Flexible ABC Inventory Classification

ABC inventory classification is a well-known approach to assign inventory item into A, B, and C groups based on their sales and usage volume. This helps inventory management become more efficient. Behind its advantage, it usually shows some problems with an inventory budget and warehouse space because the ABC assignment of SKUs are mad e without an inventory budget and space available involved. In this paper, the ABC group under restricted of an inventory budget and warehouse space to maximize the profit with optimal service level is presented. We establish this proposed model to enhance the existing ABC approach to be more applicable in real life, which has the limited inventory budget and warehouse space. Keywords: ABC Inventory Classification; Inventory Managemen

Integrated Risk-based Inventory Classification System

A report submitted by Nilesh Joshi to the Research and Creative Productions Committee in 2010 on the effectiveness of the inventory management policies adopted by organizations and their financial success

Multicriteria inventory classification using a genetic algorithm

Cataloged from PDF version of article.One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using the Analytical Hierarchy Process. @ 1998 Elsevier Science B.V

Application of Artificial Neural Networks to Multiple Criteria Inventory Classification

Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematicapproach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process

Optimizing Inventory for Profitability and Order Fulfillment Improvement

Despite the extensive research on inventory management, few studies have investigated the optimization of inventory classification and control policies for maximizing the net present value of profit and order fulfillment performance. This dissertation aims to fill the gaps, and consists of two main essays. Essay One (Chapter 1) presents a new multi-period optimization model to explicitly address nonstationary demand, arbitrary review periods, and SKU-specific lead times, with the objective of maximizing the net present value of profit. A real-world application and computational experiments show that the optimal dynamic inventory classification and control decisions obtained from the model significantly reduce both safety stock and base stock levels compared to a multi-criteria inventory classification scheme and the traditional ABC approach. Essay Two (Chapter 2) examines two order-based fulfillment performance measures: the order fill rate, defined as the percentage of orders that are completely filled from available inventory; and the average customer-order fill rate, defined as the mean percentage of total units in a customer order that can be filled from on-hand inventory. Novel optimization models are developed to maximize the order fulfillment performance. Computational results indicate that a commonly used item-based measure in general does not adequately indicate order-based performance, and the tradeoffs between profit and order-based measures vary with inventory investment. This research contributes to the existing literature by providing new approaches to optimize inventory classification and control policies with various performance criteria. It also provides practitioners with a viable way to manage inventory with nonstationary demand, general review periods and lead times, and further allows companies to quantity the tradeoffs of different performance measures

Primjena različitih kvantitativnih tehnika pri klasifikaciji zaliha

In this paper, through example, a comparison between the traditional ABC inventory classification and advanced multiple criteria inventory classification method has been given. Inventory control or management, which is one of the important techniques of operational research field, plays an important role in the company management. Application of different inventory control methods and models has a great influence on improving the company competitiveness. The traditional ABC method of inventory classification is based only on one criterion. This criterion, very often, is not the most important one, so different multiple criteria decision making methods have been developed. Some of them are described in this paper and applied for the classification of parts for the assembly of agricultural machine. The parts are classified using the traditional ABC classification based only on one criterion and analytical hierarchy process (AHP) methodology.U radu je, na jednom primjeru, prikazana usporedba klasifikacije zaliha primjenom tradicionalne ABC analize i primjenom metode višekriterijskog odlučivanja. Upravljanje zalihama, kao jedna od značajnih tehnika operacijskih istraživanja, igra važnu ulogu u upravljanju cijelim poduzećem. Primjena različitih metoda i modela upravljanja zalihama, utječe na poboljšanje konkurentnosti poduzeća. Pomoću tradicionalne ABC analize, zalihe se klasificiraju na osnovi samo jednog kriterija. Taj kriterij, vrlo često, nije najznačajniji, te se koriste i metode višekriterijskog odlučivanja. Neke od tih metoda su opisane u radu i primijenjene pri klasifikaciji dijelova za montažu poljoprivrednog stroja. Korištena je tradicionalna ABC analiza, te klasifikacija pomoću AHP (analitički hijerarhijski proces) metodologije

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

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems

Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. 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ABC Analysis in an Internet Shop: A New Set of Criteria

This article presents a model of ABC analysis tailored for internet shops. The standard set of criteria is expanded to cover e-commerce specific characteristics, such as the number of product views, search engine rankings and product links via a recommendation system..The proposed new methodology is applied to real data from an internet bookstore in Poland. A comparison with the results of a standard, not internet-oriented ABC analysis shows the advantage of using the new set of criteria.ABC analysis, internet shop, inventory control