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    CIFRE GE Medical Systems and Armines/IMT Atlantique


    Modelling and optimization of the costs of a supply chain of repairable items by exploitation of reliability data.
    Phd Student: M. Hassan EL GARRAB
    Director of thesis: Bruno CASTANIER
    Armines/IMT Atlantique manager: David LEMOINE
    Industriel manager: Adnane LAZRAK (GE Medical Systems).

    Beginning of thesis : january 2018

    Team: Reliability Engineering and Decision-Making tools

    Contacts : bruno.castanier @ univ-angers.fr


    GEHC (GE Healthcare) is the medical branch of the General Electric conglomerate, a world leader in sales and services of medical systems including medical imaging (Scanners, MRI, Mammography ...). Given the criticality of its products (medical devices) and the technological specificity of its components, GEHC offers a maintenance service for its customers. The main objective of this service is to ensure the reliability of its products (reduce the occurrence rates of breakdowns), reduce downtime while ensuring to reduce all costs associated with its interventions. In view of the criticality and cost of its products as well as the geographical dispersion of its customers, GEHC must be well versed in all maintenance and supply chain processes for spare parts.

    Spare parts used during a maintenance service intervention or following a replacement request made directly by the customer, can be of two consumable or repairable types, which follow two significantly different supply chain models:

    Consumable parts are not recoverable due to failures, they can no longer be used by other systems. Thus, in this case the supply chain model is mainly based on supply and distribution policies in new parts. In this context, research work was carried out in CIFRE thesis (in collaboration between GEHC and the Ecole des Mines de Nantes), this work has mainly proposed improvements to the forecasting and inventory management process in consumable parts [Lazrak , 2015].

    Repairable parts are recoverable due to breakdowns, and can be repaired at internal repair centers to avoid buying new parts. Thus, the management of this type of parts must not only take into account the cost and productivity aspects of the repair lines, but must also follow a supply chain model that makes it possible to synchronize between the flows into new parts and the flows into repaired parts to supply the stock or respond directly to customer demand [Lazrak et al., 2014].

    Industrial issues and general objectives of the thesis

    The problematic of this thesis concerns this second model of supply chain of repairable parts and mainly the optimization of the costs and the improvement of the performances of the repair centers of this logistic chain. Indeed, these centers must respond to random requests for repair which makes the estimation of their costs and the sizing of their stocks extremely delicate. This lack of visibility prevents these centers from proposing upstream to the customer a maintenance contract for its installed base, they are therefore limited to a responsive model to customer solicitations increasing the difficulty of aligning the repair capacity and levels. inventory with the needs of customers.

    Thus, the first objective is to propose a general approach which makes it possible to better characterize the flow of these requests for repair as a function of the evolution of the knowledge on the product that can be associated with its maturity in phase operation and knowledge of the operating environments of these spare parts specific to the products in which these parts may be used and the operating conditions specific to each customer. To do this, we will seek to support the construction of this methodology on systems reliability type approaches by differentiating the new parts for which only reliability studies carried out in the design phase are available, parts for which data from operation (frequency and type of failure, conditions of use, ...) are available in the GEHC after-sales service databases. One of the challenges at this level will be to model the effects of the evolution of system maturity and associated knowledge throughout the system operating cycle on reliability models and the estimation of effects in terms of compliance with maintenance contracts. Bayesian approaches [Aronis et al., 2004] or based on neural network learning [Karunanithi et al., 1992] or Bayesian network [Gruber and Ben-Gal, 2012] could be considered at this level. In addition, one of the expectations of this phase at the industrial is clearly the initiation of a feedback process on the reliability of parts over their entire life cycle (from design to disposal), [Zwingelstein, 1996], [Blanchard et al., 1995].

    A second objective will be to build a global cost model of the repair operations, relying on one hand on the requests previously estimated and on the other hand on the GE logistics data and the information on the client installed bases. For any new data, the model will have to estimate what is the part that belongs to the generic versus the specific. It should also be able to take into account the evolution of the system in its operational and commercial life cycle. One of the particularities will be to take into account aspects related to maintenance by redefining this life cycle according to the level of experience feedback [Aronis et al., 2004]. This will ultimately provide, through a decision support tool, maintenance contracts tailored to customer requests and the appropriate repair policies associated with these contracts, for each repair center [Tempelmeier, 2006]. ] [Jin and Liao, 2009].

    Finally, based on the previous results, a logistical optimization will be conducted in order to size the repair center stocks to ensure the continuity of the customer service in accordance with the maintenance contracts, and to optimize the inter-center flows to minimize the capital costs of spare parts [van Houtum and Kranenburg, 2015] [Muckstadt, 2004]


    [Aronis et al., 2004] Aronis, K. P., Magou, I., Dekker, R., & Tagaras, G. (2004). Inventory control of spare parts using a Bayesian approach: A case study. European Journal of Operational Research, 154(3), 730-739.

    [Blanchard et al., 1995] Blanchard, B. S., Verma, D., & Peterson, E. L. (1995). Maintainability: a key to effective serviceability and maintenance management (Vol. 13). John Wiley & Sons.

    [Grubet et Ben-Gal, 2012] Gruber, A., & Ben-Gal, I. (2012). Efficient Bayesian network learning for system optimization in reliability engineering. Quality Technology and Quantitative Management, 9(1), 7-114.

    [Jin et Liao, 2009] Jin, T., & Liao, H. (2009). Spare parts inventory control considering stochastic growth of an installed base. Computers & Industrial Engineering, 56(1), 452-460.

    [Karunanithi et al., 1992]  “Using Neural Networks in Reliability Prediction.” IEEE Software 9 : 53-59.

    [Lazrak, 2015] Lazrak, A. (2015). Amélioration des processus de prévision et de gestion des stocks dans le cadre d’une chaine logistique de pièces de rechange, Thèse de Doctorat, Mines Nantes.

    [Lazrak et al., 2014] Lazrak, A., Castanier, B., Lemoine, D., Heidsieck, R., & Thenot, C. (2014, June). Integration approaches of forecasting methods selection with inventory management indicators in the case of spare parts supply chain. In Logistics and Operations Management (GOL), 2014 International Conference on (pp. 20-26). IEEE.

    [Muckstadt, 2004] Muckstadt, J. A. (2004). Analysis and algorithms for service parts supply chains. Springer Science & Business Media.

    [Tempelmeier, 2006] Tempelmeier, H. (2006). Supply chain inventory optimization with two customer classes in discrete time. European Journal of Operational Research, 174(1), 600-621.

    [van Houtum et Kranenburg, 2015] Van Houtum, G. J., & Kranenburg, B. (2015). Spare Parts Inventory Control under System Availability Constraints (Vol. 227). Springer.

    [Zwingelstein, 1996] Zwingelstein, G. (1996). La maintenance basée sur la fiabilité : guide pratique d'application de la RCM. Hermès.