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Abstract In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R 2) better than SVR, while GRU performed 4.69% (in R 2) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas.

One of the lean tools is value stream mapping (VSM), which is used to visually map and analyze the flow of materials, information and processes required to deliver a product or service to the customer. VSM is widely used to streamline processes, reduce lead time and enhance overall operational performance. While VSM is a powerful tool, some challenges are associated with its conventional application in the manufacturing industry. Conventional VSM typically represents a snapshot of the value stream at a specific point in time. This static representation might not capture modern manufacturing environments' dynamic and evolving nature. Hence, this research addresses the problem of static representation of conventional VSM by applying Fuzzy Triangular Distribution (TFN) in the manufacturing industry by introducing a more flexible and dynamic approach. A conveyor manufacturing company was selected as a case study based on the wide variety and low volume type of manufacturing process. TFN approach was used to analyze variabilities in process parameters to identify their mean, minimum, and maximum values and remove all the outliers. Integrating TFN with VSM gives dynamic behavior to the conventional VSM. Based on the identifications, appropriate lean improvement tools were applied to develop an optimized future VSM. As a result, the future state map shows a 71.74% and 19.45% improvement ratio in terms of production lead time and value-added time, respectively, compared to the current VSM. This study can be further extended by investigating how the reduction of lead time and WIP inventory can help reduce costs for a company. [ABSTRACT FROM AUTHOR]

The increased seismic activity observed in the Himalayas, coupled with the expanding urbanization of the surrounding areas in northern India, poses significant risks to both human lives and property. Developing an earthquake early warning system in the region could help in alleviating these risks, especially benefiting cities and towns in mountainous and foothill regions close to potential earthquake epicenters. To address this concern, the government and the science and engineering community collaborated to establish the Uttarakhand State Earthquake Early Warning System (UEEWS). The government of Uttarakhand successfully launched this full-fledged operational system to the public on 4 August 2021. The UEEWS includes an array of 170 accelerometers installed in the seismogenic areas of the Uttarakhand. Ground motion data from these sensors are transmitted to the central server through the dedicated private telecommunication network 24 hours a day, seven days a week. This system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile app freely available for smartphone users and by blowing sirens units installed in the buildings earmarked by the government. The UEEWS has successfully issued alerts for light earthquakes that have occurred in the instrumented region and warnings for moderate earthquakes that have triggered in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of instrumentation, adaptation of attributes and their relation to earthquake parameters, operational flow of the system, and information about dissemination of warnings to the public. [ABSTRACT FROM AUTHOR]

Simple Summary: An important component in the study of survival in breast cancer patients is how the tumor is detected. This involves examining whether and to what extent survival differs when the tumor is detected, for example, by screening or by symptoms. In such an analysis, it is important to note that survival times are influenced by several biases. One important factor is the bias due to the so-called lead time, i.e., the time by which the diagnosis has been brought forward by screening. Therefore, survival may appear to be longer even if earlier diagnosis did not affect disease progression. We examine whether there is a remaining survival difference after correction for bias that could be attributable to the mode of detection, for example, because of higher quality of care. These results can then be used to further improve the survival of breast cancer patients. (1) Background: Screen-detected breast cancer patients tend to have better survival than patients diagnosed with symptomatic cancer. The main driver of improved survival in screen-detected cancer is detection at earlier stage. An important bias is introduced by lead time, i.e., the time span by which the diagnosis has been advanced by screening. We examine whether there is a remaining survival difference that could be attributable to mode of detection, for example, because of higher quality of care. (2) Methods: Women with a breast cancer (BC) diagnosis in 2000–2022 were included from a population-based cancer registry from Schleswig-Holstein, Germany, which also registers the mode of cancer detection. Mammography screening was available from 2005 onwards. We compared the survival for BC detected by screening with symptomatic BC detection using Kaplan–Meier, unadjusted Cox regressions, and Cox regressions adjusted for age, grading, and UICC stage. Correction for lead time bias was carried out by assuming an exponential distribution of the period during which the tumor is asymptomatic but screen-detectable (sojourn time). We used a common estimate and two recently published estimates of sojourn times. (3) Results: The analysis included 32,169 women. Survival for symptomatic BC was lower than for screen-detected BC (hazard ratio (HR): 0.23, 95% confidence interval (CI): 0.21–0.25). Adjustment for prognostic factors and lead time bias with the commonly used sojourn time resulted in an HR of 0.84 (CI: 0.75–0.94). Using different sojourn times resulted in an HR of 0.73 to 0.90. (4) Conclusions: Survival for symptomatic BC was only one quarter of screen-detected tumors, which is obviously biased. After adjustment for lead-time bias and prognostic variables, including UICC stage, survival was 27% to 10% better for screen-detected BC, which might be attributed to BC screening. Although this result fits quite well with published results for other countries with BC screening, further sources for residual confounding (e.g., self-selection) cannot be ruled out. [ABSTRACT FROM AUTHOR]

Objective: Lead time to treatment (clinical onset of epileptic spasms [ES] to initiation of appropriate treatment) is known to predict outcomes in infantile epileptic spasms syndrome (IESS). Timing the clinical onset of ES is crucial to establish lead time. We investigated how often ES onset could be established to the nearest week. We aimed to (1) ascertain the exact date or estimate the nearest week of ES onset and (2) compare clinical/demographic factors between patients where date of ES onset was determined or estimated to the nearest week and patients whose date of ES onset could not be estimated to the nearest week. Reasons for difficulties in estimating date of ES onset were explored. Methods: Retrospective chart review of new onset IESS patients (January 2019–May 2022) extracted the date or week of the clinical onset of ES. Predictors of difficulty in date of ES onset estimation to the nearest week were examined by regression analysis. Sources contributing to difficulties determining date of ES onset were assessed after grouping into categories (provider‐, caregiver‐, disease‐related). Results: Among 100 patients, date of ES onset was estimated to the nearest week in 47%. On univariable analysis, age at diagnosis (p =.021), development delay (p =.007), developmental regression/stagnation (p =.021), ES intermixed with other seizures (p =.011), and nonclustered ES at onset (p =.005) were associated with difficulties estimating date of ES onset. On multivariable analysis, failure to establish date of ES onset was related to ES intermixed with other seizures (p =.004) and nonclustered ES at onset (p =.003). Sources contributing to difficulties determining date of ES onset included disease‐related factors (ES characteristics, challenges interpreting electroencephalograms) and provider/caregiver‐related factors (delayed diagnosis). Significance: Difficulties with estimation of lead time (due to difficulties timing ES onset) can impact clinical care (prognostication), as even small increments in lead time duration can have adverse developmental consequences. [ABSTRACT FROM AUTHOR]

Lean production has been industrialized in manufacturing industry in the Global North on account of delivering quality products. The industry in the Global South on the other hand is facing problems that could easily be minimized using the production principle. This paper is organized to adapt lean production for improving productivity of garment industry in Ethiopia. The target is to alleviate variety between company's productivity and customer demand. Shop floor visit, questionnaire survey, and focus group discussion-based data mining approaches have been used for this purpose. The qualitative part was used to map out baseline data of existing manufacturing process whereas the quantitative part was aimed to construct empirical findings obtained from qualitative organization. Aggregate results accordingly show that there are substantial lean wastes such as longer lead time, high WIP inventory, longer routes, and unutilized resources. It has been meanwhile renown that if appropriate lean production tools have framed and implemented, the impact of these wastes could meaningfully be minimized and a smooth process flow can be ensured. For instance, a lead time for a single round neck t-shirt in the case study has shown an improvement of 18.3% and a 3.9% improvement of resource utilization. [ABSTRACT FROM AUTHOR]

Abstract Purpose To identify the clinical impact and potential benefits of in-house 3D-printed objects through a questionnaire, focusing on three principal areas: patient education; interdisciplinary cooperation; preoperative planning and perioperative execution. Materials and methods Questionnaires were sent from January 2021 to August 2022. Participants were directed to rate on a scale from 1 to 10. Results The response rate was 43%. The results of the rated questions are averages. 84% reported using 3D-printed objects in informing the patient about their condition/procedure. Clinician-reported improvement in patient understanding of their procedure/disease was 8.1. The importance of in-house placement was rated 9.2. 96% reported using the 3D model to confer with colleagues. Delay in treatment due to 3D printing lead-time was 1.8. The degree with which preoperative planning was altered was 6.9. The improvement in clinician perceived preoperative confidence was 8.3. The degree with which the scope of the procedure was affected, in regard to invasiveness, was 5.6, wherein a score of 5 is taken to mean unchanged. Reduction in surgical duration was rated 5.7. Conclusion Clinicians report the utilization of 3D printing in surgical specialties improves procedures pre- and intraoperatively, has a potential for increasing patient engagement and insight, and in-house location of a 3D printing center results in improved interdisciplinary cooperation and allows broader access with only minimal delay in treatment due to lead-time.

We consider a classic joint pricing and inventory control problem with lead times, which is extensively studied in the literature but is notoriously difficult to solve because of the complex structure of the optimal policy. In this work, rather than analyzing the optimal policy, we propose a class of constant-order dynamic pricing policies, which are fundamentally different from base-stock list price policies, the primary emphasis in the existing literature. Under such a policy, a constant-order amount of new inventory is ordered every period, and a pricing decision is made based on the inventory level. The policy is independent of the lead time. We prove that the best constant-order dynamic pricing policy is asymptotically optimal as the lead time grows large, which is exactly the setting in which the problem becomes computationally intractable because of the curse of dimensionality. As our main methodological contributions, we establish the convergence to a long-run average random yield inventory model with zero lead time and ordering capacities by its discounted counterpart as the discount factor goes to one, nontrivially extending the previous results in Federgruen and Yang that analyze a similar model but without capacity constraints. Funding: Research of X. Chen and L. Xin was partly supported by the National Science Foundation [Grant CMMI-1635160]. [ABSTRACT FROM AUTHOR]

Purpose: To identify the clinical impact and potential benefits of in-house 3D-printed objects through a questionnaire, focusing on three principal areas: patient education; interdisciplinary cooperation; preoperative planning and perioperative execution. Materials and methods: Questionnaires were sent from January 2021 to August 2022. Participants were directed to rate on a scale from 1 to 10. Results: The response rate was 43%. The results of the rated questions are averages. 84% reported using 3D-printed objects in informing the patient about their condition/procedure. Clinician-reported improvement in patient understanding of their procedure/disease was 8.1. The importance of in-house placement was rated 9.2. 96% reported using the 3D model to confer with colleagues. Delay in treatment due to 3D printing lead-time was 1.8. The degree with which preoperative planning was altered was 6.9. The improvement in clinician perceived preoperative confidence was 8.3. The degree with which the scope of the procedure was affected, in regard to invasiveness, was 5.6, wherein a score of 5 is taken to mean unchanged. Reduction in surgical duration was rated 5.7. Conclusion: Clinicians report the utilization of 3D printing in surgical specialties improves procedures pre- and intraoperatively, has a potential for increasing patient engagement and insight, and in-house location of a 3D printing center results in improved interdisciplinary cooperation and allows broader access with only minimal delay in treatment due to lead-time. [ABSTRACT FROM AUTHOR]

Background: In the Netherlands, the COVID-19 pandemic resulted in a temporary halt of population screening for cancer and limited hospital capacity for non-COVID care. We aimed to investigate the impact of the pandemic on the inhospital diagnostic pathway of breast cancer (BC) and colorectal cancer (CRC). Methods: 71,159BC and 48,900 CRC patients were selected from the Netherlands Cancer Registry. Patients, diagnosed between January 2020 and July 2021, were divided into six periods and compared to the average of patients diagnosed in the same periods in 2017–2019. Diagnostic procedures performed were analysed using logistic regression. Lead time of the diagnostic pathway was analysed using Cox regression. Analyses were stratified for cancer type and corrected for age, sex (only CRC), stage and region. Results: For BC, less mammograms were performed during the first recovery period in 2020. More PET-CTs were performed during the first peak, first recovery and third peak period. For CRC, less ultrasounds and more CT scans and MRIs were performed during the first peak. Lead time decreased the most during the first peak by 2days (BC) and 8days (CRC). Significantly fewer patients, mainly in lower stages, were diagnosed with BC (−47%) and CRC (−36%) during the first peak. Conclusion: Significant impact of the COVID-19 pandemic was found on the diagnostic pathway, mainly during the first peak. In 2021, care returned to the same standards as before the pandemic. Long-term effects on patient outcomes are not known yet and will be the subject of future research. [ABSTRACT FROM AUTHOR]

Supply chain coordination has been a research hot spot in supply chain management. This paper constructs a secondary supply chain system. Taking the abatement of the bullwhip effect and the double marginal effect as the coordination objective, a simulation study of supply chain decision coordination was conducted using system dynamics. First, by controlling the lead time, it was found that in the decentralized decision-making model, the profit of the supplier and the whole supply chain increases with the shortening of the lead time, and vice versa for the retailer. In the centralized decision-making model with the addition of information sharing and contract, it was found that the retailer's profit is consistent with the trend of the supplier and the supply chain as a whole, and the supplier's profit is lower than that of decentralized decision making in the pre-cooperation period. In addition, it is also found that adjusting the contract parameters can effectively improve the situation. Finally, the above models were analyzed for supply chain coordination decisions based on two scenarios: "cooperative stability" or "balance of effects". [ABSTRACT FROM AUTHOR]

A two-commodity queueing-inventory system with phase-type service times and exponential lead times is considered. There are two types of customers; Type 1 and Type 2. Demands from each customer type occur independently according to a Poisson process with different rates whereas the service times follow a phase-type distribution. Type 1 customers have a non-preemptive priority over Type 2 customers. We assume a finite waiting space for Type 1 customers whereas there is no limit on the waiting room for Type 2 customers. Type i customers demand only commodity i, i = 1 , 2 . For the ith commodity, S i and s i represent, respectively, the maximum inventory level and the reorder level. Whenever the inventory level of ith commodity drops to s i , an order is placed from retailer-i to make the inventory level S i . The lead times of the commodities are exponentially distributed with different parameters. When there is a Type i customer waiting in the queue, if the inventory level of ith commodity is zero (or reaches zero), a decision of immediate purchase is made so as not to lose the waiting customer. The queueing-inventory model in the steady-state is analyzed using the matrix-geometric method. The system performance is examined for different values of parameters. Besides, an optimization study is performed for some system parameters. [ABSTRACT FROM AUTHOR]

In the current COVID-19 era, one of the critical factors influencing the quality of hospital services is drug management, with planning and procurement being particularly pivotal to avoid drug shortages. This study aimed to examine the selection, procurement, and lead time aspects of drug management at the pharmacy installation hospital in Jakarta during the COVID-19 pandemic. Conducted as a descriptive study with retrospective data, total sampling was utilized to collect data, incorporating 1,413 drugs. The data, collected on observation sheets, underwent univariate analysis using Microsoft Excel. Results indicated that out of the seven measurable indicators, two met the standards: the percentage of allocated funds for drug procurement at 70.5%, and the frequency of errors in orders occurring five times. However, five indicators fell below the standards: the suitability of drug items with the National Formulary at 14.15%, the suitability of drug items with the Hospital Formulary at 68.15% (with an 80% benchmark), the frequency of each item's procurement in a year at a low category of 43.45%, the percentage of conformity between planning and reality at 99.61%, and the achievement of lead time for ordering drugs at 5.08 days. In conclusion, the selection, procurement, and lead time aspects of drug management at the "X" Hospital Pharmacy Installation did not fully meet the established standards. Therefore, it is recommended that healthcare management implements and adheres to robust drug management system regulations, particularly during pandemic situations, to ensure optimal hospital services.

The main challenge facing industrial companies is how to stay competitive in a fast changing world. They should adopt an effective supply chain that enables them to deliver products in a short period of time. This forced organization to change their pattern of processing and become lean. Lean manufacturing system is the best philosophy to achieve the objectives of the industrial organizations and to reduce the waste activities. The unnecessary movement, defective, waiting, inadequate processing, unnecessary inventory, excessive transport, overproduction, and the underutilization of people and facilities are the most types of waste found in the industries. Many industries have experienced the benefits of applying the lean concept in their area to improve the production processes, resources utilization, reduce production lead time, and eliminate wastes in the activities which is the goal of lean concept in the manufacturing industry. In this research, the lean concepts are presented in the leather shoes manufacturing industry in the city of Baghdad, it aimed to decrease the lead time of production, minimize the non-value time, and improve the production line. By using the most essential lean tool, value stream mapping, where the current and future map capture the flow of material and information of the production line before and after the implementation of the proposed improvement. The hybrid push/pull strategies and Kanban production were implemented to improve the production line. After adopting the suggested improvement, the production lead time reduced from 130.6 to 14.8 hours and the non-value added reduced from 118 to 10.8 minutes.

A generic way to verify asymptotic optimality of semi-open-loop policies for a wide class of MDPs with large lead times. In many real-life inventory models, order lead times can result in uncertain effects of inventory decisions. However, as the lead time grows large, one would naturally postulate that the effect of the delayed order depends weakly on the current inventory level and, thus, intuit that decoupling the delayed order with the current inventory level may provide good heuristics. Motivated by these examples, in "Asymptotic Optimality of Semi-open-Loop Policies in Markov Decision Processes with Large Lead Times," Bai et al. consider a generic Markov decision process (MDP) with one delayed control and one immediate control. For MDPs defined on general spaces with uniformly bounded cost functions and a fast mixing property, they construct a periodic semi-open-loop policy for each lead time value and show that these policies are asymptotically optimal as the lead time goes to infinity. For MDPs defined on Euclidean spaces with linear dynamics and convex structures, they impose another set of conditions under which constant delayed-control policies are asymptotically optimal. We consider a generic Markov decision process (MDP) with two controls: one control taking effect immediately and the other control whose effect is delayed by a positive lead time. As the lead time grows, one naturally expects that the effect of the delayed action only weakly depends on the current state, and decoupling the delayed action from the current state could provide good controls. The purpose of this paper is to substantiate this decoupling intuition by establishing asymptotic optimality of semi-open-loop policies, which specify open-loop controls for the delayed action and closed-loop controls for the immediate action. For MDPs defined on general spaces with uniformly bounded cost functions and a fast mixing property, we construct a periodic semi-open-loop policy for each lead time value and show that these policies are asymptotically optimal as the lead time goes to infinity. For MDPs defined on Euclidean spaces with linear dynamics and convex structures (convex cost functions and constraint sets), we impose another set of conditions under which semi-open-loop policies (actually, constant delayed-control policies) are asymptotically optimal. Moreover, we verify that these conditions hold for a broad class of inventory models, in which there are multiple controls with nonidentical lead times. Funding: Research of the first three authors was partly supported by the National Science Foundation [Grant CMMI-1635160]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0088. [ABSTRACT FROM AUTHOR]

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manufacturing system where a large number of measurements about the production state are available (e.g. sensor data). In response to this complex prediction challenge, we present a novel ensemble hierarchical deep learning algorithm comprised of three deep neural networks. One of these networks acts as a generalist, while the other two function as specialists for different products. Hierarchical ensemble methods have previously been successfully utilised in addressing various multi-class classification problems. In this paper, we extend this approach to encompass the regression task of lead time prediction. We demonstrate the suitability of our algorithm in two separate case studies. The first case study uses one of the largest manufacturing datasets available, the Bosch production line dataset. The second case study uses synthetic datasets generated from a reliability-based model of a multi-product, make-to-order production system, inspired by the Bosch production line. In both case studies, we demonstrate that our algorithm provides high-accuracy predictions and significantly outperforms selected benchmarks including the single deep neural network. Moreover, we find that prediction accuracy is significantly higher in the synthetic dataset, which suggests that there is complexity (i.e. subtle interactions) in industrial manufacturing processes that are not easily reproduced in artificial models [ABSTRACT FROM AUTHOR]

The increased seismic activity observed in the Himalayas, coupled with the expanding urbanization of the surrounding areas in northern India, poses significant risks to both human lives and property. Developing an earthquake early warning system in the region could help in alleviating these risks, especially benefiting cities and towns in mountainous and foothill regions close to potential earthquake epicenters. To address this concern, the government and the science and engineering community collaborated to establish the Uttarakhand State Earthquake Early Warning System (UEEWS). The government of Uttarakhand successfully launched this full-fledged operational system to the public on 4 August 2021. The UEEWS includes an array of 170 accelerometers installed in the seismogenic areas of the Uttarakhand. Ground motion data from these sensors are transmitted to the central server through the dedicated private telecommunication network 24 hours a day, seven days a week. This system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile app freely available for smartphone users and by blowing sirens units installed in the buildings earmarked by the government. The UEEWS has successfully issued alerts for light earthquakes that have occurred in the instrumented region and warnings for moderate earthquakes that have triggered in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of instrumentation, adaptation of attributes and their relation to earthquake parameters, operational flow of the system, and information about dissemination of warnings to the public.

Introdução: O faturamento é uma das atividades realizadas no hospital que tem grande impacto na saúde financeira de uma instituição. Dentre os diversos serviços que podem ser faturáveis, o OPME (Órteses, Próteses e Materiais Especiais) possui destaque. Usualmente a equipe de enfermagem é responsável pelo registro dos materiais que foram utilizados e o setor de auditoria e faturamento pela análise da conta. Por vezes, o processo de cobrança em conta do uso de OPME’s é de responsabilidade do setor de farmácia, que possui interface com diversas outras áreas do ciclo de cobrança. O tempo gasto nesse processo é um dos fatores que podem impactar no ciclo de receitas e ainda no relacionamento com convênios e fornecedores. A etapa de lançamento em conta pode ser um processo moroso, principalmente, se a instituição utiliza um SIH (Sistema de Informações Hospitalares) que exija o cadastro dos materiais no sistema. Objetivos: Mapear as etapas de lançamento em conta de itens de OPME que envolvem a equipe de farmácia, e o seu respectivo lead time. Para tal, foi utilizada uma ferramenta de qualidade descrita na metodologia do Kaizen. Material e Método: O fluxo de cobrança foi mapeado e identificados os ofensores do processo. Foram discutidas em conjunto com a equipe quais ações poderiam contribuir para otimizar o processo de lançamento em conta. Essas ações foram organizadas em uma matriz de esforço-impacto e avaliadas em reuniões semanais com a equipe. Resultados: Como resultado do Kaizen, foram identificadas 26 oportunidades de melhoria no processo de cobrança das contas com OPME. Dessas, houve o cancelamento de 7 ações, 10 estão em andamento, e foram 9 concluídas. Dentre as 9 ações concluídas, houve a ação de desenvolvimento de indicadores para o monitoramento do tempo de conclusão de cobrança das contas, incluído o monitoramento em etapas, como, por exemplo, o tempo de resposta de cadastro de materiais. O lead-time médio para o início de análise de uma conta foi de 3 dias; o de retorno de cadastro, de 7 dias; o de finalização do processo sem o fator cadastro, foi de 1 dia; em média cerca de 35% das contas exigiam cadastro, impactando o ciclo de receita. Discussão e Conclusões: Foi possível observar que o cadastro de material é um dos principais fatores que implicam na liberação de uma conta com OPME e, consequentemente, no faturamento (atraso na finalização da conta). Como ação de co-responsabilização entre as equipes envolvidas no processo de cobrança de uma conta, o indicador desenvolvido é compartilhado com as gestões envolvidas para a criação de processos de melhoria.

According to past research utilizing Bitcoin and other cryptocurrencies, Bitcoin has been shown to lead most other cryptocurrencies in terms of price movements. However, existing studies tend to focus on the direction of the lead-lag relationship instead of the duration of the lead-lag time. Furthermore, they are handicapped by the reliance on low-frequency data such as daily prices. This paper showcases the measurement of the lead-lag duration between cryptocurrencies using ultra-high-frequency tick-by-tick data, via the pair of Bitcoin and Cardano. Tick-by-tick data bring unique challenges in terms of methodology. The vast majority of time series econometrics methods are designed for use with data collected at regularly spaced time intervals, such as every hour, every day, etc. Tick-by-tick data, on the other hand, are not synchronized in any way and do not arrive at consistently spaced time intervals. Consequently, an asynchronous data integration methodology is utilized to estimate the Bitcoin price lead over Cardano price for each month beginning in January 2019 and continuing through May 2021. The length of the lead time ranges from 16 seconds to 118 seconds, with an average of around 57 seconds. Throughout the study period, the lengths of the lead time manifest a general trend of decline, which is shown to be statistically significant via non-parametric tests. Testing of seasonal patterns turns out to be not significant. The methodology and the findings of this paper have implications for both academics and practitioners, for example, when studying and implementing statistical arbitrage with cryptocurrencies.

Abstract Reservoir inflow (Q flow) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Q flow at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm “genetic algorithm (GA)”, for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm.

Supply chain coordination has been a research hot spot in supply chain management. This paper constructs a secondary supply chain system. Taking the abatement of the bullwhip effect and the double marginal effect as the coordination objective, a simulation study of supply chain decision coordination was conducted using system dynamics. First, by controlling the lead time, it was found that in the decentralized decision-making model, the profit of the supplier and the whole supply chain increases with the shortening of the lead time, and vice versa for the retailer. In the centralized decision-making model with the addition of information sharing and contract, it was found that the retailer’s profit is consistent with the trend of the supplier and the supply chain as a whole, and the supplier’s profit is lower than that of decentralized decision making in the pre-cooperation period. In addition, it is also found that adjusting the contract parameters can effectively improve the situation. Finally, the above models were analyzed for supply chain coordination decisions based on two scenarios: “cooperative stability” or “balance of effects”.

Rainfall forecasting is a high-priority research problem due to the complex interplay of multiple factors. Despite extensive studies, a systematic quantitative review of recent developments in rainfall forecasting is lacking in the literature. This study conducted a systematic quantitative review of statistical, numerical weather prediction (NWP) and machine learning (ML) techniques for rainfall forecasting. The review adopted the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique for screening keywords and abstracts, leading to 110 qualified papers from multiple databases. The impact of rainfall threshold, meteorological parameters, topography, algorithm techniques, geographic location, the horizontal resolution of the model, and lead time on rainfall forecast was examined. The review shows the importance of precipitable water vapor (PWV) along with other meteorological parameters for accurate nowcasting in coastal and mountainous regions. An increase in rainfall forecast uncertainty with an increase in the lead time makes the NWP model less popular for the short-term forecast. The pre-processing techniques increased the accuracy of ML techniques by considering extreme values and detecting the irregularly distributed multi-scale features of rainfall in space and time. Future research can focus on hybrid models with improved accuracy for nowcasting. The output from the hybrid model serves as input for the decision support system required for urban flood risk management. HIGHLIGHTS The PRISMA method is applied for a systematic quantitative review.; The Global Navigation Satellite System-derived precipitable water vapor (PWV) is found to be capable of analyzing the real-time profile of water vapor content.; Forecast can be improved by considering additional meteorological parameters along with the PWV.; A longer lead time in the NWP model enhances the forecast uncertainty.; There is a significant improvement in the forecast by machine learning models after pre-processing.;

The profitability of a supply chain (SC) is proportional to the stability of all its stakeholders as well as their consistent information sharing with an effective and efficient communication mechanism. Various inefficiencies, such as the bullwhip effect (BWE) and product unavailability, may be caused by a lack of coordination in an SC. The importance of sharing consumer demand has been quantified by comprehensive studies under the assumption that all SC participants will access the same information. However, only a few studies have studied the effect of minimal coordination or limited visibility of information while considering their effect on the overall efficiency of an SC. This work primarily leverages blockchain technology (BCT) to create a simulation model. To do this, an SC BWE-based model is initially developed. Following that, a blockchain-based robust information sharing system is simulated. Furthermore, information sharing is challenging, and SC stakeholders may not really trust each other and hence be reluctant to share sensitive information. Considering that, this paper propose an improved proof-of-authority (PoA) consensus algorithm that will increase trust in a decentralized SC model. Multiple experiments are carried out to demonstrate the effectiveness of our approach, and the simulation results clearly demonstrate the effectiveness of information sharing in a supply chain via blockchain, as well as that trust between partners tends to increase overall SC efficiency and reduce BWE.

In this paper, we consider a queueing inventory system with batch arrival and batch service processes. Customers arrive in batches of sizes 1 , 2 , ... , k , according to a marked compound Poisson process. We call a batch of customers as belonging to j when there are j individual customers in that batch. The service facility has waiting rooms for each category of customers and also a room to serve them. Except for category 1, all other customers have finite waiting rooms. The service room has only a limited number of seats. These seats are arranged in such a fashion that customers belonging to category j have groups of seats, each with j seats for j = 1 , 2 , ... , k . Customers are taken for service according to the availability of seats designated to each category. A category j customer can be taken for service only if j items are available in the inventory. The service time of customers of category j is exponentially distributed with parameter depending on j for j = 1 , 2 , ... , k . The number of seats available in the service room for each category of customers is restricted to a finite number. The replenishment for items follows the (s , S) policy: fill up to S at the time of replenishment. Lead time follows an exponential distribution. We analyze the system in the equilibrium state. Performance characteristics are evaluated and a number of numerical illustrations are provided. [ABSTRACT FROM AUTHOR]

Reservoir inflow (Qflow) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Qflow at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm "genetic algorithm (GA)", for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm. [ABSTRACT FROM AUTHOR]

Introduction The increasing use of teledermatoscopy in clinical practice has led to demands to evaluate the effects of this new technology on traditional healthcare systems. Objectives To study lead times from first consultation in primary care to diagnostic excision of suspected malignant melanoma lesions in traditional referrals to a tertiary hospital-based dermatology clinic compared with mobile teledermatoscopy referrals. Methods A retrospective cohort study design was used. Data on sex, age, pathology report, names of caregivers, clinical diagnosis, date for first visit to primary care unit, and date for diagnostic excision were collected from medical records. Patients managed through traditional referral (n=53) were compared with patients managed at primary care units using teledermatoscopy (n=128) regarding lead time from first visit to diagnostic excision. Results Mean time from date of first visit at primary care unit to diagnostic excision did not differ between the traditional referral and teledermatoscopy groups (16.2 vs. 15.7 days, median 10 vs. 13 days, p=0.657). Lead times from date of referral to diagnostic excision did not significantly differ (15.7 vs. 12.8 days, median 10 vs. 9 days, p=0.464). Conclusions Our study indicates that lead time to diagnostic excision for patients with suspected malignant melanoma managed by teledermatoscopy was comparable and not inferior to that of the traditional referral pathway. If teledermatoscopy is used at first consultation in primary care, it could potentially be more efficient than traditional referral.

Lean manufacturing (LM) practices and tools have played vital role in several manufacturing industries to enhance the competitive edge in in the global market. The purpose of this paper is to use the lean principles, practice and tools in Hibret Manufacturing & Machine Building Industries (HMMBI) in Ethiopia. The existence of high production lead time is getting the basic problem for HMBI that is why this research gives more attention. The study aims to improve the production lead time by minimizing non-value adding activities associated with several resources in the process. Both qualitative and quantitative data collection methods are used. The process time and floor layout are studied and mapped using Value Stream Mapping, and Spaghetti Diagram to identify wastes. The result shows that production lead time, work in process (WIP), non-value adding time (waiting time) and total distance traveled are reduced by 23.66%, 8.6%, 37.74%, 61.2% respectively. Finally, the process cycle efficiency is improved by 25.59%. The study is found significant for Hibret Manufacturing & Machine Building Industries (HMMBI) and for similar manufacturing industries.

Estimating key crop parameters (e.g., phenology, yield prediction) is a prerequisite for optimizing agrifood supply chains through the use of satellite imagery, but requires timely and accurate crop mapping. The moment in the season and the number of training sites used are two main drivers of crop classification performance. The combined effect of these two parameters was analysed for tomato crop classification, through 125 experiments, using the three main machine learning (ML) classifiers (neural network, random forest, and support vector machine) using a response surface methodology (RSM). Crop classification performance between minority (tomato) and majority ('other crops') classes was assessed through two evaluation metrics: Overall Accuracy (OA) and G-Mean (GM), which were calculated on large independent test sets (over 400,000 fields). RSM results demonstrated that lead time and the interaction between the number of majority and minority classes were the two most important drivers for crop classification performance for all three ML classifiers. The results demonstrate the feasibility of preharvest classification of tomato with high performance, and that an RSM-based approach enables the identification of simultaneous effects of several factors on classification performance. SVM achieved the best grading performances across the three ML classifiers, according to both evaluation metrics. SVM reached highest accuracy (0.95 of OA and 0.97 of GM) earlier in the season (low lead time) and with less training sites than the other two classifiers, permitting a reduction in cost and time for ground truth collection through field campaigns. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyse the waste in the production line to get an efficient production lead time. Methodology/Approach: This research uses the integration method of value stream mapping and kaizen. Based on the analysis with expert judgment, it is known that the waste comes from an inefficient factory layout. Improvements by making changes to factory layouts and merging workstations. Findings: This research has found waste caused by ineffective factory layouts based on Focus Group Discussions (FGD). Improvements by incorporating workstations and changes to the factory layout can reduce production lead time. The results showed that the production lead time decreased from 3.21 days to 2.31 days or a decrease in the ratio of 31.15%. Research Limitation/Implication: This research can also improve manufacturing performance such as cost efficiency, efficient layout, and labour reduction. So for similar companies, this research can be a reference for making improvements in reducing waste that can improve manufacturing performance so that they can compete in national and international markets. Originality/Value of paper: This paper provides benefits for the musical instrument industry regarding waste reduction. According to this research, lean production can increase the productivity and quality of the production system, because it can eliminate the waste of time or the production process. The new approach of this research is used the integration methods by involving experts through FGDs to make improvements and reduce waste in the production line. [ABSTRACT FROM AUTHOR]

Background. The time between booking an appointment and the appointment taking place, known as lead time, has been identified as a predictor of cancellation and no-show probability in health care settings. Understanding the impact of reducing permissible lead times, that is, the booking horizon, at a policy level in an outpatient care setting is important when mitigating costly cancellation and no-show rates. Few studies have researched this in an observational or experimental setting. Methods. We leveraged longitudinal observational data from an outpatient pediatric rehabilitation organization in Ontario, Canada, consisting of 73,482 visits between June 2021 and October 2023. This organization reduced its booking horizon at the policy level from 12 to 4 wk in February 2023. Using 2 interrupted time-series approaches, we estimated the change in level, slope, and variance of the weekly combined last-minute cancellation and no-show rate associated with the policy change. Results. It is estimated that reducing the booking horizon is associated with an absolute reduction in the weekly rate of last-minute cancellations and no-shows of 1.02% to 1.85% (a relative reduction of 8.07%-15.70%). Furthermore, the variance dropped by 48.18%. Conclusion. Reducing the appointment booking horizon is associated with a significant reduction in the rate and variance of costly last-minute cancellations and no-shows. The reduced variance can also help enable effective usage of strategies such as overbooking for organizations seeking further approaches to mitigating the negative effects of no-shows., Highlights: This study uses interrupted time-series approaches to assess the effects of reducing the appointment booking horizon at a policy level on last-minute cancellations and no-shows in a pediatric outpatient care setting.Reducing the permissible booking horizon from up to 3 mo to up to 4 wk is associated with a significant reduction in the rate of last-minute cancellations and no-shows.The shortened booking horizon policy is associated with a significant drop in the variance of last-minute cancellations and no-show rates, which is valuable in settings where overbooking occurs., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided in part by a grant from the Canadian Institutes of Health Research (CIHR). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report., (© The Author(s) 2024.)

Purpose: This study aims to analyse the waste in the production line to get an efficient production lead time. Methodology/Approach: This research uses the integration method of value stream mapping and kaizen. Based on the analysis with expert judgment, it is known that the waste comes from an inefficient factory layout. Improvements by making changes to factory layouts and merging workstations. Findings: This research has found waste caused by ineffective factory layouts based on Focus Group Discussions (FGD). Improvements by incorporating workstations and changes to the factory layout can reduce production lead time. The results showed that the production lead time decreased from 3.21 days to 2.31 days or a decrease in the ratio of 31.15%. Research Limitation/Implication: This research can also improve manufacturing performance such as cost efficiency, efficient layout, and labour reduction. So for similar companies, this research can be a reference for making improvements in reducing waste that can improve manufacturing performance so that they can compete in national and international markets. Originality/Value of paper: This paper provides benefits for the musical instrument industry regarding waste reduction. According to this research, lean production can increase the productivity and quality of the production system, because it can eliminate the waste of time or the production process. The new approach of this research is used the integration methods by involving experts through FGDs to make improvements and reduce waste in the production line.

Background and Aims: Gastrointestinal (GI) endoscopy is a limited health resource because of a scarcity of qualified personnel and limited availability of equipment. Non-adherence to endoscopy appointments therefore wastes healthcare resources and may compromise the early detection and treatment of GI diseases. We aimed to identify factors affecting non-attendance at scheduled appointments for GI endoscopy and thus improve GI healthcare outcomes. Methods: This was a single-center retrospective cohort study performed at a tertiary hospital gastroenterology endoscopy unit, 12 months before and 12 months after the start of the COVID-19 pandemic. We used multiple logistic regression analysis to identify variables associated with non-attendance at scheduled appointments. Results: Overall, 5,938 appointments were analyzed, and the non-attendance rate was 18.3% (1,088). The non-attendance rate fell significantly during the pandemic (22.6% vs. 11.6%, p < 0.001). Multivariable regression analysis identified the absence of deep sedation (OR: 3.253, 95% CI [2.386-4.435]; p < 0.001), a referral from a physician other than a gastroenterologist (OR: 1.891, 95% CI [1.630-2.193]; p < 0.001), a longer lead time (OR: 1.006, 95% CI [1.004-1.008]; p < 0.001), and female gender (OR: 1.187, 95% CI [1.033-1.363]; p = 0.015) as associated with appointment non-attendance. Conclusions: Female patients, those undergoing endoscopic procedures without deep sedation, those referred by physicians other than gastroenterologists, and with longer lead time were less likely to adhere to appointments. Precautions should be directed at patients with one or more of these risk factors, and for those scheduled for screening procedures during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Problem definition: Although the analytical literature extensively studies distribution channels, empirical evidence on the value of omnichannel distribution is limited, especially for the omnichannel used by manufacturing companies to fulfill retail orders. I empirically evaluate the extent to which the dual–distribution center (dual-DC) distribution channel and the factory-direct distribution channel contribute to fulfilling orders from retail stores compared with the traditional single–distribution center (single-DC) channel. Academic/practical relevance: Many manufacturing companies develop their distribution omnichannel to fulfill retail orders. To make proper decisions on various channels, they need to understand the trade-offs between different order-fulfillment measures and costs in different distribution channels. Methodology: I exploit two switches in distribution channels of a manufacturing company to its retail customers: one from single-DC to dual-DC distribution and the other from single-DC to factory-direct distribution. To account for the trade-offs between order fulfillment and the costs associated with each distribution channel, I develop three equations for fill rate, lead time, and production and distribution costs in a difference-in-difference framework and then estimate the equations using proprietary data of retail orders and delivery records. Results: The results quantify the contributions of distribution channels to order fulfillment. Compared with the single-DC distribution channel, the dual-DC distribution channel raises the fill rate by 0.4% and reduces the lead time by 9.7% without incurring additional costs, whereas the factory-direct distribution channel increases the fill rate by 0.5% and provides a 5.2% cost savings but extends the lead time by 12.5%. I further analyze these contributions to order fulfillment across demand variability and order quantity. Managerial implications: The findings provide manufacturing companies with valuable knowledge of their distribution channel choices and means to find a cost-effective distribution channel to improve order fulfillment for various customers and products. [ABSTRACT FROM AUTHOR]

This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability. [ABSTRACT FROM AUTHOR]

This study aims at optimizing lead time through effective management of resources in an equipment manufacturing industry to ensure timely completion of the projects. The concept of 'project crashing" was implemented to identify potential activities at production line of conventional two-roller machines. The developed model is optimized using modified Genetic Algorithm (GA). The results showed that the proposed model enabled to deliver products as per schedule without increasing planned cost. Sensitivity analyses were also conducted to find out the impact of various process parameters. From the analyses, it was found that the proposed model facilitates the manufacturer in delivering the products at specified time with increasing the planned cost. [ABSTRACT FROM AUTHOR]

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales. [ABSTRACT FROM AUTHOR]

Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.

In small and medium enterprises, huge numbers of reports are prepared regarding the procurement, planning, production etc on daily, weekly, monthly, quarterly and yearly basis. If the interval between preparation of same report is longer then there is the greater probability that employee can forget the procedure of preparing the report; thus it is beneficial to automate these types of reports especially when there is no cost required for automation. In the same way, material delivery analysis is a kind of report which is prepared on monthly basis and as per the employees` feedback they usually forget some of the steps of report formation; therefore, there was the chance of mistake at the end of employee. Therefore, this report was automated for to minimize the chance of error and report preparation time. All the manual tasks were enlisted and were programmed for automation by the help of VBA macros. For the execution of macros, userform was designed in visual basic editor (in MS excel) consisted on four command buttons and macros were called on command buttons` click. On pressing ‘ctrl + q’ userform used to appear on the screen. Furthermore, the comparison of old and automated methods was conducted to reflect the best suitable method. The report used to take 1.55 minutes to be made manually. Comparison of time of both methods of report formation indicated that suggested method took 70.86% less time as compared to the old method of preparing material delivery time analysis report.

Drought forecasting plays a crucial role in mitigating the severe agricultural and social consequences caused by droughts. The fluctuating nature of droughts makes it difficult to develop an effective drought forecasting model without preprocessing the input data. This paper proposes a novel approach that introduces the tunable Q-factor wavelet transform (TQWT) with the maximal overlap discrete wavelet transform (MODWT) based Fejér–Korovkin, Coiflet, and Daubechies filters in the decomposition of precipitation data for the extended lead time forecasting of the standardized precipitation evapotranspiration index (SPEI). The decomposed datasets have been coupled with Matern Gaussian process regression (MGPR), exponential Gaussian process regression (EGPR), linear support vector machine (LSVM), and coarse Gaussian support vector machine (CGSVM), and formed hybrid models to forecast SPEI-12 and SPEI-18 for several lead times (i.e., 6, 12, 18, and 24 months). Results of the study represent that the wavelet-based hybrid models are capable of predicting SPEI-12 and SPEI-18 effectively for different lead times with promising results. Both TQWT and MODWT coupled with MGPR yielded reasonable performances for the lead time of 6 months in all stations. However, for the higher lead times, TQWT coupled with MGPR outperformed other hybrid models. The results of the TQWT-MGPR for SPEI-12 are more effective than SPEI-18 in different lead times. The study highlights that preprocessing of precipitation data using TQWT is a promising direction for drought forecasting, and the findings obtained from drought forecasting can be utilized in the areas of water and agricultural resource management to effectively mitigate and alleviate the potential impacts of future droughts. [ABSTRACT FROM AUTHOR]

Background and Aims Gastrointestinal (GI) endoscopy is a limited health resource because of a scarcity of qualified personnel and limited availability of equipment. Non-adherence to endoscopy appointments therefore wastes healthcare resources and may compromise the early detection and treatment of GI diseases. We aimed to identify factors affecting non-attendance at scheduled appointments for GI endoscopy and thus improve GI healthcare outcomes. Methods This was a single-center retrospective cohort study performed at a tertiary hospital gastroenterology endoscopy unit, 12 months before and 12 months after the start of the COVID-19 pandemic. We used multiple logistic regression analysis to identify variables associated with non-attendance at scheduled appointments. Results Overall, 5,938 appointments were analyzed, and the non-attendance rate was 18.3% (1,088). The non-attendance rate fell significantly during the pandemic (22.6% vs. 11.6%, p < 0.001). Multivariable regression analysis identified the absence of deep sedation (OR: 3.253, 95% CI [2.386–4.435]; p < 0.001), a referral from a physician other than a gastroenterologist (OR: 1.891, 95% CI [1.630–2.193]; p < 0.001), a longer lead time (OR: 1.006, 95% CI [1.004–1.008]; p < 0.001), and female gender (OR: 1.187, 95% CI [1.033–1.363]; p = 0.015) as associated with appointment non-attendance. Conclusions Female patients, those undergoing endoscopic procedures without deep sedation, those referred by physicians other than gastroenterologists, and with longer lead time were less likely to adhere to appointments. Precautions should be directed at patients with one or more of these risk factors, and for those scheduled for screening procedures during the COVID-19 pandemic.

Two of the eight wastes included in the lean manufacturing concept are ‘Waiting’ and ‘Motion’. They waste precious operating time and slow down the manufacturing cycle. The major purpose of this study is to use 5S, a lean manufacturing strategy, for reducing these two wastes. The study was conducted at a plastic bag manufacturing company in Bangladesh. The manufacturing process of plastic bags consists of three operations: blowing, printing, and sealing. All activities and sub-processes were recorded, examined, and classified into value-adding and non-value-adding operations, with idle times calculated for each operation. An appropriate step-by-step 5S technique was developed and executed to improve the operational efficiency of the blowing and printing operations by eliminating extra motions and waiting time spent searching for tools. Following the deployment of 5S in these areas, the total operational time was reduced by 8% for blowing and 18% for printing. The assessment was carried out satisfactorily and is documented in this report. Through this study, a window of opportunity for improvement has been explored, which may motivate other researchers to consider applying the 5S tool in their particular research areas as a tool to reduce non-value-adding operational time.

This study investigates the effects of inventory management on customers` satisfaction with lead time as a moderator variable in government-owned hospitals in Delta State, Nigeria. It aims to contribute to the extant literature on inventory management and customer satisfaction in developing countries, focusing on Delta State in Nigeria. Two hundred and sixty-five (265) questionnaires were distributed, comprising one hundred and five to measure inventory management variables administered among Medical Doctors, Nurses, Pharmacists, and Medical Laboratory Scientist. Similarly, one hundred and sixty questionnaires designed to measure customer satisfaction were administered to Patients. The study adopts multiple regression and structural equation modeling to analyze the data. Also, studying the impact of inventory management on customers` satisfaction some marketing analysis approaches were used. The obtained results support the appropriateness of the model as lead time possesses the qualities of a moderator between strategic supplier partnership, lean inventory, and information technology that are proxies of inventory management and customer satisfaction. Besides, the results record a positive and statistically significant relationship between strategic supplier partnership, lean inventory, and customer satisfaction at a 5 percent level of significance, respectively.In addition, lead time has a positive and statistically significant relationship with customer satisfaction. Impliedly, this study concludes that inventory management proxies and lead time drive customer satisfaction. Thus, the government is recommended to focus on the lead time to avert the dearth of basic inventories in the hospitals.

We consider a joint inventory-pricing control problem in a single-product, periodic-review, dual-supplier inventory system. The two suppliers have different lead times. One expedited supplier offers instantaneous replenishment, and one regular supplier requires an L-period lead time for delivery. The supply quantity is stochastic and the demand is price-dependent. For the expedited inventory replenishment, we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting. To investigate the optimal regular inventory replenishment and pricing policy, we propose the notions of partially stochastic translation (PST) and increasing partially stochastic translation (IPST), which help in obtaining the antimultimodularity preservation in dynamic programming problems. We provide properties, sufficient conditions, and examples for PST and IPST functions. By applying PST and IPST, we obtain the antimultimodularity of the profit functions. The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities. Moreover, we reveal that as the time interval increases, the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing, respectively. PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level. However, the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities, as shown in the example.

This study evaluates the predictive performance of the North American Multi-model Ensemble (NMME) over Central Africa (CA) using the historical rainfall data. The African Rainfall Climatology Version 2 (ARC2) is used as a substitute for reference observational data to examine the capability of 11 NMME and their NMME ensemble mean (MME) in simulating rainfall. Using the Kling- Gupta efficiency (KGE), Taylor skill score (TSS), and Heidke skill score, the predictive evaluation of the models is performed from lead 0 to lead 5 of each season. The results show that the NMME models satisfactorily reproduce the bimodal and unimodal structure of rainfall in CA at the lead 0 level of different seasons: December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON). The pattern correlation coefficient (PCC) shows values of NMME and MME greater than ~0.69 and TSS > 0.60 for all four seasons. The MME presents a maximum in DJF 0.~96 between 0 and 1 month lead time. With the same time scale, just over 85% of the NMME have a KGE between 0 and 0.42. It follows that as the forecast lead time increases, the PCC and TSS of each model become small, with PCC 0:~12 in JJA and DJF, TSS<0.21 in JJA at lead 5. The NMME models exhibit an important rainfall bias and the calculated scores show the quality of the forecast decreases with increasing lead time; this may justify a constraint on the models to keep the good quality of the long-term seasonal forecast in CA. [ABSTRACT FROM AUTHOR]

Prediction of influent characteristics, before any treatment takes place, is of great importance to the operation and management of wastewater treatment plants (WWTPs). In this study, four machine-learning models, including multilayer perceptron (MLP), long short-term memory network (LSTM), K-nearest neighbour (KNN), and random forest (RF), are introduced to utilize real-time wastewater data from three WWTPs in North America (i.e., Tres Rios, Woodward, and one confidential plant) for predicting hourly influent characteristics. Input variables are selected using an autocorrelation analysis and a variable importance measure from RF. Both univariate and multivariate analyses are investigated to improve model accuracy. The performances of one- and multiple-step-ahead models are compared. With a short prediction horizon, all the models derived from both univariate and multivariate analyses show excellent performance. It was found that the performance deterioration as the prediction horizon expands could be mitigated significantly by including extra variables, such as meteorological variables. This work can provide valuable support for the high-temporal-resolution prediction of wastewater influent characteristics for WWTPs. The proposed models can also bridge the gap between data and decision-making in the wastewater sector. [ABSTRACT FROM AUTHOR]

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