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

MANSIM™ - general manufacturing

SOLSIM ™ - photovoltaics manufacturing equipment

LOGSIM ™ - 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

Little’s Law

P-K Equation

Propagation of Variability

Capacity Effectiveness

Factory Performance Indicators

2011 9/35

𝐶𝑇𝑝𝑠 = 𝐶𝐴𝑅2 + 𝐶𝐸𝑃𝑇

2

2

𝜌 2(𝑚+1)−1

𝑚(1 − 𝜌)

1

𝐸𝑃𝑅𝑝𝑠+

1

𝐸𝑃𝑅𝑝𝑠

𝑊𝐼𝑃 = 𝐶𝑇 × 𝑇𝐻

𝐶𝐷𝑅2 = 1 + 1 − 𝜌2 𝐶𝐴𝑅

2 − 1 +𝜌2

𝑚𝐶𝐸𝑃𝑇2 − 1

𝐸𝐶 = 1 −𝐷

𝑟𝑏×𝑊0 𝑄1

𝑁𝑊𝑃

• Performance Curves Cycle Time vs. Loading LACTE Profit vs. Loading

Why Simulation ?

BECAUSE … 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


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 )

http://simando.com/resources/applications/35




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

?


2011 19/35


Equipment ROI Calculation

Golden Equipment Silver Equipment Bronze Equipment

Cycle Time ………....... 30 sec MTBF_1 …..………… 5000 hrs MTTR_1 ……………........ 1 hrs MTBF_2 ……………… 7500 hrs MTTR_2 ………………… 0.5 hrs Yield ………………………. 99.6% Energy …………………. 10 kWh Price …………….… $1,500,000

Cycle Time ………....... 60 sec MTBF_1 …..………… 4000 hrs MTTR_1 ……………........ 2 hrs MTBF_2 ……………… 8500 hrs MTTR_2 ………………… 3 hrs Yield ………………………. 98.9% Energy …………………. 8 kWh Price ……………….… $850,000

Cycle Time ………....... 80 sec MTBF_1 …..………… 5000 hrs MTTR_1 ……………........ 1 hrs MTBF_2 ……………… 8000 hrs MTTR_2 ………………… 2 hrs Yield ………………………. 97.2% Energy …………………. 14 kWh Price ……………….… $450,000

2011 20/35


2011 21/35

Total Cost of Ownership

𝑻𝑪𝑶 =𝑻𝒐𝒕𝒂𝒍 𝑪𝒐𝒔𝒕𝒔 ($)

𝑻𝒐𝒕𝒂𝒍 𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑮𝒐𝒐𝒅 𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒔 𝑶𝒗𝒆𝒓 𝑺𝒚𝒔𝒕𝒆𝒎′𝒔 𝑳𝒊𝒇𝒆

𝑻𝒐𝒕𝒂𝒍 𝑪𝒐𝒔𝒕𝒔($) = 𝑭($) + 𝑳($) + 𝑹($) + 𝒀($)

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 𝒀($) = 𝑵 ∗ 𝑷($)

Where: N = number of defective product entities P ($) = value of the product entities in the specific production stage


2011 22/35


𝑻𝒐𝒕𝒂𝒍 𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑮𝒐𝒐𝒅 𝑷𝒓𝒐𝒅𝒖𝒄𝒕 𝑬𝒏𝒕𝒊𝒕𝒊𝒆𝒔 = 𝑳 ∗ 𝑻 ∗ 𝒀 ∗ 𝑼 𝑷𝒓𝒐𝒅𝒖𝒄𝒆𝒅 𝑶𝒗𝒆𝒓 𝒕𝒉𝒆 𝑺𝒚𝒔𝒕𝒆𝒎′𝒔 𝑳𝒊𝒇𝒆

Where: L = lifetime of the production system T = throughput rate Y = composite yield U = equipment utilization

𝑼 = 𝟏 − 𝑺𝑴 + 𝑼𝑺𝑴+ 𝑨+ 𝑺 + 𝑸

𝑯

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


2011 23/35


All variable/probabilistic elements in the formula can be tracked

and calculated by simulating realistically the system under study.

𝑻𝑪𝑶 =𝑭 $ + 𝑳 $ + 𝑹 $ + 𝒀($)

𝑳 ∗ 𝑻 ∗ 𝒀 ∗ 𝑼

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.


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


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


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


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


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


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 – 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

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