The topics to be studied in this part are related to “life science” considerations, as follows:

1. product lifecycle of living systems;
2. aging and death: main principle;
3. aging and death characteristics;
4. the aging of the human population;
5. product lifecycle of the brain;
6. dynamic considerations for aging and dying.

Are we moving toward bionic system design models? As stated before, advances in sciences are invading our life. Tomorrow’s world will comprise two challenges:

• – transhumanism, since we can extend all of our human functions and activities. However, we do not know yet what the limits and cost of this paradigm change are. Here, we have to address all the items relevant to life sciences and the evolution of human beings.
• – Big Data: there is so much data to be handled that accumulates quickly, that only computers can process it. This approach is a kind of artificial intelligence and business analytics. Hopes are encouraging, but possible deviations are frightening.

We also have to take into account the uncertainty and unpredictability due to system dynamics. The merge between “knowledge systematization” and nonlinear dynamics (NLD) has not been done, despite the remarkable advances made in recent approaches, such as implemented at IBM (Blue Geen and Watson machines). Concerning sustainability, we can point out that the so-called “knowledge systematization and modeling” is too formal. On the other hand, the NLD is uncontrollable and requires the implementation of complicated metamodels. This provides the grist for research and development (R&D), which has to come out of its cocoon and so its conventional approach. A sustainable solution may come from anywhere.

In decision management, which is simply an emanation of life and human activity, it can help us to refer to our physical and intellectual abilities, our physiology or health status and our psyche (e.g. emotions). This shows that the problems in industry and life sciences are intimately related. Hereafter, we just address the following items involved in the sustainability of a decision management system:

• – basic models for non-decidability;
• – system dynamic approach;
• – how a renewal process can be started?;
• – usefulness of death in biodiversity;
• – are past experiences and inheritance useful?;
• – what about the deepwater horizon?;
• – storage of facts and events; the ratchet effect.

We have entered in the era of competitivity. Industry innovates and produces many diversified solutions. There are many companies failing, but survival requires a desire for life and more diversity. Indeed, nature’s evolution is based on adaptation, selection of best-fitted solution or elements, and then reproduction.

This result on competitivity is well known. We know that innovation must apply at process level first, before focusing on product innovation [COR 15]. Cognition being increasingly important, this implies the implementation of “artifact manufacturing” (with knowledge modeling and systematization). The Japanese were the precursor in this area through the intelligent manufacturing systems IMS program; maybe the complexity of their languages helped trigger a move?

Note however that many know how to introduce new features, new and sustainable concepts (examples are efficient and smart cars, interchangeable or recycled products, “soft” machinery, alternate energies, etc.), yet still cannot tame the impact and the unpredictable behavior of the proposed solutions. For instance, concerning the Internet, we could state that it is not under control; some great democracies have been born without governance; or very complex rockets and shuttles are sent into space and blow up without knowing why. Here again, NLD is everywhere and is not under control.

Survival strategies, expansionism and evolution are the result of this competitive stage where innovation raised to process and artifacts manufacturing take up levels, due to knowledge systematization. Here, the Japanese influence was strong.

For these reasons, in the next part, we will detail several concerns such as those related to competition and cooperation, survival strategies, expansionism and evolution, etc.