A local brand forum outlet sells Micro, Small, and Medium Enterprises (MSMEs) products with inventory management still carried out manually based on employee experience. The research objectives are ...to analyze problems related to the inventory system and provide recommendations for planning and controlling inventory systems at a Local Brand Forum (FBL) outlet. Primary data was collected through observation, in-depth interviews, and Focus Group Discussion (FGD). The method used is ABC analysis to obtain the product classification. The results showed that 16 items (19.05%) are included in class A, with a total sales value of 69.55% of the total value for money. Class B has 25 product items (29.76%) with a total sales value of 20.29% of the total value for money. Products in class C are 43 items (51.19%) with a total sales value of 10.16% of the total value for money. As much as 43.75% of products in class A are nine basic commodities which are the basic needs of the Indonesian people, so they have a high priority in inventory control. These results are expected to provide planning and inventory control systems using the ABC class of products to avoid shortages and overstock inventory.
Accounting monitoring stocks of raw materials and supplies plays an important role in the business of every major company. Since inventories are one of the most expensive types of company assets, ...accounting for more than 50% of total invested capital, optimal inventory management should be an integral part of every company's business. In order to get complete results, this paper applies the ABC method, XYZ method, as well as the cross ABC XYZ method. The goal is to reduce costs of keeping the inventory as much as possible, while maintaining a level of service customers requires. The process of assortment planning, ordering and inventory management in the construction material warehouse is analyzed. The importance of monitoring inventory movements in order to achieve optimal amount of inventory, as well as the obtained results and guidelines for future operations is presented.
Background: The modern world has witnessed significant advancements across various industries such as food, healthcare, fashion, economics, and education. Among these sectors, healthcare is ...essential, given its critical role in promoting the well-being of individuals and communities.
Purpose: Pharmaceuticals are a significant part of the healthcare system, as they are a crucial factor in increasing life expectancy and are often considered the heart of the health industry. Maintaining effective inventory management for drugs is essential for pharmacists to provide efficient and reliable services to their patients.
Methodology: The study thoroughly analyzes the cost and consumption data for each type of demand, to develop a well-suited review and issuance policy for the apothecary.
Research Limitations/Implications: The paper delves into the ABC analysis, VED analysis, and trend demand for medical stores, making it a valuable resource for pharmacy stores seeking to optimize their operations and inventory management.
Originality/Value: A total of 564 drugs were included in this study, and data were collected from random strip sales between October 2022 and Mar 2023. The study's findings can be used to make informed decisions about inventory planning and classification strategies. The model utilized in this study is based on three categories of medicines: high priority, medium priority, and low priority. By analyzing the demand for these medicines, they can be categorized based on their priority within the three core groups. Pharmacists can use the model to detect shortages and take proactive measures to avoid them by analyzing demand patterns and inventory levels.
Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. ...As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.
The purpose of the article is to analyze the assortment policy of a trading company and search for directions for its improvement. The conducted analysis proved that the problems of forming an ...assortment policy are mostly connected with too huge assortment of goods on the market, with changes in customer needs and the growth of non-price competition. The specificity of the problem of the Ukrainian market is further complicated by the active hostilities in the country and related economic instability, inflation, low level of solvent demand, low level of population income growth, problems with the delivery of goods, etc. The essence of the concept of “assortment policy” as a field of activity of the marketing management of the enterprise is defined, which is a set of principles, the observance of which leads to the formation of an optimal assortment of goods from the point of view of increasing the competitiveness of the enterprise, taking into account its own capabilities, the capabilities of suppliers and partners, market needs, seasonality of demand, etc. Based on the ABC analysis for the “EVA” line of stores, it is emphasized that the most significant products are decorative cosmetics, perfumes for women, their share in the total turnover is on average almost 20%, they and other important products for the company belong to the category “A “, that is, they are the most important and significant, their share in the sum is 60%. The “B” group, which assumes medium significance, includes aromas, scented candles, atomizers, etc. Their share in total is 30%, it is the basis of the chain’s range of stores. The smallest share is occupied by goods of the “C” group, which are the least significant in terms of the turnover of the “EVA” line of stores: shaving products for men, cosmetic brushes, jewelry, etc. Their share in total is only 10%. An XYZ analysis was carried out for the “EVA” line of stores, the results of which show that the products marked X are products for which the demand is stable throughout the year (hygiene and care, home care products, home fragrances, etc.); products Y remain relatively stable throughout the year and products Z are volatile products, the coefficient of variation of sales volume by quarter exceeds 20% (sun protection, lip care, etc.). An integrated matrix of ABC-XYZ-analysis was built, which proves that the “EVA” store line carries out a balanced assortment policy, but does not saturate the assortment sufficiently with those items for which a higher level of profitability can be obtained, which leads to a rather low profit in recent years. For the company, the absence of product groups that can be attributed to the CZ classification, which are products of spontaneous demand, in the product range is noticeable, and therefore, increasing the product range in favor of such products can provide an opportunity to obtain a higher level of profit. It is recommended that the company continue to work on the development of new own brands and the creation of new product positions among the old ones, expanding the assortment in the “average” and “average minus” price segments.
The Selective Inventory Classification has popularity in industries due to its virtue of obtaining higher returns by focusing on the small group of inventories than all the thousands and lakhs of ...components. ABC classification is the most popular traditional technique which utilizes annual usage value for grouping components into A, B and C categories. However, a more accurate classification should be carried out as other parameters are also essential along with cost. So, these other criteria along with usage value are essential while categorizing the components. Hence along with ABC, multi-criteria decision-making techniques and machine learning models are applied on three parameters i.e., usage value, lead time, and unit cost. In the MCDM techniques, the A. Hadi-Vencheh model is applied for the selective classification of inventory. The K-Means clustering algorithm is also applied to cluster the items. The result of the K-Means clustering algorithm is compared to the traditional technique and multi-criteria decision-making techniques. The K-Means showed promising results when compared to MCDM techniques. The percentage accuracy of the comparison showed that K- Means clustering algorithm can be used for millions of items and gives fairly accurate results. The main advantage of the Machine Learning Algorithm is that it can handle large data efficiently. The Classification given by the algorithm also follows the Pareto rule which is the base for ABC Classification. The finding suggests the possibility of K-Means clustering Algorithm for inventory classification and the use of the algorithm will help in skipping the calculations specified in the MCDM technique. The accuracy of the algorithm can be further increased by altering the parameters.
•A new criterion for ABC analysis is initiated to classify auto spare parts.•Rough set theory detects the reliable knowledge from ABC analysis.•Base stock level in the (r,S) inventory system is ...adopted with ABC analysis.•Increasing the service level is simultaneously followed up with the operation of reducing the value and age of inventory.
There are many vague and uncertain data which affect the quality of demand forecasting and ordering decisions in the supply chain of auto spare parts. This is due to the fact that the demand is not a unique concept for all levels involved in the supply chain. The downstream level of supply chain considers its intended criteria for deciding whether to place an order to the upstream level or not. Therefore, it is very important for the upstream level to discover the main criteria that are considered at the downstream level when issuing orders. In this paper, we intend to study the number of sold cars and their mileages associated to the each of spare parts as most important criteria when retailers are going to send the new orders to the distributor. ABC analysis is done for new criteria including the demand value of spare part in comparison with increase in the total mileages of its relevant cars during the fixed period. Rough set theory helps us induce patterns and rules from uncertain information obtained by ABC analysis over the past periods. We use the extracted rules to forecast the demands of retailers in the future and then place an order based on periodic review approach. Implementation of proposed model in the one of Iranian distributors, shows the significant improvement in the key performance measures such as increasing in service level and reducing the average value and age of inventory.
Purpose: The objective of this research was to implement new inventory management in a footwear company through the analysis of indicators obtained from inventory data collection. ...Design/methodology/approach: The methods of ABC analysis, demand forecasting, safety stock, reorder point and economic order quantity were applied. The items in inventory were classified by order of financial importance through ABC analysis, and the proposed indicators were analyzed to determine the moment the inventory replenishment should be carried out as well as the purchase lot size for each item. The research also analyzed the behavior of the demand and pointed out the demand forecasting method that came closest to reality. Findings: The study presents a method of implementing inventory management based on indicators derived from the application of ABC curve methods, demand forecasting, safety stock, re-fulfillment point, and economic purchased lot. It also indicates how the ABC classification of stocks can be used to check the most representative materials in stock. The study also highlights that the rejection of modifications can be surpassed by obtaining favorable results. Research limitations/implications: The inventory management applied in this work is based on indicators that resulted in two main data which were able to define the size of the purchase lot to be ordered and the amount of material needed. Practical implications: The methods of ABC analysis, demand forecasting, safety stock, reorder point and economic order quantity were applied. The items in inventory were classified by order of financial importance through ABC analysis, and the proposed indicators were analyzed to determine the moment the inventory replenishment should be carried out as well as the purchase lot size for each item. The research also analyzed the behavior of the demand and pointed out the demand forecasting method that came closest to reality. Originality/value: In this study, a method applied is presented, highlighting the importance of the methodological application for the implementation of inventory management. The study contributes to the encouragement and adoption of methodologies to improve analysis and inventory management in companies.
•A multi-criteria inventory classification method was developed.•Machine learning algorithms are integrated with multi-criteria decision making.•A case study at an automotive company validates the ...model with its high accuracy.•The proposed method yields significantly better results than others in literature.•It is flexibly applicable to other multi-criteria inventory classification cases.
The purpose of this study is to develop a hybrid methodology that integrates machine learning algorithms with multi-criteria decision making (MCDM) techniques to effectively conduct multi-attribute inventory analysis. In the proposed methodology, first, ABC analyses using three different MCDM methods (i.e. simple-additive weighting, analytical hierarchy process, and VIKOR) are employed to determine the appropriate class for each of the inventory items. Following this, naïve Bayes, Bayesian network, artificial neural network (ANN), and support vector machine (SVM) algorithms are implemented to predict classes of initially determined stock items. Finally, the detailed prediction performance metrics of algorithms for each method are determined. The comprehensive case study executed at a large-scale automotive company revealed that the best classification accuracy is achieved by SVMs. The results also revealed that Bayesian networks, SVMs and ANNs are all capable of successfully dealing with the unbalanced data problems associated with Pareto distribution, and each of these algorithms performed well against all examined measures, thus validating the fact that machine learning algorithms are highly applicable to inventory classification problems. Therefore, this study presents uniqueness in that it is the first and foremost of its kind to effectively combine MCDM methods with machine learning algorithms in multi-attribute inventory classification and is practically applicable in various inventory settings. Furthermore, this study also provides a comprehensive chronological overview of the existing literature of machine learning methods within inventory classification problems.
The right strategy in inventory is the main point in maintaining adequate and guaranteed supply continuity. Inventory strategies are becoming important but complex when the number of items that must ...be prepared is increasing. In this regard, it is necessary to analyze the product groupings in several classifications. Prioritized products receive special attention.The same problem is faced by PT PEKA, an import raw material distribution company that has nearly 2000 product items. Based on data demand is known, that the company's demand does not have a fixed pattern. Many products have regular and irregular demand, even the number of regular demand is very volatile. Companies often experience excess stock or vice versa due to lack of stock demand according to predictions, long lead times and product self-life that varies. Related to this, alternative product groupings or classifications are needed in accordance with company conditions. The company currently has frozen and unfrozen products, has items made from natural and non-natural raw materials and has a short and relatively long lead time.PT. PEKA groups products in various stages, which lead to a combination of ABC and XYZ classifications. Demand of priority items generated from the grouping is then forecast according to the model that gives the smallest MSE / MAD. Furthermore, a case study was carried out to calculate the Total Relevance Cost (TRC) from the calculation results to be compared with the real TRC. TRC according to the classification results was Rp. 1,293,370,148, - lower than the actual condition TRC, which amounted to Rp. 1,805,887,874, -
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