Each company struggles with the same question: How can I provide – at the lowest possible costs and with an acceptable delivery time – products or services that add maximum value for my customers? Firms must develop strategic objectives which result in a competitive advantage in the market place. There are many different methods which address this problem: Lean (the value adding organization), Six Sigma (the perfect organization), TOC (the unlimited organization), TPM (the smooth organization), RCM (the reliable organization) and QRM (the cellular organization). In addition, combinations of these methods exist, like Lean Six Sigma (value adding and perfect organization) and World Class (value adding and perfect and smooth organization). The aim of this work is to present establishments of the basic model of WCM for the logistics system in the automotive industry in order to improve the work standards. The result of this research was to develop principles on strategic objectives, performance measurement systems and performance measurement system linkages for improved organizational coordination.

A crucial step in the modeling of a system is to determine the values of the parameters to use in the model. In this paper we assume that we have a set of measurements collected from an operational system, and that an appropriate model of the system (e.g., based on queueing theory) has been developed Not infrequently proper values for certain parameters of this model may be difficult to estimate from available data (because the corresponding parameters have unclear physical meaning or because they cannot be directly obtained from available measurements, etc.). Hence, we need a technique to determine the missing parameter values, i.e., to calibrate the model. As an alternative to unscalable “brute force” technique, we propose to view model calibration as a nonlinear optimization problem with constraints. The resulting method is conceptually simple and easy to implement. Our contribution is twofold. First, we propose improved definitions of the “objective function” to quantify the “distance” between performance indices produced by the model and the values obtained from measurements. Second, we develop a customized derivative-free optimization (DFO) technique hose original feature is the ability to allow temporary constraint violations. This technique allows us to solve this optimization problem accurately, thereby providing the “right” parameter values. We illustrate our method using two simple real-life case studies.

Distribution networks will experience a deep mutation concerning their planning and operation rules due to the expected increase of distributed generation (DG) interconnection to the grid. Indeed, the opening of the electricity market or the growing global concern for environmental issues will lead to a massive development of DGs. Yet, a too large amount of DGs could raise technical problems on distribution networks which have not been planned to operate with bi-directional power flow. The existing solutions to solve marginal DG connections could be no longer relevant. The distribution network definitely has to evolve towards a smarter and more flexible network. Two possible ways to reach this goal are through new architectures and developing intelligent systems. This paper focuses on new architectures and operating modes. The traditional radial distribution network could accept more DGs by introducing appropriately specific loops. A new hybrid structure enabling the coexistence of the radial and meshed operation is proposed. It is equipped with autonomous circuit-breakers and automated switches that improve its reliability. A heuristic algorithm is also proposed to build this new architecture while ensuring the equality of consumers with respect to the continuity of service and while minimizing the global cost.