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Part 7: TRIZ Combined with other Approaches; International audience; Supply chain challenges require not only effective management, but also a new innovative strategy to reduce costs and maximize its efficiency. Traditional problem-solving methodologies specific to the areas of supply chain management (SCM) find their limits when confronted with an inventive problem or a problem containing a contradiction. TRIZ (theory of inventive problem solving) is an effective theory for systematizing innovation and solving complex problems containing contradictions. Thus, the use of the theory TRIZ can be considered as a way to meet future challenges in SCM fields and get innovative solutions. This paper presents a method for solving supply chain problems and achieving a low-cost, based on complementarities between TRIZ and discrete event simulation and specific methods for solving supply chain problems. In the proposed model, a witness simulation model of the initial problem is developed to optimize the problem and find the system limits. Then, specific problem solving methods are applied to change the original description of problem and move towards a space in which a solution can be found. The discrete event simulation allows for experiments on the system to be created and analyzed. Thus, an experimental design was developed to establish the cause-and-effect relationships between the parameters of the system in order to formulate a generalized system of contradictions. And finally, ARIZ 85C, the most mature meta-methods of TRIZ, is used to address related contradictions for searching for an innovative solution, which must be subsequently implemented and evaluated in the discrete event simulation. The suitability of this new approach is finally proven through an industrial case study conducted in a company specialized in the manufacture of electronic devices for automobiles.

Problems in design of technical systems can be solved by optimization or inventive solving principles. Two representation models are studied: Generalized System of Contradictions (GSC) as inventive principle and Design of Experience (DoE) as optimisation principle. Our purpose is to improve the capacity of design problems resolution by using the both solving principles articulated to one representation model. We will show how it is possible to shift from DoE representation model to GSC representation model by using different methods. On the one side this transition can be done by the identification of Generalized System of Contradictions out of Design of Experiments based on a set of equations to resolve. On the other side methods of data analysis can be used to visualise and reorganise the DoE matrix in the form of "contradiction blocks " reflecting the set of equations. This reorganisation of representation model will be illustrated on a simple technical system.

To overcome limitations of the classical TRIZ model of contradiction, a generalization of the model has been defined. This Generalized System of Contradictions (GSC) has now to demonstrate its usefulness and the way it can be operated. To go forward an elicitation of a method to use this GSC, different ways to recognise and to identify it will be proposed in this article. These different ways are based on different strategies that will have to be tested by the analysing of the robustness of the resolution for each of the strategies.

In this paper, an approach to combine optimization methods and methods from TRIZ will be introduced. The aim is to propose a cross-fertilization of sequential experimentations, an optimized way to build Design of Experiments and to find the best solution for a given problem, and the formulation, and of course resolution, of contradictions. The idea is to search through sequential experimentation if a solution fits all the requirements of a given specs. If it does the problem is considered solved, if not, the problem is considered an inventive one. Then methods of TRIZ can be applied. But the approach proposes an extraction of contradiction out of the sequential experimentation, as these techniques enable to limit the number of elements considered in the problem and to focus only on few but main elements describing the problematic situation. As the focusing on the few but main elements of the problem is one the key objective of ARIZ methodologies, one can consider that sequential experimentation is complementary to TRIZ based methods and that it can provide a good way to make analysis of initial situation.