Manage information from multiple sources into a consolidated visualization of the supply chain operations to provide a single source of data that everyone can use with confidence.
Visual MESA® Supply Chain Scheduling delivers end-to-end analytics to support operations management of refinery and petrochemical complexes using an integrated supply chain model. It uses automated data integration to effectively deploy cutting-edge algorithms for scheduling optimization and the best alternative search options.
The Visual MESA Supply Chain Scheduling software is unique because it brings optimization and scheduling automation capabilities to simulation models. Using sophisticated algorithms, it helps schedulers to better cope with supply chain uncertainty by providing accurate and timely operation schedules that align with plan targets.
Translating the targets from the plan into feasible operations instructions is a laborious, time-consuming process involving a lot of analytics and interaction to ensure coordinated actions along the supply chain. Improving the scheduler’s performance in this process reduces the margin loss inherently due to the translation process.
Actual operation is exposed to unforeseen disruptions and unexpected events (weather conditions, forecast uncertainties, process availability issues, etc.) Increasing the response agility to tame those events expands the response options and allows the implementation of less costly corrections.
The scheduling business process typically needs to combine data from different sources, timing, and structure. Data conditioning for effective usage requires significant effort to be easily automated with current systems integration best practices. The fast consolidation of always updated visualization of supply chain operations is a fundamental requirement for effective scheduling management.
There are multiple operational paths that can deliver the same planning targets but involve different degrees of operational variability and robustness. The systematic optimization of scheduling decisions allows you to quickly explore all those alternatives and pick the most effective one.
Planned operations need to be regularly compared against actual operation execution for detection and assessment of deviations and trigger the implementation of corrective re-scheduling decisions. The process of reconciling actual operations is also a critical process to assure the quality of model-based decision-making by systematizing the analysis of variance and suggesting the need for model recalibrations.