Multiple Criteria Inventory Classification for Storage Assignment and a Case Study

Abstract. Warehouse management has been turned into a more complicated issue depending on dynamics pertain to customer, good, speed and cost. It’s an inefficient and difficult approach to control all the stored items at the same level. Based on these; the main purpose of this study is bringing in a policy for warehouse management with the help of ABC Analysis via submitting the goods to inventory based classification. The goods will be assigned to slots according to their distances to the I/O point (Input/output point) by considering their importance orders at the end. In this context, DEMATEL method is utilized besides the Multi Criteria ABC Analysis methods used in literature. Initially Multi Criteria Decision Making techniques with weighted linear optimization, and in the following in order to make these calculations more accurate, calculation of cross evaluation of goods has been made in the literature. However, when we consider the calculation of cases which has increased numbers of goods, classification will be pretty hard. Thence, only cross evaluation points of goodsexceeding a threshold value when we apply DEMATEL method are calculated and applied to classification. On a model warehouse, mentioned techniques are benchmarked and it is shown that the approach, which is offered by us, reached similar or better results than the approaches in the literature in less time.Keywords. ABC Analysis, Multi Criteria Decision Making, Warehouse Management.JEL. M10, M11, M14

DEASort: Assigning items with data envelopment analysis in ABC classes

Multi-criteria inventory classification groups similar items in order to facilitate their management. Data envelopment analysis (DEA) and its many variants have been used extensively for this purpose. However, DEA provides only a ranking and classes are often constructed arbitrarily with percentages. This paper introduces DEASort, a variant of DEA aimed at sorting problems. In order to avoid unrealistic classification, the expertise of decision-makers is incorporated, providing typical examples of items for each class and giving the weights of the criteria with the Analytic Hierarchy Process (AHP). This information bounds the possible weights and is added as a constraint in the model. DEASort is illustrated using a real case study of a company managing warehouses that stock spare parts

A comparison of multi-criteria methods for spare parts classification

Spare parts classification is a fundamental step in spare parts inventory management. Through classification, the parts are grouped using a set of relevant criteria. Methodologies and methods for multicriteria decision making are used to support the classification of spare parts. In this paper, a comparative study between the use of the multi-criteria classification based on rules and the multi-criteria classification using the Analytic Hierarchy Process is presented, showing the advantages and disadvantages of each method. The study confirmed that the multi-criteria method based on rules is more easily applied in organizations. The multi-criteria method using Analytic Hierarchy Process required more calculations, turning the implementation of the method more complicated, especially for non-Analytic Hierarchy Process specialists

In search for classification and selection of spare parts suitable for additive manufacturing: a literature review

This paper reviews the literature on additive manufacturing (AM) technologies and equipment, and spare parts classification criteria to propose a systematic process for selecting spare parts which are suitable for AM. This systematic process identifies criteria that can be used to select spare parts that are suitable for AM. The review found that there is limited research that addresses identifying processes for spare parts selection for AM, even though companies have identified this to be a key challenge in adopting AM. Seven areas for future research are identified relating to the methodology of spare parts selection for AM, processes for cross-functional integration in selecting spare parts for AM, broadening the spare parts portfolio that is suitable for AM (by considering usage of AM in conjunction with conventional technologies), and potential impact of AM on product modularity and integrality

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of ‘if–then’ decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the cross-validation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision making process

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

OR in Spare Parts Management:A Review

Abstract Spare parts are held to reduce the consequences of equipment downtime, playing an important role in achieving the desired equipment availability at a minimum economic cost. In this paper, a framework for OR in spare parts management is presented, based on the product lifecycle process and including the objectives, main tasks, and OR disciplines for supporting spare parts management. Based on the framework, a systematic literature review of OR in spare parts management is undertaken, and then a comprehensive investigation of each OR discipline's contribution is given. The gap between theory and practice of spare parts management is investigated from the perspective of software integration, maintenance management information systems and adoption of new OR methods in software. Finally, as the result of this review, an extended version of the framework is proposed and a set of future research directions is discussed

Multi-Criteria Inventory Classification and Root Cause Analysis Based on Logical Analysis of Data

RÉSUMÉ : La gestion des stocks de pièces de rechange donne un avantage concurrentiel vital dans de nombreuses industries, en passant par les entreprises à forte intensité capitalistique aux entreprises de service. En raison de la quantité élevée d'unités de gestion des stocks (UGS) distinctes, il est presque impossible de contrôler les stocks sur une base unitaire ou de porter la même attention à toutes les pièces. La gestion des stocks de pièces de rechange implique plusieurs intervenants soit les fabricants d'équipement d'origine (FEO), les distributeurs et les clients finaux, ce qui rend la gestion encore plus complexe. Des pièces de rechange critiques mal classées et les ruptures de stocks de pièces critiques ont des conséquences graves. Par conséquent il est essentiel de classifier les stocks de pièces de rechange dans des classes appropriées et d'employer des stratégies de contrôle conformes aux classes respectives. Une classification ABC et certaines techniques de contrôle des stocks sont souvent appliquées pour faciliter la gestion UGS. La gestion des stocks de pièces de rechange a pour but de fournir des pièces de rechange au moment opportun. La classification des pièces de rechange dans des classes de priorité ou de criticité est le fondement même de la gestion à grande échelle d’un assortiment très varié de pièces. L'objectif de la classification est de classer systématiquement les pièces de rechange en différentes classes et ce en fonction de la similitude des pièces tout en considérant leurs caractéristiques exposées sous forme d'attributs. L'analyse ABC traditionnelle basée sur le principe de Pareto est l'une des techniques les plus couramment utilisées pour la classification. Elle se concentre exclusivement sur la valeur annuelle en dollar et néglige d'autres facteurs importants tels que la fiabilité, les délais et la criticité. Par conséquent l’approche multicritères de classification de l'inventaire (MCIC) est nécessaire afin de répondre à ces exigences. Nous proposons une technique d'apprentissage machine automatique et l'analyse logique des données (LAD) pour la classification des stocks de pièces de rechange. Le but de cette étude est d'étendre la méthode classique de classification ABC en utilisant une approche MCIC. Profitant de la supériorité du LAD dans les modèles de transparence et de fiabilité, nous utilisons deux exemples numériques pour évaluer l'utilisation potentielle du LAD afin de détecter des contradictions dans la classification de l'inventaire et de la capacité sur MCIC. Les deux expériences numériques ont démontré que LAD est non seulement capable de classer les stocks mais aussi de détecter et de corriger les observations contradictoires en combinant l’analyse des causes (RCA). La précision du test a été potentiellement amélioré, non seulement par l’utilisation du LAD, mais aussi par d'autres techniques de classification d'apprentissage machine automatique tels que : les réseaux de neurones (ANN), les machines à vecteurs de support (SVM), des k-plus proches voisins (KNN) et Naïve Bayes (NB). Enfin, nous procédons à une analyse statistique afin de confirmer l'amélioration significative de la précision du test pour les nouveaux jeux de données (corrections par LAD) en comparaison aux données d'origine. Ce qui s’avère vrai pour les cinq techniques de classification. Les résultats de l’analyse statistique montrent qu'il n'y a pas eu de différence significative dans la précision du test quant aux cinq techniques de classification utilisées, en comparant les données d’origine avec les nouveaux jeux de données des deux inventaires.----------ABSTRACT : Spare parts inventory management plays a vital role in maintaining competitive advantages in many industries, from capital intensive companies to service networks. Due to the massive quantity of distinct Stock Keeping Units (SKUs), it is almost impossible to control inventory by individual item or pay the same attention to all items. Spare parts inventory management involves all parties, from Original Equipment Manufacturer (OEM), to distributors and end customers, which makes this management even more challenging. Wrongly classified critical spare parts and the unavailability of those critical items could have severe consequences. Therefore, it is crucial to classify inventory items into classes and employ appropriate control policies conforming to the respective classes. An ABC classification and certain inventory control techniques are often applied to facilitate SKU management. Spare parts inventory management intends to provide the right spare parts at the right time. The classification of spare parts into priority or critical classes is the foundation for managing a large-scale and highly diverse assortment of parts. The purpose of classification is to consistently classify spare parts into different classes based on the similarity of items with respect to their characteristics, which are exhibited as attributes. The traditional ABC analysis, based on Pareto's Principle, is one of the most widely used techniques for classification, which concentrates exclusively on annual dollar usage and overlooks other important factors such as reliability, lead time, and criticality. Therefore, multi-criteria inventory classification (MCIC) methods are required to meet these demands. We propose a pattern-based machine learning technique, the Logical Analysis of Data (LAD), for spare parts inventory classification. The purpose of this study is to expand the classical ABC classification method by using a MCIC approach. Benefiting from the superiority of LAD in pattern transparency and robustness, we use two numerical examples to investigate LAD’s potential usage for detecting inconsistencies in inventory classification and the capability on MCIC. The two numerical experiments have demonstrated that LAD is not only capable of classifying inventory, but also for detecting and correcting inconsistent observations by combining it with the Root Cause Analysis (RCA) procedure. Test accuracy improves potentially not only with the LAD technique, but also with other major machine learning classification techniques, namely artificial neural network (ANN), support vector machines (SVM), k-nearest neighbours (KNN) and Naïve Bayes (NB). Finally, we conduct a statistical analysis to confirm the significant improvement in test accuracy for new datasets (corrections by LAD) compared to original datasets. This is true for all five classification techniques. The results of statistical tests demonstrate that there is no significant difference in test accuracy in five machine learning techniques, either in the original or the new datasets of both inventories

Grabbing the Air Force by the Tail: Applying Strategic Cost Analytics to Understand and Manage Indirect Cost Behavior

Recent and projected reductions in defense spending are forcing the military services to develop systematic approaches to identify cost reduction opportunities and better manage financial resources. In response, the Air Force along with her sister services are developing strategic approaches to reduce front-line mission resources, commonly referred to as the Tooth . However, an underemphasized contributing source of costs are mission support activities, commonly referred to as the Tail . With the tail historically representing a sizable portion of the annual Air Force budget, strategically managing cost behavior of these indirect activities has the opportunity to generate significant cost reductions. However, very little applied or academic research have focused on advancing the knowledge behind the economics of, or the analytic techniques applied to, these activities for cost management purposes. To address this concern, this dissertation investigates i) how organizations use analytic methodologies and data sources to understand and manage cost behavior, ii) how to identify underlying cost curves of concern across tail activities, iii) how to distinguish historical relationships between the tooth and tail, iv) how to improve the performance assessment of tail activities for improved resource allocation, and v) how to provide a decision support tool for tooth-to-tail policy impact analysis