Complex Decision-Making in Economy and Finance. Pierre Massotte
context we have just studied is transdisciplinary and requires knowledge from biology, mathematics, physics as well as cognitive and social sciences. Under such conditions, it appears that conventional approaches and tools for analyzing, managing and steering industrial systems are inadequate because:
– the principle of functional decomposition is not applicable here;
– ways of thinking are compartmentalized;
– conventional modeling and simulation tools cannot be applied because such systems are unpredictable, difficult to control and have specific characteristics, etc.;
– finally, the notion of dynamic “behavior” is essential because it conditions the notions of adaptation and dynamic reallocation of new means, methods and techniques.
Given these characteristics, industrial systems cannot react to certain stimuli and controls according to predefined patterns, and we cannot react to complexity with more complexity because we would engage in an endless spiral. More specifically, some schemes recently highlighted in the Life Sciences must be considered because the mechanisms they use, which are the result of a long evolution, provide some answers to the general problem of flexibility and reactivity of industrial systems.
It is interesting to note the sequence highlighted by biologists to illustrate the evolution of cellular societies: it begins with the notion of diversity at the origin of the convergence of a cell or agent towards an attractor. This then makes it possible to bring into play the notion of complementarity, then interdependence and finally complexity. In terms of functionalities and needs, this corresponds to a specialization of agents (activation or inhibition of specific functions), the emergence of new properties at the global level, and to exploit them, the implementation of a functional organization (and by interlocking fractal type).
On the contrary, based on the fact that only a few simple mechanisms are at the origin of such an approach, the more complex the system is, the simpler the control system must remain. This fundamentally challenges CIM concepts, which, if poorly applied, lead to systems whose information flows are too constrained and too centralized, while we must rely on freer, more autonomous, more distributed systems, systems with strong interactions.
To repeat and clarify what has been previously exposed in industrial systems subject to deterministic chaos, the system is hardly controlled externally. It must therefore have self-organizing properties to adapt to new situations while remaining within a framework of freedom.
Following our observation, some of the approaches described were successfully implemented in the TCM assembly workshop at IBM France, which served as an experimental framework. They have made it possible to define appropriate tools and methods that need to be deployed (here we will simply mention the LMA product: Line Management Advisor, based on artificial intelligence techniques) [BEA 94]. Subsequent studies have further developed the use of recurrent neural networks and cellular automata based on stochastic functions to improve the approaches and results described above.
2
Designing Complex Products and Services
2.1. Complex systems engineering: the basics
2.1.1. Relationship between organization and product: basic principles
In the previous chapter, we saw that nonlinear dynamical systems (NLDS) are subject to complex behaviors. They are “programmable networks” whose functions and interactions are not necessarily linear. We encounter them in all fields: industrial, financial, economic, social, political, etc.
When we have qualitative systems, it is relatively easy to build a mathematical model of the phenomenon or system evolution, to evaluate it and study its behavior. When we have quantitative information, the development of the model is much more difficult; so is the study of the model.
In a manufacturing system dedicated to the assembly and testing of complex technological sets, the problem is what will determine the quality of the product and the performance of the manufacturing process: “will it remain stable? Will productivity be optimal? Does the production system remain under control?” So many questions that a production manager asks himself or herself.
First, it should be noted that in a conventional system, most tasks are often performed directly. These same tasks to be performed are under the control of a human being and several elements must be taken into account to characterize the level and nature of an organization in which human resources are involved, namely:
– Competences: these are linked to a constituent entity of a system and correspond to a task, function or mission entrusted to it. Competences refer to concepts such as aptitude, talents, skill, knowledge and experience or know-how necessary to ensure the successful completion of this task. These are the competences, available at the level of an entity, that will bring added value to the product or service being transformed. In the context of this study, competency is strongly correlated with the autonomy of this entity.
– Culture: this refers to all the uses, traditions and customs, shared beliefs and convictions, ways of seeing, doing and knowing how to that ensure an implicit code of behavior and cohesion within a system or organization. As we can see, the cohesion of a system implies that a certain number of entities are linked together in order to form a network. The cohesion of the network is then ensured by links and interactions.
– Emulation and motivation: the first term refers to a state of mind or willingness to equal or surpass someone or something. Similarly, motivation is a process that triggers, continues or stops a behavior. These two concepts are used to express the activation or inhibition of a link, the reinforcement or not of an action or interaction.
The functioning and behavior of such an organization depends of course on these three elements and their combination. The French mathematician René Thom examined this problem through his theory of “catastrophes”, which allowed him to highlight transition phenomena and discontinuities, of which we will mention only two examples:
– the distribution of competences and the communication system between groups of operators are fundamental. Some imbalances always end up resulting in an explosion or implosion of behavior, which inevitably has an impact on the result;
– the interactions that condition the feedback effects are essential. Similarly, the interaction force will be the result of learning sessions, progressive and iterative reinforcement or inhibition of links between entities.
2.1.2. Reminder of the operating rules of an organization
As we have just seen, the effectiveness and efficiency of an organization carries with it the skills, culture and motivation of the system. A good distribution between these skills and good coordination between the different entities is based on a system of links with the four main characteristics.
2.1.2.1. Zero delay
This is synonymous with responsiveness and adaptability. Processes must be able to be linked and interact with each other as quickly as possible. The definition of needs and their characteristics must be rapid and lead to the immediate design and development of the required products and services. Current IT tools combined with customer relationship management (CRM), technology monitoring, computerized and integrated modeling and design techniques make it possible to meet this demand. This is currently the case in the automotive industry, electronics, high technologies, etc. Large companies such as Dell, IBM, Peugeot, Renault or even the Airbus EEIG consortium, etc. are structured in this way and can ensure a rapid introduction of new products to the market.
2.1.2.2. Zero cost
Computer technologies and the Internet have brought about two major advantages:
–