Stock Level Research Articles

General equilibrium and dynamic inconsistency

We study the role of different sources of naivete in a general equilibrium version of the Ramsey model with quasi-hyperbolic discounting. When agents are aware of others’ naivete, as strongly suggested by empirical evidence, they revise consumption paths, correctly anticipating prices in a resulting sliding equilibrium (perfect foresight). When agents are unaware of others’ naivete, as is typically assumed in the theoretical literature, they revise both consumption paths and price expectations (quasi-perfect foresight). We prove the existence of sliding equilibrium under perfect foresight for the class of isoelastic utility functions. We show that generically quasi-hyperbolic discounting matters for saving behavior: sliding equilibrium under perfect foresight is observationally equivalent to some optimal path in the standard Ramsey model only if utility is logarithmic. By comparing sliding equilibria under different types of foresight we show that perfect foresight implies a higher saving rate, long-run capital stock and consumption level than quasi-perfect foresight.

Neutrosophic Inventory Management: A Cost-Effective Approach

Abstract Classical inventory models (IM) serve as quantitative tools for determining the optimal order quantities, timing of orders, and safety stock levels for specific inventory items or item groups. Zadeh (1965. Fuzzy sets. Information and Control, 8, 338–353) introduced fuzzy theory and Dubois and Parade (1988. Fuzzy logic in expert systems: The role of uncertainty management. Fuzzy Sets and Systems, 28, 3–17) presented the study of fuzzy inventory model, which, however, exhibits limitations in effectively handling uncertainty, inaccuracies, and imprecise data. In 1999, Smarandache presented the idea of neutrosophic set theory to handle uncertainty. Using trapezoidal neutrosophic numbers, this study extends the idea of neutrosophic sets to inventory management, concentrating on resolving the uncertainty associated with holding costs, ordering costs, and shortage costs. First time within the literature of the neutrosophic set, our new method not only addresses existing problems but can also tackle other issues that no other authors have successfully resolved so far. Additionally, we conduct a comparative analysis of our proposed model against existing models in this article. Based on this comparative study, our findings assert the superior performance of our proposed model in relation to some of the existing models. In conclusion, we wrap up our research by presenting graphical, logical, and tabular comparisons with the existing methods.

Mixed-integer programming models for a multi-period integrated reverse logistics network with multiple recovery options and different quality levels of returns

This paper addresses an integrated reverse logistics (RL) model with a production route and multiple recovery options, including repair, refurbishing, remanufacturing, recycling, and selling spare components. We aim to use the quality grading system method to analyse returns comprehensively. In this regard, quality levels are used to classify returns and quality thresholds are utilised to categorise them into certain quality groups. The proposed model deals with different activities such as locating, purchasing, transporting, processing, inventory holding, recovering, and production in a planning horizon with multiple periods. A two-stage optimisation model is proposed to facilitate the decision-making process. In Stage 1, a mixed integer linear programming (MILP) model is proposed to minimise the costs and determine the number of returned products directed into different recovery routes. In Stage 2, another MILP is proposed to maximise the network’s profit. Furthermore, 350 scenarios are defined to examine the network’s performance and decide the optimal quality thresholds. Finally, the proposed models are applied to a real dataset and solved by the CPLEX solver through Pyhton. According to the results, scenarios that use multiple recovery options earn higher profits and lower costs as most demands are satisfied and the stock level of inventories decreases.

Optimal reserve policies for emergency medical supplies: Joint consideration of reserving safety stocks, production capacity, and capital

The tragic consequences of the COVID-19 epidemic highlight to governments the importance of keeping sufficient medical supplies. Given that the physical stocks are subject to considerable obsolescence risk, existing studies have considered combining holding safety stocks with keeping production capacity or capital reserve. Pioneering the exploration of keeping all three resources of safety stocks, production capacity, and capital in reserve, we derive closed-form optimal reserve policies under different scenarios. One crucial insight is that the optimal reserve policies should be determined by considering both the cost and utilization efficiency, e.g., solely adopting safety stocks is optimal when the utilization efficiency of safety stocks exceeds that of production capacity and capital reserves, and when the expected benefit of utilizing safety stocks outweighs the cost. Additionally, we find that the optimal safety stocks level is impacted by the combination of demand uncertainty and epidemic outbreak probability, i.e., the optimal safety stocks level decreases (resp., increases) with demand uncertainty in case with a sufficiently low (resp., high) epidemic outbreak probability, whereas the optimal levels of production capacity and capital always increase with demand uncertainty. We show that coordinating the three resources brings extra benefits compared with keeping fewer resources. By exploring the practical scenario where the government faces multiple potential epidemics, we find that below a threshold, a lower outbreak probability of a large-scale epidemic leads to more capital and production capacity reserves.

Improved energy retrofit decision making through enhanced bottom-up building stock modelling

Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinctbuildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies.To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework’s effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.

Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities

Pre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable; therefore, stockpiled items may not be used. To avoid over-stocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.

Mixed-integer linear programming for project scheduling under various resource constraints

Project scheduling is an important management task in many companies across different industries. Generally, projects require resources, such as personnel or funds, whose availabilities are limited, giving rise to the challenging problem of resource-constrained project scheduling. In this paper, we consider the scheduling of a project consisting of precedence-related activities that require time and two types of resources for execution: storage resources representing, e.g., the project budget; and renewable resources representing, e.g., personnel or equipment. Storage resources are consumed by activities at their start or produced upon their completion, while renewable resources are allocated to activities at their start and released upon their completion. The resource-constrained project scheduling problem with consumption and production of resources (RCPSP-CPR) consists of determining a minimum-makespan schedule such that all precedence relations are respected, the demand for each renewable resource never exceeds its capacity, and the stock level of each storage resource never falls below a prescribed minimum. Due to the consideration of storage resources, the feasibility variant of this problem is NP-complete. We propose a novel compact mixed-integer linear programming (MILP) model based on a novel type of sequencing variables. These variables enable us to identify which activities are processed in parallel and whether a sequencing of activities is necessary to respect the resource capacities. Our computational results indicate that our novel model significantly outperforms state-of-the-art MILP models for all considered scarcity settings of the storage resources. Additionally, our results indicate a superior performance for instances of the well-known resource-constrained project scheduling problem (RCPSP).

Impacts of dual carbon emission reduction technology and technology spillovers of deterioration reduction on supply chain system's performances considering government incentives and contract design

Investments in carbon emission reduction and deterioration reduction are important aspects for the firms to construct a low-carbon supply chain systems of deteriorating products with the increasing preferences of customers for environmentally friendly and fresh products. In this study we develop a two-echelon supply chain system consists of one manufacturer and one retailer. The manufacturer implements dual carbon emission reduction technology to strengthen the carbon emission reduction efficiency under cap-and-trade regulation and both manufacturer and the retailer invest in deterioration reduction technology to reduce the deterioration and increase the stock level of the fresh items. To increase the demand, the retailer takes the responsibility of promoting the low-carbon and fresh products among the customers. Deterioration reduction technology spills over from the manufacturer to the retailer. Then a differential game model is formulated and the equilibrium strategies are analysed by Stackelberg differential game approach in long term scenario. Numerical analysis has been done to analyse the theoretical results more rigorously by MATLAB software and sensitivity of some major parameters on the demand and profits of the manufacturer and the retailer have also been done to obtain some managerial insights. The impacts of implementing dual carbon emissions reduction technology and the effects technology spillovers are discussed. The results show that when the manufacturer invests in dual carbon emission reduction technology over single reduction technology, it increases the emission reduction level and hence the environmental performance and also the demand and hence economic profits of the both the supply chain members. If the deterioration reduction technology spills over from the manufacturer to retailer, it increases the profit of the manufacturer but the retailer will be beneficial when his/her marginal profit will be high. In addition, we have incorporated government subsidy on manufacturer's emission reduction investment and a cost-sharing contract on the promotional effort of the retailer in our work and then found that joint impacts of them on the supply chain system. We have shown that these two incentives provide the pareto improved solution for the supply chain system and take the supply chain members to a win-win situation as economically and environmentally. The discussions of the results of this study can be beneficial for the industries which produced deteriorating products and emits high carbon during the production process in terms of taking optimal decisions. It also gives a lesson to the government how to inspired the practitioners towards developing a sustainable system.

EOQ Optimization Revolutionizes Inventory Management, Delivering Cost Savings Globally

Efficient inventory management is crucial for companies to minimize costs while ensuring adequate supply to meet demand. PT Samawa Tirta Alam, a bottled sweet drinking water company, faces significant inventory management issues, notably with refined sugar raw materials. High transportation costs and fluctuating demand contribute to excessive inventory accumulation and increased storage costs, necessitating an optimization strategy. This study implements the Economic Order Quantity (EOQ) method to address these challenges. Through a mixed-method approach, qualitative data were gathered via observations and interviews, while quantitative analysis utilized historical data to calculate optimal order quantities, frequencies, safety stock levels, and reorder points. Results demonstrate substantial cost savings, with optimal order quantities determined, reducing order frequencies from 12 to 2 times per year, and minimizing safety stock levels. The findings underscore the effectiveness of the EOQ method in enhancing cost-effectiveness and operational performance in raw material inventory management for similar companies. Highlights: 1. Cost Efficiency: EOQ method reduces total inventory costs effectively.2. Operational Precision: Minimizing safety stock enhances operational efficiency.3. Strategic Insight: Integrated approach provides practical solutions for inventory management challenges. Keywords: Inventory Management, Economic Order Quantity (EOQ), Cost Optimization, Raw Materials, Transportation Costs

Inventory Management Optimization: Balancing Cost and Service Levels

Efficient management of inventory plays a role, in the success of businesses across sectors. Finding the equilibrium between keeping inventory costs low and meeting customer needs is a challenge that requires constant exploration and creativity. This study delves into the complexities of optimizing inventory management focusing on strategies that effectively handle the trade offs between minimizing expenses and fulfilling customer expectations. By delving into an analysis of existing literature this study consolidates approaches, models and best practices implemented by companies to enhance the efficiency of inventory management. It thoroughly examines aspects such as improving demand prediction accuracy reducing lead times optimizing safety stock levels and integrating supply chains. Additionally it explores how advanced technologies like intelligence data analytics and automation are transforming inventory management processes.Moreover this research delves into how industry landscapes, market dynamics and business goals influence inventory management tactics. Through real world examples and empirical data analysis it offers insights, on how organizations can tailor their strategies to meet needs while balancing the objectives of cost control and service level improvement. In the end this study adds to what we know about improving inventory management by providing advice and tips for companies to gain lasting edges in todays ever changing market. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

Demand Driven Material Requirements Planning: An Inventory Optimization Model

When dealing with stochastic multi-state production and distribution systems, determining the appropriate quantity of safety stocks on hand may be tricky. We often limit existing solutions for maximizing safety stock levels while keeping cost, objectives and service level constraints in mind to a subset of multi-stage inventory challenges, such as strictly serial architectures or specialist two-stage production/distribution systems. This may make achieving ideal stock levels while meeting cost objectives and sustaining service levels problematic. This is due to the underlying assumption in these systems that cost and service level are directly proportional to one another. Controlling unpredictability and satisfying customers are inventory management?s most important tasks. DDMRP (Demand-Driven Material Requirements Planning) dynamically adjusts inventories to handle unpredictability and improve customer service. This is done to improve crisis management. This achieves ?demand-driven material requirements planning.? This study presents a DDMRP-specific safety stock calculation. The inclusion of this formula aids in the preservation of mathematical consistency. When dealing with a model based on normal probability distributions, the major focus is discovering and applying the parts necessary for safety stock computation. One of the most well-known fast-moving consumer goods (FMCG) firms in South East Asia is the major focus of a case study to validate the proposed strategy. To build a scientific model, the study adopts a probabilistic strategy. The primary goal of this model is to aid in the process of identifying appropriate levels of safety stock and inventory at the distributor level, which will ultimately allow for a customer fill rate of 99%. A distribution network will be employed to achieve this purpose. The model provides a complete framework for estimating optimum safety stock levels and reorder points while accounting for the stochastic nature of demand and including critical elements. This enables more precise forecasting of future demand. This contributes to better inventory management practices and promotes customer satisfaction.. KEYWORDS :DDRMP, Demand level, Distribution network, Safety Stock Model.

Blood Bank Inventory Control

Abstract: "Rakt Sewa" be advanced blood bank inventory portal having been designated for optimizing and streamlining the management of blood within hospitals and blood banks. This innovative system provide real-time tracking!! and reporting capabilities, enabling healthcare professionals to efficiently monitor availability and the usage of different blood groups. The portal facilitate instant access to vital inventory data, assist medical staff in making informed decisions quicklie, particularly in emergency situations where time is of an essence. The core function of "Rakt Sewa" include automated inventory updates, expiry tracking, and integration with hospital management systems to ensure data accuracy and reduce manual entry errors. It also feature customizable alerts for low stock levels and expiring units!!!!, aiding in maintaining optimal stock levels and minimizing waste. Moreover, "Rakt Sewa" support detailed reporting and analytics tools that offer insights into usage patterns, demand forecasting, and supply chain management, allowing for better planning and resource allocation.

Machine Learning Enabled Inventory Prediction

Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging historical sales data and other relevant factors to forecast future inventory levels. This project demonstrates the potential of machine learning, specifically the XGBoost algorithm, to revolutionize inventory management practices, enabling businesses to achieve greater efficiency and profitability. Keyword : Machine Learning, Non-linear Relations, Cost Minimization, Stock levels, Profitability, Inventory Prediction.

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Hierarchical modeling of irregularly spaced financial returns

Volatility modeling is crucial in finance, especially when dealing with intraday transaction‐level asset returns. The irregular and high‐frequency nature of the data presents unique challenges. While stochastic volatility (SV) models are widely used for understanding patterns in volatility of daily stock returns which constitute regularly spaced time series, new classes of models must be introduced for analyzing volatility in irregularly spaced intraday data. Specifically these models must accommodate the random gaps between successive transactional events. By modeling the gaps using autoregressive conditional duration (ACD) models, we describe a hierarchical irregular SV autoregressive conditional duration (IR‐SV‐ACD) model for estimating and forecasting intertransaction gaps and the volatility of log‐returns. We carry out the analysis in the Bayesian framework via the Hamiltonian Monte Carlo (HMC) algorithm with No‐U‐turn sampler (NUTS) in R using the cmdstanr package. The fits and forecasts are obtained using Monte Carlo averages based on the posterior samples. We illustrate this approach using simulation studies and real data analysis for intraday prices available at microseconds level of health stocks traded on the New York Stock Exchange (NYSE). The log‐returns and gaps are calculated for the stocks and are used for modeling.

A Comprehensive Analysis of Inventory Management and Its Potential Implications on Financial Reports

Abstract: Inventory management plays a pivotal role in businesses' operational efficiency and financial success, encompassing processes aimed at maintaining optimal stock levels while minimizing costs and meeting customer demand. This paper presents a comprehensive analysis of inventory management practices, focusing on their impact on financial performance, challenges, opportunities, and best practices. Through a literature review, the study examines existing research on inventory management, highlighting its significant positive relationship with organizational performance. The research methodology includes correlation analysis, descriptive statistics, questionnaire analysis, and hypothesis testing to explore the intricate dynamics of inventory management. Findings reveal a strong correlation between efficient inventory management practices and improved financial metrics, emphasizing the importance of implementing advanced inventory management systems, enhancing forecasting accuracy, and fostering collaboration within supply chains. The study concludes with practical recommendations for businesses seeking to optimize their inventory management processes and enhance financial performance in today's competitive marketplace

Сервисное обслуживание пневматических шин на самоходные машины для подземных горных работ

The article presents the issue of short life of pneumatic tyres for mobile underground mining machines. It is noted that the correct choice of pneumatic tyres will increase the tyre life, but for a greater increase it is necessary to control the entire life cycle of the tyre, i.e. to carry out its service. The life cycle stages to be taken into account during tyre maintenance are presented. Control at each stage will minimise damage to the pneumatic tyres. The maintenance phase should include the records of tyre usage history and stock management. Analysis of the records will help to determine the reasons for tyre rejection and the wear conditions, the tyre condition before and after repair, and before and after retreading. Service maintenance of pneumatic tyres will allow planning the approximate level of stock, replacement periods and effective stock management. The article justifies the relevance of holding joint meetings of service specialists, tyre designers, and the company’s. In conclusion, it is noted that the issue of creating a national standard of service maintenance of pneumatic tyres for mobile underground mining machines is relevant.

Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method

The research presents a novel approach to inventory modelling, emphasizing stochastic demand and dynamic pricing strategies for a seasonal sales framework. The methodology divides the sales season into intervals, each associated with distinct pricing strategies influenced by stochastic factors. The study employ exponential smoothing for demand forecasting, optimizing inventory replenishment and dynamic pricing strategies through developed algorithms. Notably, the study determine the optimal smoothing parameter for demand forecasting, balancing responsiveness to recent demand patterns with long-term stability. Proposed research achieves a comprehensive framework empowering businesses to enhance competitiveness and profitability by addressing challenges of stochastic demand and dynamic pricing. Dynamic pricing strategies outperformed classical strategies, allowing businesses to respond promptly to demand fluctuations and optimize profit margins during sales seasons. Incorporating stochastic demand models enabled organizations to implement safety stock and buffer inventory levels effectively, mitigating risks associated with uncertain demand patterns. Real-time data analysis was crucial for adjusting price dynamics and making effective management decisions, leading to improved financial performance. The iterative nature of dynamic pricing strategies emphasized the importance of continuous refinement to adapt to evolving market dynamics. This approach enables data-driven decisions, adaptation to market fluctuations, and improved inventory management despite unpredictable demand. Ultimately, this study provides valuable insights and methodologies applicable across industries for efficient and profitable inventory management.

Internal control effectiveness and stock price crash

This study focuses on the problems of imperfect internal control effectiveness, insufficient information transparency, and plummeting stock prices. The study selects the data of non-financial main board listed companies in China’s Shanghai and Shenzhen A-shares from 2012 to 2021 as a sample, and adopts an empirical research methodology, which reveals that the effectiveness of internal control is negatively related to the trend of share price crash, and efficient internal control is positively related to the transparency of corporate information environment. The findings suggest the impact of internal control on the risk of stock price crash at the individual stock level and provide empirical support for listed companies to manage their risks. This study has practical value in guiding listed companies to strengthen internal control, improve information transparency, mitigate the risk of stock price crashes, and provide a decision-making basis for the healthy and stable development of the capital market.

Co-creation of a novel approach for improving supply chain management for SARS-CoV-2 point of care diagnostic services in Mopani District, Limpopo Province: nominal group technique.

Effective supply chain management (SCM) of point-of-care (POC) tests for diseases like severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) requires active participation from diverse stakeholders, government entities, and regulatory bodies. The responsibility for overseeing various aspects of POC tests, including procurement, quality assurance, storage, inventory management, distribution, and human resource capacity, lies with national, provincial, and local levels of government. This study aimed to collaboratively develop an innovative approach to enhance SCM for SARS-CoV-2 POC diagnostic services in resource-limited settings, using the Mopani District in Limpopo province, South Africa, as a case study. Key stakeholders were invited to participate in an online workshop using purposive sampling. The study employed the nominal group technique (NGT) for data collection, which consisted of two phases. Phase 1 focused on identifying barriers in the supply chain of COVID-19 rapid tests, while phase 2 aimed to devise strategies to overcome the priority barriers identified in phase 1. Participants used a Likert scale of 1-5 to rank barriers and strategies, and an overall ranking score was calculated for each. The participants were provided with the results of the ranking exercise for their feedback. Eleven key stakeholders from national (n = 1), provincial (n = 4), and local government (n = 2) levels, research entities (n = 3), and non-governmental organizations (n = 1) took part in the study. Participants identified significant barriers in the supply chain, such as the availability of testing kits, unknown demand, information on SCM during a pandemic, methods of controlling stock, and procurement processes. Strategies suggested by key stakeholders included monitoring stock levels and optimizing stock visibility systems to improve test availability, enhancing information visibility and consistent data updates to address unknown demand and improve SCM during a pandemic, employing data capturing and digitization for effective stock control, and implementing demand planning and standardized procurement processes at the national level to enhance stock procurement. The successful collaboration with key stakeholders, facilitated by the NGT, resulted in the co-creation of a novel approach to enhance SCM for COVID-19 diagnostic services in resource-limited settings. This study holds the potential to support the provision of COVID-19 diagnostic services in such settings. A recommended follow-up study would assess the feasibility of implementing this approach.