Can AI magically solve SMEs’ stock management problems? No. Because magic has nothing to do with it

For smaller businesses, knowing how much stock to keep on hand is a constant battle. Unsold products eat into cash reserves, but running out of something customers want can be just as costly.

Many SMEs still rely on historical sales figures, spreadsheets and the experience of staff who know their customers inside out. But these methods become harder to manage as businesses grow, product ranges expand, and supply chains become more complicated.

Artificial intelligence (AI) offers another approach. AI-powered forecasting tools can analyse sales patterns, seasonal demand, supplier lead times and other factors to help businesses make better purchasing decisions.

So to the big question: how can they help your business?

Why better forecasting matters for cash flow

Inventory is a significant financial commitment, particularly for smaller businesses with limited working capital. Every pound spent on products that aren’t selling is money that can’t be used elsewhere, whether that’s paying suppliers, hiring staff or investing in growth.

Rupert Bedell, Managing Director at UK technology provider Fasthosts, says AI-enhanced forecasting can help SMEs strike a better balance.

“For an SME, that probably means fewer nasty surprises rather than a perfect crystal ball prediction,” he says. “Think less cash tied up in stock that isn’t moving, and fewer lost sales because something sold out. For a business that’s keeping an eye on cash flow week to week, even the tiniest improvement matters.”

Businesses also have to factor in the cost of storing excess stock, particularly when products risk becoming obsolete or passing their expiry dates. More accurate forecasting can help them identify slow-moving items and cut back on orders before unsold inventory becomes a bigger problem.

What AI brings to inventory planning

Traditional forecasting often starts with historical sales data. Small retailers will often look at how many units they sold last Christmas and use that figure to decide how much to order this year.

But a promotion might unexpectedly increase demand, a supplier delay could leave a business short of stock, or changing customer preferences could affect sales.

“Traditional forecasting methods typically assume that the future will look like the past,” Bedell explains. “And while that’s a fine method to follow in stable conditions, it can become significantly less reliable when a lot of things change at once.”

AI can weigh factors such as seasonality, promotions, trends, and lead times together, updating predictions as new data becomes available.

For a growing distributor supplying several retailers, predictive planning could help identify which products are likely to see increased demand, which are moving more slowly, and where supplier delays might cause problems.

An online retailer might use AI to work out how much stock to order ahead of Black Friday or Christmas. For food and drink suppliers, it could help avoid buying too much of a product that needs to be sold before it expires.

The groundwork matters more than the algorithm

Even the most sophisticated forecasting software will struggle if the information feeding into it is wrong.

A product that appears to have sold poorly might actually have been unavailable for several weeks. Without accurate stock records, a forecasting system could mistake lost sales for low customer demand.

Harvey Dhillon, Founder and CEO of Zmartly, a UK accountancy firm specialising in e-commerce sellers, says businesses need to address these issues before expecting AI to improve their purchasing decisions.

“Before using it, a seller needs clean historical sales data, accurate stock counts and warehouse processes that record receipts, dispatches and returns promptly,” he says. “The misconception is that AI will fix the underlying records. It can help decide what to buy, but buying more precisely is only useful if the starting stock count is right.”

For SMEs, this means checking that stock records match what’s physically in the warehouse, product information is consistent, and employees follow clear procedures for receiving deliveries, recording stock movements, and processing returns.

Without these basics, AI risks producing forecasts based on unreliable information.

Getting started without overcomplicating things

Adopting AI-enhanced forecasting doesn’t have to mean overhauling the entire business at once.

Businesses could start by looking at where their stock management is causing the most problems. Perhaps certain products keep selling out, or they’re regularly left with unsold seasonal stock. The next step is to check whether their existing sales and inventory data is reliable enough to help forecast demand more accurately.

Bedell recommends starting small, testing the results and retaining human oversight.

“In reality, the best results appear when someone who knows the business well reviews what the AI suggests and performs a sanity check,” he says.

An experienced stock manager may know about an upcoming supplier disruption or new customer contract that isn’t yet reflected in the data. AI should support their decisions, not replace them.

Advanced planning with AI

Businesses should also consider whether their existing systems can provide the information needed for more advanced planning. When sales, purchasing, finance and inventory records are spread across disconnected applications, gathering consistent data can be difficult.

Integrated business management platforms – examples include Acumatica, Oracle NetSuite and SAP Business One – can help by bringing these functions together, giving SMEs a more consistent view of stock levels, purchasing activity and financial performance. This provides a foundation for more advanced forecasting and analytics, whether through integrated tools or additional solutions.

Ultimately, SMEs should focus on what they want to improve, whether that’s reducing excess stock, avoiding shortages or freeing up working capital, and measure results against those objectives.

AI won’t compensate for poor stock records or purchasing decisions that nobody reviews. But with the right foundations in place, it can help businesses keep less money tied up on warehouse shelves. No magic required.

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