Furniture retailers face an unusually difficult inventory equation: products are bulky, demand is seasonal, imports can take months to arrive, and a missed forecast can leave expensive stock sitting in a warehouse or popular items unavailable when customers want them. Australian retailer Amart Furniture is addressing that challenge by selecting RELEX Solutions to replace its legacy planning system with AI-driven demand forecasting and replenishment across its store and distribution network. The move highlights a broader shift in retail technology toward AI-powered supply chain planning as retailers look to improve availability without carrying excessive inventory.
For a furniture retailer managing thousands of products, inventory planning is fundamentally different from managing fast-moving consumer goods.
A sofa, dining set or home décor collection can occupy substantial warehouse space and may arrive through international supply chains with long and variable lead times. Seasonal demand adds another layer of uncertainty. When planning systems depend heavily on spreadsheets and manual adjustments, responding to those variables can become increasingly difficult.
That is the problem Amart Furniture says it is trying to solve with RELEX Solutions, selecting the Finnish supply chain software company to replace its existing planning system.
The Australian furniture retailer plans to deploy RELEX’s AI-native planning platform across 66 stores and four distribution centers, according to the announcement. The system will support demand forecasting, replenishment, inventory planning and import-container optimization.
The objective is straightforward: improve forecast accuracy and product availability while reducing unnecessary inventory and automating repetitive planning work.
The announcement also illustrates where AI is becoming particularly practical in retail technology. Rather than using generative AI to create customer-facing content, Amart is applying machine learning and optimization to operational decisions that directly affect inventory, purchasing and logistics.
Why furniture supply chains are difficult to forecast
Amart sells more than 2,000 products, ranging from large furniture items to home décor and curated collections. The company says its previous planning process relied heavily on manual workflows and spreadsheets.
That model can work at smaller scale, but the complexity grows quickly when planners must account for seasonal purchasing patterns, international shipping schedules, supplier lead times and store-level demand.
For Amart, imported inventory creates a particularly important planning challenge. A forecast error made today may not become visible until a shipment is already in transit.
RELEX’s system is designed to forecast demand at both distribution-center and store levels. The platform can account for delivered-sales timing and pre-sold quantities, allowing inventory decisions to reflect when products are expected to actually reach customers rather than simply when an order is placed.
That distinction could be significant for furniture retail, where large orders and long fulfillment windows can distort conventional demand signals.
AI moves from forecasting to replenishment
Demand forecasting is only one component of the deployment.
RELEX will also optimize purchase orders based on container fill, weight and volume constraints. For an importer, that turns logistics optimization into a direct inventory-planning problem.
A purchase order is not necessarily efficient simply because it matches forecasted demand. If products are shipped internationally in containers, how those products fit together can affect transportation economics.
Automating that calculation can allow planners to consider product demand and shipping constraints simultaneously rather than manually reconciling separate spreadsheets.
The platform will also learn from differences between expected and actual supplier lead times. When a supplier consistently delivers later or earlier than planned, the system can adjust safety-stock levels and ordering schedules.
That creates a feedback loop between supply-chain performance and future planning.
Instead of planners manually changing safety-stock assumptions every time a supplier misses a target, the system can continuously incorporate observed lead-time behavior into future recommendations.
Retail AI is becoming an operational technology
The Amart deployment fits into a broader retail technology trend in which AI is increasingly being used behind the scenes.
Companies such as Amazon, Walmart and Carrefour have invested heavily in demand forecasting, automated replenishment, warehouse optimization and other forms of supply-chain intelligence. Enterprise software vendors including SAP, Oracle and Microsoft are also integrating AI capabilities into supply-chain and enterprise planning platforms.
The competitive landscape has consequently shifted from basic inventory management toward more connected planning systems that can combine demand signals, supply constraints and operational decisions.
RELEX competes in this market with established platforms such as Blue Yonder, o9 Solutions, Kinaxis, SAP and Oracle. The differentiator for retailers is increasingly not whether a platform offers AI, but how deeply that intelligence is embedded into the planning workflow.
For Amart, the emphasis appears to be on connecting forecasting with the realities of furniture importing rather than treating forecasting as an isolated analytics function.
What the deployment means for retail planning teams
For enterprise retailers, replacing a legacy planning platform is not simply a software upgrade. It changes how planners interact with inventory decisions.
A system that automates forecasting and replenishment can reduce the amount of time teams spend maintaining spreadsheets, manually adjusting forecasts and reacting to exceptions. But the value depends on whether planners trust the recommendations and whether the underlying data is reliable.
That makes explainability and operational control important considerations.
Amart’s planning teams will still need to understand why demand forecasts change, where supplier uncertainty is affecting inventory and when the system’s recommendations require human intervention. The strongest AI planning deployments are therefore likely to be collaborative rather than fully autonomous.
The retailer says the new platform should allow its teams to spend less time managing supply uncertainty and more time making higher-value decisions.
That is an increasingly common goal across enterprise supply-chain technology.
Inventory optimization has become a strategic issue
Retailers have spent years balancing two competing pressures: maintaining enough stock to protect customer experience while avoiding the working-capital burden of excess inventory.
That balance became particularly difficult during the global supply-chain disruptions of recent years. Retailers learned that historical demand patterns alone were insufficient when shipping times, supplier reliability and consumer behavior changed simultaneously.
AI-driven planning platforms are designed to respond to that uncertainty by continuously recalculating forecasts and replenishment decisions as new information becomes available.
For a furniture retailer like Amart, the benefits could extend beyond inventory accuracy. Better planning can influence warehouse utilization, import economics, store availability, supplier relationships and ultimately customer fulfillment.
The deployment also represents a broader transition away from static planning cycles.
Instead of relying on periodic spreadsheet-based forecasting, retailers are moving toward systems that continuously ingest demand and supply information, identify deviations and recommend adjustments.
Amart’s rollout is still an implementation rather than a demonstrated outcome, so improvements in forecast accuracy, inventory costs and product availability will need to be measured after deployment.
But the strategic direction is clear. As retail supply chains become more complex, AI-powered planning is moving from an experimental technology to a core component of enterprise retail infrastructure.
Market Landscape
AI-driven supply-chain planning is becoming increasingly competitive as retailers look for ways to improve inventory efficiency without sacrificing customer availability.
RELEX Solutions competes with platforms from Blue Yonder, SAP, Oracle, o9 Solutions and Kinaxis, among others. These vendors increasingly combine demand forecasting, replenishment, inventory optimization and supply-chain analytics rather than offering isolated planning tools.
The shift is particularly relevant to retailers dealing with long lead times and complex distribution networks. AI systems can continuously evaluate demand patterns and operational constraints, while optimization engines can turn those forecasts into purchasing and replenishment recommendations.
For retailers, the strategic question is moving beyond whether to use AI to where AI can make operational decisions more reliable.
Amart’s deployment is a useful example because its planning problem combines seasonal demand, imported products, supplier variability and physical container constraints. Those conditions create a strong use case for AI-based forecasting and optimization.
The next phase will likely involve deeper integration between planning platforms, warehouse management, transportation systems, supplier data and retail execution, creating increasingly automated feedback loops across the supply chain.
Top Insights
- Amart Furniture selected RELEX Solutions to replace legacy planning with AI-driven forecasting and replenishment across its Australian store and distribution network.
- The deployment targets seasonal demand, long import lead times and container constraints, combining demand forecasting with purchase-order and inventory optimization.
- RELEX will use actual supplier lead-time performance to adjust safety stock and ordering decisions, reducing manual planning intervention and supply uncertainty.
- The move reflects retail’s broader transition from spreadsheet-based planning toward AI systems capable of continuously adapting forecasts and replenishment recommendations.
- Enterprise retailers will increasingly evaluate AI planning platforms on measurable inventory, availability, logistics and working-capital outcomes rather than AI features alone.
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