What We’ll Unpack in This Article (TL;DR)
Artificial intelligence in the supply chain is powerful – it can create highly accurate demand forecasts, help reduce inventory distortion, and handle huge amounts of data. However, these tools are not “set it and forget it.” Humans still need to guide supply chain strategy, and in some aspects, humans know better than AI. This overview breaks down the state of AI inventory management tools, and discusses where humans still play a leading role.
Inventory management has always been a balancing act, as businesses try to avoid both overstocking and stockouts while meeting customer demand. Historically, organizations leaned on manual spreadsheets, gut feelings, and legacy ERP systems to get things right. Now, one technology is transforming the supply chain as a whole, and improving the way leaders approach their inventory: artificial intelligence (AI).
AI inventory management technology is powerful. It can generate highly accurate demand forecasts, help reduce inventory distortion, crunch enormous volumes of data, and analyze external demand signals. However, even the smartest tools and most sophisticated algorithms have blind spots. From risk tolerance to leading indicators, these tools are not “set it and forget it.” While AI handles heavy lifting, provides insight, and assists workflows, humans still need to make key decisions and decide strategy.
This overview breaks down the state of AI inventory management tools, and discusses where humans still play a leading role.
What Are The Benefits of AI-Driven Inventory Management?
Artificial intelligence is no longer a fringe or futuristic technology: it’s here, deployed in the workforce, and rapidly advancing. Research from McKinsey shows that organizations’ use of AI has accelerated significantly in the past years – after a long stretch of “little meaningful change.” When it comes to inventory management, AI is bringing more precision and visibility to every step of the process.
Key benefits of deploying AI tools in your supply chain include:
- Forecasting demand with more accuracy: To predict future demand for products, AI goes far beyond simple forecasts with historical data. By incorporating external signals (like market conditions and seasonal demand), AI can generate demand projections while reducing forecast errors, which allow decision-makers to reduce inventory costs and improve service levels.
- Reducing safety stock requirements: Safety stock is a critical buffer that helps prevent stockouts. But to get it right, this calculation needs to be based on good forecasting data. By leveraging AI, leaders can quickly see optimal safety stock reordering quantities.
- Granular inventory views: The visibility AI algorithms and dashboards provide is unparalleled. It allows you to see data and projections down to the SKU level, so you can understand ROI and optimal inventory levels.
- Distills raw data: Raw supply chain data is helpful, but the sheer volume alone can often become overwhelming – even for financial executives. AI-supported tools don’t just display data: they help you address the root cause of issues and simulate different scenarios (like trade-offs between service level and cost).
When supply chain leaders use AI, it shows: data from Gartner found that top-performing supply chain organizations are using AI to optimize processes at more than twice the rate of low-performing peers.
What New AI Tools & Tech Are Changing in Inventory Management?
AI inventory management tools are being deployed at a breakneck pace. The next wave of models and tools is already pushing beyond traditional forecasting, helping businesses tackle ongoing challenges like product obsolescence, volatility, and imbalanced demand patterns.
Cutting-edge AI innovations which are transforming the supply chain include:
1. New forecasting algorithms
As we’ve discussed, AI demand forecasting is already powerful. But there are novel types of forecasts being developed which can address ongoing problems, and help you see even further into your data. For example, one of the toughest problems in inventory planning is knowing when a product is about to stop selling. Traditional forecasts assume something will sell, but typically don’t help with end-of-life or long-tail SKUs. However, emerging zero-demand forecasts predict when an item is going to drop to zero sales, so you can stop ordering products before they become obsolete.
2. Executive dashboards
This one-stop-shop for key financial insights is designed for CFOs and other top-level decision-makers who need to spot trends and problems as quickly as possible. While every dashboard functions differently, StockIQ’s AI-powered executive dashboard combines ERP data (such as stock on-hand) with StockIQ metrics (excess, slow/dead, service levels) to give executives clear visibility into:
- Planned excess versus overstock.
- Future inventory projections.
- Time to sell through current stock if trends hold (burndown).
- Behavior-based SKU classes (recurring/sporadic/slow).
3. Supplier monitoring tools
Your vendors play a central role in whether your inventory plans succeed – even the most accurate forecasts will do little to move the needle if vendors consistently miss lead times or are overcharging you. AI-driven supplier monitoring tools (like vendor scorecards) track metrics like on-time delivery, lead time trends, and fill rate, so you can quickly identify suppliers that meet your needs. If you have multiple partners, these tools also allow you to compare elements like cost versus lead times, so you can choose a vendor that meets your current needs (whether you need speedier fulfillment or a more cost-effective option).
4. Advanced supply chain metrics
Artificial intelligence shines at digesting and assessing huge amounts of data. Now, AI is fueling unparalleled visibility into supply chain performance metrics, which leaders can leverage to improve cash flow, margins, and growth.
Top AI-driven metrics include:
- Cost of goods sold: Cost of goods sold (COGS) is heavily influenced by inventory and supply chain factors, and can shift rapidly. For example, changes in supplier pricing, supply chain tariffs, and ordering decisions can put a serious dent in margins. But AI tools give you real-time insight into COGS, while providing you with additional contextual data (like stratification, usage patterns, and margin) so you can clearly see the value of your inventory.
- Carrying costs: These “hidden taxes” of holding inventory include storage, insurance, depreciation, shrinkage, and obsolescence. Monitoring this metric allows leaders to prune low-margin or low-demand items that don’t contribute to profit, and avoid over-ordering items that aren’t moving.
- Economic order quantity: EOQ determines the optimal order size based on costs. If you order small quantities too frequently, costs typically rise, while ordering too much can balloon inventory value and associated costs. This metric tells you, at a glance, what amount of stock you should order for things to be as cost-effective as possible.
The Human Touch: Where People Still Have the Edge
Artificial intelligence and machine learning have transformed inventory planning, but that’s not the end of the story – there’s plenty these tools still can’t do. While AI inventory management tools excel at analyzing data, spotting patterns, and generating forecasts, they can’t necessarily understand or incorporate real-world context that shapes demand and decisions.
Here’s where people still remain indispensable in inventory management:
- Risk attitude and trade-offs: Every business weighs risks (like stockouts versus overstocks) differently. For some, a lost sale is unacceptable. For others, excessive carrying costs are a bigger concern. AI can present data and statistics, but it takes a human to decide how to act to best protect margins and service.
- Reading signals AI can’t see: There are some leading indicators that only humans will know about – like if there’s a new upcoming marketing campaign or promotion that will cause demand to spike. While some emerging tools allow planners to inject custom variables directly into the forecast, for now, humans are best at evaluating these signals.
- Creative problem-solving: With AI handling the burden of heavy data analysis and formulas, humans get to shift their focus to much more complex and subtle problems that require creative solutions.
Ultimately, AI can simulate outcomes, quantify risks, and even highlight specific SKUs that deserve a second look. But it’s the human decision-makers who set the risk appetite, decide which scenarios matter most, and align inventory policy with broader business goals.
StockIQ: Where AI and Humans Work Together in Inventory Management
AI has undeniably changed the inventory management playbook. These tools reduce waste, free up working capital, and help companies remain nimble and resilient. As powerful as AI inventory management tools are, outcomes are still shaped by human decisions. AI can map the probabilities – but people decide the path.
The future of inventory isn’t about replacing humans with machines. It’s about achieving a state of augmented decision-making, where AI provides speed, scale, and insight, while planners bring context, judgement, and strategy. And if you’re ready to seamlessly blend AI and human expertise in your supply chain, StockIQ is here to help.
Unlike ERP systems that just skim the surface, StockIQ digs deep into pre-warehouse supply and demand planning, giving decision-makers the AI-powered data and analytics they need to make critical decisions. Think: demand forecasts, advanced inventory analysis, supplier performance monitoring, and inventory replenishment planning that leverages cutting-edge artificial intelligence.
Are you interested in learning how StockIQs AI-powered supply chain planning suite can improve your inventory management practices? Contact us today or request a StockIQ demo.