Dedicated maintenance and repair shop control for spare parts networks

We study a repairable inventory system dedicated to a single component that is critical in operating a capital good. The system consists of a stock point containing spare components, and a dedicated repair shop responsible for repairing damaged components. Components are replaced using an age-replacement strategy, which sends components to the repair shop either preventively if it reaches the age-threshold, and correctively otherwise. Damaged components are replaced by new ones if there are spare components available, otherwise the capital good is inoperable. If there is free capacity in the repair shop, then the repair of the damaged component immediately starts, otherwise it is queued. The manager decides on the number of repairables in the system, the age-threshold, and the capacity of the repair shop. There is an inherent trade-off: A low (high) age-threshold reduces (increases) the probability of a corrective replacement but increases (decreases) the demand for repair capacity, and a high (low) number of repairables in the system leads to higher (lower) holding costs, but decreases (increases) the probability of downtime. We first show that the single capital good setting can be modelled as a closed queuing network with finite population, which we show is equivalent to a single queue with fixed capacity and state-dependent arrivals. For this queue, we derive closed-form expressions for the steady-state distribution. We subsequently use these results to approximate performance measures for the setting with multiple capital goods

Forecasting of compound Erlang demand

Intermittent demand items dominate service and repair inventories in many industries and they are known to be the source of dramatic inefficiencies in the defence sector. However, research in forecasting such items has been limited. Previous work in this area has been developed upon the assumption of a Bernoulli or a Poisson demand arrival process. Nevertheless, intermittent demand patterns may often deviate from the memory-less assumption. In this work we extend analytically previous important results to model intermittent demand based on a compound Erlang process, and we provide a comprehensive categorisation scheme to be used for forecasting purposes. In a numerical investigation we assess the benefit of departing from the memory-less assumption and we provide insights into how the degree of determinism inherent in the process affects forecast accuracy. Operationalised suggestions are offered to managers and software manufacturers dealing with intermittent demand items

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Politiques de pilotage de flux dans les chaînes logistiques : impact de l'utilisation des prévisions sur la gestion de stocks

The efficient management of supply chain flows is an important concern that is considered by enterprises as a lever enabling to improve the customer service level at low costs. Several investigations deal with this issue by developing tools for a better flow management. Within this framework, our thesis proposes new flow management policies.In the first part of this work, we provide a synthesis of the existing flow management policies by highlighting their similarities and their differences. This enables us to propose a classification of these policies based on the type of available information on the demand. Such a classification constitutes a tool that enterprises may use to choose the best flow management policy for a given context.In the second part of this work, we propose an extension of the classical inventory control policies, based on an inventory consumption approach, to future requirements based policies where the requirements are expressed in the form of uncertain forecasts. Thus, we develop new dynamic forecast based inventory control policies based on the concept of forecast uncertainty. A numerical study compares these policies and enables us to show the benefit of using forecasts in managing flows. Moreover, we study the equivalences that exist between the different flow management policies presented in this thesis. This enables us to give a more coherent global vision of these policies and to highlight the relations that exist between them.Le pilotage de flux dans les chaînes logistiques représente un enjeu majeur pour les entreprises qui leur permet d'améliorer la qualité du service vis-à-vis des clients tout en réduisant les coûts. Plusieurs travaux s'intéressent à cette problématique en proposant des outils pour un meilleur pilotage. Cette thèse s'inscrit dans le cadre de ces travaux et a pour objectif de proposer de nouvelles politiques de pilotage de flux.Dans la première partie de ce travail, nous avons effectué une synthèse des politiques existantes en mettant en évidence leurs similarités et leurs différences. Ceci nous a permis de proposer une classification de ces politiques en se basant sur le type de l'information disponible sur la demande, ce qui représente un outil d'aide au choix de la meilleure politique de pilotage dans un contexte industriel donné.Dans la deuxième partie de ce travail, nous avons effectué une extension des politiques de gestion de stock classiques, basées sur une approche de renouvellement de la consommation, à des politiques basées sur une approche de pilotage par les besoins futurs, ces besoins étant exprimés sous forme de prévisions. Ceci nous a permis de développer des nouvelles politiques dynamiques de gestion de stock sur prévisions basées sur la notion d'incertitude prévisionnelle. Nous avons également effectué une étude numérique comparative de ces politiques qui met en valeur les bénéfices de l'utilisation des prévisions de la demande dans le pilotage de flux. Par ailleurs, nous avons analysé les équivalences qui existent entre les différentes politiques de pilotage de flux, traitées dans le cadre de cette thèse, ce qui nous a permis de donner une vision globale plus cohérente de ces politiques et de mettre en exergue les relations qui existent entre elles

On the performance of adjusted bootstrapping methods for intermittent demand forecasting

International audienceA plethora of parametric and non-parametric methods have been developed in the last decades to deal with the inventory forecasting of intermittent demand items. Most of the parametric methods represent variations of the Croston method and the non-parametric ones are based on bootstrapping. When the inventory performance is considered, these methods often result in an under-achievement of the target service level. A study by Teunter and Duncan (2009) has shown that when the lead-time demand forecast is adjusted by assuming that the first period in the lead-time bucket corresponds to a non-zero demand, the service performance improves considerably. The study has been conducted by considering two Croston type methods and a parametric (with Normal distribution) bootstrapping method. However, the study has not included two well performing bootstrapping methods that are commonly considered in the literature to deal particularly with the inventory forecasting of intermittent demand items. A first method that samples demand data by using a Markov chain to switch between no demand and demand periods and a second method that samples separately demand intervals and demand sizes. In this paper, we propose variations of the two bootstrapping methods where the lead-time demand is adjusted by considering that a demand occurs in the first period of each lead-time bucket. Through an empirical investigation based on the spare parts demand of more than 9000 stock keeping units, we show that the proposed adjusted methods result in a higher service-cost efficiency compared to the original methods

Editorial Special issue: Operations Management in Service Systems Downloaded from

International audienceThe service sector is the largest sector of the economy in most industrialized nations, and is fast becoming the largest sector in developing nations as well. Driven by today's new business environment, including advanced telecommunications, accelerated business globalization, increased automation and highly on-demand and competitive innovations, the complexity of the operations management of service systems is continuously increasing. Managers of service systems are wrestling to deliver both traditional conflicting objectives of low operating costs and high service quality. Consequently, the problems of operations management in service systems have recently attracted a lot of attention. The most popular applications relate to Services for Supply Chains, Health Care Systems and Call Centres. Given the size and the complexity associated with their operations, service systems have emerged as a fertile ground for academic research. We were delighted therefore to be offered the opportunity to edit this special issue of the IMA Journal of Management Mathematics on operations management in service systems. We hope that through this special issue we have achieved our objective which is to advance the current state of knowledge in this area, adding in parallel significant value to relevant real-world practices. This special issue demonstrates that all the key areas addressed in Manufacturing Operations (such as demand forecasting, acquiring capacity, deploying resources and managing delivery) are now also reflected in the Services Operations agenda. However, issues such as behavioural factors, the emerging need for increased standardization and automation of the operations, handling technological advances (such as the digitalisation of systems) and cultural issues (such as those resulting from the globalization of systems, e.g. big call centres) that introduce many interesting research opportunities have not yet sufficiently explored and more remains to be done in this area to gain new interesting managerial insights. The call for this Special Issue has attracted many submissions. All manuscripts underwent a rigorous review process which resulted in the six papers presented in this special issue. This process required the cooperation of both authors and reviewers, and we thank all of them for their efforts and diligence; they all made our role as Special Issue Editors easy and enjoyable