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This paper investigates a single-vendor, multiple-buyer coordinated supply chain under stochastic demand. Each buyer implements a continuous review policy, and the shortage quantity is fully back-ordered. The lead time for the first shipment includes setup, production, and transportation times, while the lead time for the remaining shipments includes only transportation time. The production time is a function of production lot and production rate, which are both decision variables, and the setup and transportation times are controllable as well. For each buyer, the safety stock in the first replenishment cycle is not necessarily identical to that in the remaining cycles. Two successive shipments from the vendor to a buyer may have different sizes, and their ratio is a decision variable. The problem is finding the production and inventory replenishment policy, production rate, and lead times that minimise the long-run expected total cost per time unit. We demonstrate properties satisfied by the cost function and develop an optimisation algorithm, whose performance is compared to a benchmark algorithm based on a commercial solver in a numerical experiment. The experiments also investigate the benefits stemming from the proposed model when compared to models reproducing situations that leverage some or any of the controlled factors. [ABSTRACT FROM AUTHOR]

PT Krakatau Steel (Persero) Tbk (PTKS) is a State-Owned Enterprise engaged in the production of steel and related services. The MRO Procurement Department is a procurement function to process MRO Material purchasing at PTKS. The main problem currently faced is the lead time for the MRO material procurement process which takes a long time, the reason is that procurement is often conducted by spot method and repeatedly for the same material. Too long lead times pose a risk to the smooth production process due to the unavailability of MRO materials when needed. Referring to the MRO Procurement Department's KPI, >85% of the number of POs (Purchase Orders) issued must have a lead time of <41 days, however, based on the actual historical data for 2022, the PO lead time achievement for the MRO material procurement process does not meet the target. Department's KPI, >85% of the number of POs (Purchase Orders) issued must have a lead time of <41 days, however, based on the actual historical data for 2022, the PO lead time achievement for the MRO material procurement process does not reach the target. An alternative solution used to speed up procurement lead times is implementing a Blanket Purchase Agreement (BPA) and/or Consignment Contract for MRO routine/consumable material types. The results obtained are that the long and complicated MRO material procurement process for the same type of material can be eliminated, which means that with the improvements made, the MRO Procurement Department can implement a more efficient procurement process. [ABSTRACT FROM AUTHOR]

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]

This paper makes an attempt to develop a periodic review inventory system with imperfect quality of items. In this system, there are two resupply modes, namely regular mode and emergency supply mode. After reviewing the rest quantity at the review epoch decisions are taken that which supply mode should be used. And hence, the order is placed. Regular supply case has a higher lead time and shorter ordering cost compared to emergency supply case. Items are received with imperfectness which is random. After receiving the items, an immediate screening process has been started and imperfect items are separated. This problem is analyzed through the order-up-to R H inventory control system framework. Hence, there must be suitable units of items that raise the inventory position to R H . In this circumstance, it can be shown that either the regular supply case is used or there is an indifferent inventory position E at the review position such that, a higher quantity than E carries out the regular supply case and a lesser quantity than E carries out emergency supply mode. Thus models are developed and optimized through some analytical methods. Finally, numerical examples are taken and concluded through analytical discussions. [ABSTRACT FROM AUTHOR]

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.

Currently, the development of the health care industry is very rapid. The rapid development of health services will have an impact on increasing the elements of their supporting activities. One of the supporting activities is the procurement of medical equipment. Procurement cases often do not go well, so procurement is not on time. This is due to the very long lead time. This has an impact on decreasing customer satisfaction. Companies try to overcome this problem to be able to compete in the market. This study aims to help companies eliminate waste that occurs in the procurement process so that customer satisfaction is obtained. The approach used is Lean Service with VSM as a process flow mapping tool. Focus Group Discussion was used in the analysis of problems and solutions for improvement. Based on the FGD, there are three problems, namely, the delivery of goods, project creation, and billing documents. The results showed that the overall lead time of the critical care procurement process decreased from 216 days to 110 days, or by 45. [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]

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]

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]

In this paper, we consider a two commodity queueing inventory system with random common lifetimes for each commodity, positive service time, positive lead time, and individual ordering policy. Inventory is managed by the continuous review (s i , S i) policy for the commodity i , i = 1 , 2 . On realization of the common lifetime or inventory level less than or equal to s i , i = 1 , 2 by service completions, a replenishment order is placed for S i , i = 1 , 2 items to bring the inventory level back to S i , i = 1 , 2 . There are two types of customers called priority and non-priority. Customers arrive according to two independent Poisson processes. A single server processes the inventory before delivery. Lifetimes and lead times of each commodity and service time for each customer category follow an independent exponential distribution with different rates. No customer will be allowed to join when the inventory levels become zero. System performance measures are derived. We also examine the effect of the lead time parameters and common lifetime parameters on the system performance measures. The model was examined in the steady state by using the matrix geometric approach. Further, we analyzed an associated optimization problem and carried out numerical illustrations. [ABSTRACT FROM AUTHOR]

This paper analyzes the optimal level of discount coupons and a point when to introduce it in a situation when firms provide discount coupons to minimize its loss; order cancellations occurred by difficulty in delivering products on time due to an increase in orders. This paper first finds the expected number of order cancellations using the nonhomogeneous Poisson process. Second, the profit function is structured by introducing discount coupons with a multivariate probability function. Findings reveal that offering coupon helps a firm to minimize its loss of profit, but if popularity does not settle down, discount coupon is no use. [ABSTRACT FROM AUTHOR]

Due to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991–2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods. [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]

Organisations in the hospitality sector face problems in meeting customers' demands of food items within an expected time and cost. However, some materials planning and procurement techniques may help process food items within acceptable lead times. The organisation needs to know what and how much it has on hand and what and how much to purchase from external sources. This paper applied the MRP and JIT concepts in a medium-sized restaurant named that creates various types of food items using raw materials from local and overseas sources. The paper has used actual data over ten weeks of sales of the most popular food items to forecast future demand using a moving average forecasting model. By using the forecast of end products and other information (viz. product structure, bill of materials, inventory on hand, lead-time), an MRP spreadsheet for various raw materials was generated, which would be supplied using the JIT scheduling to meet current and future customer demands within the expected time and at the lowest cost. [ABSTRACT FROM AUTHOR]

The oil and gas industry faces perennial challenges related to cost reduction, product quality enhancement, and operational efficiency. To remain competitive in the market, companies must optimize their production lead times, reduce costs, and ensure high levels of customer service. This study aims to identify and prioritize critical wastes in OCTG production lines to inform operational improvements. This research thoroughly evaluates the existing state of operations and identifies critical areas for improvement by utilizing a combination of Value Stream Mapping (VSM) and Borda Count Methods (BCM). The VSM provides a clear understanding of the complex movement of resources and activities in manufacturing and distributing products, making it easier to identify any inefficiencies. The BCM offers a systematic method to prioritize discovered waste according to its impact and severity. The study's findings expose crucial obstacles and inefficiencies in the production lines of OCTG, with waiting times and defects surfacing as notable areas of concern. By employing BCM, it can prioritize these concerns and derive valuable insights to inform strategic decision-making and operational improvements. The long-term goal of this research is to advance the current efforts of the oil and gas industry in enhancing production processes, reducing costs, and enhancing overall operational efficiency. [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.

Many periodic review inventory studies make the simplifying assumption that orders do not crossover, that is, that they are delivered in the same sequence as they were issued. In many real-life situations, however, long international shipping routes result with frequent crossovers. Accordingly, we investigate a periodic review inventory system with backlogged compound Poisson demand in which order may crossover. We develop an exact formula for the window fill rate, i.e. the probability for customers to receive service within their tolerable wait. Evaluating the window fill rate using the exact formula is very time consuming and therefore an efficient approximation formula that assumes that orders do not crossover is considered. Lead times of actual global supply lines demonstrate that this approximation results in considerable overstocking when customers' tolerable wait is high, or understocking when the tolerable wait is low. Replacing the actual lead times distribution with the effective lead times distribution improves the approximation formula's accuracy considerably. This result allows us to show how the window fill rate depends on the model parameters and make practical observations about the trade-off between the tolerable wait and stock levels needed to maintain a required level of window fill rate. [ABSTRACT FROM AUTHOR]

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]

Inventory management is one of the most important components of Alibaba's business. Traditionally, human buyers make replenishment decisions: although artificial intelligence (AI) algorithms make recommendations, human buyers can choose to ignore these recommendations and make their own decisions. The company has been exploring a new replenishment system in which algorithmic recommendations are final. The algorithms combine state-of-the-art deep reinforcement learning techniques with the framework of fictitious play. By learning the supplier's behavior, we are able to address the important issues of lead time and fill rate on order quantity, which have been ignored in the extant literature of stochastic inventory control. We present evidence that our algorithms outperform human buyers in terms of reducing out-of-stock rates and inventory levels. More interestingly, we have seen additional benefits amid the pandemic. Over the last two years, cities in China partially and intermittently locked down to mitigate COVID-19 outbreaks. We have observed panic buying from human buyers during lockdowns, leading to the bullwhip effect. By contrast, panic buying and the bullwhip effect can be mitigated using our algorithms due to their ability to recognize changes in the supplier's behavior during lockdowns. [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.

For a complex product production, any flexible manufacturing system with a work-in-process inventory is recommended for a supply chain management (SCM) system. Building a flexible manufacturing system increases the total cost of the supply chain; for this reason, a discrete investment is important. For flexible production systems, production rate within a finite specific interval of production rate as work-in-process inventory is calculated. The aim of the supply chain is to reduce the total cost when demand during the lead time is a random variable with a normal distribution. A crashing cost is utilised to reduce the duration of lead time within the supply chain system. A model is proposed to obtain the optimal flexible production rate with the reduced total cost of the supply chain. A classical optimisation technique is employed to obtain the closed-form and quasi-closed-form solutions of the decision variables. An improved algorithm is designed to obtain the global minimum cost of SCM under the framework of a flexible production system. An illustrative numerical example and sensitivity analysis are given to test the model. A numerical study proves that this model obtains the minimum cost with the optimal decision variables. [ABSTRACT FROM AUTHOR]

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.

Companies aiming to upgrade their supply chains to meet operational and sustainability objectives are often required to redesign their supply networks. However, there is scarce empirical evidence on how to redesign existing supply chains, especially global supply chains. This longitudinal case study examined the aquaculture supply chain redesign process with the aim of improving market, operational, and sustainability performance. Aquaculture, although characterized as the blue revolution, has received little attention in management studies. Action research collected evidence via informal interviews, participant observation, on-site visits, and secondary data selection. The redesign process included three phases: (i) physical distribution network mapping, risk-uncertainty analysis, and hierarchical goal setting, (ii) implementation of physical network re-design, and (iii) performance monitoring, control, and continuous improvement. The company achieved significant lead time reduction and fast response to the markets while, at the same time, reducing environmental pollution and meeting sustainable targets. The study uncovers that supply chain redesign should not be limited to physical network restructuring but also include facilities value extension and integrated information management. It shows how companies can mitigate different types of risks (supply, production, logistics, demand) via specific supply chain redesign interventions. [ABSTRACT FROM AUTHOR]

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”.

The Material Requirements Planning (MRP) Theory celebrates 50 years of its development. Because delays are Inevitable, Grubbström developed a theory where delays are calculated in the space of complex variables. Using his approach to evaluate time and exposure to risk in complex production-inventory systems is much easier when dealing with multiple delays. Working in the space of complex variables, which describe production flows and cash flows associated with items, it is easier to derive the NPV of activities in the system. This approach allows for a better evaluation of the impacts on the exposure to risks and rescheduling. We presented the development of this theory since 1967 when Grubbström published his first economic article on Laplace Transforms in production and investments. We discuss possible directions for further progress towards age management, environmental sustainability and closed-loop control, risk evaluation, and agile rescheduling in the framework of Industry 4.0. The overview includes the canon of Grubbström's work to be used as a reference for researchers, who are aware of the importance of correct and accurate timing for the economic evaluation of technical systems. It also underlines an open question: Can this approach support the development of more transparent and resilient financial systems for financing global inventories? [ABSTRACT FROM AUTHOR]

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]

Lean literature suggests that the attitude of “wait and see” has not yet allowed textile professionals to adopt Lean in production, especially in the laboratory. To fill this gap, the author illustrated a case study in a chemical laboratory of a renowned knit composite industry in Bangladesh. A team of textile and industrial engineers observed all processes in the laboratory from start to end for two years to detect problems and then conducted four brainstorming sessions with Lean experts to generate solutions to those issues which are finally presented for capacity improvement through dyeing machines’ capacity optimization, lead time evaluation, lab room temperature optimization, facility layout redesign, quick worksheet preparation, and workplace organization. This study proves that Lean applies to any process improvement. Also, data obtained in this research is highly valuable for textile engineers for future research and readily available for Value Stream Mapping. [ABSTRACT FROM AUTHOR]