How do Japanese and French firms in steel industry address the institutional change and the globalization? Employment adjustment and age management in a downsizing context

Steel industry has been engaged for a very long time in a downsizing process which has deeply transformed social and industrial relations, work and employment management. Once, these industry was owned and managed by big national groups (sometimes public) and employed a lot of workers at different levels of qualification. Now, a large movement of concentration leads to the emergence of transnational leader. Steel industry has become more and more a footloose industry, with high technological level. After several downsizing operations, firms must adopt now more flexible strategies which integrate the aging of workforce (with the retirement of baby- boom generation), and the question of transmission of skills. The age management represents now the main way – and the cheapest one in the short term– to reduce and optimize the firm workforce, but also a crucial issue for the preservation of knowledge and skills, required by the activity. In these conditions, how do firms manage the new context of financial and economic crisis? What are the consequences on labour and industrial relations, and work organisation in two important plants belonging to two international leaders? We intend to discuss the hypothesis of the convergence of firms strategy and employment system. We will wonder if, and how, historical background and the nature of labour market in which firms are embedded, influence the downsizing strategy and the age management of firms. We will focus our comparative analysis on two steel plants, localised in France and in Japan. We will examine changes that have occurred in labour and industrial relations and human resource development in two steel industry plants after the 1980s, in Japan and in France. The paper presents the intermediate results of a comparative research on new dynamics of labour markets in France and in Japan . It has been led both by Japanese and French researchers which used statistical databases on Japanese and French Steel industry and qualitative methodology (semi-directive interviews).downsizing, steelmaking industry, labour markets, age management, comparison, France, Japan

A Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning

In Conformance Checking, alignment is the problem of detecting and repairing nonconformity between the actual execution of a business process, as recorded in an event log, and the model of the same process. Literature proposes solutions for the alignment problem that are implementations of planning algorithms built ad-hoc for the specific problem. Unfortunately, in the era of big data, these ad-hoc implementations do not scale sufficiently compared with well-established planning systems. In this paper, we tackle the above issue by presenting a tool, also available in ProM, to represent instances of the alignment problem as automated planning problems in PDDL (Planning Domain Definition Language) for which state-of-the-art planners can find a correct solution in a finite amount of time. If alignment problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with advantages in term of versatility and customization. Furthermore, by employing several processes and event logs of different sizes, we show how our tool outperforms existing approaches of several order of magnitude and, in certain cases, carries out the task while existing approaches run out of memory

Science-Industry Links and the Labour Markets for Ph.D.s

The aim of this research was twofold: firstly to highlight how the current “hybridisation” of the academic and industrial rationales exerts its influence over the new production of young scientists; secondly to compare, between five OECD countries (USA, France, Great Britain, Japan and Germany), the ways that PhDs and doctoral students are socialised within a specific -societal- set of institutional arrangements. The production of PhDs brings into play a multiplicity of institutions at various national or local levels and mobilises the various resources available to them. The interaction between them requires the agents to adopt a variety of different behaviours based on a diversity of animating principles. Thus in order to reveal the various - societal - modes of the construction of new scientific knowledge and competence, we were led to analyse simultaneously the socialisation of young scientists and the various institutional configurations. To this end, we attempted to analyse some of the essential elements that structure this process, such as the funding system, the nature of the contract between doctoral students and their supervising institutions, the rules governing the academic community, training-job transition, career paths etc.Ph.D.s; mobility; labour market for scientists; international comparison; OECD countries

Mécanismes de reproduction des entrepreneurs de PME et dynamique des districts industriels italiens

National audienceThe purpose of this article is to understand the dynamics of Italian production systems composed of Small and Medium Entreprises; in particular, we study the industrial district of Bassano del Grappa, characterised by a craft production process, and localised in the northeast of Italy. The results show that this district, thanks to competitiveness, resists to economic fluctuations. These competitive factors are not explained by technological determinants, but by its capacity to generate organisational and social mechanisms, which are necessary to the reproduction of local professional skills. The actual crisis of the Italian districts may be, in this perspective, interpreted as a crisis of the reproduction system of skills and actors.Notre article vise à comprendre la dynamique des systèmes de production composés de PME sur la base de l'analyse empirique d'un district artisanal italien du Nord-Est - le district de Bassano. Son objectif est de montrer que la compétitivité et la capacité de ce district artisanal à perdurer au gré des différentes conjonctures économiques, ne s'expliquent pas par la mobilisation de technologies de pointe, mais par sa capacité à engendrer des mécanismes organisationnels et sociaux nécessaires à la reproduction des compétences professionnelles locales. Quand ces mécanismes ne fonctionnent plus, la dynamique du système productif local est menacée

Comment justifier de la nécessité d'une « rencontre productive » ? Les enseignements de la littérature des districts industriels.

L'idée que la recomposition des systèmes productifs locaux doit être considérée dans l'optique de leur capacité collective à produire les connaissances nouvelles nécessaires au renouvellement des activités productives, est largement reconnue. Elle participe largement dans le champ des problématiques sur le local au renouvellement des outils traditionnels et des hypothèses fondamentales de l'économie, expliquant les déterminants contemporains de la compétitivité des firmes et des économies. Ainsi, la notion de « rencontre productive » (Colletis, Pecqueur, 1993) s'inscrit tout à fait dans l'idée d'un nouveau modèle productif fondé sur la connaissance et la coordination des activités qui conduirait notamment à un nouveau rapport entre organisation industrielle et organisation de l'espace. Il apparaît bien que derrière l'idée d'une « rencontre productive », on trouve celle d'une transformation des déterminants de l'activité productive, des modalités efficaces des organisations industrielles et in fine de la compétitivité. Cela implique, d'une part, de reconsidérer les modalités de l'organisation industrielle centrée sur la firme comme lieu élémentaire de la production et de la division du travail, et d'autre part, d'intégrer le rôle de la connaissance dans les déterminants des positions compétitives des firmes et des économies.. Dans la mesure où la division du travail n'est pas circonscrite aux frontières de la firme, émerge la question de l'articulation entre l'organisation du travail et l'organisation du territoire que la notion de « rencontre productive » entend souligner. Cependant, si chacun s'accorde à reconnaître la réalité des bouleversements récents survenus dans l'organisation du travail et de l'entreprise, bien des interrogations subsistent sur leur portée et leur signification. L'objectif de notre communication est d'envisager les diverses justifications mobilisées pour expliquer ce qui constitue aujourd'hui, pour nous, un nouveau paradigme productif. Dans ce contexte, les districts industriels, tant du point de vue empirique que théorique, peuvent être mobilisés pour souligner les caractéristiques de ce nouveau modèle productif. Ils constituent en effet une première manifestation des transformations de l'ordre productif capitaliste. A ce titre, les analyses proposées par les auteurs « districtualistes » et leurs justifications, au-delà du phénomène circonscrit à l'économie italienne, doivent être envisagées tout particulièrement à la lumière de la généralisation du modèle de « l'entreprise étendue », notamment. L'objet de cette communication est donc de confronter les hypothèses mobilisées afin de caractériser les transformations des déterminants des organisations industrielles : si « la nouvelle économie industrielle » et de la connaissance se place dans un renouvellement radical des catégories classiques, les auteurs qui ont développé le concept de district industriel à partir de l'observation des cas du Nord est et du Centre de l'Italie, parviennent à expliquer un phénomène semblable en demeurant dans le cadre des catégories classiques de la Science Economique

Higher Education Systems and Industrial Innovation

This text discusses the approach adopted in a European research project concerning the relationships between science and industry. The analysis uses the notion of actors as vectors for the creation and diffusion of competences and knowledge throughout the innovation process. From this perspective, the article presents some results on the strategic behaviour of firms at the micro-level in five countries. An analytical framework in terms of “conventions” addresses the interplay between micro and macro levels. Finally, we present some significant insights into national public policies in the field of science-industry collaboration.Science-industry collaboration; knowledge creation; learning; institutions; state intervention; policy-making

Inter– multi- and trans-disciplinary approaches in astronomy education research

Looking at human and natural reality, based on experience and awareness of its complexity, the western style of knowledge was divided into disciplines. These developed their own language and methods in relation to their objects of study. The separation, useful in some stages of study and in their specific development, was often simplistic and damaging both in scientific elaboration, to meet the challenges that nature and the future offers us, and in didactic transposition of knowledge. Studies in general education and cognitive psychology, and more recently neurosciences, show that aspects of different disciplines are formed and stimulated in parallel, and also motion and cognition are linked in the brain. The research confirms that cognitive experience is linked to the body and to emotions, more than school organizations often wanted to recognize. Therefore, inter-, multi- and trans-disciplinary approaches better relate to the objects of study, to teaching methodologies, and to teaching research methods. To analyze these issues, I present reflections from my Astronomy teaching experiences with students of different ages in Italy and elsewhere, and I present open questions about teaching and learning, in and out of school, and about teacher training

Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications

Multilayer networks are a powerful paradigm to model complex systems, where multiple relations occur between the same entities. Despite the keen interest in a variety of tasks, algorithms, and analyses in this type of network, the problem of extracting dense subgraphs has remained largely unexplored so far. In this work we study the problem of core decomposition of a multilayer network. The multilayer context is much challenging as no total order exists among multilayer cores; rather, they form a lattice whose size is exponential in the number of layers. In this setting we devise three algorithms which differ in the way they visit the core lattice and in their pruning techniques. We then move a step forward and study the problem of extracting the inner-most (also known as maximal) cores, i.e., the cores that are not dominated by any other core in terms of their core index in all the layers. Inner-most cores are typically orders of magnitude less than all the cores. Motivated by this, we devise an algorithm that effectively exploits the maximality property and extracts inner-most cores directly, without first computing a complete decomposition. Finally, we showcase the multilayer core-decomposition tool in a variety of scenarios and problems. We start by considering the problem of densest-subgraph extraction in multilayer networks. We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and exploit multilayer core decomposition to approximate this problem with quality guarantees. As further applications, we show how to utilize multilayer core decomposition to speed-up the extraction of frequent cross-graph quasi-cliques and to generalize the community-search problem to the multilayer setting

Data-Driven Methods for Data Center Operations Support

During the last decade, cloud technologies have been evolving at an impressive pace, such that we are now living in a cloud-native era where developers can leverage on an unprecedented landscape of (possibly managed) services for orchestration, compute, storage, load-balancing, monitoring, etc. The possibility to have on-demand access to a diverse set of configurable virtualized resources allows for building more elastic, flexible and highly-resilient distributed applications. Behind the scenes, cloud providers sustain the heavy burden of maintaining the underlying infrastructures, consisting in large-scale distributed systems, partitioned and replicated among many geographically dislocated data centers to guarantee scalability, robustness to failures, high availability and low latency. The larger the scale, the more cloud providers have to deal with complex interactions among the various components, such that monitoring, diagnosing and troubleshooting issues become incredibly daunting tasks. To keep up with these challenges, development and operations practices have undergone significant transformations, especially in terms of improving the automations that make releasing new software, and responding to unforeseen issues, faster and sustainable at scale. The resulting paradigm is nowadays referred to as DevOps. However, while such automations can be very sophisticated, traditional DevOps practices fundamentally rely on reactive mechanisms, that typically require careful manual tuning and supervision from human experts. To minimize the risk of outages—and the related costs—it is crucial to provide DevOps teams with suitable tools that can enable a proactive approach to data center operations. This work presents a comprehensive data-driven framework to address the most relevant problems that can be experienced in large-scale distributed cloud infrastructures. These environments are indeed characterized by a very large availability of diverse data, collected at each level of the stack, such as: time-series (e.g., physical host measurements, virtual machine or container metrics, networking components logs, application KPIs); graphs (e.g., network topologies, fault graphs reporting dependencies among hardware and software components, performance issues propagation networks); and text (e.g., source code, system logs, version control system history, code review feedbacks). Such data are also typically updated with relatively high frequency, and subject to distribution drifts caused by continuous configuration changes to the underlying infrastructure. In such a highly dynamic scenario, traditional model-driven approaches alone may be inadequate at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust data-driven methods to support their decisions based on historical information. For instance, effective anomaly detection capabilities may also help in conducting more precise and efficient root-cause analysis. Also, leveraging on accurate forecasting and intelligent control strategies would improve resource management. Given their ability to deal with high-dimensional, complex data, Deep Learning-based methods are the most straightforward option for the realization of the aforementioned support tools. On the other hand, because of their complexity, this kind of models often requires huge processing power, and suitable hardware, to be operated effectively at scale. These aspects must be carefully addressed when applying such methods in the context of data center operations. Automated operations approaches must be dependable and cost-efficient, not to degrade the services they are built to improve. i