Inventory Control Methods Research Articles

Sistem Pengendalian Persediaan Barang pada Toko Mulia Jaya Elektronik Karawang

This research was motivated by a less than optimal inventory control system at the Mulia Jaya Elektronik store, in the process it may also have an impact on the security of stock. Procurement of merchandise at the Mulia Jaya Elektronik store does not yet use standard methods, procurement is still carried out using calculations based on estimates and there is a lack of control over the inventory of goods at the Mulia Jaya Elektronik Store. The purpose of this study is to analyze the appropriate inventory control system implemented at the Mulia Jaya Elektronik store. The inventory control method that researchers will use is the ROP (Reorder Point) method in procuring merchandise and increasing inventory security through inventory management. The work of writing this study article uses a quantitative descriptive method. The results of this research can make a positive contribution to better procurement of inventory goods through the implementation of an effective inventory control system.

Buffer Stock Inventory Control Mechanism: An Approach of Minimizing the Buffer Stock Level Through Segmentation at a Tertiary Care Rural Hospital.

Background Efficient management of hospital inventories, particularly pharmaceuticals, is essential for ensuring timely patient care and optimizing resource allocation.Buffer stock is the backup stock, which is kept for providing a supply of drugs when the main stock is consumedand new stock is in the process of procurement.The buffer stock is sometimes kept in excess, which causes unnecessary overutilization of the financial resources. Therefore, the author's team aimed to optimize the inventory control of the buffer stock.This study addressed these challenges by integrating always better control (ABC) and vital, essential, and desirable (VED) analysis to categorize drugs based on consumption patterns and clinical importance to minimize buffer stock and ensure optimum resource allocation. To overcome these challenges, there is a need to integrate financial and clinical factors into inventory management decisions. Methods This study was done at the pharmacy of an apex hospital situated in a rural region of western India. The study aimed at the categorization of drugs that require strict inventory management control. The drugs and consumables used in the month of April 2024 in the hospital pharmacy were considered for the study& inventory control methods were applied. Results The study discovered that a subgroup of drugs accounted for a significant portion of the pharmacy's budget, with 12.18% categorized as high-value (Category A), 22.07% as moderate-value (Category B), and 65.74% as low-value (Category C) items. ABC analysis showed that Category A drugs consumed 70.1% of the average daily expenditure (ADE) of the pharmacy, while Category B and Category C drugs contributed 19.9% and 9.98% of the ADE, respectively. VED analysis classified drugs into vital (11.34%), essential (58.26%), and desirable (30.39%) categories. The ABC-VED matrix further categorized drugs into subgroups, with class I items constituting 20.62% of the inventory and accounting for 73.82% of the ADE. Class II items comprised 56.69% of the inventory, consuming 22.91% of the ADE, while class III items constituted 22.68% of the inventory and consumed 3.28% of the ADE. Conclusion ABC and VED analysis integration provides a comprehensive framework for optimizing drug inventory management in healthcare facilities. By considering both financial and clinical factors, this approach enables tailored management strategies, minimizing buffer stock, eliminating stockouts, and enhancing patient care. The rural hospital's location is responsible for maintaining the buffer stock level and the reorder level quantity. This study highlighted the role of inventory control techniques in healthcare facilities, particularly in pharmacies, to ensure the availability of essential medications while optimizing resource utilization. By integrating segmentation techniques such as ABC and VED analysis, this study provided valuable insights into categorizing drugs based on their consumption patterns, criticality, andimportance in the clinical area.

Time series imaging-based deep learning method for inventory control

ABSTRACT Data-driven newsvendor methods offer promising solutions for estimating replenishment quantity within complex markets by leveraging demand-related features. However, most features utilized by these methods rely on manual selection, and some important unobservable features cannot be thoroughly utilized. These issues might result in the loss of some crucial information, thus incurring considerable cost in these decision-making results, meaning that there is still much room for improvement. To remedy this, we propose a time series imaging-based deep learning method, which automatically extracts crucial features alongside those selected manually. This method maximizes the advantages of time series imaging, convolutional neural networks, bidirectional long short-term memory networks, and multilayer perceptron networks. Experiments with four diverse product time series and real-world data show that our approach reduces the average newsvendor problem cost by 16.33% to 33.43% compared to the best benchmark, highlighting its robustness. Additionally, each component within our method significantly contributes to decision-making.

Tackling inventory losses of small accessories in engineering manufacturing: a case study of Euro Manufacturing

Learning outcomes After completion of the case study, the students will be able to understand ABC analysis and develop a systematic approach using PDCA, analyze processes, technology, employee training and supplier relationships when analyzing shrink and developing solutions, evaluate how technology improves production inventory control and visibility and recognize the importance of fostering a culture of employee accountability and ownership to minimize inventory loss and improve overall operational efficiency. Case overview/synopsis On June 2, 2023, sitting in his office in Karachi, Pakistan, Khan Aamir, the manager of store and inventory at Euro Manufacturing, found himself immersed in a cloud of confusion. The incessant loss of inventory items, particularly the nut bolts and small accessories, had become a perplexing challenge. To address these losses and provide a cycle count report to the director of supply chain, Aamir, manager of store and inventory, was given the responsibility to take action. He was looking for a comprehensive approach to address the current problems and prevent further losses in the future. This case study examines the various reasons for the losses, including theft, inadequate inventory control methods, human error and problems with suppliers. It highlights the importance of established procedures, the use of technology (such as barcode scanning, radio-frequency identification tagging and inventory management software) and the cultivation of a culture of accountability among employees. Complexity academic level This case study is developed for class discussion in the course of operations management or supply chain management. This case study is suitable for use with undergrad students. This case study can be taught in a module on operations management or supply chain management, as part of a broader course in business management or industrial engineering. Supplementary materials Teaching notes are available for educators only. Subject code CSS: 9: Operations and logistics.

Analysis of Raw Material Inventory Control with A Tabular Approach and Formula Approach Economic Order Quantity (EOQ) to Optimize The Cost of Soybean Raw Material Inventory At The Rizqy Putra Fried Tofu Factory

Inventory management is a crucial aspect in the operations of the manufacturing industry, including in fried tofu factories such as the Rizqy Putra Fried Tofu Factory. The main raw material, namely soybeans, must be managed efficiently to ensure smooth production and minimize the costs incurred. This research aims to determine the inventory control of soybean raw materials applied by Rizqy Putra Fried Tofu Factory, the total inventory cost, and the optimal quantity of soybean raw material orders. This research is quantitative research with a comparative descriptive analysis. The data processed are the inventory purchase and usage reports of soybean raw materials at Rizqy Putra Fried Tofu Factory in 2023. The data analysis method used in this research includes two approaches: the tabular approach and the formula approach with the calculation technique of Economic Order Quantity (EOQ). Based on the results of the research, the inventory control method applied by Rizqy Putra Fried Tofu Factory is the traditional or conventional method, where the total inventory cost of soybean raw materials is Rp. 18,720,000 with details of storage costs amounting to Rp. 18,000,000 and ordering costs of Rp. 720,000 per year, and an ordering frequency of 48 times with an average order quantity of 5,875 kg. Meanwhile, according to the calculation of the formula approach EOQ, the optimal order quantity is 2,880 kg, with an ordering frequency of 24 times and the total inventory cost incurred is Rp. 734,387. The conclusion of this research indicates that based on the calculation of the EOQ formula approach, the total inventory cost of soybean raw materials is more optimal compared to the conventional method applied by Rizqy Putra Fried Tofu Factory.

A Pilot Study on the Assessment of the Knowledge, Practice, and Challenges of Pharmaceutical Inventory Management among Community Pharmacists

Pharmacies and related supplies must always be available at the required location, in the requisite amount, and with the necessary quality for health facilities to offer comprehensive care. Efficient inventory management improves pharmaceutical product profitability by reducing procurement costs and preventing long-term storage, while poor inventory management hinders essential drug availability. The current study aims to assess the knowledge, practice, and challenges in applying inventory management methods. A mixed-method study design with sampling techniques of purposive and snowball methods was used. A semi-structured questionnaire was implemented for quantitative data and an open-ended question was employed to explore more information as qualitative data. The collected qualitative data were transcribed and identified as five major themes knowledge about inventory management, challenges faced by pharmacists in managing inventory, purchasing methods, stocking, and expiry, and the purpose of inventory control management. Most pharmacists employ the inventory management method based on their experience and the customer needs. Among the basic inventory management tools like ABC, VED, FSN, or XYZ, the use of VED and FSN methods for purchasing and stocking is high. It is noted that they lack professional training but gained more knowledge and skills through the experience. When they were asked about the inventory control methods, about 70% of them were not aware, and of those who said yes 20 % were able to answer at least 1 method and 10 % were not able to correctly mention the name. To conclude the pharmacist should start considering the modern methods along with the current methods for effective marginal profit and competitive management of the pharmacies.

Fuzzy Logic Optimizes Global Inventory Management

Inventory control is crucial for balancing customer demand and inventory levels, especially in make-to-order production systems like those in refrigeration manufacturing. This study addresses the challenges faced by a company producing sandwich panels, where inefficiencies in inventory processing led to overstock and outstock issues. By applying the fuzzy inventory control method using Python, the study aimed to optimize inventory levels. The results showed a 6% reduction in inventory, from 191,307 m² to 182,619.4627 m², demonstrating the method's effectiveness. This approach can improve inventory management in similar industrial settings, aligning production with demand and reducing excess inventory. Highlight: Efficiency Boost: Fuzzy logic optimizes inventory, minimizing overstock and outstock. Precise Management: Python aids accurate inventory analysis for informed decisions. Cost Savings: Aligning inventory with demand reduces excess, enhancing profitability. Keywoard: Inventory control, Fuzzy logic, Optimization, Production management, Industrial engineering

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

Research on inventory control method based on demand response in power system fuzzy hybrid particle swarm optimization algorithm

Research on inventory control method based on demand response in power system fuzzy hybrid particle swarm optimization algorithm

Fuzzy demand-driven material requirements planning: a comprehensive analysis of fuzzy logic implementation in DDMRP

ABSTRACT Demand-Driven Material Requirements Planning (DDMRP) is a new method of inventory control designed to address the challenges of today's complex and volatile supply chain environment. DDMRP replaces traditional Material Requirements Planning (MRP) with a more agile and responsive approach that is based on a set of colour-coded buffers. DDMRP ensures that the right inventory is in the right place at the right time, enabling companies to respond quickly and effectively to changing customer demand. DDMRP requires the setting of various parameters, such as variability factor (VF) and lead time factor (LTF), which can have a significant impact on systems’ performance. Pre-assumed fixed values of DDMRP parameters are used in most of the existing studies. This may lead to either high stockout levels or excess inventory especially in environments involving high level of variability. In this study, we proposed a fuzzy logic-based approach which dynamically adjusts the values of VF and LTF parameters considering demand and lead time variability. The effectiveness of the proposed approach was investigated by comparing its performance with DDMRP based on several numerical experiments. The results showed that the proposed Fuzzy Demand-Driven Material Requirements Planning (FDDMRP) outperforms DDMRP in terms of backorder rate and total cost.

Multi-Item Inventory Control Using Economic Order Quantity (EOQ) Model with Safety Stock, Reorder Point, and Maximum Capacity in Retail Business

The presence of retail businesses in Indonesia has many positive impacts on the community, especially in improving the economy. The existence of buying and selling transactions involving suppliers, retailers, and the community as consumers can play a role in improving the national economy. Retail can be called a bridge for suppliers and consumers to meet their needs. The diversity of consumer needs requires retailers to provide a variety of products from many suppliers. Efforts that need to be made by retail businesses in order to minimize costs incurred are by controlling inventory. To prevent excessive expenditure, the inventory control method used is to apply the Economic Order Quantity (EOQ) model. The EOQ model can provide the optimum total inventory cost by adjusting the frequency of orders placed over a period of time. After obtaining the total inventory cost, the calculation of safety stock, reorder point, and maximum capacity can also be applied so that the inventory costs incurred can be minimal.

IMPLEMENTASI METODE EOQ DAN POQ PADA PENGENDALIAN PERSEDIAAN BAHAN BAKU ALAT UJI KESEHATAN PT. X

Manufacturing companies essentially transform raw materials into finished products through various production operations. This research aims to evaluate the inventory control of raw materials for health diagnostic test device products (HBSAG & HIV) at PT X. The method used is Material Requirement Planning (MRP) with lot sizing techniques using EOQ and POQ methods. The calculated actual inventory costs for the company are Rp 25,357,043 (HBSAG) and Rp 23,973,320 (HIV). The research results using the EOQ lot sizing method show reduced costs to Rp 17,486,791 (HBSAG) and Rp 13,243,617 (HIV). Meanwhile, the POQ lot sizing method yields inventory costs of Rp 16,403,312 (HBSAG) and Rp 12,556,514 (HIV). In the final analysis, the most optimal inventory control method is the Period Order Quantity (POQ) method, as it can save inventory costs by Rp. 8,953,731 (HBSAG) and Rp. 11,346,116 (HIV) annually.

Controlling Raw Material Inventory using the Economic Order Quantity (EOQ) Method at PT. ICI Paints Indonesia

The inventory control system comprises a set of control policies to determine the level of inventory to be maintained, when orders to replenish inventory should be placed, and how large orders should be. This system determines and ensures the availability of inventory in the right quantity and at the right time. The aim of inventory is in the right quantity and at the right time. Inventory management aims to provide the necessary raw materials for the production process to run smoothly without shortages or excess inventory, thus minimizing inventory control of raw materials, specifically Titanium Dioxide and Calcium Carbonate. The company often faces shortages and excess inventory of these raw materials. The company must estimate the future demand for Titanium Dioxide and Calcium Carbonate raw materials. This prompts the research question of how to analyze the implementation of the Economic Order Quantity (EOQ) method for inventory control of these specific raw materials at PT. ICI Paints Indonesia. This research employs qualitative techniques and analyzes data using the Economic Order Quantity (EOQ) method, Lead Time, Re-Order Point, and Total Inventory Cost Order. The results of this study indicate that inventory control of raw materials using the Economic Order Quantity (EOQ) method is more efficient for managing Titanium Dioxide and Calcium Carbonate raw materials at PT. ICI Paints Indonesia. The company can optimize the inventory of Titanium Dioxide and Calcium Carbonate raw materials and reduce purchasing costs. By establishing a reorder point, the company can anticipate excess or shortage of Titanium Dioxide and Calcium Carbonate raw materials, thus minimizing inventory-purchasing costs.

Peningkatan Pengelolaan UKM di Era Digitalisasi pada Nisa Bakery di Kelurahan Surodinawan Kecamatan Prajurit Kulon Kota Mojokerto

In the Era of Digitalization, SMEs must keep up with developments. The problems faced by SMEs Nisa Bakery include the production process not being optimal because there is no mixer with the required capacity, a lack of understanding regarding the production space, where the production space is still one with a storage area for both raw materials and auxiliary materials, which is also integrated with packaging of finished goods (bread), financial recording is still simple, there is no separation between recording household and business finances, while marketing still depends on orders. The aim of this community service is to help solve the problems that occur at Nisa Bakery. The method used to solve the problem is providing equipment grants in the form of a mixer with a capacity of 10 liters, training on the use and maintenance of the mixer, expanding marketing using online media, business management assistance which includes training and mentoring on business management, finance/accounting. The results of this community service were the provision of a standing mixer machine with a capacity of 10 liters, understanding of inventory control methods, technical training in simple bookkeeping and marketing digitalization training.

Inventory Management Under Uncertainty Condition with Fuzzy Logic: A Literature Review

Fuzzy set theory, or fuzzy logic, has been in use in inventory systems since the 1980s, with vaguely defined, ill-defined, or imprecise values, or decisions based on individual subjective beliefs. Provides a framework for describing the parameters that can be passed. There are many uncertainties in inventory management that can arise due to many things, such as: Order Changes, Random Supplier Capabilities, or Unexpected Events. Fuzzy logic as a method of inventory control that provides a framework by considering parameters that are vague or poorly defined, or whose values are imprecise and determined based on individual subjective beliefs make it available. The purpose of this white paper is to review previous studies using fuzzy logic in inventory management to see the uncertainty variables used. The method used is a thematic analysis of articles on the application of fuzzy logic in various industries. The results of the review show that variables like fuzzy logic make it easy to obtain results from uncertainty variables that can be applied to industrial activities to enable manufacturing activities to be carried out effectively and efficiently increase.

Integrating K-Domain and Robust Optimization Methods of Inventory Control for Sustainable Enterprises in Perishable Food Supply Chain

Integrating K-Domain and Robust Optimization Methods of Inventory Control for Sustainable Enterprises in Perishable Food Supply Chain

Off-design performance and control strategies of sCO2 recompression power systems considering compressor operating safety

The supercritical carbon dioxide (sCO2) power cycle is a promising power cycle in the field of thermal power generation. The system is often operated under off-design conditions, which would lead to the unsafe or unstable operation of the sCO2 power system. The performance of the sCO2 recompression cycle system under off-design working conditions by different control strategies is investigated in this paper. A one-dimensional design prediction model for centrifugal compressors is introduced to predict off-design system performance and the operating conditions and safety of centrifugal compressors under the 5 control strategies are simulated and compared. To show the difference between the control strategies, the thermal efficiency was compared, the highest thermal efficiency of the system is 11.2 % by the inventory control method, and the lowest thermal efficiency is 5.45 % by the turbine bypass control method. The compressor backflow control method makes the main compressor has a choking risk and the recompressor has a surging risk when the output power of the system is small. The speed variations of centrifugal compressors are 39.55 % (MC) and 34.71 % (RC) using the inventory control method. A hybrid control strategy (the compressor backflow and inventory control strategies) is proposed based on the performance maps of centrifugal compressors. The turbine efficiency is improved by 4.5 % and the system thermal efficiency is improved by 7.1 % by using the hybrid control strategy. The hybrid control strategy can solve the problem of dangerous working conditions of centrifugal compressors. The results of this study can guide the system control and operational safety of sCO2 recompression cycles.

Analysis of Coffee Raw Material Inventory Control Using the EOQ (Economic Order Quantity) Method in SME Sido Luhur

SME Sido Luhur is a producer that processes raw coffee beans who have problems ordering coffee beans every month which is considered to result in higher inventory costs. The sample in this study were SME Sido Luhur owner who were determined based on judgment sampling. The research was conducted at SME Sido Luhur starting September 2022. This research used primary data obtained through interviews and secondary data obtained from the cost and quantity of raw materials in 2018. Data for 2018 can represent 2019, 2020, 2021, 2022, and so on, because starting in 2017 SME Sido Luhur have entmered the maturity phase so the 2018 data can represent the following year based on the relatively stable use of raw materials and having entered the business maturity phase. The analysis method uses EOQ to find the optimum inventory quantity with the lowest possible cost and the right reorder time. A comparison of robusta coffee bean inventory control methods shows that the Economic Order Quantity method is more effective in controlling raw material inventory in SME Sido Luhur as evidenced by the cost of raw material inventory being economical and efficient. After using the EOQ method, orders are made every 2 months to save costs for ordering raw materials compared to before using the EOQ method, which is done every month.

PENGENDALIAN PERSEDIAAN BAHAN BAKU BROWNIES DENGAN ANALISIS PERBANDINGAN METODE MIN-MAX, ECONOMIC ORDER QUANTITY DAN PERIOD ORDER QUANTITY

This research examines raw material inventory control at Hendhy's Brownies. Inventory management analysis is carried out using the Min-Max, Economic Order Quantity (EOQ) and Period Order Quantity (POQ) methods. Based on data analysis and processing, it is known that raw material inventory management using the min-max method has an order quantity of 13,231 kg with an ordering frequency of 5 times and a total ordering cost of Rp. 5,325,387. By using the Economic Order Quantity (EOQ) method, an order quantity of 9001 kg was obtained with an order frequency of 5.3 times and a total order cost of Rp. 3,420,527 Using the Periodic Order Quantity (POQ) method, the order quantity is 4252 kg, the order frequency is 20 times, and the total order price is IDR 3,071,030. Based on the results of the three methods, the EOQ method is the most optimal inventory control method compared to the other two methods. So that the results obtained through the EOQ method can improve forecasting of raw material shortages for companies, so that the use of raw materials becomes optimal

A Review of Biomass-to-Bioenergy Supply Chain Research Using Bibliometric Analysis and Visualization

Based on current trends and policies aimed at decarbonizing energy systems, the conversion of biomass to bioenergy has the potential to grow rapidly, but such growth depends on the development of efficient, sustainable, and competitive biomass supply chains. As a result, the biomass supply chain has stimulated the interest of a diverse group of researchers across academia, government, and industry, and there is a need to synthesize and categorize the rapidly expanding literature in this field. We conducted a literature review using advanced bibliometric analysis and visualization of 1711 peer-reviewed articles published from January 1992 to August 2022 with the aim of promoting impactful research in both growing and neglected areas of investigation. The results show that there are potential research gaps and opportunities in six critical areas: globalization of supply chain research; incorporation of uncertainty, stochasticity, and risk into supply chain models; investigation of multi-feedstock supply systems; strengthening supply chain resilience; application of inventory control methods; and broader use of machine learning and artificial intelligence in this field. By providing a holistic examination of how biomass-to-bioenergy supply chain research has grown and evolved over this period, our results and subsequent framework and recommendations can aid researchers in developing future studies and can guide stakeholder strategies to identify, diagnose, and address modern challenges that face the bioenergy industry.