Inventory Model Research Articles

Efficiency of Inventory in Thai Hospitals: Comparing Traditional and Vendor-Managed Inventory Systems

Background: Traditional procurement and price negotiation methods in public hospitals in Thailand involve group purchasing agreements and bulk orders, posing challenges to improving inventory management efficiency. The vendor-managed inventory (VMI) model is a promising alternative for enhancing hospital performance, especially during crises. This study aimed to assess the potential cost savings in implementing VMI in a large general hospital in Thailand. Methods: A simulation modeling approach was used to compare the current inventory system with three VMI models: VMI1, focused on improving inventory turnover rate (ITR); VMI2, emphasized frequent replenishment with a 1-month supply; and VMI3, eliminated safety stock. Results: The results demonstrated significant cost savings, with potential reductions in total inventory management expenses. Specifically, VMI1 improved ITR from 6.31 to 7.76, reducing average inventory by 36% and cutting management costs by 40%. VMI2, with an ITR of 12.80, reduced inventory by 44% and saved 47% in management costs, while VMI3 achieved a 70% reduction in inventory and a 69% saving in management costs. Conclusions: This study highlights the VMI’s transformative potential in hospital inventory management, demonstrating significant cost savings. However, in the public sector, the feasibility of procurement regulations requires further exploration.

Bi-Objective Inventory Policy with Comprehensive Environmental Factors Formulation and Service Level Constraints

As firms and consumers engage with environmental issues, decisions for inventory control need to entail this perspective of sustainability. Most green inventory models employ methods such as carbon caps or taxes for dealing with environmental sustainability. This problem can be more generally tackled via an explicit estimation of the environmental drivers of maintaining inventory in a warehouse, paired with the economic perspective within a transparent multi-objective optimization framework. With this goal, this paper builds on a detailed estimation of environmental and cost factors for a continuous-review inventory policy. The bi-objective problem is tackled by keeping the objective functions separate. In particular, the modeling of greenhouse gas emission or cost performance factors for the inventory encompasses factors that can depend on the decision variables, taking into account aspects such as warehouse location, building characterization, energy usage, and transport requirements. The effects of the emission drivers on the multi-objective optimization decisions are analyzed, considering that the problem can be constrained by multiple service level measures. Stockout response can be multifaceted and different service level measures capture different aspects of inventory shortages, affecting the resulting efficient solutions differently. The results highlight the impact of aspects such as warehouse location and supply capacity on solutions for the multi-objective inventory problem. Managerial decisions are thus influenced by warehousing and supply attributes via a traceable link to specific cost and emission determinants.

Code and Data Repository for Deep Stacking Kernel Machines for the Data-Driven Multi-Item, One-Warehouse, Multiretailer Problems with Backlog and Lost Sales

The one-warehouse multi-retailer (OWMR) problem is an inventory control problem in a two-echelon supply chain. This study extends the OWMR problem to the data-driven multi-item OWMR problems with backlog and lost sales, designs a new deep network structure that is suitable for the specific multi-item OWMR problems, and develops the LRTO algorithm and the greedy heuristic.

Perancangan Kebijakan Persediaan e-Commerce dengan Pertimbangan Permintaan Probabilistik dan Performance Score

Julis, a local company focused on the production and sale of perfumes through e-commerce platforms, is one of the SMEs facing such challenges. Julis has encountered difficulties in managing its product inventory, leading to high inventory costs and low customer order fulfillment rates (fill rate). These issues risk hindering the company's growth and diminishing consumer trust. This research aims to design optimal inventory policies for Julis by considering probabilistic demand and the company's performance score. This performance score is a measure of performance that integrates two key criteria: profit, calculated based on total inventory costs, and fill rate. With the right inventory policies, Julis is expected to improve inventory efficiency, reduce costs—particularly ensuring inventory costs meet the target of less than 40% of the company's total revenue or income—and achieve a minimum fill rate target of 95%, which is considered crucial for maintaining customer satisfaction and supporting business growth.The approach used in this research is an inventory optimization model that combines probabilistic methods in determining Order Quantity (Q) and Reorder Point (ROP). This design aims to address demand uncertainty, which often poses a major challenge in inventory management within the e-commerce sector. Additionally, this research introduces the integration of inventory classification based on performance score, enabling the company to prioritize certain products based on relevant performance criteria. As a result, each product category can be managed more efficiently and strategically according to business priorities. The results of this research indicate that the implementation of the designed inventory policies can significantly improve Julis' performance score. Specifically, in terms of cost efficiency, these policies can reduce inventory costs and increasing the fill rate, which directly positively impacts the company's profitability. Furthermore, the integration of inventory classification based on performance score has also proven effective in helping Julis identify the most critical products to prioritize in its inventory management.

Optimizing substitution of two products model for exponentially increasing demand under inflation and shortages

This study addresses the limited attention given to product substitution in inventory models. Incorporating product substitution is crucial for determining reorder points, and safety stock, enabling businesses to optimize inventory levels, reduce costs, and maintain customer satisfaction. This study introduces an economic order quantity model tailored to an inflationary environment with shortages and one-way substitution between two deteriorating product types. Through comprehensive testing, this study evaluates the model under various substitution scenarios, including partial substitution. Findings highlight the significance of product substitution in inventory management, allowing businesses to optimize inventory levels, manage costs, and ensure customer satisfaction in dynamic environments with inflation and fluctuating product availability. This model provides the firm with the necessary information to determine the optimal ordering quantity of both products to optimize total benefit and enhance supply chain efficiency. The model demonstrates substantial cost advantages, with partial substitution resulting in an average cost reduction of approximately 9% compared to no substitution and about 45% compared to full substitution. Numerical experiments validate the applicability of the proposed model.

A closed-loop supply chain inventory model with stochastic demand, exchange rate, green investment, and carbon tax

In this paper, a mathematical inventory model for a closed-loop supply chain consisting of a single manufacturer and a single retailer is investigated under a stochastic environment and imperfect production. The manufacturer adopts a remanufacturing policy to recover returned products from the market. The model takes into account exchange rate disparities because the supply chain’s participants are in separate countries. The model also considers carbon emissions, which are expected to be produced from storage, transportation, and production. A carbon policy, namely carbon tax, is implemented to manage the overall emissions resulting from the supply chain. To cope with carbon restrictions, the manufacturer has an opportunity to invest in green technologies. The objective of the study is to find optimal shipment quantity, number of deliveries, safety factor, and green investment such that the joint total cost is minimized. An iterative procedure is suggested to solve the proposed problem, and a numerical example is presented for model validation. The findings imply that variations in the production rate and carbon tax have a significant influence on the model’s performance. Aside from that, the results reveal that uncertainty in the exchange rate and production flaws are important factors that must be considered by managers in making inventory decisions, especially those related to the number of deliveries, frequency of deliveries, and the amount of money invested in green technology. Finally, a sensitivity analysis is provided to explain the behavior of the model.

An update on the landslide inventory and susceptibility modelling in the Illawarra with an analysis of the 2022 rainfall events

An update on the landslide inventory and susceptibility modelling in the Illawarra with an analysis of the 2022 rainfall events

Fuzzy Non-Linear Programming for Fuzzy Inventory Model with Storage Space and Budget Constraint

Fuzzy Non-Linear Programming for Fuzzy Inventory Model with Storage Space and Budget Constraint

Minitab 20 and Python based-the forecasting of demand and optimal inventory of liquid aluminum sulfate supplies

In a company, inventory management is crucial due to the significant impact on various aspects of the business. Similarly, the Indonesian water supply company (PDAM) requires effective inventory management to ensure the supply of liquid aluminum sulfate chemicals. The probabilistic statistical inventory control (SIC) model is commonly used for inventory management. However, previous research on chemical inventory models in PDAMs often relied on simple linear regression to forecast demand data, which fails to capture the inherent volatility in demand. Therefore, this research aimed to predict demand data using the seasonal autoregressive integrated moving average (SARIMA) method and determine the optimal policy for supplying liquid aluminum sulfate chemicals. The results showed that the best demand forecasting model was SARIMA (2,1,2) (1,1,0)12 with a mean absolute percentage error (MAPE) value of 8.19%. The finding of the optimal inventory policy showed a safety stock value of 11,922.35 kg, a reorder point value of 49,511.20 kg, and an order quantity of 21,526.59 kg, leading to a total cost of IDR 11,132,034,145.45. The sensitivity test also showed that variations in lead time, price, μ, and σ parameters directly influence changes in total cost, reorder point, and safety stock. These calculations were conducted using Minitab and Python software.

Expected value of generalized trapezoidal bipolar fuzzy number to solve a multi-item marketing planning inventory model with allowable shortages

Decision makers often grapple with the complexity of multi-faceted real-life problems, where uncertainty arises from both positive and negative states of mind. This duality reflects the decision maker’s optimistic and pessimistic perspectives during the decision-making process. To address this, our paper introduces innovative concepts and frameworks for generalized bipolar trapezoidal fuzzy numbers.We propose novel arithmetic operations on these fuzzy numbers, computing the negative α-cut and positive β-cut methods. Furthermore, we uniquely compute the convex combination of expected values from both the positive and negative membership parts.These theoretical advancements are applied to a practical case study: a multi-item marketing planning inventory model with allowable shortages. Our proposed method’s efficacy is highlighted through detailed numerical illustrations, sensitivity analyses and comparative studies complemented by compelling graphical presentations

Sustainable inventory, pricing, and marketing strategies for imperfect quality perishable items with inspection errors and advertisement-, expiration date- and price-dependent demand under carbon emissions policies

The demand for perishable products is affected by freshness levels, advertising efforts and selling prices, among other factors. By virtue of being consumed mostly by humans, quality control is also an important factor in perishable production systems. Moreover, inventory management related operations in such production systems release sizeable quantities of carbon emissions that are often regulated by carbon policies. To study the interactions of all these attributes in the context of a perishable inventory system, this paper proposes four sustainable inventory models for a perishable product with imperfect quality, inspection errors and whose demand depends on the advertising frequency, expiration date and selling price. The emissions released are assumed to be governed by carbon tax and carbon cap policies. Two of the models are developed under the assumption that the quality inspection process is 100% effective while the other two models consider the possibility of committing inspection errors, and additionally, for each pair of models, one is developed under a carbon tax policy and the other under a carbon cap policy. All the models are aimed at jointly optimising the perishable product’s lot-size, advertising frequency and selling price. The numerical results show that the presence of inspection errors leads to 28% and 23% lower profits under carbon tax and cap policies, respectively. Moreover, profit can be maximised by either stocking perishable products with longer shelf lives or targeting customers that engage with advertising mediums and those that are not price-conscious, while emissions can be minimised via the enforcement of carbon policies.

Psychometric Evaluation of the Brief-COPE Inventory and Exploration of Factors Associated with Perceived Stress among Peruvian Nurses.

This study aimed to analyze the psychometric properties of the Brief-COPE Inventory and to determine its concurrent validity by examining its association with perceived stress among Peruvian nurses. A psychometric study was conducted with 434 Peruvian nurses to evaluate the psychometric properties of the Brief-COPE Inventory through confirmatory factor analysis. Three stepwise variable selection regression models were implemented. The three-factor model of the Brief-COPE Inventory demonstrated adequate fit indices (root mean square error of approximation = 0.052, standardized root mean square residual = 0.068, and both the comparative fit index and the Tucker-Lewis index = 0.95). Additionally, the factors were significantly correlated (p < 0.001), and the reliability was adequate (ω = 0.90). Nurses reported a medium level of perceived stress, with associated factors including having received stress management training, fear of COVID-19, and problem-focused coping strategies (p < 0.05). This study confirms that the Brief-COPE Inventory is a valid tool for measuring coping strategies among Peruvian nurses due to its good model fit, excellent reliability, and concurrent validity with perceived stress. However, further research is needed to assess its validity in the specific areas of performance perceived by nursing professionals.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Disease Phenotypes in Refractory Musculoskeletal Pain Syndromes Identified by Unsupervised Machine Learning.

Overlapping chronic pain syndromes, including fibromyalgia, are heterogeneous and often treatment-resistant entities carrying significant socioeconomic burdens. Individualized treatment approaches from both a somatic and psychological side are necessary to improve patient care. The objective of this study was to identify and visualize patient clusters in refractory musculoskeletal pain syndromes through an extensive set of clinical variables, including immunologic, psychosomatic, wearable, and sleep biomarkers. Data were collected during a multimodal pain program involving 202 patients. Seventy-eight percent of the patients fulfilled the criteria for fibromyalgia, 77% had a concomitant psychiatric-mediated disorder, and 22% a concomitant rheumatic immune-mediated disorder. Five patient phenotypes were identified by hierarchical agglomerative clustering as a form of unsupervised learning, and a predictive model for the Brief Pain Inventory (BPI) response was generated. Based on the clustering data, digital personas were created with DALL-E (OpenAI). The most relevant distinguishing factors among clusters were living alone, body mass index, peripheral joint pain, alexithymia, psychiatric comorbidity, childhood pain, neuroleptic or benzodiazepine medication, and response to virtual reality. Having an immune-mediated disorder was not discriminatory. Three of five clusters responded to the multimodal treatment in terms of pain (BPI intensity), one cluster responded in terms of functional improvement (BPI interference), and one cluster notably responded to the virtual reality intervention. The independent predictive model confirmed strong opioids, trazodone, neuroleptic treatment, and living alone as the most important negative predictive factors for reduced pain after the program. Our model identified and visualized clinically relevant chronic musculoskeletal pain subtypes and predicted their response to multimodal treatment. Such digital personas and avatars may play a future role in the design of personalized therapeutic modalities and clinical trials.

Deterministic Two-Storage Inventory Model for Deteriorating Items with Salvage Value Under Inflation

Deterministic Two-Storage Inventory Model for Deteriorating Items with Salvage Value Under Inflation

Prospective Life Cycle Assessment of Hydrogen: A Systematic Review of Methodological Choices

This systematic review examines methodological choices in assessing hydrogen production and utilisation technologies using prospective life cycle assessments (LCA) between 2010 and 2022, following PRISMA guidelines. The review analysed 32 peer-reviewed articles identified through Scopus, Web of Science, and BASE. The study reveals a significant gap in the consistent application of prospective LCA methodologies for emerging hydrogen technologies. Most studies employed attributional approaches, often lacking prospective elements in life cycle inventory (LCI) modelling. Although some initiatives to integrate forward-looking components were noted, there was often lack of clarity in defining LCA objectives, technology readiness level (TRL), and upscaling methods. Of the 22 studies that focused on emerging hydrogen technologies, few detailed upscaling methods. Additionally, the review identified common issues, such as the limited use of prospective life cycle impact assessment (LCIA) methods, inadequate data quality evaluation, and insufficient sensitivity and uncertainty analysis. These findings highlight the substantial gaps in modelling low-TRL hydrogen technologies and the need for more robust, comprehensive approaches to assess uncertainties. The review also identified common practices and areas for improvement to enhance the reliability and relevance of hydrogen technology environmental assessments.

Probabilistic Inventory Models and Productivity of Selected Manufacturing Firms in Port Harcourt

This study examines the relationship between probabilistic inventory models and productivity of selected manufacturing firms in port Harcourt. Correlational survey research design was adopted for this study as this study seek to determine the relationship between the two variables. The population of this study is thirty-two (32) manufacturing companies in Rivers State which are registered with the Rivers State branch of Manufacturers Association of Nigeria (MAN). This gave us a total of ninety-two (92) for the study. Structured questionnaire instrument title” Probabilistic inventory models and productivity of selected manufacturing firms in Port Harcourt. questionnaire was developed on five- point likert scale. The result of the Cronbach's Alpha reliability test indicates .800 which is above .70 which implies that the items are reliable. Pearson product moment correlation was used to test the hypotheses using SPSS (statistical package social sciences). The study revealed that there is a significant relationship between Continuous Review Models and Productivity of manufacturing firms in Port Harcourt, Rivers State. There is a significant relationship between Periodic Review Models and Productivity of manufacturing firms in Port Harcourt, Rivers State. There is a significant relationship between Single-Period Models and Productivity of manufacturing firms in Port Harcourt, Rivers State. The study concluded that Probabilistic inventory models play a crucial role in optimizing inventory management for manufacturing firms. The study recommended that Manufacturing firms in Port Harcourt should consider implementing probabilistic inventory models such as continuous review, periodic review, and single-period models to optimize their inventory management processes.

Product refurbishment in an imperfect production inventory system under carbon regulations: An interval type-2 fuzzy environment

This study develops an imperfect production inventory model focusing on the proper handling of imperfect products and addressing carbon emissions in different stages of a production process. The proposed inventory system consists of three stages: production, screening, and refurbishing. The produced products undergo a screening process, with perfect quality items being delivered to the primary market, while imperfect quality products are routed through a refurbishing process, subsequently sold in the secondary market. It is observed that in the existing literature on refurbished products, all such types of products have been sold at the end of the business period, this means that there is no continuous demand of refurbished product at the remaining time points of the business period which does not support the practical situation in a real business system. In this regards, our main objective in this study is consideration of continuous demand of refurbished products along with perfect ones. Again, we incorporate interval type-2 fuzzy uncertainty in both markets’ demand to better reflect market fluctuations introducing a new defuzzification method. In this framework, we evaluate the effectiveness of two emission regulatory strategies: carbon tax and carbon cap and trade policies. The findings of this study offer valuable insights into various aspects. Notably, our analysis reveals that if the difference between the primary and secondary markets’ demands reduces to a large extent, a negative impact on the profit is observed. It is also shown that an increase in demand for refurbished products allows for a higher permissible rate of imperfections in production. Comparing the outcomes under two emission regulations, we find that the cap and trade policy is beneficial from both economic and environmental perspectives. Additionally, it becomes evident that the manufacturer should carefully consider the duration of each inventory stage, as it leads to a significant increase in overall emissions. Regarding the uncertain nature of the market, it is observed that demand volatility in the secondary market negatively affects the profit.

An integrated inventory and order pick-up model considering the vendor's capacities and the vehicle trips' duration for the MVSB system

Purpose: This research develops a model integrating inventory with order pick-up that considers both the vendor’s capacities and the pick-up time for the multi-vendor single-buyer (MVSB) system.Design/methodology/approach: A model of integrating inventory with pick-up of order considering both the vendor’s capacities and the pick-up time for the MVSB’s system is formulated as a mixed integer non-linear programming, and a heuristic-metaheuristic algorithm is proposed to solve the problem. This model applies a decomposition approach to minimize total relevant costs, which consist of the buyer’s ordering, vendor’s set-up, vendor’s and buyer’s holding, and pick-up.Findings: This research proposes an inventory model integrated with the pick-up of orders, considering both the capacity of the vendor and the pick-up time for the MVSB system. This research also proposes a hybrid heuristic-metaheuristic algorithm that can obtain a solution in realistic computational time. Originality/value: This paper proposes a mathematical model for the inbound inventory routing problem, considering both the capacities of vendors and the pick-up time for the MVSB system.

Analyzing how inflation affects non-instantly decaying goods with demand linked to ads and selling price in a dual-warehouse setup

This study explores inventory management strategies specifically designed for non-instantaneous deteriorating goods under inflationary conditions, utilizing a dual-warehouse system—one owned and the other rented with limited storage capacity. We examine the effects of advertising frequency and product selling price on demand rates, taking into account the gradual decline in customer patience, which leads to partial backlogging of shortages. The primary objective is to determine optimal replenishment policies for retailers that effectively minimize total costs per unit time. In real life, managing oil inventory is crucial for industries where factors like gradual deterioration, demand fluctuations due to advertising and pricing, and sensitivity to inflation require sophisticated inventory models to optimize replenishment policies and minimize costs. To validate our proposed inventory model, we provide a numerical. A sensitivity study using MATLAB R2024a software highlights the impact of parameter changes, providing significant information for decision-makers across various industries.