[系列活動] 製造資料科學：從預測性思維到處方性決策

Data Science in Manufacturing

From Predictive to Prescriptive

Dr. Chia-Yen Lee ( )

Institute of Manufacturing Information and Systems () Dept. of Computer Science and Information Engineering ()

Engineering Management Graduate Program () National Cheng Kung University ()

@NCKU ()

Outline

2

1

Intelligent Manufacturing Systems

2

Characteristics of Manufacturing Dataset and Data Preprocessing

3

Cases, Issues and Myths

4


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Intelligent Manufacturing Systems

3

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Overview

Intelligent

Manufacturing

Systems

What is Systems?

What is Manufacturing?

What is Intelligent?

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What is Systems?

Systems is

Process Input Output

Feedback & Control

Environment

Boundary

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Human Body System

Human Body

Inputs (Five Sense)

Hearing

Sight

Touch

Smell

Taste

Process

Learning

Boundary

Environment

Good Outputs

Mental Growth

Physical Growth

Bad Outputs:

Pressure

Urine, Excrement

Exhale: CO2

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What is Manufacturing?

Manufacturing is Manufacturing is

Manufacturing is the realization () of product. Swann (2003)

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How to throw a paper farther?

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Manufacturing is

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Idea? (by brain-storming and association)

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Manufacturing is

Manufacturing Process (Handmade)

For your needs (function)

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(2011).. . http://www.zwbk.org/MyLemmaShow.aspx?zh=zh-tw&lid=150882

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obj1. How to throw a paper farther?

obj2. The product should be delightful.

(this is what we call the customer requirements)

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?

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Valued added?

Material change

12

google

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Valued added?

Process change

google

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Valued added?

Process change

google

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Valued added?

Process change

google

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?

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Customer Requirements

Obj1. How to throw a paper farther?

Obj2. The product should be delightful.

Obj3. Low cost ()

Obj4. Specification ()

Obj5. Firmness ()

.

...

..

KPI

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Performance Index

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Yurdakul, Mustafa (2002),"Measuring a manufacturing systems performance using Saatys system with feedback

approach", Integrated Manufacturing Systems, Vol. 13 Iss 1 pp. 25 - 34

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What is Intelligent?

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Intelligent

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Shutterstock (www.shutterstock.com)

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Knowledge? Experience?

Any others?

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Knowledge

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: ? ?

email

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Advanced Analytics Intel: SETFI: Manufacturing data: Semiconductor tool fault isolation. Causality Workbench Repository,

http://www.causality.inf.ethz.ch/ repository.php (2008)

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: ? ?

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! !

! !

! !

!

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Transformation from Data, Information, to Knowledge

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Etu (2013)

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Experience

Experience Decision Problem

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()

http://www.herogamingjobs.com/2014/01/07/experience-vs-knowledge/

Wisdom is

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Combine together is

Intelligent + Manufacturing + Systems = ?

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Definitions

IMS is a system which improves productivity by systematizing the

intellectual aspect involved in manufacturing and flexibility by integrating

the entire range of corporate activities so as to foster the optimum in

relationship between men and intelligent machines (H. Yoshikawa,

University of Tokyo).

An intelligent manufacturing process has the ability to self-regulate

and/or self-control to manufacture the product within its design

specifications (Nam Suh, MIT)

Definition in this lecture

is a decision-oriented system which has the computational

intelligence and self-learning ability to optimize the

manufacturing process.

Definition of IMS

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IMS Framework

Tolga Bozdana (2012)

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Manufacturing Intelligence

()

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Purpose

The purpose of business is to create and keep a customer.

(Peter Drucker)

The social responsibility of business is to increase its profits.

(Milton Friedman)

Method

Transform the resource to product (or service) and by-product

Nature

a portfolio of resources (land, building, labor, equipment, electricity, etc.)

deployed through a network of activities

to create value for a customer

in order to create economic profit () for its owners

The Manufacturing Enterprise

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Value and Profit

Value

Manufacturing is value-added process for the realization of the product

Value Price Cost (VPC) framework

Customer Surplus vs. Producer Surplus

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Price

Value

Cost

Consumer

Surplus

Producer

Surplus

Wikipedia (2016)

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Business Decision Framework

Pricing

Strategy

Demand

Planning

Capacity

Portfolio

Cost

Structure

Price Elasticity

Product Mix

Utilization Demand

Fulfillment

Cost Parity

Capital Expenditure

Structure

Profitability Margin Return

ROIC, Payback

(Chien and Zheng, 2011)

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e-Manufacturing Hierarchy

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International SEMATECH (2002)

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Source: International SEMATECH e-Diagnostics and EEC Guidance 2003

Equipment Health Monitoring,

Predictive Maintenance (PdM)

Statistical Process Control,

Virtual Metrology, RMS

Carbon

Emission

RFID, Vision Guidance

Energy

Mgmt

Proactive Decision

(Forecasting)

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Manufacturing Networks

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Stage1 Stage2 Stage3 Stage4 Stage5

Material

Warehouse

Stocker

(Buffer) Shipments

(Inspection)

work-in-process

(WIP)

Picking

Feeding

Pack&Ship

Bottleneck ()

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Layout and AMHS

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Interbay Transport

Interbay Transport

ITS

ITS

ITS

ITS

ITS

ITS

Inter-floor

Transfer Stocker

EQ1

EQ1

EQ1

EQ2

EQ1

EQ1

EQ1

EQ2

EQ4

EQ4

EQ3

EQ2

EQ4

EQ4

EQ3

EQ2

EQ5

EQ5

EQ5

EQ2

EQ5

EQ5

EQ5

EQ2

Lee et al. (2014)

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Strategic Level

Financial PlanningStrategic Business

Planning

Marketing

Planning

Make-or-Buy

Strategy

Tactical Level

Aggregate Production

Planning

Vendor Selection &

Order Allocation

Master Production

Schedule

Resource Planning

Rough-Cut Capacity

Planning

Capacity Require.

Planning

Material Require.

Planning

Monthly Material

Planning

Capacity Planning

Productivity &

Efficiency Analysis

Vendor Relationship

Mgmt.Demand Mgmt.

Outsourcing Planning

Purchasing

Vendor Scheduling &

Daily Assignment

Order Releasing

Shop Floor Operation

Scheduling

Shop Floor Control &

Data CollectionFollow-Up

Production

Activity ControlIn-house Outsourcing

Operational Level

Logistics Mgmt.

Long Term

Mid Term

Short Term

Facility Layout

Production Management

Lee and Johnson (2013)

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Rapidly changing uncertain environment

Demand and market conditions

Diversity of customer requirements in global markets

Demands for mass customization/product variety

Short product life cycles

Need flexibility and shorter time-to-market

Short Innovation/Product Development Cycles

Inventory becomes a major risk

Push the manufacturing industry service industry

Cost of Capacity

A new 12 semiconductor fab may cost US$3-5billion

Idle capacity is a CEOs nightmare

Why Need Production Management?

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Manufacturing system vs. Service system Manufacturing: sales quantities and prices are defined before production

due to a longer production lead time (Internal Demand)

Service: non-storable commodities which once transformed from inputs, must be consumed by customers immediately (External Demand)

Main difference is Inventory!

Inventory

Raw materials, Components, Work-in-process (WIP), Finished goods

Lead Time + Uncertainty = Inventory

Inventory Reduction

Reduce material and production lead time (includes transport)

Reduce information delay times (, Bullwhip effect)

Improve quality of information (reduce uncertainty)

Manufacturing system vs. Service system

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Difference between Manufacturing and Service

Now, manufacturing system moves toward service system.

Manufacturing vs. Service

System Manufacturing Service

Input Material Labor

Process Capital-intensive Build SOP

Labor-intensive Building SOP is difficult

Output Physical product

Intangible Non-separable Non-stable Non-storable Time-concerned

feedback Quality Control Performance criteria

Difficult to measure quality and performance

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Chien et al. (2004)

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Why is it difficult to manage a

manufacturing systems?

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Diverse Resources and KPIs (KPI)

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(2011)

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(ICT )

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Prof.Dr. Engelbert Westkmper, Fraunhofer IPA Stuttgart, Germany, Factories of the Future beyond 2013: The role of ICT

http://cordis.europa.eu/fp7/ict/micro-nanosystems/docs/fof-beyond-2013-workshop/westkaemper-manufuture_en.pdf

!? ()

()

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Variability is anything that causes the system to depart from

regular, predictable behavior.

Sources of Variability:

setups workpace variation

machine failures differential skill levels

materials shortages engineering change orders

yield loss customer orders

rework product differentiation

operator unavailability material handling

Variability ()

Variability from Resource!

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: ??

(variability)?

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Transparency- showing the uncertainties in MFG

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Lee, Jay and Lapira, Edzel (2013). Recent Advances and Trends in Predictive Manufacturing in Industry 4.0

Environment. http://reliabilityweb.com/articles/entry/recent_advances_and_trends_in_predictive_manufacturing/

Internal Uncertainties

Visible

machine failure

product defects

long time delays

drops in overall equipment

effectiveness (OEE)

standardization

Invisible

machine degradation

component wear, etc.

predictive and monitoring

External Uncertainties

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MES

APC

FDC

SPC

VM

R2R

RMS

Preventive Maintenance

Predictive Maintenance

Yield Management

etc

Demand Forecast

Long-Run Capacity Planning

Short-Run Capacity Planning

CapEx

Cost Structure

MPS & MRP

Scheduling & Dispatching

Inventory Management

Order Releasing

etc

Quality

Productivity

Cost Down

Cycle Time

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CIM and Automation

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(Wang, 2012)

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Lean (identify non-value-added process and remove it)

Value

Value Stream Mapping (VSM)

Waste elimination (7 muda, Womack and Jones, 2003)

1. Transportation

2. Inventory

3. Motion

4. Waiting

5. Overproduction

6. Overprocessing

7. Defects

Continuous flow

Line Balancing

Pull production system

Lean Manufacturing

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PDCA cycle

PDCA cycle (Deming, 1950s)

PLAN: Establish the objectives and processes necessary to deliver

results in accordance with the expected output (the target or goals).

DO: Implement the plan, execute the process, make the product.

CHECK: Study the actual results (measured and collected in "DO"

above) and compare against the expected results (targets or goals

from the "PLAN") to ascertain any differences.

ACT: Request corrective actions on significant differences between

actual and planned results.

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Continuous Improvement by PDCA

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(standardization) (institutionalization)

cost down

Wikiwand (2016). http://www.wikiwand.com/en/PDCA

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Cost Down?

Cost Down!?

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Intelligent Factory is a decision-oriented system which has the

computational intelligence and self-learning ability to optimize the

manufacturing process.

Based on Data ()

Real-time Feedback Control ()

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generated by Wordle (2012)

Really!?

KNOWLEDGE DISCOVERY IN DATABASES (KDD)

Data

Warehouse

Knowledge

Source :From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, AAAI Press/The MIT Press 1996.

Selection

Preprocessing

Target Data

Preprocessed

Data

Pattern

Transformed

Data

Data Mining

Transformation

Interpretation/

Evaluation

ETL (Extract-Transform-Load)

Storage and Calculation

IT Infrastructure

Pattern Extraction

Value Interpretation for Profitability

Business Applications

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In God we trust, all others must bring data - Edward Deming (1900-1993)

What gets measured, gets managed - Peter Drucker (1909-2005)

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A Short Break

Q&A

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Characteristics of Manufacturing

Dataset and Data Preprocessing

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Manufacturing Evolution

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3.04.0 1. 2.() 3.()

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How to improve a complex

manufacturing system?

Pittsburgh Technology Council (2014). http://www.pghtech.org/media/64942/panoupdated.jpg

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Get to the Bottom of Our

Problems

()

(The Characteristics of Manufacturing)

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Characteristics of Manufacturing

The Nature of Manufacturing Systems

Multiple Resources Management

Cause: diverse product

Effect: complicated management (eg. inventory, human resource, etc.)

Network Systems

Cause: different and multiple processes

Effect: interrelationship between upstream and downstream

(eg. process control, WIP, setup)

Dynamic Adjustment

Cause: demand fluctuation product-mix change

Effect: bottleneck shift, WIP

Dynamic bottleneck shift

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The Nature of Manufacturing Systems

Production Lead-Time (cycle time, CT)

Cause: material procurement and processing time

Effect: time-to-market

Throughput (TH) of Production Line

Cause: Different process or new-and-old machines (i.e., vintage issue)

Effect: bottleneck and machine capability ()

Littles Law: WIP = TH x CT

Cost-based system (for the same product)

Cause: limited resource to satisfy the customer requirement

Effect: driving productivity and cost down

Note: process or design improvement value-based (for new product)

Note: pure MFG doesnt involve the pricing decision

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Manufacturing Field (problem clarification)

Ask Why in shop floor level ()?

()!?

No support (labor/machine/material/management/supplier/inventory)

Manufacturing Dataset (data for analysisbut no data?)

Ask Why?

I dont knowno idea

Long long time ago

No one take over the

None of my business

Please ask somebody/window/dept./supplier/business(competitor!?)

I am the newcomer

Data is not accurate because

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Characteristics Issues

Batch size () Lot ID decomposition, trace back, yield calculation

Parallel machine () Missing value

Golden machine () Utilization, class imbalance Inference bias

Recipe and parts ()

Nominal or categorical variable too many class too many dummy variables

Sampling testing () Missing value

Engineering or R&D lot ()

Outlier, machine contamination, setup capacity loss

Maintenance ()

When? How ( or )? How much? Capacity loss, Reliability

Changeover () Setup time, capacity loss

Bottleneck shift() Different treatment, WIP transfer

Queue time limit() Defects, WIP

Data imbalance () Inference bias Inventory = Lead Time + Uncertainty

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() Data Preparation

Data Merge (trace back for recipe diagnosis)

Lot ID Sub Lot ID Final inspection Pass or Fail

Lot001 Lot001 Pass

Lot002 Lot002_1 Pass

Lot002 Lot002_2 Fail

Lot003 Lot003_1_1 Pass

Lot003 Lot003_1_2 Pass

Lot003 Lot003_2 Pass

Lot ID WS1_A WS1_B WS2_A WS2_B

Lot002

Sub Lot ID WS7_A WS7_B

Lot002_1

Lot002_2

R&D

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() (yield calculation)

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Lot ID Pass or Fail

Lot001 Pass

Lot002 Pass

Lot003 Fail

Lot004 Pass

Lot ID Pass or Fail Lot size

Lot001 Pass 20

Lot002 Pass 25

Lot003 Fail 10

Lot004 Pass 15

75%

85.7%

R&D

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Parallel Machine

Not identical () Tool Matching

Data Preparation

Missing Value

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Lot ID WS_A_ Mach_1_Temp

WS_A_ Mach_2_Temp

Lot001 820 N/A

Lot002 820 N/A

Lot003 N/A 840

Lot004 N/A 840

Lot ID WS_A_ Temp

WS_A_ Mach_Type

Lot001 820 1

Lot002 820 1

Lot003 840 2

Lot004 840 2

WS_A_Mach_1

WS_A_Mach_2

R&D

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Golden Machine

(inference bias)

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WS_A_ Mach_2_Temp

Lot001 820 N/A

Lot002 820 N/A

Lot003 820 N/A

Lot004 830 N/A

Lot005 820 N/A

Lot006 820 N/A

Lot007 825 N/A

Lot008 830 120

Lot009 820 130

Lot010 N/A 120


WS_A_ Mach_2_Temp

Mach_1 Mach_2

Lot001 820 Avg. 1 0

Lot002 820 Avg. 1 0

Lot003 820 Avg. 1 0

Lot004 830 Avg. 1 0

Lot005 820 Avg. 1 0

Lot006 820 Av.g. 1 0

Lot007 825 Avg. 1 0

Lot008 830 120 1 1

Lot009 820 130 1 1

Lot010 Avg. 120 0 1

R&D

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Recipe/ Parts- Nominal () or Categorical () Variable Transfer to dummy variable (, )

Given N levels, the method will generate N-1 dummy variables.

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Lot ID WS_A_ Mach_1_Parts

Lot001 PartsA

Lot002 PartsB

Lot003 PartsA

Lot004 PartsC

Lot005 PartsD

Lot006 PartsE

Lot007 PartsE

Lot008 PartsA

Lot009 PartsC

Lot010 PartsE

Lot ID WS_A_ Mach_1_ PartsA

WS_A_ Mach_1_ PartsB

WS_A_ Mach_1_ PartsC

WS_A_ Mach_1_ PartsD

Lot001 1 0 0 0

Lot002 0 1 0 0

Lot003 1 0 0 0

Lot004 0 0 1 0

Lot005 0 0 0 1

Lot006 0 0 0 0

Lot007 0 0 0 0

Lot008 1 0 0 0

Lot009 0 0 1 0

Lot010 0 0 0 0

R&D

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level (Recipe or Parts) Dummy Variables

Issue: Curse of Dimensionality

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R&D

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R&D

()

NO.001 A 13.723 7.235

NO.002 B NA NA

NO.003 B 13.728 7.237

.

.

.

NO.099 B NA NA

NO.100 A 13.726 7.236

X Y ()

Skills of missing value imputation:

1. // 2. K-Nearest Neighbours

3. Prediction Model http://www.theanalysisfactor.com/seven-ways-to-make-up-data-

common-methods-to-imputing-missing-data/

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Outlier

LotIDLotIDrecipe

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()

R&D

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(10,000)

updown (Overall Equipment Effectiveness, OEE)

queue time

(eg. status variable identification, SVID)

()

80

STA

GE

x_S

VID

x

0 5

10

1

5

2

0

2

5

0 200 400 600 800 1000 Lot or

Time

R&D

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/

/1product

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ID Product

Type

Process

Time (hrs)

Start

Time

Lot1 A 1 2016/12/26 14:50

Lot2 A 1 2016/12/26 15:50

Lot3 B 2 2016/12/26 17:50

Lot4 B 2 2016/12/26 19:50

Lot5 B 2 2016/12/26 21:50

Lot6 C 1 2016/12/27 00:50

Lot7 C 1 2016/12/27 01:50

R&D

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(bottleneck)

WIP

//

//

/

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R&D

120

units/day

140

units/day

80

units/day

120

units/day

100

units/day

80 units/day

()

(2016)

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(Queue Time Limit)

(defect)

Queue Time LimitFOUP

(, furnace)(Form Batch)(recipe)

Qtime(x)(y)Qtime

estimated by the difference between check-out of A and check-in of B

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R&D

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(Queue Time Limit) Astage1

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ID Product

Type Stage

Process

Time (hrs)

Start

Time

Lot1 A 1 1 2016/12/26 14:50

Lot1 A 2 1 2016/12/26 16:00

Lot1 A 3 0.5 2016/12/26 17:20

Lot1 A 4 1 2016/12/26 18:00

Lot1 A 5 0.5 2016/12/26 20:30

1 Alarm!!

R&D

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, i.e., KEY

KeyLot ID

?

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Time SVID 101 SVID102 ..

2/11 00:00:00

2/11 01:00:00

2/11 02:00:00

.

.

.

2/11 23:00:00

Time SVID 1 SVID 2 ..

2/11 00:06:29

2/11 00:10:41

2/11 03:41:09

.

.

.

2/11 23:11:57

Periodic-based record Event-based record

R&D

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Data Merge

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R&D

Dong and Lee (2017)

Event Periodic

1

()

(Event)

Nearest time

Rolling Forward

Rolling Backward

Nearest time

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Date A

1 2016-01-01 A1

2 2016-04-01 A2

3 2016-07-01 A3

4 2016-09-15 A4

Date B

1 2016-02-20 B1

2 2016-05-01 B2

3 2016-06-15 B3

4 2016-07-01 B4

5 2016-12-31 B5

Date A B

1 2016-01-01 A1

2 2016-04-01 A2 B1

3 2016-07-01 A3 B4

4 2016-09-15 A4 B4

A1 A2 A3 A4

B1 B2 B3 B4 B5

A

B

B

B1 B4 B4

- Rolling forward

R&D

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Date A B

1 2016-01-01 A1 B1

2 2016-04-01 A2 B2

3 2016-07-01 A3 B4

4 2016-09-15 A4 B5

A1 A2 A3 A4

B1 B2 B3 B4 B5

A

B

B

B1 B2 B4 B5

- Rolling backward

R&D

Date A

1 2016-01-01 A1

2 2016-04-01 A2

3 2016-07-01 A3

4 2016-09-15 A4

Date B

1 2016-02-20 B1

2 2016-05-01 B2

3 2016-06-15 B3

4 2016-07-01 B4

5 2016-12-31 B5

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Data/Class Imbalance # of qualified product extremely dominates the # of defective product

() /

? For the two classes (0 and 1), rule of thumb

10% vs. 90%? 5% vs 95%? or 1% vs. 99%?

It depends on your industry applications.

From a theoretical viewpoint, it occurs if it skews the model training for

prediction

Overfitting? Class Imbalance?

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R&D

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Lot ID X1 X100 Inspection

Lot01 PASS

Lot02 PASS

Lot03 PASS

Lot04 PASS

Lot05 PASS

Lot06 PASS

Lot07 FAIL

Lot08 PASS

Lot09 PASS

Lot10 PASS

Lot11 PASS

Lot12 PASS

Inspection

1FAIL

PASS

X1~X100

11/12 = 91.7%

R&D

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200100?

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(Scale)

93

(nominal scale)

(categorical

scale)

(ordinal scale)

Bin

(interval scale)

(ratio scale)

(absolute scale)

(2014)

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()

(2014)

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: ?

?

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: ?

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11

1 0.0886 0.0886 0.0886 0.0886

2 0.0684 0.0684 0.0684 0.0684

3 0.3515 0.3515 0.3515 0.3515

4 0.9874 0.9874 0.9874 0.9874

5 0.4713 0.4713 0.4713 0.4713

6 0.6115 0.6115 0.6115 0.6115

7 0.2573 0.2573 0.2573 0.2573

8 0.2914 0.2914 0.2914 0.2914

9 0.1662 0.1662 0.1662 0.1662

10 0.44 0.44 0.44 0.44

11 0.6939 ? 0.3731 0.6622

0.4023 0.3731 0.3731 0.3994

0.2785 0.2753 0.2612 0.2753

0.3208 0.0317

vs.

(2014)

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: ?

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(N/A 0 !!)

(eg. N/A)

: / (Nearest-Neighbor Estimators)

(2014)

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?

Difficulty in Data Collection

Multiple sources

Different data type/format

Redundant dataset

Difficulty in cross -functional/interdepartmental data collection ()

? ???

()

8am-6pm24hrs

363636

defectsdefects

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(problem) (analysis) Shop-floor: defective product, trouble shooting, yield prediction, virtual

metrology, predictive maintenance (PdM), etc.

Management: demand forecast, price prediction, market share, etc.

The complexity of manufacturing significantly depends on product-mix.

? trouble shooting based on the same product-mix

tune

robust forecasting based on the different product-mix

MSExrecipey(!?)( or )

(coefficient of independent variable)

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A Short Break

Q&A

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Cases, Issues and Myths

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Process Diagnosis / Trouble Shooting

()

Yield Prediction

(/)

Feature Selection

()

Process Adjustment

()

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1: Process Diagnosis/ Trouble Shooting ()

AMHS

(cycle time)(sampling)

105

Over 1000 stages!

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Wafer Bin Map () Wafer bin maps that show specific spatial patterns can provide clue to

identify process failures in the semiconductor manufacturing.

In practice, most companies rely on experienced for troubleshooting.

However, as IC feature size is continuously shrinking, WBM patterns

become complicated and thus human judgments become inconsistent

and unreliable.

In the semiconductor fabrication process, the circuit probe (CP) test is

used to detect specific failures of each die and thus indicate the test

results with the corresponding bin values.

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(Liu and Chien, 2013)

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108

(Liu and Chien, 2013)

Root Cause

@NCKU () 109

Map clustering for

systematic defects

12 defect patterns

and build WBM bank

Decision tree for root

cause classification

(Hsu and Chien, 2007)


@NCKU ()

110

@NCKU ()

2: Feature Selection ()

Feature Selection

In a large-scale dataset, select a few important variables (i.e. columns)

In particular, # of variables (p) much larger than # of observations (n),

i.e. p >> n issue

Address Curse of Dimensionality

The number of observations required exponentially grows to estimate the

function or model parameters.

111

A n

p

X n

k k

@NCKU ()


Objective (Guyon and Elisseeff, 2003)

improving the prediction performance of the predictors

providing faster and more cost-effective predictors

providing a better understanding of the underlying process that

generated the data. (eg. for process monitoring)

Types

Variable selection: select the best subset of the existing

variables/features without a transformation.

Supervised Learning () with Y as label

Eg. stepwise regression, LASSO, random forest, etc.

Feature extraction (variable transformation): transforming the existing

variables into a lower dimensional space

Unsupervised Learning () with only X

Eg. independent component analysis (ICA), principal component analysis

(PCA), etc.

112

@NCKU ()


MECE Feature Selection (Taiwan Patent: I564740)

claims that the important variables should not contain overlapping

information and should provide sufficient information.

113

Lee and Chen (2017)

@NCKU ()


114

Prepared

Dataset

Feature

Selection

LASSO SVM Stepwise

Regression

Voting

by Sampling or K-fold Cross-Validation

Pivot

Analysis

Engineering

Experience

Feature

Validation

Boosting Random

Forest PCA Random

Sampling

Data

Reduction

(p>>n)

Imbalance?

Correlation

Coefficient

Independence

Test

Data

Preprocessing

Yes

No

Knowledge

Management (KM)

Documentation

Tsai and Lee (2017)

@NCKU ()


Data Reduction (Tsai and Lee (2017)

(!) (i.e., main effect)

xy

z (x->z->y)zyx

(counter)z

: ()

: x ()

115

@NCKU ()


Voting () When using several selection methods, calculate the selecting

frequency for robust variable selection.

116

SVID Stepwise LASSO Random

Forest Boosting Voting

SVID_003 4

SVID_101 3

SVID_021 3

SVID_040 3

SVID_002 3

SVID_128 2

SVID_062 2

SVID_077 2

@NCKU ()


K-fold Cross Validation

eg. 10-fold cross validation

117

training fold testing fold

1st iter.

2nd iter.

3rd iter.

10th iter.

Error1

Error2

Error3

Error10

Minimize Error=1

10 Error10=1

@NCKU ()


118

Class Imbalance

Random sampling deals with the issue. We focus on undersampling

which samples a subset of the majority class.

The main deficiency is that many majority class examples are ignored.

Thus, we sample several subset from the majority class (resampling).

Example

For Y label, vs. = 1000 : 50

Samples 50 at a time for model training

# of replications: 20 times

Rank the variables by the voting

SVID Voting by

Undersampling

SVID_003 19

SVID_101 18

SVID_021 18

SVID_040 18

SVID_002 17

SVID_128 17

SVID_062 17

SVID_077 17

@NCKU ()


Pivot Analysis ()

119

SVID _Avg _avg Range/Avg.

SVID_003 0.9487 0.2583 1.1439

SVID_101 0.2078 0.5434 0.8934

SVID_021 0.4105 0.8377 0.6845

SVID_040 52.08 71.69 0.3170

SVID_002 2.7256 2.1025 0.2581

SVID_128 8.0523 8.5336 0.0580

SVID_062 0.9430 0.9747 0.0330

SVID_077 1569 1603 0.0216

Mach_ID FAIL TOTAL RATIO

Photo_06 12 384 0.0313

Etch_10 3 295 0.0102

Photo_32 10 1011 0.0099

Photo_17 4 410 0.0098

PVD_02 1 123 0.0081

CVD_14 9 3456 0.0026

Diff_09 1 495 0.0020

Etch_07 5 2769 0.0018

1!

.

@NCKU ()


Engineering Experience Validation

Y

120

BuzzHand (2014)http://www.buzzhand.com/post_118913.html

@NCKU ()


Engineering Experience Validation

30

3~4

: : SVID_051SVID_064 38

: SVID_034SVID_082 11

: SVID_115 1

121

3~4

@NCKU ()


(KM)

122 122

@NCKU ()

()

?

123

@NCKU ()

/

124

@NCKU ()

3: Yield Prediction (/)

(recipe)(Y)eg.

? /

(thickness)(Critical Dimension, CD)(Overlay)(defects)(area)(layer), (yield)

Benefits

(predictive maintenance)

125

@NCKU ()


126

Dataset with

selected variable

Prediction/

Classification

Model

PLS BPNN CNN

K-fold Cross-Validation

R-Squared

MSE

Confusion

Matrix

Model

Evaluation

Boosting Decision

Tree SVR

Random

Sampling

Data

Transformation

Imbalance?

Categorical to

Dummy Var. Z-scores

Feature

Selection

Yes

No

Knowledge

Management (KM)

Documentation

Hung, Lin and Lee (2017)

Data

Discretization

Data Partition (training vs. testing)

@NCKU ()


Testing Dataset with R2, Adjusted R2, Mean Squared Error(MSE), etc.

127

Yie

ld /

Pre

cis

ion /

Metr

olo

gy V

alu

e

Lot

Hung, Lin and Lee (2017)

@NCKU ()


Statistical Control Chart for Classification

128

UCL

LCL

Yie

ld /

Pre

cis

ion /

Metr

olo

gy V

alu

e

Lot

@NCKU ()

- Confusion Matrix

FNFPTNTP

TNTP

Accuracy


1 () 2 ()

1 ()

TP (true positive)

FN (false negative)

(Type II error)

(miss)

2 ()

FP (false positive)

(Type I error)

(false alarm)

TN (true negative)

129

@NCKU ()


(Sensitivity, Recall) 1()

Miss () Rate = 1 Sensitivity

(Specificity) 2()

False Alarm () Rate = 1 Specificity

130

TPSensitivity

TP+FN

TNSpecificity

TN+FP

1 2

1 TP FN

2 FP TN

@NCKU ()


1()

1() (Type I error)

FP Rate = 1 Specificity

TP Rate

= Sensitivity

Model A

Model B

ROC Receiver Operating

Characteristic curve

FP RateTP Rate

TP rateFP rate

TP RateFP RateTrade-off ()

!

131

@NCKU ()


132

Testing

Accuracy TP Rate TN Rate AUC

BPN 71.9% 89.7% 51.7% 70.2%

PLS 78.1% 69.1% 88.3% 78.9%

Model Performance (an example)

BPN

Bad Good

Bad 61 7

Good 29 31

PLS

Bad Good

Bad 47 21

Good 7 53

AUC: Area under the Curve of ROC

@NCKU ()

?

133

@NCKU ()

134

@NCKU ()

4: Process Adjustment ()

Process Adjustment

Manipulate the compensating variables of a process to achieve the

desired process behavior (eg. output close to a target)

135

Time

Y

UCL

LCL

Time

Y

UCL

LCL

@NCKU () 136

Shooting Problem


Step1x?

Step2x?

@NCKU ()


Step1: x? + (Pivot Analysis)

YX1,,X50

137

Time

Lot ID

Yield

@NCKU ()


Pivot Analysis ()

138

@NCKU ()


X1X2?

Simple ratio? X1: 5.67/5.33 = 1.0638 , X2: 460/410=1.1220

Range/Avg? X1: 0.33/5.5 = 0.06, X2: 50/435 = 0.1149

()

!? One-way analysis of variance (ANOVA)

!?

139

Row Labels Average of

X1

Average of

X2

0 5.33 410

1 5.67 460

Grand Total 5.50 435

@NCKU ()

- 123

p

http://datasci.tw/event/engineer_statistics_170114/

140

(Ling-Chieh Kung)

!!

@NCKU ()


Step2: x? Exponentially Weighted Moving Average(EWMA) controller

(Montgomery, 2013)

Assume: a process output gradually goes up and away from the target ()

is widely used to compensate for process shift and noise.

where is a constant (called process gain) like regression coefficient.

+1 is a disturbance and usually autocorrelated.

where = is the prediction error at time t and is weighting factor for EWMA.

This type of process adjustment scheme is called integral control.

141

+1 = + +1

+1 = + 1 = +

= 1 = 1

( +1 ) =

( )

@NCKU ()


An Example of Integral Control (Montgomery, 2013)

Figure shows 100 observations on the number average molecular

weight of a polymer, taken every four hours.

the target value T = 2000

The drifting behavior of the process is likely caused by unknown and

uncontrollable disturbances in the incoming material feedstock and

other inertial forces, but it can be compensated for by making

adjustments to the setpoint of the catalyst feed rate x.

142

@NCKU ()


An Example of Integral Control (Montgomery, 2013)

Now, we build a equation for the adjusted process

We will forecast the disturbances with an EWMA having = 0.2.

143

+1 2000 = 1.2 + +1

= 1 =

+1 =

1

6 2000

@NCKU ()


Temperature less than 588.5

Temperature great than 589.5

144

Lot #

Over time

Lot #

Over time

Thic

kness

Thic

kness

Tsai and Lee (2017)

3.7

2 3

.75

3

.78

3

.81

3

.72

3

.75

3

.78

3

.81

Decrease x

(temperature)

by 1.5

@NCKU ()

Multivariate Control Limits

The information is found in the

correlation pattern - not in the

individual variables!


Chang (2012), IBM

The Need for Multivariate Data Analysis - Correlation

145

@NCKU ()

?

146

@NCKU ()

?

!

()

()

147

@NCKU ()

148

@NCKU ()

149

R&D

(2015)

@NCKU ()

2017/02/11, 2pm3 ()

A3

= () / ()

eg. (56+160+138 mins)/(180+180+180 mins)

=65.6%

150

()

()

()

()14:00-14:11 11 11

14:11-14:16 5

14:16-14:27 11

14:27-14:30 3

14:30-14:43 13

14:43-14:50 7

14:50-14:54 4

14:54-15:00 6

15:00-15:15 15

15:15-15:28 13

15:28-15:33 5

15:33-15:43 10

15:43-15:48 5

15:48-16:01 13

16:01-16:10 9

16:10-16:17 7

16:17-16:22 5

16:22-16:31 9

16:31-16:40 9 9

16:40-16:47 7

16:47-16:52 5

16:52-16:55 3

16:55-17:00 5

43

140

20

88

42

50

(B)

(D)

(C)

13

124

BA C

(A)

(B)

(C)

@NCKU ()

80% 85%

(80% 85%)

200 * 80% = 160 () 200 * 85% = 170 ()

(Dashboard)160Run ()

(eg. )

151

@NCKU ()

=

=/

/

=

=

(Labor Eff)

1(Machine Util)

=0.8

10.656= 2.33 ()

152

@NCKU ()

( / time-motion study)

()

(/)

()

~

( )

153

@NCKU ()

?

?

?

(robust)?

Training datasettesting dataset?

?

?

?

(re-train)?

?

?

?

..

154

@NCKU ()

WIP?

?

throughput?

?

?

?

target?

product-mix?

? ?

Continuous Delivery??

?

?

..

155

?

@NCKU ()

!!

(Dec. 31, 2016) "2016"

156

@NCKU ()

A Short Break

Q&A

157

@NCKU ()


158

@NCKU ()

159

?

?

?

@NCKU ()

160

@NCKU ()

161

http://thewhen.pixnet.net/blog/post/42532195-

%E5%A1%94%E7%BE%85%E5%8D%A0%E5%8D%9C%3A%E6%88%91

%E8%A9%B2%E5%A6%82%E4%BD%95%E5%A2%9E%E5%8A%A0%E8

%B2%A1%E9%81%8B%3F

http://www.zou1.com/news/show-97765.html

@NCKU ()

162

@NCKU ()

163

http://www.ettoday.net/news/20150924/569618.htm

@NCKU () 164

@NCKU ()

.

XD

165

@NCKU ()

()

!

166

@NCKU ()

167

@NCKU ()

(Decision under Certainty)

(Decision under Risk)

(Decision under Strict Uncertainty)

168

@NCKU ()

169

1. () 2. ()

1. () 2. () 3. !! ()

@NCKU ()

170

(Decision under Certainty)

God2GO

(100%)

@NCKU ()


171

0.2

0.8

()

Weather2GO

0.2 +0.8 (!?)

-

1/6, 3.5

3.5 (!?)

@NCKU ()


172

0.2

0.8

()

Weather2GO

$0 $100 ()

$200 ()

$0

0.2x0

+0.8x200

=160

0.2x100

+0.8x0

=20

@NCKU ()


173

$0 $100 ()

$200 ()

$0

0.2x0

+0.8x200

=160

0.2x100

+0.8x0

=20

0.8 ()

100,000 ?

@NCKU ()

()

()

174

@NCKU ()


175

Based on your

Preference

Structure

1

2~

I2GO

@NCKU ()


50

176

42 21 8

BBQ 35 28 13

KTV 28 25 23

? Revised from Chien (2005)

@NCKU ()


(The Maximin Payoff Criterion)

177

Min

Return

42 21 8 8

BBQ 35 28 13 13

KTV 28 25 23 23

@NCKU ()


(The Maximax Payoff Criterion)

178

Max

Return

42 21 8 42

BBQ 35 28 13 35

KTV 28 25 23 28

@NCKU ()


(The Minimax Regret Criterion) (Savage, 1951)

Step 1

Step 2(minimax)

179

Max

Regret

0 7 15 15

7 0 10 10

14 3 0 14

42 21 8

BBQ 35 28 13

KTV 28 25 23

@NCKU ()


(preference structure)

180

42 21 8

BBQ 35 28 13

KTV 28 25 23

?

@NCKU () 181

Capacity Planning Problem (Newsboy problem over time)

Capacity Plan: A, B, C or D (demand)?

Two Types of Risk

Capacity Shortage : loss of sales, loss of market share

Capacity Surplus : machine idleness, inventory, holding cost

Time

Demand

Demand

A

B

C

@NCKU ()

Newsboy problem

0 ()

: ?

182

@NCKU () 183

Capacity Planning Problem (more demand scenarios)

Two demand forecasts: Marketing vs. Sales

Should capacity level follow marketing, or sales, or ?

How about more than two demand scenarios?

Time

Demand

Marketing Forecast

Sales Forecast

0

Capacity Level?

@NCKU ()

Empirical Study (Lee and Chiang, 2016)

Empirical Study- Taiwan TFT-LCD Manufacturer

We introduce a two-phase framework to decide the best capacity level

reducing the losses (i.e., regret) of capacity surplus and capacity

shortage.

1st stage: Demand Forecast () Linear Regression

Autoregression

Neural Network

2nd stage: Capacity Decision () Expected Value (EV, )

Minimax Regret (MMR, )

Stochastic Programming (SP, )

184

@NCKU ()

Empirical Study

2-stage approach

Demand Forecast

Capacity Decision

VDGP

LI: latent information

(Chang et al., 2014)

185

Lee and Chiang (2016)

@NCKU ()

Empirical Study

Capacity Regret Assessment

Analytic Hierarchical Process (, AHP) (Satty, 1980)

186

Capacity Regret

Assessment

CustomerInventory

Market

Share

Profit

Loss

Loss of

Falling Price

Inventory

Loss

Holding

CostReputation

Lower

Capacity

Level

Higher

Capacity

Level

ALTERNATIVE

OBJECTIVE

ATTRIBUTE


@NCKU ()

Empirical Study

A Tradeoff Between Two Risks

187

Capacity Decision

@NCKU ()

Empirical Study

Data Source

Nov. 2013 to Oct. 2014

aggregate demand for a specific technology node

Dataset: training data (9 months) and validation data (3 months)

Objective: determine the capacity level for the future 3 months (Aug.

2014 to Oct. 2014) based on historical demand until July 2014.

188

0

10,000

20,000

30,000

Training

Dataset

Validation

Dataset

Technology

Node A

Time

Demand


@NCKU ()

0

10,000

20,000

30,000

Training

Dataset

Validation

Dataset

Technology

Node A

Time

Demand

Empirical Study

189

1st phase: Demand Forecast

linear regression (OLS), autoregression (AR), neural network (NN)

Criterion: Mean Squared Error (MSE): NN < AR < OLS

Linear Regression

Autoregression

Neural Network

@NCKU ()

Empirical Study

2nd phase: Capacity Decision

AHP gives pair-wise comparisons of the capacity surplus and shortage,

and calculate the eigenvector () as weights of two risks.

31 individuals with TFT-LCD industrial backgrounds

2 for 0-2 yrs, 9 for 2-5 yrs, 13 for 6-9 yrs, 7 for more than 10 yrs of service

one third have undergraduate degrees and the others for graduate degrees

= 0.521 and = 0.479.

190

*Current policy builds up the capacity level based on the maximal forecast demand.

**Avg. Reg.: the weighted sum of squared error (WSSE) over the number of validation periods

Month Actual

Demand

Capacity Decision Current Policy

EV MMR SP

Aug. 2014 19499 20207 16941 17157 18922 Sep. 2014 18110 21305 17921 18306 19135 Oct. 2014 15059 22404 18536 19291 18885

Standard Deviation 2271.3 1098.2 804.7 1068.1 134.8

Average Regret - 1796.0 1032.2 1113.8 874.8

@NCKU ()

Empirical Study

191

0

500

1000

1500

2000

0 300 600 900 1200

StandardDeviation

AverageRegret

Current

Policy

EVMMR

SP

Poor

Better

Better Poor


@NCKU ()

(scenarios)

Butwhat is the next step?

100%

A95%

B90%

AB?

192

@NCKU ()

~

193

Testing

Accuracy TP Rate TN Rate AUC

BPN 71.9% 89.7% 51.7% 70.2%

PLS 78.1% 69.1% 88.3% 78.9%

3: Yield Prediction (/) Classification Confusion Matrixtrade-off

type-I and type II

BPN

Bad Good

Bad 61 7

Good 29 31

PLS

Bad Good

Bad 47 21

Good 7 53

AUC: Area under the Curve of ROC

@NCKU ()

?

194

@NCKU ()

195

170cm

165cm 175cm

172cm

@NCKU ()

.

196

@NCKU ()

(! !!)

()

~ ()

Manufacturing Hand Make ~

()

()

(scenarios)

()

()

197

@NCKU ()

- (tornado diagram) Adjustment range: 10%

198

UCL LCL

increase

decrease

SVID_003

SVID_101

SVID_021

SVID_040

SVID_002

SVID_128

Variable

Y or Metrology

@NCKU ()

(Analytics)?

Hillier and Lieberman

Analytics is the scientific process of transforming data into insight for

making better decisions

Descriptive analytics () Using innovative techniques to locate the relevant data and identify the

interesting patterns in order to better describe and understand what is

going on now.

Predictive analytics () Using the data to predict what will happen in the future.

Prescriptive analytics () Using the data to prescribe what should be done in the future. The

powerful optimization techniques of operations research are what are

used here.

199

@NCKU ()

200

Source: CRISIL GR&A analysis

Operations Research ()

@NCKU ()

vs.

201

Yield

Time

Cost

Yield

New Tape-out (NTO)

/

Device

optimization

during process

integration

Engineering

Parameter

Optimization

Tool Matching

Equipment Health

Monitoring (EHM)

Spec Setting/ SPC

Virtual Metrology

R2R Control/ FDC

PM/PdM

Virtual Material Quality Investigation

Yield ramp

during pilot

production

Process turning

during volume

production

@NCKU ()

?

() ()

202

http://www.supergameasset.com/male-warrior-game-asset.html

https://68.media.tumblr.com/618419c9c129cdd1176855d202334e25/tumblr_o829ynvbH61udl64oo1_400.gif

VS

@NCKU ()

The Wyndor Glass Co. products high-quality glass products,

including windows and glass doors. (Hillier and Lieberman, 2010)

It has three plants. Plant 1 makes aluminum frames and hardware

Plant 2 makes wood frames

Plant 3 produces the glass and assembles the products

Top management has decided to revamp the product line.

Production capacity are released to launch

two new products having large sales potential:

Product 1: An 8-foot glass door with aluminum framing

Product 2: A 46 foot double-hung wood-framed window

203

?

http://img.archiexpo.com/images_ae/photo-g/55456-4334799.jpg

https://www.andersenwindows.com/

@NCKU ()

This problem can be recognized as LP problem of the classic product mix () type.

Linear Programming (, LP)

Formulation as a Linear Programming Problem

x1: number of batches of product 1 produced per week

x2: number of batches of product 2 produced per week

Z: total profit per week from producing these two products

204

?

Production Time Available

Time Plant Product 1 Product 2

1 1 0 4

2 0 2 12

3 3 2 18

Profit ($1,000) $3 $5 0,

1823

122

4 s.t.

53

21

21

2

1

21

xx

xx

x

x

xxZ

@NCKU () 205

Graphical Solution

The resulting region of permissible values of (x1, x2), called the

feasible region.

?

0,

1823

122

4 s.t.

53

21

21

2

1

21

xx

xx

x

x

xxZ

@NCKU () 206

Graphical Solution

Move the objective function with fixed slope through the

feasible region in the direction of improving Z.

?

21 (x1) 62 (x2)

!

@NCKU ()

?

()

KPI ()

solution

207

@NCKU ()

208

DS in

MFG

1. 2. (eg.

) 3. ()

@NCKU ()

(Throughput)?

209

...

@NCKU ()

210

@NCKU ()

Big Data?

Pattern?

211

@NCKU ()

?

212

@NCKU ()

Market Structure and Game Theory (external analysis)

Productivity driving Price reduction (!?)

Lee and Johnson (2015)

Curse of Productivity!?

213

@NCKU ()

The source of complexity in manufacturing is variability (). Automation () is a powerful way to reduce variability.

Big Science facilitates automation and real-time decision.

However

1. (panacea)

2. IT"

3.

4. data ()

5. ~

()().

214

@NCKU ()

KPI Hierarchy

215

Dupont

ROE

Profit

Margin

Asset

Turnover

Financial

Leverage

Profit

Sales

Assets

Cost Accounts Receivable

Cash + Inventory +

Bottleneck

Machine

Other

Assets +

Labor Material Floor

Space

Non-bottle.

Machine

Fin

an

cia

l Ind

ex

Ma

nu

factu

ring In

de

x

Reso

urc

e

Decis

ion

TAT = Turn Around Time

CVP = Customer Volume Performance

CIP = Customer (Line) Item Performance

@NCKU () 216

(USnews2017)

USNews (2017).

http://money.usnews.com/careers/best-

jobs/operations-research-analyst

@NCKU () 217

(2014)

@NCKU ()

~

~

~

218

@NCKU ()

References 2015

2014

Dong, Z.-H. and C.-Y. Lee, 2017. Equipment Health Monitoring of Analytic Heretical Process Index in Semiconductor

Manufacturing. 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2017), 27-

30 June 2017, Modena, Italy.

Guyon, I. and A. Elisseeff, 2003. An Introduction to Variable and Feature Selection. Journal of Machine Learning

Research, 3, 11571182.

Han, J., M. Kamber, and J. Pei, 2011. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann ()2008

Hillier, F. S.and G. J. Lieberman, 2010. Introduction to Operations Research, 9th ed., McGraw-Hill, New York. ()

Hsu, S. and C. Chien, 2007. Hybrid Data Mining Approach for Pattern Extraction from Wafer Bin Map to Improve

Yield in Semiconductor Manufacturing. International Journal of Production Economics, 107, 88103.

Hung, S.-Y., Y. L. Lin, C.-Y. Lee, 2017. Data Mining for Delamination Diagnosis in the Semiconductor Assembly

Process. 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2017), 27-30

June 2017, Modena, Italy.

Lee, C.-Y. and M.-C. Chiang, 2016. Aggregate Demand Forecast with Small Data and Robust Capacity Decision in

TFT-LCD Manufacturing. Computers & Industrial Engineering, 99, 415422.

Lee, C.-Y. and A. L. Johnson, 2013. "Operational Efficiency", book chapter edited in: Badiru, A. B. (Editor),

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Contact Information:

name: (Chia-Yen Lee) phone: 06-2757575 34223 email: [email protected]

web: https://polab.imis.ncku.edu.tw/

mailto:[email protected]

[系列活動] 製造資料科學：從預測性思維到處方性決策

Data & Analytics

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