All Dataleo news, jobs, analyses and tutorials around Retail Planning in Supply Chain and Operations.
MediumHighMediumMediumMediumMedium
LowSKU Science remains a useful Radar signal for lightweight demand planning and S&OP, especially for SMB, e-commerce and retail teams that need structure without a large enterprise planning suite.
The practical relevance is Demand Planning, SKU-level forecasting, inventory planning and planning analytics. For smaller teams, the first AI-enabled planning step is often not agentic automation, but replacing fragile spreadsheets with repeatable forecast review and decision routines.
More details are available on the SKU Science website.
This product signal matters because Supply Chain AI adoption also happens in small and mid-sized companies. SKU Science should be tracked where accessible planning tools help teams build forecast discipline, planning ownership and Planning Governance.
MediumVekia continues to focus on automatic replenishment in retail, a practical AI planning use case where machine learning supports store and warehouse stock decisions. The product signal matters because replenishment is one of the domains where planning recommendations quickly affect service, waste and working capital.
For retailers, the relevant capabilities are Automatic Replenishment, demand forecasting, store replenishment, warehouse replenishment and inventory optimization. The governance issue is how planners monitor exceptions, adjust recommendations and measure the impact on availability and margin.
More details are available on the Vekia automatic replenishment page.
This product signal is relevant because Retail Planning is becoming increasingly operational and AI-supported. Vekia should be tracked where Supply Chain AI improves replenishment decisions while keeping human control over service, stock and margin trade-offs.
HighRELEX Solutions introduced agentic AI capabilities for supply planning diagnostics. The announcement matters because it moves AI closer to planning root-cause analysis, exception explanation and recommended action in retail and consumer goods supply chains.
For Supply Planning, the practical value is faster diagnosis of constraints, shortages and planning exceptions. The governance requirement is that agentic diagnostics remain explainable and that planners retain control over high-impact decisions affecting inventory, availability and service.
This is a meaningful signal for Agentic AI in retail planning. RELEX users should evaluate whether diagnostics improve planner speed while preserving auditability, override logic and Human-in-the-Loop controls.
MediumSlimstock highlighted how Freedom of Movement uses AI-powered planning to support retail growth. The item is relevant because it connects planning technology to practical retail outcomes: stock availability, inventory productivity and replenishment decisions.
For Inventory Planning teams, the signal is that mid-market and retail companies are using AI-supported planning to improve everyday operational decisions, not only strategic forecasting. The governance focus should be planner trust, exception logic and measurable service-cost impact.
This is a practical Supply Chain AI signal because it shows AI planning value in routine retail replenishment and inventory decisions. Slimstock’s relevance is strongest when recommendations are explainable and tied to service, stock and working-capital outcomes.
MediumDOUGLAS is expanding its use of RELEX into additional European markets, including the Netherlands, Belgium and Eastern Europe. The rollout supports a more unified planning model across regional retail operations.
Regional scale requires common planning definitions while preserving local decision rights for assortment, promotions and service targets.
MediumSwedish furniture retailer Mio is adding AI-assisted diagnostics and machine-learning forecasting through RELEX. The capabilities are intended to help planners detect issues earlier and move from reactive analysis toward more proactive retail planning.
Diagnostic AI needs transparent root-cause logic and clear ownership of corrective actions so alerts improve decisions rather than add noise.
MediumRELEX Solutions published research on how AI is moving into core supply chain planning decisions as volatility continues. The signal is particularly relevant for retail, grocery and consumer goods organizations where forecasting, replenishment, pricing and waste reduction are directly connected.
For Retail Planning, the relevant shift is from AI as analytics support to AI as decision support for availability, margin, replenishment and inventory placement. That makes AI Governance important as decisions become more granular and frequent.
RELEX’s research is a useful market signal because retail planning is one of the clearest operational test beds for Supply Chain AI. The challenge is not only prediction quality, but safe autonomy in replenishment, pricing and inventory decisions.
HighMETRO is working with RELEX and Accenture to transform fresh-food planning. The initiative targets forecast accuracy, availability, inventory management, agility and food-waste reduction across global operations.
Fresh-food AI requires reliable shelf-life data, local overrides and clear accountability when service, margin and waste objectives conflict.
MediumSymphonyAI announced its inclusion in the Forrester Retail Planning Platforms Landscape, reinforcing its position in Retail Planning, CPG Planning and AI-supported merchandising workflows.
The signal matters because retail planning platforms are increasingly expected to connect forecasting, replenishment, merchandising, store execution and analytics. For teams evaluating Supply Chain AI, this points to the convergence of planning intelligence and operational retail decisions.
More details are available in the SymphonyAI announcement.
This recognition is useful for the Radar because Retail Planning is becoming one of the most operationally concrete domains for AI Planning. The key evaluation criterion is whether platforms improve decisions across merchandising, replenishment and store execution without creating opaque automation.
HighSymphonyAI announced next-generation CINDE Merchandising Agents for retail, positioning Agentic AI inside weekly sales, promotions, new item launches and merchandising reset workflows.
For supply chain and retail planning teams, the practical signal is that AI is moving from analytics support toward workflow-level assistance. This matters for Retail Planning, Merchandising and replenishment decisions where recommendations need to be explained, validated and coordinated across stores, categories and supply chain operations.
More details are available in the SymphonyAI announcement.
This is relevant because Agentic AI is beginning to enter concrete retail decision workflows, not only dashboards. The governance question is how Retail Planning teams manage recommendations, approvals and exceptions when AI agents influence merchandising and replenishment actions.
HighThe Body Shop selected RELEX to replace manual forecasting and replenishment processes across stores, franchises and distribution centers.
The transition should retire parallel spreadsheets and define ownership of forecasts, replenishment parameters and overrides.
HighRELEX demonstrated agentic AI systems operating within no-code business rules to support unified retail and Supply Chain planning at NRF Europe 2025.
No-code rules need formal ownership, testing and version control before agents use them in live planning decisions.
MediumRELEX reported increased investment in Diagnostics, True Inventory and agentic AI capabilities during the first half of 2025, alongside customer expansion.
The useful test is whether diagnostics and agents improve specific planning decisions rather than simply increasing alert volume.
HighPetco Mexico selected RELEX to improve forecasting, replenishment, seasonal planning and inventory efficiency across 145 stores and two distribution centers.
Retail AI needs store-level data quality, transparent replenishment rules and controlled local overrides.
MediumAutone raised $17 million to expand its AI-powered retail inventory planning platform. The round included investors such as General Catalyst, Speedinvest, Y Combinator, Seedcamp, Motier and Financière Saint James.
The announcement is relevant for Retail Planning because fashion and lifestyle brands need faster decisions on buying, allocation, replenishment and stock rebalancing. Public coverage and company materials connect Autone to brands such as Roberto Cavalli, Courrèges and Benoa.
More details are available in the Startups Magazine report.
This is relevant because Inventory Planning is one of the clearest near-term use cases for AI in retail. Autone’s funding shows continued momentum around tools that help planners translate demand and stock signals into governed actions.
A BCG and Consumer Goods Forum survey finds that CPG and retail companies remain far more ambitious about AI than their scaled deployments suggest, particularly across idea-to-market and <a href="/search?q=Assortment%20Planning">offer-to-assortment</a> decisions.
Read more →