All Dataleo news, jobs, analyses and tutorials around Demand Planning in Supply Chain and Operations.
The role is relevant to planning operating-model design, decision rights, technology implementation and measurable process outcomes.
The role is relevant because support incidents can affect forecasts, supply plans and operational decisions across the planning environment.
The role is relevant to enterprise architecture connecting forecast decisions, fulfillment policies, data and execution systems.
The role is relevant to end-to-end planning architecture, scenario governance and cross-functional decision ownership.
The role is relevant to governing forecast logic, solution architecture and integration across planning environments.
The role translates planning policy into governed system logic and user routines.
The role translates planning requirements into credible demonstrations, assumptions and governed solution designs.
The role is relevant because it connects SAP IBP, S&OP, demand, inventory and supply planning with governed decision processes.
This is a concrete example of generative AI entering demand planning work. The governance challenge is to distinguish controlled prototyping from an industrial replenishment engine, with explicit assumptions, versions, validation rights and production controls.
The role is relevant where product strategy must translate into governed demand, stock and replenishment decisions across the operating network.
The role is relevant to forecast ownership, data quality and the governance of recurring demand-planning decisions.
This job is a useful signal for Supply Chain Planning because it focuses less on system implementation alone and more on the operational end-user layer of SAP IBP. The role sits where planning tools, recurring routines and business decisions meet.
For planning leaders, the interesting point is governance of the weekly and monthly decision cycle: which alerts matter, which KPIs trigger escalation, who owns the short-term response plan, and how planning data becomes trusted enough to support S&OE and S&OP decisions consistently.
HighMediumMediumHighMediumHighMediumFlowlity has made its Co-planner available through the ChatGPT Apps directory, extending conversational access to live Supply Chain Planning data.
According to Karim B., Flowlity’s CTO and co-founder, planners can use ChatGPT to ask operational questions such as which sites have coverage alerts, how a product forecast is evolving and where late orders are located. Flowlity then retrieves and analyzes the relevant planning data.
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.
MediumFlowlity continues to position probabilistic planning, demand forecasting and inventory optimization as a practical path away from Excel-based supply chain workflows. The product signal is especially relevant for manufacturers and mid-market teams managing supplier variability and demand uncertainty.
The planning relevance is clear: Probabilistic Forecasting and Inventory Optimization can help teams prioritize replenishment, purchasing and supplier decisions under uncertainty. The operational question is how recommendations are explained and reviewed before they influence inventory commitments.
More details are available in the Flowlity resource hub.
This is a useful Radar signal because Supply Chain AI is increasingly about uncertainty management, not only forecast automation. Flowlity should be tracked where probabilistic recommendations help planners move from spreadsheet firefighting to governed inventory decisions.
MediumColibri S&OP is positioning AI Agents inside supply chain planning workflows covering demand planning, supply planning, strategic planning, safety stock optimization and constrained plan optimization.
This is relevant for mid-market and local planning teams because it shows how agentic planning ideas are moving beyond global mega-suites. The practical question is how S&OP teams use agents to accelerate scenarios and exceptions while keeping human ownership of planning decisions.
More details are available on the Colibri S&OP website.
This product signal matters because Agentic AI in planning is not only a large-enterprise trend. Colibri S&OP should be tracked where AI helps business users structure demand, supply and scenario decisions inside a governed Planning Governance process.
HighA new wave of Supply Chain AI experimentation is emerging across the planning community. Inspired by initiatives such as Knut Alicke’s AI-assisted S&OP application, supply chain professionals are increasingly using vibe coding tools to build operational applications without traditional software development teams.
What started as isolated experiments is becoming a broader movement. Examples now span S&OP, demand planning, inventory management, scenario modeling, supplier risk monitoring and planner copilots. Recent community examples include AI-generated planning applications shared by practitioners such as Mahmoud Moursy, alongside other public discussions around IBP engines, manufacturing dashboards and supply-chain planning automation.
The emergence of these tools creates a new layer between Excel and enterprise APS platforms. Rather than replacing established planning solutions, these lightweight applications allow domain experts to rapidly test ideas, automate workflows and address local planning challenges that may never justify a large transformation project.
However, the opportunity comes with significant risks. As more planners become application builders, organizations must address AI governance, data quality, model transparency, business ownership, security, auditability and integration with enterprise systems. Without controls, companies risk creating a new generation of planning silos and shadow applications powered by AI rather than spreadsheets.
The most important signal is not that planners can now build software. It is that the economics of solution creation have changed. A planner with deep business expertise and access to modern AI tools can now prototype a functional Supply Chain Planning solution faster than many traditional software projects can complete requirements gathering.
For leaders, the question is no longer whether employees will build AI-powered planning applications. They already are. The strategic question becomes how to govern them through version control, testing standards, approval workflows, data lineage, user permissions, documentation and lifecycle management.
This points to the emergence of a middle layer between Excel and enterprise ERP or APS environments. It can accelerate controlled prototyping, but it also creates operational risk when business logic, data flows and decision ownership are not explicit.
The Dataleo team is currently working on a practical framework to help companies evaluate, govern, industrialize and scale vibe-coded Supply Chain AI applications. More details will be shared soon.
MediumFlowlity has presented Forecast Studio, a workspace intended to help planners understand, analyze and adjust AI-generated demand forecasts before they influence inventory and supply decisions.
The workspace displays AI forecasts alongside actual demand and historical trends, supports manual adjustments based on business context, enables comparison of alternative scenarios and highlights forecast drivers such as seasonality, trends and external signals.
Forecast transparency should go beyond a visual explanation. A governed Supply Chain Planning workflow must retain the statistical baseline, the AI-generated version, manual adjustments, scenario assumptions and the final approved forecast as separate, traceable objects.
The critical controls are ownership of each adjustment, supporting evidence, comparison against the baseline through Forecast Value Added, and controlled publication into APS, ERP or BI.
HighWipak has selected RELEX to connect demand, inventory and production planning. The manufacturing group will use machine-learning forecasting and shared planning processes to improve end-to-end visibility and coordination.
Connected planning needs aligned product, capacity and inventory definitions, with clear ownership of production trade-offs and forecast overrides.
HighBISSELL has expanded its use of o9 Solutions to rebuild planning across demand, supply, inventory and supplier collaboration. The transformation replaces a fragmented operating model built around spreadsheets, basic MRP outputs and manual coordination with a more integrated planning environment.
According to BISSELL’s supply chain leadership, scenario analysis that previously took weeks can now be completed in days or, for some questions, hours. The company uses o9 capabilities across Demand Planning, supply planning and multi-echelon inventory optimization to evaluate demand changes, component constraints, tariffs and other sources of volatility before decisions become urgent.
The implementation has also produced reported inventory benefits. o9 states that BISSELL reduced safety stock while improving service levels, and earlier customer material cited a $20 million safety-stock reduction alongside lower forecast bias. The wider signal is that integrated planning value comes from connecting scenarios, inventory policies and supplier decisions rather than optimizing each function separately.
Mediumo9 Solutions has reported expanding enterprise-planning deployments during the first quarter of 2026. The update highlights continued customer adoption of connected planning capabilities across demand, supply, inventory and broader enterprise decision processes.
Deployment growth should be assessed through operating outcomes: scenario speed, inventory, service, planner adoption and the governance of models and overrides.
HighLogility has launched DemandAI+ through Aptean AppCentral, combining AI-first forecasting with prebuilt planning agents designed for rapid activation. The offer targets demand-planning teams seeking faster deployment of automated analysis and recommendations.
HighLogility, now part of Aptean, announced DemandAI+ capabilities that position agentic AI inside supply chain planning workflows. The signal is relevant because planning vendors are moving from AI-assisted forecasting toward AI-supported exception analysis and recommended action.
For Demand Planning, the key question is whether agentic AI improves planner productivity while maintaining traceability of forecast drivers, overrides and business assumptions. This makes Planning Governance central to adoption.
DemandAI+ is a useful signal for the move from forecasting tools toward Decision Intelligence. Logility customers should assess whether agentic AI outputs are explainable, reviewable and connected to controlled planning workflows.
HighTeleflex has completed a global go-live of integrated demand and supply planning on o9 Solutions. The deployment connects visibility into demand, inventory, capacity and operational trade-offs within one planning environment.
The value depends on shared assumptions, model ownership and disciplined override governance across demand and supply teams.
MediumSKU Science introduced a product-lifecycle module designed to transfer historical data across product transitions and preserve forecast continuity during phase-in and phase-out.
Lifecycle forecasting requires governed predecessor-successor links, ownership of history-transfer rules and validation before the new product forecast is published.
MediumSKU Science outlined 2026 platform updates including forecasts in multiple business units, faster trend detection, stronger product-lifecycle management, dynamic ABC/XYZ classification and multilingual tutorials.
The updates strengthen usability, but governance still depends on controlled classifications, validated lifecycle links and traceable forecast adjustments.
LowArkieva published practical supply chain planning content that frames AI as a way to improve forecasting, inventory decisions and planning collaboration rather than replace planners outright. The signal is useful because many mid-market planning teams still need process maturity before advanced automation.
For Demand Planning, inventory and S&OP teams, Arkieva’s relevance is pragmatic: better forecast discipline, structured exceptions, supply-demand balancing and planning routines. This connects Planning Governance with realistic AI adoption.
Arkieva is a useful Radar signal because not every company is ready for autonomous agents. Many need a reliable planning layer first, with clear owners, calendars, exception rules and human review before scaling Supply Chain AI.
MediumSKU Science joined Supply Chain Event 2025 in Paris to present customer projects using AI models for demand forecasting and planning.
The relevant test is whether AI improves forecast accuracy and planner productivity without obscuring model choice, overrides or accountability.
MediumGanacos announced its participation in Supply Chain Event 2025 in Paris, where it planned a joint session with Carrefour on the retailer’s unified and collaborative private-label S&OP model.
The session was presented by Carrefour planning leaders Manuella Maignan Pierson and Juliette de Brisson, connecting demand and supply planning with private-label growth, supplier coordination and shared planning data.
The relevant signal is not the event presence itself, but the operating model behind the case. A unified S&OP process requires authoritative data, clear ownership of assumptions and controlled movement from commercial forecasts into supply and supplier decisions.
Leaders should assess whether the planning layer remains a lightweight collaborative environment or becomes sufficiently critical to require deeper integration with ERP, APS and supplier workflows.
MediumIntuiflow detailed its integration with SKU Science, adding demand-planning capabilities to a platform spanning S&OP, material planning, scheduling and execution.
MediumNetstock launched AI Pack to improve supply chain visibility and decision-making for small and mid-sized businesses. The announcement highlights capabilities such as Predictor Inventory Advisor and AI-supported planning guidance.
This matters because SMB and mid-market supply chain teams often need practical AI support for Inventory Planning, demand planning, replenishment and exception prioritization before they need a heavy enterprise APS. Netstock’s positioning is therefore relevant to accessible AI adoption in planning.
More details are available in the Netstock announcement.
This is relevant because accessible AI Planning tools can improve daily replenishment and inventory decisions in companies that are not ready for large planning transformation programs. The governance focus should remain planner review, recommendation traceability and master data quality.
Demand-planning AI should be judged by whether it improves a defined planning decision, not by whether a model or assistant has been introduced.
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Forecast enrichment is valuable only when humans or agents contribute material information unavailable to the baseline model.
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July PMI data shows production growth alongside worsening delivery times, stockpiling and rising input costs.
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US manufacturing activity rose as companies front-loaded orders against shortage and inflation risks, while factory employment weakened. The divergence complicates interpretation of <a href="/search?q=Demand%20Planning">demand</a> and capacity signals.
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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.
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Moving planners from manual reporting toward interpretation and strategic influence
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