Keynote at the 3rd Annual International Conference of the Immersive Learning Research Network, iLRN 2017
Today’s shop floor, the area of a factory where operatives assemble products, is a complex and demanding work environment. The employed and produced technology becomes ever more complex, and employees are responsible for an increasing amount of tasks. As a consequence, the employee is under constant pressure to solve problems occurring on the shop floor as fast as possible, and simultaneously to improve his work-related knowledge, skills, and capabilities. This keynotes presents the outcome of the APPsist project, which investigated how adaptive technology can support the employee on the shop floor in this challenging environment.
Introduction to ArtificiaI Intelligence in Higher Education
Workplace-based Learning in Industry 4.0 -- Multi-perspective approaches and solutions for the shop floor
1. Workplace-based Learning
in Industry 4.0
Multi-perspective approaches and solutions
for the shop floor
Carsten Ullrich
Associate Head
Educational Technology Lab (EdTec),
German Research Center for Artificial
Intelligence (DFKI GmbH)
2. Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence
• One of the largest research institutes in the field
of innovative software technology based on AI
methods
• Focusing on complete cycle of innovation - from
world-class basic research through prototypes
to product functions and commercialization.
• Research and development projects are
conducted in 10 research departments, 10
competence centers and 5 living labs
• Educational Technology Lab
– Support of education and qualification processes
through innovative software technologies
– Research, development and consulting
– Focus on technologies that intelligently adapt and
adjust learning environments and learning materials
to individual learners
– http://edtec.dfki.de/
Carsten Ullrich, Workplace-based Learning in Industry 4.0
3. Towards Industry 4.0
tEnd of
18th Century
Start of
20th Century
First
Mechanical
Loom
1784
1. Industrial Revolution
through introduction of
mechanical production
facilities powered by
water and steam
2. Industrial Revolution
through introduction of mass
production based on the division
of labor powered by
electrical energy
Start of
70ies
4. Industrial Revolution
based on Cyber-Physical
Production Systems
today
010001101
001010100
100101010
010010101
Industry 1.0
Industry 2.0
Industry 3.0
Industry 4.0
DegreeofComplexity
3. Industrial Revolution
electronics and IT and heavy-
duty industrial robots for a
further automation
of production
Wahlster, 2012
Carsten Ullrich, Workplace-based Learning in Industry 4.0
4. The Workplace is
Transforming
• Challenges for Europe's manufacturing industry:
– Accelerating innovation
– Shorter product cycles
– Ever increasing number of
product variants
– Smaller batch sizes
(batch size 1)
– … while keeping/increasing level
of competitiveness
– … with fewer and fewer employees
Carsten Ullrich, Workplace-based Learning in Industry 4.0
5. Human Operators at
Tomorrow’s Workplace
• Despite the increasing automation, human operators have
place on shop floor with changed roles
• Contradictory predictions:
"Optimistic view"
– Job losses compensated by new jobs
– “Better” work, increased qualifications
– Higher autonomy and self-organization
"Pessimistic" view
– Major job losses
– Polarization as middle layer disappears
– Advanced control
Carsten Ullrich, Workplace-based Learning in Industry 4.0
(Source: Hirsch-Kreinsen, 2017)
What do we want?
What does our
technology enable?
6. Sociotechnical Perspective
• Technological innovation cannot be considered in isolation, but
requires an integrated approach drawing from technical,
organizational and human aspects.
Carsten Ullrich, Workplace-based Learning in Industry 4.0
Technology
OrganizationHuman
7. Assistance- and Knowledge-Services
for Smart Production
• Challenges
– Industry 4.0 increases complexity on the shop floor
– Employee under constant pressure
• to solve problems occurring on the shop floor as fast as possible,
• to improve work-related knowledge, skills, and capabilities
• Chances
– Industry 4.0: sensors, actors, data
• Opportunity to build tools that
– adapt themselves intelligently to the knowledge level and tasks of the
human operators
– integrate and connect the knowledge sources available in the company
– generate useful recommendations of actions
– enable recording of work processes and applied knowledge
– support the migration towards smart manufacturing
Carsten Ullrich, Workplace-based Learning in Industry 4.0
11. Partly automated assembly
line
Support for maintenance
5-axis drill
Support for machine usage
Pilot Scenarios
Partner
Pilot Area
Pilot Scenario
Production line
Support for failure detection
Carsten Ullrich, Workplace-based Learning in Industry 4.0
12. 3 manual assembly
stations
Main host computer
Monitoring and analysis
SPS
Controlling the machines
Coarse control and
monitoring granularity
System detects status and
faults
Classification on level of
stations, not components
Activities
Preventive maintenance
Resolving disabled states
and faults
Manual assembly
Goal
Increase scope of actions of
workers
Increase workers’
understanding of process,
product, manufacturing
Automated processes
Machine user
Machine operator
(plus)
Machine operator
Competence
Pilot Study: Festo
Carsten Ullrich, Workplace-based Learning in Industry 4.0
13. Pilot study Festo: Refill Loctite
Carsten Ullrich, Workplace-based Learning in Industry 4.0
14. Characteristics of Support
Carsten Ullrich, Workplace-based Learning in Industry 4.0
MENSCH-
MASCHINE-
INTERAKTION
• Knowledge discovery:
Recommend relevant
information
• Notification: Inform
employee that relevant
information is available
For the employee:
• Support work
procedures
• Widen range of actions
• Gain experience
• Gain insights
• Make work meaningful
Company:
• Increase flexibility
• Increase productivity
Translation into concrete
requirements: joint work
with work council and I4
experts from union
Control lies in hands of employee
15. APPsist‘s Assistance- and
Knowledge Services
• APPsist: First general applicable service-oriented architecture, with company specific
specializations
– Machinery, job profiles, learning materials, documents, ...
Smart Services: Use of existing infrastructure to implement new functionalities
• User-centered: Focus on support, qualification, further training of the employee
• User-adaptive, context-based support through formalized expert knowledge
Carsten Ullrich, Workplace-based Learning in Industry 4.0
Databases
Machinery Employees
Devices
AR
Smart Services
Basic
Services
16. Assistance in
carrying out activities
• Objective: Perform work activities as efficient and effective as possible
– Achieve production targets (OEE, Overall Equipment Effectiveness)
• Contextual recommendations by displaying
– Relevant work activities
– Relevant information (circuit diagrams, construction blueprints, manuals, ...)
• Assistance during activity
– Display of the individual steps of an action (step-by-step instructions)
– Augmented Reality: superimposition of information in the field of vision
– Adaptation using sensor data
Carsten Ullrich, Workplace-based Learning in Industry 4.0
17. Supporting Learning
• Performing a work procedures does not automatically
lead to learning
• Goal: Support targeted knowledge acquisition
– Display relevant work procedures
– Display of relevant content and information (learning materials,
manuals, ...)
• product
• production
• process
• Taking into account
– Performed work procedures
– Development goals
Carsten Ullrich, Workplace-based Learning in Industry 4.0
22. Artificial Intelligence in Education
• Intelligent Tutoring Systems and
Adaptive Learning Environments
provide adaptive and
contextualized support of learners
• Significant body of research on
adaptive support in university and
highly structured domains such
as mathematics, physics and
computer science
• Methods
– Knowledge-based systems:
Modelling human experts
– Statistical approaches
Carsten Ullrich, Workplace-based Learning in Industry 4.0
Domain
Model
Learner
Model
Pedagogical
Model
23. APPsist Ontology
• Describes relevant
concepts for and their
relationships
• User
• Content
• Manufacturing
• Representation in
OWL (Semantic Web
standard)
• Used for
communication
between services and
for reasoning by
intelligent services
Carsten Ullrich, Workplace-based Learning in Industry 4.0
24. User Model
• Connection to domain-model concepts
• Concepts from domain-model are enriched with user specific
values
– Number of executions (for process-steps)
– Number of views (for contents/documents)
– Number of usages (manufacturing/production objects)
• Relevant user properties
• Workplace-groups
• Permissions
• “State“: main activity (KPI), secondary activities
• Development goals
• Mastered measures
Carsten Ullrich, Workplace-based Learning in Industry 4.0
25. Adaptivity in Smart
Manufacturing
• Main activity: Fulfill Key Performance Indicators (KPI)
Assistance: Depending on the context
a) Reacting to the current situation on the shop floor, e.g.,
Loctite is empty
• Secondary activity: Time for Learning
Learning: Depending on the employee
b) Reacting to recently occurring events (e.g., a large number
of correctly or incorrectly performed measures)
c) Long-term development goals (e.g., working towards a
new job position)
Carsten Ullrich, Workplace-based Learning in Industry 4.0
26. If employee is in state “main work activity” and asks for assistance, then
select work procedures relevant for current station und machine state:
1. WU = workplace unit to which employee is assigned to.
Determined through request to user-model-service.
2. S = sort(stations ∪ installation) of AG. Determined by querying
domain model: There, each workplace unit is assigned to work with
specific installations. An installation consists of stations. Sort the
stations according to priority of each station.
3. MS = machine state of S, sorted according to priority of machine
state. Determined through request to machine-information-service.
4. P = Procedures for MS. Determined through query of domain
model: Procedures are applicable to machine states.
5. P_a = those procedures of M the employee is authorized to
perform (with or without assistance). Determined through request
to user model.
Result: P_a
Select Measures, Main Activity
Examples
1. WU = (Production
of standard
cylinders)
2. I =
(DNC_DNCB_DSB
C, …) . Stations =
(S10, S20, …).
Pri(DNC)=8
3. MS = (LociteEmpty,
GreaseFew, …)
4. P = (ChangeLoctite,
ChangeGrease, …)
5. P_a =
(ChangeLoctite)
Carsten Ullrich, Workplace-based Learning in Industry 4.0
27. If the employee is in state secondary activity (“time for learning”) and asks for
procedures, then select procedures relevant to development goals (content C_A,
and/or position PO, and/or production items PI_A).
1. PO = agreed future position of employee. Determined by query to user model.
2. P = relevant work procedures for PO. Determined through query to domain
model: Each position has tasks, and work procedures perform tasks.
3. P_U = P without mastered procedures. Determined through query to user model
(which keeps track of mastered procedures).
Result = P_U.
Select Measures, Secondary Activity
Carsten Ullrich, Workplace-based Learning in Industry 4.0
28. If the employee is in state “main work activity” and asks for information, then select
content relevant for the stations assigned to and their machine states:
1. WU = workplace unit to which employee is assigned to; P = position of
employee. Determined through request to user-model-service.
2. S, MS = Machine states and stations/installations relevant for WU (see
previous rule)
3. I = Content about S∪MS for target-group = P or without target-group.
Determined by querying domain model, which contains metadata that relates
content to domain model entities and specifies its target-groups, if any.
Result = Content I.
For instance: operation manuals, circuit diagrams, and other content that provides
information about the current situation enabling the employee to overcome
occurring problems.
Select Content, Main Activity
Carsten Ullrich, Workplace-based Learning in Industry 4.0
29. If employee is in state secondary activity (“time for learning”) and asks for content, then select
content relevant to current work history (machines and procedures worked with). Development
goals: content C_A, and/or position PO, and/or production items PI_A.
1. PI = production items with which employee has worked with in the last four weeks, P_S the
procedures that she performed successfully and P_N those not performed successfully.
This information is stored in the learner-record-service.
2. C_P_N = content about P_N and production items used by P_N, with already seen content
sorted to the back (this information is stored in the learner-record-service).
3. C_P_S = content about P_S or about production items used by P_S or about PI.
4. C_P = Content that covers one/several of the following: position PO, tasks of PO, or
production entities PI_A.
5. C_PI_PO = Content that describes production entities relevant for PO.
6. C_P_PO = Content that describes production entities used for performing procedures
relevant for PO.
7. C_T = C_P_S ∪ C_P ∪ C_PI_PO ∪ C_P_PO, with already seen content sorted to the
back.
Result: Content C_P_N + C_A + C_T, with duplicates removed.
Select Content, Secondary Activity
Carsten Ullrich, Workplace-based Learning in Industry 4.0
30. ?? State of the Art ??
Carsten Ullrich, Workplace-based Learning in Industry 4.0
Are rule-based
systems state of the
art?
31. AIED in Industrial Production
• AIED in Mathematics / Physics: More than 30 years of research
– Principles well understood
– Proven architectures
• Learning at the industrial workplace:
– Multitude of single systems, no common basis
• APPsist:
– First general ontology (domain description) with focus on learning in
production environments
– First general rules to support the employees
• Rule-based systems have proven themselves, well-understood for
which problems they are suitable
• Statistical approaches require data…
Carsten Ullrich, Workplace-based Learning in Industry 4.0
32. Digital Education Space
• Learning systems can easily
capture actions of the learner
• Data is simple but usable (click
data, performance)
• Learning Analytics:
Real-time recognition of
learning progress, motivation,
correlations between
navigation behavior and
learning success
Update of learner model
Feedback to learners and
teachers through a pedagogical
model
Carsten Ullrich, Workplace-based Learning in Industry 4.0
33. Analogue Education and
Work-Spaces
Analogue Spaces out of reach
for learning systems
No learner modelling and
adaptive reactions possible
Carsten Ullrich, Workplace-based Learning in Industry 4.0
34. Stepping from the
Analogue into the Digital
• APPsist:
– First steps towards the
use of data signals from
"analogue" world (sensor
data of the production
plants),
– their interpretation
regarding the actions of
the employees,
– and their usage for
automated support
Carsten Ullrich, Workplace-based Learning in Industry 4.0
35. Internet of Things for the
Digitization of Existing Spaces
• Increasing penetration
of environments with
sensors / actors
– Smart Factory
– Smart City
– Smart Home
– Smart Energy
• Usage of Smart Data
also for user-centered
support
• Coupling between
work- and education
spaces
Carsten Ullrich, Workplace-based Learning in Industry 4.0
36. Coupling between Work- and
Education Spaces
• In the education space:
learning adapted to
activity and goals
• In the workspace: during
the execution of activities
references to relevant
training materials
• Authoring support (EdTec
Project DigiLernPro)
• Data collection
statistical methods!
Smart Training Services
Carsten Ullrich, Workplace-based Learning in Industry 4.0
Required:
• privacy and data
protection
• design principles:
enable good work
and good learning
Sociotechnical
Perspective!