Inventory Stock Research Articles

A-205 Evaluating the Cost and Importance of Supply Chain Resilience in the Clinical Laboratory

Abstract Background Recent pandemics, epidemics and outbreaks continually underscore the critical importance of resilient supply chains in healthcare, particularly in clinical laboratories where timely access to supplies is essential for patient care. Supply chain disruptions, whether due to global crises or localized challenges, can significantly impact laboratory operations, leading to delayed diagnoses, compromised patient outcomes, and increased costs. It is estimated that 70%-80% of a patients’ EMR is clinical laboratory results. Despite this, supply chain resilience in clinical laboratories remains an under-addressed issue and remains a buzzword. Methods To evaluate the cost and importance of supply chain resilience in the clinical laboratory, we conducted a comprehensive review and interviews with 9 Healthcare Supply Chain experts analyzing the impact of supply chain disruptions on laboratory operations and patient care. Data was also collected from a diverse range of clinical laboratories, including hospitals, independent labs, and research institutions, spanning different geographic regions as well as a survey of 50 Laboratory experts with a 50% response rate. Key metrics assessed included: -Frequency and duration of supply chain disruptions. -Financial costs incurred due to disruptions, including expenses related to expedited shipping, alternative sourcing, and inventory management. -Impact on laboratory workflow, turnaround times, and patient care outcomes. -Strategies employed to mitigate supply chain risks and enhance resilience. Results Our review revealed findings regarding the cost and importance of supply chain resilience in the clinical laboratory: -Supply chain disruptions were frequent, with 85% of laboratories experiencing at least one significant disruption in the past year and 100% in experienced disruptions 2022. -The average duration of disruptions was 2-3 weeks, leading to substantial delays in test processing and patient care. -Financial costs associated with disruptions averaged $50,000 per laboratory per annum, including expenses for rush orders, and premium-priced alternatives. -Common strategies employed were increased inventory or safety stocks, multi-vendor sourcing, and supply chain mapping. Conclusions The findings of this study underscore the critical importance of supply chain resilience in the clinical laboratory. Supply chain disruptions are not just inconveniences; they pose significant risks to patient safety, operational efficiency, and financial sustainability. Investing in supply chain resilience is imperative for laboratories to fulfill their mission of delivering timely and accurate patient results. To continue to enhance supply chain resilience, laboratories must adopt a proactive approach, leveraging data-driven insights and collaborative partnerships. This includes diversifying supplier networks and fostering closer collaboration between laboratory and procurement teams.

Applying Neural Networks to Predict Ventilator Demand: A Study of Pakistan's Healthcare Sector

The distribution companies that deal with ventilators in Pakistan face challenges related to inventory control because of inadequate product shelf life, shortages, excess inventory, and unnecessary stock. This study, which focuses on Pakistani ventilator distribution companies, aims to offer a novel approach to sales estimation, avoid unnecessary stock expenditures, and stop clientele loss brought on by ventilator shortages. The results of this study will help determine key elements and standards that Pakistani distributors of ventilators might employ to boost sales. Most ventilator distribution businesses in Pakistan are independent wholesalers that purchase stock from their stores and distribute it to customers. To maximize ventilator distribution firms' sales for various products, this study examined distribution and sales data from 2019 to 2024 for many locations and dates. To create an accurate sales forecasting model for a ventilator distribution company, this research also aims to apply artificial neural networks (ANN) for effective sales prediction. An Artificial Neural Network (ANN) model was trained using a dataset from ventilator distribution businesses and the proposed model produced an accuracy of 90%, which is good for early prediction.

The Utilization of Time Series Data Forecasting Techniques on Hospitals Pharmaceutical Inventory

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.

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.

Mise en place d’une antenne pharmaceutique de blocs opératoires dans un centre hospitalier universitaire

An operating room pharmaceutical unit centralizes medical devices and drugs for various surgical specialities. The aim of this work is to present the methodology used in our establishment to set up the operating room pharmaceutical unit. This approach involved the formation of multi-professional working groups. The needs of operating theatres were defined based on an analysis of healthcare product consumption and stock inventories. Material sheets were defined for each procedure. On the basis of simulations, material supply arrangements were selected, specifying material flows, equipment, workstations and information systems. Over 3200 healthcare product references were identified and 862 equipment files were created. Local stocks have been limited to medical trolleys for nursing staff. Emergency operating packs have been deployed for unforeseen operations. Cabinets have been dedicated to transporting re-sterilizable medical devices, and carts have been purchased for programmed operating packs. The equipment is made available by logistics agents and pharmacy assistants under pharmaceutical responsibility. This innovative approach is a model for facilities desiring to centralize and secure the logistics of healthcare products in the operating room. Ongoing adjustments will be required to meet new operating rooms needs.

Comparison of Chen's Fuzzy Time Series and Triple Exponential Smoothing in Forecasting Medicine Stocks at the Blang Cut Kuala Community Health Center

Forecasting is estimating future conditions by examining conditions in the past. In social life, everything is uncertain and difficult to predict precisely, so forecasting is needed. Efforts are always made to make forecasts in order to minimize the influence of this uncertainty on a problem. In other words, forecasting aims to obtain forecasts that can minimize forecast errors, which are usually measured by the mean absolute percentage error. This method is usually used for time series-based forecasting and uses data or information from the past as a reference when predicting current data. This research will compare the application of the Fuzzy Time Series Chen method and the Triple Exponential Smoothing method in forecasting drug stock determination at the Kuala Community Health Center, Blang Mangat District, Lhokseumawe City Regency, Aceh. The research results showed that the Triple Exponential Smoothing method was better in forecasting drug stock inventories compared to Chen's Fuzzy Time Series method. Chen's Fuzzy Time Series method produces a MAPE value of 17.67%, which means it has an accuracy of 82.33%, while the Triple Exponential Smoothing method produces a MAPE value of 9.842%, which means it has an accuracy of 90.158%

Application of the K-Nearest Neighbor Method to Predict Demand for Goods from Customers at PT Sinergi Prima Enjineering

PT Sinergi Prima Enjineering, which is engaged in services, has been trusted as a contractor in several companies facing challenges in handling the large number of requests for goods and stock inventory management. This research aims to improve the prediction of demand for goods and inventory management using the calculation of the K-Nearest Neighbor (KNN) method and RapidMiner tools. With the comparison of calculations between KNN and RapidMiner using ten test data, the results are appropriate where the categorical grouping is often ordered totaling five data, moderately ordered totaling two data and rarely ordered totaling three data. The test results show that K = 3 produces a prediction accuracy of 91.98%. These results show that K-Nearest Neighbor can accurately anticipate future stock inventory and items that will be ordered by customers and it is hoped that the company can improve customer satisfaction and overall operational performance.

Low Usage of Modern Contraceptives Among Women of Reproductive Age in Tanzania: Analysis Beyond Availability and Awareness Factors

Most studies on contraceptives have linked low use with awareness and problems of availability. However, these studies have not considered low or discontinuation of use of contraceptives to be a positive phenomenon where women exercise their reproductive rights or autonomy. Hence, this article has adopted a Marxist-Feminist theory of contraceptives to examine the association between socio-demographic factors and low usage of contraceptives among women of reproductive age. It also evaluates the influence of the availability and awareness on the use or non-use of contraceptives among such a group of women. The article is based on a cross- sectional case study conducted in Kigoma Rural District, Tanzania. The data were collected through questionnaires modified from TDHS, interviews with nurses, and an in-charge of a health facility. Secondary data were collected from TDHS (2016, 2022), Tanzania Service Delivery Point report, Stock Inventory Charts and Our World data sets. Quantitative analysis was performed through STATA 14, and secondary data was processed through content analysis. The study found that though the majority of the reproductive age are aware of contraceptives, most (80%) have not used them. We do not treat this as a problem, but an exercise of reproductive rights and autonomy by women. In addition, there is no significant evidence to prove any influence of socio-demographic factors on low use of contraceptives. Low use and discontinuation of contraceptives are associated with the need for more children by women, and the side effects of contraceptives. Therefore, we conclude that women should be left to decide when and how to use contraceptives: they should not be forced through direct or indirect health services.

EQONOMIC ORDER QUANTITY (EOQ) METHOD FOR INVENTORY CONTROL OF SOYBEAN RAW MATERIALS IN TOFU INDUSTRY FAUSI BONDOWOSO

tofu industry is an industry engaged in the food sector, namely processing agricultural products in the form of soybeans that sell food needs in the form of tofu. The process of making tofu in the Fausi tofu industry with a large amount every day makes the supply of soybean raw materials must be maintained optimally. Recording for the purchase of soybean raw materials which is still done manually by writing in a daily book makes it difficult for the owner or employees involved to review stock inventory. Descriptive analysis method with a quantitative approach is done by identifying existing data, comparing the results of the data obtained with the existing theory, producing an overview of the problem to be studied and drawing conclusions. The results of the analysis of soybean raw material inventory control in the fausi tofu industry, namely the Economic Order Quantity method can be more optimal than the manual method. Reorder point (ROP) which determines the point at which raw material inventory must be added before running out of inventory in 2023 is at least 1449 Kg. And the calculation of Total Inventory Cost in the Fausi Tofu Industry is more effective using the EOQ method than the manual method because it can reduce total inventory cost by Rp 6,710,485 so that the costs incurred are lower.

Pengendalian Kualitas Produk Gula Studi Kasus PT Madubaru PG. PS. Madukismo Yogyakarta

PG Madukismo is a sugar factory that was established in 1955 and is the only sugar factory in the province of the Special Region of Yogyakarta. This factory is located in Padokan Village, Tirtonirmolo Village, Kasihan District, Bantul Regency, Special Region of Yogyakarta. PT Madubaru, a company engaged in sugar cane processing, is a sugar and spirits factory located in the Yogyakarta area. Companies that produce cane sugar with SHS IA quality. Product defects are often found, resulting in a decrease in quality, therefore it is necessary to carry out quality control. This study uses the Failure Mode and Effect Analysis (FMEA) method to identify failure modes, Logic Tree Analysis (LTA) to determine the consequences of failure arising from failure modes and determine effective preventive maintenance policies for each machine. For the production quality control section, the Statistical Processing Control (SPC) method is used to analyze product defects to determine whether product defects are still within controllable limits or not. While the Failure Mode and Effect Analysis (FMEA) method is used to determine the failure mode in sugar production. For the quality control section on raw materials using the Economic Order Quantity (EOQ), Re-Order Point (ROP), Safety Stock (SS), and Total Inventory Cost (TIC) methods. The conclusion of this study is regarding the results of the analysis and calculations carried out with several settlement methods and there are several causes of production defects from 5 factors, such as: human factors, work methods, environment, raw materials and machines used. And provide solutions to companies in order to reduce defects by controlling product quality.

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods

Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. A review of the remote sensing data and deep learning tree species classification methods is lacking in its analysis of unimodal and multimodal remote sensing data and classification methods in this field. To address this gap, we search for major trends in remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss some limitations of tree species classification.

Analisis Pengendalian Persediaan Bahan Baku Dengan Menggunakan Metode Economic Order Quantity (Eoq) Pada Pt. Bazt Inti Bersama

A very important component in the production process at the company is the raw material. To deal with market demand, accurate raw material inventory planning is needed to determine optimal and economical purchasing levels. This is to ensure that there are no excess or shortage of inventory, which allows for cost savings and disruption of raw material stockouts in the production process. The purpose of this study is to determine the management of existing inventory stock at PT. Bazt Inti Bersama, as well as to find out the optimal number of orders at the most economical cost using the Economic Order Quantity (EOQ) method, and the application of safety stock. This study uses a qualitative descriptive method with data collection techniques through library research, observation, interviews, documentation related to the research problems studied. The results showed that the raw material inventory control for fresh salmon at PT. Bazt Inti Bersama is quite good, but controlling raw material inventories using the economic order quantity (EOQ) method has been proven to be able to reduce the cost of inventories used with a difference in costs of 19,074,116 rupiah. So it can be more economical. Meanwhile, the safety stock needed by the company requires 5,324 kg.

PENERAPAN DATA MINING TERHADAP DATA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA TOKO CITRA UTAMA

Citra Utama Store is a retail and wholesale business aimed at meeting the needs of local residents for their daily household shopping. The problem lies in the difficulty of identifying consumer shopping trends for products in the store, and the inefficiency of the stock placement system. In a competitive business environment, it is crucial for us to obtain information that can help develop our business. One source of such information is the sales transaction history. Using data mining with the apriori algorithm and association rule, we can extract sales transaction data to derive information that can be used to identify frequent itemset combinations, which can then be analyzed to determine products frequently sold together, the most popular items, and customer preferences. The analysis of association rules formed from the apriori algorithm calculation yielded the highest itemset combination pattern, namely EGGS → AQUA GLN, with a support value of 2.84% and a confidence value of 34.13%. By setting minimum support >1.5% and minimum confidence >30%, this research can assist the store in devising sales strategies and managing stock inventory, and uncover association rules that can be used as purchasing patterns by consumers

Optimizing food pantry stocking through client preferences: a novel elicitation and classification process

PurposeThis study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.Design/methodology/approachThis study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.FindingsThis study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.Research limitations/implicationsThis research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.Practical implicationsThis study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.Social implicationsAn emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.Originality/valueThis study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

Implementation of the First In First Out (FIFO) Algorithm in the Sandal and Shoe Product Inventory (Stock) Application

This study addresses the optimization of inventory management for sandal and shoe products, at CV Diva Karya Mandiri Warehouse, which covers of five key features: a dashboard, master data management, transaction data, reporting, and user management. The First In First Out (FIFO) algorithm is specifically applied to the transaction feature, ensuring timely disbursement in line with the order of receipt. It is implemented using Rapid Application Development (RAD) methodology, which consists of Planning Requirements, User Design, Construction, and Cutover phases. The developed inventory application offers two access levels: administrators with comprehensive access and warehouse managers with limited access for viewing, searching, and filtering item data. This study successfully implementing the FIFO algorithm, with 95% Blackbox testing result achieved through boundary value analysis approach.Top of Form

Inventory Management Improvement at PT Inalum, Kuala Tanjung, Sumatra Utara

This research aimed to analyze the inventory issue in PT Inalum. The method used in this research is a quantitative method to collect inventory historical data from January 2022 to December 2022 obtained from the company’s ERP SAP. The data contains information of 6028 items of the MRO inventory. The collected data was analyzed using Economic Order Quantity (EOQ), Reorder Point (ROP), Safety Stock (SS) and Average Inventory Level (AIL). By using Fixed Order Quantity model, the MRO inventory is calculated to compare the ideal and actual average inventory levels. From the analysis, the researcher found that the MRO inventory in Kuala Tanjung, PT Inalum, has a service level of 0.52, which is still far below the service level target of 0.95. At the same time, PT Inalum has the potential to reduce inventory by USD 5,402,389 or 58.2% of the inventory value. This research aims to provide guidance for the management of PT Inalum in making inventory policies that can increase service levels as well as minimize inventory costs.

Analisis Peramalan dalam Menentukan Perencanaan Persediaan pada Toko Seragam Sekolah Hermul Jaya

Forecasting is an estimation of future events based on systematically compiled past data using certain methods. Forecasting is necessary as a basis for planning, therefore an appropriate forecasting method is needed to see a lower level of error so that inventory planning can be optimal and efficient. This study aims to find a more suitable method to be used at Hermul Jaya Clothing Store to determine the predicted amount of stock in the store. This research uses a descriptive quantitative approach and POM QM application to calculate the inventory forecast of uniform stores based on two indicators, namely Moving Average, Weighted Moving Average and Expponential Smoothing. From the results of this study, the stock inventory at Hermul Jaya Uniform Store is not yet effective because stock purchases or inventory are made without prior planning or it can be said that this store does not use forecasting methods

Analisis Peramalan Persediaan Bahan Baku dan Penentuan Stok Penyangga (Buffer Stock) Udang Vaname (Littopenaeus Vannamei)

This study examines the analysis of raw material inventory forecasting and buffer stock of vaname shrimp at one of the fishery companies in Makassar Industrial Estate (PT Bogatama Marinusa Makassar). The analysis method used to determine the supply of raw materials needed by the company is the ARIMA Box-Jenkins method. This method is used to forecast raw material inventory on time series data. Determination of buffer stock is done using standard deviation and policy factors. Vaname shrimp (Littopenaeus vannamei) raw material data was obtained from 2020 to 2022 (156 weeks). The results showed that the highest amount of raw material inventory occurred in October week 147 in 2022, amounting to 152,792 tons, while the lowest amount of raw material inventory occurred in May week 122 in 2022, amounting to 13,102 tons. The best vaname shrimp raw material inventory model is the ARIMA (1,1,1) model which has a Sum Square Error (SSE) value of 1.70250, Mean Square Error (MSE) value of 0.0112007. This model is used to forecast raw material inventory for the next 48 weeks. The forecasting results show that there will be a decrease in week 1 to week 6 and a relative increase in raw materials in week 7 to week 48 with a MAPE value of 4.54%. The amount of buffer stock that must be owned by the company is 37.311 tons.

Android Based Inventory Stock Design Case Study On The North Jakarta Transportation Service Subject

Study This aim For visualize design general Android- based Inventory Stock system for the North Jakarta Transportation Sub – Department. Referring to Law Number 1 of 2004 concerning state treasury , goods or assets belonging to the state/ region in is all purchased items _ or obtained on burden Budget State/Regional Revenue and Expenditure (APBN/D) or originate from acquisition other valid ones , or items obtained _ based on decision powerful court _ law fixed ( provisions of Article 2 paragraph (2) of the Regulation Government Number 27 of 2014). Analysis the need will used in do development of "Android Based Inventory Stock Design Case Study On The North Jakarta Transportation Service Sub". Planning application , at stage This done making structure navigation , UML diagrams (use case diagrams, activity diagrams and class diagrams) for use make it easier stage planning to features that will made on the website and also the design appearance for the UI (User Interface) website version using Prototype High Fidelity (Figma). Planning system mean For something method after assessment from movement development , stage preparation For development or creation of application programs . With hope the system will made later can become solution on problems that exist in the North Jakarta Transportation Sub-dept. The Android- based Inventory Stock application provides solution to problem human error in recording inventory Manually. With automated and structured systems , risks _ error write , loss sheet paper , and errors calculation can minimized in a way significant . With connected system _ in a way direct with inventory database , information stock goods can accessed with fast and accurate , enabling takers _ decision For Act in a way more appropriate time . Application This give convenience in tracking inventory , even moment happen error or lost goods . Facility integrated tracking_simplify the process of searching and recovering accurate inventory data.

On the interdependence of strategic inventories and new product generation introduction

The seminal study by Anand et al. (2008) demonstrates that retailers have an incentive to strategically stock inventories as bargaining chips against upstream manufacturers. To counteract the impact of such strategic inventories, the manufacturer may wish to adjust or introduce products of different qualities in the second period. Shall he then upgrade or downgrade the product’s quality? In our model, the manufacturer produces a single product of a given quality in the first period, and the retailer may carry strategic inventories. In the second period, the manufacturer may choose to produce only the existing product, replace it with a new version, or may choose to produce both generations of products. We find that in the absence of strategic inventories, as the quality of the existing product increases, the manufacturer transitions from offering the existing product along with an upgraded generation to offering a downgraded generation next to the existing one, and ultimately offering only the existing version. However, in the presence of strategic inventories, we uncover a new domain: if the quality of the existing product is low, the manufacturer should offer only the upgraded product. In such a scenario, strategic inventories can benefit the manufacturer and retailer as it weakens the magnitude of double marginalization. The volume of strategic inventories decreases in the existing product quality until the retailer carries a sufficiently high volume such that only the upgraded product is offered by the manufacturer because of the retailer’s ability to price discriminate between consumers based on their preferences for the two product generations.