Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.

In literature, the majorities of paper on the subject of modeling pooled distribution supply chain (SC) are based on vehicle routing problem and propose models for, only, the operational level of the supply chain. Modeling pooled SC is a problem including a strategic decision consisting of designing a network by locating a number of logistical platforms and tactical /operational decisions dealing with the customer allocation and vehicle routing. So, it will be interesting to propose and study a model as two echelons Location Routing problem (2E-LRP) for this type of chain for a more comprehensive resolution that separate decision levels. We opt for a multi-sourcing and multi-products 2E-LRP in which routes can end at different depots than the starting one in contrary of classical formulations. We add to usual constraints of 2E-LRP those specific to pooling. We propose a mixed integer linear programming (MILP) formulation for this problem. To measure the performance of our method, we have used Matlab solver to solve the MILP using extended known 2E-LRP instances and different configurations of costumers. The results obtained by comparing a collaboration scenario and non-collaboration one, show that collaboration leads to a reduction in transport costs, travel distances, and CO2 emissions in addition to the improvement of the vehicle load rate. This work opens up new lines of research in this area.

The information systems conducted by the requirements of the internationalization of activities, by the organization of firms in networks and by the evolution of Information and Communication Technology, tend more and more toward the sharing of information in real time. The Electronic Data Interchange (EDI) is among the tools that guarantee this exchange. EDI is a quick and effective means of transfer of business documents and ensures the optimization of the information flows of and their synchronization with the physical flows in the Supply chain. The objective of this study is to answer this research question "how the automobile industry in Morocco uses the EDI technology to optimize its flows?" We chose to adopt a case study approach to observing the reality of the practice of the EDI and its use as a tool for optimizing information flows. Thus, this article is the result of literature review and of the content analysis of semi-structured interviews conducted with professionals in the automotive industry. Keywords : EDI, Information flows, Automotive Industry, Supply chain, Logistic IntroductionThe evolution of technology and the structural changes in the global economy had led the organizations to recreate their value chain and to allocate more resources to the information system. Indeed, the emergence of their strategic and organizational role has made information technology investment an obligation and not an option. Thus, the information mastery that is an issue in the competitive struggle, the companies wishing to integrate distribution or supply networks into their logistic system, turn to

No abstract

No abstract