| dc.description.abstract | Company XYZ (XYZ) operates a government contract in south Kansas City Missouri.
XYZ is most notably engaged in the manufacturing of high-precision components for
national security. XYZ operations are heavily dependent on complex, custom-built test
equipment which is spread throughout their 3.1 million square foot facility.
XYZ has occupied their current space for over 60 years, but as the infrastructure has aged
and the workload requirements have changed dramatically, they are now in the midst of
relocating to a new site in Kansas City that allows for today’s highly flexible, rapid
response manufacturing needs. The massive XYZ relocation has been phased into 142
unique individual moves that will take over 18 months to complete.
The manufacturing floor at XYZ holds over 2,000 unique pieces of test equipment. An
entire work group at XYZ is dedicated to the routine calibration and maintenance of this
fleet of test equipment. In addition to this routine work that supports production, all test
equipment must be “close loop calibrated” prior to relocation to the new facility to ensure
product quality levels are being met. These 2,000 pieces of equipment belong to any one
of the 142 move phases, but the volume of test equipment in each move phase is highly
dependent on the technical area being relocated in a given phase.
In February 2014, Test Equipment Calibration & Maintenance (TECM) will be tasked
with completing close loop calibrations for the Assembly and Electrical Fabrication
(AEF) area of XYZ that includes 16 different technology areas. This specific move
phase holds 191 unique pieces of test equipment, and easily dwarfs all other move phases
that have calibrated test equipment. Only 10 technicians in TECM are cross-trained for
calibration work within any of the given 16 technology areas, which severely limits
TECM workload capacity.
After individual close loop calibration job assignments were made, initial capacity
planning indicated that TECM did not have the resources needed to complete the
assigned work in the AEF move phase on time. By creating a linear optimization model
that included 50 decision variables and 26 constraints, the researcher has identified key
cross-training needs and specific workload assignments that align individual technician
efficiencies within specific technology areas to successfully deliver all AEF calibration
needs within the relocation timeframe.
Based on individual technician efficiencies, the output of the final linear optimization
model yields the exact number of work hours each technician needs to work in a given
technology area during the AEF timeframe. Technician availability and technology area
demands are used as constraints to ensure all other technician commitments and all AEF
technology area work is completed while ensuring AEF close loop calibration
requirements are met within the identified timeframe. | |
