1. Manufacturing Simulation Case Studies
Chapter 11
Manufacturing Simulation Case Studies

2. Chapter objectives
Explore the manufacturing applications of simulation modeling
Apply a complete simulation process on a real-world manufacturing system
Learn how simulation project management practices are used in real-world projects
Validate the value and benefits of simulation modeling

3. Chapter Content Chapter Overview
Hybrid Simulation of Titanium Manufacturing Process
Paint capacity study of an aviation company
Simulation of a seamless pipe facility
Chapter Summary

4. Manufacturing Process Simulation
Simulation can be used as an effective platform to model several manufacturing applications
WITNESS modeling techniques can be used to develop realistic representation of complete real-world simulation projects.
It is important to learn the practical aspects of simulating manufacturing systems, execute the various phases of the simulation process, and practice simulation project management.

5. Hybrid Simulation of Titanium Manufacturing Process
A summary of a simulation project conducted at an automotive supplier of high-quality Titanium Metal Products referred to as TMP.
The project analyzes a hybrid simulation of one of the supplier’s manufacturing processes.
The study aims at improving the manufacturing system throughput (productivity) in order to meet the growing demand requirements.

6. The Schematic of TMP Plant

7. This project will allow TMP manufacturing teams to:
Study benefits
This project will allow TMP manufacturing teams to:
develop system-level strategies to increase sponge throughput and reduce costs
test these strategies in a “safe” environment before implementation
understand the robustness of these strategies to changing market conditions
The primary deliverable of the titanium sponge simulation will be a constraint-based throughput improvement road map. 

8. Modeling Process
Identify questions/issues to be addressed by the model
List and define operational / financial evaluation metrics
Develop a block diagram of the process to be modeled
Collect, scrub, and organize the input data set
Program and validate the simulation model
Run what-if experiments to evaluate alternatives to increase throughput

9. Model Assumptions
A number of assumptions were made to define the model scope, simplify the process logic, and provide repeatable conditions for model start up and experimental runs.
The set of simplification assumptions include:
Raw materials are always available (chlorine, coke, rutile, etc.)
Finished goods are never blocked
Mass balances are calculated every hour
Cranes and forklifts are black-boxed and operators are always available
Plant operates continuously (24 hours per day, 7 days per week)

10. Model Data Inputs facility layout annual volumes
routings / flow logic / operating practices
processing times
chemical process yields and material balance relationships
set up times and batch sizes
mean cycles between failure and mean times to repair
shift hours / preventive maintenance
WIP inventories and move times

11. Output 1: Reductions (starts)
Model Outputs
Output 1: Reductions (starts)
Per day
Per week
Output 2: Mass Production (lbs/day)
TiCl4
MgCl2 Mg Ti
Output 3: Time Between VDP Feed Starts
Time Between MgCl2 Taps

12. Fitting distributions to collected data

13. Baseline Plant Performance Summary

14. Baseline Time-In-State report chart

15. Baseline Mg Production Histogram

16. Simulation Output Analysis
The largest value-added of simulation modeling is achieved from analyzing simulation outputs and optimizing the model parameters.
This is typically achieved through statistical analysis, experimental design, and optimization.
DES models provide a flexible and cost-effective platform for conducting statistical analysis and running experimental design, what-if analysis, and optimization methods.
Decision-makers can draw better inferences about the model behavior, compare multiple alternatives, and optimize performance.

17. Paint capacity study of an aviation company
This case study presents a WITNESS application for a paint process in an aviation company.
The paint process is referred to as SP3.
Study objectives include the following:
Develop a detailed process map (Value Stream Map) of the current state paint process
Illustrate all of the current positions in the paint process
Perform a “sharp pencil” analysis to determine precise painting capacity
Identify opportunities to reduce waste and increase throughput

19. Airplane paint shop Layout

20. Study Assumptions
The following assumptions were made when developing the WITNESS simulation model:
Planes are always available to be prepped
There is no downstream blocking after Detailing station
Labor resources are not the constraint
Equipment PM and Downtimes as provided

21. Data collected from paint capacity study

22. Data collected from paint capacity study …
POS #
Description
Mean
Min
Max
Distribution
POS 1 or 3
Prep Work
14.5
8.34
19.8
Triangular (8.34,14.539,19.8)
POS 2 or 4
Base Paint
14.4
18.8
Triangular (8.34,14.439,18.8)
POS 5
Layout
14.9
Triangular (8.34,14.905,18.8)
Paint Stripes
POS 6
Detailing
16.0 34
Triangular (8.34,15.959, 34.0)

23. Process Flow and Base Model

25. Time-In-State chart for paint operations

26. KPIs results

27. Simulation of a seamless pipe facility
This case study presents a WITNESS application for a seamless pipe facility simulation. The facility includes a Hot Mill Area, a QA area, and the area of finishing lines
Study objectives include:
Study operation of proposed tube & pipe plant in Saudi Arabia using a simulation model
Validate capability of lines to achieve expected production level of 600,000 mt/yr
Optimize intermediate storage area size and capacities in the facility
Test the effect of various schedules and product mixes on operation metrics
Analyze and optimize shift patterns for each plant area to ensure minimal blocking of the Hot mill area.

28. System Description Plant is expected to achieve 600,000 mt/yr target
Plant produces tubes, casings & line pipe
Range of OD: 60 to 365 mm
Three main areas
Hot Mill
QA – Heat Treatment and EMI
Finishing Lines – LP and OCTG
Product is heat-treated depending on grade
WIP Storage
Two intermediate storage areas (serves as a decouple between each pair of areas)
Two heat treatment storage areas
Number of in-process buffers (mostly cooling beds)
Material Handling System
Overhead cranes (4 each) in the Intermediate storage areas.

29. Model Input Parameters
Product Mix Schedule and Sequence
Product description: Product Code, OD, WT, Length, Type, Grade, Thread
Processing rates of each part type on each equipment
Down times of each equipment (randomized)
Cycle, time or product based setups
Buffer and storage area capacities
Plant working hours – shift patterns
Crane cycle times
All input parameters are read into the model through an Excel Interface

30. Process flow diagram and the data collected for the Hot Mill area

31. Process flow diagram and the data collected for the QA area

32. Process flow diagram and the data collected for the Finishing Lines

33. The simulation results of the baseline model

34. TIS Graph of
Facility Operations