End User
LMS
Description
This service utilizes AI to optimize the scheduling* of a production in manufacturing. The input comprises the orders, the machines available and their data. The output envelopes the schedule plan of the manufacturing itself. Energy efficiency will be one of the criteria used in decision making, along-side time-related criteria. Real-time data on the orders could potentially be used to extend this service towards being an adaptive scheduler.
Core Capabilities
Optimization & Decision Support
Business Need
Manual or inefficient scheduling processes in industrial operations often lead to resource conflicts, downtime, overstaffing, or underutilization of assets. These inefficiencies increase operational costs, reduce productivity, and introduce human error into time-sensitive decision-making. Also, traditional scheduling systems often overlook energy efficiency. A scheduler service automates and optimizes the allocation of tasks and resources, ensuring that operations run smoothly and efficiently. By minimizing idle time and improving throughput, it directly supports cost savings, scalability, and better service delivery. Incorporating energy efficiency as a scheduling criterion enables smarter allocation of resources not only based on availability or speed, but also on minimizing energy use. This reduces operational costs, supports sustainability goals, and helps companies meet regulatory or ESG (Environmental, Social, Governance) targets.
Key Performance Indicators
Response time for real-time adjustments
Increase in energy efficiency
Reduction in time-related KPIs
Applicability & Adaptation
Data Provided
Proprietary datasets from plant operations (API, CSV, JSON, depending on the case) (unknown datasize) (existing documentation for structure) (data resolution per hour)
Proprietary datasets for orders (API, CSV, JSON, depending on the case) (unknown datasize) (existing documentation for structure) (data resolution per hour)
TEF
TEF IND