Most supply chain data lives in silos — SAP, TMS, WMS, MES, PLM, and countless spreadsheets, each holding one piece of the picture. Planners spend their time toggling between systems and reconciling numbers, when what they need is simply to see the whole network at once.
The AIO Supply Chain Control Tower brings it together. It ingests data from every layer of your supply chain — tier-x suppliers, contract manufacturers, logistics partners, customers, and your own manufacturing and distribution network — and surfaces it in a single, unified view. Instead of stitching sources together by hand, planners see one consistent picture of inventory positions, order status, and supply commitments across the entire network, in near real time.
The payoff is a shared source of truth: everyone working from the same current view, with no blind spots between tiers and no time lost assembling the picture before the real work can begin.

End-to-end Visibility

A supplier delay doesn't stay at tier-x. It ripples through the network and eventually lands on production schedules, customer commitments, and revenue — often weeks later, when the room to react has already closed. By the time the problem is visible where it hurts, it is usually too late to prevent it.
AIO closes that gap. It aggregates risk signals from across the network and models how they propagate, projecting the downstream impact of an upstream event before it materializes as a missed delivery. Planners see not just what is failing right now, but what will fail if no action is taken — and where. That foresight is what turns a warning into a decision. Instead of reacting to disruptions after they surface, teams get meaningful lead time to intervene while options are still open and cheap.
PREDICTIVE RISK

No planner can watch hundreds of open orders at once, and the issues that matter rarely announce themselves. By the time a problem surfaces in a manual review, it has often already become a firefight.
The AIO Supply Chain Control Tower watches continuously. It scans the network for deviations as they emerge — a supplier delivery that will miss its window, an inventory position drifting below safety stock, a demand spike the plan hasn't yet absorbed — so nothing slips through unnoticed. But detection alone would just add to the noise. That's why each exception is scored by business impact and routed to the right planner with a recommended action already attached.
The effect is a shift from reactive firefighting to structured triage: the most important issues first, in front of the right person, with a clear next step. Planners spend their time deciding and acting, not hunting for what needs attention.
AUTOMATED MONITORING

When planning, procurement, and logistics each work from their own systems and their own exports, coordination breaks down into version disputes: whose numbers are right, and how current are they. The disagreement isn't really about judgment — it's about data.
The AIO Supply Chain Control Tower removes that friction. It operates as a decision layer on top of your existing systems — connecting to SAP ERP, MES, PLM, WMS, TMS, and external data sources without replacing them. The AIO semantic data model harmonizes and reconciles data across these sources into one consistent operational picture, so every function works from the same numbers at the same time.
The result is genuine cross-functional collaboration: no more version disputes between teams, no more decisions made on stale exports. When everyone shares a single, current view, the conversation shifts from arguing about the data to deciding what to do about it.
DATA INTEGRATION

Visibility inside your own four walls is only part of the picture. The biggest uncertainties usually sit at the edges — what customers will actually pull, and what suppliers can genuinely deliver. When those signals arrive late or not at all, planners have no choice but to hedge with conservative buffers and excess inventory.
The AIO Supply Chain Control Tower extends visibility across those boundaries by integrating directly with supplier and customer data feeds. Vendor Managed Inventory (VMI) programs, collaborative demand signals, and supply confirmations flow into the same decision environment — so planners see downstream customer demand and upstream supply capacity in a single view.
Closing that information gap changes the economics of buffering. With real demand and supply signals in hand, teams can replace guesswork and safety stock with confidence — holding less inventory while protecting service, because the plan reflects what partners on both sides are actually doing.
DATA INTEGRATION

Sales & Operations Planning is exclusively about tactical decisions and trade-offs. How much working capital should I invest in stock to safeguard our ability to deliver? Should I build up stock through pre-production to alleviate a future capacity overload? In what cases is it worth accepting higher material costs from a second source to make the supply chain more resilient? Is it necessary to expand the capacity of a production line to avoid a potential bottleneck in 8 months' time?
AIO S&OP models the entire S&OP process in a flexible monthly cadence and includes, e.g., Product Review, Demand Review, Supply Review and Executive S&OP. Decisions are prepared using intelligent reports and AI support (e.g. agenda generation) and are digitally represented in AIO S&OP. The implementation of S&OP decisions is supported and monitored by workflows. Results are fed back into AIO S&OP. Using machine learning methods, AIO S&OP learns and provides insights into the quality and likelihood of success of decisions. AIO S&OP - the AI-based closed-loop S&OP system tailored to the needs of your supply chain.
S&OP · Scenario Planning

For decades, forecasting relied on statistical methods such as exponential smoothing and ARIMA models. These approaches are robust and interpretable, but they assume relatively stable patterns and struggle to capture complex, non-linear dependencies or the influence of many interacting drivers. Machine learning has changed what is achievable. Our AIO ML-based Forecast Engine learns directly from large volumes of historical and environmental data, captures non-linear relationships, seasonality, and the effects of external factors that classical methods cannot. In practice, this translates into a substantial leap in forecasting accuracy — a step change rather than an incremental gain — which is why ML-based forecasting has become the new benchmark in the field. 
Our AIO ML-based Forecast engine is among the best in the field: in productive use cases across Automotive and Consumer Goods, we have improved forecast accuracy by up to 20 percentage points. AIO learns continuously and automatically, and it segments planning items into those that can be planned fully automatically and those where human judgment is still required — focusing your planners' attention exactly where it adds value.
ML FORECASTING · GRADIENT BOOSTING

As the volatility in the world is growing, just-in-time principles no longer work smoothly. We need to build strategic buffers to be prepared for supply disruptions. However, we also want to control and optimize the locations and the quantity of stock in multi-echelon networks. Determining inventory parameters based on fixed rules or gut feeling leaves money on the table — either through excess inventory or avoidable stock-outs. 
The AIO MEIO engine calculates the right safety stock in the network models every SKU, location, and supply tier to determine the optimal safety stock level and replenishment policy for each node, accounting for actual demand variability, lead time uncertainty, and supply reliability. The result is a dynamically maintained inventory policy that adapts as conditions change, without requiring manual recalibration.
MEIO · Inventory · Optimization

Even with accurate forecasts and well-positioned buffers, planners still face a hard question: given all the limits the real world imposes, what is the best possible plan? Supply chains are full of competing constraints — production capacities, sourcing limits, minimum order quantities, lead-time windows, and regulatory restrictions — that interact in ways no spreadsheet or manual process can fully resolve. The number of feasible combinations explodes quickly, and good-enough heuristics leave value on the table.
AIO's Constraint Optimization module solves this directly. It is built on Gurobi, a commercial-grade mathematical optimizer that handles this combinatorial complexity, evaluating all relevant constraints simultaneously rather than one at a time. Planners define the objective — minimize cost, maximize service level, or balance the two — and AIO finds the feasible optimum: the plan that best meets the goal while respecting every constraint in the network.
GUROBI · MIXED-INTEGER OPTIMISATION

Production planning has traditionally answered one question: can we make the plan work? Feasibility matters, but a plan that is operationally sound is not necessarily the right plan financially. Two feasible schedules can have very different consequences for the bottom line — and that difference usually stays invisible until it shows up in the P&L.
AIO's Master Production Planning module closes that gap. It builds feasibility-checked production schedules that optimize simultaneously across procurement, manufacturing, and distribution constraints — and it models the EBIT impact of every scheduling decision as it does so. Profitability is no longer a downstream surprise but an explicit input to the plan, visible at the moment decisions are made.The result changes the conversation. Supply chain teams can bring a production plan to finance and commercial leadership with the P&L consequence already calculated — not a feasible plan that finance must later assess, but a financially optimized one they can act on with confidence. 
EBIT-AWARE PLANNING

AIO's Supply Chain Planning Assistant is a large-language-model interface embedded directly in the planning environment. Instead of building reports or navigating complex screens, planners simply ask questions in natural language — "Which suppliers are at risk of missing next month's commits?" or "What's driving the safety stock increase for this SKU family?" — and get clear, structured answers in seconds.
What sets the Assistant apart is that every answer is grounded in the AIO Dataverse, drawn from your actual supply chain data rather than generated from a language model's assumptions. The result is trustworthy, verifiable responses, not plausible-sounding guesses.
Beyond answering questions, the Assistant drafts exception summaries, generates recommended action plans, and surfaces relevant precedents from past disruption responses. It augments planner judgment rather than replacing it — handling the legwork of gathering and structuring information so planners can focus on deciding what to do..
LLM · AGENTIC AI

When a stock-out or OTIF failure occurs, the instinct is to fix the nearest visible cause — a late delivery, a forecast miss — and move on. But proximate causes are usually symptoms, not the real problem. Treat the symptom and the failure returns in a different form next quarter.
AIO's Root Cause Analyzer goes deeper. It uses causal AI models — not correlation analysis — to trace failures back to their structural drivers across the full causal chain: why the delivery failed, what caused the production delay, what drove the procurement shortfall, and what changed in the demand signal upstream. Instead of a list of things that happened to co-occur, planners get a clear picture of what actually caused the outcome — and therefore what to change to prevent it recurring.
This is the first production-grade causal AI root cause analytics tool available in the supply chain software market.
CAUSAL AI

Before committing to a corrective action, planners need to know what it will actually do — not just to the immediate problem, but to related KPIs across the supply chain. Expediting one order can starve another; raising safety stock protects service but ties up cash. Every fix has ripple effects, and they are rarely obvious in the moment.
AIO's Impact Prediction engine makes those effects visible in advance. It models the relationships between supply chain decisions and their operational and financial outcomes — combining causal and correlation-based methods — so planners can simulate an intervention and see its projected downstream impact before executing. Trade-offs that were once discovered after the fact become inputs to the decision.The effect is a shift from reactive firefighting to informed decision-making with known risk profiles — acting deliberately, with a clear view of the consequences, rather than reacting and hoping for the best.
DECISION SIMULATION

A large share of planning work is high-volume and rule-governed: generating replenishment orders, updating safety-stock parameters, aggregating demand consensus, routing exceptions. These tasks are necessary, but they consume capacity that planners should be spending on judgment calls — the decisions where experience genuinely matters.
AIO's Agentic Supply Chain Planning layer takes them on. AI agents act on defined policies, execute the routine work at scale, and flag anything that falls outside their confidence threshold for human review. Over time they learn from planner overrides, so the boundary between what runs automatically and what needs a human keeps moving in the right direction.The result is reliable, auditable automation of demand, material, and capacity planning at a scale no manual process can match — with planners freed to focus on the exceptions and decisions that create the most value.
AGENTIC AUTOMATION