From Fixed Rules to Intelligent Optimisation
Manufacturing scheduling has never been simple, and the complexity continues to grow. Demand changes, material constraints, resource availability, and operational pressures all compete for attention.
For decades, scheduling systems have relied on predefined rules to build achievable plans. Today, AI makes it possible to explore thousands of alternatives and identify opportunities that traditional approaches may never uncover.
What is Evo APS?
Manufacturing planning and scheduling has become increasingly complex. Demand changes, material availability fluctuates, customer expectations continue to rise, and planners are often required to make critical decisions under significant time pressure. Traditional scheduling systems rely on predefined rules to create achievable production plans, but as complexity increases, finding the best possible schedule becomes increasingly difficult.
Evo APS takes a different approach.
Powered by advanced optimisation technology and artificial intelligence, Evo APS is designed to explore thousands of potential scheduling scenarios in minutes. Rather than simply generating a schedule, it evaluates alternative options while balancing capacity, materials, resources, delivery commitments, changeovers, and operational priorities to identify the most effective outcome.
The result is a more intelligent approach to scheduling that helps manufacturers improve throughput, increase resource utilisation, reduce waste, enhance service levels, and make better-informed decisions faster. Evo APS enables planners to move beyond firefighting and manual schedule adjustments, giving them greater confidence that they are working from the best possible plan.
As an authorised reseller of Evo APS, Kudos Solutions combines over 30 years of manufacturing planning and scheduling expertise with innovative optimisation technology. Together, we help manufacturers take a practical step towards smarter, faster, and more intelligent scheduling in an increasingly complex manufacturing environment.
The Challenge With Traditional Scheduling
Every schedule is a series of trade-offs. Improve one area and another may suffer. Reduce setup time and delivery dates may be impacted. Improve throughput and resource utilisation may fall elsewhere. The challenge isn’t creating a schedule. The challenge is finding the best schedule.
Complexity is Growing
Manufacturers are managing more products, shorter lead times, and increasing operational variability than ever before.
Decisions Need To Be Faster
When disruption occurs, planners need to understand the impact quickly and respond with confidence.
Every Minute Counts
Time spent manually rebuilding schedules is time not spent improving performance.
Putting Evo APS To The Test
To validate the technology, Evo APS was tested using scheduling models specifically designed to replicate the complexity of real-world manufacturing environments.
The objective wasn’t simply to create a schedule. The objective was to determine whether an AI-driven approach could identify opportunities for improvement that traditional scheduling methods might overlook.
These models incorporated many of the challenges manufacturers face every day, including capacity constraints, material dependencies, changeovers, and shared resources competing for availability.
The models included:
- Sequence-dependent changeovers
- Secondary constraints
- Material dependencies
- Bills of Materials
- Resource constraints
- Multi-operation processes
- Shared equipment such as ovens and curing resources
By combining multiple constraints into a single scheduling environment, Evo APS was able to evaluate realistic manufacturing scenarios rather than simplified examples.
This allowed the testing to focus on what really matters to manufacturers: improving operational performance while maintaining achievable and practical schedules.
What The Results Showed
One demonstration model contained 71 operations and represented a typical manufacturing scheduling environment.
The results were significant.
70% Less Setup Time
Setup time reduced from almost five hours to just over ninety minutes during testing.
Late Orders Eliminated
Schedules that previously contained late orders were completed with no late orders present.
Shorter Schedules
The overall schedule length was reduced while maintaining operational constraints.
Thousands Of Possibilities Every Minute
One of the most powerful aspects of Evo APS is the speed at which it evaluates options.
During testing, strategies were run across 100,000 generations and analysed an average of approximately 7,500 schedules every minute.
This allows Evo APS to continually refine schedules and explore opportunities that may never be considered using traditional methods.
Rather than accepting the first acceptable answer, the software continues searching for a better one.
Better Scheduling Isn’t About Replacing People
The goal of AI scheduling isn’t to replace planners.
It’s to give planners more options.
The software does the heavy lifting of evaluating thousands of alternatives, allowing planners to focus on making informed decisions based on the best available outcomes.
This reduces firefighting, improves confidence, and helps organisations respond faster when conditions change.
The Future Of Scheduling
For years, manufacturers have focused on creating schedules faster. The next opportunity may be creating better schedules. The Evo APS testing demonstrated that AI-driven optimisation can improve setup time, reduce schedule length, eliminate late orders, and continually search for better outcomes. The question is no longer whether AI can create a schedule.
It’s whether your current scheduling approach is finding the best one.
Ready to Rethink Scheduling?
Discover how Evo APS helps manufacturers explore thousands of scheduling possibilities, reduce planning effort, and uncover opportunities hidden within their operation.
👉 Contact Steve Ward, Sales & Marketing Manager
📧 steve.ward@kudossolutions.co.uk
📧 enquiries@kudossolutions.co.uk
