Inventory Control Research Articles

The effects of learning styles and attention control on P300 test in young adults

BackgroundLearning style is a set of characteristics that an individual uses while perceiving, learning, and reacting. There are basically three types: visual, auditory, and kinesthetic. Attentional control is the capacity of an individual to choose what to pay attention to and what to ignore. P300 is one of the event-related potential responses recorded by recognizing the infrequent stimulus in frequent stimuli. Research indicates that the P300 is associated with the differences in learning and can be utilized alongside standard psychometric assessments for evaluation. The differences between learning styles and attentional control in cortical regions are not yet fully understood. The aim of this study was to evaluate the relationship of different learning styles and attentional control with P300 in normal hearing individuals.MethodForty-nine participants aged 18–30 years normal hearing university-educated individuals were administered P300 test, Maggie McVay Lynch Learning Style Inventory and Attentional Control Scale. One-Way ANOVA or Kruskal–Wallis test was used for comparisons between groups according to data normality, and Pearson or Spearman analysis was used for correlation analyses.ResultsAlthough shorter P300 latencies were obtained in auditory learning style subjects and larger P300 amplitudes were obtained in visual learning style subjects, P300 latencies and amplitudes of visual, auditory, and kinesthetic learning style subjects did not differ statistically. In addition, the attention controls of the groups were similar. No significant correlation was found between attentional control and P300 latencies and amplitudes.ConclusionAlthough some questions in the questionnaires were associated with P300, the presence of different learning styles did not have a significant effect on P300. Furthermore, the level of attentional control was not found to be related to the P300.

Analysis of Soybean Raw Material Inventory Control Using the Economic Order Quantity (EOQ) Method at the Terang Tofu Factory

Analysis of Soybean Raw Material Inventory Control Using the Economic Order Quantity (EOQ) Method at the Terang Tofu Factory

Inventory Control in Modern Supply Chains: Integrating Advanced Technologies for Optimal Performance

The evolution of supply chain management has positioned inventory control as a critical determinant of operational efficiency and competitive advantage. This comprehensive article examines the intersection of traditional inventory management principles with emerging technologies, particularly focusing on the integration of predictive analytics, machine learning algorithms, and real-time monitoring systems. It explores how modern inventory control systems are adapting to address contemporary challenges while leveraging technological advances to enhance decision-making processes. The article investigates traditional methodologies like Economic Order Quantity and Just-In-Time systems alongside modern approaches utilizing IoT-enabled sensors and advanced analytics for real-time tracking. Through examination of cloud-based platforms, data analytics integration, and sustainability considerations, the article demonstrates how digital transformation is reshaping inventory management practices. It also addresses implementation challenges and future directions, including emerging technologies and integration trends, while providing insights into how organizations can develop resilient and efficient inventory systems that align with both operational and strategic goals in an increasingly complex supply chain landscape.

Platelet Inventory Management with Approximate Dynamic Programming

We study a stochastic perishable inventory control problem with endogenous (decision-dependent) uncertainty in shelf life of units. Our primary motivation is determining ordering policies for blood platelets. Hospitals typically order their required platelets from a central supplier, and as such, the shelf life of units at the time of delivery can be subject to significant variability. Determining optimal ordering quantities is a challenging task because of the short maximum shelf life of platelets (three to five days after testing) and high uncertainty in daily demand. We formulate the problem as an infinite-horizon discounted Markov decision process (MDP). The model captures salient features observed in our data from a network of Canadian hospitals and allows for fixed ordering costs. We show that with uncertainty in shelf life, the value function of the MDP is nonconvex and key structural properties valid under deterministic shelf life no longer hold. Hence, we propose an approximate dynamic programming (ADP) algorithm to find approximate policies. We approximate the value function using a linear combination of basis functions and tune the parameters using a simulation-based policy iteration algorithm. We evaluate the performance of the proposed policy using extensive numerical experiments in parameter regimes relevant to the platelet inventory management problem. We further leverage the ADP algorithm to evaluate the impact of ignoring shelf-life uncertainty. Finally, we evaluate the out-of-sample performance of the ADP algorithm in a case study using real data and compare it with the historical hospital performance and other benchmarks. After tuning the parameters, the ADP policy can be computed online in a few seconds and results in more than 50% lower expiration and shortage rates compared with the historical rates. In addition, it performs better or as well as other benchmarks, including an exact policy that ignores uncertainty in shelf life and becomes hard to compute for larger instances of the problem. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: V. Sarhangian was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2018-04518]. H. Abouee-Mehrizi was supported by the Natural Sciences and Engineering Council of Canada [Grant RGPIN-2019-05625]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0245 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0245 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

On non-negative auto-correlated integer demand processes

Abstract Methods to generate realistic non-stationary demand scenarios are a key component for analyzing and optimizing decision policies in supply chains. Typical forecasting techniques recommended in standard inventory control textbooks consist of some form of simple exponential smoothing (SES) for both the estimates for the mean and standard deviation. We study demand generating processes (DGPs) that yield non-stationary demand scenarios, and that are consistent with SES, meaning that SES yields unbiased estimates when applied to the generated demand scenarios. As demand in typical practical settings is discrete and non-negative, we study consistent DGPs on the non-negative integers. We derive conditions under which the existence of such DGPs can be guaranteed, and propose a specific DGP that yields autocorrelated, discrete demands when these conditions are satisfied. Our subsequent simulation study gains further insights into the proposed DGP. It demonstrates that from a given initial forecast, our DGP yields a diverse set of demand scenarios with a wide range of properties. To show the applicability of the DGP, we apply it to generate demand in a standard inventory problem with full backlogging and a positive lead time. We find that appropriate dynamic base-stock levels can be obtained using a new and relatively simple algorithm, and we demonstrate that this algorithm outperforms relevant benchmarks.

Inventory routing optimization for pharmaceutical cold chain logistics from economic and environmental perspective

The effectiveness and efficiency gains or losses in logistics are directly influenced by inventory control and distribution management, which have a significant impact on the synchronisation and overall performance of the supply chain. This paper presents one of the initial studies that introduces the inventory routing problem within a two-tier pharmaceutical cold chain logistics network, considering economic, environmental and social criteria. The model takes into account real-life characteristics such as uncertain demand, carbon emissions, and service level. To implement this model in practice, a comparative analysis is conducted between sequential decision-making and integrated decision-making approaches. Additionally, sensitivity analysis is performed on inventory boundaries and carbon-trade price using Python and Gurobi. The results offer valuable insights for optimising operational and tactical performance in pharmaceutical cold chain logistics management.

LEVERAGING ARTIFICIAL INTELLIGENCE IN HEALTHCARE SUPPLY CHAINS: STRENGTHENING RESILIENCE AND MINIMIZING WASTE

AI is reshaping the future of healthcare supply chain management, increasing operational performance and reducing the level of wastage, to explore efficiency and cost benefits. This paper used quantitative forecast models, which have the potential to support sustainable economic growth of supply chain management. There are many issues and challenges that are associated with healthcare supply chains such as inventory control, demand forecasting, and resources. Management. These problems remain unsolved by traditional supply chain solutions leading to problems such as overstock, stockout, and wastage. AI technologies and applications like machine learning algorithms and predictive analytics offer solutions through their aptitude for forecasting, inventory management, and making the right decisions. Econometric techniques such as time series and econometric modeling quantitative techniques are used in assessing the economic implications of AI in healthcare supply chain innovation. These techniques involve studying past trends to forecast future behavior and these are beneficial in organizational demand changes, resource utilization, and general waste can be better estimated. Integrating AI with quantitative forecasting enables healthcare organizations to strengthen their operational resilience, adjust to changing market conditions, and realize cost savings. The study emphasizes several key advantages of AI adoption, including enhanced accuracy in demand forecasting, lower operational costs, and improved resource utilization efficiency. Furthermore, AI-powered tools assist organizations in managing uncertainties and responding proactively to disruptions, fostering overall economic stability and growth. Through utilizing quantitative forecasting methods, healthcare organizations can optimize their supply chain operations, promote sustainable economic growth, and improve service delivery. KEYWORDS: Artificial Intelligence, Healthcare Supply Chains, Operational Resilience, Waste Reduction, Predictive Analytics, Demand Forecasting, Resource Optimization, Machine Learning, Sustainability.

An Adjustment Method for Re-adhesion Control of Electric Rolling Stock Using Particle Swarm Optimization and Its Validation by a Scale Model Railway Vehicle

An Adjustment Method for Re-adhesion Control of Electric Rolling Stock Using Particle Swarm Optimization and Its Validation by a Scale Model Railway Vehicle

IMPLEMENTASI METODE FUZZY TIME SERIES UNTUK PERAMALAN PENJUALAN PADA RESTORAN COCO BISTRO TANJUNG

As competition in the culinary industry becomes increasingly intense, restaurant companies are required to focus more on product management and inventory control to maintain customer satisfaction and ensure smooth operations. Coco Bistro Tanjung, part of the Tropical Group, still relies on historical sales data without using scientific methods to forecast sales, which often leads to difficulties in determining the optimal amount of raw material inventory. This study aims to forecast sales for the upcoming period using the Fuzzy Time Series method, which is considered effective in addressing sales fluctuations. The research process begins with problem identification, a literature review, and the selection of the method, followed by the collection of sales data from Coco Bistro Tanjung. Forecasting is performed manually using the Fuzzy Time Series method, and the accuracy of the forecasts is evaluated using MAPE, MSE, and MAE. The results show that the Fuzzy Time Series method can provide accurate predictions for six main food categories, with the Balinese Main Course category achieving the best accuracy (MAPE 8.72%), classified as "Very Good," while the Pizza category showed the lowest accuracy with a MAPE of 38.75%, classified as "Fair." This research proves that the Fuzzy Time Series method is effective in forecasting sales trends for most food categories, which can help the company optimize inventory management and improve operational efficiency.

Evaluating the Effectiveness of Inventory Management Systems in Local Government

This study analyzes the effectiveness of the inventory management and control system at Nchelenge Town Council, targeting critical issues such as data inaccuracies, stockouts, and challenges in demand forecasting. The research employs qualitative methods, including semi-structured interviews and focus group discussions with 24 council staff members, to evaluate the system using key performance indicators (KPIs) such as accuracy rates, order fulfillment times, cost-effectiveness, and stockout rates. Findings indicate that, despite the use of advanced tools like inventory tracking software, barcode scanners, and RFID technology, data inaccuracies persist, with 80% of participants highlighting this as a major obstacle. Furthermore, 70% reported difficulties in demand forecasting, leading to operational inefficiencies. However, strategies such as Just-In-Time Ordering and Cycle Counting have shown effectiveness in reducing excess stock and improving inventory accuracy. The study recommends implementing stringent data validation protocols, adopting advanced forecasting models, and enhancing system integration. Continuous staff training and exploring IoT and RFID technologies for real-time tracking are also proposed to improve accuracy and efficiency. These measures aim to enhance service delivery and operational effectiveness, providing valuable insights for other local government councils facing similar challenges. By adopting the proposed strategies, councils can significantly improve their inventory management systems, reduce costs, and better meet community needs.

Analyzing exposure risks in warehousing due to the presence of phthalate contamination

Phthalate esters (PAEs) are widely used in plastic consumer products and many studies published to date have associated these chemicals with severe human health problems. Particularly, the risks within warehouses, which involve large quantities of PAE sources stored in closed spaces, have not been addressed. This article presents an integrated inventory control model to determine the periods within the supply cycle where concentrations of PAEs are likely to represent a risk for the warehouse personnel. This model considers the dynamic aspect of the warehouse supply and consumption mechanisms, and links it to the release patterns of PAEs in closed environments which depend on the type of materials, temperature and time. Numerical analysis corroborates that, in certain periods of time, concentrations of PAEs in the warehouse can exceed permissible levels for humans, and thus the use of appropriate protective wear and decontamination procedures should be established.

Unit dose drug dispensing systems in hospitals: a systematic review of medication error reduction and cost-effectiveness

BackgroundMedical errors pose significant risks to patient safety and public health. Automated unit dose drug dispensing systems (UDDSs) have emerged as valuable tools to reduce medication errors while optimising economic...

Innovation and Performance Of Retail Small To Medium Enterprises In Harare Metropolitan Province Zimbabwe

The integration of innovation into various aspects of society and the economy has the potential to transform industries and re-define business models. The study aims at assessing effects of innovation and the performance of retail Small to Medium Enterprises (SMEs) in Harare Metropolitan Province. The study is guided by Pragmatism Research Philosophy. Structured questionnaires and structured interview guides were used to collect quantitative and qualitative data from 60 respondents. Quantitative data were analysed using descriptive statistics and correlations while qualitative data were analysed using NVivo. The main study findings establish that there is a positive relationship between innovation and performance of retail SMEs. Study findings show that innovation promotes the adoption of advanced technologies within the retail sector. This includes technologies such as artificial intelligence (AI), big data analytics, the Internet of Things (IoT) and cloud computing. These technologies can enhance the efficiency and effectiveness of retail operations, streamline supply chains, improve customer experience and support data-driven decision-making. The study also reveals that innovation optimises retail operations and improves efficiency. By automating manual processes and integrating systems, SMEs reduce costs, minimise errors and enhance productivity. For instance, adopting inventory management systems and point of sale (POS) solutions can streamline inventory control, reduce stock outs and improve overall operational performance. The recommendations proffered by the study is that government should provide the necessary support such as digital skills training and favourable policies that enhance retail SMEs productivity.

Computational Complexity of Discretized Markov Decision Processes*

Discretization is an important way to handle the difficulty caused by continuous spaces in Markov decision processes (MDPs). While the existing studies have obtained the order of magnitude of complexity for common discretization algorithms, more precise complexity analyses are called for in order to guide the design and selection of such algorithms to meet the often demanding nature of real-world problems. This paper studies the discretization and iterative schemes for solving discrete-time continuous MDPs (MDPs with continuous state and action spaces) with an infinite-horizon expected discounted criterion, as well as the computational complexity resulted therefrom. Specifically, we consider and analyze the procedure of two stages: the uniform discretization of continuous MDPs and the value iteration of discretized MDPs. Concrete forms of complexities, including the iteration complexity and the computational complexity for the single-grid and multigrid methods, are derived. We prove that the multigrid method leads to a lower computational complexity than that of the single-grid method under certain conditions. Finally, we conduct numerical experiments about inventory control to validate our main results, which present some insights into the parameter selection of algorithms for solving continuous MDPs. The accuracy and tightness of our complexity bounds are also demonstrated.

Deep Learning for Demand Forecasting: A Framework Incorporating Variational Mode Decomposition and Attention Mechanism

Accurate demand forecasting is crucial for modern supply chain management, forming the foundation for inventory optimization, cost control, and service level improvement. However, demand time series data often exhibit high volatility and diverse patterns, further complicated by the rapid expansion and heterogeneity of data sources. These challenges can result in significant degradation in predictive accuracy when traditional models are applied to complex demand datasets. To address these challenges, this study proposes an end-to-end demand forecasting framework leveraging Variational Mode Decomposition (VMD) and attention mechanisms. The framework first employs VMD to decompose raw demand time series into multiple modes to extract hierarchical features, including trends, seasonal patterns, and short-term variations. Subsequently, an attention mechanism is introduced to dynamically capture and integrate demand sequences alongside contextual information, enhancing the focus on critical features and improving predictive performance. Experimental results demonstrate that the proposed method achieves superior predictive accuracy compared to conventional approaches, with a 37% reduction in Mean Absolute Error (MAE) relative to baseline models. This substantial improvement in demand forecasting accuracy provides actionable insights for decision-makers, enabling more efficient inventory control, production planning, and overall supply chain optimization.

The impact of resource management practices on the performance of micro and small enterprises in Werabe Town, Ethiopia

IntroductionEfficient resource management is essential for enhancing organizational performance; however, its implementation in micro and small enterprises (MSEs) has received limited academic attention. Taking into account these obvious reasons, this study aims to investigate the influence of resource management practices on the performance of micro and small enterprises in Werabe Town, Ethiopia.MethodsWe distributed a structured questionnaire to 265 randomly selected small business operators. Both descriptive and inferential statistical methods, including correlation and regression analysis were used to analyse the collected data.ResultsThe findings indicate low overall MSE performance, driven by inadequate financial, human resource, material, and information management practices. Based on regression analysis, these four factors explain 64.3% of the variations in performance, showing that they have a positive and statistically significant effect.ConclusionTherefore, improving financial planning, cash flow, and working capital management is crucial for maintaining efficient financial operations. However, financial efficiency alone is not enough; a formal HR planning process, a well-defined job description, carrying out a recruiting and selection, and establishing a pay system are equally crucial. Additionally, streamlining material requirement planning, procurement processes, and inventory control systems ensures resource availability and operational efficiency. Furthermore, pre-made and streamlined information management software is appropriate to improve the flow of information for better decision-making across financial, human, and material resources.

Inventory Control Analysis Using The EOQ Method At CV Sudut Cantik

This research aims to determine the inventory control carried out by CV Sudut Cantik. The data used in this research is primary data obtained from interviews and secondary data obtained from company records for one year (January – December 2023). The data analysis method used in this research is Economic Order Quantity (EOQ) method, which aims to find out economical orders, Total Inventory Cost (TIC), Safety Stock (SS), and ReOrder Point (ROP). Using a descriptive quantitative research approach. Data were analyzed using POM-QM For Windows V5.3. The research results obtained are the Total Inventory Cost according to company policy for products namely Daviena Skincare Glowing Series Package is IDR 337,725, while based on the EOQ method is IDR IDR 34,155.

Integrating Data Analysis and Predictive Analytics into an Advanced Inventory Management System using Machine Learning and Deep Learning to Address Backorders

The evolution of ICT technology has benefited various businesses and applications in a variety of ways. The inventory management system is one of those that has used a variety of techniques to improve logistics and inventory management performance. The novel research introduces an advanced inventory management system that intends to improve operational efficiency in the retail industry by providing an intuitive interface for managing purchases, invoicing, client information, product catalogues, and sales numbers. The underlying inventory control systems automatically compute and apply reorder levels and safety stock quantities. Machine learning and deep learning are new technologies employed in current analytics due to their enormous benefits.As a result, the purpose of this research paper is to highlight a combined approach of providing analytical business intelligence via data visualization platforms, as well as predictive analytics via machine learning and deep learning, in order to demonstrate a significant advancement toward the automation and optimization of inventory governance processes. The proposed approach demonstrates how combining traditional software development with cutting-edge data analysis techniques can result in a more efficient, scalable, and informative inventory management solution. It addresses common retail difficulties like stockouts and overstocking by utilising powerful machine learning (ML) and deep learning (DL) techniques. Finally, empirical testing and real-world applications are used to evaluate the suggested approach, which shows significant advances in inventory optimisation as well as other benefits such as enhanced demand forecasting, cost reductions, and operational efficiency. The findings show that machine learning and deep learning have the potential to transform inventory management practices.

Analysis of inventory control of label sticker raw materials using the continuous review (q) and periodic review (p) methods

Abstract X is a manufacturing company that produces label stickers. For this production, they require key raw materials, one of which is a 22.5cm Raflacoat type substrate. Currently, the company X has difficulty in controlling the inventory of this type of substrate as its demand often fluctuates. This often results in stock shortages (stock outs) which cause many customers to cancel orders due to insufficient inventory. This study aims to control the inventory of Raflacoat 22.5 cm substrate in order to minimize the occurrence of shortages and excess stock and reduce the total cost of inventory at company X. The methods used are Continuous Review (Q) Lost Sales and Periodic Review (P) Lost Sales. The calculation results show that the minimum total inventory cost with the Continuous Review (Q) Lost Sales method is Rp2,160,302,178, while the company method is Rp2,396,962,073. Thus, the cost savings achieved are Rp236,659,895 or 9.87%. After forecasting the needs in the period March 2023 to February 2025, the total need is estimated to reach 2304 rolls. Based on the inventory control of Raflacoat 22.5 cm substrate raw materials, the optimal number of orders is 193 rolls per order with a total inventory cost of Rp2,739,423,128.

Role of Ecommerce in Reducing Operational Cost

E-commerce plays a predominant role in the process of buying and selling of goods and services over the Internet. It helps in establishing businesses and sales with a wide range of customers across the globe, offering the customers to shop online 24/7. This study helps to explore various aspects that contribute to the reduction of operational costs in ecommerce such as inventory management, automation of business processes, logistic and supply chain management, labor cost, warehousing, etc. Additionally, processing orders, payments, and customer services through automation, it helps business to gain more profit by reducing the costs associated with it. Through digital marketing, it can reach customers across the globe efficiently. Data analytics helps to make decisions effortlessly by providing valuable insights. The findings in the article help to understand how the adoption of e-commerce in businesses helps to achieve a significant reduction of administration costs, storage costs, shipping costs, etc by helping the business earn more profitability. The study looks at a number of important ways that e-commerce can save costs, such as through automating corporate processes, enhancing inventory control, requiring less physical infrastructure, having a worldwide reach and being scalable, streamlining communication, and producing less paperwork.