Inventory Control Research Articles

Integration of a new Cryogenic Liquefier into the IB-1 Cryogenic Test Facility

The increase over the last years of the testing activities related to quantum systems, SRF cavities for the PIP-II and the LCLS-II projects, as well as superconducting magnets for the HL-LHC project and Fusion research activities, has required the addition of a new Helium cryogenic plant into the existing IB-1 Industrial Cryogenic Test Facility. The new cryogenic plant is composed of a cryogenic liquefier (Cold Box) able to provide up to 340 L/h, a 4 kL Dewar and two Mycom® compressors providing up to 120 g/s. AL-AT (Air Liquide Advanced Technologies) has taken part of this project by designing and manufacturing the cryogenic liquefier. This new cryogenic plant is connected through a cryogenic distribution system to a 10 kL Dewar, which is part of the existing cryogenic test facility, itself composed of another Cold Box and a Sullair® compressor. The new cryogenic plant has two main operating modes: one allows to transfer liquid helium at 1.7 bar between the two Dewars, the other allows to transfer supercritical Helium at 2 bar or more between the new Cold Box and the 10 kL Dewar. The entire industrial cryogenic facility is handled by a common Inventory Control System, composed of three control valves, and 9 tanks giving a total buffer volume of more than 1000 m3. This paper presents the technical features of the new Helium cryogenic plant, as well as the main results of the liquefier commissioning phase and details of the helium transfer between the two Dewars, making the connection between the cryogenic plants at the IB-1 Industrial Cryogenic Test Facility.

Deterioration and age governed greenhouse gas emissions from the product itself: an optimum inventory control problem

Greenhouse gas emissions contribute significantly to global warming. This occurs during various operations, including the production, storage, and transportation of an inventory, but in some cases, the inventory itself becomes the emission source. Emissions from cattle dunk cakes, livestock slurry, manure, crude oil, and gasoline can be considered examples of this type of emissions. This paper proposes a mathematical formulation for a deteriorating inventory model utilizing the inventory as a source of emissions. The model is developed under inflation. Other activities, such as energy consumption for warehousing, are considered to be contributing to greenhouse gas emissions here. The rate of emissions from the product is exponentially governed by the rate of deterioration and the age of the material. The trapezoidal-type demand rate is considered using the Heaviside step function. Shortages are permitted but partially backlogged, and the backlogging rate is supposed to be decreasing exponentially with the increased waiting time. The numerical illustration of the model is provided to illustrate the mathematical expressions, and the effect of parametric variation is reported to give managerial insights. The results reveal that the greenhouse gas emissions are proportional to the variable rate of emissions cost and the deterioration rate. However, the increment in total emissions with respect to variable emissions cost increases with the deterioration rate. If the deterioration rate is negligible, total carbon emissions do not fluctuate much, but it increases rapidly if it is relatively high. The preservation technology is used to reduce deterioration and helps reduce emissions.

Propagation of best practices in value and supply chain management: Demand forecasting and planning

This paper is a discussion article that focuses on the propagation of best practices in value and supply chain management. Specifically, the article discusses the application of prudent demand analysis and forecasting techniques as tools for effective and efficient business management. The use of quantitative models such as time-series and econometric methods in forecasting prices, sales and demand volumes have formed the subject matter of the paper. The expeditious adoption of the techniques presented herein can equip the entrepreneur or manager with essential knowledge of future demand conditions that can be extremely useful when planning production schedules, inventory control, advertising campaigns, output in future periods, and investment, among other things. The ultimate expected impacts would be to facilitate the enhancement of economic and financial profitability, sustainability and other micro-economic and socio-economic performance variables.

A Study on E-Commerce Warehousing Dynamics Navigating Order Fulfilment and Returns

The e-commerce over the past decade has experienced remarkable growth, reshaping shopping habits and challenging traditional supply chain methods. This surge in online shopping has compelled e-commerce firms to enhance their warehousing and order fulfilment strategies to meet rising consumer expectations for swift deliveries and hassle-free returns. Effective management of order fulfilment and reverse logistics has become essential for competitiveness in this industry. This study investigates e-commerce warehousing dynamics, emphasizing the optimization of order speed, delivery times, operational costs, and return management. Through industry reports, academic literature, and case studies, we identify best practices in five key areas: warehouse design and automation, demand forecasting, inventory control, order picking and packing processes, transportation strategies, last-mile delivery models, and reverse logistics for returns. Our research underscores the significance of highly automated warehouses equipped with advanced technologies such as robotics, automated storage and retrieval systems (ASRS), automated guided vehicles (AGVs), and intelligent software for material flow management. These innovations empower e-commerce firms to boost throughput, enhance operational flexibility, and adapt to changing demands effectively.

Optimal information disclosure in a production-inventory queueing system

This study considers a production-inventory queueing system, where customers decide whether to wait for the item or leave without a purchase if a stockout occurs. The system manager determines the joint pricing and inventory control policy. In addition, the system manager can choose whether or not to provide information about the system state to arriving customers. Our strategic production-inventory queueing system includes the observable, unobservable, and almost unobservable systems, which are obtained by appropriately choosing the information disclosure policy by the system manager. In this study, we find the optimal information disclosure policy, as well as the optimal price and the optimal inventory control policy for the profit maximization and social welfare maximization. Under the optimal policy, the system manager should disclose the system state if and only if the queue length exceeds a certain threshold.

PERANCANGAN SISTEM INFORMASI CONTROL STOCK SPARE PART BEBASIS WEB

The spare part stock control recording system is more effective in triggering the company's performance to be better than before. Therefore, we need a system that can handle the process of recording and creating spare part stock control reports using the waterfall model, which is an SDLC method. The hope is that this application can help the performance of the agency. In this thesis the author creates a spare part stock control recording system that will help the administration process at PT. Ichii Industries Indonesia. The spare part stock control system is a system that will record incoming goods, outgoing goods, remaining stock and also prepare spare part stock reports. With this system at PT. Ichii Industries Indonesia can reduce recording errors that may occur. This system can also speed up the process of creating reports which in the end can help PT. Ichii Industries Indonesia.

The Effect of Integrated Warehouse Operation Efficiency on Organizations Performance

In the contemporary business landscape, warehouse efficiency has emerged as a cornerstone of organizational prowess, serving both as a centre of expertise and a strategic tool for competitive advantage. A well-functioning warehouse not only facilitates swift fulfilment of customer needs but also significantly enhances overall business performance. This study seeks to investigate the impact of warehouse attributes on efficiency, focusing particularly on layout and operations. Through a comprehensive literature review, we explore the role of warehouse design and operational practices in driving efficiency gains. Our analysis reveals that the configuration of a warehouse plays a pivotal role in determining its operational effectiveness, with optimized layouts facilitating rapid response to customer demands. Furthermore, effective warehouse operations are identified as critical components in enhancing overall efficiency, working synergistically with layout design to bolster performance. Concurrently, inventory management has emerged as a paramount concern for organizations worldwide, directly influencing operational success. Efficient inventory practices not only enhance customer service but also mitigate stock-related challenges.. By synthesizing insights from both warehouse efficiency and inventory management domains, this study aims to provide a comprehensive understanding of the strategies employed by organizations to optimize operational performance and drive sustainable growth. Keywords: Seamless integration of inventory practices, Efficient warehouse operations, Technological advancements in inventory control, Inventory tracking systems, Optimization of holding costs Technology-driven warehouse management

A Bi-Objective Inventory Model for Strategic Items in a Stochastic Environment with Partial Shortage: A Base-Stock Approach

This paper suggests an ordering policy for strategic items. Based on the base stock policy, we modeled a bi-objective, multi-product inventory control system with partial back-ordering. The model aims to minimize total costs and maximize the average service level, considering stochastic constraints such as storage space, maximum allowable shortage, and maximum allowable lost sales to match real-world conditions. We used three Multi-Objective Decision-Making methods, namely Individual optimization, Desirability function, and Goal attainment, to solve the problem. TOPSIS method showed the Goal attainment method’s superiority over the others. The sensitivity analysis revealed that the mean of storage space had the most significant impact on objective functions. Also, increasing the back-order percentage can reduce the system’s total costs.

Evaluation of BPJS Drug Planning Controls in Regional General IFRS dr. Soehadi Prijonegoro Sragen

Service in the pharmaceutical sector at hospitals is one that must be carried out in a timely and high-quality manner in order to support good service. Controlling drug supplies has a major impact on the quality of service to patients. The aim of this research is to evaluate the BPJS drug inventory control system for the planning and procurement stages with ABC, VEN, ABC-VEN, EOQ. The research design is a type of non-experimental descriptive research with retrospective data collection. The data used for the period January-December 2021. The drugs analyzed are oral drugs and the most needed drugs are included in the ABC, VEN categories so that the BPJS ABC-VEN drug group is obtained. The results of ABC analysis with a total of 464 drugs were classified into groups. Group A 19%, group B 25.4% and group 55.6%. The VEN group analysis was Vital 19.4%, Essential 77.8% and non-Essential 2.8%. Based on the results of EOQ with a total of 113 items.

Analisis Penerapan Metode Material Requirement Planning (MRP) dalam Perencanaan dan Pengendalian Bahan Baku pada PT. BSM

PT. BSM, is a company that operates in the manufacturing sector by producing Mechanical Seals and is also a distributor of the Nippon Pillar Japan brand. Production planning and control is necessary to ensure that all processes function smoothly and production efficiency and effectiveness are achieved. PT.BSM's production system implements Make To Order (MTO), in which production activities are carried out when the customer places an order. This process will take place from raw materials, and semi-finished goods to finished products. This will pose a risk to the production process if the raw materials to be produced are not yet available. So it is necessary to have an analysis of planning and inventory control to expedite the production process. Therefore, the purpose of this study is to plan and control raw materials in accordance with requests from customers in the future using the Material Requirement Planning (MRP) method with the Lot For Lot (LFL) technique, after calculating the MRP with The Lot For Lot (LFL) technique obtained the planning results for mechanical seal raw material requirements for 2023 every month. Based on MRP calculations using the Lot For Lot technique, mechanical seal raw materials can be planned and controlled for the next 12 periods. Key Words: LFL, Mechanical Seals, MRP, Supplies

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

Review Paper: “A Study on Effective Inventory Management And Control

This study delves into the critical role of inventory management and control in enhancing operational efficiency for businesses across various sectors. With globalization and technological advancements shaping contemporary market dynamics, efficient inventory management has become imperative for organizations striving to maintain competitiveness and profitability.

Fusing SVR with PSO Improves E-commerce Sales Prediction with 8.98% MAPE

This study explores the integration of Support Vector Regression (SVR) with Particle Swarm Optimization (PSO) to forecast clothing product sales at Nara Gallery Collection Boutique, addressing the challenge of achieving high forecast accuracy in e-commerce. Through literature review, direct observation, and interviews with a textile SME owner, SVR parameters are optimized using PSO. Results indicate a Mean Absolute Percentage Error (MAPE) value of 8.98% with optimized parameters (C = 34.3642, ε = 0.0110, σ = 0.3677, cLR = 0.1062, λ = 0.0117), enhancing decision-making in inventory management and strategic planning for e-commerce businesses. This research highlights the potential of integrating SVR with PSO for accurate sales forecasting and suggests avenues for further exploration in alternative forecasting methods and optimization techniques. Highlight: Enhanced Forecasting Accuracy: SVR and PSO integration improves e-commerce sales predictions. Parameter Optimization: PSO optimizes SVR parameters, reducing Mean Absolute Percentage Error. Strategic Inventory Management: Accurate forecasts aid in effective e-commerce inventory control. Keywoard: Support Vector Regression, Particle Swarm Optimization, Sales Forecasting, E-commerce, Inventory Management

A smart inventory management system with medication demand dependencies in a hospital supply chain: A multi-agent reinforcement learning approach

In light of the intense need for quality health and well-being, the healthcare industry must improve its operations by strengthening its service delivery and reducing overall costs. A smart inventory management system for managing medicines in a hospital supply chain (HSC) is one of the best solutions as its advantages ensure availability of affordable medicines as well as inventory cost reduction. Significant effort is made on inventory management from literature; however, smart inventory systems for HSC considering the various complexities and uniqueness of healthcare systems are limited. This paper attempts to fill this gap by developing a stochastic semi-Markov decision process model which is solved by a multi-agent reinforcement learning method. Data collected from a multispecialty hospital in India’s eastern region serves to validate the proposed model. The results present the optimal order quantities and optimal inventory control policy for the HSC considering the uncertain medication demand at the hospital pharmacy and multiple point-of-care units, and demand dependencies among medicines prescribed to the patients admitted in the hospital. The study also has significant managerial implications for enhancing HSC’s inventory performance, improving the healthcare service delivery, reducing healthcare cost, thereby ensuring the affordable, accessible, and quality healthcare to the society.

Inventory control for reusable express packaging with under sharing policy

AbstractOnline shopping and e‐commerce have contributed significantly to the environmental pollution caused by the generation of packaging waste. In response to this issue, reusable express packaging has emerged as a promising solution. However, the widespread adoption of reusable express packaging faces significant challenges, namely, the initial investment burden and the complexities associated with recycling. To address these challenges and enhance resource utilization efficiency while minimizing supply–demand discrepancies, inventory sharing has emerged as an effective measure. We first establish an inventory model for reusable express packaging, which takes into account stochastic loss rates and customer service level. We further extend our research to scenarios involving both inventory sharing and competition. We substantiate the value of inventory sharing and the impact of competition through comprehensive numerical analyses. Our findings demonstrate that inventory sharing contributes to enhancing the profits of express delivery companies, especially with a more pronounced effect in companies where competition exists.

A prescription for efficiency: optimizing the surgical item inventory management practices in a healthcare facility

PurposeThe primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.Design/methodology/approachThis study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.FindingsIt was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.Research limitations/implicationsThis study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.Practical implicationsThis study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.Originality/valueThis study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

Hair Saloon Management Application

The Hair Saloon Management Application, developed by Mr. Zende Pratik Santosh and team, offers a digital solution for saloon administration. Aimed at modernizing saloon operations, the application streamlines tasks like appointment scheduling, client management, and inventory control. Leveraging technologies like Android and Java, it provides scalability and performance. The project methodology involved needs assessment, design, and testing. Results showcase its user-friendly interface and future scope includes AI integration and platform expansion. This application represents a significant advancement in saloon management, fostering efficiency and growth in the beauty industry.

A STUDY ON LOGISTICS RISK EVALUATION INVOLVED IN PIGMENT INDUSTRY

In the current worldwide economy, logistics are essential to guaranteeing the smooth movement of commodities among different businesses. The pigment business, which is necessary for colouring a wide range of goods, including paints and cosmetics, has particular difficulties with logistical management. The objective of this research is to assess the logistical risks that are common in the pigment business and provide ways to reduce them. A mixed-methods approach is used in the research, integrating quantitative and qualitative analysis. In order to identify important risk variables in pigment logistics, qualitative data is gathered through surveys and interviews with stakeholders, logistics managers, and industry experts. These elements include supplier connections, inventory management, transportation, warehousing, and regulatory compliance. Moreover, quantitative analysis evaluates the frequency and seriousness of hazards revealed by statistical techniques. This entails the creation of a methodology for evaluating logistics risk that is specific to the pigment sector and incorporates variables including regulatory changes, supply chain interruptions, demand volatility, and shipping delays. The report suggests a thorough risk-reduction plan for the logistics of the pigment sector based on its results. This plan includes preventative actions including expanding the variety of transportation routes, implementing sophisticated inventory control systems, creating backup plans, and improving supplier cooperation. This study's consequences go beyond the pigment business; it provides insights that may be applied to other areas of logistics management. Through the investigation of the particular logistical issues encountered by pigment producers, this study enhances the resilience, efficiency, and overall sustainability of the supply chain. Keywords: Supply Chain Management, Risk Mitigation Techniques, Quantitative Analysis, Pigment Industry, Logistics Risk Assessment.

Adaptive Lagrangian Policies for a Multiwarehouse, Multistore Inventory System with Lost Sales

Adaptive Lagrangian Policies for Inventory Control Consider the problem of managing inventory in a multiwarehouse, multistore setting where each store can be periodically replenished from potentially a set of nearby warehouses. This is a practically relevant problem, and its optimal policy is not easy to compute because of the curse of dimensionality. Existing works in the literature have proposed a policy based on Lagrangian relaxation of the original stochastic problem. This vanilla policy applies the control parameters derived from the Lagrangian model and has been shown to perform well in some cases. In “Adaptive Lagrangian Policies for a Multiwarehouse, Multistore Inventory System with Lost Sales,” we go one step farther and develop adaptive Lagrangian policies that update (at certain update times) the control parameters of the vanilla policy based on the historical demand realizations. We analytically show that our proposed adjustment significantly improves the performance of the vanilla policy.

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.