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Criteria selection significantly impacts the reliability and utility of multicriteria decision making (MCDM) models. While criteria may vary across industries, a formalised criteria selection process is influential in determining MCDM model outcomes. This article analyses and compares the criteria selection approaches used in 62 articles that apply MCDM-based inventory classification models, contrasting them with methodologies outside the field. Our findings reveal a conspicuous absence of formal criteria selection methods within MCDM-based inventory classification research. The limited application of quantitative and qualitative approaches indicates that this field has not kept pace with methodological advances in criteria selection. To bridge this gap, we advocate for further research aimed at developing a conceptual framework for criteria selection tailored to inventory classification. We also suggest evaluating the impact of formal criteria selection processes on inventory management decisions and exploring the benefits of integrating artificial intelligence into criteria selection for inventory classification studies. Additionally, this article identifies several limitations related to criteria selection for practitioners employing MCDM-based inventory classification models. [ABSTRACT FROM AUTHOR]

Mishra and Raghunathan (2004) and Kim (2008) conducted a study on the same model but reached conflicting results on under what circumstances vendor-managed inventory (VMI) benefits the retailer as opposed to retailer-managed inventory (RMI) in a setting where two products are partially substitutable. However, we note that their conflicting results were due to different assumptions about unmet demand after product substitution, which is influenced by consumers response to retail stockouts. This paper then extends their studies by incorporating consumer response to retail stockouts and re-examining the impacts of VMI on the retailer's performance as well as on each manufacturer's. Therefore, this paper reconciles the conflicts in the existing scholarly work and provides a more complete understanding of when each supply chain member is better off with VMI than RMI in a two-product setting. Additionally, the incorporation of consumer response to retail stockouts sheds light on how product substitution and consumers' store loyalty affect the value of VMI to each supply chain member. [ABSTRACT FROM AUTHOR]

We consider a general assembly system that produces variations on a single product. Customers can choose to customize any aspect of the product which corresponds to one or more stages of the assembly system. We assume that demand for the original product and the demand for any customization at any stage are all random with known probability distributions. We model this problem as a multi-echelon inventory management problem. This problem has not been studied before. Allowing for customization at any stage in an assembly system makes this problem significantly more complex than similar problems considered in the past. To solve this problem, we 'split' the assembly line into two parallel assembly lines which we call uncommitted and committed lines. We show that the usual method of reducing the assembly system to a series is not possible in general. We develop a heuristic policy which can be used to then reduce the system into a series system. We also present numerical calculations which highlight the efficacy of the heuristic policy. [ABSTRACT FROM AUTHOR]

Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry. [ABSTRACT FROM AUTHOR]

Relief supply inventories are important in disaster response operations. To address the problem of dead stocks and avoid relief supply expiration, such inventories must be renewed periodically. Different from the traditional shelf-life rotation mechanism (i.e. renewal of the entire inventory at the expiration date), which pays more attention to decreasing the quantity of dead stocks, the novel period rotation mechanism that we design in this study will gradually rotate relief supplies according to their shelf life. We propose a mathematical programming model to minimise the total cost of relief organisations and conduct a case study and analyse the sensitivity of key parameters using real-world data. Results show that our rotation mechanism can substantially reduce the financial burden on relief organisations and improve the quality of relief supplies for victims. The novelty of our study includes: (1) We introduce a new approach that can rotate multiple times, through which we consider the quality of relief supplies; (2) We incorporate recycling and remanufacturing into the rotation process; and (3) We adopt an inductive reasoning method to develop the model with demand and time uncertainties. We prove the feasibility of our model and contributions of our study to humanitarian logistics practice and research. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems. [ABSTRACT FROM AUTHOR]

This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels. [ABSTRACT FROM AUTHOR]

This study focuses on the stochastic modelling and analysis of a serial production network consisting of two manufacturing stations operating under a make-to-stock inventory policy. The outcome is a single type of product and every manufacturing station includes a machine and an output buffer. Both machines are gradually deteriorating during their operation. Deterioration results in a reduced production rate. Continuous-time Markov chain was used to model all the possible states the network transits over time due to the occurrence of certain events, such as client arrival, deterioration failure, production or repair completion. The structure of the Markov chain was thoroughly studied providing useful information, supporting the effort of numerical solving to determine the steady-state probabilities enabling the calculation of useful performance metrics like equipment availability, down time, idle time, utilisation and average inventory. Through a series of numerical experiments, the behaviour of the serial production network was examined while alternating its parameters. Interesting conclusions emerged regarding the factors affecting the operation of such production systems subjected to gradual deterioration. [ABSTRACT FROM AUTHOR]

Scheduling is a complex task due to the need to optimise multiple competing objectives and react to unpredictable events that may occur during production execution. The strategy of predictive-reactive scheduling can be used to reconcile the conflict between the original schedule and the current shop floor situation. This study seeks to present an inventory data-driven predictive-reactive production scheduling model that supports the evolving concepts of the Industry 4.0. Periodically, a machine learning technique provides predictive scheduling considering a best-case scenario according to an established Key Performance Indicator (KPI). Then, material non-availability causes disruptions in production, which triggers the Simulation-Based Optimization (SBO) method to handle these events. Thus, SBO provides a reactive schedule with the best set of priority rules to sequence jobs on each machine according to the data on the shop floor. This model was validated with a real case study using data collected from a metal-mechanical company. Considering the service level KPI, the results showed that the model is able to find a better solution in the compared scenarios. Therefore, even in a dynamic and stochastic scenario, with machine breakdowns, quality problems, raw material delays, and accuracy issues, the model proved efficient in mitigating these variations' effects. [ABSTRACT FROM AUTHOR]

This paper studies the multiple period inventory replenishment problem for a capacitated warehouse when procurement prices and market demands are both uncertain, with the goal to minimise the procuring and holding costs. We analyse this problem under the framework of competitive analysis, where neither probabilistic distributions nor sets are available to characterise the unknown price and demand parameters. An efficient online real-time replenishment algorithm is developed, which is free of any distribution assumption, and the decisions are made based entirely on past and present information. We derive an instance-independent competitive ratio of the algorithm, which provides a worst-case theoretical performance guarantee, and shows that the proposed algorithm performs well for situations with high volatility uncertainty over time and is naturally risk-averse. Finally, a set of numerical experiments further verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The inventory levels of pickup points play an important role for the same-day or next-day pickup and delivery services. The previous inventory optimisation research usually makes an assumption about demand distribution, does not use the real dataset or consider shipping strategies for this problem. In this study, we introduce a new strategy, mixture of anticipatory and emergency shipping, and propose forecasting-optimisation integrated approach to optimise multi-items' inventories in each pickup point based on big data analysis. We explore a real dataset including 23,808,261 records with 54 pickup points and 4018 items. We first cluster the dataset based on the distances between pickup points and the warehouse, then, implement the forecasting-optimisation integrated algorithms to select the more profitable strategy for each group. The result indicates that compared with the original algorithms, our proposed approach can effectively increase the profits, particularly, the novel algorithm, Long Short-Term Memory networks – Quantile Regression, performs better. Additionally, we find that the 100% anticipatory shipping is not necessarily superior to emergency shipment, when the pickup point is farther from the warehouse, the advantage of emergency shipment is more significant. However, the mixture of anticipatory and emergency shipping can contribute to higher profits for online retailers. [ABSTRACT FROM AUTHOR]

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Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston. However, these methods are generally applied at the observed frequency, ignoring any additional information, such as trend that becomes prominent at higher levels of aggregation. We evaluate established Temporal Aggregation (TA) methods: ADIDA, Forecast Combination, and Temporal Hierarchies in the said context. We further employ restricted least-squares estimation and propose two new combination approaches tailored to decreasing demand scenarios. Finally, we test our propositions on both simulated and real datasets. Our empirical findings support the use of variable forecast combination weights to improve TA's performance in intermittent demand items with a risk of obsolescence. [ABSTRACT FROM AUTHOR]

Operations management researchers and practitioners have shown increasing interest in incorporating sustainability into supply chain (SC) design models. This means that sustainability must be considered in all aspects of the SC, including location, inventory and transportation (LIT) decisions. Hence the aim of this article is to propose an optimisation model that incorporates: (i) LIT decisions in an integrated manner; (ii) the three sustainability (3S) aspects, i.e. economic, environmental and social, into each named decisions; and (iii) a closed-loop supply chain (CLSC) structure. The proposed formulation is a multi-objective mixed integer non-linear programming (MO-MINLP) model whose objectives consider minimisation of economic and social costs (economic aspect) and carbon emissions (environmental aspect), and maximisation of the social impact of SC operations (social aspect). A transformation technique is applied to one of the objective functions, which results in an MO-MILP model solved by the lexicographic method. This article focuses on commodity industries where only one finished product is manufactured. Hence the 3S-LIT model is validated with a randomly generated dataset and against a recently published alternative model applied to the copper mining industry. [ABSTRACT FROM AUTHOR]

A continuous-review, two-echelon distribution system with one central distribution centre (CDC) and multiple regional distribution centres (RDCs) is studied. The CDC jointly replenishes its inventories of multiple items from an external supplier, while each RDC replenishes its inventories of the items from the CDC. Each RDC faces a Poisson demand for each item, and the inventories of each stock in the system are controlled by a (Q, S) policy. Under this policy, an order is placed by a stock whenever its aggregate demand since the last order reaches a given quantity, and the inventory position of each item is raised up to its order-up-to level after the order placement. The objective is to optimise these (Q, S) policies so that the expected total cost of this system is minimised. We propose a decomposition and coordination method for this optimisation after deriving analytically the cost function of the system. Our extensive numerical experiments demonstrate the effectiveness of the proposed method. Furthermore, a parameter sensitivity analysis is conducted to analyse the impacts of some key system parameters on the performance of the method, and managerial insights are provided for optimising distribution systems with joint replenishment and real applications of the method. [ABSTRACT FROM AUTHOR]

Urged by the necessity to establish sustainable supply chains (SCs), this study focuses on exploring the closed-loop inventory routing problem (CIRP) for perishable food packed by multi-type returnable transport items (RTIs). The selling revenue of perishable food is dependent on food's remaining shelf life and the specific type of RTIs used for packaging. RTI selection decisions need to be jointly considered in the CIRP to weigh the potential benefits against associated costs. For this problem, we first develop an integer linear programme (ILP) to maximise the total profit of the holistic SC. Subsequently, we design a tailored kernel search (KS) matheuristic as an efficient solution. A real CIRP with multi-type RTIs for fresh strawberries is used to demonstrate the practicality of the ILP. For this case study, we perform extensive sensitivity analysis of the relevant parameters, extracting valuable managerial insights. Finally, experiments are conducted on 170 randomly generated instances. Computational results show that the proposed KS manages to achieve competitive solutions for instances with up to 10 retailers much more efficiently than CLPEX. For instances with up to 40 retailers, the KS algorithm significantly outperforms CPLEX in terms of solution quality, improving the obtained profit by 80.03% on average under the same computational time. [ABSTRACT FROM AUTHOR]

Prior research on inventory control has been wide ranging, yet the environmental implications of an $ ({s,S}) $ (s , S) inventory policy remain uninvestigated. This paper seeks to bridge the gap by characterising a firm's voluntary environmental policy in the setup of an $ ({s,S}) $ (s , S) inventory control policy. We suggest a mixed model structure wherein, due to the presence of fixed production costs, the inventory is determined continuously by sales and impulsively with ordering decisions obeying an optimal stopping process, while the uncertain sales process is controlled by continuous-time environmental goodwill-related decisions. We show that a firm should successively use voluntary environmental efforts to stimulate its sales when there is inventory and to increase backlogging to improve its production efficiency. Given the recurrent pattern of this policy, we conclude that voluntary environmental efforts under an $ ({s,S}) $ (s , S) inventory control is not compatible with using these efforts as a means to generate ephemeral reputation insurance. [ABSTRACT FROM AUTHOR]

Facility location, supply inventory and distribution, and evacuation planning are key operational functions in a humanitarian relief network, it is critical to integrate these three functions and schedule their activities jointly in a coordinated manner. Considering uncertain demands and evacuation rates of injured people, we develop a distributionally robust model for the multi-period humanitarian relief network design with multiple types of relief supplies. To solve the problem, we reformulate the proposed model into a mixed integer linear programme, and develop an enhanced branch-and-Benders-cut algorithm that incorporates some algorithm enhancements to solve the resulting model. Extensive numerical experiments show that: (i) the distributionally robust model provides more reliable and flexible solutions that perform the best when faced uncertainty over the deterministic and stochastic models; (ii) the algorithm enhancements are very effective to enhance the performance of the proposed algorithm, which can reduce the CPU time by up to 9.75%∼41.64% on average; (iii) the integrated solution approach is more beneficial to solve the problem when comparing with a sequential solution approach; and (iv) some model parameters have significant impact on the solution structure, which can help decision maker set proper parameters to achieve the desired trade-off among the considered metrics. [ABSTRACT FROM AUTHOR]

An inventory stacking decision assigns positions to items which are stacked vertically, such as containers in container terminals and steel plates in steel plants. The performance of stacking decisions is greatly affected by the arrival and departure information on items. We study an inventory stacking problem with partial information based on industrial observation in a steel plant. On the inbound side, we investigate three levels of information on future arriving items and their effect on performance. On the outbound side, we study the impact of the retrieval sequence, which is often random. We develop models incorporating different availabilities of information and determine stacking strategies. The study shows that the stacking strategy and stacking performance depend highly on information quality and space utilisation. Especially, when the space utilisation is high, low-quality information deteriorates the performance and such information should be ignored. This contradicts the general belief that more information should bring better performance. The study further proposes a time window allocation approach to reduce the uncertainty in retrieval, and it is effective in improving stacking performance. [ABSTRACT FROM AUTHOR]

Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study examines the empirical outcomes of some existing forecast combination methods and proposes a generalised feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions. [ABSTRACT FROM AUTHOR]

In capital-intensive industries, it is becoming common practice for operators to outsource the mission-critical equipment maintenance to service suppliers. Performance-based contracts (PBCs) are widely used and the equipment availability is considered as an important performance indicator. In this paper, we studied a kind of mission-critical equipment that has a major component. When a failure occurred in the major component, it is replaced by a spare part and repaired offline. We consider suppliers' risk attitudes (i.e. risk-averse, risk-neutral, and risk-seeking) and the spare parts inventory cost-sharing between the supplier and the operator to design a PBC. Specifically, from the operator's perspective, we use a principal-agent analysis framework to construct a contract model which considers different suppliers' risk attitudes. We further explore the impact of inventory cost-sharing proportion on the operator's profit and contract parameters. The results show that it is more incentive to provide larger fixed payments and performance penalties (rewards) for risk-seeking (risk-averse) suppliers as the increasing of risk coefficient under no cost-sharing. Moreover, a large cost-sharing proportion has no impact on the operator's optimal profit, but affects the contract option of different risk attitudes' suppliers. Finally, a numeric example is used to illustrate the proposed models. [ABSTRACT FROM AUTHOR]

We considered two periodic review perishable inventory systems. The first system is controlled by a periodic review (T , S) policy, where orders are triggered at each review period T and when the stock is less than S. The second one is controlled by a periodic review (s , S) policy, where orders are triggered only if the stock level is below s. For both models, the lifetime of each product was assumed to follow a general distribution, whereas the lead time was constant. The demands followed a Poisson process, and all unsatisfied demand was lost. Using an approximate solution of the steady-state probabilities of the inventory level, we obtained a closed form of cost expression. Next, we conducted a simulation study to identify our solution accuracy. Finally, we performed a sensitivity analysis to evaluate the effect of the lifetime variation and cost parameters on the best policy and its cost. The obtained results were close to those of the optimal policy with an average accuracy of 3.7% for the periodic review (T , S) policy and 3.8% for the periodic review (s , S) policy. For the worst cases, where the accuracy is higher than 7%, we provided an analytical approach to improve the system accuracy. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to enlarge the scope of existing EOQ-type models by incorporating uncertain product returns that may include time or quantity restrictions. In this inventory control problem, the item's demand is deterministic and occurs at a known constant rate over time. Product returns arrive according to a Poisson process. The amount returned to the seller at each return epoch is a random or fixed fraction of the accumulated number of units (net of previous returns) that have been demanded so far within a stock cycle. The ordering policy is a continuous review policy that orders a fixed quantity every time an order is placed. The replenishment lead time is instantaneous, and shortages are not allowed. The objective is to find optimal order quantities by minimising the long-run average total costs per unit time. Costs include a fixed plus proportional cost per order, and a holding cost per unit held in inventory per unit time. A level-crossing approach is used to derive a closed-form expression for the expected ordering and inventory holding costs as a function of the order size and other relevant parameters. [ABSTRACT FROM AUTHOR]

The clothes inventory balancing scheduling problem (CIBSP) among branch stores with the allowance of lateral transshipments has gained increasing attentions in fast-fashion apparel industry, especially for the trial sale of new products. To solve the CIBSP faced by a leading apparel company in China, a mixed integer linear programming (MILP) model is first formulated, based on which a relax-and-fix (R&F) method is developed. Several heuristic cuts based on practical experience and observations are further integrated into the R&F method to speed up the searching. The effectiveness of the method is demonstrated through extensive computational experiments: it is able to provide near-optimal solutions with average optimality gap 1.15% with less computation time, compared to solving the MILP model directly in a commercial solver. Case studies also demonstrate that the developed R&F method can obtain high-quality solutions with average optimality gap 0.09% with much less computation time. [ABSTRACT FROM AUTHOR]

This paper documents that U.S. equity returns are large and positive during the opening hours of European markets. These returns are pervasive and highly economically and statistically significant. Consistent with models of inventory risk, we demonstrate a strong relationship with order imbalances at the close of the preceding U.S. trading day. Rationalizing unconditionally positive "overnight drift" returns, we uncover an asymmetric reaction to demand shocks: market sell-offs generate robust positive overnight reversals, while reversals following market rallies are much more modest. We argue that demand shock asymmetry can arise in inventory management models with time-varying market maker risk-bearing capacity. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Sexual abuse of adolescent girls often leads to maladaptive behavioural patterns, variable anxiety responses, and dysfunctional coping mechanisms. This case report aims to investigate the prevalence of early maladaptive schemas (EMSs), anxiety, and coping strategies among five adolescent girls who are survivors of sexual abuse. The participants had a mean age of 16.6 years (±1.14 years) and were residing at the Ibtissama Social Complex Support Association (ISCSA) in Meknes, Morocco. Data were collected using a suite of standardised Likert-scale instruments. The State and Trait Anxiety Inventory (STAI), Young Schema Questionnaire-Short Form (YSQ-SF), and Ways of Coping Checklist (WCC) were used. Findings reveal pervasive EMS activation and high anxiety levels among all participants, with emotion-focused coping as the predominant mechanism. These results emphasise the critical roles of EMS, anxiety, and coping strategies following sexual abuse, highlighting the need for effective, scalable therapeutic interventions for this vulnerable demographic. [ABSTRACT FROM AUTHOR]

This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach. [ABSTRACT FROM AUTHOR]

Objective: In a randomized clinical trial, we evaluated whether the STIC (Systemic Therapy Inventory of Change) measurement and feedback system (MFS), the first MFS to explicitly integrate the family systems perspective, improved outcomes in individual, couple and family therapy. Method: Nine hundred and seventy clients seeking individual, couple or family therapy, entered therapy with 93 therapists at four sites in the Chicago metropolitan area. All therapists were trained with the STIC and participated in both Treatment as Usual (TAU) and TAU with the STIC (STIC). After agreeing to participate, clients were randomly assigned to TAU or STIC. Therapists did not know the condition to which a case was assigned, until just prior to the first session. Therapy was not time-limited or constrained, except for the use of the STIC in the STIC condition. All clients were evaluated on a non-STIC multi-systemic battery of widely used outcome measures pre-and-post therapy. Results: STIC clients improved more than TAU clients regardless of treatment modality or outcome measure. Clinically significant change was also greater for STIC than TAU clients across outcome measures. Conclusion: The STIC MFS holds promise for improving outcomes beyond TAU in individual, couple, and family therapy. [ABSTRACT FROM AUTHOR]

Since the 1990s, the Norwegian management for cultural heritage has increased its focus on finding effective solutions for protecting Norway's wooden cultural heritage from fire damage. The medieval churches in general, including the wooden stave churches, with their interiors and inventories, are of special interest. However, the usefulness of protecting valuable interiors and inventories when fighting fire has been questioned. An experiment was carried out to find manageable solutions for protecting large inventories by using fire covers in case of fire. An experiment using seven commercially available products was conducted by fire fighters to investigate whether these products could protect historic interiors from water and fire. The preliminary results show that it is possible to find manageable, large format covers for the protection of large, immovable historic inventories. [ABSTRACT FROM AUTHOR]

In the current study, we aimed to investigate the mediating roles of paternal self-efficacy and relationship quality as serial mediators in associations with sexist attitudes and paternal involvement. For that purpose, a total of 420 married adult Turkish men with at least one child between the ages 4 and 10 participated, and the Ambivalent Sexism Inventory, Child-Oriented Sexist Attitude Scale, Parenting Sense of Competence Scale, Positive-Negative Relationship Quality Scale, Inventory of Father Involvement, and Personal Information Form were administered. Following the testing of six different serial mediation models, it came to light that a) paternal self-efficacy and relationship quality (both positive and negative aspects) were found to mediate the relationship between benevolent sexism and paternal involvement and b) paternal self-efficacy and relationship quality (both positive and negative aspects) were found to mediate the relationship between gender-biased attitudes towards the child and paternal involvement. The research findings are discussed in the context of relevant literature. [ABSTRACT FROM AUTHOR]

In behavioral and social research, questionnaires are an important assessment tool, through which individuals can be categorized according to how they classify themselves in respect to a personal trait. One example is the Major Depression Inventory (MDI), which is widely used for the assessment of depression. It can also be used as a depression severity scale, with scores ranging from 0 to 50 constructed considering the same weight for each item in the MDI. However, the dependence among the items of the questionnaire suggests that a score with better properties could be obtained through factor models, which besides allowing to reduce the dimensionality of multivariate data, provides the estimation of common factors and factor loadings that often have an interesting theoretical interpretation. Additionally, auxiliary information could be available and, the effect of these variables in the latent factor could be estimated and provide interesting results. Thus, the main aim of this paper is to propose a factor model for ordered categorical data which incorporates auxiliary variables to explain the latent factors. The proposed model provides an alternative score to MDI based on the estimated latent factors that takes the uncertainty in the data and auxiliary information into account. [ABSTRACT FROM AUTHOR]

Assessing the credibility of symptoms is critical to neuropsychological assessment in both clinical and forensic settings. To this end, the Inventory of Problems-29 (IOP-29) and its recently added memory module (Inventory of Problems-Memory; IOP-M) appear to be particularly useful, as they provide a rapid and cost-effective measure of both symptom and performance validity. While numerous studies have already supported the effectiveness of the IOP-29, research on its newly developed module, the IOP-M, is much sparser. To address this gap, we conducted a simulation study with a community sample (N = 307) from the United Kingdom. Participants were asked to either (a) respond honestly or (b) pretend to suffer from mTBI or (c) pretend to suffer from depression. Within each feigning group, half of the participants received a description of the symptoms of the disorder to be feigned, and the other half received both a description of the symptoms of the disorder to be feigned and a warning not to over-exaggerate their responses or their presentation would not be credible. Overall, the results confirmed the effectiveness of the two IOP components, both individually and in combination. [ABSTRACT FROM AUTHOR]

Co-locating primary care and immediate care boosted practice revenue and physician compensation, improved patient access, and mitigated burnout in these clinics. Co-locating primary care and immediate care boosted practice revenue and physician compensation, improved patient access, and mitigated burnout in these clinics. [ABSTRACT FROM AUTHOR]

Purpose: The Purpose of the study was to investigate the construct validity and internal consistency of the LaMI among staff in the context of elderly care in Sweden. Design/methodology/approach: Questionnaire data from a longitudinal study of staff working in elderly care were used. Data were collected using the Leadership and Management Inventory. First data collection was for explorative factor analysis (n = 1,149), and the second collection, one year later, was for confirmatory factor analysis (n = 1,061). Findings: The explorative factor analysis resulted in a two-factor solution that explained 70.2% of the total variance. Different models were tested in the confirmatory factor analysis. The final model, a two-factor solution where three items were omitted, showed acceptable results. Originality/value: The instrument measures both leadership and management performance and can be used to continually measure managers' performances as perceived by staff to identify areas for development. [ABSTRACT FROM AUTHOR]

With rapid development of live-streaming e-commerce, an increasing number of firms are collaborating with live-streamers (e.g., online influencers and celebrities) to host online live-shows for selling their products. To reduce the firm's risk and motivate it to stock more, live-streamers can commit a minimum sales volume and/or the effort devoted to attracting fans and promoting the upcoming live-show. In this article, we study the roles of such sales and effort commitments on the performance of a "supply chain" in which a seller sells its product via a live-streamer. Product demand at the live-show is random while depending on the live-streamer's pre-show effort and the seller needs to decide the amount of inventory for sale at the live-show. We consider three scenarios, in which the live-streamer makes sales-only, effort-only, and hybrid commitments, and compare the decisions and profits of both parties. We show that the commitment on sales volume or effort is mutually beneficial to the seller and the live-streamer. Moreover, the hybrid commitment can motivate the seller to stock more inventory and induce more effort from the live-streamer compared to sales-only or effort-only commitment. The live-streamer always prefers the hybrid commitment whereas the seller may prefer the sales-only commitment, for example, when the penalty is at a medium level. [ABSTRACT FROM AUTHOR]

ABSTRACT Both classical and contemporary definitions of education emphasise the development of learners and the discovery of their potential. The period of systemic education at all stages is an opportunity to seek this potential both at school and outside it. These are the circumstances of the development of abilities, interests and passions. In the educational process and in the modern world, the most valued are people who are creative, entrepreneurial, open to new experiences and have a passion—since it provides energy, gives wings, is a source of a sense of freedom and meaning and helps maintain well‐being. This study aimed to explore passion profiles, considering their dimensions and categories, and the respondents' field of study and gender, in a sample of Polish university students (n = 2720). The online Self‐Report Passion Inventory (SRPI) was used. Cluster analysis identified three latent profiles, differentiated by the perceived benefits of passion, its origin and its balance with other life activities. In addition, we identified categories of passionate activities typical of each profile. Field of study and gender were represented in similar ways in the distinguished profiles, indicating the universal nature of passion. The qualitative analysis of the results revealed potential reasons for the difficulties respondents experienced while categorising passionate activities. [ABSTRACT FROM AUTHOR]

Vocational interest inventories are widely used in both research and practice to help match people to well‐fitting work environments. However, because there are many different methods to operationalize interest fit, a debate remains regarding the best ways to do so. To empirically inform this debate, our study compared the predictive power of four widely used interest fit indices (i.e., matching scale scores, profile deviance scores, profile correlations, and polynomial regression scores) for predicting career choice satisfaction. Using a large and diverse U.S. sample (N = 257,320), results indicated that among the three single‐term interest fit measures, profile correlations (R2 = .04) explained more variance in career choice satisfaction than matching scale scores (R2 = .02) and profile deviance scores (R2 = .00). By comparison, the full 30‐term polynomial regression model explained the most variance in career choice satisfaction (R2 = .09); in this case, however, the nonlinear terms that capture fit effects only accounted for about 22% (R2 = .02) of the total variance explained by the model. Overall, these results indicate that researchers and practitioners should be cautious of the greater criterion‐related validity of polynomial regression models as fit information may not be a substantial contributor to their predictive capacities. In addition, our findings support the use of profile correlations as a predictive, single‐term measure of interest fit. [ABSTRACT FROM AUTHOR]

AbstractIn the event of a disruption at a primary supplier, the manufacturer’s sourcing and production operations are inevitably affected, necessitating a response strategy based on the estimated disruption duration. This paper derives the optimal production policy for each response strategy, aiming to minimize total costs over a planning horizon within a dynamic programming framework. We characterize these production policies across various model parameters, particularly focusing on the estimated disruption duration and initial inventory level. Our analysis identifies when the passive acceptance strategy and the backup strategy each hold an advantage, and how the cost benefits of each strategy vary with different model parameters. Additionally, we examine the consequences of overestimating or underestimating the disruption duration, providing insights into how the chosen response strategy and production policy, based on the estimated duration, diverge from the true optimal ones as the actual duration unfolds. We also highlight the cost implications of these discrepancies. Finally, in scenarios where the true duration follows a probabilistic distribution, our numerical experiments demonstrate how strategy selection and the cost of misestimation are influenced by the mean of the true disruption duration and the initial inventory level. These analyses offer decision-makers valuable guidance on when to act or wait in the face of disruptions. [ABSTRACT FROM AUTHOR]

In this paper, we develop a multi-item inventory model with stochastic demand, and the demand is considered as a random variable following a power demand pattern. A cumulative cycle demand is first identified for each product, and then the demand is gradually released to the inventory system by the power patterns within a cycle. The replenishment schedule is predetermined and well-known. Shortages are permitted but partially backlogged, and the rest are taken as lost sales. An effective inventory policy is utilized to reduce the total expected cost per unit of time by acquiring the optimal amount of stock to be stored at the start of the inventory cycle, which may protect against excessive lost sales and also avoid overstocking. This model is better suited for use in online purchases and the offline market where the buyer is assured that the goods will be shipped within a few days rather than at the time of purchase. We evaluated the system with items deteriorating instantaneously, which makes it appropriate to have a larger initial demand with a power pattern index greater than one. Numerical illustrations and sensitivity analysis provide an efficient tool for the insights of the theoretical solutions over the managerial foundation. [ABSTRACT FROM AUTHOR]

Wetlands face global threats with estimates suggesting a loss ranging from 21% to 85% of their original extent. Africa’s wetlands, covering about 4.4% of the continent, provide crucial services to millions of people and harbour significant biodiversity. This study assesses African countries’ readiness for the Kunming−Montreal Global Biodiversity Framework (GBF) through wetland inventories. It examines the progress made under the Aichi Targets for 2020, revealing 39% of reporting African countries indicating being on track for Aichi Target 11. Subsequently, the study explores wetland inventorying trends from national reports made to the Ramsar Convention from COP07 (1999) to COP14 (2022), showcasing increased membership and progress in African countries. Notable patterns emerge, revealing challenges in maintaining accurate inventories. Despite varying responses, 67% of African countries reported having a comprehensive wetland inventory at one point in the past 23 years. However, the wetland inventories are out of date, and critical information is not easily accessible, hampering accurate reporting on the current state of wetlands, and hindering informed decision-making for conservation and protection initiatives. Global datasets have contributed to reporting on wetland ecosystems; however, biodiversity-focused wetland typology systems like the IUCN Global Ecosystem Typology are crucial for a comprehensive understanding of wetland ecosystems. [ABSTRACT FROM AUTHOR]

Understanding of past droughts has been mostly shaped by meteorological data, with relatively less known about the human aspects of droughts, their socio‐economic impacts, as well as choices people make in response to droughts in different environmental and socio‐political contexts. The lack of data that systematically record and categorize drought impacts is an important reason for this disparity. In this paper, we present an Irish drought impacts database (IDID) containing 6094 newspaper reports and 11,351 individual impact records for the island of Ireland, covering the period 1733–2019. Relevant articles were identified through systematic searching of the Irish Newspaper Archives, and recorded impacts were categorized using a modified version of the classification scheme employed by the European drought impact inventory (EDII). Drawing on the wealth and diversity of content provided by the newspapers, the IDID database provides information on the documented temporal and geographical extent of drought events, their socio‐economic and political contexts, their consequences, mitigation strategies employed and their change over time. The IDID also facilitates analysis of long‐term patterns in drought incidence, individual impact categories, as well as detailed insight into the impacts of individual drought events over nearly three centuries of Ireland's history. In addition, by allowing an examination of the coherence between meteorological records and identified impacts, it advances our understanding of the influences that contemporary economic, political, environmental and societal events had on the human experience, perception and impact of droughts. This new open‐access database, therefore, provides opportunities for improving understanding of drought vulnerability and is an important step in developing greater capacity to cope with and respond to future droughts on the island of Ireland. [ABSTRACT FROM AUTHOR]

Evaluation and strengthening of existing bridges – historical outlines and findings regarding the Hessian road bridge inventory Two short histographical sketches introduce the concepts of refurbishment, condition and load‐bearing capacity assessment between 1700 and 2010 and take a look at the development of the recalculation of existing bridges. Based on project sketches, the hidden safety features of existing road bridges are worked out and procedures for achieving efficient structural bridge strengthening are presented. The focus is on the determination of real object‐specific traffic load models, calibrated structural analyses and additive and non‐destructive strengthening methods. Some critical comments on planning acceleration and the current approval system, together with the demand that bridge construction should be reflected in the common objective of "traffic", broaden the definition of strengthening holistically. This offers the opportunity to stimulate a discussion about the sensible handling of existing bridges as part of our road infrastructure. [ABSTRACT FROM AUTHOR]

Phonotacticon is a cross-linguistic database that contains syllabic phonotactic information about spoken lects (linguistic varieties), including the possible forms of the onset, nucleus and coda of each lect, as well as the phonemic and tonemic inventories. In this paper, we present Phonotacticon 1.0, which contains the phonotactic profiles of 516 Eurasian lects retrieved from descriptive literature. The later versions of Phonotacticon will extend beyond Eurasia and will ultimately cover spoken lects in all macroareas. As an example of the research potential of this database in future studies, we have generated from Phonotacticon several descriptive visualizations, such as the distribution of the maximal onset length, to demonstrate the visually discernible areal distribution of certain phonotactic patterns. [ABSTRACT FROM AUTHOR]

This paper presents a thorough assessment and classification of different uncertain environments used by researchers to analyze inventory management(IM) systems across various sectors, such as ABC analysis, Last In, First Out (LIFO), and batch tracking. Moreover, it introduces the concepts of the neutrosophic principle and fuzzy principle in inventory management. it also investigates the difficulties associated with the traditional inventory model. The primary focus of the study lies in inventory management under the Neutrosophic principle, specifically addressing uncertain demand and imprecise data. By shedding light on the potential of neutrosophic principle, this manuscript contributes valuable insights into overcoming the challenges posed by fuzzy models and enhancing decision-making in the realm of inventory control system. [ABSTRACT FROM AUTHOR]

Key Takeaways: In the United States, all community and nontransient noncommunity water systems are required to submit an inventory describing the location and makeup of all service lines in their service areas. While the material can be designated as lead, galvanized requiring replacement, nonlead, or unknown, the last two designations require additional steps to be taken in the proposed Lead and Copper Rule Improvements (LCRI). Some utilities have few or no lead service lines, and this inventory requirement, issued by the US Environmental Protection Agency (EPA), may be burdensome to those utilities. This article demonstrates the methodology in EPA's proposed LCRI for verification of construction material, and an alternate validation procedure is proposed. [ABSTRACT FROM AUTHOR]

Humanitarian action saves lives by delivering supplies following a disaster. To effectively prepare, large humanitarian organizations solve optimization models to allocate inventory minimizing expected time‐to‐respond. However, these organizations also rely on transportation carriers to deliver this inventory, a feature often omitted from such models. Part of the reason is that capacity data on third‐party carriers are scant. Nevertheless, there is value in humanitarian organizations asking themselves the following: how should such data be incorporated into a model; how important is it to incorporate transportation capacities; where is it worth increasing transportation capacity? Building on previous inventory optimization models, we partner with the United States Federal Emergency Management Agency (FEMA) to answer these questions by incorporating transportation market capacity. Using public and proprietary data sets, we first approximate the trucking capacity near FEMA's warehouses. We develop intuitive metrics that guide practitioners to make decisions, understand trade‐offs in complex networks, and negotiate contracts. We show that when optimizing inventory, ignoring trucking capacity can lead to up to 18% higher response times. In discussions with FEMA, our research has guided strategic inventory deployment and has been a catalyst in a new initiative for transportation contracting. [ABSTRACT FROM AUTHOR]

Through analyses of Russian voicing assimilation and German dorsal fricative assimilation, this article argues for a restricted version of structure preservation and a stratal model of Optimality Theory. Structure preservation (Kiparsky 1985) prohibits the creation of allophones early in the phonological computation. The parallel architecture of OT undermines the assumptions of structure preservation, which has been widely rejected within OT. This article demonstrates that processes that are both neutralizing and non-structure-preserving, and which involve overlapping sets of targets and triggers, such as Russian voicing assimilation, result in a ranking paradox in parallel OT. Purely allophonic processes, such as German fricative assimilation, do not pose the same difficulties. Analyses of both processes are proposed within the framework of Stratal OT. The use of multiple strata eliminates the ranking paradox illustrated in Russian voicing assimilation and accounts for the interaction of German fricative assimilation and umlaut. This account predicts that non-structure-preserving neutralization cannot take place at the earliest level of evaluation but must apply after the rich base is filtered to the language-specific inventory and stem-level processes are applied. In the case of Russian, this is substantiated by application of assimilation across clitic boundaries, requiring phrase-level application. [ABSTRACT FROM AUTHOR]

Inventory accuracy is critical for the survival and success of supermarket chains, and there is increased search and application for new solutions and modern technologies to achieve operation inventory accuracy. Accuracy of inventory management is vital for the success of supermarkets for example, it results into lower inventory investment, ensures customer satisfaction and maintain profitability for the supermarket. This is because inventory accuracy enables supermarkets to avoid stock-outs, overstocking and unnecessary carrying costs. This paper aims to explore and describe inventory ERP systems and their capabilities in the achievement of inventory accuracy in supermarket chains hinging on continuous improvement theory and Technology-Organization-Environment (TOE) model. An empirical investigation, using a qualitative case study design was conducted. Collective case study approach was used studying cases of two leading supermarkets (C-ug and N-ke) in Uganda and Kenya. Data was obtained by interviewing two key informants from each supermarket (ERP system controller and stores Manager). Content Analysis and Multiple Response Analysis were used to analyze unstructured questions and structured questions respectively. The findings indicated that an ERP system operates through the integration of internal sections of the supermarket and external players such as the suppliers. Secondly, it was found that a good ERP system should be user friendly, fit for the nature of business of supermarkets with key security features for better functioning in supermarket chains. Finally, the study provides that ERP systems have great capabilities for achieving inventory accuracy in the operations of supermarket chains through real-time inventory monitoring, automated notifications about the status of the goods and the stock levels, provision of demand reports on products and automated inventory tracking In this study, a case study approach was taken when studying the capabilities of ERP in supermarket chains on two supermarkets hence the findings need to be validated by widening the scope of the supermarket chains. Most Previous research takes a wholistic approach when examining the influence of ERP on inventory & warehousing and supply chain disregarding its direct impact on inventory accuracy yet other inventory management Technology applications such RFID have been largely tested on inventory accuracy hence the need to test ERP capabilities on inventory accuracy. [ABSTRACT FROM AUTHOR]

Background/Objectives: The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts. Methods: This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems. Results: We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses. Conclusion: In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat. [ABSTRACT FROM AUTHOR]