MODULE4_20221212_rev1.pptx

TRAINING
Programme
INDUSTRY 4.0 SPECIALIST
1
MODULE 4 : Work and Organization Design in the
Age of digital transformation

INDUSTRY 4.0
SPECIALIST
2
MODULE 4:
Work and Organization Design in the Age of digital transformation
OBJECTIVE:
Module 4 teaches participants how work changes with the introduction of Industry 4.0 technologies
and what challenges and opportunities for the employees may result (e.g. changed work content
and new skills needs, digitized work equipment, other forms of cooperation, new forms of
leadership). Participants learn how to use these approaches systematically changes and where it
makes sense to involve employees at an early stage in the process of introducing Industry 4.0. The
participants deal actively with examples from the operational practice and apply the learned basic
knowledge. They open opportunities to prepare for the digital transformation, to recognize risks at
an early stage and to exploit potential for oneself.
CONTENTS:
Modules Themes
4.1 Computer Integrated Manufacturing (CIM) /IOT Systems
4.1 Case studies related to interdependency of human & technology
4.2 Benchmarking Semiconductor CIM/IOT solution
4.3 Organization Challenges

INDUSTRY 4.0
SPECIALIST
3
SUB MODULE 4.1
Computer Integrated Manufacturing (CIM) /IOT Systems
OBJECTIVE:
The participants recognize the interdependency of human-technology organization.
Participants can systematically record, present and classify the change in work as part of
Industry 4.0. They learn about design approaches and can present them.
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
4
SUB MODULE 4.1
OBJECTIVE:
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
1. Leader Engagement
a. Need an appointed secretary
b. Secretary need to have list of participance and make additional 4 columns with
D,A,R,N
2. Data Collection
a. Get all the gear, pencil / excel sheet ready
b. Review the questions
c. Answer the questions within 3 min ? To improve accuracy from thought process vs
immediate identity
d. Summarized the score
3. Data classification
a. Plot bar chart
4. Data summarization
a. Participant will update the score and secretary will note down the scoring
accordingly and shared the information
5. Summarize and recommendation
a. Review the answer
Personality Traits
5

INDUSTRY 4.0
SPECIALIST
1. There will be 2 pages
a. Page 1, is where the participant need to fill scoring with 1 to 5
b. Page 2, is where we need to do bar chart *
2. The most important is on Page # 1.
3. The participant need to fill scoring with 1 to 5. 1 means less matching, 5
means most matching
4. This is not right or wrong answer, instead, fill up rationally which later it
will be most beneficial to all of us. See table preparation
Quick Briefing
6

INDUSTRY 4.0
SPECIALIST
Page 1
7

INDUSTRY 4.0
SPECIALIST
Plot Bar Chart
8
Group 1 Group 2 Group 3 Group 4 Group 5

INDUSTRY 4.0
SPECIALIST
Plot Bar Chart
9

INDUSTRY 4.0
SPECIALIST
Participants Scooring
MODULE 1: UNDERSTANDING INDUSTRY 4.0 10

OPENESS
IMAGINATIVE
CURIOS
LIKES NEW THINGS
DIFFERENT
EASILY BORED
IMPULSIVE
NEUTRAL
CALM
NICE
GOOD LISTENER
EASY GOING
EASILY HURTS BY OTHERS
LACK OF SELF CONFIDENCE
RELATIONAL
FRIENDLY
OPTIMISTIC
FUN
EXCITING
TALKATIVE EASILY JUSTIFY
WITHOUT FACTS
ANALYTICAL
CAREFUL
CAUTIOS
ACCURATE
LIKE TO DO THINGS CORRECTLY
WRONG EASILYT
MAY FIND IT HARD TO EXPRESS
EMOTION
DECISIVE
FAST
DECISIVE
RESULT ORIENTED
CONFIDENT
IMPATIENT
FIND OTHERS SLOW
SCORES REVIEW

Typical Engineering Ranks vs. Function that need our
personality adjustment
Degree
of
Tolerance
&
Self
Discipline

INDUSTRY 4.0
SPECIALIST
A case study to take on with comprehensive good profile.
Lets watch short movie related to our exercise

INDUSTRY 4.0
SPECIALIST
Source: Business Insider Malaysia
Read more at http://www.businessinsider.my/personality-conscientiousness-and-success-
2015-3/#GC1IIX7rOm2LkBmm.97
Success Personality Trait

INDUSTRY 4.0
SPECIALIST
• According to Thomas Davenport in his book, big data @ work (2014), “big data is
important to you and your organization, you would have to do something about it.
You would need to decide which aspects of it make the most sense to apply to your
business and get to work on them. You’d need to hire, rent, or start to develop the
kind of people who make big data work. And you’d need to change your technology
architecture as well…This field is likely to be booming for many years.”
• Differences between conventional analytics and big data:
Big Data is important to you & your organization
Item Conventional analytics Big data
Type of data Data formatted in rows & columns Unstructured formats
Volume of data Tens of terabytes or less 100 terabytes or petabytes
Flow of data Static pool of data Constant flow of data
Analysis methods Hypothesis-based Machine learning
Primary purpose Internal decision support & services Data-based products
•A terabyte (TB) is approximately 10^12 bytes or 1 trillion bytes of data, or 1,000 gigabytes (GB)
•A petabyte (PB) is approximately 10^15 bytes of data, 1,000 terabytes (TB) or 1,000,000 gigabytes (GB)

INDUSTRY 4.0
SPECIALIST
• Below are the terminology evolves for the activity involves in using and analyzing
the data with some new elements in each generation:
Time frame Term Specific Meaning
1970~1985 Decision support Use of data analysis to support decision making
1980~1990 Executive support Focus on data analysis for decisions by senior executives
1990~2000 Online analytical processing (OLAP) Software for analyzing multidimensional data tables
1989~2005 Business intelligence Tools to support data-driven decisions, with emphasis on reporting
2005~2010 Analytics Focus on statistical and mathematical analysis for decisions
2010~present Big data Focus on very large, unstructured, fast-moving data
Thomas Davenport, big data @ work, 2014

INDUSTRY 4.0
SPECIALIST
• The book “big data @ work” also describes that “analysts estimate that 50 billion
sensors will be connected to the internet by 2025 (“the Internet of Things”). The
bulk of data from sensors come from “the industrial internet”—a very large
number of networked devices in plants, transportation networks, energy grids, and
so forth. GE estimates that gas blade monitoring in energy-producing turbines
alone can produce 588 gigabytes of data a day.”
• The book also states that, “it’s the people who really make big data work. The role
of data scientist, is the primary gating factor in whether big data succeeds within
an organization. The data is often free or cheap, the hardware and software are
free or inexpensive, but the people are expensive and difficult to hire.”

INDUSTRY 4.0
SPECIALIST
• At current time of equipment, there are 5 products now arriving according to the
table below. As the equipment operation/operator which product that need to
select first and why ?
CASE #1 (1 of 8)

INDUSTRY 4.0
SPECIALIST
• At current time of equipment, there are 5 products now arriving according to the
table below. As the equipment operation/operator which product that need to
select first and why ?
CASE #1 (2 of 8)
Lets do exercise with boundary of FIFO, SPT, EDD and CR
•FCFS ( First Come First Serve)
•SPT ( Shortest Processing Time)
•EDD (Earliest Due Date)
•CR* (Critical Ratio) *

INDUSTRY 4.0
SPECIALIST
CASE # 1 FIFO : (3 of 8)
Together lets calculate this:
1) FCFS (First-come, first-served), Jobs are processed in the sequence in which
they entered the shop
Sequence Arrival Product Processing Time Completion Time Due Date Delay
1 A 11 61
2 B 29 45
3 C 31 31
4 D 1 33
5 E 2 32
Avrage Completion
Time =
Average Delay =
Number of Prod
Delay =

INDUSTRY 4.0
SPECIALIST
CASE # 1 SPT : (4 of 8)
2) SPT (Shortest Processing Time), Jobs are sequenced in increasing order
of their processing times. The job with the shortest processing time is
first, the job with the next shorter process time is second and so on.
1 A 11 61
2 B 29 45
3 C 31 31
4 D 1 33
5 E 2 32
Avrage Completion
Time =
Average Delay =
Number of Prod
Delay =

INDUSTRY 4.0
SPECIALIST
CASE # 1 EDD : (5 of 8)
3) EDD (Earliest due date), Jobs are sequenced in increasing order of their due
dates. The job with the earliest due date is first, the job with the next
earlier due date is second and so on.
1 A 11 61
2 B 29 45
3 C 31 31
4 D 1 33
5 E 2 32
Avrage Completion
Time =
Average Delay =
Number of Prod
Delay =

INDUSTRY 4.0
SPECIALIST
4) CR (Critical ratio), Critical ratio scheduling requires forming the ratio of the
processing time of a job divided by the remaining cycle time until due date,
and scheduling job the job with the largest ratio next.
Note :
CR< 1 - Behind schedule
CR=1 - On time
CR>1 - Ahead of schedule
Thus, smaller CR is of higher priority
CASE # 1 CR : (6 of 8)
1 A 11 61
2 B 29 45
3 C 31 31
4 D 1 33
5 E 2 32
Avrage Completion
Time =
Average Delay =
Number of Prod
Delay =

INDUSTRY 4.0
SPECIALIST
In this exercise, SPT has better cycle time (Avg Complete Cycle Time) with lowest
late lot delivery
CASE # 1 OVERALL SUMMARY : (7 of 8)

INDUSTRY 4.0
SPECIALIST
Critical Ratio :
• To meet customer delivery date
Starvation Avoidance :
• To ensure bottleneck tools always have WIP to run
DISPATCHING RULE WITH 2 OBJECTIVES: CRITICAL
RATIO WITH STARVATION AVOIDANCE

INDUSTRY 4.0
SPECIALIST
Critical Ratio = (Due Date – Current Time) / Remaining Plan Cycle Time
Starvation Avoidance = Time Required at the bottleneck / Lot Plan cycle time to bottleneck
Time Required at Bottleneck :
(( (Time_to_BN x WIP)) - (Time_to_BN x WIP))
+ (Bottleneck TCT x Bottleneck WIP))
- Buffer
It Means………..
(Total Work Time for WIP to BN)
+ (Work Time at BN )
– WIP Buffer Time
FORMULATION CALCULATION

INDUSTRY 4.0
SPECIALIST
Constraint Requirements
Customer Requirements
Lower values represent higher
need for each category.
Ranking Values can pull lots to
different end of the dispatch list.
Lot ID SA
SADXXXXX.7 -10
SADXXXXX.8 -10
SADXXXXX.27 -2
SADXXXXX.2 -0.5
SADXXXXX.11 -0.5
SADXXXXX.15 -0.5
SADXXXXX.18 -0.5
SADXXXXX.9 -0.01
SADXXXXX.3 0
SADXXXXX.12 0
SADXXXXX.14 0
SADXXXXX.16 0
SADXXXXX.19 0
SADXXXXX.4 0.1
SADXXXXX.13 0.1
SADXXXXX.17 0.1
SADXXXXX.20 0.1
SADXXXXX.10 0.5
SADXXXXX.5 8
SADXXXXX.21 8
SADXXXXX.23 8
SADXXXXX.25 8
SADXXXXX.6 10
SADXXXXX.22 10
SADXXXXX.24 10
SADXXXXX.26 10
SADXXXXX.1 15
Lot ID CR
SADXXXXX.1 -60
SADXXXXX.2 -26
SADXXXXX.3 -26
SADXXXXX.4 -26
SADXXXXX.5 -26
SADXXXXX.6 -26
SADXXXXX.9 -4
SADXXXXX.10 -3
SADXXXXX.11 -1
SADXXXXX.12 -1
SADXXXXX.13 -1
SADXXXXX.21 -1
SADXXXXX.22 -1
SADXXXXX.7 -0.01
SADXXXXX.14 0
SADXXXXX.15 1.2
SADXXXXX.16 1.2
SADXXXXX.17 1.2
SADXXXXX.23 1.2
SADXXXXX.24 1.2
SADXXXXX.8 5
SADXXXXX.18 5
SADXXXXX.19 5
SADXXXXX.20 5
SADXXXXX.25 5
SADXXXXX.26 5
SADXXXXX.27 45
COMPARING THE NEED OF BOTH WORLD

INDUSTRY 4.0
SPECIALIST
• Initial Setting :
(0.5 x CR) + (0.5 x SA)
A negative number in the CR field
means that the lot is late.
A negative number in the SA field
means that the bottleneck that the
lot feeds is hungry.
Lot ID SA CR Final Ranking
SADXXXXX.1 15 + -60 = -45
SADXXXXX.2 -0.5 + -26 = -26.5
SADXXXXX.3 0 + -26 = -26
SADXXXXX.4 0.1 + -26 = -25.9
SADXXXXX.5 8 + -26 = -18
SADXXXXX.6 10 + -26 = -16
SADXXXXX.7 -10 + -0.01 = -10.01
SADXXXXX.8 -10 + 5 = -5
SADXXXXX.9 -0.01 + -4 = -4.01
SADXXXXX.10 0.5 + -3 = -2.5
SADXXXXX.11 -0.5 + -1 = -1.5
SADXXXXX.12 0 + -1 = -1
SADXXXXX.13 0.1 + -1 = -0.9
SADXXXXX.14 0 + 0 = 0
SADXXXXX.15 -0.5 + 1.2 = 0.7
SADXXXXX.16 0 + 1.2 = 1.2
SADXXXXX.17 0.1 + 1.2 = 1.3
SADXXXXX.18 -0.5 + 5 = 4.5
SADXXXXX.19 0 + 5 = 5
SADXXXXX.20 0.1 + 5 = 5.1
SADXXXXX.21 8 + -1 = 7
SADXXXXX.22 10 + -1 = 9
SADXXXXX.23 8 + 1.2 = 9.2
SADXXXXX.24 10 + 1.2 = 11.2
SADXXXXX.25 8 + 5 = 13
SADXXXXX.26 10 + 5 = 15
SADXXXXX.27 -2 + 45 = 43
FINAL RANKING

INDUSTRY 4.0
SPECIALIST
Disadvantage:
• Time Consuming
• Possibility of missing important lots
• Might filter on wrong steps when looking at incoming WIP
Decide necessity
of altering PM
Schedule
Estimate when
lots will arrive
Calculate/consider
Q-Time after
process
Plan/Decide
batching of lots
If multiple lots
have same
priority, use FIFO
Check availability
of Test Wafers
Decide final
ranking of lots
Dispatch LOT
Is this Right ?
TRADITIONAL LOT PLANNING

INDUSTRY 4.0
SPECIALIST
The best to start all project is to understand overall big big big
picture and connecting them to the project that we are planning
to engage.
The word to remember as for today is “Characterization”
1st Step to Finding Big picture and
connecting the Dot…

INDUSTRY 4.0
SPECIALIST
• The law between utilization and cycle time is exponential when the utilization is above
85%. requires effective optimization tools like dispatching and scheduling
Improve the cycle time curve
to allow additional loading at
respective cycle time
Scheduling
FAB Wide
Equipment
Avail /Flex
Throughput
Flows/Yield
*
Cycle time versus utilisation presented by
TSMC researchers (Source: Wang, 1997)
Equipment performance curve.
(Source: Martin, 2000).
Cycle Time vs Throughput (Source: Delp et al., 2006) and 2011.
Chien-FC et al. 2012
Transportation
Delivery (AMHS)
FACTORY CHARACTERISTIC

INDUSTRY 4.0
SPECIALIST
REAL NEEDS
WHAT DO WE LEARNT
• Method use to find better solution
• Many more solution available and can we make the process faster, How ?

INDUSTRY 4.0
SPECIALIST
Whenever, we are asked to validate an improvement or we need to check improvement results of
our own, traditionally we use average as key analysis. Many of those then added with subjective
justification.
Instead, we were taught in academic, poly, colleges and university that based on study started as
early as in 1908, without proper validation, the validation that we do, will not concluded the
results as expected.
We regularly see re-occurrence of problem raises after the implementation and in accuracy in our
justification.
In our case, all analysis that we do MUST have statistical validate process.
Example for validation ;
• Please validate new WPH for fo Machine A new recipes if its has achieved more than 24.5
WPH
• Lets change new scheduling rule at Machine B so we can get moves more than 6K per day.
Use current method vs. statistical approach, do we get the same results? Let see next slide
Background of statistical analysis

INDUSTRY 4.0
SPECIALIST
Background: Student-t history
The t statistic approach will be mostly used in our analysis and
data. A brief of t statistic is, it was introduced by William Sealy
Gosset in 1908, a chemist that is working with the Guinness
brewery in Dublin, Ireland. Student was his pen name. The
student’s t-est work was submitted to and accepted in the journal
Biometrika and published in 1908
(Wikipedia, extracted on 25th July 2017)

INDUSTRY 4.0
SPECIALIST
Background: Martin Wilk

INDUSTRY 4.0
SPECIALIST
Background: Samuel Sanford Shapiro

INDUSTRY 4.0
SPECIALIST
•
Hypothesis Testing Confidence Intervals
Hypothesis testing relates to a
single conclusion of statistical
significance vs. no statistical
significance.
Confidence intervals provide a
range of plausible values for your
population.
Use hypothesis testing when you
want to do a strict comparison
with a pre-specified hypothesis
and significance level.
Use confidence intervals to
describe the magnitude of an
effect (e.g., mean difference,
odds ratio, etc.) or when you
want to describe a single sample.
STATISTICAL DATA VALIDATION

INDUSTRY 4.0
SPECIALIST
HYPOTHESIS EXPRESSION
CONFIDENTIAL
Symbol Define Equation Explanation
H0 H null =, ≥, ≤ Null = 0, means zero change or
current state or as it is
HA H Alternate ≠, <, > New proposal or New improvement
claim. The burden of proof shall fall
into this category

INDUSTRY 4.0
SPECIALIST
One sided test (≥, ≤, <, >)
• A test is concerned to find greater than or less than but not both
• This is a test for at sigma σ at 0.05, at one tail
Two sided test (=, ≠)
• A test that is for inequality
• This is a test for sigma for two tail, at σ at 0.025
One sided vs. two sided Tests
CONFIDENTIAL

Click to edit Master title style
• Type 1 Error: Rejecting the null hypothesis when it is true. Probability of
this error equal α
• Type II Error:
• Accepting the null hypothesis when it is at false. Probability of this
error equal β
• Or famously quoted as, Type II error also known as rejecting the
alternative hypothesis when it is true
CONFIDENTIAL

Click to edit Master title style
CONFIDENTIAL
H0: µ = µ0 HA: µ ≠ µ0
(1- σ)
Correct Decision
β
Type II Error
Power at 10%, 20%
σ
Type 1 Error
(1- β)
power
Correct Decision

INDUSTRY 4.0
SPECIALIST
Based on given data below, what is the conclusion to validate new WPH for
CMP new recipes if its better than 24.5 WPH ? What is our proposal then. Lets
discuss
CASE STUDY 2: VALIDATING IMPROVEMENT

INDUSTRY 4.0
SPECIALIST
• Based on the data alone, data shows insufficient evidence to reject H0 (wph =24.5).
Based on the true means results may lies between
• CI interval at 99% 24.01 to 26.6
• CI interval at 95% 24.3 to 26.3
• Further investigation required further to validate the data and understand the caused
of outliers, which later may fit into CI interval regions beyond 24.5 wph, therefore we
can accept the proposal.
CASE STUDY 2: VALIDATING IMPROVEMENT

INDUSTRY 4.0
SPECIALIST
• Big amount of data
• Data Analysis Skills
CASE STUDY 2: LESSON LEARNT

INDUSTRY 4.0
SPECIALIST
• Tool moves /output is very sensitive towards the load port exchange time. The loadport
exchange time is the time where the product replacement needed to be done. Please
study how the loadport exchange time impact the output based on the below data.
CONFIDENTIAL
CASE STUDY 3: IDENTIFY THE CONSTRAINT POINT

INDUSTRY 4.0
SPECIALIST
CASE STUDY 3: MOVE IN CUBS HAS NO DIFF FOR
LOAD PORT EXCHANGE TIME AT EITHER 1,2 AND 3.

INDUSTRY 4.0
SPECIALIST
Problem Statement:
• Unaware on capacity loss due to wafer per hour (WPH) drop below target
• IE need to do the same thing (WPH calculation and tool to tool WPH variation study)
repeatedly
Improvement:
Created an automated WPH monitoring report to ease WPH monitoring.
• Email sent weekly to CMP and manufacturing. Outcome as follows:
• module get updated on their tools’ throughput performance and can use the
report to measure their performance
• Always aware on WPH deviation and can quickly rectified the issue
• Always aware on opportunity for capacity expansion by benchmarking best tool
WPH
CASE STUDY 4: MONITORING BOTTLENECK AREA

INDUSTRY 4.0
SPECIALIST
CASE STUDY 4: MONITORING BOTTLENECK AREA

INDUSTRY 4.0
SPECIALIST
CASE STUDY #4: CMP WPH MONITORING
-RESULT-
• Able to detect and rectified multiple WPH deviation events

INDUSTRY 4.0
SPECIALIST
• WIP Arrival Forecast gives guidelines to Manufacturing and Equipment engineers
to select suitable window to perform tool maintenance
• Data updated daily and the GUI application for each Modules can be
downloaded from website
CASE STUDY 5: WIP ARRIVAL FORECAST

INDUSTRY 4.0
SPECIALIST
• The chart below describes next 7-day Forecast of potential area with
Queuing WIP
CASE STUDY 5: 7-DAY FORECAST QUEUING AREA

INDUSTRY 4.0
SPECIALIST
• 30-day Stage Move accuracy > 95 % and qualified for actual manufacturing
operation analysis
CASE STUDY 5: MODEL VALIDATION

INDUSTRY 4.0
SPECIALIST
• Downtime of critical machinery can cause loss of revenue, especially for a
giant company like GE
• GE installed sensors in machinery in every sector to reduce downtime and
losses
• Each gas power station turbine can generate data around 500 GB per day
• GE analysed the data to know how the machinery operates and to monitor
the effect of making minor changes, such as operating temperatures or fuel
levels, on performance
• Each of the 22,000 wind turbines continues to stream operating data to the
cloud, where GE analysts can change the direction and pitch of the blades
to ensure as much energy as possible is being captured
• GE uses Intelligent learning algorithms so that an individual turbine can
adapt its behaviour to mimic nearby more efficient turbines
CASE STUDY 6:
GE: Big Data and the industrial internet
Bernard Marr, Big Data in Practice: How 45 Successful Companies Used
Big Data Analytics to Deliver Extraordinary Results, 2016

INDUSTRY 4.0
SPECIALIST
• Since the first Volvo car with internet connectivity in 1998, it has been working to
develop its data strategy, initially working to combine warranty claims data with
telemetry to predict when parts will fail or when a vehicle will need service
• Volvo's Early Warning System analyzes over one million events each week to see
how they relate to failure rates and breakdown
• Today, Volvo cars are equipped with sensors to detect driving conditions and
monitor the performance of vehicles in dangerous situations, such as on icy roads.
Data is uploaded to the Volvo Cloud and shared with the Swedish highway
authorities.
• The third focus of Volvo's analytical strategy is to improve driver and passenger
comfort. This involves monitoring application usage and comfort features to see
what customers find useful, and what is less used or ignored. These include
entertainment features such as built-in connectivity with streaming media services,
as well as practical tools such as GPS, traffic incident reporting, parking locations and
weather information.
• Upcoming hot topics in the car world is autonomous vehicles and Volvo sees safety
as a key factor & Volvo is developing its own in-house AI algorithm
CASE STUDY 7: Volvo: Machine learning-enabled
analytics on a large scale
Bernard Marr, Big Data in Practice: How 45 Successful Companies Used
Big Data Analytics to Deliver Extraordinary Results, 2016

INDUSTRY 4.0
SPECIALIST
• Recap our learning
• Short Quiz
OVERALL RECAP

INDUSTRY 4.0
SPECIALIST
56
SUB MODULE 4.1
OBJECTIVE:
Modules Themes
4.3 Systems Proposal
CONTENTS:

INDUSTRY 4.0
SPECIALIST
Information Handling Automation
Integrated Yield Systems (IYS)
Manufacturing Execution System (MES)
SPC
WIP Mgt
(include
NPW)
Carrier Mgt
(FOUP)
User Mgt
Equipment
Mgt
Flow Mgt FDC &
eDiagnostics
CV Vehicles
Wafer Sleuth
System
Yield /
Defect Mgt
System
APC
System
Business Rules
MES Database
Equipment Integration
Manager (EIM) & Data
Collection
Alarm & OCAP Management
Real Time Dispatcher
Automation/ Workflow
Manager
Recipe Management System
Real-time databases Off-line databases
Scheduling
Data
Analysis
System
Reticle
Management
System
Preventative
Maintenance
System
Mfg
Reporting
System
Special
Processing
Yield
Reporting
System
Material Handling Automation
Material Control
System (MCS)
Inter-bay
AMHS
OHT
Support Eqpt
Load
Port
Load
Port
Setup Metrology
Eqpt
Load
Port
Load
Port
In-line Metrology
& in-line E-test
Eqpt
Load
Port
Load
Port
Process Eqpt
Load
Port
Load
Port
Integrated
Stocker-Sorter
SMS
Load
Port
Load
Port
Reticle Stocker
(Pod & Bare)
Intra-bay AMHS
Controller
Inter-bay AMHS
Controller
Stocker
Controller
Stocker Intra-bay AMHS
OHT
AMAT Meeting 2007
ADVANCED MANUFACTURING ARCHITECTURE

INDUSTRY 4.0
SPECIALIST
Real time
Customer Report
WIRELESS
Quality System
Oracle
ERP System
Fab 1
MES
Manufacturing Execution
System
CMMS
Computerized
Management
Maintenance System
Electronic
Data Collection
Recipe
Management
System
AMHS
Automated Material
Handling
AutoSched AP
Plant Planner
Fault Detection and
Classification
Yield
Management
System
Advance
Productivity Family
TOTAL FACTORY AUTOMATION SYSTEM

INDUSTRY 4.0
SPECIALIST FAB VIDEO : WINDBOND

INDUSTRY 4.0
SPECIALIST
MES
Manufacturing Execution Systems deliver information that enables the optimization
of production activities from order launch to finished goods.
MANUFACTURING EXECUTION SYSTEM

INDUSTRY 4.0
SPECIALIST
Computerized maintenance management system (CMMS) is a software package that
maintains a computer database of information about an organization's
maintenance operations
CMMS
COMPUTERIZED MAINTENANCE MANAGEMENT
SYSTEM

INDUSTRY 4.0
SPECIALIST
Fault detection and classification (FDC) transforms sensor data into summary statistics
and models that can be analyzed against user defined limits to identify process excursions.
FDC
FAULT DETECTION AND CLASSIFICATION

INDUSTRY 4.0
SPECIALIST
dataPower is a powerful, comprehensive yield data analysis tool that enables IC
producers to rapidly identify losses due to problems at design, fabrication and test -
so they can take informed actions to improve yield
DataPower
Klarity Defect is a fully integrated defect data management system that
automatically collects defect data and images in real time from multiple sources
throughout a fab—including wafer inspection systems, review stations—then integrates
this information for advanced excursion monitoring, yield correlation, and reporting.
Klarity
Wafer Sleuth is use to perform analysis
by findings correlations between
observational and test result to wafer
process order, allowing quick
identification of the process step and
equipment causing a variation
Wafersleuth
YIELD MANAGEMENT SYSTEM

INDUSTRY 4.0
SPECIALIST
Quality Assurance System Suite
QA
QUALITY SYSTEM

INDUSTRY 4.0
SPECIALIST
Myfab MyFab provides secure online access to transactions, reports and other useful
information to customers
REAL-TIME CUSTOMER REPORT

INDUSTRY 4.0
SPECIALIST
APF Real Time Dispatcher (RTD) and Reporter are the only real-time, high-performance
dispatching and reporting solutions that help manufacturers identify and implement process
improvements without complex application programming. RTD directs pre-staging, releases
lots and adjusts load balancing of production equipment through “what next, where next and
when next” rules to improve the use of product, carriers, equipment and labor.
APF RTD
ADVANCED PRODUCTIVITY FAMILY

INDUSTRY 4.0
SPECIALIST
APF Activity Manager is a visual development environment designed to deploy decision, execution
and exception logic within a fully integrated workflow automation framework. Activity Manager
provides fab managers with the ability to manage and control resources, equipment, software
applications and personnel to improve utilization and increase productivity. It frees up unrealized
manufacturing capacity through improved “pull” scheduling (customer demand), shorter production
lead-times and reduced work-in-process inventory
AMA
ADVANCED PRODUCTIVITY FAMILY

INDUSTRY 4.0
SPECIALIST
Previous:
Operator: monitor
Oper. will regularly looks at
the dispatch list and comply
to the suggestion
Dispatching
Systems
Systems integrate with
Product, routes and equip info
then suggest oper. to execute
task in the systems
Oper. Perform
route adjustment
Oper. to execute task in the
systems, with buddy check to
confirm action validity
Load
Operator Carry the product
to equipment
Current:
AMA Virtual Oper.
monitor
Trigger Event Based conf.
for specifics parameter
Dispatching
Systems
Integrate the dispatching
Systems with virtual oper to get
Info of where to run a product
AMA Virtual Oper.
Perform route
adjustment
Virtual oper. to execute task in
the systems with fool proof
- accurate sampling
- WIP processing opportunity
- Quality verification
Load
Operator Carry the product
to equipment
AMA APPLICATION

INDUSTRY 4.0
SPECIALIST
69
SUB MODULE 4.1
OBJECTIVE:
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
Organization Challenges
A. Talented people in
1. Data analytical cum developer
2. Systems integration
3. Engineer with programmer/systems background
B. Users Transformation
1. Trusting the information
2. New Job and skill upgrading
3. Managing Information & Intervention
KEY ORGANIZATION CHALLENGES WHEN WORKING
WITH CIM/IOT SYSTEMS

INDUSTRY 4.0
SPECIALIST
Data Analytical
a. Recognizing problem statement
b. Setting up the scope and the amount of information required
c. Able to convert the problem into mathematical equation
d. Practice DMAIC and Agile management
e. Perform Statistical analysis
f. Perform correlation and optimize solution
g. Integrate the solution in the systems
TALENTED PEOPLE : DATA ANALYTICAL CUM
DEVELOPER

INDUSTRY 4.0
SPECIALIST
System Integrator
a. Equipment with application to provide information
b. Various Application / systems
c. Data Indexing
d. Provide alternative solution for old hardware
TALENTED PEOPLE : SYSTEM INTEGRATOR

INDUSTRY 4.0
SPECIALIST
Engineer with programmer/systems background
a. Troubleshooting capability with systematic approach
b. Implemented the solution in the systems
c. Limit the amount of information required
d. Practice DMAIC and Agile management
e. Perform Statistical analysis
f. Perform correlation and optimize solution
g. Integrate the solution in the systems
TALENTED PEOPLE : ENGINEER WITH SYSTEMS/
PROGRAMMER BACKGROUND

INDUSTRY 4.0
SPECIALIST
Trusting the information
a. Accuracy of information vs. manual validation
b. People perception
c. Frequent data miss match and how to move on into stages
d. Losing control and empowered systems to perform
e. Data Center management
f. Security protection
USER TRANSFORMATION: TRUSTING THE
INFORMATION

INDUSTRY 4.0
SPECIALIST
New Job and Skill Upgrading
a. Repetition Job replace with automation and systems
b. Job upgrade to supervisor. New job include supervise the systems and
feedback for loophole
c. Able attention to detail for continues improvement
d. Train in statistical analysis
e. Train in basic systems capability and integration
USER TRANSFORMATION: NEW JOB AND SKILL
UPGRADING

INDUSTRY 4.0
SPECIALIST
Managing Information & Intervention
a. Understand the information capability and know how to intervene
b. Ensure data integrity and lagging scope
c. Data relationship between systems and its type of historical & temporal profile
d. Able to use right data for right programming / development scope
e. Allow manual or “security “ or health check intervention during OCAP
USER TRANSFORMATION: MANAGING
INFORMATION & INTERVENTION

INDUSTRY 4.0
SPECIALIST
SYSTEMS FMEA CASE STUDY
77
RESTRICTED VIEW
NOT FOR
PRINTING

INDUSTRY 4.0
SPECIALIST
SOLUTION FMEA CASE STUDY
78
RESTRICTED VIEW
NOT FOR
PRINTING

INDUSTRY 4.0
SPECIALIST
Believe it or not,
“Your machinery could actually be twice as large as
you think”
In most factories every single machine
operates alongside an identical hidden
machine
The art is to make this hidden machine
visible and to use it
Arno Koch
Author: OEE For The Production Team
2011
OEE CASE STUDY:
The Mystery of the Hidden Machine

INDUSTRY 4.0
SPECIALIST
• OEE was first described –as a central component of the TPM
methodology- in Seiichi Nakajima’s book ‘TPM tenkai’ (1982,
JIPM Tokyo).
• Nakajima, the “father of TPM” who brought his passionate vision
and methods, passed away on April 11, 2015. He was 96 years old.
• Pioneering founder of the Total Productive Maintenance system.
Nakajima was honored by the Emperor of Japan with the Ranju
Ho-sho, or Medal with Blue Ribbon. The award recognizes
individuals with significant lifetime achievements, and was given
to Nakajima by the Emperor "to show gratitude for the
dedication to improving the manufacturing industry through
TPM.“
• Started in Toyota and Fan out to almost all in mfg company in
Japan
• Commercialize the OEE term from Fuji Film in US, a Japanese
company operating outside japan. Assignment to Steven Blom &
Arno Koch
OEE CASE STUDY:
OEE Origin

INDUSTRY 4.0
SPECIALIST
• Most industry in Japan
• US and Europe Manufacturing companies
• Semiconductor manufacturing company that comply to SEMI Standard worldwide
• All Automotive industries in Japan, Europe, US and Malaysia (Improvement of OEE
through Implementation of TPM in Mfg Industries, UMP 2015)
CONFIDENTIAL
OEE CASE STUDY:
Literature Review – OEE Implementation

INDUSTRY 4.0
SPECIALIST
CONFIDENTIAL
OEE CASE STUDY:
Literature Review - General
( Talinn Univ of Tech, Depart of Mechanical and Industrial Engineering Tallinn, Estonia,Int. Conference on
Innovation Technologies, Prague 2016)

INDUSTRY 4.0
SPECIALIST
• Salt Company (Emisal) in Egypt produces anhydrous Sodium Sulphate and Sodium Chloride
refined salt), Magnesium sulphate Heptahydrate (Epsom salt), Sodium chloride Pure.
• The big six losses in any industry (quality, availability and speed) are also presented. The data
were collected through reviewing the technical documents available in Emisal Company.
• As a result, the Company achieved about 93% in average quality rate of overall equipment
effectiveness equation and about 87% in availability in October 2012 where in average
performance efficiency in October 2012 it achieved about 87.5 %.
• (Source: Islam H. Aftefy IE Dept, Al Fayoum Univ Egypt, International Journal of Mechanical &
Mechatronics Engineering IJMME-IJENS Vol:13 No:01)
OEE CASE STUDY:
Literature Review – Other Industry

INDUSTRY 4.0
SPECIALIST
CONFIDENTIAL
Worldwide studies indicate that
the average OEE rate in
manufacturing plants is 60%.
World Class OEE is considered to
be 85% or better.
Source: http://www.oeetoolkit.com
OEE CASE STUDY

INDUSTRY 4.0
SPECIALIST
CONFIDENTIAL
OEE CASE STUDY:
OEE Evaluation Mechanism

INDUSTRY 4.0
SPECIALIST
CONFIDENTIAL
NonScheduled Time*
UnScheduled Downtime
Scheduled Downtime
Engineering Time
Standby Time
Productive Time
Operations
Time
Total
Time
Equipment
Downtime
Manufacturing
Time
Equipment
Uptime
OEE CASE STUDY:
Semi E10 and Equipment Status

INDUSTRY 4.0
SPECIALIST
CONFIDENTIAL
Category X-Site Status
Total
Time
1. Non-scheduled
Time
EQENG, EQMOD, SHUTDOWN
Operation
Time
Down Time
2. Unscheduled
Downtime
ENHOLD, ENQUAL, ENWAIT,
ENWIP, FACWIP, MMWAITE,
MMWAITU, UMDOWN,
UMQHOLD, UMPART, UMQUAL,
UMWAIT, UMWIP
3. Scheduled
Downtime
MMQUAL, MMDELQ, MMWAITP,
SETUP, PMWIP, PMDELQ,
PMHOLD, PMQUAL, PMWAIT
Equipment
Uptime
Engineering Time 4. Engineering Time ENDEV
Manufacturing
Time
5. Standby Time
IDLE, UMAUTO, UMBLOCK,
WAIVI
6. Productive Time RUN, MMWIP, WAIVA
OEE CASE STUDY:
Semi E10 and Equipment Status

INDUSTRY 4.0
SPECIALIST
Total Time 1 week = 7 days = 168 h
Non Scheduled Time Operations Time
Load
lock
A
B
* holidays
* Installation/rebuild/
shutdowns
* training
Down Time Up Time
Unscheduled
Down Time
* run out
op.material
* out of specs
* Repair
Scheduled Down
Time
* maintenance
* maintenance delay
* Material refill
* Setups
0 h
10h
4h
2h
12h
8h
Engineering
Time
* process tests
* software tests
* experiments
Manufacturing Time
)
Productive Time
* regular production
* rework
* engineering runs
Standby Time
*no operator
*no product
*no support
5h
24,6h
20,4h 427 w
68,7h 1033 w
A
B
3h 47 w
A
5,6h 65+3w
B
4,7h 97+2w
A
0h 0w
B
 of all wafers = 1´674
The theoretical process times are due to the equipment supplier: A: 2.5min/wafer und B: 3.3 min/wafer
68 wafer processed,
but 3 are scrap
99 wafer processed,
but 2 are scrap
cluster tool
h: hours
w: wafers
In a cluster-tool for metal deposition two processes A,B are performed.
For calculation of OEE using E10 the following times (and processed wafers) are recorded:
OEE CASE STUDY:
Example

INDUSTRY 4.0
SPECIALIST
CONFIDENTIAL
Data:
Run = 18 hrs
Idle = 2 hrs
PM = 2 hrs
Qual = 1.5 hrs
UM = 0.5 hrs
WPH = 10
Wafer output = 160
Wafer scrap = 10
Calculation:
Availability = (Run + Idle) = (18 + 2) = 83%
Total time 24
Performance = Actual output = 160____
Potential output (10 wph x20 hrs)
= 80%
Quality = Good output = (160-10) = 93%
Total output 160
OEE = Availability x Performance x Quality
= 83% x 80% x 93%
= 61%
OEE CASE STUDY:
OEE calculation

INDUSTRY 4.0
SPECIALIST
Manufacturing
Engineering Module
OEE CASE STUDY:
Waterfall Chart sample (Each bar has its own ownership)

INDUSTRY 4.0
SPECIALIST
91
END OF SESSION 4.1

INDUSTRY 4.0
SPECIALIST
92
SUB MODULE 4.2 :
Development of Digitized Work System
OBJECTIVE:
Participants understand how digitalization can affect work. They are able to classify these
changes, evaluate them and apply design approaches.
The participants interactively develop an example for the design of digitized work
systems. They are able to apply the learned design approaches and know how to
implement them in everyday operations.
Modules Themes
4.2 Development of Digitized Work System
4.2.1 Design approach to integrate the requirements into digitization projects.
4.2.2 Influences of Digitization on the work.
4.2.3 Development of digitized work systems.
4.2.4 Practical example from the context of Industry 4.0.
CONTENTS:

INDUSTRY 4.0
SPECIALIST
93
SUB MODULE 4.2.1 : Design approach to integrate the
requirements into digitization projects
OBJECTIVE:
Modules Themes
4.2 .
CONTENTS:

INDUSTRY 4.0
SPECIALIST
Analytics Landscape for Design approach to integrate the
requirements into digitization projects
Improvement Stages Descriptions Accuracy Update Status
Stochastic Optimization To achieve the best results with the effects
of variability Prescriptive Minimal in practices
Optimization To achieve the best results
Predictive Model What if scenario of What will happen,
Predictive Seen in applied in
Wafer FAB
Simulation Affirmation test What if Analysis of what
could happen
Probability and Statistic Evaluate data confirmation based on high
degree of accuracy
Triggers and Alerts To notify the action required
Query/ Drill Down Classify the problem or what exactly is the
problem Descriptive Many in practices
Reporting Provide status and problem
Competitive advantage
Competing on Analytics Davenport and Harris
2007

INDUSTRY 4.0
SPECIALIST
Next session class Planning
Improvement Stages Descriptions Accuracy Update Status
Stochastic Optimization To achieve the best results with the effects
of variability Prescriptive Minimal in practices
Optimization To achieve the best results
Predictive Model What if scenario of What will happen,
Predictive Seen in applied in
Wafer FAB
Simulation Affirmation test What if Analysis of what
could happen
Probability and Statistic Evaluate data confirmation based on high
degree of accuracy
Triggers and Alerts To notify the action required
Query/ Drill Down Classify the problem or what exactly is the
problem Descriptive Many in practices
Reporting Provide status and problem
Competitive advantage
Competing on Analytics Davenport and Harris
2007

INDUSTRY 4.0
SPECIALIST
• Real world Factory operations can be represented or digitized in a
computer model using a simulation software
• The simulation model allows the user to perform tasks such as
Capacity analysis, Capacity planning and Scheduling
• In general, a simulation model requires set of input data and user can
perform analysis based on the output data generated from the
simulation run
Design approach to integrate the requirements into
digitization projects
Input
Data
Simulation
Output
Data
Analysis

INDUSTRY 4.0
SPECIALIST
Simulation
Model
Current WIP profile information
• Lot identification number
• Product
• Lot start date
• Lot due date
• Lot quantity
• Lot priority
• Current process step
Route information
• Product
• Process step
• Equipment family
• Process time**
• Equipment dedication*
Tool availability information
• Equipment family
• Equipment availability**
Tool information
• Tool family
• Tool name
• Tool quantity & capacity**
• Task selection rules**
• Batching parameters
• Processing efficiency
Order information**
• Product
• Product order date
• Product due date
• Product order quantity
• Product priority
Typical Simulation model Input configuration
Source : CREST RnD lecturer series 2015

INDUSTRY 4.0
SPECIALIST
98
SUB MODULE 4.2.2 :
Influences of Digitization on the work
OBJECTIVE:
Modules Themes
4.2 .
CONTENTS:

INDUSTRY 4.0
SPECIALIST
• Manufacturing Engineers do not have visibility for the WIP Arrival Forecast
needed for planning especially during meeting with Engineering Module
Engineers
• WIP Arrival Forecast from the Simulation model gives guidelines to the
engineers to determine potential dates for tool downtime requests
• Forecast data by each modules are updated daily and can be downloaded
through a website
• The WIP Arrival GUI application (written in Visual Basic for Applications
codes) can be accessed anywhere & anytime
Application of Simulation Model Results:
WIP Arrival Forecast

INDUSTRY 4.0
SPECIALIST
• Simulation model can analyze the impact of re-prioritization on the manufacturing
monthly output.
• Batches of products (approximately 5% of the total products) are simulated with
higher processing priority compared to the rest of the products. These products
need to expedite at faster cycle time speed to ensure the deadline are met which
stop other products at shared equipment.
• As a result, other products will be delayed causing monthly output for the current
activity month to be reduced and cycle time on the consequent month to
deteriorate.
• Strong justification can be made using this simulation model result.
“What-if” analysis

INDUSTRY 4.0
SPECIALIST
• Customized Auto Email System
• Sent out daily to publish Simulation Forecast results
• Monthly/Quarterly Output & Cycle Time by product
• Easy to read format on mobile device
• Detailed Excel report can be downloaded from intranet
• Replaced daily manual report preparation
Daily Facility Performance Forecast Report

INDUSTRY 4.0
SPECIALIST
• Simulation Model Forecast can be used for the following:
• To forecast the Factory performance. Examples of output reports are Output
per month or Quarter or year and cycle time
• To provide WIP arrival forecast that will help equipment engineers to select
suitable window to perform tool maintenance
• To test “what-if” scenarios to learn how changing the input variables such as
Product Mix or Product re-prioritization can impact the Factory performance
Influences of Digitization on the work

INDUSTRY 4.0
SPECIALIST
103
SUB MODULE 4.2.3 :
Development of digitized work systems
OBJECTIVE:
Modules Themes
4.2 .
CONTENTS:

INDUSTRY 4.0
SPECIALIST
1. Descriptive. Explaining on the past through the ability to simplify data from
compressing and summarize the information into valuable information.
2. Predictive. Use to estimate what potential to occur by using all the current data
with minimum statistical modeling techniques,
3. Prescriptive. Use to estimate What potential to occurs with breakdown of specific
scenarios from each cost of actions and classification of specific data, so decision
can be made, which involve DOE , responds surface methodology (RSM) and others
techniques
* Response surface methodology (RSM) is an experimental strategy that was developed in the 1950’s36. RSM is comprised of a group of mathematical and
statistical techniques that are based on fitting experimental data generated from studies established using an experimental design, to empirical models and that
are subsequently used to define a relationship between the responses observed and the independent input variables37, 38. RSM is able to define the effect of
independent variables alone and in combination with the manufacturing processes under investigation.
Development through Data Analytics Approaches

INDUSTRY 4.0
SPECIALIST
1. Our exposure in IR 4.0 is focusing right decision making which we today we will
expose our self into Predictive + partial of Prescriptive concept.
2. An Operation Research (OR) or also known as “the science is better” approach will
use advance analytical approach concept to narrow choices to the best solution
with optimization, specially for
a. Quantitative, repeatability decision making
b. Bold decision for everyday activities for less risk and better decision
3. The above helps due to through analysis, large amount data being analyze, main
problem can be identified.
Making estimation with reasonable accuracy
Operations Research: The Science of Better .
http://www.scienceofbetter.org/
Adopt from

INDUSTRY 4.0
SPECIALIST
1. Making decision of type of market pricing to offers and how much will
it be
2. Making decision of best class arrangement for lecturer and students
which minimize travel time, or consolidation of courses
3. Making decision of what routes to go for vacuum cleaner robot so it
can finish before the battery dried.
4. Find best schedule options
Example of the OR

INDUSTRY 4.0
SPECIALIST
108
SUB MODULE 4.2.4 :
Practical example from the context of Industry 4.0
OBJECTIVE:
Modules Themes
4.2 .
CONTENTS:

INDUSTRY 4.0
SPECIALIST
1. Finewood is a furniture manufacturer and exporter
2. The company wants to introduce two new high-quality bedroom sets,
BR_1 & BR_2 using 3 existing equipments to produce the components.
BR_1 requires production capacity from EQP1 & EQP3 while BR_2
needs EQP2 & EQP3
3. The company wants to sell as much of either bedroom set, however it
is not clear which mix of the two sets would be most profitable
4. A team has been formed to find what is the production rate (batches
per week, at 10 sets per batch) for both sets to maximize the total
profit, subject to the capacity constraint from the equipments
Example 1: Finewood
• Adapted from example in Textbook Introduction to Operations Research (Hillier & Lieberman)

INDUSTRY 4.0
SPECIALIST
1. Decision variables:
x = the number of batches of BR_1 produced per week
y = the number of batches of BR_2 produced per week
2. The objective is to maximize the total profit (in thousand $) per week from both
sets
Maximize Z = 3x + 5y
3. Constraints:
a. BR_1 requires 1 hr of EQP1 but only 4 hrs per week is available; x <= 4
b. BR_2 requires 2 hrs of EQP2 but only available 12 hrs per week; 2y <= 12
c. Both require EQP3 but it only has 18 hrs per week available; 3x + 2y <= 18
d. Nonnegative production rate; x >= 0 and y >= 0
Example 1: Linear Programming Formulation

INDUSTRY 4.0
SPECIALIST
1. This problem has 2 decision variables (2 dimensions) so a graphical
solution can be applied
2. The method is to find the permissible (x,y) values that meet the given
restrictions by drawing their border lines
3. Begin with the nonnegative restrictions; x >= 0 and y >= 0
4. Then consider the restriction x <= 4 means that (x,y) values cannot
reside to the right of line x=4
Example 1: Graphical Solution

INDUSTRY 4.0
SPECIALIST
1. Then, restriction 2y <= 12 requires adding a line 2y = 12 as another
boundary
2. The last restriction requires a line 3x + 2y = 18 as the boundary (note
that, only points below or under this line meeting the restriction 3x +
2y <= 18)
3. The resulting shaded area of permissible values (x,y) are called
feasible region

INDUSTRY 4.0
SPECIALIST
1. Then find a point in this feasible region that maximizes the Z=3x+5y
2. Begin by trial & error for example by drawing a line Z=10=3x+5y and can see that there are points on this
line within feasible region
3. Next try a larger Z value such as 20 and there are points on this line within the feasible region
4. Notice that both lines are parallel, formed by equation y=-3/5x+1/5Z, with slope of -3/5
5. Draw more parallel lines with at least a point residing in feasible region until getting a line that the gives
the highest value of Z
6. A parallel line that passes the point (2,6) gives the optimal solution with Z=36
7. Another method is by moving a ruler from the same fixed slope to the direction of increasing Z within
the feasible region until finding the last optimal point (decreasing direction when objective is to
minimize Z)

INDUSTRY 4.0
SPECIALIST
1. The solution from this Linear Programming model using graphical
method shows that Finewood should produce 2 batches of BR_1 and 6
batches of BR_2 per week, with expected profit of $36,000 per week
2. Another approach using Excel Solver gives similar solution
Example 1: Conclusion

INDUSTRY 4.0
SPECIALIST
1. Let’s use another example, to select a set of food that will satisfy a set
of daily nutritional requirement at minimum cost
2. The problem is formulated as a linear program where the objective is
to minimize cost and the constraints are to satisfy the specified
nutritional requirements
Example 2: Food menu selection

INDUSTRY 4.0
SPECIALIST
Objective is to find the serving size for the food that minimizes the meal cost, while
meeting the nutritional and serving size requirement:
Example 2: Linear Programming Formulation
• Adapted from: The Diet Problem: A WWW-based Interactive Case Study in Linear Programming: https://ftp.mcs.anl.gov/pub/tech_reports/reports/P602.pdf

INDUSTRY 4.0
SPECIALIST
1. One method to solve a Linear Programming problem (especially more
than 2 decision variables) is by using Microsoft Excel Solver (free
limited Excel Add-In)
2. Requires basic knowledge on function SUMPRODUCT
3. Key in the example into Excel
Example 2: Excel Solver

INDUSTRY 4.0
SPECIALIST
1. Enable Solver Add-In
2. Select Solver from Excel menu
3. Enter the Solver Parameters
• Target cell is the Objective function
• Select the Objective (Min)
• By Changing Cells (Decision Variables)
• Add the Constraints one-by-one
• Click Solve & OK on the next dialog box
Example 2: Excel Solver

INDUSTRY 4.0
SPECIALIST
The results show optimal serving size for each food menu with cost of
$3.15
Example 2: Results

INDUSTRY 4.0
SPECIALIST
120
SUB MODULE 4.3 :
Agile Work Methods
OBJECTIVE:
The participants come to know why new working methods become increasingly important in the
professional world.
Participants will get an overview of the conventional versus the agile work method in the context of
Project management.
Participants learn about the framework conditions in terms of management and organization in a
company, so that agile working methods in the respective environment lead to success.
The participants realize the decision-making criteria, when to use Agile working methods and when
to maintain with the conventional Project management methods.
Modules Themes
4.3 Agile Work Methods
4.3.1 Agile working – why?
4.3.2 Overview of conventional & Agile work methods in Project management.
4.3.3 Agile approach, Tools & Methods.
4.3.4 Criteria for selecting conventional or agile working methods.
CONTENTS:

INDUSTRY 4.0
SPECIALIST
121
SUB MODULE 4.3.1 :
Agile working – why?
OBJECTIVE:
professional world.
Project management.
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
• Study by Oracle Corporation on the development of digital technologies and
Industry 4.0, shows that the Fourth Industrial Revolution will have a strong
impact on the way the business, especially industry, is conducted and
organized
• Future businesses will have a global network of connected machinery,
warehousing systems and production facilities bringing a new approach
called smart factories, according to Industry 4.0 work group. This trend is
also reported by General Electric on implementation of internet in
production systems in “Industrial Internet – Pushing the Boundaries of
Minds and Machines”.
• PricewaterhouseCoopers “World survey of the implementation of the
concept of Industry 4.0 in industrial companies for 2016", highlighted that
• annual benefit from cost optimization and efficiency amounted to USD421B
• digital solutions gains annual revenue amounted to USD493B
• digital technology needs annual investment amounted to USD907B
Agile Work Methods: Connection of Digitization of the
professional work and the globalization of markets
• Source: Agile manufacturing as a promising concept for Russian industry, E S Balashova and E A Gromova
• Source: The Agile approach in industrial and software engineering project management, Miloš Jovanović et al

INDUSTRY 4.0
SPECIALIST
• The industrial development under Industry 4.0 condition requires an
effective model
• Agile manufacturing meets the modern needs of the Fourth
Industrial Revolution to organize the most advanced and dynamic
production process, while adapting to all kinds of unpredictable
changes
• Product development is a complex set of activities that requires
careful coordination and attention to the smallest details. However,
the plan should be regarded as an initial hypothesis that is constantly
reviewed as evidence presented, economic assumptions change, and
opportunities are re-evaluated.
Agile Work Methods: Why product & solutions
development becoming complex & less predictable
• Source: Agile manufacturing as a promising concept for Russian industry, E S Balashova and E A Gromova
• Source: Harvard Business Review: Six Myths of Product Development, Stefan Thomke & Donald Reinertsen

INDUSTRY 4.0
SPECIALIST
• Conventional sequential project management methods led to
frustrations to software development. In 2001, a group of leading
software developers gathered in Utah, USA and started Agile
Manifesto.
• The software industry needed new methods that allow greater agility
for changes without sacrificing cost and production schedules.
• Agile methods divide production into small components or iterations
that allow for quick development and testing, thus modifications can
be made before the end product completed.
• Now agile methods are adopted by various industries beyond
software development, including telecommunications, aerospace and
construction.
History of Agile
• Source: Project Management Institute, Inc. (PMI): https://www.pmi.org

INDUSTRY 4.0
SPECIALIST
•Agile is an approach that can utilize a variety of methodologies, such as
SCRUM, Kanban, Lean, extreme programming (XP) and Test-Driven
development (TDD)
•According to Project Management Institute, Inc. (PMI), its fastest growing
certification is the PMI Agile Certified Practitioner (PMI-ACP)
•Based on PMI 2015 Pulse of the Profession® report, highly agile
organizations that are responsive to market dynamics, complete more of
their projects successfully than their slower-moving counterparts — 75
percent versus 56 percent
Agile Project Management

INDUSTRY 4.0
SPECIALIST
126
SUB MODULE 4.3.2 :
Overview of conventional & Agile work methods
OBJECTIVE:
professional world.
Project management.
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
•Two significant differences between conventional and Agile are the
approach to the frameworks and the attitudes people bring:
• Conventional Project Management Professionals refer to large set of
standard tools and select the best ones for the project. In other words
these selected rules will help to achieve the benefits that the approach
has to offer.
• However, this approach may not be applicable to Agile methodologies. For
example, Scrum process is designed to be very light and economical.
• The second one is the culture difference created by project managers
applying waterfall versus from those applying Agile. The complaints for
waterfall projects seem to revolve around the burden of project
administration or the ways in which this process can consider the ability to
accomplish something.
• The complaints for Agile projects complaints tend to be due to lack of
documentation, which leads to a lack of efficiency and accountability.

INDUSTRY 4.0
SPECIALIST
•There is also a difference between process and principles.
•Traditionally, the project manager is trained that if a formal suite of formal processes is
established, and the team holds on to the process, then the project will succeed.
•In Agile, such thinking is however based on four key principles that can be applied in any
project environment, regardless of methodology.
• (1) people matter more than process
• (2) deliverables matter more than documentation
• (3) collaboration matters more than contracts
• (4) planning matters more than any given plan.
• Source: Digite.com: https://www.digite.com/blog/waterfall-to-agile-with-kanban/

INDUSTRY 4.0
SPECIALIST
129
SUB MODULE 4.3.3 :
Agile approach, Tools & Methods
OBJECTIVE:
professional world.
Project management.
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
Agile Tools and Methods:
SCRUM & Kanban
• Media source 1: Organize Agile: https://www.organizeagile.com/what-is-scrum/
• Media source 2: Digite.com: https://www.digite.com/kanban/what-is-kanban/
• Media source 1: Organize Agile (Scrum in under 5 minutes)
• Media source 2: Digite.com (What is Kanban? - An Introduction to Kanban System)

INDUSTRY 4.0
SPECIALIST
131
SUB MODULE 4.3.4 : Criteria for selecting
conventional or agile working methods
OBJECTIVE:
professional world.
Project management.
Modules Themes
CONTENTS:

INDUSTRY 4.0
SPECIALIST
•Stacey matrix provides decision-
making process that proposes
appropriate management actions and
defines four areas: simple,
complicated, complex and chaotic.
•The x-axis deals with the HOW. We are
on the left side when the team knows
the technology well and has used it
many times previously. Otherwise, we
are on the right dimension if the
technology is completely new to the
team.
•The y-axis deals with the WHAT. On
the bottom of the axis, project
stakeholders all agree on the goal and
have the same understanding of the
expected outcome. On top it’s the
opposite, no agreed requirements and
no alignment on expectations.
Criteria for selecting conventional or agile working
methods
• Source: Agile-Minds: http://www.agile-minds.com/when-to-use-waterfall-when-agile/

INDUSTRY 4.0
SPECIALIST
•Projects in the simple zone could be handled in a check-list style.
•The complicated zone segment is socially and technologically complicated. Complicated means less
convenient but still a bit predictable. In technically complicated contexts it’s clear on WHY and WHAT to
achieve. Still, the HOW is not clear. An agile iterative approach helps to get feedback from the project
team on the achievements that make the adaptation possible
•Complexity zones mean high risk and uncertainty and require high frequency of feedback. Requirements
and execution are not clear. SCRUM approach is suitable here. It improves transparency with small
iterations and frequent checkpoints allowing for easy customization. Team planning is the starting point
for each new iteration and enables instant feedback from stakeholders to the team to customize the next
iteration.
•In chaotic zone, requirements and implementation pathways are both unclear and high risk. Kanban is
suitable here as it is the most flexible project management method. With no structure like sprints and the
only focus on work in progress (WIP), Kanban focuses on continuous results to enable further
customization in direction of backlog items. The goal is to move from chaotic zone to complex by dividing
the problems. The principle “Act, Sense and Respond” helps to move to complex zone.
Criteria for selecting conventional or agile working
methods
• Source: Agile-Minds: http://www.agile-minds.com/when-to-use-waterfall-when-agile/

INDUSTRY 4.0
SPECIALIST
END OF SESSION 4.3

INDUSTRY 4.0
SPECIALIST
135
SUB MODULE 4.4 :
SCRUM Approach
OBJECTIVE:
The participants learn the methods of SCRUM as well as the responsibilities, processing
cycle (Sprints) and the retrospectives.
Based on case studies and practical examples the participants get an overview and a
sense of where and in which cases have agile work methods been implemented
successfully.
Modules Themes
4.4 SCRUM Approach
4.4.1 Introduction to SCRUM
4.4.2 Process of SCRUM
4.4.3 Progress Control in SPRINT
4.4.4 Case Studies and practical examples for agile working with
SCRUM approach
CONTENTS:

INDUSTRY 4.0
SPECIALIST
136
SUB MODULE 4.4.1 :
Introduction to SCRUM
OBJECTIVE:
successfully.
Modules Themes
4.4 SCRUM Approach
SCRUM approach
CONTENTS:

INDUSTRY 4.0
SPECIALIST
•SCRUM is a framework that manages projects with
better flexibility and speed
•SCRUM is usually used in software development, but it
is also suitable for almost all projects and organizations
Introduction to SCRUM
Traditional SCRUM
Rely on plans,
documentation and
meetings
Work with dedicated team in
short sprints to achieve end
results, while getting
feedback from product
owners along the way

INDUSTRY 4.0
SPECIALIST
• The SCRUM methodology was developed in 1993 by Jeff Sutherland
and formalized in 1995 with Ken Schwaber
• Initially, it was developed to manage and develop products and now
is widely used for products, services, and organization management
• It has been used not only by majority of software development
companies around the globe, but also by other industries such as
finance, healthcare, higher education and telecommunications
History and origins of SCRUM
• Source: Scrum.org: https://www.scrumguides.org/

INDUSTRY 4.0
SPECIALIST
139
SUB MODULE 4.4.2 :
Process of SCRUM
OBJECTIVE:
successfully.
Modules Themes
4.4 SCRUM Approach
SCRUM approach
CONTENTS:

INDUSTRY 4.0
SPECIALIST
The Process of SCRUM:
A Brief Overview of the SCRUM Framework
Product
Backlog
Product
Owner
Initial
Planning
Sprint
Planning
(1d)
Sprint
Backlog
(1d)
Daily Scrum
(Standup)
(15min)
+
Scrum
Master
Scrum
Team
2 - 4
wk
Sprint
Review
(2-4hr)
Sprint
Retrospective
(1-3hr)
Analysis,
Design,
Build
Testing
Sprint
Deployment
Potential Shippable
(working increment of)
Software
• Diagram Source: Agile project management with Scrum: A case study of a Brazilian pharmaceutical company IT project (adapted from
Agile Project Management with Scrum, Ken Schwaber)
• Media source: Scrum.org: https://www.scrum.org/resources/brief-overview-scrum-framework
• Media source: Scrum.org (A Brief Overview of the Scrum Framework)

INDUSTRY 4.0
SPECIALIST
• Product owner
• Defines products, manages Product Backlog, decides priorities, accepts or
rejects work results
• Responsible to maximize the value of the product that Development Team
delivers
• Development Team
• Self organized, ideally full-time cross-functional employees, small size (5-9
persons)
• At the end of each Sprint, the team delivers a potentially releasable
Increment of “Done” product
• Scrum Master
• Facilitator, ensures Scrum is applied by the team
• Responsible to promote and support Scrum. Helps everyone understand
Scrum theory, practices, rules, and values
Roles of the SCRUM Team
• Source: Scrum.org: https://www.scrum.org

INDUSTRY 4.0
SPECIALIST
142
SUB MODULE 4.4.3 :
Progress Control in SPRINT
OBJECTIVE:
successfully.
Modules Themes
4.4 SCRUM Approach
SCRUM approach
CONTENTS:

INDUSTRY 4.0
SPECIALIST
• A burndown chart shows the
amount of work remaining across
time, in a Sprint, a release, or a
product
• The source of the raw data is the
Sprint Backlog and the Product
Backlog
• Helps to visualize the correlation
between the amount of work
remaining and the progress of the
Team in reducing this work
Progress Control in SPRINT: Burndown chart
• Source: Agile Project Management with Scrum, Ken Schwaber

INDUSTRY 4.0
SPECIALIST
144
SUB MODULE 4.4.4 : Case Studies and practical
examples for agile working with SCRUM approach
OBJECTIVE:
successfully.
Modules Themes
4.4 SCRUM Approach
SCRUM approach
CONTENTS:

INDUSTRY 4.0
SPECIALIST
• This short video describes the example of agile working with SCRUM
approach, based on the author’s experience on software
development projects at the following companies:
Example of agile working with SCRUM
• Media source: Axosoft: https://www.axosoft.com/scrum

INDUSTRY 4.0
SPECIALIST
EXERCISE 4.3 & 4.4
Please complete Exercise for Sub Module 4.3. & 4.4

INDUSTRY 4.0
SPECIALIST
END OF SESSION 4.4

INDUSTRY 4.0
SPECIALIST
THANK YOU.

MODULE4_20221212_rev1.pptx

Recommended

Recommended

More Related Content

Similar to MODULE4_20221212_rev1.pptx

Similar to MODULE4_20221212_rev1.pptx (20)

Recently uploaded

Recently uploaded (20)

MODULE4_20221212_rev1.pptx