Greybox Schedule Optimisation
Greybox offers a scheduling solution to manufacturers seeking maximum value from their resources. It is a joint cognitive system where human and machine intelligence collaborate. The results are exceptional.
Up to 40% Labour Reduction
10% Output Improvement
Estimated 15% Energy Savings
Up to 40% Tool Set up/Product Changeover Reductions
Current scheduling technologies are inadequate
The two technologies currently used for scheduling both have advantages and limitations.
|Humans and/or excel-based templates||Black box optimisation algorithms|
|Has done a “good” job||Does a “very good” job|
|Often not good enough, especially with the onset of mass customisation/one-off products||Can comprehend all of system that can be entered into data model|
|Flexible||Inflexible – minimal interaction allowed|
|Limited in scope and speed of response||Requires extensive training for planners to use and sustain|
|Does not clearly represent business KPIs|
|Is difficult for end users to understand and amend|
|Relies completely on the data in underlying systems|
|Does not comprehend nuances|
Greybox Scheduling – beyond state of the art
Creates a joint cognitive system for businesses where human and machine intelligence collaborate for success.
The system's algorithms can suggest solutions to support the end user, increasing user confidence and enabling easier deployment
It offers a clear and simple user interface with a focus on the manufacturing KPIs
User interaction is a key component, allowing access to human expertise and tacit knowledge
It is robust at handling system changes
Intuitive Complex Schedule Management
Merging Human Expert Knowledge and Computer Computational Power
How Greybox works
Observability & interaction is necessary to increase user’s confidence in decision support systems.
This requires seamless transition between human and automated planning.
|Multi-objective, genetic algorithm optimisation of production and energy||Optimisation outputs are visualised as a weekly run-plan|
|Ability to accept ad-hoc user defined goals based on expert knowledge||Users can intuitively generate dynamic rules by dragging and dropping events|
|Scheduled updates and re-optimisation triggered by critical events||Interaction with the run-plan automatically updates projected performance indicators|
|Scheduled updates and re-optimisation triggered by critical events|