a production system has been studied [BAR 96] in terms of the organization to be put in place to compensate for the disruptive effects of chaos (in the sense that they are unpredictable). Here, the chaos is essentially due to interactions between cells: oscillations created by calls, supply orders that propagate from cell to cell, going up the “line” of manufacture. They are also production or launch orders from the production management system, which will spread from one cell to another and vice versa. This will generally be done from downstream to upstream, if we operate in a pull flow. When these orders respond to nonlinear functions or influences and the phenomena are amplified, making the system sensitive to initial conditions (SIC), this induces many possible states for the production system, which can be the result of deterministic chaos. This is observed, for example, in the dynamic variation of stocks (WIP or Work In Process) throughout the “line”. The cause is due to the sequencing and amplification effects specific to the physical and logical structure of the production system. We will call this the caterpillar effect. Under these conditions, we cannot predict the behavior of such a system. Moreover, the model corresponding to a real workshop is relatively complex and cannot integrate all the parameters and assumptions: it cannot be used for steering purposes. Simulation can therefore be used to “gauge” a complex system, to evaluate trends and define the least bad strategies.
In terms of the strategies adopted, one of them consists of returning the production system to a stable state, which amounts to placing it in an area of the known and “reassuring” phase space corresponding to an area of weak bifurcations. However, its adaptability is reduced because, to reconfigure it and bring it into a new given state, it will require a lot of energy (since its inertia is greater). On the contrary, we will recommend exploiting the chaotic nature of such a system. More generally, we must seek conditions that place the production system at the limit of stable and unstable states. In an area of “weak chaos”, which is easy to achieve in nonlinear workshops, its flexibility is maximum. These concepts formed the basis of an international project called GNOSIS-VF (EU ESPRIT 28448 project) as part of the Intelligent Manufacturing Systems (IMS) program with Japan.
In this example, behavioral complexity is related to the presence and importance of interactions between the different agents that make up the production system. Here, simple deterministic functions applied to strongly linked systems can generate chaos. In this case, it is possible to control its effects by decoupling the cells from the system through a double Kanban system; this way, the value of the work in progress can be limited while leaving each cell its own elasticity and having disturbances that compensate themselves. With reduced buffer stocks, the adaptation of inputs and outputs is rather rapid thanks to self-regulatory effects; the best strategy is then to let the system evolve freely, maintaining the parameters within certain control limits.
1.4.4. Message flows in complex information systems
1.4.4.1. Distributed information processing
This case study was taken as a typical example of a situation that many people are familiar with: it is a Global Information System Network of which the Internet, or Intranet, is one of the elements. This heterogeneous network includes a large number of server and client centers. Each center or agent has its own strategies and can perform different routing or control tasks, as shown in Figure 1.3.
Knowledge and information are distributed throughout the network [MAI 94]. The evolution and growth of such a system with tens of thousands of nodes cannot be ensured, controlled or planned from a central computer. In such a network there is an “apparent” anarchism; each node (agent) is an autonomous computer system: it has the possibility to direct traffic according to predefined rules and the saturation state of the network. It can also manage information flows according to their nature and the state of nearby cells. Indeed, and as we have seen, even though some cells, or a group of cells, have chaotic behavior, there is often a smoothing of chaos at the global level.
Figure 1.3. A dynamic industrial system with nonlinear interactions
In this figure, we see that it is cellular automata (CA) with independent but interacting agents who do not have knowledge of the overall consequences of their actions. The probabilistic data and incomplete or inaccurate information they manipulate, combined with processing delays, result in the emergence of various attractive states such as fixed points, oscillations or even deterministic chaos and auto-catalytic mechanisms that converge them into particular collective states and behaviors. There is the emergence of a collective “intelligence” that cannot be predicted and controlled in advance and that highlights the fact that reductionist approaches cannot be referred to. For these industrial, dynamic and nonlinear interaction systems, the development of models based on evolution equations makes it possible to characterize and study them.
1.4.4.2. Emergence of collaborative work
As already mentioned, chaos and fractals are part of the same field of mathematics and underlie the principles of autonomy and self-organization. These properties are exploited in cellular automata, involving stochastic functions; solutions can therefore emerge from systems composed of communicating entities and functions that rapidly evolve into simple – periodic or quasi-periodic – and strange attractors. Their properties can therefore directly influence control systems, management methods and organization. The impact on new skills requirements, people’s education, structure and social aspect in the firm have been particularly studied in industry in Germany [WAR 93].
1.5. Applications of new concepts in industrial systems
1.5.1. New features and functionalities to consider
In the field of complexity, the presence of deterministic chaos in electronic circuits and signal processing is known to many automation engineers [EEA 95]. Without going into detail, we have simplified the representation of the new paradigms to be implemented and positioned them in a graph. The aim here is to switch from the “standard” system to “OKP” (One-of-a-Kind Production). In this context, the concepts developed above will have an impact on the methods and tools used to control and manage production systems. As indicated in Figure 1.4, there is an opposition of characteristics, resulting in a conflict between capabilities and goals.
Figure 1.4. Positioning of paradigms in production management
In view of the new constraints observed in industry and the changing needs of consumers, it will be necessary to increase both the possibility of producing specific devices, also known as “attributes” (and no longer “finished products”), personalized, in small quantities and on demand, with maximum efficiency.
In short, and this is a change, clients are becoming inflexible, while production systems and products must be more flexible and adaptable. In case of difficulties, we will even say that it is a supply crisis and not a demand crisis. The initial approach consists of developing and using information technologies as a factor of innovation and resolution. However, these only concern process automation and are based on concepts and information theories that have certainly evolved and led us to JIT (Just-In-Time), CIM (Computer-Integrated Manufacturing), FMS (Flexible Manufacturing Systems) and so on. Thanks to robotics
