Exploitation des informations de traçabilité pour l'optimisation des choix en production et en logistique

The recent product traceability requirements demonstrate an industrial need to improve the information management strategies within traceability systems in order to evolve from reactivity to proactivity. The aim of this work is to exploit the recently available real-time access to traceability information. We propose the utilization of artificial intelligence and operational research techniques to analyse the information and therefore suggest improvement actions. This research project is composed of two main activities: first, the diagnosis of the criticality value associated to a production regarding the traceability information and second, the actions to undertake as a result of this diagnosis. One of the issues studied in this thesis is the problem of minimizing the size of products recall. Initially the problem of raw materials dispersion minimization is analysed. Then a result of the dispersion rate along with other production criteria are evaluated in order to determine a risk level criterion in terms of quality and security that we name “production criticality”. This criterion is used subsequently to optimize deliveries dispatch with the purpose of minimizing the number of batch recalls in case of crisis. This is achieved by implementing flexible and reactive toolsDans le cours des dernières années, la traçabilité s’est positionnée au cœur de plusieurs enjeux fondamentaux pour les entreprises. Cependant, cette notion est encore aujourd’hui vue comme une contrainte, servant uniquement à respecter des impositions légales et à rappeler des produits non-conformes. Dans ce projet, nous nous sommes attachés à élargir la définition de traçabilité aux domaines de la prévision et de la protection, pour qu’elle ne soit plus perçue comme une obligation supplémentaire à assumer, mais comme un véritable argument d’avantage concurrentiel. Ces travaux de recherche sont consacrés à l’exploitation des informations de traçabilité par l’utilisation des techniques d’intelligence artificielle et de recherche opérationnelle, afin de proposer des actions d’amélioration en production et en logistique. Ils ont été menés en collaboration avec la société ADENTS International, experte en traçabilité. Ce projet est composé de deux principaux axes de travail : l’un portant sur le diagnostic de la criticité d’une production, en fonction des informations de traçabilité et l’autre sur les actions à entreprendre par rapport à ce diagnostic. Dans le premier, nous remarquons l’importance de la notion de dispersion de matières premières et des composants, ainsi que celle des écarts en termes de qualité et de sécurité. Dans le second, nous nous intéressons d’avantage à la notion de rappel de produits, visant une gestion de transformations adaptée en aval de la production, afin de minimiser ces rappels. Pour la mise en place de ces deux grandes activités, nous nous sommes engagés à proposer des modèles et des méthodes flexibles et réactives, pouvant s’adapter à la versatilité ontologique des flux d’informations de traçabilit

Exploiting traceability information in order to optimize production and logistic choices

Dans le cours des dernières années, la traçabilité s’est positionnée au cœur de plusieurs enjeux fondamentaux pour les entreprises. Cependant, cette notion est encore aujourd’hui vue comme une contrainte, servant uniquement à respecter des impositions légales et à rappeler des produits non-conformes. Dans ce projet, nous nous sommes attachés à élargir la définition de traçabilité aux domaines de la prévision et de la protection, pour qu’elle ne soit plus perçue comme une obligation supplémentaire à assumer, mais comme un véritable argument d’avantage concurrentiel. Ces travaux de recherche sont consacrés à l’exploitation des informations de traçabilité par l’utilisation des techniques d’intelligence artificielle et de recherche opérationnelle, afin de proposer des actions d’amélioration en production et en logistique. Ils ont été menés en collaboration avec la société ADENTS International, experte en traçabilité. Ce projet est composé de deux principaux axes de travail : l’un portant sur le diagnostic de la criticité d’une production, en fonction des informations de traçabilité et l’autre sur les actions à entreprendre par rapport à ce diagnostic. Dans le premier, nous remarquons l’importance de la notion de dispersion de matières premières et des composants, ainsi que celle des écarts en termes de qualité et de sécurité. Dans le second, nous nous intéressons d’avantage à la notion de rappel de produits, visant une gestion de transformations adaptée en aval de la production, afin de minimiser ces rappels. Pour la mise en place de ces deux grandes activités, nous nous sommes engagés à proposer des modèles et des méthodes flexibles et réactives, pouvant s’adapter à la versatilité ontologique des flux d’informations de traçabilitéThe recent product traceability requirements demonstrate an industrial need to improve the information management strategies within traceability systems in order to evolve from reactivity to proactivity. The aim of this work is to exploit the recently available real-time access to traceability information. We propose the utilization of artificial intelligence and operational research techniques to analyse the information and therefore suggest improvement actions. This research project is composed of two main activities: first, the diagnosis of the criticality value associated to a production regarding the traceability information and second, the actions to undertake as a result of this diagnosis. One of the issues studied in this thesis is the problem of minimizing the size of products recall. Initially the problem of raw materials dispersion minimization is analysed. Then a result of the dispersion rate along with other production criteria are evaluated in order to determine a risk level criterion in terms of quality and security that we name “production criticality”. This criterion is used subsequently to optimize deliveries dispatch with the purpose of minimizing the number of batch recalls in case of crisis. This is achieved by implementing flexible and reactive tool

Mining serialized data: Opportunities in the pharmaceutical supply chain

International audienceThe serialization of pharmaceutical products enables the detailed monitoring of pharmaceutical flows that should lead to improvements in the performance of healthcare institutions. This article provides an analysis of the opportunities for data mining and data visualization in the pharmaceutical industry. It yields two main contributions (i) it lists the main potential usage of data mining and data visualization when applied to serialized pharmaceutical data; and (ii) it identifies the data requirements for implementation. The proposed analysis reveals that serialization analytics should help reduce the distribution of counterfeit drugs, improve demand forecasting and provide insights into improving inventory management decision

Global Survey of Outcomes of Neurocritical Care Patients: Analysis of the PRINCE Study Part 2

BACKGROUND: Neurocritical care is devoted to the care of critically ill patients with acute neurological or neurosurgical emergencies. There is limited information regarding epidemiological data, disease characteristics, variability of clinical care, and in-hospital mortality of neurocritically ill patients worldwide. We addressed these issues in the Point PRevalence In Neurocritical CarE (PRINCE) study, a prospective, cross-sectional, observational study. METHODS: We recruited patients from various intensive care units (ICUs) admitted on a pre-specified date, and the investigators recorded specific clinical care activities they performed on the subjects during their first 7 days of admission or discharge (whichever came first) from their ICUs and at hospital discharge. In this manuscript, we analyzed the final data set of the study that included patient admission characteristics, disease type and severity, ICU resources, ICU and hospital length of stay, and in-hospital mortality. We present descriptive statistics to summarize data from the case report form. We tested differences between geographically grouped data using parametric and nonparametric testing as appropriate. We used a multivariable logistic regression model to evaluate factors associated with in-hospital mortality. RESULTS: We analyzed data from 1545 patients admitted to 147 participating sites from 31 countries of which most were from North America (69%, N = 1063). Globally, there was variability in patient characteristics, admission diagnosis, ICU treatment team and resource allocation, and in-hospital mortality. Seventy-three percent of the participating centers were academic, and the most common admitting diagnosis was subarachnoid hemorrhage (13%). The majority of patients were male (59%), a half of whom had at least two comorbidities, and median Glasgow Coma Scale (GCS) of 13. Factors associated with in-hospital mortality included age (OR 1.03; 95% CI, 1.02 to 1.04); lower GCS (OR 1.20; 95% CI, 1.14 to 1.16 for every point reduction in GCS); pupillary reactivity (OR 1.8; 95% CI, 1.09 to 3.23 for bilateral unreactive pupils); admission source (emergency room versus direct admission [OR 2.2; 95% CI, 1.3 to 3.75]; admission from a general ward versus direct admission [OR 5.85; 95% CI, 2.75 to 12.45; and admission from another ICU versus direct admission [OR 3.34; 95% CI, 1.27 to 8.8]); and the absence of a dedicated neurocritical care unit (NCCU) (OR 1.7; 95% CI, 1.04 to 2.47). CONCLUSION: PRINCE is the first study to evaluate care patterns of neurocritical patients worldwide. The data suggest that there is a wide variability in clinical care resources and patient characteristics. Neurological severity of illness and the absence of a dedicated NCCU are independent predictors of in-patient mortality.status: publishe