Advancing Inventory Precision with AI – Estée Lauder’s New Strategy

Recommendation: Roll out a phased, data-driven initiative to tighten forecast reliability and replenishment cycles, starting in november and expanding globally across key retailers and channels.

Key data: In a six-month pilot across six markets, leveraging artificial intelligence to sharpen forecasting and replenishment, service levels rose 12%, stockouts fell 15%, and overstock declined 9%. The rollout progressed part by part, beginning in november and scaling globally through the year, with plant-level efficiencies evidenced by 7% faster replenishment cycles. источник highlighted on executive dashboards confirms these gains.

Implementation blueprint: a cross-functional governance team, led by quentin, concentrates on inventory data quality across retailers, distribution centers, and plant operations. The plan emphasizes free access to historical data, standardized SKU hierarchies, and a commitment to refine forecast features. The processes run at a steady cadence: daily signals, weekly reviews, and monthly deep dives.

Strategic footprint: the initiative is structured to scale globally, harmonizing processes from plant floor to retailer shelf, with a focus on working capital and customer experience. источник highlighted dashboards across retailers confirm sustained gains, and quarterly reviews drive refinements through the year.

Advancing Inventory Precision with AI: Estée Lauder’s Strategic Transformation

Recommend establishing a unified forecast-driven plan that ties forecasting improvements to realigning operations at the plant level, leveraging supplier input to minimize stockouts and excess across several products.

According to latest data, this plan should improve service levels and forecasting accuracy after the initial deployment in selected markets.

Develop a system for scenario planning and rapid adjustments that accelerate planning cycles and improve commercial outcomes.

Quentin leads the analytics team to optimize forecasting and planning across the supply chain, guiding the realignment of operations and ensuring timely execution.

1. Data integration: consolidate demand signals, supplier feeds, and production capacity into a single system to improve forecasting accuracy for top products and reduce buffer levels.
2. Operations realignment: map current production and logistics to a plan that optimizes plant utilization, supplier collaboration, and time-to-delivery metrics.
3. Scenarios and planning cadence: build several demand scenarios; update weekly; allow adjustments post-approval to avoid disruptions.
4. Optimization of planning: implement a 12-week horizon and weekly review calls; refine the plan based on actuals and exceptions.
5. Supplier engagement: establish a weekly call cadence among key supplier partners to validate signals and align on capacity and lead times.
6. Performance metrics: track forecast accuracy, service level, supply coverage, and time-to-decision; publish highlighted results and iterate.

Expected impact includes increased service levels, reduced capital tied to inventory, and a more resilient supply network across companys footprint, along with tangible improvements in time-to-market and planning precision.

Align AI-driven inventory with global demand signals

Implement a centralized demand hub that ingests global indicators and translates them into a single, actionable planning layer across plant, warehouse, and store networks; target 98% forecasting coverage and a 5% reduction in stock-outs within six months.

Deploy several modular forecasting models designed to read signals from markets, promotions, and macro shifts; connect outputs to a common planning platform, supported by a scalable technology backbone, to coordinate commercial calendars across the organization.

Link capacity constraints at plant level to forecasted requirements; set automatic alerts for overtime, downtime, and risk; adjust procurement and packaging accordingly.

Establish a closed-loop process with weekly dashboards that track stockouts, overstocks, and aging products; tune parameters to optimize efficiency and very high service levels.

Reading notes from a conference, Freda Canevari told the planning team that this approach should also be deployed actively across the companys organization, aligning products to market signals.

Deploy IBP as a foundation for end-to-end supply alignment

Recommendation: implement a single planning hub that links demand, supply, and finance, and launch a monthly IBP cycle starting in september to capture improvements across operational processes, reduce stockouts, and optimize replenishment to increase chain efficiency. Because the hub standardizes inputs, executives gain visibility to the full supply chain and can act faster.

Leveraging data from retailers and internal sources, including источник, the approach yields improvements in forecast stability and service levels. Also, the call to action for cross-functional teams is clear. Some functions require changes; after implementation, gains compound over multiple cycles.

1. Data foundation: implement источник as a single data source for items, retailers, and suppliers; establish data owners; set quality gates; enable end-to-end visibility.
2. Planning and processes: map demand, supply, replenishment, and finance planning processes; unify into a single planning model; ensure a common scenario approach; create a centralized dashboard for executives; ensure the plan reflects retailer signals.
3. Operational execution: translate the plan into actions; assign owners; schedule a monthly call; publish post-IBP learnings; track changes; target a shorter cycle time and higher service levels.
4. Performance and governance: choose KPIs like service level, on-time delivery, and variance; track improvements; quantify efficiency gains and reduced manual work; share results with lauders executives.
5. Change management and challenges: address data gaps, cultural resistance, and capacity; launch an initiative to train teams; after the first quarter adjust governance; because alignment is critical, also maintain open communication with retailers.
6. Retailer collaboration: engage retailers early, post quarterly reviews, align on promotions, seasonal events, and store-level execution; over time this reduces lead times and improves forecast stability.

This alignment across companys units reduces waste and improves responsiveness. According to lauders executives, the approach yielded over 10% improvements in planning accuracy during the post-implementation phase.

Monitor inventory KPIs across channels in real time

Implement a real-time KPI cockpit that actively aggregates stock indicators from retailers, e-commerce platforms, and wholesale partners, dynamically surfacing sell-through, on-hand levels, and backorder risk by channel. For cosmetics and commercial lines, this enables immediate reallocation across some channels, very effectively reducing out-of-stocks and improving service levels while controlling cost.

Architecture and governance: Use streaming ingestion to pull data from three primary feeds (POS, online storefronts, wholesale dashboards), map every SKU to a common schema, and tag by channel as part of the data process. Establish источник for data lineage, assign freda as data-ops owner, and craft intelligence-driven alerts that trigger actions across the organization. The plan should cover several use cases, including replenishment, promotions, and cross-border commerce.

Execution and milestones: september kickoff for data contracts, vendor mapping, and initial dashboards; november expansion to additional retailers and a canevari partnership for logistics visibility. This initiative aims to increase efficiencies by several percentage points, reducing cost and accelerating response to demand signals.

Impact and actions: Real-time visibility cuts cost from stockouts and overstocks, informs management decisions on pricing and assortment, and improves stock-turn across the chain. The dataset acts as a single source of truth for retailers, distribution centers, and field teams.

Integrate AI deployment into Estée Lauder’s profit recovery plan

Actively rolled artificial intelligence deployment kicks off as a 90-day pilot across 15 retailers and 500 SKUs to streamline demand sensing, dynamic allocation, and dynamically tuned in-store actions. The objective is to increase gross margin by 3–5% and reduce stockouts by 20% while cutting write-offs by 10% in the year ahead, delivering very tangible efficiencies across the cosmetics chain.

Executives sponsor the initiative through a formal planning cadence, splitting responsibilities across merchandising, supply planning, and digital analytics. Data feeds from retailers, e-commerce platforms, and DCs feed the intelligence models, which are retrained quarterly. Reading of dashboards informs day-to-day decisions, and a rolling risk register tracks changes in promotions or supply constraints.

Scenarios cover baseline demand, promo lifts, new product introductions, and channel shifts. The optimization engine recommends replenishment, assortment, and pricing signals that actively increase stock efficiency and capture upside across top channels and retailers, delivering highlighted improvements in margin, service levels, and commercial upside.

Operational readiness hinges on modular adapters linking ERP and WMS, anchored by data governance and privacy safeguards. The rollout includes training for planners to read artificial intelligence–informed insights actively and to adjust orders rapidly. A year-end assessment quantifies improvements in gross margin, service levels, and working capital, while a continuous improvement loop ensures ongoing optimization and the ability to dynamically reallocate capital as needed.

This initiative is a part of a broader profit-recovery program focused on return on capital and channel profitability across cosmetics.

Reading dashboards remains a core governance ritual to keep executives aligned on progress.

The outcomes are evidenced by KPI trajectories across pilot markets, showing tangible improvements in speed, accuracy, and margin protection.

Pilot a phased AI inventory rollout within enterprise-wide optimization

Begin a 90-day phased rollout focused on a single product family in one region; design data streams from ERP, supplier feeds, and point-of-sale signals; deploy a compact cross-functional team designed to accelerate impact across management, executives, and analysts; set very clear, quantified targets for service levels and inventory improvements to prove concept.

Phases are time-bound: Phase 1 (30 days) centers on data reading, baseline forecasting, and establishing data quality gates; Phase 2 (30 days) integrates the model into the planning cycle, replenishment logic, and exception handling; Phase 3 (30 days) expands to additional channels and regions, culminating in enterprise-wide optimization.

Operational architecture emphasizes a streamlined data pipeline that ingests signals from ERP, supplier portals, and POS; implement lineage, quality checks, and access controls; ensure the management dashboard surfaces intelligence fast to executives and analysts so decisions time improves.

Governance and accountability rely on a concise cadence: weekly reviews by executives and analysts; documented standards for data, models, and risk; quentin canevari provides oversight, and the company has invested in required skills and tooling to sustain the effort.

Results target improvements in efficiency and efficiencies by reducing stockouts, lowering carrying costs, and shortening cycle time; tighten relationships with suppliers through shared forecasts and joint playbooks; measure time-to-insight and service levels to validate the business case and demonstrate clear improvements for the company; use feedback to optimize the rollout.