Plum Living

Plum Living's inventory journey did not stop at the initial 21% reduction. As more demand and lead time data flowed through Flowlity, planners progressively refined buffer policies and supplier replenishment cadences, which pushed the long-term reduction to 38% (€598k → €367k target). Inventory turnover lifted by roughly 60% at unchanged demand, freeing working capital that the company can redeploy into growth. The supplier portal extended the 9-month replenishment horizon to Plum's ~10 suppliers, which turned ordering into collaborative planning and reduced lead time variability on both sides.

Once Flowlity went live at Plum Living, the system produced a rolling 9-month replenishment plan that gets shared continuously with the brand's ~10 suppliers, refreshed as new orders and forecasts come in. The 9-month horizon gives suppliers time to plan raw materials, capacity, and shipments rather than reacting to last-minute purchase orders, which reduces lead time variability and stockout risk on both sides of the relationship.

For a DTC furniture brand where manufacturing lead times stretch over weeks and customer expectations are set by next-day e-commerce, this kind of visibility is what unblocks growth without piling up inventory. The horizon also changes the conversation with suppliers from transactional ordering to collaborative planning: suppliers can flag capacity constraints early, propose batching that reduces unit cost, and align their own raw material procurement to Plum Living's catalogue evolution. The 9-month window is long enough to cover most supplier upstream cycles in the furniture industry, which is what makes it operationally meaningful rather than just a forecasting exercise.

Flowlity replaced Plum's manual replenishment with AI-sized buffers that account for demand variability and supplier lead times rather than relying on flat coverage rules. Excess safety stock that had built up during the Excel era as a defensive reflex was released as the system right-sized policies SKU by SKU, which translated directly into a 21% inventory drop at go-live.

Over time, the long-term reduction reached 38% (from a €598k baseline to a €367k target) as planning strategies continued to mature. At unchanged demand, this lifted inventory turnover by roughly 60%, which freed working capital that Plum Living redeployed into growth investments rather than warehouse storage. Service levels held steady throughout the transition because the AI buffers, while smaller in aggregate, were positioned more intelligently across the catalogue: small movers no longer carried defensive overstock while fast movers gained the protection they actually needed. The 38% long-term gain compounded as more data flowed in and as planners refined the AI parameters with their domain knowledge.

Plum Living went live in 3 months across two warehouses and 630 SKUs, then progressively extended the scope. The implementation phases were typical of a Flowlity DTC rollout:

• a few days for scope definition and business mapping
• around six weeks for data synchronization between Plum and Flowlity systems
• two months for model training and calibration using Plum's historical data
• a three-month phase of live testing and KPI monitoring before full deployment.

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A single customer success manager on the Flowlity side and a small core team on the Plum side were enough to drive the rollout, which kept coordination overhead low. Speed of value matters more for DTC brands than feature breadth, and Plum Living's case demonstrates that AI planning is now a realistic option even for fast-growing digital businesses without large IT teams. Similar phased rollouts have been documented in industrial contexts, notably Groupe Lemoine's multi-site project, where the same logic applies: start narrow, prove value, then scale.

Excel had been flexible at the early stage of Plum Living but stopped scaling once SKU count, supplier complexity, and demand variability all rose together. Planners spent more time updating formulas and reconciling sheets across the team than analyzing exceptions. Supplier visibility flatlined because nobody had bandwidth to share rolling forecasts, and stock decisions became reactive: orders went out when shortages appeared rather than ahead of demand. The cost was paid in overstock on slow movers and stockouts on fast movers simultaneously, which is the worst possible inventory position for a DTC brand built on customer experience. Working capital climbed faster than revenue because safety stock kept being added defensively across the board, and inventory turnover deteriorated silently until cash became a real constraint on growth. Plum Living recognized the limit when the planning team's workload was scaling linearly with SKU count, which is the opposite of what an asset-light digital brand model is supposed to enable. The shift to AI-powered Supply Chain platforms was what restored operating leverage.

Founded in Paris in 2020, Plum Living specializes in customizable kitchens, wardrobes, and bathrooms sold through a digital-first model focused on design and personalization. As the company grew across Europe, its Supply Chain became more complex: more products, more suppliers, more demand variability. At the time of the Flowlity project, Plum Living operated with around 45 employees, approximately 1,000 SKUs combining make-to-stock and make-to-order products, around 10 suppliers, and two warehouses managing inventory flows. The planning team relied heavily on spreadsheets, which worked at small scale but gradually broke down as the catalogue and supplier panel expanded. Several operational issues appeared simultaneously: high inventory tying up working capital, poor stock balance between product categories with overstock on some lines and shortages on others, limited visibility on future demand, manual replenishment that made decision-making slow and error-prone, weak supplier visibility, and stockouts not systematically tracked. These are typical signs of a fast-growing brand whose planning processes have not scaled with the business: the Supply Chain becomes reactive, with planners spending most of their time correcting problems rather than anticipating them. Plum Living needed a planning approach built for variability and growth, not a bigger spreadsheet.