Top 5 AI Features for Managing Excess Inventory

Struggling with excess inventory? AI can help you save money, reduce waste, and optimise stock levels. Here's a quick look at the top tools transforming inventory management today:

• Demand Forecasting: Predict future demand with up to 95% accuracy to avoid overordering and stockouts.

• Anomaly Detection: Spot unusual sales patterns early to prevent inventory imbalances.

• Real-Time Inventory Tracking: Monitor stock levels live to make informed decisions and reduce surplus.

• Automated Reorder Alerts: Trigger timely restocking based on real-time data, cutting stockouts by over 40%.

• SKU-Level Analysis: Analyse individual product performance to identify slow movers and optimise stock allocation.

These AI-driven solutions not only reduce excess inventory but also improve cash flow and operational efficiency. Read on to see how UK businesses are using these tools to cut costs and stay competitive.

How to reduce potential stock-outs and excess stock with AI-based inventory optimization?

1. Demand Forecasting

AI-powered demand forecasting has become a game changer in modern inventory management, revolutionising how businesses predict their stock needs. Unlike older methods that relied on simple historical averages, AI systems dive deep into a mix of historical and external data, producing far more precise forecasts. By analysing past sales trends, customer behaviour, weather conditions, and even local events, businesses can better anticipate demand shifts and make more informed inventory decisions.

Accuracy in Reducing Excess Inventory

AI-driven forecasting significantly cuts down errors in supply chain predictions - by as much as 30 to 50%. This precision allows businesses to order exactly what’s needed, reducing surplus stock. Take Lenovo, for example: the company saw a 4.8% boost in revenue, a 5% improvement in on-time delivery performance, and a near 20% reduction in manufacturing and logistics costs - all thanks to AI-powered forecasting. Similarly, Deloitte research highlights that demand planning can improve forecast reliability by 10–20% while slashing inventory costs by up to 20%.

Real-Time Responsiveness

One of the standout features of AI forecasting is its ability to adapt in real time. As market conditions shift, these systems fine-tune their predictions, catching changes that traditional methods might miss. For instance, Walmart uses real-time sales data, local events, and weather updates to dynamically adjust its forecasts. During hurricane season, the system anticipates increased demand for essentials like bottled water and snacks, ensuring shelves stay stocked without creating unnecessary surplus. McKinsey notes that businesses with agile supply chains can respond to demand changes twice as fast as their competitors. Similarly, Zara leverages real-time sales data and customer feedback to adjust production and inventory, maintaining a fast turnover rate.

Granular Insights for Targeted Actions

AI forecasting doesn’t just offer broad predictions - it provides highly detailed, product-specific insights. Instead of generalising at the category level, these systems deliver SKU-level forecasts, enabling businesses to fine-tune inventory based on local demand. For example, a store in Manchester might stock different quantities of an item compared to a store in Edinburgh, reflecting the unique preferences of each location. This level of precision allows businesses to manage their inventory more effectively.

"The right way to think about a Demand Planning model is what is the right level to model each of the inputs. It should be model at the level it makes sense... Supply chain forecasts or demand forecasts make sense of the level at which supply chain requires the forecast." – Chakri Gottemukkala, Co-Founder and CEO of o9 Solutions, Inc.

By focusing on granular data, businesses not only avoid overstocking but also optimise their entire supply chain. This targeted approach naturally leads to substantial cost savings.

Cost-Saving Potential

The financial impact of precise demand forecasting goes far beyond just cutting excess inventory. Research shows that effective demand planning can reduce inventory holding costs by 20–25% annually. Companies also report up to a 50% drop in forecasting errors and a 10–15% reduction in overall supply chain costs . These savings translate into lower storage costs, fewer markdowns, and better cash flow management.

For businesses looking to implement AI-driven demand forecasting, platforms like Forthcast offer advanced six-month projections, blending statistical analysis with machine learning. These tools provide the detailed insights and accuracy businesses need to cut costs and improve profitability.

2. Anomaly Detection

Anomaly detection serves as an essential tool for inventory managers, offering early warnings by analysing both historical and real-time sales and inventory data. By spotting deviations from normal trends, it enables retailers to act swiftly when sales unexpectedly surge or plummet, helping to prevent stock issues before they spiral out of control.

Real-Time Responsiveness

What makes anomaly detection so effective is its ability to monitor inventory flows continuously. Take the example of a manufacturing client of Bear Cognition: by implementing a system that identifies anomalies in real time, they cut down their manual inventory tasks by 13 weeks each year and eliminated delays entirely. This real-time monitoring is critical in fast-changing supply chain environments, allowing businesses to react quickly to unexpected shifts, avoid operational bottlenecks, and make better use of their resources.

Granular Insights for Targeted Actions

Traditional dashboards often miss the finer details, but anomaly detection systems dig deeper to provide actionable insights across various dimensions. These systems can pinpoint the root causes of changes at the product, category, or location level.

"If you can detect anomalies in real time and fix them early, that's the best thing that can happen." – Gaurav Dixit, Head of Data and AI Products at DigitalRoute

With this level of detail, retailers can identify which products or categories are causing inventory imbalances. This enables precise actions, such as adjusting orders for specific SKUs or investigating supply chain disruptions, to resolve issues efficiently.

Accuracy in Reducing Excess Inventory

Anomaly detection also plays a key role in improving inventory accuracy by identifying data points that deviate from expected patterns. This process enhances data quality overall. By flagging outliers - whether they relate to sudden changes in KPIs, unusual customer behaviour, or unexpected traffic patterns - businesses can dig into the root causes, which might stem from data entry mistakes, system glitches, or actual market shifts. Acting on these insights helps not only to refine inventory accuracy but also to reduce surplus stock and cut costs.

Cost-Saving Potential

The financial benefits of anomaly detection are hard to ignore. The global market for these solutions is projected to hit £6.8 billion by 2026, growing at a rate of 15.8% annually. By ensuring data accuracy, businesses can avoid costly mistakes like incorrect orders, stockouts, and delays. Additionally, predictive maintenance enabled by these systems can save manufacturers up to 1.6 million hours of downtime each year, translating to approximately £580 billion in savings through a 6% boost in productivity.

Platforms like Forthcast incorporate anomaly detection as a core feature, automatically flagging unusual sales patterns and alerting businesses to potential inventory challenges. This proactive approach not only safeguards inventory accuracy but also streamlines operations, reducing the need for manual monitoring of complex data sets.

3. Real-Time Inventory Tracking

Building on demand forecasting and anomaly detection, real-time inventory tracking offers businesses immediate insights into their stock levels. This technology revolutionises inventory management by providing live updates as transactions occur. Unlike older systems that refresh data periodically, real-time tracking captures every transaction the moment it happens, giving retailers the information they need to avoid overstocking or running out of key items.

Just like demand forecasting and anomaly detection, real-time tracking supports a proactive approach to managing inventory.

Real-Time Responsiveness

The strength of real-time tracking lies in its ability to adapt instantly to changing stock conditions. AI-powered systems take this a step further by continuously analysing data and adjusting to shifts in market trends and fulfilment requirements. These systems don’t just count stock - they integrate live demand data and lead times to dynamically adjust inventory levels.

A great example of this is Toyota's real-time supplier monitoring and inventory analytics system. By identifying potential bottlenecks and maintaining just-in-time inventory practices, Toyota has managed to sidestep many of the overstocking challenges faced by other manufacturers.

This constant flow of data doesn’t just improve responsiveness - it also provides detailed insights into inventory at a product level.

Granular Insights for Targeted Actions

Real-time inventory tracking offers a detailed view of stock, breaking it down by individual products, SKUs, and store locations. This level of detail helps businesses identify problems at their root, rather than weeks later through outdated reports. For instance, the system can monitor item-location stock levels, days of supply, seasonal trends, and key metrics like inventory days on hand. These insights allow businesses to fine-tune procurement and production decisions with greater accuracy.

"Real-time inventory tracking enables abnormal stock position changes to be flagged as transactions occur. Sudden or unexplained shrinkage and stock-outs can be spotted early before issues multiply and cascade across the supply chain." - Versa Cloud ERP

In 2025, Direct Plants demonstrated the value of this approach, streamlining multichannel sales by automating inventory updates across platforms.

Accuracy in Reducing Excess Inventory

Modern tracking systems can achieve inventory accuracy rates as high as 99%. This level of precision helps businesses identify slow-moving or surplus stock before it becomes a costly problem. With real-time data, companies can make informed decisions about reordering, discounting, or redistributing inventory, improving turnover rates and freeing up warehouse space. Live data also enables businesses to adjust orders on the fly, ensuring they remain aligned with current demand.

When paired with AI-powered demand forecasting, real-time tracking can cut inventory forecasting errors by up to 50%. This improvement is critical, especially when you consider that 75% of businesses lose sales due to poor inventory control.

Cost-Saving Potential

The cost-saving benefits of real-time inventory tracking are undeniable. Businesses can cut inventory carrying costs by up to 30% with better stock management, while delivery times can be improved by around 25%. Automated reordering, driven by live data, further reduces operational costs and prevents expensive stockouts. Labour costs also drop as manual inventory checks and reconciliations become less necessary. Additionally, more accurate fulfilment planning helps lower delivery and return shipping expenses. These savings are particularly relevant when you consider that shrinkage cost U.S. retailers around £75 billion in 2024, often due to theft, damage, or data errors.

Platforms like Forthcast exemplify how real-time inventory tracking can transform operations. By providing instant stock visibility and automated alerts, these systems ensure that businesses make inventory decisions based on up-to-date information rather than outdated reports. This helps maintain optimal stock levels and avoid the financial pitfalls of overstocking or understocking.

4. Automated Reorder Alerts

Building on the advantages of real-time tracking, automated reorder alerts seamlessly connect inventory monitoring with smart replenishment strategies. These systems constantly track stock levels and automatically trigger orders when inventory falls below predefined thresholds, ensuring businesses are always prepared to meet future demand.

By replacing traditional manual ordering methods, automated alerts remove the guesswork and reduce human errors in inventory decisions. These systems determine reorder points using factors like lead times, daily usage rates, and safety stock needs. This ensures businesses maintain enough inventory to meet demand without overinvesting in slower-moving items. Together with real-time tracking, automated reorder alerts create a proactive approach to inventory management.

Real-Time Responsiveness

One of the standout features of automated reorder alerts is their ability to react instantly to inventory changes. This rapid response helps prevent minor issues from escalating into major problems like stockouts or overstocking. On average, businesses using these systems have reduced stockouts by over 40% and shortened cycle times by more than 30%. They adapt quickly to shifts in demand, keeping fast-selling items replenished while avoiding excess orders for slower-moving products.

These systems rely on advanced technology, such as RFID tracking, IoT sensors, and cloud platforms, to continuously monitor inventory and capture data automatically. This ensures reorder decisions are always based on up-to-date information rather than outdated reports.

Granular Insights for Smarter Decisions

Automated reorder systems go beyond broad inventory management by offering detailed, SKU-level insights. Instead of applying a blanket rule for all products, these systems tailor alerts based on specific product characteristics, sales trends, and historical turnover data. For instance, fast-moving items might trigger restocking when inventory dips to 30% of normal levels, while slower-moving products might only require reorder alerts at 15%. This precise approach prevents overordering of slow-moving items and prioritises stock for high-demand products.

In April 2025, Infizo Stock highlighted the success of one of their clients, a retail chain with 25 stores. After implementing automated reorder alerts, the retailer reduced stockouts by 35% and cut excess inventory by 20% within just three months. By setting alert thresholds based on historical data and lead times, the system helped the company maintain balanced stock levels, improve customer satisfaction, and lower holding costs.

Reducing Excess Inventory with Precision

The accuracy provided by automated reorder alerts plays a key role in cutting down excess inventory. By integrating multiple data sources and using algorithms to calculate optimal reorder quantities, companies have achieved inventory cost reductions of over 20%. These systems analyse sales trends and adjust restocking levels automatically, minimising the need for manual intervention and reducing the risk of stock shortages. Businesses using such systems are 50% more likely to hit financial targets and can adapt to market changes 20% faster.

Financial Advantages

The cost-saving potential of automated reorder alerts goes well beyond inventory reduction. These systems improve operational efficiency, lower labour costs, and enhance cash flow. Companies using online inventory systems with automated alerts have reported a 25% boost in efficiency and a 30% drop in stockouts. Savings come from reduced carrying costs due to lower average inventory levels, less manual effort in ordering processes, and more predictable inventory spending. Additionally, avoiding stockouts protects revenue and ensures customer satisfaction by keeping products consistently available.

Platforms like Forthcast leverage AI-driven forecasting alongside smart reorder alerts to help businesses maintain optimal stock levels while maximising efficiency and profitability.

5. SKU-Level Analysis

After automated reorder alerts, diving into SKU-level analysis offers a sharper lens for managing excess inventory. This AI-driven tool examines each SKU (Stock Keeping Unit) individually, providing actionable insights that go beyond broader inventory trends. By building on demand forecasting and real-time tracking, it allows businesses to fine-tune their inventory management at the product level.

What makes SKU-level analysis so impactful is its ability to uncover patterns that are often hidden in aggregated data. By monitoring metrics like turnover rates, profit margins, storage costs, and sales velocity for each SKU, businesses can make smarter decisions about their product catalogue. It helps pinpoint slow-moving items that tie up valuable capital and highlights top-performing products.

Granular Insights for Smarter Decisions

SKU-level analysis delivers precise insights into inventory levels, purchasing needs, and storage allocations. This allows businesses to categorise SKUs into fast-moving and slow-moving items, reflecting real consumer demand while distinguishing profitable products from those underperforming.

With this granular tracking, businesses gain a clearer picture of stock performance. They can identify top-selling items, analyse customer buying habits, and adjust inventory levels accordingly.

"SKU-level transaction data is important because it allows businesses to understand the performance of individual products, identify top-selling items, analyse customer buying patterns, and optimise inventory levels." - Techsalerator

In practice, this analysis can even uncover location-specific trends. For example, an apparel retailer might discover that floral print dresses are popular in city centre stores, while striped patterns sell better in suburban areas. Such insights allow for more targeted stocking decisions, improving customer satisfaction and sales.

Cutting Excess Inventory with Precision

Focusing on individual SKU performance makes it easier to tackle overstock issues. Research shows that for many brands, just three SKUs can account for 50% of sales. This highlights why it’s crucial to prioritise resources on high performers while identifying products with poor turnover rates that drain profitability.

Instead of applying generic inventory rules, SKU-level analysis enables businesses to adjust stock levels based on actual demand for each product. This tailored approach ensures inventory aligns more closely with customer needs, reducing waste and improving efficiency.

The Cost-Saving Edge

The financial benefits of SKU-level analysis can be considerable. For example, an apparel retailer trimmed its catalogue from 20,000 to 12,000 SKUs, cutting holding costs by 15% and boosting profit margins by 10%.

Other examples underscore its potential. A tech company reduced its accessory range by 30%, leading to a more focused product strategy and happier customers. Similarly, a global FMCG leader consolidated overlapping product variants, cutting inventory waste by 20% and improving its ability to adapt to consumer trends.

Streamlining SKUs doesn’t just save money - it also simplifies operations. By eliminating underperforming products, businesses can optimise warehousing, distribution, replenishment, and order processing. This frees up resources and shelf space for high-demand, high-margin items, driving both sales and profitability.

Given that retailers and CPG companies lose an estimated £850 billion globally each year due to out-of-stock and overstock issues, SKU-level analysis is a powerful tool for addressing both challenges at once.

Platforms like Forthcast make this process easier by offering comprehensive SKU-level analysis features. These tools break down complex inventory data into actionable insights, helping businesses cut costs and increase revenue across their product catalogue.

Feature Comparison Table

To give you a clear picture of how AI features contribute to managing excess inventory, here's a detailed breakdown of their key benefits and practical applications:

These features work together to tackle excess inventory challenges while enhancing overall efficiency. By integrating them, businesses can cut costs by 20% and improve operational efficiency by up to 50%. For consumer packaged goods companies, these tools can lead to a 20% reduction in inventory.

Platforms like Forthcast bring all these features under one system, ensuring they complement each other for optimal inventory management. Each feature not only addresses specific issues but also contributes to a unified strategy, making inventory optimisation seamless across the board.

Conclusion

AI-powered inventory management is no longer just a tech upgrade - it's becoming a financial necessity for UK businesses navigating today’s unpredictable markets. The benefits are clear: McKinsey research highlights that companies can cut logistics costs by 15%, lower inventory levels by 35%, and improve service levels by 65% through these solutions.

This level of unpredictability requires smarter, more agile inventory strategies. UK retailers are already reaping the rewards. Take ASOS, for instance - this online fashion giant uses machine learning to forecast demand, allowing it to adjust stock levels on the fly. This approach not only reduces waste but also ensures products are available when customers need them. As Andrei Newman, CEO of Designer Home Spas, puts it:

"With better demand forecasting, we've minimised both stockouts and overstocking. We no longer waste money on excess inventory that sits unsold or face the high costs of rush orders to meet unexpected demand. This efficiency helps maintain steady cash flow and reduces financial waste."

The potential for efficiency gains is enormous. AI-driven tools can significantly reduce excess stock and streamline operations. And the push for adoption is gaining momentum. Gartner predicts that by 2024, half of all supply chain organisations will invest in AI and analytics. Similarly, Statista forecasts that by 2025, 38% of supply chain and manufacturing businesses globally will consider AI "critical" to their operations. Delaying adoption could leave UK companies trailing behind competitors already leveraging these advantages.

The five key AI features discussed earlier form a powerful framework for tackling inventory challenges. Together, they can slash forecasting errors by up to 50% and reduce lost sales from out-of-stock issues by as much as 65%. For UK businesses, this translates into savings of 5-10% on transportation and warehousing costs, alongside a 25-40% cut in supply chain administration expenses.

What’s more, these advanced AI tools are accessible to businesses of all sizes, equipping them with the resources needed to stay competitive in today’s fast-moving market.

FAQs

How does AI-driven demand forecasting outperform traditional methods in inventory management?

AI-powered demand forecasting is transforming inventory management by offering real-time, precise insights into consumer demand. Unlike older methods, AI can process extensive and varied data sources - like sales records, social media activity, and even weather conditions - allowing businesses to adapt swiftly to market shifts.

This modern approach reduces forecasting mistakes, limits surplus inventory, and helps avoid expensive stock shortages. By fine-tuning stock levels and streamlining supply chains, companies can reduce waste, lower storage expenses, and increase profits, helping them maintain an edge in a rapidly evolving marketplace.

How does anomaly detection help prevent inventory imbalances, and how can businesses use it effectively?

Anomaly detection plays a key role in keeping inventory levels in check by spotting unusual patterns like sudden spikes or drops in stock, irregular sales trends, or disruptions in the supply chain. Catching these anomalies early allows businesses to act quickly, avoiding problems like overstocking or running out of stock.

To make the most of anomaly detection, companies can rely on AI-driven algorithms to process both historical and real-time data. These systems highlight deviations and offer actionable insights, helping teams make swift and informed decisions. Fine-tuning these systems is crucial to reduce false alarms and ensure accurate detection, which supports balanced inventory management and smoother operations.

How does SKU-level analysis help businesses optimise inventory and reduce excess stock?

SKU-level analysis offers businesses a precise way to track the performance of individual products. By pinpointing slow-moving items, predicting demand with greater accuracy, and fine-tuning reorder quantities, companies can steer clear of overstocking and make better use of storage space.

This level of detail boosts inventory efficiency, reduces waste, and ensures resources are focused on the products that truly drive value, leading to improved profitability.

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