Search

The rapid and accurate prediction of earthquake Strong-Shaking Zone (SSZ) is crucial for issuing precise early warnings to regions at high risk of strong ground shaking. Generally, the SSZ is derived from the real-time spatial distribution of observed ground motions. However, during the initial stages of large earthquakes, the SSZ is often underestimated and provide alerts without enough lead-time (the time interval between the alert declaration and the S-wave arrival to the target area). In this study, we propose an innovative approach termed Near-epicenter-based Partial Matching Crossover. Leveraging the characteristic that reliable magnitude estimates for large earthquakes are available earlier than accurate predictions of the peak ground velocity (PGV) distribution, this approach utilizes near-epicenter station data to rapidly estimate the SSZ. It achieves this by matching a segment of the fault, defined by a predetermined length, with the predicted PGV map within a 120 km radius centered at the epicenter. Application of our method to strong motion data from China, Japan and Turkey demonstrates its efficacy in quickly anticipating the post-earthquake intensity distributions for large earthquakes. Specifically, it offers a lead time of 5 s or more for 51.5% (39,354 km2), 43.3% (5772 km2), 31%(47,107 km2) and 75.3% (81,966 km2) of the IMM = V region during the M 8 Wenchuan earthquake, the M 7.3 Kumamoto earthquake, the M 7.8 Syria earthquake and M 7.6 Turkey earthquake, respectively. The presented approach introduces a novel methodology to extend the lead time for earthquake early warnings. [ABSTRACT FROM AUTHOR]

Manufacturing key metrics are a useful approach for evaluating shop floor operations. The collaboration between operators and robots is essential in maintaining a resilient performance within smart and flexible manufacturing systems. For effective collaboration, both operators and robots must possess varying degrees of resilience, including full resilience, partial resilience and the ability to handle total disruptions. In this paper, lead time is considered a significant key metric. When the system is fully resilient and dependable, it achieves the optimal lead time. Consequently, lead time serves as a benchmark for evaluating the system's performance. However, if the robot experiences significant performance issues, it can negatively impact the cycle time, resulting in longer lead times. The discrepancy between the optimal lead time and the lead time obtained during partial or complete disruption is subtracted from the optimal lead time. To ensure the validity of the findings, mathematical equations are utilized in combination with other relevant data. This approach contributes to the knowledge base in the field. Finally, the paper will provide suggestions for future research endeavors.. [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]

The Garhwal region, situated in the central segment of the Himalayas, stands as one of the most seismically active areas in the Uttarakhand Himalaya. The newly established earthquake early warning (EEW) system in the Garhwal region emphasizes the critical need for magnitude scaling relations to enable real-time estimation of earthquake size. This study employs P-wave onset data from both observed and simulated records to develop magnitude scaling relations specific to the Garhwal region. Initially, we modified the semi-empirical technique (SET) for P-wave simulation and validated this modification using the 1991 Uttarkashi (Mw6.8) and 1999 Chamoli (Mw6.4) earthquakes in the Garhwal region. The modified semi-empirical technique (MSET) demonstrates its reliability, as evidenced by the comparatively low root mean square error (RMSE) between observed and simulated records for both earthquakes. Subsequently, we apply MSET to simulate seven additional future earthquakes (Mw7.0–8.5) at the hypocentral locations of the 1991 Uttarkashi and 1999 Chamoli earthquakes. Moreover, conventional EEW parameters, such as average period (τc) and peak amplitude displacement (Pd), are extracted from 107 observed and 63 simulated P-wave onsets, utilizing a 3 s time window. Using this combined dataset of observed and simulated data, we introduce magnitude scaling relations based on the τc and Pd parameters. Subsequently, the developed scaling relations are tested to predict earthquake magnitude using the average values of estimated EEW parameters. The relatively small errors, measuring at 4.47–0.75%, and 2.45–0.60% for τc–Mw and 5.71–1.60% and 4.90–1.57% for Pd–Mw scaling relations, respectively for the Uttarkashi and Chamoli earthquakes suggest the applicability of these proposed relations. However, the scaling relations developed tend to underestimate the magnitude of earthquakes larger than 7.5, potentially attributed to the saturation observed in these relations for big earthquakes. Additionally, the calculated lead time for future earthquakes will range from 4 to 60 s for the sites situated within epicentral distances of 56–200 km. This lead time has the potential to play a substantial role in disaster mitigation in the Garhwal region, especially in the Indo-Gangetic plains and Himalayan foothills. Hence, the development of scaling relations specific to the region is imperative for effective disaster mitigation and risk reduction in the Garhwal region. [ABSTRACT FROM AUTHOR]

Supply chain performance at PT. ABC still needs to be developed because often logistics disruptions influenced by suppliers still occur so that the issue of supply chain resilience in meeting customer needs is a concern that needs to be solved. Supply chain management analysis, especially on the resilience aspect, is important for PT. ABC continues to strive to develop the performance and productivity of its company. The purpose of this study is to determine the extent of supply chain resilience at PT. ABC in meeting customer needs and producing strategies that must be carried out to improve supply chain resilience at PT. ABC. The approach in this study uses the SCOR-DS (Digital Standard) Model in the scope of resilience and reliability as its performance attributes. The results of the analysis show the resilience of the supply chain at PT. ABC is still unstable with the percentage of leadtime supply achievement by suppliers only obtaining a value of 67.29%, while the target achievement is 90% which means suppliers have not been able to supply within a maximum of 7 days. Project improvement that must be done includes procurement terms & conditions review, freight carrier delivery performance evaluation, and regular reconciliation meetings as the first priority, then consignment inventory as the second priority. [ABSTRACT FROM AUTHOR]

• The survival benefit from screening for bladder cancer (BC) may be due to the "lead time" (LT) bias. • LT is the interval between the detection of BC in asymptomatic and symptomatic persons. • LT may be derived from the ratio between the prevalence of pre-symptomatic BC and its incidence. • These ratios are 3.3 to 12.1 years if derived from screening for microhematuria. • LT may, therefore, account for the survival benefit from early treatment of BC. • The benefit of screening for BC should be confirmed by randomized trials. Some studies have suggested a survival benefit from early treatment of bladder cancer (BC). This benefit may be due in part to a "lead-time" bias (LT), i.e., the time interval between the detection of BC in asymptomatic individuals and the development of symptoms ("backward prolongation of survival"). To estimate the LT of BC, it was assumed that LT corresponds to the ratio between the prevalence of pre-symptomatic BC and the incidence of symptomatic BC. Data on the prevalence of pre-symptomatic BC were derived from published screening studies. Data on the annual incidence of symptomatic BC at the age and gender of the study populations were derived from national registries in the countries in the years in which the screening studies were conducted. The ratios of the prevalence of presymptomatic BC to the incidence of symptomatic BC ranged from 3.3 to 12.1 years when derived from screening for microhematuria, and from 1.8 to 5.3 years when derived from screening for urine cytology and cell markers. The estimates of the LT of BC derived from the ratios between its prevalence in asymptomatic persons and its incidence in the corresponding population were consistent with those previously reported in retrospective and prospective cohort studies. Since these estimates may account for the survival benefit from early treatment of BC, the gain of screening for BC remains uncertain and should be confirmed by controlled randomized trials. [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]

Dynamical prediction of monsoon rainfall has been an important topic and a long-standing issue in both research and operational community. This paper provides a comprehensive evaluation of the subseasonal-to-seasonal (S2S) prediction skill of the East Asian summer monsoon (EASM) rainfall using the hindcast record from the Beijing Climate Center Climate System Model, BCC CSM1.1m, during the period 1983–2019. The model exhibits reasonable skills for predicting the EASM rainfall at all lead times with the skill dropping dramatically from the shortest lead time of about 2 weeks (LM0) to 1-month lead (LM1), and then fluctuating remarkably throughout 2-month to 12-month lead times. Over the EASM domain, the rapid decline of the S2S rainfall prediction skill from LM0 to LM1 is mainly caused by the inferior skills over Central China in July and over Northeast China in August. Composite analysis based on hindcast records suggest that these inferior skills are directly tied to the model's difficulties in capturing above-normal precipitation over eastern Central China and Northeast China in the respective months, which are further shown to be associated with anomalous weakening and meridional movement of the Northwestern Pacific subtropical high and the activity of large-scale teleconnection pattern hard to be predicted over northeastern Asia in summer, respectively. These findings inform the intrinsic limits of the S2S predictability of the EASM rainfall by a dynamical model, and strongly suggest that the level of confidence placed upon S2S forecasts should be stratified by large-scale circulation anomalies known to significantly affect the prediction skill, e.g., the subtropical high and high-latitude teleconnection patterns for summer monsoon rainfall prediction in this region. [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]

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]

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]

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]

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]

Flood forecasting using hydrological and hydraulic models is an efficient non-structural flood management option. This paper presents a real-time flood forecasting approach for the data-scarce Jhelum basin, using MIKE 11 Nedbor-Afstromings Model (NAM), hydrodynamic (HD), and flood forecasting (FF) models. European Centre for Medium-Range Weather Forecast (ECMWF) model-forecasted precipitation and temperature data were used to forecast floods and increase the forecast lead time. The model performance was evaluated using the Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE) and indicators of agreement between the simulated values and the observed values. These indicators show good consistency between the observed and simulated hydrographs for the calibration and validation periods, resulting in a level of reliability sufficient for flood forecasting purposes. Errors in forecasting were corrected using an automatic updating routine MIKE 11 FF with the ECMWF input. The forecast results were acceptable for lead times up to 7-days. The study concluded that the developed model was effective for flood forecasting in the data-scarce, snow-dominated Jhelum basin, and that it might be extended to other similar basins throughout the world as part of an early flood warning system. [ABSTRACT FROM AUTHOR]

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]

Amelioration occurs when the utility, value, quality, or quantity of items increases with time. In contrast, deterioration occurs when the quality or quantity of items decreases with time. This study is a purview for both manufacturers and retailers of ameliorating and deteriorating items. In this paper, an integrated inventory model is developed under the influence of amelioration with time and the price varying demand pattern over a fixed time interval. The amelioration is supposed to follow the Weibull distribution, taking into account the infinite time horizon and lack of shortage. Mathematical expressions for the total profit are developed. The proposed inventory model optimizes the length of time during which there is no demand, the length of time during which the inventory level becomes zero, and it optimizes the initial inventory level to maximize the total profit. We have shown that the profit function is concave with respect to time. Next, we have provided an algorithm to find the optimal solution. Finally, we have delivered numerical examples, conducted sensitivity analysis and provided graphical representations for each case to illustrate the theory of the integrated inventory model for ameliorating items and how the inventory model reacts to changes in parameters. [ABSTRACT FROM AUTHOR]

Lead time—the duration of construction and commissioning—is an important determinant of the capital cost of nuclear power plants (NPPs). For an industry dominated by a handful of multinational firms, the degree of cross-national variation is surprising. NPP lead times have historically trended upward over time in Western nations, and yet they are comparatively quick and stable in East Asia. I theorize that the institutional capacity and autonomy of subnational governments can partially explain these patterns in the data. Having assembled a novel data set on the design specifications of the global population of NPPs, I empirically document a positive association between political decentralization and NPP lead time that is not explained by observed cross-country differences in NPP design. The results are suggestive of the hypothesis that political decentralization creates conditions that slow NPP construction for nontechnical reasons. However, the findings are not robust to certain robustness checks and fail to rule out the possibility that unobserved differences in design explain this association. [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]

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]

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]

Pharmaceutical inventory management is critical because insufficient pharmaceutical items and their misusage affect the lives of humans and cause financial losses. Conversely, deteriorating items and lead-time have special importance in pharmaceutical inventory issues, especially as uncertainty appears in both. Different procedures are considered to deal with uncertainty, including fuzzy systems as well as probabilistic techniques. The present study develops a novel technique according to the evidential theory approach, aimed at solving an inventory model with stochastic deterioration rates and lead-times appearing in a single-vendor (pharmaceutical company) multi-buyer (hospitals) inventory system. It seems that the evidence approach is effective in dealing with interval, imperfect, inaccurate, and missing (ignorance) data. The lead time demand of the hospitals is log-normal, and the shortage of hospitals is thoroughly back-ordered. Particle swarm optimization (PSO) and genetic algorithm (GA) solve this problem. Ultimately, the results are illustrated using the numerical example and the impacts of the main parameters. Highlights Develop the new pharmaceutical supply chain models based on the evidential theory approach Design single-vendor (pharmaceutical company) multi-buyer (hospitals) system with stochastic deterioration rates and lead times Considering interval, incomplete, imprecise and missing (ignorance) data in healthcare systems for make decision in the inventory problem Using genetic algorithm (GA) and particle swarm optimization algorithm (PSO) [ABSTRACT FROM AUTHOR]

The Quality Control Division plays an important role in Inbound and production activities by ensuring that incoming goods are received in the form of raw materials properly without any defects or called zero defects. Relevant previous research is one of the supports that can be used as a key for the research to be started. In this study, the previous research variables used as a reference are Quality Control, Lead-time, and Inbound Activities. In the midst of very tight competition between companies, it is influenced by competent Human Resources so that the company's productivity remains stable and even increases and can achieve the company's own goals. The method used in this study is a quantitative research method. The sample of this study were 40 respondents, employees of PT. Syngenta Indonesia production and operations division. The results showed that Quality Control had a positive and significant effect on inbound logistics activities and the Lead Time variable also had a positive and significant effect on inbound logistics activities. It can be concluded that the applied quality control process has proven to have a positive effect on the activities of receiving and delivering goods to PT. Syngenta Indonesia GP Site through the dimensions of material control, process control, finished product inspection. [ABSTRACT FROM AUTHOR]

We study a periodic-review inventory system with multiple capacitated suppliers. These suppliers have different lead times and unit costs, and suppliers with shorter lead times charge higher unit costs. As the optimal ordering policy is unknown and expected to be complicated, we propose a multi-index base-stock (MIBS) policy for the system based on n tailored inventory positions. Orders are placed with each supplier according to a modified base-stock policy. We analyze the policy's system dynamics and establish separability properties of the optimal policy parameters. We also provide a simulation-based optimization procedure for the policy. Because the procedure involves an exhaustive search across n - 1 parameters, we construct a heuristic for computing near-optimal policy parameters. We numerically demonstrate the performance of the MIBS policy and the effectiveness of the heuristic. The results show that the performance of the MIBS policy is very close to that of the dynamic programming policy and that the performance of the heuristic is close to the optimal MIBS policy, with much faster computation. We also explore the performance of the MIBS policy when the number of sources is increased and find that increasing the number of sources means decreasing the average cost, while the marginal returns of adding a supplier diminish. Importantly, given sufficient capacity, by carefully selecting two of the suppliers—often the fastest and the slowest—it is possible to achieve an adequate balance between cost efficiency and responsiveness. [ABSTRACT FROM AUTHOR]

We report on the opinions of respondents to a survey of native plant material (NPM) users east of the Mississippi River. We sought respondents who would have a sufficient depth of experience and interest to be able to answer the survey questions. To find potential respondents, we first built a geographically diverse list of NPM-user organizations and then asked them to help us promote the survey through their social networks. Survey respondents expressed a preference for local ecotypes (74%) and almost no interest in cultivars (0.3%). Respondents identified commercial availability as the greatest barrier to their use of local ecotypes. Of the respondents, 92% use native seeds, and those who prefer local ecotypes are shopping farther afield than their concept of "local" would support. The most popular seed vendor is on average 584 km (363 mi) away from the respondent's location, and the second most popular is 1296 km (805 mi) away. Respondents who think of local as being in-state buy out-of-state 85% of the time. Of the respondents, 90% have less than 2 year's lead time before acquiring NPM, which is not enough time to have wild seeds agronomically increased or plants contract grown. Given those circumstances, 83% would be willing to pay a premium to obtain the ecotypes they want. Among potential solutions to the commercial shortage problem, 99% of respondents supported creation of an online marketplace for sharing supply-and-demand information. Respondents expect their demand for NPMs to increase, highlighting the importance of addressing these issues now. [ABSTRACT FROM AUTHOR]

A horse learning about the entrance to narrow, cage-shaped places may be challenging both for the horses as well as for the owners. For some behaviors, such as loading into a trailer or climbing onto a treadmill, the final behavioral goal can be achieved by working towards it in stages. This study compared the successive approximation of horses to their first work on a treadmill with horses hardly ever loaded (HE L) and regularly loaded (R L) into a trailer. Fourteen horses were divided into two groups (HE L n = 7 and R L n = 7) based on their experiences of entering into a trailer. All horses were taught using four stages of successive approximation. The average lead time was longer in the HE L than in the R L group, both in the first (HE L: 33.8 ± 12.4 s; R L: 17.6 ± 12.9 s; p = 0.035) and last stages (HE L: 12.0 ± 10.3 s; R L: 3.7 ± 1.0 s; p = 0.032) of trials. With the subsequent repetitions of each step, the heart rate decreased in both groups. Very few behaviors indicating fear or unwillingness (“rearing,” “sideways,” and “backwards”) were observed. Horses that were regularly loaded exhibited signs of relaxation. The successive approximation of horses to the first work on a treadmill differed and may depend on the previous experiences with loading and travelling in the confined space of a trailer. [ABSTRACT FROM AUTHOR]

• The study investigates how driver SA was influenced after exposed to CVSs. • Drivers experienced early warnings in CVSs showed degraded SA than other groups. • The driver SA are impaired with CVSs may be due to their reliance on the warnings. Vehicles equipped with connected vehicle technologies are able to communicate with each other and with infrastructures. Compared to Advanced Driving Assistance Systems (ADAS) using camera systems and sensor technologies, the Connected Vehicle Systems (CVS) leverage the wireless communication networks to detect hazards with a greater range, alert drivers of hazards much earlier, and therefore enhance driving safety. However, drivers' reliance on the CVS to detect critical situations could negatively affect them maintaining situation awareness (SA) in noncritical situations when no warning is issued by the CVS. The present study conducted a driving simulator experiment with 40 participants to investigate the effect of connected vehicle systems on driver SA in normal, noncritical driving scenarios after they were exposed to the CVS with different designs of collision warning lead time (3 s, 6 s, and no warnings). After drivers experienced the CVS-supported warnings with the assigned design of lead time in critical situations, driver SA was measured in normal driving conditions using the freeze probe technique. Results revealed that drivers who experienced the CVS with early warnings (6 s) showed lower SA for normal driving events compared to those who experienced the CVS with late warnings (3 s) or no warnings. Although early warnings of CVS brought more safety benefits to drivers in critical situations, the degraded driver SA due to drivers' reliance on such warning systems could endanger drivers when a system failure occurred. These findings highlight the importance of balancing the effects of warning lead time on driver SA and driving performance in designing connected vehicle systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Shortening the lead time for Product Development (PD) provides enterprises with a competitive advantage. Given the iterative nature of PD projects, two aspects are regularly considered to shorten the PD lead time, that is, conducting faster or fewer iterations. However, executing faster iterations usually causes more iterations and vice versa. Therefore, suitable coordination between faster and fewer iterations is necessary to minimize the PD lead time. We investigate this coordination from a strategic perspective, whereby a PD project is considered as a sequence of stages and characterized by the design rates and rework probabilities of those stages. We model the coordination as a decision to choose the appropriate design rates for each stage, wherein the rework probabilities are negatively related to the design rates. An absorbing Markov process is applied to calculate the expected lead time of a PD project. Further, we formulate a geometric programming model to determine the optimal design rates of the stages with respect to the minimal expected lead time. Several insights are extracted from the model to provide general guidance on the coordination, including the effect of the acceptance check rate of the project, rework risk of the stages on the optimal design rates, and decomposability of the coordination. Inspired by these insights, an efficient heuristic algorithm is designed. The algorithm performs well in numerical experiments, which in turn validates the insights. Additionally, a field case proves the effectiveness of our model. Compared with the current policy, 12.25% of the PD lead time is saved through appropriate coordination between faster and fewer iterations. [ABSTRACT FROM AUTHOR]

The equatorial Pacific warm water volume (WWV) and western equatorial Pacific WWV (WWVw) are two important long‐lead time El Niño‐Southern Oscillation (ENSO) event precursors. Here, we utilize the recharge‐discharge oscillator framework to obtain the analytical solution of WWV and WWVw lead times to ENSO sea surface temperatures (SSTs). Both WWV and WWVw lead times show a strong dependence on the inherent ENSO periodicity, whereas WWV lead time is further modulated by the air‐sea coupling strength and ENSO SST spatial pattern. The derived solution is able to explain observed decadal variations in lead times of WWV to SST in terms of variations in ENSO periodicity and air‐sea coupling strength. Plain Language Summary: Predicting El Niño‐Southern Oscillation (ENSO) events in advance has profound socio‐economic benefits because of ENSO's impacts on global weather and climate. The basin‐wide equatorial Pacific heat content (also known as warm water volume, WWV) and western Pacific WWV (WWVw) are well‐known predictors of ENSO events several seasons ahead. In this study, we present an analytical expression of WWV and WWVw lead times to ENSO sea surface temperature (SST) anomalies. The analytical solution can explain the observed decadal changes in WWV lead times. We demonstrate that the recent shortened WWV lead time observed after the year 2000 can be largely explained by a decrease in the inherent ENSO periodicity, and second by an increase in the air‐sea coupling strength. The latter is associated with an ENSO SST spatial pattern change with more frequent occurrences of Central Pacific El Niño events in recent decades. Key Points: Theoretical lead times of basin‐wide warm water volume (WWV) and western Pacific WWV (WWVw) to El Niño‐Southern Oscillation (ENSO) events are validatedWWV lead time depends on both ENSO periodicity and the ENSO spatial pattern, whereas WWVw lead time depends solely on ENSO periodicityObserved decadal variations of WWV lead times result from variations in ENSO periodicity and air‐sea coupling strength [ABSTRACT FROM AUTHOR]

The equatorial Pacific warm water volume (WWV) and western equatorial Pacific WWV (WWVw) are two important long‐lead time El Niño‐Southern Oscillation (ENSO) event precursors. Here, we utilize the recharge‐discharge oscillator framework to obtain the analytical solution of WWV and WWVw lead times to ENSO sea surface temperatures (SSTs). Both WWV and WWVw lead times show a strong dependence on the inherent ENSO periodicity, whereas WWV lead time is further modulated by the air‐sea coupling strength and ENSO SST spatial pattern. The derived solution is able to explain observed decadal variations in lead times of WWV to SST in terms of variations in ENSO periodicity and air‐sea coupling strength. Plain Language Summary: Predicting El Niño‐Southern Oscillation (ENSO) events in advance has profound socio‐economic benefits because of ENSO's impacts on global weather and climate. The basin‐wide equatorial Pacific heat content (also known as warm water volume, WWV) and western Pacific WWV (WWVw) are well‐known predictors of ENSO events several seasons ahead. In this study, we present an analytical expression of WWV and WWVw lead times to ENSO sea surface temperature (SST) anomalies. The analytical solution can explain the observed decadal changes in WWV lead times. We demonstrate that the recent shortened WWV lead time observed after the year 2000 can be largely explained by a decrease in the inherent ENSO periodicity, and second by an increase in the air‐sea coupling strength. The latter is associated with an ENSO SST spatial pattern change with more frequent occurrences of Central Pacific El Niño events in recent decades. Key Points: Theoretical lead times of basin‐wide warm water volume (WWV) and western Pacific WWV (WWVw) to El Niño‐Southern Oscillation (ENSO) events are validatedWWV lead time depends on both ENSO periodicity and the ENSO spatial pattern, whereas WWVw lead time depends solely on ENSO periodicityObserved decadal variations of WWV lead times result from variations in ENSO periodicity and air‐sea coupling strength [ABSTRACT FROM AUTHOR]

Just-in-time (JIT) delivery is one of the key delivery strategies that enable the right quality product to reach the point of demand at the right time without creating unnecessary costs in the supply chain network. The concepts of quality and time are two main factors to be considered in this issue. Particularly because the variability in quality directly affects cost and time, it is very important for JIT delivery. In this study, the effect of raw material quality within the supply chain network which tries to ensure JIT delivery is examined. For this, a multi-stage, multi-period mixed-integer mathematical model is proposed in the study. The model aims to minimize the total supply chain cost by trying to balance the purchasing and quality costs for manufacturers. In addition to standard constraints, the stock balance and time constraints have been developed for JIT delivery. As a result of the model resolved with the GAMS Distribution 21.6 program, 34.5% of the raw material supplied from suppliers is provided from 1st quality raw materials, while 65.5% is provided from 2nd quality raw materials. Despite its low purchasing costs, 3rd quality raw material has not been preferred by the producers in any period because it causes a delay in JIT delivery. Besides, it is seen that deliveries are made very close to the maximum delivery time of distribution centers, depending on the variability in raw material quality. It is understood that raw material quality is an important concept in a JIT supply chain network due to its significant impact on time. [ABSTRACT FROM AUTHOR]

One of the main aims of models using cancer screening data is to determine the time between the onset of preclinical screen-detectable cancer and the onset of the clinical state of the cancer. This time is called the sojourn time. One problem in using screening data is that an individual can be observed in preclinical phase or clinically diagnosed but not both. Multistate survival models provide a method of modeling the natural history of cancer. The natural history model allows for the calculation of the sojourn time. We developed a continuous-time Markov model and the corresponding likelihood function. The model allows for the use of interval-censored, left-truncated and right-censored data. The model uses data of clinically diagnosed cancers from both screened and nonscreened individuals. Parameters of age-varying hazards and age-varying misclassification are estimated simultaneously. The mean sojourn time is calculated from a micro-simulation using model parameters. The model is applied to data from a prostate screening trial. The simulation study showed that the model parameters could be estimated accurately. [ABSTRACT FROM AUTHOR]

This research presents the data obtained in a metalurgical company, from the implementation of VSM - Value Stream Mapping tool - in agreement with the Lean Manufacturing - LM system. Through a quali-quantitative approach, and the research-action method was used as a research strategy. The starting point was based on a bibliographical survey, it was sought to identify concepts to develop and implement VSM in a manufacturing unit located in the industrial district of Guarulhos (Brazil). The participation of employees and researchers, we test the hypothesis that with the VSM method implementation is possible to obtain a productivity gain without investment in assets. The results demonstrated an improvement in productivity, increasing the man, hour, machine (hhm) ratio by 26.74%, a 21% reduction in processing time (PT) and 31.5% in Lead Time (LT). Was concluded that with the application of the VSM tool an improvement of the planning, programming and production control (PPCP) management is obtained, resulting in actions that lead to the productivity gain. This study contributes to demonstrate that managing the production based on real data structured in mathematical tools is as relevant as the improvement of the productive infrastructure of the factory; leading to the effective reduction of waste, to improving productivity and increasing the competitiveness. [ABSTRACT FROM AUTHOR]

Purpose: This study aims at implementing lean six sigma to evaluate the productivity and manufacturing wastes in the production line of a paper company Methodology/Approach: The study is a case study in nature. The method illustrates how lean six sigma (LSS) is used to evaluate the existing production process in a paper production company with focus on productivity and manufacturing wastes. The study considered a real-time problem of customer's dissatisfaction. Gathered data were based on machine functionality (up time, down time and cycle time); materials and labour flow at every process stage of the production line. The optimization of the production process was based on lean tools like value stream mapping, process cycle efficiency, Kaizen, 5S and pareto chart Findings: Based on lean six sigma application, it was discovered that the present production performance was below standard and more manufacturing wastes were generated. The present productivity and manufacturing wastes are reported as low process cycle efficiency (23.4 %), low takt time (4.11 sec), high lead time (43200sec), high number of products not conforming to six sigma values, high down time (32.64 %) and excess labour flow (33). After the implementation of the lean six sigma tools for certain periods, there were lots of improvements in the production lines in terms of all the parameters considered. Research Limitations/ Implications: The study has demonstrated application of lean six sigma in the case of solving real-time problems of productivity and manufacturing wastes which have a direct implication on customer's satisfaction. The lesson learnt and implications presented can still be further modeled using some lean based software for validity Originality/Value: The study has contributed to the body of knowledge in the field of LSS with focus to process-based manufacturing, unlike most literature in the field which concentrated more on discrete based manufacturing. [ABSTRACT FROM AUTHOR]

• Propose a fully numerical iterative process to optimize the maintenance threshold for systems with non-monotonic degradation behavior subject to competing failures. • Consider lead time before maintenance implementation in the numerical framework. • Assess the impact of shock detection threshold on the expected cost. Shocks have attracted considerable interest in reliability and maintenance engineering because of their impact on vulnerable systems. Most industrial systems suffer from both internal degradation caused by fatigue and wear-out, and external shocks that often occur randomly due to harsh weather conditions, overloading, etc. Developing maintenance optimization models without taking these stochastic shocks into account is often ineffective. This paper develops a model to optimize the maintenance alarm threshold for a single-component continuously monitored system which is exposed to both fatal and non-fatal shocks in the presence of lead time for hard time maintenance. The shocks occur randomly according to a homogeneous Poisson process during the whole degradation process and have a stochastic impact on the degradation level, while the system resistance to shocks decreases as the system approaches failure. We propose a new numerical maintenance optimization model to find the solution without Monte-Carlo simulation and the model is compared to the Wiener process. A numerical example and a real-time experimental case study on roller bearings are used to demonstrate the effectiveness of the model. The results show that the model is capable of improving maintenance decision-making in terms of failure probability and risk perspective. [ABSTRACT FROM AUTHOR]

The Transcatheter Aortic Valve Replacement (TAVR) procedure requires an initial consultation and a subsequent procedure by an interventionalist (IC) and surgeon. The IC-surgeon pair coordination is extremely challenging, especially at Mayo Clinic due to provider time commitments distributed across practice, research, and education activities. Current practice aims to establish the coordination manually, resulting in a scheduling process that is cumbersome and time consuming for the schedulers. We develop an algorithm for pairing ICs and surgeons that minimizes the lead time (days elapsed between the clinic consult and procedure). As compared to current practice, this algorithm is able to reduce average lead time by 59% and increase possible IC-surgeon pairs by 7%. The proposed algorithm is shown to be flexible enough to incorporate practice variations such as lead time upper bound and two procedure days for a single consult day. Algorithm alternatives are also presented for practices who may find the proposed algorithm infeasible for their practice. [ABSTRACT FROM AUTHOR]

Interval cancers are cancers detected symptomatically between screens or after the last screen. A mathematical model for the development of interval cancers can provide useful information for evaluating cancer screening. In this regard a useful quantity is MIC, the mean duration in years of progressive preclinical cancer (PPC) that leads to interval cancers. Estimation of MIC involved extending a previous model to include three negative screens, invoking the multinomial‐Poisson transformation to avoid estimating background cancer trends, and varying screening test sensitivity. Simulations show that when the true MIC is 0.5, the method yields a reasonably narrow range of estimated MICs over the range of screening test sensitivities from 0.5 to 1.0. If the lower bound on the screening test sensitivity is 0.7, the method performs considerably better even for larger MICs. The application of the method involved annual lung cancer screening in the Prostate, Lung, Colorectal, and Ovarian trial. Assuming a normal distribution for PPC duration, the estimated MIC (95% confidence interval) ranged from 0.00 (0.00 to 0.34) at a screening test sensitivity of 1.0 to 0.54 (0.03, 1.00) at a screening test sensitivity of 0.5 Assuming an exponential distribution for PPC duration, which did not fit as well, the estimated MIC ranged from 0.27 (0.08, 0.49) at a screening test sensitivity of 0.5 to 0.73 (0.32, 1.26) at a screen test sensitivity of 1.0 Based on these results, investigators may wish to investigate more frequent lung cancer screening. [ABSTRACT FROM AUTHOR]

An appropriate material-management strategy requires estimation of, and classification based on, the lead time of construction materials. To estimate the lead time precisely, the identification of the factors that influence it is essential. Previous studies in material management have not comprehensively examined the factors that influence lead time and have not emphasized the need to classify materials accordingly. To address these gaps, this study collected a large sample of procurement data from 16 building construction projects. A two-step cluster analysis resulted in three clusters, namely, long lead (L), moderate lead (M), and short lead (S), referred to here as LMS based on the average lead time of the materials. Combining the LMS classification with the existing ABC classifications can facilitate more reliable control over the inventory of materials. Furthermore, the regression analysis of the procurement data revealed that the lead time of construction materials is positively correlated with the unit price and order value but negatively correlated with the project value. Therefore, the lead time increases with the increase in unit price and order value of materials while it decreases as project value increases. Furthermore, the capacity of a supplier was found to have a negative influence on the lead time of bulk materials. Therefore, bulk materials delivered by a supplier with higher capacity will have a shorter lead time than those delivered by a supplier with lower capacity. The findings of this study enable construction practitioners to precisely estimate the lead time of materials and to adopt the appropriate strategies for managing construction materials, thereby enhancing the availability of materials for the project. [ABSTRACT FROM AUTHOR]

In this paper we analyzed an inventory model with two-suppliers, finite life times, multiple working vacations and customers who arrive according to RCE process. Perishable and replenishment rates of two-suppliers are exponentially distributed. The server takes exponential working vacations when the queue is empty. Arrival process follows Poisson distribution and the probability for an ordinary customer is p and for negative customer is q. Limiting distribution of the assumed model is obtained. Numerical results are presented for cost function and various system performance parameters. The impact of two-suppliers on the optimal reorder points will be useful in developing strategies for handling various perishable inventory problems with replenishment rates. [ABSTRACT FROM AUTHOR]

This article aims to propose a fuzzy model for closed-loop material requirement planning (MRP) systems considering uncertain parameters like production capacity, on-hand inventory and lead times. For this, a deterministic closed-loop MRP model is proposed, and then fuzzy coefficients in the constraints of the model are used to establish the fuzzy MRP model, which depends on the degrees of satisfaction (?) of the decision-maker. Data from a production plan of a company dedicated to the manufacture of electrical transformers are employed to verify the proposed fuzzy MRP model, minimizing inventory holding costs, production setup costs, and extra capacity costs. The results show the fuzzy model performs better than the deterministic model, especially for low? values, providing better performance in terms of the total cost, total inventory, service level, and computational efficiency. [ABSTRACT FROM AUTHOR]

It is seen that the trade credit period has an important role in real business world. In this article, an inventory model has been developed by considering stochastic lead time demand with lead time crashing cost. Here, also to get the impact between credit period and lead time, a lead time dependent credit period has been considered. In this model, considering partial back-order, the effect of lost sale has been included. Under the above considerations, an inventory model has been optimized in the parlance of infinite time horizon. Here, three objective functions have been developed on the basis of the position of credit period and business period. Finally, to study the feasibility of the model, three different numerical examples have been illustrated with sensitivity analysis with respect to a parameter. [ABSTRACT FROM AUTHOR]

Timely delivery is the major issue in Fast Moving Consumer Good (FMCG) since it depends on the lead time which is stochastic and long due to several reasons; e.g., delay in processing orders and transportation. Stochastic lead time can cause inventory inaccuracy where echelons have to keep high product stocks. Such performance inefficiency reflects the existence of the bullwhip effect (BWE), which is a common challenge in supply chain networks. Thus, this paper studies the impact of stochastic lead time on the BWE in a multi-product and multiechelon supply chain of FMCG industries under two information-sharing strategies; i.e., decentralized and centralized. The impact was measured using a discrete event simulation approach, where a simulation model of a four-tier supply chain whose echelons adopt the same lead time distribution and continuous review inventory policy was developed and simulated. Different lead time cases under the information-sharing strategies were experimented and the BWE was measured using the standard deviation of demand ratios between echelons. The results show that the BWE cannot be eliminated but can be reduced under centralized information sharing. All the research analyses help the practitioners in FMCG industries get insight into the impact of sharing demand information on the performance of a supply chain when lead time is stochastic. [ABSTRACT FROM AUTHOR]

In today's market environments, where globalization and competition conditions are getting harder, it is possible for companies to continue their existence with effective supply chain activities. In addition to delivering the ordered product at the right place and at the right time, a supply chain network that is directly related to stakeholders should be designed through a quality, sustainable and well-managed network mechanism. In supply chain network management, chain activities should be maintained by taking advantage and adapting to current, dynamic, and existing developments. In this study, the factors affecting lead time in a food company's supply chain network management were determined, and the relationship between effective factors and the lead time was investigated. Accordingly, an integrated design of experiment was conducted with analysis of variance (ANOVA) and analysis of covariance (ANCOVA), and the obtained results were compared to each other. Duncan and Tukey tests were used to see differences between levels of effective factors. According to the obtained results, it was aimed to improve the supply chain performance and aid the company in order to make decisions at the tactical and strategic levels. [ABSTRACT FROM AUTHOR]

Poor stock management has been identified as one of the causes of vaccine stock-outs in health facilities. This study assessed the occurrence of vaccine stock-outs and vaccine stock management practices in primary health care facilities in OR Tambo district of the Eastern Cape province, South Africa. Descriptive cross-sectional study design was used in this study to assess both the stock management practices and the availability of vaccines in the facilities. The study was conducted in 64 primary health care facilities across all sub-districts in OR Tambo. Data were collected using a questionnaire administered by the researcher, record checks and through observation. The occurrence of stock-outs of six tracer vaccines on the day of the visit and in the preceding 24 months were assessed. The data were captured into REDCap tool and analysed using STATA version 14. Most standard stock management procedures were not adhered to, as these procedures were mostly handled by health care workers who either required formal training or refresher training on vaccine management. Cold chain capacity was not adequate and some vaccines were exposed to freezing. Both stock cards and the stock visibility solution (SVS) device were used in all the facilities for vaccine stock management. Less than half of the facilities visited 27 (44%) filled their stock cards regularly. Delays in receiving supplies from the pharmaceutical depot were commonly reported by facilities, which contributed to stock-outs. A total of 49 (77%) health facilities had at least one stockout for at least one vaccine on the day of the visit. Furthermore, BCG and OPV were the most affected vaccines in 37 (58%) and 28 (44%) of facilities, respectively. Interventions for improving vaccine availability should be considered, especially those targeting human resources and the entire stock management system. [ABSTRACT FROM AUTHOR]

Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2-3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias. [ABSTRACT FROM AUTHOR]

An emergency storage battery (ESB) is an important asset to ensure the reliability and safety of complex technological systems. Even if an asset owner decides to replace a deteriorated ESB in expressway transportation systems , a lead time (LT) from ordering replacement parts to delivery is inevitable because the replacement parts cannot be stockpiled. During the LT, the ESB continues to deteriorate, i.e., the risk of deterioration continuously increases. If the LT has a non-negligible length, it is necessary to find intervention policies that appropriately consider the risk during the LT. In this study, we propose a methodology to derive optimal intervention policies by explicitly considering deterioration risk during the LT in a stochastic control framework. Specifically, the geometric Brownian motion is statistically estimated as the deterioration process of the ESB, and an optimization model is proposed to find the optimal intervention policy. The proposed methodology is applied to a case study on an expressway transportation system. In the case study, a sensitivity analysis is also performed to obtain practical implications through a life cycle cost (LCC) comparison. Our findings show that the proposed methodology can reduce the LCC by 5.49% compared with when the LT is not considered. • Optimize intervention policy of emergency storage batteries. • Consider deterioration risk during lead time. • Use stochastic control. • Apply the proposed methodology to real world case study. • Demonstrate that the proposed methodology can reduce life cycle costs. [ABSTRACT FROM AUTHOR]

The socioeconomic benefits associated with informative water quality forecasts for large lakes are becoming increasingly evident. However, it remains an enormous challenge to produce forecasts of water quality variables that are accurate enough to meet public demand. In this study, we developed and evaluated a new forecast framework for real-time forecasting of daily dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations at lead times from one to six days for Lake Chaohu, the fifth largest freshwater lake in China. The forecast framework is based on a 3-D hydrodynamic ecological model referred to as EcoLake. We used hydrological, meteorological and water quality data from multiple sources to generate initial conditions and forcing functions. Solar radiation and inflows from tributaries which are not readily available were calculated using forecasted cloud cover and rainfall. Forecast skill was evaluated based on 122 forecasts produced on different days in 2017 and for each of the 12 sampling sites. Results indicate that the skill of the forecast framework varies considerably across water quality variables, sampling sites, and lead times. Generally, the forecast framework is more skillful than the persistence forecasts, which use the most recent observations as forecasts. The TN forecasts tend to be the most skillful with a mean RMSE skill score of 28.5% averaged across the six lead times. The DO forecasts tend to have the lowest skill with an average value of 10.9%. Model sensitivity experiments further revealed that errors in the raw air temperature and wind speed forecasts have a noticeable impact on the overall skill of DO and NH forecasts. The forecast framework proposed here could be a useful operational forecasting tool to enhance the effectiveness of the drinking water supply and public health protection based on the water quality management of Lake Chaohu. Unlabelled Image • A framework for real-time forecasting daily water quality variables for Lake Chaohu is developed and evaluated. • The forecast framework is based on a 3-D hydrodynamic ecological model. • Average RMSE skill score for TN forecasts is 28.5%, compared to persistence forecasts. • Forecast errors of meteorological variables have noticeable impact on the skill of proposed forecast framework. • The water quality forecast framework can enhance the security of drinking water supply for Lake Chaohu. [ABSTRACT FROM AUTHOR]