Inventory has long been one of the most awkward levers in business finance. Carry too much and cash is trapped on the shelf, warehousing costs rise and deeper planning flaws can be concealed. Carry too little and the result can be missed sales, operational disruption, emergency freight and, in industrial settings, downtime that is costly to recover from.
That tension is becoming sharper. Across retail, consumer goods, industrials and heavy industry, senior leaders are confronti...
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ng tighter working capital, more demanding customers, volatile supply and less predictable demand. In that climate, inventory can no longer be left to fixed thresholds, rough heuristics or precautionary buffers built on habit. It has to be managed with far more precision.
The point is not simply to trim stock. Effective inventory optimisation is about holding the right product, in the right location, at the right time, for the right purpose. In retail and consumer goods, that means protecting availability without creating excess, markdown exposure or obsolescence. In asset-heavy sectors, it means releasing cash while still safeguarding maintenance schedules, operational continuity and equipment uptime.
The old playbook is increasingly inadequate because volatility exposes its weaknesses. Forecast error, long and variable lead times, minimum order quantities, production schedules, capacity limits, shelf-life constraints and service targets all interact. When they are considered in isolation, businesses can end up with shortages in one area and surplus in another. That is a poor outcome at any time, but it is especially problematic when finance teams are under pressure to unlock cash and operations teams are judged on resilience and service.
Across the market, that balance is now being reconsidered with the help of more advanced tools. AI-enabled inventory systems, including platforms such as Iris by Argon & Co, are being positioned as decision-support engines rather than replacements for planners. Their role is to give organisations a clearer picture of how inventory is performing, what is driving safety stock and where the biggest improvements may lie.
According to Argon & Co, such tools draw on historical demand, future forecasts, item-location lead times, minimum order quantities, service levels and segmentation policies to establish a baseline. They can then test different scenarios, including alternative service-level structures, safety-stock settings and lead-time assumptions, to identify where cash can be released with the least risk to service or operations.
That matters because the value of optimisation lies not only in the model itself, but in how quickly it informs action. A rapid diagnostic can identify where inventory is concentrated and where the opportunity sits. More detailed modelling can then quantify the trade-offs between service, cost, cash and risk. From there, businesses can revise planning parameters, embed them into existing systems and create governance processes that keep settings current rather than static.
Chase for Business has similarly argued that better inventory control can improve cash flow by freeing up money for payroll, rent and other expenses, while understocking can damage sales and disrupt operations. The emphasis is the same: inventory is not just an operational issue, but a financial one.
The most effective programmes also break down internal silos. Finance, supply chain, procurement, sales, operations and maintenance all need a common understanding of what inventory is there to do. In retail and consumer goods, that means linking demand planning, promotions, supply planning and customer service. In industrial environments, it means aligning stock policy with maintenance strategy so that service levels do not come at the expense of idle capital.
Industry commentary on AI inventory tools points in the same direction. Recent round-ups of vendors such as RELEX Solutions, Netstock and Llamasoft highlight the growing use of predictive analytics, real-time data and ERP integration to improve replenishment decisions, reduce stock-outs and lower carrying costs. The message is consistent: businesses that use data more intelligently can usually make better decisions than those relying on static rules and periodic review cycles.
That is particularly relevant because inventory carrying costs can be substantial. Some finance advisers estimate they may run at 20 to 30 per cent of inventory value each year, which means even modest reductions in excess stock can have a meaningful effect on cash.
Volatility is unlikely to disappear. The organisations best placed to cope will be those that can make inventory decisions dynamically, based on evidence rather than instinct. They will know where stock protects service, where it protects uptime, where it supports resilience and where it merely ties up capital.
For businesses under pressure to improve cash, service and reliability at once, the conclusion is clear: make inventory visible, understand the trade-offs and manage it with intent.
Source: Noah Wire Services
