Manufacturing Simulation Case Studies Chapter 11 Manufacturing Simulation Case Studies

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

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

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.

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.

The Schematic of TMP Plant

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. 

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

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)

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

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

Fitting distributions to collected data

Baseline Plant Performance Summary

Baseline Time-In-State report chart

Baseline Mg Production Histogram

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.

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

Airplane paint shop Layout

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

Data collected from paint capacity study

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)

Process Flow and Base Model

Time-In-State chart for paint operations

KPIs results

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.

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.

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

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

Process flow diagram and the data collected for the QA area

Process flow diagram and the data collected for the Finishing Lines

The simulation results of the baseline model

TIS Graph of Facility Operations