize AI on the road

There is a growing opportunity for supply chains to leverage AI for real-time logistics tracking and signaling, revolutionizing transportation management. 

Traditional logistics systems operate with significant blind spots between shipping and receiving, creating costly inefficiencies when reality deviates from the schedule. Whether truck drivers miss delivery windows, and are stuck at distribution centers for extended periods, or receiving teams are misinformed and understaffed, causing bottlenecks, inefficiencies can ripple throughout the supply chain, impacting inventory levels, labor costs, and ultimately customer satisfaction.

AI-powered tracking and signaling transforms this outdated model by creating intelligent, adaptive logistics networks. A ready data foundation (as described above), can ingest and process EDI shipment and order information, real-time location information, inventory levels, and even warehouse staffing levels, to empower workforces to receive the most critical shipments just as they arrive.

Beyond improved scheduling, change orders, which can historically get lost in communication silos between sales, operations, and logistics teams, can now be captured and signaled earlier in the process. AI systems can assess the impact of these changes on inventory positions and transportation needs, recommending optimal adjustments to maintain service levels while minimizing costs.

Intelligent, adaptive logistics networks powered by EDI shipment and order information, real-time location information, inventory levels, and even warehouse staffing levels, empower workforces to receive the most critical shipments just as they arrive.

3. Forecast with precision: AI-powered trend detection

AI-driven product attribution transforms how supply chains understand and predict consumer behavior by customizing, organizing, and enriching master data management (MDM). What’s historically been a highly manual process that varies by data source, AI-powered attribution is made seamless by solutions like Crisp’s AI Blueprints, and enables teams to track sales patterns with unprecedented granularity across attributes such as packaging formats, flavor profiles, nutritional content, and more.

For example, the onset of GLP-1 medication trends is seeing consumers choose smaller portion sizes, protein-rich foods, and reduced-sugar options – creating a dynamic sales impact across grocery categories – and even apparel, with athleisure reporting an uptick in smaller clothing size sales. Supply chain teams can leverage AI-powered attribution to identify GLP-1 trend-aligned products based on nutritional profiles, portion formats, and ingredient compositions, closely monitor performance, and then adjust forecasts and inventory positions accordingly, staying ahead of competitors that rely solely on category-level data.

Complementing attribution intelligence, AI-powered anomaly detection continuously monitors sales against expected baselines. Offering a pre-built, customizable data science model, Crisp’s Anomaly Detection AI Blueprint flags deviations in demand and helps pinpoint root causes, preventing small discrepancies from escalating into out-of-stocks (OOS) or excess inventory situations – keeping forecasts tight and profitable. 

Supply chain teams can leverage AI-powered attribution to identify GLP-1 trend-aligned products, for example, based on nutritional profiles, portion formats, and ingredient compositions, closely monitor performance, and then adjust forecasts and inventory positions accordingly, staying ahead of competitors that rely solely on category-level data.

4. Activate conversational insights

Even for the most experienced planners, separating signal from noise in retail data can be a time-consuming and complex task. Large language model (LLM) analytics solutions allow users to query historical and real-time data using natural language (NLP), delivering instant insights down to the SKU and store level. 

Instead of writing complex queries or sorting through endless spreadsheet rows, NLP enables users to simply ask questions like “Which distribution centers show the most significant seasonal demand fluctuations?” directly in AI tools. When powered by clean, structured retail data, platforms like Snowflake Cortex, and Databricks Genie can generate accurate responses to natural language supply chain inquiries, enabling non-technical decision-makers to act fast and with confidence – whether optimizing replenishment schedules, identifying regional demand shifts, or fine-tuning inventory in real-time.

Large language model (LLM) analytics solutions allow users to query historical and real-time data using natural language (NLP), delivering instant insights down to the SKU and store level. 

5. Execute in-stock, on-shelf success

The ultimate test of supply chain excellence is ensuring products are actually available for purchase when and where consumers seek them. On-shelf availability (OSA) challenges like phantom inventory can silently erode sales and customer loyalty despite flawless execution elsewhere in the supply chain.

AI transforms OSA monitoring from reactive to predictive by analyzing patterns that human teams would struggle to process manually, by processing and computing sales velocity data, with on-hand inventory levels, and historical performance metrics.

In doing so, and with customizable conditions and thresholds defined, Crisp’s On-Shelf Availability AI Blueprint can detect and flag product availability issues down to the regional, store, and SKU level, keeping teams proactive and responsive in maintaining ideal in-stock positions across their retail network.

With in-stock and OTIF rates as critical success metrics for retailers, brands that leverage AI-powered OSA solutions can secure valuable shelf real estate and maximize sales potential across every point of distribution (PODs).

Crisp’s On-Shelf Availability AI Blueprint can detect and flag product availability issues down to the regional, store, and SKU level, keeping teams proactive and responsive in maintaining ideal in-stock positions across their retail network.

Win the race to AI-driven supply chain excellence

AI is already reshaping CPG supply chain strategies. Concrete applications demonstrate the ability of an AI-driven approach to inform decision-making, optimize fulfillment, and proactively address disruptions, empowering teams to stay ahead of shifts, and scale operations for sustained growth and competitive advantage. Learn how to build the foundation for AI success in supply chain planning by integrating clean, structured data with Crisp – book a demo today.