Читать книгу Complex Decision-Making in Economy and Finance - Pierre Massotte - Страница 34

On the functional and logistical level, an industrial system has a network structure, as found in ecosystems or biology, with medium-sized non-hierarchical aggregations. At the information system level, this system contains positive and negative feedback loops: this is the case when several activity centers (workshops) are interconnected to form a complete plant. Such a system, with its interaction and feedback loops, is presented in Figure 1.1.

Figure 1.1. An industrial MRP system with its feedback loops

More formally, it is a cybernetic system with strong interactions between distributed functions: a given function will influence the activity or “inhibition” of a neighboring function. The problem involves analyzing the overall behavior resulting from such a system; a systems dynamics approach is appropriate. We have applied it to study the dynamic influence of interactions between various manufacturing plants of the type: Semiconductor – Electronic boards – Computers.

This set is subject to stepwise requests, at the final product “computers” level. The objective here is to analyze the impact of disruptions (demand level, yield variations, “over-reaction of production agents”) on the evolution of inventories and work in progress upstream, at the card and then semiconductor level. Modeling in the form of differential equations was carried out in a simple way on such systems, and it was possible to easily integrate the parameters and variables (size of buffer stocks, response times, etc.). In this case, it has been shown [MAS 95c] that the variation in component stocks, i.e. upstream, becomes chaotic when the system is subjected to simple control variations. As the capacity of the production system is limited, some products are penalized compared to others. This situation occurs more precisely when the flow of information is amplified and when we try to “recover” the drifts because the production agents tend to anticipate disruptions. On the contrary, in the case where the input variable is chaotic, it has not been possible to demonstrate with certainty that the system was chaotic: because of the buffer stocks (number, size, response time, etc.), the results can be smoothed, attenuated or even amplified. When the size of the buffer stocks is large and the yields are low, it has been found that the system can even diverge: in this case, the noise is amplified and directly influences the evolution of the system. However, this is also not possible due to the lack of sufficient reliable data.

Conventional management systems are essentially “planning”: they are intended to prepare a production system but not to manage it as well as possible. In some cases, they are even useless because they are too restrictive, sometimes unable to control a complete production system. In addition, they are subject to the normal reaction of a planning agent who reacts in the opposite direction to certain trends in an attempt to compensate for variations. In this case, the overly “planning” and “rigid” management system leads to dangerous, unexpected and unpredictable variations, such as changes and jolts that are amplifying disturbances. On another level, conventional production management systems are not designed to handle chaotic programs. In such production systems, reactive management tools can only degrade their overall performance. We therefore deduce that planning management systems, which are so reassuring and very useful in other respects, are costly in terms of procedures (many steering meetings, production readjustments, multiple planning steps, etc.) and are not the best suited to certain types of dynamic behavior.

According to the well-known principles of stability, it is sufficient to introduce compensation loops or decision-making centers whose action will change the direction of variation, positive or negative, of the product or information flow. It is also possible to control unstable systems by injecting “noise” and uncertainty into control parameters as well as stimuli and input variables. This allows the possibility of compensating or even eliminating knocks and counteracting the effects of pumping. For example, it is common for decision-makers to introduce noise: they can constantly modify product priorities in the workshop, depending on changes in the situation and conflicting demands. In fact, the stimuli generated are based on their perception of the problem and tend to create more disruptions and blows.