This document discusses applying a dynamic systems approach to production management in the automotive industry. It argues that complex systems like automotive supply chains can be modeled as networks of interacting elements. Small events can trigger unpredictable behavior, so it is important to identify early signals of change. The authors propose using phase space analysis to recognize patterns in production, sales, and other industry data that may act as early warnings. They conducted an exploratory study applying phase space tools to real manufacturing and market data. The results showed how patterns in the data could help predict system transitions and improve forecasting for decision making in unpredictable environments like the automotive industry.

1. A Dynamic Systems Approach to Production
Management in the Automotive Industry
Vasco Teles 1,2, Francisco Restivo 1,3
vascoft@gmail.com, fjr@fe.up.pt
1 University of Porto – Faculty of Engineering
2 MIT Portugal Program – Engineering Design and Advanced Manufacturing
3 LIACC – Artificial Intelligence and Computer Science Laboratory
APMS 2010 International Conference
Cernobbio, Italy, October 12th 2010

2. Background
Context
Relevance
The impact of
individual decisions
What are dynamic
systems
Agenda
Setting up
The challenge:
Identifying signals
The need for data
The study
How to identify
hidden patterns
Applications
Method and Analysis
Conclusions

3. Context
Background

4. In our world, systems are growing
in size and scale,
becoming more complex and
difficult to manage.

5. Supply chain systems
Healthcare
Industry
Retail
Transport systems
Military and security
Environmental networks

6. thousands of platforms,
sensors
decision-making nodes
linked together and interacting
using rules
that far exceed the engineering models
and solutions we are used to.

7. Relevance
Background

8. People are now looking at these
ultra-large scale systems as interdependent
webs of software-intensive systems,
people,
policies,
cultures,
and economics
 systems of systems
This new approach of dynamic systems is
relevant in such complex industries
like the automotive.

9. networks of individuals, either knowing or not each
other, sharing knowledge, information and advice
(Thun & Hoenig 2009).

10. The impact of
individual decisions
Background

11. we can easily recognize that the impact
of the lower economic and social
expectations of population
an individual decision to postpone one
year the replacement of the family car

12. The impact may be much stronger that the
losses resulting from running a somehow poorly
optimized production management system.

13. As a such dynamic system,
many times in this industry a small event,
previously identified or not,
can trigger the system to
unpredictable,
extreme or
chaotic behaviours
(Barabasi et al. 2000)

14. Complex networks: vertices elements
the edges  their interactions
Decentralized source  a highly connected
element  characteristics of human behaviour
Decision-making  may trigger a deterministic-
chaos situation
Feedback  higher / lower unpredictability
Agents concurring to limited resources

15. Understanding these networks may allow
the identification of the possible source of
phenomena, to tackle critical
management questions of planning, in
environments of low predictability.
(Salganik & Watts, 2009)
(Makridakis & Taleb 2009)

16. What are
dynamic systems?
Background

17. Static systems: social sciences, independent
outcome
Dynamic systems: better represent reality
(complexity, initial and previous states of the
system, memory), depend on previous events.
The path of the system depends on its initial conditions.
The development of a dynamic system: sequence of
shifts between stability and instability.

18. To decrease the unpredictability and to
understand early signals of phenomena, we
need to understand the “driving
forces”, transitional events that disrupt stable
phases, either internal or external to the system.
Howe & Lewis (2005)

19. The challenge:
identifying the signals
Setting up

20. It is getting clear that complex systems present critical
thresholds, at which the system shifts abruptly from
stability to instability.
(Scheffer et al. 2009)
Traditional models are not sufficiently precise to reliably predict
where critical thresholds may occur and to forecast change.
Statistical processes
Test autocorrelation changes are significant.
Signal analysis methods and filters
Prevent from false positives
Results depend on parameter choices in filtering
But which series should be identified as relevant and how to identify
them, to optimize the use of data and analysis methods?

21. The need for data
Setting up

22. rich and detailed
 extract the relevant information
 better decision making
large amounts of data can be gathered and analyzed
Production
Customer
Marketing
Logistics
Sales and after-sales
Top management
sales, revenues, costs price inquiries, information inquiries,
complaints
exchange/repair of parts
lead time
labour accidents and diseases
absence
efficiency
internal failure cost (scrap)
inventory
deliveries

23. How to identify
the hidden patterns?
The study

25. Proposal:
Analyse signals in the Phase Space
It is a graph representation of a system‟s possible states or
outcomes, each corresponding to one unique point in the
referential whose coordinates represent the state of the system at
any moment.
(Weigend 1994)
(Sivakumar et al. 2007)
 recognize the existence of some kind of coherence
 if a consistent trajectory is be found, then a
deterministic chaos phenomenon can be interpreted

26. Figure 1 – Signal and phase space plot for “noise” and the “logistic map function”

27. Applications
The study

28. Climatology
Medicine
Engineering
Management
Geology
…

29. Method and analysis
The study

30. Exploratory study and method
We believe that the application of phase space tools
can assist in improving the predictability of systems‟
analysis
Represents the history of the system
The need to better forecasting  how to develop new or other
types of tools and methods to study dynamic change, using
behavioural data
The „region‟ of these trajectories (attractor) may be
used to obtain useful qualitative information on
complexity, and may lead to system classification
(Sivakumar et al. 2007).

31. Searched for data within the automotive industry...
… but had to analyse data from other fields
Applied "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 an
international company
Stocks variation (the market)
Daily adjusted close value of 4 different stocks
Different industries
2 countries: Portugal and United States of America
“General Electric” data since 1962
“Energias de Portugal” and “Portugal Telecom”, data since 2003
“Google”, data since 2004
Visits to a website (the consumers).
Visits to a Portuguese travel website in 2009.

32. Similar patterns
GE and EDP, two tech
companies
Figure 3 – Phase space
representations Matlab®-based
Similar patterns
Google and PT, two ICT
companies
Scattered dots
Parts produced by a
manufacturing company and
visits to a website

33. Scattered dots  Low or no interaction: values are
independent from the previous period.
Manufacturing (planned) and website visits (not planned)
Patterns with clusters of dots  phases or periods in the
life: recursivity and dependency from previous states
Stocks‟ phase spaces
If the attractor it is “clear”  simple dynamics and the
system as low dimensional.
If the attractor is “blurred”  complex dynamics and the
system as high dimensional.

34. Conclusions
The study

35. Perturbations in complex systems trigger a transition before
change occurs towards a potential deterministic chaos
A pattern in the indicators may act as a warning, but the
actual moment of a transition remains difficult to predict
Early-warning signals are one of the tools for predicting
critical transitions and forecast behaviours

36. In the phase space diagram, plotting data can lead to
those patterns  non-random events.
Its simplicity in representing behavioural patterns has
potential, namely concerning the dynamics of the
automotive industry.
Further steps:
 collect and analyse automotive industry data
 employ the space phase tool
 understand its results, applied to decision making

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39. vascoft@gmail.com