Supply chain management 4.0: a literature review and research framework

This article presents a review of the existing state-of-the-art literature concerning Supply Chain Management 4.0 (SCM 4.0) and identifies and evaluates the relationship between digital technologies and Supply Chain Management. A literature review of state-of-the-art publications in the subject field and a bibliometric analysis were conducted. The paper identifies the impact of novel technologies on the different supply chain processes. Furthermore, the paper develops a roadmap framework for future research and practice. The proposed work is useful for both academics and practitioners as it outlines the pillar components for every supply chain transformation. It also proposes a range of research questions that can be used as a base to guide the future research direction of the field. This paper presents a novel and original literature review-based study on SCM4.0 as no comprehensive review is available where bibliometric analysis, motivations, barriers and technologies’ impact on different SC processes have been considered

Goal-Driven Approach to Model Interaction between Viewpoints of a Multi-View KDD process

International audienceA data mining project is usually held by several actors (domain experts, data analysts, KDD experts ...), each with a different viewpoint. In this paper we propose to enhance coordination and knowledge sharing between actors of a multiview KDD analysis through a goal driven modeling of interactions between viewpoints. After a brief review of our approach of viewpoint in KDD, we will first develop a Goal Model that allows identification and representation of business objectives during the business understanding step of KDD process. Then, based on this goal model, we define a set of relations between viewpoints of a multi-view analysis; namely equivalence, inclusion, conflict and requirement

Contribution a l'etude de l'estimation sequentielle par approximation stochastique d'un parametre statistique

SIGLET 55355 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Rank-based tests for autoregressive against bilinear serial dependence

Optimal (signed and unsigned) rank-based procedures are derived for the problem of testing autoregressive AR(1) dependence, with unspecified autoregressive parameter and innovation density, against first-order diagonal bilinear dependence. The proposed test statistics rely on rank-based versions of the residual spectrum and bispectrum. The resulting tests are asymptotically invariant, hence asymptotically distribution-free, and locally asymptotically most powerful. Their local asymptotic powers and asymptotic relative efficiencies with respect to the Gaussian Lagrange multiplier procedure of Saikkonen and Luukkonen (1988) are provided explicitly.info:eu-repo/semantics/publishe

Locally asymptotically optimal tests for AR(p) against diagonal bilinear dependence

info:eu-repo/semantics/publishe

Optimal rank-based tests against first-order superdiagonal bilinear dependence

info:eu-repo/semantics/publishe

Locally asymptotically optimal tests for autoregressive against bilinear serial dependence

info:eu-repo/semantics/publishe

PLM (Product Lifecycle Management) Model for Supply Chain Optimization

Part 2: PLM EcosystemInternational audienceProduct Lifecycle Management (PLM) is an integrated business approach to the collaborative creation, management and dissemination of engineering information throughout the extended enterprise.Concretely, PLM enables a supply chain to become much more competitive by an effective collaboration among customers, developers, suppliers and manufacturers at various lifecycle stages of a product.Our objective is to propose a PLM model for a supply chain in order to increase its overall performance through better control of products at all stages of their lives. Thus, we will track product’s information on a supply chain composed, as a first step, by five actors (supplier, enterprise, warehouse, transporter and customer). Indeed, by integrating the logistics constraints in the early stages of product development, this will avoid additional costs and time waste caused by a product unsuitable for its supply chain

Reduced Complexity Iterative Decoding of 3D-Product Block Codes Based on Genetic Algorithms

Two iterative decoding algorithms of 3D-product block codes (3D-PBC) based on genetic algorithms (GAs) are presented. The first algorithm uses the Chase-Pyndiah SISO, and the second one uses the list-based SISO decoding algorithm (LBDA) based on order- reprocessing. We applied these algorithms over AWGN channel to symmetric 3D-PBC constructed from BCH codes. The simulation results show that the first algorithm outperforms the Chase-Pyndiah one and is only 1.38 dB away from the Shannon capacity limit at BER of 10−5 for BCH (31, 21, 5)3 and 1.4 dB for BCH (16, 11, 4)3. The simulations of the LBDA-based GA on the BCH (16, 11, 4)3 show that its performances outperform the first algorithm and is about 1.33 dB from the Shannon limit. Furthermore, these algorithms can be applied to any arbitrary 3D binary product block codes, without the need of a hard-in hard-out decoder. We show also that the two proposed decoders are less complex than both Chase-Pyndiah algorithm for codes with large correction capacity and LBDA for large parameter. Those features make the decoders based on genetic algorithms efficient and attractive