A Dynamic Systems Approach to Production Management in the Automotive Industry


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A Dynamic Systems Approach to Production Management in the Automotive Industry

  1. 1. A Dynamic Systems Approach to ProductionManagement in the Automotive IndustryVasco Teles 1,2, Francisco Restivo 1,3vascoft@gmail.com, fjr@fe.up.pt1 University of Porto – Faculty of Engineering2 MIT Portugal Program – Engineering Design and Advanced Manufacturing3 LIACC – Artificial Intelligence and Computer Science LaboratoryAPMS 2010 International ConferenceCernobbio, Italy, October 12th 2010
  2. 2. BackgroundContextRelevanceThe impact ofindividual decisionsWhat are dynamicsystemsAgendaSetting upThe challenge:Identifying signalsThe need for dataThe studyHow to identifyhidden patternsApplicationsMethod and AnalysisConclusions
  3. 3. ContextBackground
  4. 4. In our world, systems are growingin size and scale,becoming more complex anddifficult to manage.
  5. 5. Supply chain systemsHealthcareIndustryRetailTransport systemsMilitary and securityEnvironmental networks
  6. 6. thousands of platforms,sensorsdecision-making nodeslinked together and interactingusing rulesthat far exceed the engineering modelsand solutions we are used to.
  7. 7. RelevanceBackground
  8. 8. People are now looking at theseultra-large scale systems as interdependentwebs of software-intensive systems,people,policies,cultures,and economics systems of systemsThis new approach of dynamic systems isrelevant in such complex industrieslike the automotive.
  9. 9. networks of individuals, either knowing or not eachother, sharing knowledge, information and advice(Thun & Hoenig 2009).
  10. 10. The impact ofindividual decisionsBackground
  11. 11. we can easily recognize that the impactof the lower economic and socialexpectations of populationan individual decision to postpone oneyear the replacement of the family car
  12. 12. The impact may be much stronger that thelosses resulting from running a somehow poorlyoptimized production management system.
  13. 13. As a such dynamic system,many times in this industry a small event,previously identified or not,can trigger the system tounpredictable,extreme orchaotic behaviours(Barabasi et al. 2000)
  14. 14. Complex networks: vertices elementsthe edges  their interactionsDecentralized source  a highly connectedelement  characteristics of human behaviourDecision-making  may trigger a deterministic-chaos situationFeedback  higher / lower unpredictabilityAgents concurring to limited resources
  15. 15. Understanding these networks may allowthe identification of the possible source ofphenomena, to tackle criticalmanagement questions of planning, inenvironments of low predictability.(Salganik & Watts, 2009)(Makridakis & Taleb 2009)
  16. 16. What aredynamic systems?Background
  17. 17. Static systems: social sciences, independentoutcomeDynamic systems: better represent reality(complexity, initial and previous states of thesystem, memory), depend on previous events.The path of the system depends on its initial conditions.The development of a dynamic system: sequence ofshifts between stability and instability.
  18. 18. To decrease the unpredictability and tounderstand early signals of phenomena, weneed to understand the “drivingforces”, transitional events that disrupt stablephases, either internal or external to the system.Howe & Lewis (2005)
  19. 19. The challenge:identifying the signalsSetting up
  20. 20. It is getting clear that complex systems present criticalthresholds, at which the system shifts abruptly fromstability to instability.(Scheffer et al. 2009)Traditional models are not sufficiently precise to reliably predictwhere critical thresholds may occur and to forecast change.Statistical processesTest autocorrelation changes are significant.Signal analysis methods and filtersPrevent from false positivesResults depend on parameter choices in filteringBut which series should be identified as relevant and how to identifythem, to optimize the use of data and analysis methods?
  21. 21. The need for dataSetting up
  22. 22. rich and detailed extract the relevant information better decision makinglarge amounts of data can be gathered and analyzedProductionCustomerMarketingLogisticsSales and after-salesTop managementsales, revenues, costs price inquiries, information inquiries,complaintsexchange/repair of partslead timelabour accidents and diseasesabsenceefficiencyinternal failure cost (scrap)inventorydeliveries
  23. 23. How to identifythe hidden patterns?The study
  24. 24. Proposal:Analyse signals in the Phase SpaceIt is a graph representation of a system‟s possible states oroutcomes, each corresponding to one unique point in thereferential whose coordinates represent the state of the system atany moment.(Weigend 1994)(Sivakumar et al. 2007) recognize the existence of some kind of coherence if a consistent trajectory is be found, then adeterministic chaos phenomenon can be interpreted
  25. 25. Figure 1 – Signal and phase space plot for “noise” and the “logistic map function”
  26. 26. ApplicationsThe study
  27. 27. ClimatologyMedicineEngineeringManagementGeology…
  28. 28. Method and analysisThe study
  29. 29. Exploratory study and methodWe believe that the application of phase space toolscan assist in improving the predictability of systems‟analysisRepresents the history of the systemThe need to better forecasting  how to develop new or othertypes of tools and methods to study dynamic change, usingbehavioural dataThe „region‟ of these trajectories (attractor) may beused to obtain useful qualitative information oncomplexity, and may lead to system classification(Sivakumar et al. 2007).
  30. 30. Searched for data within the automotive industry...… but had to analyse data from other fieldsApplied "parallel data"Employed a tool based on Matlab® (Pinto 2009)Manufacturing industry (the production)Parts produced during 2009 in three cells of a Portuguese plant from aninternational companyStocks variation (the market)Daily adjusted close value of 4 different stocksDifferent industries2 countries: Portugal and United States of America“General Electric” data since 1962“Energias de Portugal” and “Portugal Telecom”, data since 2003“Google”, data since 2004Visits to a website (the consumers).Visits to a Portuguese travel website in 2009.
  31. 31. Similar patternsGE and EDP, two techcompaniesFigure 3 – Phase spacerepresentations Matlab®-basedSimilar patternsGoogle and PT, two ICTcompaniesScattered dotsParts produced by amanufacturing company andvisits to a website
  32. 32. Scattered dots  Low or no interaction: values areindependent from the previous period.Manufacturing (planned) and website visits (not planned)Patterns with clusters of dots  phases or periods in thelife: recursivity and dependency from previous statesStocks‟ phase spacesIf the attractor it is “clear”  simple dynamics and thesystem as low dimensional.If the attractor is “blurred”  complex dynamics and thesystem as high dimensional.
  33. 33. ConclusionsThe study
  34. 34. Perturbations in complex systems trigger a transition beforechange occurs towards a potential deterministic chaosA pattern in the indicators may act as a warning, but theactual moment of a transition remains difficult to predictEarly-warning signals are one of the tools for predictingcritical transitions and forecast behaviours
  35. 35. In the phase space diagram, plotting data can lead tothose patterns  non-random events.Its simplicity in representing behavioural patterns haspotential, namely concerning the dynamics of theautomotive industry.Further steps: collect and analyse automotive industry data employ the space phase tool understand its results, applied to decision making
  36. 36. Barabasi, A., Albert, R. & Jeong, H., Scale-free characteristics of random networks: the topology of the world-wide web. Physica A: Statistical Mechanics and its Applications, 281, 69-77 (2000).Barabási, A., The Architecture of Complexity. IEEE Control Systems Magazine, (August), 33-42 (2007).Bresten, C.L. & Jung, J., A study on the numerical convergence of the discrete logistic map.Communications in Nonlinear Science and Numerical Simulation, 14(7), 3076-3088 (2009).Gottwald, G. & Melbourne, I., Testing for chaos in deterministic systems with noise. Physica D: NonlinearPhenomena, 212(1-2), 100-110 (2005).Halbiniak, Z. & Jóźwiak, I.J., Deterministic chaos in the processor load. Chaos, Solitons & Fractals, 31(2), 409-416 (2007).Howe, M. & Lewis, M., The importance of dynamic systems approaches for understanding development.Developmental Review, 25(3-4), 247-251 (2005).Makridakis, S. & Taleb, N., Decision making and planning under low levels of predictability. InternationalJournal of Forecasting, 25(4), 716-733 (2009).Pinto, R. A., Phase Space Tool, available at http://paginas.fe.up.pt/~ee02208/ (2008)Salganik, M.J. & Watts, D.J., Web-Based Experiments for the Study of Collective Social Dynamics in CulturalMarkets. Topics in Cognitive Science, 1(3), 439-468 (2009).Scheffer, M. et al., Early-warning signals for critical transitions. Nature, 461(7260), 53-9 (2009).Schittenkopf, C., Identification of deterministic chaos by an information-theoretic measure of the sensitivedependence on the initial conditions. Physica D: Nonlinear Phenomena, 110(3-4), 173-181 (1997).Sivakumar, B., Jayawardena, A.W. & Li, W.K.., Hydrologic complexity and classification : a simple data.Hydrological Processes, 2728(July), 2713- 2728 (2007)Thun, J. & Hoenig, D., An empirical analysis of supply chain risk management in the German automotiveindustry. International Journal of Production Economics (2009).Watts, D., A simple model of global cascades on random networks. Proceedings of the National Academyof Sciences of the United States of America, 99(9) (2002).Weigend, A., Paradigm change in prediction. Philosophical Transactions of the Royal Society London A348, 405-420 (1994).
  37. 37. vascoft@gmail.com