All Dataleo news, jobs, analyses and tutorials around APS in Supply Chain and Operations.
The role is relevant to APS implementation governance, data readiness, configuration ownership and the transition from sales commitments to controlled operational use.
The role translates planning requirements into credible solution designs and decision workflows.
The role is central to owning planning logic, workflow design and user validation.
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.
MediumOMP launched Unison Express as a faster deployment path for supply chain planning capabilities. The announcement is relevant for companies that want structured planning modernization without waiting for long, heavy implementation cycles.
For Supply Chain Planning teams, the signal is time-to-value. Faster deployment packages can help organizations move from spreadsheet-based or fragmented planning toward more controlled planning workflows, especially when paired with strong Planning Governance.
This matters because implementation speed is becoming a competitive factor in APS and advanced planning adoption. The practical question is whether accelerated deployment still preserves data quality, planning ownership and Human-in-the-Loop decision controls.
HighPlanetTogether released APS V12.3.1 with new process-manufacturing capabilities covering inventory visibility, tank scheduling, clean-in-place operations and drum-buffer-rope scheduling.
These capabilities matter because high-level plans often simplify the operational constraints that determine whether a production schedule is actually executable.
MediumIn 2020, Jean-François Nordmann joined o9 Solutions to help develop the French market. His role covered market opening, qualification of planning needs and discussions with Supply Chain, IT and transformation leaders around integrated planning.
The move reflected growing French interest in Supply Chain Planning, S&OP, IBP and APS platforms able to connect business planning with enterprise data and execution processes.
The decisive architectural question is not whether a platform uses AI, but whether the customer can inspect, configure, version and govern the planning logic.
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Manufacturers should model constraints and stabilize planning discipline before automating scheduling decisions with AI.
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Planning and execution platforms increasingly share capacity, material and sequencing logic—raising questions about which system is authoritative.
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Community and OEM planning models reduce licensing friction while increasing customer responsibility for logic, integration, security and support.
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A more accurate forecast creates value only when it changes inventory, capacity, service or financial decisions in a measurable way.
Read more →Decision capacity, data reliability and explainability as practical filters for AI investment
Read more →AI forecasting, automated replenishment and agentic assistance now sound similar across vendors, while implementation and ownership models remain materially different.
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The recurring issue is not the lack of AI vocabulary, but the lack of public evidence connecting architecture to operating results.
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Capability maps do not reveal how many agents exist, which decisions they influence or who owns their actions.
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Supply Chain AI fails when companies automate a weak process, fragmented data and unclear decision rights.
Read more →Category planners increasingly manage inventory, forecast and service policies that shape commercial outcomes.
Read more →When supply is scarce, planners are deciding where the company places capacity, inventory and risk.
Read more →Simulation adds value when approved assumptions become part of the same capacity model used for operational planning.
Read more →Planning platforms need more than technical ownership: they need someone who connects product choices to business decisions, data and controls.
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Separate maintenance and production models create competing versions of capacity and late operational conflicts.
Read more →The framework improves material readiness only when lead times, sourcing rules, order policies and effective dates remain governed.
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As AI prioritizes exceptions and diagnoses root causes, planner work shifts from recalculating every plan to governing which decisions deserve action.
Read more →Counting agents, use cases or automated workflows does not prove that planning decisions improved.
Read more →Technology can replace spreadsheets without replacing weak ownership, hidden parameters or fragmented decision rights.
Read more →Every sequence, changeover and capacity decision reshapes margin, working capital and customer service. Yet production planning is still often managed as a back-office scheduling task.
Read more →The next planning failure will not come from a bad forecast. It will come from an AI agent making a plausible decision that nobody clearly owns.
Read more →Why continuous orchestration is becoming a strategic capability
Read more →Using generative AI to prototype adaptive planning without replacing the underlying methodology
Read more →AI can accelerate decisions, but it cannot repair fragmented data, unclear ownership or dysfunctional processes
Read more →Why one forecast KPI cannot represent every product, audience and planning decision
Read more →Why scaling AI in integrated planning depends on data, governance and decision ownership
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Why AI should expand professional capacity without transferring decision accountability
Read more →Moving planners from manual reporting toward interpretation and strategic influence
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From better forecasts to explainable, executable decisions
Read more →A fixed lead time hides uncertainty, calendar effects and process-stage variability that directly affect inventory and service decisions.
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A CMO appointment to monitor for shifts in how o9 frames planning decision value
Read more →Learning AI methods is easier than defining decision ownership
Read more →Emerging logistics technologies require governance before scale
Read more →Why faster answers do not automatically create faster decisions
Read more →Separating AI marketing from decision capability
Read more →Why forecast-led replenishment may not solve the working-capital problem
Read more →Spending expectations are accelerating, but many organizations are still trying to automate planning without redesigning decision rights, data ownership or workflows.
Read more →From forecasting and procurement to governed operational decision support
Read more →From decision-ready insights to governed planning actions
Read more →Evaluating planning outcomes instead of marketing claims
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Agents can investigate and recommend, but decision rights must remain explicit
Read more →Automation may reduce manual planning work while increasing the need for people who design decision rules, govern agents and connect business priorities to planning systems.
Read more →Planning expertise increasingly sits alongside SQL, modelling and data-structure skills
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