Le wip concurrent : une proposition de file d'attente du point de vue du produit pour caractériser le temps de cycle

International audienceNous nous intéressons à des modèles de théorie des files d'attente pour caractériser les temps de cycle (délais de fabrication à différentes étapes de production) de produits dans des productions complexes. Des modèles de théorie de file d'attente sont régulièrement utilisés dans l'industrie pour cela, mais en dehors de leurs hypothèses de modélisation. Nous montrons tout d'abord dans cet article l'incidence d'une utilisation de ces modèles hors hypothèses sur la qualité de l'estimation du temps de cycle. Nous proposons alors un nouveau type de représentation des files d'attente, du point de vue des produits et sans hypothèses sur les équipements. Nous montrons sur un cas d'étude réel d'équipements complexes de microélectronique comment cette nouvelle représentation des files d'attente permet, en plus d'une première caractérisation du temps de cycle, d'extraire des informations fondamentales de n'importe quel groupe d'équipements traitant un même flux de produits. Enfin, nous discutons des étapes à venir pour intégrer cette représentation dans des outils de simulation ainsi que dans des modèles totalement génériques de files d'attente

A study of variability induced by events dependency in microelectronic production

-Complex manufacturing systems are subject to high levels of variability that decrease productivity, increase cycle times and severely impact the systems tractability. As accurate modelling of the sources of variability is a cornerstone to intelligent decision making, we investigate the consequences of the assumption of independent and identically distributed variables that is often made when modelling sources of variability such as down-times, arrivals, or process-times. We first explain the experiment setting that allows, through simulations and statistical tests, to measure the variability potential stored in a specific sequence of data. We show from industrial data that dependent behaviors might actually be the rule with potentially considerable consequences in terms of cycle time. As complex industries require strong levers to allow their tractability, this work underlines the need for a richer and more accurate modelling of real systems. Keywords-variability; cycle time; dependent events; simulation; complex manufacturing; industry 4.0 I. Accurate modelling of variability and the independence assumption Industry 4.0 is said to be the next industrial revolution. The proper use of real-time information in complex manufacturing systems is expected to allow more customization of products in highly flexible production factories. Semiconductor High Mix Low Volume (HMLV) manufacturing facilities (called fabs) are one example of candidates for this transition towards "smart industries". However, because of the high levels of variability, the environment of a HMLV fab is highly stochastic and difficult to manage. The uncontrolled variability limits the predictability of the system and thus the ability to meet delivery requirements in terms of volumes, cycle times and due dates. Typically, the HMLV STMicroelectronics Crolles 300 fab regularly experiences significant mix changes that result in unanticipated bottlenecks, leading to firefighting to meet commitment to customers. The overarching goal of our strategy is to improve the forecasting of future occurrences of bottlenecks and cycle time issues in order to anticipate them through allocation of the correct attention and resources. Our current finite capacity projection engine can effectively forecast bottlenecks, but it does not include reliable cycle time estimates. In order to enhance our projections, better forecast cycle time losses (queuing times), improve the tractability of our system and reduce our cycle times, we now need accurate dynamic cycle time predictions. As increased cycle-time is the main reason workflow variability is studied (both by the scientific community and practitioners, see e.g. [1] and [2]), what follows concentrates on cycle times. Moreover, the "variability" we account for should be understood as the potential to create higher cycle times, even though "variability" may be understood in a broader meaning. This choice is made for the sake of clarity, but the methodology we propose and the discussion we lead can be applied to any other measurable indicator. Sources of variability have been intensely investigated in both the literature and the industry, and tool down-times, arrivals variability as well as process-time variability are recognized as the major sources of variability in that sense that they create higher cycle times (see [3] for a review and discussion). As a consequence, these factors are widely integrated into queuing formulas and simulation models with the objective to better model the complex reality of manufacturing facilities. One commonly accepted assumption in the development of these models is that the variables (MTBF, MTTR, processing times, time between arrivals, etc.) are independent and identically distributed (i.i.d.) random variables. However, these assumptions might be the reason for models inaccuracies as [4] points out in a literature review on queuing theory. Several authors have studied the potential effects of dependencies, such as [5] who studied the potential effects of dependencies between arrivals and process-times or [6] who investigated dependent process times, [4] also gives further references for studies on dependencies effects. In a previous work [3], we pinpointed a few elements from industrial data that questioned the viability of this assumption in complex manufacturing systems. Figure 1: Number of arrivals per week from real data (A) and generated by removing dependencies (B)Comment: International Conference on Industrial Engineering and Systems Management, Oct 2017, Saarebr{\"u}cke, German

Workflow variability modeling in microelectronic manufacturing

Dans un contexte où l’industrie du semi-conducteur explore de nouvelles voies avec la diversification des produits et le paradigme de « More than Moore », les délais de livraison et la précision de livraison sont des éléments clés pour la compétitivité d’entreprises de semi-conducteur et l’industrie 4.0 en général. Les systèmes de production sont cependant sujets à de la « variabilité », qui crée des embouteillages dans la production de manière incontrôlée et imprévisible. Cette thèse CIFRE (partenariat entre le laboratoire GSCOP et STMicroelectronics) s’attaque à ce problème de la variabilité dans la fabrication en environnement complexe. La première partie de cette thèse offre une étude approfondie de la variabilité: nous mettons d’abord en avant les conséquences de la variabilité pour mieux la définir, puis nous clarifions que la variabilité concerne les flux de production en introduisant la notion de variabilité des flux de production et en apportant des éléments de mesure associés, et nous clôturons cette première partie par l’étude des sources de variabilité à travers une étude bibliographique et des exemples industriels. La seconde partie est dédiée à l’intégration de la variabilité dans les outils de gestion de production: nous montrons comment une partie des conséquences peut être mesurée et intégrée aux projections d’encours pour améliorer le contrôle et la prévisibilité de la production, proposons un nouvel outil ((the WIP Concurrent) pour mesurer plus précisément les performances des systèmes en environnement complexe, et mettons en avant des effets de dépendances prépondérants sur la variabilité des flux de production et pourtant jamais pris en compte dans les modèles. La troisième et dernière partie de la thèse couvre les perspectives de réduction de la variabilité : en se basant sur les éléments présentés dans la thèse, nous proposons un plan pour réduire la variabilité des flux de production sur le court terme, et une direction pour la recherche à moyen et long terme.In the context of Industry 4.0 and the More than Moore’s paradigm, delivery precision and short cycle times are essential to the competitiveness of High Mix Low Volume semiconductor manufacturing and future industries in general. So called “variability” however creates uncontrolled and unpredictable “traffic-jams” in manufacturing systems, increasing cycle times and decreasing the systems’ tractability. This research, a CIFRE PhD between the GSCOP laboratory and STMicroelectronics, addresses this issue of variability in complex manufacturing environment. We first conducted, in the first part of the manuscript, an in-depth study of “variability”: we approached the notion through its consequences in manufacturing systems, clarified that the variability was about the workflow, introducing the notion of workflow variability and measures that come with it, and identified the main sources of variability through a literature review and real-world examples. We focused in the second part of this manuscript on the integration of workflow variability in production management tools: We showed how integrating the stable consequences of workflow variability can improve WIP projections in complex systems and increase the control on such systems, proposed a new tool (the Concurrent WIP) to better measure the performances of systems subject to high workflow variability, and showed that complex “dependency” mechanisms play a key role in workflow variability yet are not integrated in any model. Finally, the third and last part of the manuscript organized perspectives for variability reduction: based on the work of this manuscript, we showed a framework for variability reduction on the short term, and proposed a direction for medium and long-term research

Modélisation de la variabilité des flux de production en fabrication microélectronique

In the context of Industry 4.0 and the More than Moore’s paradigm, delivery precision and short cycle times are essential to the competitiveness of High Mix Low Volume semiconductor manufacturing and future industries in general. So called “variability” however creates uncontrolled and unpredictable “traffic-jams” in manufacturing systems, increasing cycle times and decreasing the systems’ tractability. This research, a CIFRE PhD between the GSCOP laboratory and STMicroelectronics, addresses this issue of variability in complex manufacturing environment. We first conducted, in the first part of the manuscript, an in-depth study of “variability”: we approached the notion through its consequences in manufacturing systems, clarified that the variability was about the workflow, introducing the notion of workflow variability and measures that come with it, and identified the main sources of variability through a literature review and real-world examples. We focused in the second part of this manuscript on the integration of workflow variability in production management tools: We showed how integrating the stable consequences of workflow variability can improve WIP projections in complex systems and increase the control on such systems, proposed a new tool (the Concurrent WIP) to better measure the performances of systems subject to high workflow variability, and showed that complex “dependency” mechanisms play a key role in workflow variability yet are not integrated in any model. Finally, the third and last part of the manuscript organized perspectives for variability reduction: based on the work of this manuscript, we showed a framework for variability reduction on the short term, and proposed a direction for medium and long-term research.Dans un contexte où l’industrie du semi-conducteur explore de nouvelles voies avec la diversification des produits et le paradigme de « More than Moore », les délais de livraison et la précision de livraison sont des éléments clés pour la compétitivité d’entreprises de semi-conducteur et l’industrie 4.0 en général. Les systèmes de production sont cependant sujets à de la « variabilité », qui crée des embouteillages dans la production de manière incontrôlée et imprévisible. Cette thèse CIFRE (partenariat entre le laboratoire GSCOP et STMicroelectronics) s’attaque à ce problème de la variabilité dans la fabrication en environnement complexe. La première partie de cette thèse offre une étude approfondie de la variabilité: nous mettons d’abord en avant les conséquences de la variabilité pour mieux la définir, puis nous clarifions que la variabilité concerne les flux de production en introduisant la notion de variabilité des flux de production et en apportant des éléments de mesure associés, et nous clôturons cette première partie par l’étude des sources de variabilité à travers une étude bibliographique et des exemples industriels. La seconde partie est dédiée à l’intégration de la variabilité dans les outils de gestion de production: nous montrons comment une partie des conséquences peut être mesurée et intégrée aux projections d’encours pour améliorer le contrôle et la prévisibilité de la production, proposons un nouvel outil ((the WIP Concurrent) pour mesurer plus précisément les performances des systèmes en environnement complexe, et mettons en avant des effets de dépendances prépondérants sur la variabilité des flux de production et pourtant jamais pris en compte dans les modèles. La troisième et dernière partie de la thèse couvre les perspectives de réduction de la variabilité : en se basant sur les éléments présentés dans la thèse, nous proposons un plan pour réduire la variabilité des flux de production sur le court terme, et une direction pour la recherche à moyen et long terme

Concurrent WIP and an application to clearing functions for complex heterogenous systems

International audienceComplex manufacturing systems are challenging to study because of the high level of information required and the inaccessibility of most of it. Their tractability is however essential for the efficiency of state-of-the-art industries. This is particularly the case in the semiconductor industry that faces high mix and low volume conditions, and for which traditional methods fail to capture the high complexity and require continuous actions and corrections to adjust to heterogeneous toolsets and product-mix. We present the Concurrent WIP (CWIP), a new way of studying such systems at the level of a process-cluster by identifying each job's queue from its own perspective. CWIP is designed to be practical, with a low level of resource investments, yet informative. We explain how CWIP can be computed based on historical data and then used to derive capacity estimates and clearing functions without any assumptions on the system or on the form of the functions. In the process, we derive not only an average workload-dependent capacity, but also a confidence interval on this capacity. The relevance and efficiency of the proposed estimates are experimentally tested on a simulated system mimicking a small but complex process-cluster of the semiconductor industry. The estimates are used to predict WIP absorption times and we show how they characterize well not only the average behavior but also the full range of possible behaviors of the system. Finally, we discuss further applications of CWIP, that could be used to compute refined clearing functions or to monitor complex systems

A literature review on variability in semiconductor manufacturing: the next forward leap to Industry 4.0

International audienceSemiconductor fabrication plants are subject to high levels of variability because of a variety of factors including re-entrant flows, multiple products, machine breakdowns, heterogeneous toolsets or batching processes. This variability decreases productivity, increases cycle times and severely impacts the systems tractability. Many authors have proposed approaches to better model the impact of variability, often focusing on specific aspects. We present a review of the sources of variability discussed in the literature and the methods proposed to manage them. We discuss their relative importance as seen by the authors as well as the limits current theories face. Finally, we emphasize the lack of research on some critical aspects related to High Mix Low Volume fabs. In this setting, the ability of practitioners to predict and anticipate the effects of changing product mix and client orders remains challenging, delaying the transition of semiconductor manufacturers towards Industry 4.0