638 research outputs found

    Forecasting of Sporadic Products: Practical and Theoretical Considerations

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    This paper focuses on forecasting of products with sporadic demand. The demand for such products is not continuous but diffused seemingly at random, with a large proportion of zero values in the analyzed time series. The sporadic character of demand patterns actually means that the information available on the demand for previous selling periods is patchy, resulting in lower quality of data available. Under such circumstances demand forecasting is a challenging task. We present the results of a case study, where forecasting practice of a pharmaceutical wholesaler firm –we call it Pharma– is analyzed and developed. We present state-of-the-art knowledge related to demand forecasting of sporadic products and test suggestions related to them. We show that these suggestions can only partly be backed. We extend therefore the suggested product classification scheme and recommend using the concept of demand data aggregation. This will reduce sporadicity and result in higher quality forecasting. Aggregation also helps to specify the recommended forecast period, the length of time recommended to calculate the forecast for. The managerial consequences of these suggestions are also discussed, and future research directions are highlighted

    Forecasting spare part demand with installed base information: A review

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    The classical spare part demand forecasting literature studies methods for forecasting intermittent demand. However, the majority of these methods do not consider the underlying demand-generating factors. The demand for spare parts originates from the replacement of parts in the installed base of machines, either preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast the future demand for spare parts. This paper reviews the literature on the use of such installed base information for spare part demand forecasting in order to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) the value of using installed base information to improve forecasting; and (4) the limits of the existing methods. This serves as motivation for future research

    Framework for spare parts management. Methods to improve decision making. (Marco de referencia para la gestión de repuestos. Métodos para la mejora del proceso de toma de decisiones)

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    Este trabajo surge en un marco de colaboración con la Universidad de Brescia, mediante el intercambio de información con uno de sus alumnos y el seguimiento en paralelo de los departamentos homo logos correspondientes. Este intercambio se facilita tanto por el uso del inglés para la realización del trabajo, como por los conocimientos de italiano adquiridos en el Politécnico de Milán durante una estancia anual donde el tema de investigación presentado aparece de forma recurrente. En el sector industrial, especialmente en aquellas empresas con activos que requieren grandes inversiones y alto grado de especialización, nos encontramos con el problema de la gestión de repuestos, cuya gestión tiene un notable impacto en el desempeño final de la organización. Cualquier fallo en la maquinaria conlleva un paro en la producción principal, y este paro n depende del tiempo de diagnóstico, reparación o cambio de pieza. Siendo piezas de alta especialización esto puede implicar fabricar el repuesto desde cero con sus implicaciones: Caí da del nivel de servicio, menores tasas de producción, roturas de stock, perdidas... Se podrí a pensar que la solución es mantener un nivel de stock de piezas de repuesto de manera que se reduzcan estos Lead Time, pero se debe tener en cuenta que mantener un stock de piezas de repuestos con riesgo de obsolescencia alto, costes de mantenimiento de inventario elevado y de alguna manera “secundarios” para la producción, puede significar unos costes excesivos. La clave está en buscar el balance que permita minimizar los costes conjuntos y encuentre el modo de proceder óptimo. En la literatura se estudia el problema de gestión de repuestos, pero se hace de manera demasiado focalizada, estudiando y actuando sobre cada pequen o proceso decisional en vez de tratar de atacar el todo. Este ha sido el objetivo de este trabajo, ofrecer una metodología completa, que englobe todos los procesos de gestión, que aporte herramientas y modelos, que encontrando su justificación en la literatura, proporcionen un respaldo y un soporte objetivo a los protocolos de acción a la hora de la toma de decisiones.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías Industriale

    Determining inventory base stock levels of expendable spare parts under service level agreement for on-time delivery

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    Availability of service parts is critical to have adequate equipment maintenance in order to avoid costs associated with unplanned shut downs, loss of production, and increase safety among others. Determining an adequate quantity of service parts to have is a challenging situation that companies have to deal with because service parts encompass intermittent demand; this type of demand is of variable size and occurring at irregular intervals. As consequence of the nature of service parts, companies have to have large quantities of parts in stock increasing their holding cost, or companies have to place expedited order to avoid late deliveries and avoid penalty fees. In this research, a model is developed in order to determine the inventory base level for all parts in order to minimize holding cost, penalty cost for late delivery and shipment cost while satisfying an agreed service level for on-time equipment delivery. Scenario based approach is utilized to provide a robust result. Given that constraints and variables increase dramatically, pre-processing techniques are utilized to reduce the model and obtain a solution for the large scale model within a reasonable time

    A Comparative Study of Bootstrapping Techniques for Inventory Control

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    Setting correct inventory levels is an important business consideration in order to minimise inventory investment while at the same time ensuring sufficient inventory levels to meet customer demand. Inventory management has a significant impact on both financial and customer service aspects of a business. Selecting appropriate inventory levels requires that products’ lead time demand be accurately estimated in order to calculate the reorder point. The purpose of this study was to empirically determine whether bootstrapping methods used to estimate the lead time demand distribution and reorder point calculation could match or even outperform a standard parametric approach. The two bootstrapping methods compared in this research included variations of those presented by Bookbinder and Lordahl [1989] and do Rego and de Mesquita [2015]. These were compared to the standard parametric approach common in practice which makes use of the Normal distribution for modelling lead time demand. The three reorder point calculation methods were each incorporated into the inventory policy simulations using data supplied by a South African automotive spare parts business. The simulations covered a period of twelve months and were repeated for multiple service levels ranging from 70 to 99 percent. Results of the simulations were compared at a high level as well as for groups of items identified using segmentation techniques which considered different item demand and lead time characteristics. Key findings were that the Normal approximation method was far superior in terms of the service level metric, while the variation of the Bookbinder and Lordahl [1989] method adopted in this study presented possible cost benefits at lower service levels

    Exploring Computing Continuum in IoT Systems: Sensing, Communicating and Processing at the Network Edge

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    As Internet of Things (IoT), originally comprising of only a few simple sensing devices, reaches 34 billion units by the end of 2020, they cannot be defined as merely monitoring sensors anymore. IoT capabilities have been improved in recent years as relatively large internal computation and storage capacity are becoming a commodity. In the early days of IoT, processing and storage were typically performed in cloud. New IoT architectures are able to perform complex tasks directly on-device, thus enabling the concept of an extended computational continuum. Real-time critical scenarios e.g. autonomous vehicles sensing, area surveying or disaster rescue and recovery require all the actors involved to be coordinated and collaborate without human interaction to a common goal, sharing data and resources, even in intermittent networks covered areas. This poses new problems in distributed systems, resource management, device orchestration,as well as data processing. This work proposes a new orchestration and communication framework, namely CContinuum, designed to manage resources in heterogeneous IoT architectures across multiple application scenarios. This work focuses on two key sustainability macroscenarios: (a) environmental sensing and awareness, and (b) electric mobility support. In the first case a mechanism to measure air quality over a long period of time for different applications at global scale (3 continents 4 countries) is introduced. The system has been developed in-house from the sensor design to the mist-computing operations performed by the nodes. In the second scenario, a technique to transmit large amounts of fine-time granularity battery data from a moving vehicle to a control center is proposed jointly with the ability of allocating tasks on demand within the computing continuum

    Spare parts planning and control for maintenance operations

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    This paper presents a framework for planning and control of the spare parts supply chain inorganizations that use and maintain high-value capital assets. Decisions in the framework aredecomposed hierarchically and interfaces are described. We provide relevant literature to aiddecision making and identify open research topics. The framework can be used to increasethe e¿ciency, consistency and sustainability of decisions on how to plan and control a spareparts supply chain. This point is illustrated by applying it in a case-study. Applicability of theframework in di¿erent environments is also investigated
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