Connect production, inventory, orders, supply chain, logistics and fulfilment to understand the complete operational picture rather than isolated departmental metrics.
Use predictive analytics, risk indicators and external data to identify emerging supply, demand, operational and manufacturing risks earlier.
Bring KPIs, real-time information, predictive insights, scenarios and recommended actions together so decision-makers can evaluate situations with greater context.
Use intelligent order scheduling and connected supply chain information to prioritise critical orders and coordinate production, inventory and logistics activities.
Gain earlier visibility into supply constraints, demand fluctuations, logistics conditions and external events that may affect manufacturing performance.
Connect inventory information with production requirements, customer orders and supply conditions to support more informed inventory and replenishment decisions.
Analyse supplier and third-party service-provider information to understand performance, profitability, sourcing requirements and commercial relationships.
Use spend analytics, source-to-contract processes, e-sourcing capabilities and tail spend management to improve transparency across procurement activities.
Connect IIoT, predictive maintenance, real-time tracking and operational performance information to identify conditions that can contribute to prolonged downtime and idle time.
Give manufacturing stakeholders a shared environment to investigate operational abnormalities, discuss scenarios, and coordinate responses.
Use configurable intelligence and adaptive machine learning models to support changing manufacturing priorities, information requirements and operational conditions.
Bring AI, IoT, Digital Twin, predictive analytics, simulation, enterprise integration and cyber-physical capabilities together within the Cerexio One framework.
