Nicole RadziwillINTELEX

Email: nicole.radziwill@gmail.com - Twitter: @nicoleradziwill

Quality 4.0 & the Future of Manufacturing:It's All About the Data

Medvedev, G. (1991). The truth about Chernobyl. IB Tauris.

Medvedev, G. (1991). The truth about Chernobyl. IB Tauris.

“You reported to Kiev on 26 April that the radiation situation at the plant in Pripyat was within normallimits?” – “Right. That’s what the instruments we had at the time showed.” p. 226

1. What is being measured & how2. Context of the measurement3. Knowledge of the instrument

1. What: Exposure to ionizing radiation - 3.6 R/hour2. Context: 800-1500 R/hour near exposed reactor;

300 R accumulated for radiation sickness3. Instrument: 3.6 R/hour is the maximum

measurement on the standard device used at thosefacilities at the time

Every Data Point is Important

• Ironmaking (ore+lime+coke pig iron)• Steelmaking (pig iron liquid steel)• Casting• Rolling

Video: https://www.youtube.com/watch?v=NlAoaajyjpM (2:48)

Video: https://www.youtube.com/watch?v=-RYCXDUt2m8 (1:42)

Temperature sensorsGas pressure monitors

Wind rateWater ingress detection

PermeabilityTuyere Kinetic Energy

Flame TemperatureBosh Gas

Descending BurdenHot Metal %Si

Operator uses HMIs tomonitor process andproduct quality in the

SCADA system &perform interventions

• Uncontrolled cooling

• Water leak (explosive)

• Incorrect amount of gas introduced toreaction

• Temperature too high

• Temperature too low

• Carbon monoxide release

• Slag solidifying

What Can Go Wrong?

From https://insights.globalspec.com/article/7809/high-performance-slag-materials-a-steel-industry-byproduct

• Fanning (capacity)

• Backdrafting (repair)

• Banking (short outages)

• Blowing out (end of life)

• Blowing down (inspection)

• “Salamander tipping” (afterblow-down)

• Regular shutdowns are rare

• Unplanned shutdowns have seriousconsequences

Turning Off a Blast Furnace

From https://insights.globalspec.com/article/7809/high-performance-slag-materials-a-steel-industry-byproduct

1.

2.

3.

Loss of Product – Loss of Assets – Loss of $ - Loss of Life

Forensics Report Revealed…

This is a data-intensive real-time classification problem

Current Statevs.

Nominal State

Root Cause:• Software bug?• Hardware issue?• Process out of control?• Human error?• Cyberattack?

Telemetrydata

INT

ER

VE

NT

ION

?Y

ES

OR

NO

Supplychain info

Network logs,SIEM, cameras

ERP Data

QMS/SPCProcess Data

RPA data

M2M data

BLOCKCHAIN

“Smart Manufacturing”

Data Science & Machine Learning

Unfortunately, Both Sides Can Play

From https://arxiv.org/pdf/1709.06397.pdf

“… a leading car manufacturer… was experiencingrecurrent faults, and therefore high warranty costs, onthe rear tail-lights. The engineers had been looking atthe rear of the vehicle for the answer (and notsucceeding) however the software quickly found theroot cause of the fault to be located in the roof of thevehicle, a part of the car that had not even beeninvestigated as a possible source. Sometimes priorexperience, or being too close to a problem, can inhibita solution if an old hypothesis is applied to a newproblem.”

From http://www.qmtmag.com/display_eds.cfm?edno=9665074

1. Augment (or improve upon) human intelligence

2. Increase the speed and enhance the quality of decision-making

3. Improve transparency, traceability, and auditability

4. Anticipate changes, reveal biases, adapt to new circumstances/ data sources

5. Reveal opportunities for continuous improvement

6. Learn how to learn: cultivate self-awareness and other-awareness

How Do We Get There?

Quality through the Lens of Innovation

https://www.yumpu.com/en/document/read/38080390/asq-innovation-think-tank-executive-summary

http://asq.org/future-of-quality/

1: The Future of LeadershipFROM EFFICIENCY to ADAPTABILITY

• Top-down management

• Short to moderate horizon (5-20 yr)

• Performance measured byproductivity and efficient use ofresources

• Quality (meeting stated and impliedneeds of stakeholders) andeffectiveness of value-adds are alsocritical

• Increase transparency to empowerimmediacy in decision making

• “Networked” management: leadersbecome boundary spanners inservice to everyone else

• Long-horizon view (100 yr)

• Performance measured by SELF-AWARENESS & CONTINUOUSLEARNING

TRADITIONAL EMERGING

2: The Future of Customer ExperienceCONTINUOUS VOICE OF THE CUSTOMER

• Customer-centric operational model

• Organizations invest in “return on relationships” –including social design, increasing agility andresponsiveness, resilience

• Limited boundaries between customers andcompanies

• Analytics available everywhere, by request

• Voice of the Customer (VOC) will come not only fromthe customer, but from his or her products andenvironment – through all phases of product lifecycle

• AI/ML techniques for these tasks subject to humanbias and inappropriate use (e.g. OKCupid, Facebookemotional contagion, Tay.ai)

3: The Future of ManufacturingOPERATIONS & SUPPLY CHAIN OMNISCIENCE

• New technologiesautomate, redefine,possibly eliminateseams and transitions

• Tighter integration ofinformation, materials,and workforce acrosspreviously perceivedboundaries

• Innovation will requireattention to data qualityand data managementover the lifetime of thedata (curation)

• Not about individualinterests, butecosystem viability

From Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods andapplications. Journal of Manufacturing Systems.

4: The Future of EducationFEWER BOUNDARIES, MORE DATA CONSCIOUSNESS

• Digital technology, big data, andcustomization is impactingeducation of all levels and types

• Leverages natural variability inhuman capabilities and interests

• Personalized learning bringsdiversity, accessibility, choice

• More flexible credentialing

• New models (Lambda School)

• Manufacturing workforcedevelopment must be adapted toemphasize cognitive skills

From Kovach, J. V., & Fredendall, L. D. (2013). The influence of continuous improvement practiceson learning: An empirical study. The Quality Management Journal, 20(4), 6.

5: The Future of EnergyCONSUMERS BECOME PRODUCERS

• Energy is a commodity

• Nonrenewable options

• Buildings will be hubs of energyproduction and storage as well asconsumption

• More efficient energy production &more effective (renewable) choices

• New business models and newsources of data

OLD NEW

Radziwill, N. M. (2018, October). Let’s Get Digital: The many waysthe fourth industrial revolution is reshaping the way we think about

quality. Quality Progress, p. 24-29. http://qualityprogress.com

Connectedness

Intelligence

Automation

Quality 4.0 = C I A

for improving performance

Information is only useful when it’s valid, andyou are Connected to it

Automation helps bring it to you when youneed it, and frees up time and effort

Intelligence helps you understand and respond

• Cyber-physical systems produce Big Data

• Network infrastructure is robust; data can be shared for real-time decision support

• Software libraries for advanced analytics are accessible,comprehensive, and reliable

• People, machines, and data all connected in near real-time

1. Lots of it (volume)

2. It’s coming at you fast (velocity)

3. Different formats and sampling frequencies (variety)

4. Huge variations in data quality (veracity)

5. Different people/organizations produce it or own it (governance)

6. It could easily change or disappear (control)

7. There may be restrictions on how you use it (policy)

What is Big Data?

“… Big Data is anything bigger or more complex thanwhat your organization is currently prepared to handle.”-- one of the world’s experts on cyberinfrastructure for Big Data at a National Science Foundation panel (2013)

Quality Maturity

Adapted from Carlson, R. O., Amirahmadi, F., & Hernandez, J. S. (2012). A primer on the cost of quality for improvementof laboratory and pathology specimen processes. American journal of clinical pathology, 138(3), 347-354.

Profit

ProfitProfit

OpportunityCapture

Value Vision forQuality 4.0

ExternalFailure

InternalFailure

Appraisal

Error- andwaste-free

service

Siloed/ Struggling

Quality as:1.0 - Inspection

2.0 - Design3.0 - Engagement

4.0 - Discovery

AI/ML evolves data-driven decision making to be self-aware & adapt tochanging environments, circumstances, and customer/stakeholder needs

When people learn,they update:

POLICIESPROCEDURES

PRACTICESHEURISTICS

When AI/ML algorithmslearn, they update:

PREDICTIONSKEY PREDICTORSCLASSIFICATIONS

PATTERN IDENTITIESOPTIMAL PATHS

Artificial Intelligence

Machine Learning

Neural Networks

Deep Learning

Big Data

Blockchain

Enabling Tech/Cybersecurity

Statistics & Data Science

Solid Process-Driven Management System

Transparent Data Architecture

Cybersecurity as a Habit

Strategy for Discovery

Shorten Loops

Nicole Radziwill – Text: (703) 835-6336

Email: nicole.radziwill@gmail.com

https://www.linkedin.com/in/nicoleradziwill/

Questions?

Muffins

1. Prediction

2. Classification

3. Pattern Identification

4. Data Reduction

5. Anomaly Detection

6. Pathfinding

+ =

99.8% chance ofblueberry muffin

96.6% chance of notblueberry muffin

Deep Learning =Building a stack of

components toprocess inputs

Deep Learning

Optics