Download - Factory performance optimization
Factory Performance Optimization
Methods and tools for continuous significant improvement of production and operations
SIMANDO is a global management and technology consulting firm with a high focus on decision support systems and operational excellence. We partner with client organizations in all industrial sectors to address their most important challenges and develop complete solutions that will enable them to achieve their objectives and make significant improvements in their performance. Our customized approach combines innovative technology, systems thinking and passion for operational excellence. This ensures that our solutions enable our clients to achieve sustainable competitive advantage by optimized operations and responsiveness to the current dynamic business environment. Founded in 2009, SIMANDO is a private company with its headquarters in Timisoara, Romania. For more information, please visit: www.simando.com
About SIMANDO
Services and Products
Factory Performance Optimization Framework
Analytical Methods for Performance Analysis & Improvement
Simulation in Manufacturing
Lean Six Sigma for Manufacturing
2011 3/35
Outline
About SIMANDO
2011 4/35
Mission
Vision
At SIMANDO, our primary mission is to help our clients make substantial, continuous improvement in their performance. We accomplish this by providing them with outstanding technology solutions and consulting services to increase their excellence degree at all levels.
We strive to be the company that understands perfectly its clients' objectives, always delivers quantifiable results and maximizes the financial and trust investments made by its clients.
Certified Six Sigma Black Belt (American Society for Quality)
Project Management Professional (Project Management Institute)
Oracle Certified Professional Java Programmer (Oracle Corporation)
Certificate in Finance (New York Institute of Finance)
Our Certifications
Company founded in 2009
Privately owned, LLC
Headquarters: Timisoara, ROMANIA
Expertise
Software Applications Development Advanced algorithms and design patterns
Software architecture
Software development lifecycle methodologies
Functional and object oriented programming
Database Management Systems
MRP/ERP Systems
2011
Industrial
Project and product development management
Computer Integrated Manufacturing
Industrial engineering and factory planning
Manufacturing, logistics, supply chain design
Transport and distribution systems
Operational Excellence
Lean Six Sigma Transformation
Design for Six Sigma
Toyota Production System
Theory of Constraints
Product Development
Modeling and Simulation
Systems modeling, simulation and optimization
All simulation paradigms - discrete events, agent-based and system dynamics
Statistics
5/35
Services and Products
Services
Production, logistics, supply chain, healthcare engineering , modelling and simulation
Operations optimization
Lean Six Sigma/Design For Six Sigma training and implementation
Training and assistance in simulation models development
Product development and project management
Computer Integrated Manufacturing
Products
Modeling and simulation component libraries
MANSIMโข - general manufacturing
SOLSIM โข - photovoltaics manufacturing equipment
LOGSIM โข - warehousing and logistics
Specialized software applications for Lean Six Sigma, planning and scheduling
2011 6/35
Factory Performance Optimization
2011 7/35
Factory Performance
Industrial Engineering
Six Sigma
Lean
Theory of Constraints
TRIZ
Information Technology
Synergistic framework for continuous significant improvement of production and operations
Factory Performance Leverage Points
2011 8/35
The 3 Dimensions of Manufacturing
1st Dimension Products and processes selection Factory location, size and layout
2nd Dimension Factory workstations and machines Factory personnel Material handling systems Supplies and spare parts inventory Degree of automation
3nd Dimension Jobs starts protocol Preventive maintenance protocols Personnel allocation protocols Batching protocols Dispatching rules and scheduling Waste reduction programs
Investment
Manufacturing Performance
1st Dimension
2nd Dimension
3rd Dimension
Littleโs Law
P-K Equation
Propagation of Variability
Capacity Effectiveness
Factory Performance Indicators
2011 9/35
๐ถ๐๐๐ = ๐ถ๐ด๐ 2 + ๐ถ๐ธ๐๐
2
2
๐ 2(๐+1)โ1
๐(1 โ ๐)
1
๐ธ๐๐ ๐๐ +
1
๐ธ๐๐ ๐๐
๐๐ผ๐ = ๐ถ๐ ร ๐๐ป
๐ถ๐ท๐ 2 = 1 + 1 โ ๐2 ๐ถ๐ด๐
2 โ 1 +๐2
๐๐ถ๐ธ๐๐2 โ 1
๐ธ๐ถ = 1 โ๐ท
๐๐ร๐0 ๐1
๐๐๐
โข Performance Curves Cycle Time vs. Loading LACTE Profit vs. Loading
Why Simulation ?
BECAUSE โฆ SIMULATION GIVES US ANSWERS!
2011 10/35
The future is of greater interest to me than the past, since that is where I intend to spend the rest of my life. ~ Albert Einstein When?
Where?
What?
Who?
Why?
How?
!
Simulation Study Types
Simulation
Studies
System Design
New processes
New facilities
New concepts
Structural Design
Elements
Layout
Logic
Logical Design
Flow logic
Operations sequences
Priority rules
Parametric Design
Cycle times
Reliability requirements
Velocities, rates
Problem Solving
Diagnosis
Problem definition
Solution finding
Diagnosis
Problem definition
Testing Schemes
What-if scenarios analysis
Solution Validation
Sensitivity analysis
Continuous Improvement
Opportunity definition
Performance measurement
Performance improvement
Opportunity Definition
Benchmarking
Test Plans
Feasibility check
Plan Validation
Sensitivity analysis
2011 11/35
Simulation Benefits
2011 12/35
Convince clients of your operational capabilities
Safely analyze dangerous scenarios Implement your decisions
with confidence
Make prompt and correct decisions
Experiment and get fast feedback
Analyze the behavior of complex systems
Communicate ideas efficiently and credibly
Discover alternatives to unexpected roadblocks
Teach new concepts easily
Save money in short and medium term
Test fast, fail fast, adjust fast. ~ Tom Peters
Applicability Areas
Manufacturing
Key Performance Indicators FMEA Production flow design Planning and scheduling Resource estimation Capacity planning Total cost of ownership
2011 13/35
Lean Six Sigma
Stochastic process simulation Statistical analysis Variability elimination Pull mechanism design QOS metrics Dynamic VSM Benchmarking
Logistics and Supply Chain
Transport networks design Fleet planning & maintenance Warehouse design Operations optimization Supply chain planning
IT & Telecom
Wireless networks topology
Protocols design
Agent-based emergent behaviour analysis
QOS
Urban Development
Public utilities planning
Evacuation plans creation
Disaster recovery
Anti-terrorist measures
Healthcare
Resource estimation
QOS
Epidemics dynamics
Operations optimization
How We Do It ?
Problem formulation
Objectives and plan definition
Model development
Data collection
Model conceptualization
Code verification
Model validation
Control
Implementation
Reporting
Experiments run and analysis
Design of experiments
Your trajectory to success with simulation
2011 14/35
Continuous improvement is better than delayed perfection. ~ Mark Twain
Modeling
2011 15/35
Specialized component libraries
Domain specific library components
2D/3D customizable animation
Fast and easy drag-and-drop layout modeling
Reusable models and components encourage continuous improvement!
Simulation Models Input/Output Data
2011 16/35
Text
Excel
Database
Webservice
XML
Text
Excel
Database
Webservice
XML
Run-time Charts
Simulation Model
Input Data
Output Data
CAD
Simulation in Manufacturing
2011 17/35
Assembly line simulation model
Creativity is thinking up new things. Innovation is doing new things. ~ Ted Levitt ( http://simando.com/resources/applications/35 )
Simulation in Manufacturing
Optimal plant layout
? 2011 18/35
Detection and management of bottlenecks
60 sec
Rework Loop
Rework Loop
Rework Loop
A
B
30 sec
60 sec
60 sec
120 sec
120 sec
120 sec
120 sec
120 sec
120 sec
?
Simulation in Manufacturing
2011 19/35
Simulation in Manufacturing
Equipment ROI Calculation
Golden Equipment Silver Equipment Bronze Equipment
Cycle Time โฆโฆโฆ....... 30 sec MTBF_1 โฆ..โฆโฆโฆโฆ 5000 hrs MTTR_1 โฆโฆโฆโฆโฆ........ 1 hrs MTBF_2 โฆโฆโฆโฆโฆโฆ 7500 hrs MTTR_2 โฆโฆโฆโฆโฆโฆโฆ 0.5 hrs Yield โฆโฆโฆโฆโฆโฆโฆโฆโฆ. 99.6% Energy โฆโฆโฆโฆโฆโฆโฆ. 10 kWh Price โฆโฆโฆโฆโฆ.โฆ $1,500,000
Cycle Time โฆโฆโฆ....... 60 sec MTBF_1 โฆ..โฆโฆโฆโฆ 4000 hrs MTTR_1 โฆโฆโฆโฆโฆ........ 2 hrs MTBF_2 โฆโฆโฆโฆโฆโฆ 8500 hrs MTTR_2 โฆโฆโฆโฆโฆโฆโฆ 3 hrs Yield โฆโฆโฆโฆโฆโฆโฆโฆโฆ. 98.9% Energy โฆโฆโฆโฆโฆโฆโฆ. 8 kWh Price โฆโฆโฆโฆโฆโฆ.โฆ $850,000
Cycle Time โฆโฆโฆ....... 80 sec MTBF_1 โฆ..โฆโฆโฆโฆ 5000 hrs MTTR_1 โฆโฆโฆโฆโฆ........ 1 hrs MTBF_2 โฆโฆโฆโฆโฆโฆ 8000 hrs MTTR_2 โฆโฆโฆโฆโฆโฆโฆ 2 hrs Yield โฆโฆโฆโฆโฆโฆโฆโฆโฆ. 97.2% Energy โฆโฆโฆโฆโฆโฆโฆ. 14 kWh Price โฆโฆโฆโฆโฆโฆ.โฆ $450,000
2011 20/35
Simulation in Manufacturing
2011 21/35
Total Cost of Ownership
๐ป๐ช๐ถ =๐ป๐๐๐๐ ๐ช๐๐๐๐ ($)
๐ป๐๐๐๐ ๐ต๐๐๐๐๐ ๐๐ ๐ฎ๐๐๐ ๐ท๐๐๐ ๐๐๐๐ ๐ถ๐๐๐ ๐บ๐๐๐๐๐โฒ๐ ๐ณ๐๐๐
๐ป๐๐๐๐ ๐ช๐๐๐๐($) = ๐ญ($) + ๐ณ($) + ๐น($) + ๐($)
Where: F ($) = fixed costs for purchasing the system L ($) = fully burdened labor cost R ($) = recurring costs (consumables, maintenance, specialized support etc.) Y ($) = yield loss cost ๐($) = ๐ต โ ๐ท($)
Where: N = number of defective product entities P ($) = value of the product entities in the specific production stage
Simulation in Manufacturing
2011 22/35
Total Cost of Ownership
๐ป๐๐๐๐ ๐ต๐๐๐๐๐ ๐๐ ๐ฎ๐๐๐ ๐ท๐๐๐ ๐๐๐ ๐ฌ๐๐๐๐๐๐๐ = ๐ณ โ ๐ป โ ๐ โ ๐ผ ๐ท๐๐๐ ๐๐๐๐ ๐ถ๐๐๐ ๐๐๐ ๐บ๐๐๐๐๐โฒ๐ ๐ณ๐๐๐
Where: L = lifetime of the production system T = throughput rate Y = composite yield U = equipment utilization
๐ผ = ๐ โ ๐บ๐ด + ๐ผ๐บ๐ด+ ๐จ+ ๐บ + ๐ธ
๐ฏ
Where: SM = scheduled maintenance USM = unscheduled maintenance A = assist time S = standby time Q = qualification time H = total number of scheduled production hours per week
Simulation in Manufacturing
2011 23/35
Total Cost of Ownership
All variable/probabilistic elements in the formula can be tracked
and calculated by simulating realistically the system under study.
๐ป๐ช๐ถ =๐ญ $ + ๐ณ $ + ๐น $ + ๐($)
๐ณ โ ๐ป โ ๐ โ ๐ผ
Due to variable costs and probabilistic events associated with complex production systems, only simulation-based methods of calculating the TCO can provide correct and accurate estimates therefore.
Simulation in Manufacturing
Detailed modeling of components and manufacturing scenarios
Accurate timing and behavior of the modeled systems
Manual work, worker-machine and fully automated manufacturing modeling possibilities
Any type of production environment: jobbing, intermittent, mass production
Resources behavior controlled by highly detailed state machines according to machine specs
Any type of Key Performance Indicator can be defined and tracked
Ramp-up scenarios analysis
Inbound/outbound logistics and supply chain analysis and integration
Declustering of job starts and maintenance
Load management scheme design
2011 24/35
Simulation in Manufacturing
Line balancing and materials handling
Dispatching rules:
critical ratio, shortest processing time, FIFO, due date, etc.
Conveyors vs. Automated Guided Vehicles vs. Humans
Material flow optimization
Buffers capacities & policies (FIFO, LIFO, FEFO, custom)
2011 25/35
Simulation in Manufacturing
Lean manufacturing speed and quantity control and Six Sigma quality
Simulation offers support in finding solutions to reduce: Transport time
Inventory and buffers
Employee motion
Waiting
Overproduction
Defects
2011 26/35
Simulation in Manufacturing
Optimization of Key Performance Indicators
Work in process (WIP)
Load-adjusted cycle time efficiency
Manufacturing lead time
Equipment cycle times
Queuing, blocking, waiting, transport time
Throughput
Equipment and human resources utilization
Energy, consumables, spare parts, waste
Spares and supplies inventory levels and variability
2011 27/35
Simulation in Manufacturing
Design and optimization of complex equipment
Utilization, throughput, cycle time for cluster tools
Equipment with M:N mapping of process resources to handling units
Optimization of handling units movement and process resources allocation
2011 28/35
Process Chamber
Process Chamber
Process Chamber
Process Chamber
Process Chamber
Process Chamber
Process Chambers
IO Ports Multiple handling units on the same rail
Simulation in Manufacturing
Production planning and scheduling
2011 29/35
Production Planning
Forecast
Simulation
Feedback
Simulation in Lean Implementation
Static Value Stream Map
Dynamic Value Stream Map (Simulation)
2011 30/35
Nature does constant value stream mapping โ it's called evolution. ~ Carrie Latet
Simulation in Lean Implementation
Single piece flow vs. batch processing analysis
Kanban (pull) mechanism design
Production leveling (heijunka)
Cycle, safety and buffer stocks calculation
Just In Time (JIT), Just in Sequence (JIS) inventory strategy design
Cellular operations design
Overall Equipment Effectiveness (OEE) calculation
Relation between demand and takt time analysis
2011 31/35
Define
Measure
Improve
Control
Analize
Simulation in Lean Six Sigma
Define Project Scope
Define Lean
Measures
Define Structure
and Variables
Develop Current State
VSM
Develop Simulation Model
Develop Dynamic
VSM
Identify Sources of Variation and
Waste
Optimize Process Parameters
Apply Lean
Techniques
Validate Improvement
Develop Future State
VSM
Develop DOE Plan Run Simulation
Experiments Analyze Process
Flow
Develop Control Strategy
Test Control Plans
Implement Control Plans
Monitor Performance Over
Time
2011 32/35
Simulation-based Lean Six Sigma Project Roadmap
Design For Six Sigma
Design Produce/Build Deliver Support
Time
Cost vs. Impact
Impact
Potential is positive
(Impact > Cost)
Cost
Potential is negative (Impact < Cost)
2011 33/35
Impact of design stages on life cycle
Simulation in Design For Six Sigma
2011 34/35
Data collection Model building
Simulation model
Model analysis
Conclusions and reporting
Valid ?
Verified ?
Identify
Conceptualize
Optimize
Validate
Yes
No
No
Simulation-based DFSS Project Roadmap
SIMANDO Team
Thank you for your attention!
SIMANDO LLC 9 Republicii Blvd Timisoara, TM 300159 ROMANIA Tel: +40 356 172 021 Fax: +40 356 172 017 E-mail: [email protected] Web: www.simando.com