Demand For Items Research Articles

The role of information communication technologies on carbon emissions in OECD countries: new evidence from method of moments quantile approach.

This paper aims to investigate the effects of information and communication technologies (ICT) on carbon emissions (CO2) in the OECD area. For this purpose, a comprehensive panel data set is utilized covering the 1994-2018 period for 38 countries and a novel method of moments panel quantile regression model which allows to account for fixed effects and endogenous explanatory variables. Results suggest that the ICT, represented by the percentage of individuals using the Internet in the total population, contributes positively to CO2 emissions up to 0.40th quantile and has no effect after this level. The results imply that countries with relatively low per capita emissions are susceptible to the rebound effects, in which better energy efficiency results in increased demand for energy and ICT-related items, hence increasing carbon emissions. At this juncture, one policy idea would be to include a carbon tax into the per-unit purchase price of smartphones, tablets, smart gadgets, and any other relevant ICT items. Additionally, this legislation can assist decrease conspicuous consumption, which can be viewed as a trigger for the demand for ICT products. Additionally, these countries should encourage enterprises to invest in and employ energy-efficient technologies through tax incentives or subsidies.

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

Recent literature has revealed a growing interest in methods for anticipating the demand for medical items and personnel at hospital, especially during turbulent scenarios such as the COVID-19 pandemic. In times like those, new variables appear and affect the once known demand behavior. This paper investigates the hypothesis that the combined Prophet-LSTM method results in more accurate forecastings for COVID-19 hospital Intensive Care Units (ICUs) demand than both standalone models, Prophet and LSTM (Long Short-Term Memory Neural Network). We also compare the model to well-established demand forecasting benchmarks. The model is tested to a representative Brazilian municipality that serves as a medical reference to other cities within its region. In addition to traditional time series components, such as trend and seasonality, other variables such as the current number of daily COVID-19 cases, vaccination rates, non-pharmaceutical interventions, social isolation index, and regional hospital beds occupation are also used to explain the variations in COVID-19 hospital ICU demand. Results indicate that the proposed method produced Mean Average Errors (MAE) from 13% to 45% lower than well established statistical and machine learning forecasting models, including the standalone models.

Spillover connection between oil prices, energy risk exposure, and financial stability: implications for the COVID-19 pandemic.

The aim of the study is to test the nexus between oil prices, energy risk exposer, and financial stability to recommend the implications for the period of COVID-19 crises. The study findings show that a systemic macroeconomic simulation that combines with the 17% oil prices and 26% energy risk exposure at household item demand gives a rise to energy subsidies at 18.14% and it contributes to make energy financing as efficient as 38.3% in study context. By this, the oil prices and energy risk exposure repercussions caused significant connection with financial stability. Utilization of oil-importing and oil-exporting economies necessitates the use of energy. Energy and capital are complementary in manufacturing. Following the study findings, we suggested and adjusted the energy risk exposure framework to take into account. The findings show that allocating oil price-related subsidy to enterprises yields the best policy results. However, the benefit to society as a whole is quite small. Additional analysis results indicate that in a less energy-dependent sector, having no subsidies would be the best strategy. On such benefits, different policy implications are also suggested for associated individuals to sustain financial stability.

A new solar energy-based system integrated with hydrogen storage and heat recovery for sustainable community

This study presents a unique solar energy-based system using green hydrogen as energy storage option developed to meet the electricity, heat and cooling demands for a potentially sustainable community. The energy and exergy analyses are implemented to analyse and assess the proposed system and its performance. In this regard, a community is identified in the Southern Ontario in Canada with 1500 residential units. The hydrogen produced in the system is stored in the underwater balloons, called “the underwater hydrogen balloon”. The heat recovery from the sewage is included in the system in order to enhance the performance of the air conditioners and heat pumps. The results of the present study indicate that in order to dedicate the off-grid power system, 288,000 m2 of PV panels is required in the month of December when the lowest solar radiation is obtained. On the hand, it is possible to provide 65,474 kWh of energy can be provide the grid with that much of PV panel in the month of June. Both energy and exergy efficiency values tend to decrease with the increasing both PV panel area and solar radiation due to increasing solar energy input against to constant demand items. The heat recovery from the sewage helps increase the performance of the air contioning system with heat pump by about 33%.

Exploring the application of Industry 4.0 technologies in the agricultural food supply chain: A systematic literature review

The need for Industry 4.0 (I4.0) is felt across “agriculture food supply chain (AFSC)” to tackle the global demand for food items and concerns regarding food safety and security. Further, this transformation is possible due to recent advancements in I4.0 technologies, including “Internet-of-Things (IoT), Blockchain, Big Data (BD), Information and Communication Technology (ICT), Cloud Computing (CC) and Cyber-Physical System (CPS)” etc. Each of these technologies has a specific role in making the AFSC eco-system smart enough to address today’s world challenges. Thus, this paper reviews these major technologies in AFSC to understand their applications and recent trends. The review is based on 146 articles from the “Scopus and Web of Science” database. The articles include peer-reviewed journals published between 2010 and 2020. The applications of reviewed technology in AFSC are analysed under five research dimensions, namely traceability and food safety, information system management, food waste, control and monitoring, decision making and agribusiness, and other miscellaneous-based applications. The study suggests that the integration of reviewed technologies can be more useful to provide low-cost solutions and empower sustainability in AFSC. Further, blockchain can emerge as a game-changer to ensure food safety and security. The current challenges and future research agenda in concerned themes are also identified to further motivate researchers to develop this area. This is the first paper that summarises the recent developments of six technologies for AFSC research.

Modeling and simulation for inventory management of repairable items in maintenance systems

Simulation models have become promising methods to investigate and improve real-world systems. This paper describes the development of effective simulation-based models for maintenance systems in a manufacturing environment whereby production lines send their faulty but repairable items to a repair shop. The proposed models aim at investigating the centralized storage of repaired items with three replenishing strategies, lot-for-lot ( L4L), economic order quantity and reorder point ( Q-R), and minimum-maximum order level ( s-S) reordering policies of repairable items with backordering allowance in a risk-free and flexible manner to effectively manage the procurement of new repairable items and control three inventories of repairable items, repaired ready-for-use, new, and faulty awaiting repair. The models take into account the uncertain environment of maintenance systems and the probabilistic nature of the failures, repair times, and lead times of repairable items. The demand for repairable items in these three inventories is both stochastic and dynamic. A simulation case problem is presented to demonstrate the suitability and applicability of the proposed simulation models and to study the influence of different parameters on performance measures using parametric analysis. The main results are the vital decision-making insights provided by the developed models about the three reordering policies and the three inventories of repairable items. These managerial insights are essential for achieving minimum inventory costs and maximum service levels.

Brazilian consumer perceptions towards second-hand clothes regarding Covid-19

Second-hand clothes (SHC) emerge as an alternative for responsible consumption, towards sustainability, as a new opportunity for the fashion industry. This study aims to understand the perceptions and attitudes of Brazilian consumers regarding SHC on a comparative basis before and during the occurrence of the Covid-19 pandemic. The empirical investigation was divided into two stages. Firstly, a survey was conducted before Covid-19 (November 2019) with a sample of 513 Brazilian fashion consumers, where exploratory factorial and cluster analysis were performed. Using a structural equation model, five hypotheses were generated based on literature to test the influence of some factors in the intention to buy SHC: socio-environmental awareness, preconception with SHC, need for uniqueness, brand awareness and social prestige. Secondly, another survey was conducted during Covid-19 (August 2020) with a sample of 392 Brazilian fashion consumers. The comparative analysis demonstrated some differences in the intention to buy SHC regarding the pandemic. “Preconception with SHC” remained non-significant, “need for uniqueness” and “social prestige” remained positively correlated. “Socio-environmental awareness” has become positively correlated and “brand awareness” intensified negatively during the pandemic. New research can investigate the current transfiguration of consumer behavior in other countries considering the outbreak of coronavirus, as well as analyze the actions of institutions towards the fashion sector in view of conscious consumption, which is increasingly in vogue among the consumer population. This research contributes to the knowledge about SHC consumer behavior regarding the Covid-19 pandemic. The results assist in marketing strategies for the fashion industry, considering the growing demand for second-hand items during the pandemic, and contribute to a higher level of sustainability in the sector.

Craniodental traits predict feeding performance and dietary hardness in a community of Neotropical free‐tailed bats (Chiroptera: Molossidae)

Abstract Form–function studies have established a strong link between dental morphology and the mechanical properties of food items, with animals evolving tooth shapes theoretically ideal for their diets. However, information on how teeth perform under natural conditions is rare, which limits the understanding of how dental morphology influences dietary ecology and niche partitioning within animal communities. Free‐tailed bats (Chiroptera: Molossidae) are a diverse clade of aerial insectivorous mammals that exhibit an outstanding variation in size and craniodental traits, which have been directly related to ecological segregation among sympatric species. We investigate the mechanisms that allow functional dietary specialization and trophic segregation among sympatric free‐tailed bat species inhabiting a Neotropical forest. To do so, we coupled data on 3D dental topographic metrics, head and skull dimensions, field‐collected feeding performance and dietary hardness measurements. We found that evolved differences in molar topography and skull size vary in tandem with the mechanical demands of prey items naturally consumed by sympatric molossid species. This may be explained by feeding performance capabilities resulting from both molar shape and the overall size of the feeding apparatus, which seem to allow efficient processing of prey items with specific mechanical properties. For instance, smaller bats with higher molar topographic values (sharper, more complex molars) and more gracile heads mainly feed on softer insects, whereas bigger bats with lower molar topographic values (blunter, less complex molars) and more robust heads mostly feed on tougher insects. Species with a broader range of sizes, craniodental morphologies and insect hardness are also present in the community. These results illustrate how the morphology and size of feeding structures, and how they perform, may facilitate trophic segregation among sympatric insectivorous bats. Similar mechanisms may help structure other communities of insectivorous mammals, therefore the approaches presented here could be used to generate a better understanding of the ecomorphological traits and processes that underlie their diversity. Read the free Plain Language Summary for this article on the Journal blog.

근육병 장애인의 Arm Support 사용에 따른 작업 부하의 변화와 심리·사회적 영향 : 사례연구

Objective : The purpose of this study was to investigate the change of workload and the psychosocial impact of arm support according to use in people with muscular disorders, who are restricted from performing activities of daily living due to the decrease in upper extremity muscle strength. Methods : The subjects were three people with muscular disabilities living in the community, all diagnosed with spinal muscular atrophy. Subjects performed eating, wearing a mask, brushing the hair before and after the active arm support was applied. After that, we measured and compared the workload of activities before and after using active arm support through NASA-TLX, and measured the psychosocial impact of arm support using the K-PIADS. Finally, we investigated the subjective feeling of the subjects after using the arm support through an interview. Results : As for the change in workload, the average score of all items decreased after using arm support, and the average score of the physical demand item decreased the most. The results of the psychosocial impact were 2.08 points ('positive impact ~ very positive effect') for competence, 2.06 points for adaptability ('positive effect ~ very positive effect'), and 1.71 points for self-esteem ('slightly positive effect ~ positive effect'). Through the interview, we confirmed that the subjects felt confidence, satisfaction, and comfort when performing the activity by using arm support, but also felt uncomfortable due to the arm support not being fixed correctly and the interference of wheelchair structure. Conclusions : Through this study, we found that arm support lowered the workload of the people with disabilities and had a positive psychosocial impact. In the future, we hope that a study based on the community will be conducted for various types of people with muscular disabilities. And also customized services should be conducted through systematic evaluation. we expected that the results of this study will be used as basic data for research and services that utilize arm support as an intervention strategy.

Welcome to the era of vague news: a study of the demands of statistical and mathematical products in the COVID-19 pandemic media

In this article, we report on a typology of the demands of statistical and mathematical products (StaMPs) embedded in media items related to the COVID-19 (coronavirus) pandemic. The typology emerged from a content analysis of a large purposive sample of diverse media items selected from digital news sources based in four countries. The findings encompass nine categories of StaMPs: (1) descriptive quantitative information, (2) models, predictions, causality and risk, (3) representations and displays, (4) data quality and strength of evidence, (5) demographics and comparative thinking, (6) heterogeneity and contextual factors, (7) literacy and language demands, (8) multiple information sources, and (9) critical demands. We illustrate these categories via selected media items, substantiate them through relevant research literature, and point to categories that encompass new or enhanced types of demands. Our findings offer insights into the rich set of capabilities that citizens (including both young people and adults) must possess in order to engage these mass media demands, critically analyze statistical and mathematical information in the media, evaluate the meaning and credibility of news reports, understand public policies, and make evidenced-informed judgments. Our conclusions point to the need to revise current curricular frameworks and conceptual models (e.g., regarding statistical and probability literacy, adult numeracy), to better incorporate notions such as blended knowledge, vagueness, risk, strength of evidence, and criticality. Furthermore, more attention is needed to the literacy and language demands of media items involving statistical and mathematical information. Implications for further research and educational practice are discussed.

Fad2Vec: 판매 데이터 임베딩 기반 패션 산업 단발성 유행 탐지

In the fashion industry, fads, a phenomenon in which demand for specific items surges in a short time and then disappears, are often observed. However, the conventional demand forecasting methods have limitations in predicting fads because it focuses on long-term trends. This paper presents a new approach called Fad2Vec, which adapts the Item2Vec approach to detect fads effectively. Hot periods composed of a hot point and a preceding period for each product are identified based on time-series sales data. Products are embedded into vectors representing the time points when they are temporarily fashionable. Then, they are clustered into a fad group where fashion items belong to the same cluster and show similar fad patterns. A case study of four famous fashion brands is provided to show how Fad2Vec works and verify its validity. The proposed Fad2Vec approach is expected to be practically utilized for the production planning of fast fashion companies.

Minimizing conditional value‐at‐risk under a modified basestock policy

Consider a single‐product, make‐to‐stock, risk‐averse company that manages its finished goods inventory by a basestock policy. Item demand is modeled as a Poisson process, and unit production time is modeled as an exponential random variable. Annual cost is equal to the sum over one year of the inventory‐holding and back‐ordering costs. The company wants to minimize the annual cost conditional value‐at‐risk by an appropriate basestock selection called the optimal basestock. After offering evidence that the optimal basestock is analytically intractable, we derive a closed‐form approximation of the optimal basestock. In two independent simulation experiments, we found that the average penalty for using the approximation is less than 1%; we believe this accuracy would be acceptable for practical applications. Leveraging the approximation's accuracy and simplicity, we establish several results that may serve as predictors of the company's appropriate basestock adjustments to changes in operational, economic, and risk aversion parameters. We illustrate how our research results were successfully applied in a Fortune 10 energy company. We extend the problem setting in two different directions to widen the applicability of our research results.

Cost of Nutritious Diet for Children in Pakistan and Effects of Imminent Afghan Refugees on Existing Consumption Pattern

The fall of Kabul after the US withdrawal from Afghanistan may have serious repercussions for Pakistan if the influx of refugees accelerates, as additional demand for food items and resulting inflationary pressures can jeopardize the diets of children. To examine this issue, we estimate the gap between actual and desired per capita expenditures on a least cost nutritious diet consumed by Pakistani children aged 3-10 years. The study finds the gap across income groups and regions for two provinces of Pakistan i.e., KP and Balochistan. We find that under-consumption is highest in children of Balochistan Urban and KP Urban, as households in both locations spend only 21 percent of the recommended dietary expenditures. We estimate the decrease in dietary expenditures coming from an increase in food prices after the refugees’ influx. We found the dietary gap is high if the influx of refugees exceeds one million by the first quarter of 2022. However, the dietary gap does not increase much if the number of refugee arrival remains under 700,000, albeit the large nutrition gap does not improve.

How to Cover Additional Cooling Load Needs During COVID-19 Pandemic?

Abstract It has been crystal clear to everyone that the fresh air supply is one of the highest priorities to keep people under safe indoor conditions during the COVID-19 pandemic. In many buildings, either there are no mechanical ventilation systems or the air conditioning systems do not have adequate capacity to meet the additional cooling loads for the increased fresh air requirement. Providing fresh air without disturbing the thermal conditions in the building requires a substantially increased cooling load and operating costs. On the other hand, there may not be adequate power infrastructure to meet this additional energy demand item in many regions. In this technical brief paper, we aim to emphasize the importance of heat storage systems and provide a quick solution to illustrate that they can meet the fresh air demands without requiring additional chiller or chiller capacity. In addition, it is observed that heat storage systems can be a convenient solution to meet the urgent and additional cooling loads in a more sustainable way.

Applying the zero-inflated Poisson regression in the inventory management of irregular demand items

ABSTRACT Managers often need to face the problem to define the inventory level of items having an irregular demand. Difficulties arise when the existing methods cannot be applied due to some hypotheses, leading to wrong inventory levels, and , higher holding costs . In this paper, a methodological framework, based on the Zero-Inflated Poisson regression, and called Spare Part Oriented ZIP Approach (SPOZA) is developed. It aims to simplify the inventory-level evaluation procedure when items have an irregular demand pattern The SPOZA output is the evaluation of the optimal theoretical protection level to fulfil to reduce inventory total costs as a percentage value. Aiming to evaluate the benefits of applying SPOZA, a real-case application is presented. Several scenarios are analyzed, and comparisons with standard forecasting methods are conducted. Results are very promising since SPOZA leads to an inventory costs reduction and performs better than other approaches, especially when the items demand increases.

Analysis of an optimal policy in dynamic bipartite matching models

A dynamic bipartite matching model is given by a bipartite matching graph which determines the possible matchings between the various types of supply and demand items. Both supply and demand items arrive to the system according to a stochastic process. Matched pairs leave the system and the others wait in the queues, which induces a holding cost. We model this problem as a Markov Decision Process and study the discounted cost and the average cost problem. We assume that the cost function is linear on the queue sizes. We show that for the N-shaped matching graph, an optimal matching control prioritizes the matchings in the pendant edges and is of threshold type for the diagonal edge. In addition, for the average cost problem, we compute the optimal threshold value. We then show how the obtained results can be used to characterize the structure of an optimal matching control for a quasi-complete graph with an arbitrary number of nodes. For arbitrary bipartite graphs, we show that, when the cost of the pendant edges is larger than in the neighbors, an optimal matching policy prioritizes the items in the pendant edges. We also study the W-shaped matching graph and, when the cost of the pendant edges is larger than the cost of the middle edge, we conjecture that an optimal matching policy is also of threshold type with priority to the pendant edges; however, when the cost of the middle edge is larger, we present simulations that show that it is not optimal to prioritize items in the pendant edges.

Interval valued demand and prepayment-based inventory model for perishable items via parametric approach of interval and meta-heuristic algorithms

This work mainly focuses on the mathematical formulation of an imprecise inventory model with partial prepayment policy in interval environment. In the proposed model, two different cases (without shortage and with partially backlogged shortages) are taken into consideration. Here, the demand and deterioration rates are considered as interval valued. Also, all the inventory costs, viz. ordering cost, purchase cost, holding cost, shortage cost and lost sale cost are considered as interval valued. Then, the continuous change of inventory level with time of the proposed model is defined mathematically by the interval differential equations. For both cases of the proposed model, the interval valued average profits are obtained by using parametric approach of interval. Here, the selling price and business period are taken as decision variables of the corresponding interval maximization problem. Finally, two numerical examples are considered and solved by using different meta-heuristic algorithms for checking the validity of the proposed model.

A study of multi-objective restricted multi-item fixed charge transportation problem considering different types of demands

In this paper, we formulate a multi-objective fixed charge transportation problem (FCTP) for multiple items, considering availability of multiple modes of transport along each pair of origin and destination. The variable cost of each item, the fixed cost and the transport time are considered to be different for each mode of transport. It is also considered that some items are mutually incompatible and cannot be transported in the same mode of transport. Two models of the multi-item FCTP are presented, considering the demands of items as crisp and interval numbers, respectively. The transportation problem is then posed as a multi-objective optimization problem (MOOP), in which the objectives are to minimize the total cost and total transport time. For the model with interval demands, the instances are only solved, in which, for each item, the sum total of demands of an item at different destinations is at least the sum total of availabilities of the item at different origins. For each model, a set of four numerical examples are solved using two multi-objective evolutionary algorithms (MOEAs), namely, Non-dominated Soring Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). A comparative study of the computational results are made in terms of four performance index, namely, RNI value, Hyper-volume (HV), Inverted Generational Distance (IGD) and Spread. The computational results clearly indicates that NSGA-II outperforms SPEA2 for all the numerical examples, in terms of generating better approximate Pareto front with respect to dominance, diversity and spread.

Modeling and solution algorithm for a disaster management problem based on Benders decomposition

Pre-disaster planning and management activities may have significant effects on reducing post-disaster damages. In this article, a two-stage stochastic programming model is provided to design a resilient rescue network assuming that the demands for relief items and the network functionality after the disaster are affected by uncertainty. Locations and capacities of relief centers, the inventory of relief items, and strengthening vulnerable arcs of the network are among the main decisions that must be taken before the disaster. Servicing the affected points is decided after the disaster, and the risk of not satisfying demands is controlled by using the conditional-value-at-risk measure. Since the direct resolution of the model is intractable and time-consuming over actual large-sized instances, an improved Benders decomposition algorithm based on the problem structure is proposed to overcome this difficulty. Computational results highlight the effectiveness of the proposed method compared to the existing approaches.

Application of expected value and chance constraint on uncertain supply chain model with cost, risk and visibility for COVID-19 pandemic

ABSTRACT In today’s world, the role of supply chain management including transportation plays a vital role in the financial and economic growth of the country. The supply chain management is most seriously impacted by a new global pandemic, known as coronavirus disease 2019 (COVID-19). This effect becomes more challenging for the manufacturers as well as suppliers due to the high demand for most essential items. The twin effects of uncertain interval supply chain visibility (SCV) and supply chain risk (SCR) on the efficiency of the supply chain management are explored in this paper. In this investigation, two different models namely the expected value model (EV model) and the chance-constrained model (CC model) using uncertain interval programming techniques have been developed. These models are converted into their corresponding deterministic forms using goal programming method and linear weighted method. Hence, our model incorporates the objectives of SCV maximization, SCR minimization, and cost minimization under the constraints of budget, demand, capacity, supply risk, supply availability, etc. The sensitivity analysis and managerial insight of the models are also discussed to examine the variation in the objective function for deciding the case of emergency environment like pandemic causes by COVID 19. Finally, a real-life example is given to exhibit the effectiveness and usefulness of our proposed study.