Industrial Consulting for Change
Words and concepts
Big data – big amount of data that needs non conventional analysis tools to be
processed
Data mining - automatic or semi- automatic methods aimed to find correlations or
patterns from big data
Machine learning - performance improvement of software by self learning
Cognitive technologies – technologies that implie deeply the cognitive processes and
thoughts of their users
Cognitive computing – human – like man to machine interaction (IBM Watson)
Cloud computing – informatics resources access through the internet
Industrial Consulting for Change
Words and concepts
Virtual reality- emulation of sensitive reality by machines
Deep learning – part of machine learning aimed to take out information from a set of
data, using efficient algorithms.
Internet of things (iot) - extension of the internet to physical objects, so that they
become capable among them and men.
Industrial internet of things (iiot) – part of iot concerning industrial “objects” only.
Cyber security – security of data exchanged om the internet.
Industrial Consulting for Change
Areas of interest
industry
Product development
Design
Production
Maintenance
Logistics
Energy
Energy efficiency
Smart grid
Agricolture and environment
Sustainability
Trasportation
Autonomous guide vehicle
Tertiary
Consultancy and services
Finance (banks assurances)
Media (journalism)
Public administration
Building
Buildings
Domotics
Health
Hospitals
Telemedicine
Army
Attack, défense, logisticsupport
Industrial Consulting for Change
The three previous industrial revolutions
The three previous industrial revolutions were triggered by
new technologies
then they were beared by
organizational models
and then they had outcome
social - economical
and the fourth?
Industrial Consulting for Change
The three previous industrial revolutions
Source: Forschungsunion, acatech, Abschlussbericht Arbeitskreis Industrie 4.0
Industrial Consulting for Change
The three previous industrial revolutions
revolution period technologis organization outcome
1^ 1760/70 -
1830
Steam machine First industrial
organizations
From farmer to
workman
2^ 1860/70 –
early C20th
Electricity and
chemical products
Fordism,
Taylorism
Rise up of
mass
production
3^ 1950 -
today
Electronics and
informatics
Toyota
Production
System
Service
society
4^ Today Digitalization and
ICT
? Cyber-physical
products
Industrial Consulting for Change
Melting pot of know - how
TRADITIONAL
INFORMATION
&
TELECOMMUNICATION
STATISTICS
(Mathematics)
INDUSTRY
4.0
Industrial Consulting for Change
Maintenance outcome – strategical planning (life cycle)
Consistancy between company and maintenance objectives
Identification of critical elements (machines, systems) to focus
maintenance activities:
statistical analysis (RAM, productivity, costs)
Montecarlo simulation (under development in FESTO)
Identification e quantification of KPI’s
Choice of maintenance policies
FMEA
cost to benefit analysis
make or buy (contracts)
Maintenance organization
Choice of CMMS
Results are on SW made
available to the client for
use
Industrial Consulting for Change
Maintenance outcome – corrective and scheduled
On site interventions support
augmented reality
interactive virtual documentation
remote support
scheduled interventions (scheduled maintenance only
Data Collection and archiving
data analysis and processing
outcome in overhauling and continuous improvement of
maintenance policy,
outcome on machine builder (Early Equipment
Management)
Cloud computing and
decision support
Data mining
Industrial Consulting for Change
Maintenance outcome – predictive
Choice of monitoring and analysis technics
Significant inputs (signals, parameter, etc.) to collect
Signal analysis and processing(time/frequency)
Setting of acquisition intervals (continuous / periodic)
Setting of sensors positioning locations
Definition of notification criteria (sms, e-mail, etc.)
Definition and optimization of notification thresholds
Reporting and management interfaces of operating systems (CMMS; other sw)
SW available to client for
statistical analysis of
data, Smart Reporting
System
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Maintenance outcome – predictive
Data acquisition and analysis
Data conditioning and processing
Data comparison vs thresholds
Triggering of warnings
Support to diagnosis and prognosis
SW available to client for
data analysis ,
development of neural
networks, reports, useful
life evaluation.
Industrial Consulting for Change
maintenance outcome – measurements and improvement of machine
performances
Big data
Data mining
Machine learning
Analytics
Cloud computing
Advanced maintenance engineering
Data repository
Failure data analysis and RCA
Suggestion of next machine design
improvement and organizational model
Design / optimization of diagnostic and
prognostic model
Industrial Consulting for Change
maintenance outcome – definition and control of budget
Lay-down of budget on the basis of technical
system and organization model and of production
plan
Budget vs actual costs gap analysis and finding of
countermeasures to be verified by simulation Tools of
economical
analysis
Simulators
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maintenance outcome – spare parts management
Mathemathical model of spare parts warehouse, by means of
reliability and Montecarlo simulators in order to optimize spare
parts amount
Automatic accountability of spare upload / download
Automatic coding of spares, 3D scan of spares
3D printing of particular items
Spares distribution via robots
Mathematical model
3D acquisition
Machine learning
3D printing
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maintenance outcome – third part management and contracts
Cognitive computing IOT
Automatic call for service (IOT)
Computerized evaluation of offers (software Emma)
Computerized legal consultacy (digital sollicitor)
Consultancy to company (Amelia from ITsoft, Watson from IBM)
Industrial Consulting for Change
maintenance outcome – competence improvement and management
Training on virtual machines and systems
Competence gap analysis coming out of
maintenance interventions (recorded on CMMS)
and setting up of suitable training
Distance learning Virtual rality
Big data
Data mining
Training tools and equipments
(FESTO EDUCATION)
Industrial Consulting for Change
maintenance outcome – CMMS
CMMS userfriendly, eccess from popular tablet and smartphone.
Integration between CMMS and other applications (predictive maintenance,
RCM, technical – economical optimizer) to offer a global view of maintenance
management according to Physical Asset Management
Text recognition CMMS able to classify information coming from intervention
reports
Big data
Data mining
Cloud computing
Cognitive computing
Deep learning
IIOT
Cyber security
Industrial Consulting for Change
Typical scenario
DCS
Screens
Historian
Data
Local
instrumentation
Thermographies
Vibration
Measurements
Lab
Analysis
Industrial Consulting for Change
How Technology Can Help (1)
DCS Historian
Data
Local
instrume
ntation
Thermog
raphies
Vibration
Measure
ments
Lab
Analysis
Cloud
Repository
Industrial Consulting for Change
How Technology Can Help (2)
Big Data
Statistics
Root Cause
Analysis
Neural
Networks
KPI
Calculation
Object Based
Models
Notifications
CMMS
HTML
5
Web
Services
Cloud
Repository
Industrial Consulting for Change
apmOptimizer is a Decision Support System for the assets management, suitable for ISO 55000
series standard applications. apmOptimizer method works with the departments of:
Production: by plant RDB diagram, Asset availability;
Maintenance: by LORO, FTA, Asset reliability, PM, PIO, CO;
Logistic: by Spare parts selection, Transportations and Stores;
Financial: by Life Cycle Cost.
apmOptimizer presentation
* LORO= Level Of Repair Optimization from MIL -HDBK-1390 LORA
apmOptimizer is a Decision Support System for the assets management, suitable for ISO 55000 series standard applications. The apmOptimizer method works with the departments of:
Production: by plant RDB diagram, Asset availability;
Maintenance: by LORO*, FTA, Asset reliability, PM, PIO, CO;
Logistic: by Spare parts selection, Transportations and Stores;
Financial: by Life Cycle Cost.
dat
a fl
ow
D
SS
* LORO= Level Of Repair Optimization from MIL -HDBK-1390 LORA
Exemple
Industrial Consulting for Change
dat
a s
ourc
e
f
ucti
onal
ity
• Stochastic simulation of failures conditions versus time.
• Failure distribution estimation using apmOptimize software with source failure db or failure Company data.
• Reliability, Availability and Maintenance models of apmOptimizer.
• Preventive maintenance optimization with apmOptimizer.
• Inspections optimization with apmOptimizer.
• Performance models of apmOptimizer taking into account the effects of failure interruptions and storage capacity.
• Life Cycle Cost calculation with apmOptimizer for each scenario.
Short Name Full Name Source of failure rate data base
Pipe Corrosion
Pipelines bio-corrosion models, Berkeley, University.
Bloch Heinz P. Bloch, Fred K. Geitner Pratical machinery management for process plant.
RADC RADC, Not electronic reliability note book, US Department of Commerce.
NPRD Not electronic parts reliability data. Reliability. US Department of Defence.
Company Failure data from Client
OREDA Onshore & Offshore Reliability Data.
ApmOptimizer function. & data source Exemple
Industrial Consulting for Change
apmOptimizer by source data and its algorithms identifies the predicted failure and recommends the optimal maintenance time and the optimal spare ordering time.
Process Method (one) Exemple
Industrial Consulting for Change
• Pipelines (OCAB, OSDUC, CE/REDUC)
• Flow control valves (manual and motorized)
• Main pumps with motors, transfer pumps with motors, auxiliary pumps with motors
• Flotation roof tanks with oil level meters
• Pressure safety valves
• Flow rate meters, pressure meters
• Transformers of sub station
Type of assets involved in the analysis
Optimization system example Exemple
Industrial Consulting for Change
Situation before optimization study Situation after optimization study
Total Life Cycle Cost =5.938 mln USD
Availability = 97.65%; Unvailability 2.35%;
Production loss =1600 –1581 = 19 m3/h
Total Life Cycle Cost reduction for 50 years = 38.1% (45.2mlm per year)
Unavailability may be reduced by (2.35 - 1.02) / 2.35 = 56.7 %.
Performance lost may be reduced by (19-10)/19= 47,36%
Summary of achievable results applying the optimization study
Total Life Cycle Cost =3.676 mln USD
Availability = 98.98 %; Unvailability 1,02%;
Production loss =1600 –1590 = 10 m3/h
Optimization Results Exemple
Industrial Consulting for Change
maintenance outcome – early equipment management
Retrofit equipments and system (fit for 4.0 – retrofit for 4.0)
Industry 4.0 machine design
New typre of equipments to maintain, more and more robots, more
and more electronics (casual failures)
Macchines more flexible (availability and reliability of production
systems and utilities as well assume different values with respect
the actual ones)
Industrial Consulting for Change
Social and economical outcome
Production come back to western countries, but not employment
Value creation / loss
Creation / loss of employment
Distribution of wealth among people non dependent on employment
(citizenship income)
Subjects dealt with at the Economical Internatinal Forum held in Davos
Industrial Consulting for Change
What can we do – FESTO Training and Consultancy proposal
Seven new courses
Machines and systems design according to i4.0
Cyber security in industrial communications
Maintenance toward i 4.0
Machines and systems revamping according to i4.0
Smart system integration and intelligent product RFID in action
Remote troubleshooting – remote failure finding and intervention
Business model innovation in Industry 4.0
……
and then consultancy