Production equipment maintenance planning at Commercial Metals Company Steel Texas is currently done weekly without the benefit of equipment performance data. A group of consultants was tasked with 1) creating a simulation model of the shredding process that incorporates equipment failure modes and costs of downtime and 2) providing recommendations for preventive maintenance planning. The consultants analyzed downtime data, identified the most frequent and problematic sources of scheduled and unscheduled downtimes, and used this information to construct a prioritization plan intended to help with preventive maintenance planning and reduce costs from equipment failures.

1. Production equipment maintenance planning is a weekly practice
within all production departments at Commercial Metals
Ø Production equipment maintenance planning is a weekly practice within
all production departments at Commercial Metals Company Steel Texas.
Prioritizing equipment repairs and preventive maintenance work is a
challenge because there is no equipment performance data to support a
plan of action.
Ø Deliverables:
1. A simulation model with equipment failure modes costs’ on production
and maintenance
2. Recommendations regarding preventive maintenance plan
Problem The Shredding Process
Shredder Equipment Failure Modes and
Maintenance Planning
Logan Hanson, Matt Mitcham, Jeremy DiGiovanni, Doga Hacisalihoglu
Isidro Rosas, Richard Welfel, Adrain Ramos, Dillon Sterling
Project Process Map
Gantt Chart
Time Studies
Data Analysis
Simulation Findings and Future WorkCost Analysis
Consultants
• Based on the data given, 15 different types
were identified for scheduled downtime and
41 for unscheduled downtime
• Pareto Chart shows the frequencies for all of
these types of downtime
• Using the 80/20 rule, top 3 most problematic
downtimes were selected for both scheduled
and unscheduled
• Coefficients of Variances
with values over 1 were
used to determine
which down times were
out of control.
• Based on this finding, a
list of prioritization was
constructed with the
intention of helping
preventive maintenance
planning.
• The pie chart shows the contribution of each of
41 unscheduled downtimes to total downtime
• Using Arena’s Input Analyzer, probability
distributions for each 6 selected downtimes
were calculated
Dr. Jesus Jimenez – Department of Engineering
Dr. Francis Mendez - Department of Computer Information Systems
Dr. Ricki G. Ingalls – Department of Computer Information Systems
• 29% of all processed
scrap metal is sent
through the Non-Ferrous
side passing through the
Eddy Current System.
• Using this project’s
methodology of Data
Analysis and through the
use of simulation, more
conclusions regarding the
Non-Ferrous side could
be found.
• This project’s methodology could also be implemented to find
differences between times where changes are applied upon the
system such as machine replacements.
• Since the design of data analysis is already constructed, the user
would only need to change data points at any given time to
dictate the findings of the simulation
Daily Down Time Costs
Weekly Down Time Costs
Monthly Down Time Costs
*
**Barko Crane 9 was
down during data
collection
*Time Studies on Barko Cranes and Meatball
Sorters were conducted at three different times
to calculate the infeed and exiting rates
• Time Studies
data
collected for
Barko Cranes
and
Meatball
Sorters
**