Case Study of Integrated Industrial Engineering Methods with Simulation
Phases of Simulation
Define the problem
Design the study
Design the conceptual model
Formulate inputs, assumptions, and process definition
Build, verify, and validate the simulation model
Experiment with the model and look for opportunities for design of experiments
Document and present the results
Define the model life cycle
Simulation Methodology
The eight phases of simulation provide a recipe for analysis success
Each phase has from 4 to 13 activities for completion
Each phase has a documentation deliverable associated with it
Phase 1: Define the Problem
Focus:
What questions will we ask the model?
What do we want to achieve?
What is the scope/boundary?
How much work/time will it take?
Deliverable:
Formal proposal document
Phase 2: Design the Study
Focus:
Estimate model life cycle
Describe how performance will be measured
Determine project timing and priority
Deliverable:
Project functional specification document
Phase 3: Conceptual Model
Focus:
Describe the “real” system in abstract, modeling terms
Determine the level of detail
Decide on statistical output interpretation
Deliverable:
General model specifications document
Phase 4: Formulate Inputs, Assumptions and Process
Focus:
Process logic definition
Analysis of input data
List modeling assumptions
Deliverable:
Detailed model specifications document
Phase 5: Build, Verify and Validate the Model
Focus:
Construction and coding
Verification and validation
Calibration
Deliverable:
Validated base model
Phase 6: Experiment with the Model
Focus:
Determination of cause and effect relationships
Identification of major influences
Analysis of results
Deliverable:
Simulation Results documentation
Phase 7: Documentation and Presentation
Focus:
Communication of results
Communication of methods
Maintenance and user documentation
Deliverable:
Final report documentation
Phase 8: Model Life Cycle
Focus:
Field validation tests
User friendly I/O interfaces
Model training and responsibility
Deliverable:
Formal proposal document
Input Data Analysis
Why is it important?
G-I-G-O (Garbage In …)
Need to accurately capture individual component behavior
Need to identify “patterns” that describe the variability of system components
Simulation Output Analysis
Run Length
Replications
Output Analysis
Bottleneck Analysis
Warm-up Plot, JPH/Time
Bottleneck Analysis
Compare the busy, idle, down and blocked time of each work station
Compare the average number of parts in each buffer and on each conveyor segment
Perform sensitivity analysis to identify which parameter has to most impact
General characteristics of a bottleneck work station
Lowest blocked time
Lowest idle time
Highest busy time
Upstream buffers are mostly full
Downstream buffers are mostly empty
Upstream workstation are blocked
Downstream workstations are idle
Simulation Guidelines
Technical guidelines
Managerial guidelines
Elements of failure
Elements of success
Technical Guidelines
Clearly define objectives
Diagram process flow
Understand the model life cycle
New vs. existing systems
Start with a simple model, add complexity later as needed
Get users involved in model building
Be familiar with the data collection process – question the data
Verify the model by making deterministic and extreme
condition runs
Validate the model against actual data
Be conservative in determining the experimental conditions
Use ranges (based on statistical calculations) rather than point estimates
Use time based plots for the major performance metrics
Start documentation from day one of the study
Managerial Guidelines
The project team should involve all key decision-makers on the problem
Identify one main user for the study and get his/her time commitment for the study
Make sure the main user (engineer) is involved with the study in all phases of the simulation project
Make it clear to the project team what type of results can and cannot be expected from the study
Report results as soon as possible and as often as possible
Work with many milestones throughout the project
Make sure all parties involved with the study hear about the results
Get input and resolve conflicts before going to the next step of the study
Control and document changes to the project
Focus more on the objective than on the model
There is no end to more detail and experimentation
Stop at the detail level necessary to produce accurate estimation of performance measures
Elements of Failure
Modeling for animation only
Modeling for the model’s sake
No predefined performance metrics
No documentation of communication of underlying assumptions and logic
Improper input data statistical analysis (or none)
Improper statistical methods (or none) for comparison of alternatives
No pre-definition of scope and objective
Improper level of detail (usually too much)
No pre-definition of system boundaries
Elements of Success
Ask the question:
What do I want to know from this simulation and how will I measure it?”
Draw firm system boundaries
Determine the correct level of detail
Decide what scenarios you want to evaluate
Project Management
Use a proven, structured methodology
Stick with it
Use a PM tool for planning and tracking
Keep notes on what you did right and what you did wrong
Document everything
Case Study: Material Flow and Indirect Labor Study
Agenda
Overview
IE Studies
Static and Dynamic Simulation
Results and Conclusions
Overview
A major study was performed at a manufacturing plant to identify opportunities for efficiency improvements:
The study generated recommendations for indirect labor and material flow improvements for current and future state operations.
The recommendations provided input to management about resource improvement proposals for local Union contract negotiations.
Various indirect labor assignments were evaluated including material handling equipment, i.e. forklift drivers, tugger drivers, crane operators, die setters, and stock chasers.
Various data collection and analysis techniques were used in the project:
Traditional IE studies for the development of time standards.
Material flow analysis using static simulation.
Dynamic resource simulation under varying production schedules.
Resource evaluation using forklift and tugger monitoring system.
Bar coding techniques for improved operational efficiencies.
The planning and operation simulation tools developed by PMC for this study provide the following benefits:
Allows change impact evaluation for various indirect resources for both the short-term and long-term planning horizons.
Tools are usable by trained plant personnel for what-if studies related to both current-state schedule fluctuations and future-state program changes.
Material flow model interfaces with plant AutoCAD layout and can be effectively updated as changes occur to the layout.
Project Savings
Summary
(21) Worker Assignments could be re-allocated on an immediate basis.
(14) Worker Assignments could be re-allocated with implementation of infrastructure improvement recommendations and analysis of ‘residual functions’.
(24) Worker Assignments could be re-allocated with Union consent, if classification changes are implemented.
These re-allocations represent a potential savings of $4.15 Million, in addition to the associated equipment and maintenance cost savings.
IE Studies
Extent & Areas Covered
Validated and updated plant’s existing databases pertaining to Material Handling
Developed time-elements and established time-standards where applicable.
Time studies covered (7) different Indirect Labor classifications throughout the plant.
(95) time studies executed over all 3 shifts.
Developed & updated standard work instructions based upon equipment used and required work practices.
Static and Dynamic Simulation
Simulation Overview
Diverse sources for input data:
Part numbers (Bill of Material, Pressroom Line-Up)
Container Information (Online Systems, Plant personnel)
Shift Schedules and Available Minutes Per Shift (Plant personnel)
Static Simulation Overview
Static Model uses Flow Path Calculator in conjunction with AutoCad
Provides capability to incorporate all material flow data into single database and calculates material handling utilizations.
Generates graphical output for flow analysis useful for identifying wasteful long-distance moves.
Creates congestion plots to highlight heavy traffic areas in the plant.
Output useful for identifying and evaluating opportunities to combine driver assignments and reallocate drivers.
Provides a tool for performing what-if scenarios to evaluate both short-term and long-term opportunities.
Static Simulation Flow – Subassembly Hilos
Straight FlowCongestion Diagram
Static Simulation What-If Scenario Example
Simulation
Dynamic Simulation Overview
Dynamic Model uses discrete-event simulation software with Excel Interface
Useful for evaluating the press room material handling resources including forks and tuggers where utilization fluctuates widely depending upon the press schedule.
Generates time-based charts that quantify the utilization of resources over time thus highlighting opportunities for material handling improvement in a dynamic environment.
Provides tool for evaluating and planning manpower required for current and future changes to the pressroom lineup schedule.
Dynamic Model Output Example
Sample Model Output for HiLo Group servicing two lines
Dynamic Model What-if Example
What-if: Combine coverage for two Press Lines
Before
After
Results and Conclusions
Opportunity to reallocate 59 material handling people presents potential savings of $4.15 Million.
Integrated approach utilized various analytical and IT tools in a comprehensive manner to evaluate indirect labor resources, including personnel and equipment
Tools can be utilized for ongoing efficient analysis of indirect labor resources required by changing production conditions in the plant from both short-term pressroom schedule changes to long-term program changes.
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