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Workplace-based Learning in Industry 4.0 -- Multi-perspective approaches and solutions for the shop floor

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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.

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Workplace-based Learning in Industry 4.0 -- Multi-perspective approaches and solutions for the shop floor

  1. 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. 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. 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. 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. 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. 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. 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
  8. 8. APPsist Consortium Application& Validation Research& Development Consulting *Subcontracts * Duration 1.1.2014-31.12.2016 Carsten Ullrich, Workplace-based Learning in Industry 4.0
  9. 9. Carsten Ullrich, Workplace-based Learning in Industry 4.0
  10. 10. Carsten Ullrich, Workplace-based Learning in Industry 4.0
  11. 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. 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. 13. Pilot study Festo: Refill Loctite Carsten Ullrich, Workplace-based Learning in Industry 4.0
  14. 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. 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. 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. 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
  18. 18. Carsten Ullrich, Workplace-based Learning in Industry 4.0
  19. 19. Carsten Ullrich, Workplace-based Learning in Industry 4.0
  20. 20. Carsten Ullrich, Workplace-based Learning in Industry 4.0
  21. 21. Carsten Ullrich, Workplace-based Learning in Industry 4.0
  22. 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. 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. 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. 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. 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. 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. 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. 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. 30. ?? State of the Art ?? Carsten Ullrich, Workplace-based Learning in Industry 4.0 Are rule-based systems state of the art?
  31. 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. 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. 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. 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. 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. 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!
  37. 37. Thank you Carsten Ullrich carsten.ullrich@dfki.de
  • rodrigobeckecabral

    Aug. 1, 2017
  • NisheshPunia

    Jul. 8, 2017

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.

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