Scheduling Complex HMLV Applications
Scheduling High Mix Low Volume (HMLV) Applications
Written by: Bill Kirchmier 5/7/2012
Scheduling complex non-linear HMLV applications is difficult; due to the many diverse scheduling options available. Persons interested in understanding how Finite Capacity Scheduling (FCS) systems simulate functions to efficiently schedule HMLV applications are invited to join us at this comprehensive seminar.
In any HMLV application; jobs pass through work centers in a non-linear sequence as specified by production routings. This creates complex scheduling environments. Regardles of the technology
applied to solve feasible HMLV applications; the following functions are required, the technique must:
- Define “resource demand” for material and capacity for all WO operations throughout the scheduling horizon.
- Define material and capacity “resource availability” by work center by shift by date/time
- The scheduling foundation must be simulation based and data should be deterministic for schedules to be realistic
The following FCS features supported by your FCS developer should apply:
- Reduced cycle times by smoothing product flow patterns
- Reduce job lead time which is often a high percentage of total wait time of ICBP
- Simulation reduces WIP caused by unnecessary simultaneously processing jobs
- Simulation supports setup matching to reduce setup times
- Minimizes changing job priority as jobs progress through the shop
- Improves on-time delivery
The primary difficulty in scheduling HMLV with traditional methods are:
- Inability to specify the release date/time of work orders
- Inability to efficiently and logically coordinate and syncronize resources
- Inability to efficiently coordinate jobs/operations moving through the plant
ERP based on ICBP scheduling aggravates several factors including:
- User modeling is minimal or non-existing scheduling logic is based on predetermined assumptions
- Manual changes required for job due dates and priorities; that are not based on logic
- Delays in material supply due to poor predictability