High Lead Time Research Articles

Study and optimization of process parameters for deformation machining stretching mode

Monolithic thin-structure parts with intricate geometric designs are employed in a variety of aeronautical, medical, marine, and automotive applications, which include the moldlines of the fuselage, turbine blades, impellers, avionic shelves, irregular fins, prostheses, bone and joint support, and skull plates. The deformation machining process is the solution to this challenging and difficult-to-manufacture high-quality components with intricate narrow geometries at competitive prices. The aim of the present study is to assess the effect of process parameters of the deformation machining process wherein a thin, floor-like structure is created by milling and is then formed using a single-point incremental forming tool. Investigation involves the design and development of tooling required for the process followed by feasibility checking of the process. To examine the impact of different process parameters on the process response, the experiments were carried out using the design of experiments. The findings of this study indicate that different process parameters, including spindle speed, tool diameter, incremental step depth, and feed rate, have a substantial impact on the process response, like thickness, surface finish, and hardness. Uneven and non-uniform surface patterns during SEM indicate that it is needed to examine the impact of process parameters. This research involves the feasibility study of a new hybrid technique of deformation machining. Conventionally, a metallic structure is produced by joining various components through welding or by fastening. These methods require additional expenditure on equipment, storage, floor space, human resources, etc., with higher lead time. Joining increases weight and reduces fatigue strength. The creation of monolithic structures can eliminate all these disadvantages.

Global multi-sourcing network design with inventory planning under uncertainty

ABSTRACT In the current era of turbulence, demand uncertainty and significant supply delays have become recurrent challenges for every global supply chain, which are accentuated by geopolitical issues, regional conflicts, natural disasters, etc. Companies are keen to decide when and where nearshoring and offshoring their supply locations and to adapt their inventory control policies to cope with significant perturbations. To deal with these challenges, we investigate the integration of inventory planning at the strategic level through a novel setting of the location-inventory problem under uncertainty. This problem is characterised by inbound multi-sourcing, order splitting among multiple suppliers, and optimised inventory decisions from sourcing to customers under uncertain demand and lead time. A two-stage stochastic optimisation model is proposed and solved using a Benders decomposition algorithm, supported by a sample average approximation approach. The results show that both lead time and demand uncertainties impact strategic and tactical decisions of the global supply network. Specifically, multi-sourcing and inbound order splitting policies have proven beneficial when the network faces a high lead time uncertainty. Based on an illustrative case of a global automobile spare parts supply network under different supply-side scenarios, managerial insights are gained on the benefit of onshore, nearshore, and offshore decisions. When facing a high lead time uncertainty, as in the pandemic case or any geopolitical episode, our findings suggest the necessity to rely on nearshoring for the supply side.

Integration of Process Modeling and Six Sigma for defect reduction: A case study in a wind blade factory

The energetic and environmental crisis currently being experienced around the world has brought an exponential growth on the demand for wind energy, and, consequently, real challenges for companies in this sector. Since this industry is relatively new, its production processes require a significant amount of human intervention and have a high variability in the process and high lead times. Therefore, there is a need to focus on the quality of the processes and their improvement. This paper presents the use of the Six Sigma DMAIC approach integrated with Business Process Management, to reduce the number of defects and the repair time in a leading wind blade manufacturing company in Portugal. During the measurement and analysis phases, data was collected to understand the performance of the process and to identify the main problems and their causes. The data was analysed through several graphical and statistical techniques. In the analysis phase, process modeling was also utilized, using Business Process Model and Notation, to identify the critical steps that are responsible for producing the majority of defects. One month after beginning to use this approach, it was possible to notice a reduction in the number of defects by more than 30%, in the repair time by 14%, and an increase in the process sigma level by more than 100%. Thus, this hybrid approach has allowed the company to become more competitive and responsive to the constant growth in demand.

A Case Study of Customer Relationship Management in J&K Grameen Bank Samba of J&K

This study aims at making contribution to the literature on understanding the relationship between customer relationship management, customer satisfaction and customer loyalty in context to J&K Grameen Bank. This study attempts to investigate the dimensions of customer relationship management (CRM) and Customer satisfaction (CS) that influence customer loyalty (CL). Today the banking sector is making significant contribution to the economic development and redefining the consumption habits of Indians. The constantly changing consumer attitudes and buying behavior is making it difficult for bankers and service providers to take their customers for granted. The highly demanding and knowledgeable consumers are compelling banks to stock a huge product range, offer attractive discounts, in an aesthetically set up environment and so on- all in the hope of having a loyal customer base. But high lead times, seasonal fluctuations, evolving consumer preferences are some of the issues creating a stiff completion in the business environment. So, banks and service providers have realized that it is very essential to work towards getting into customer relationship management programmes. For this, banks and service providers are working out ways to understand the opportunity and having preferences of individual customers that chart out strategies and programs so as to develop a base of loyal customers who will patronize the banks and service providers frequently. The study is based on descriptive and exploratory style using primary data.

Supply chain cash-flow bullwhip effect: An empirical investigation

The cashflow bullwhip effect (CFB) is the amplification of working capital variance along a supply chain, analogous to the bullwhip effect (BWE) for order variance. High CFB is an indication of inefficiency in working capital management. This study investigates the occurrence of CFB and BWE in a sample of 763 U.S. public companies from 2010 to 2019. The results indicate that CFB is experienced by 37% of retailing, 43% of wholesaling, and 81% of manufacturing firms. Additionally, firms with conservative payment policies have smaller CFB and BWE, while high liquidity ratios are associated with smaller BWE. Predictability in demand seasonality is linked with smaller CFB and BWE, while high lead time is associated with larger CFB and BWE. Higher demand autocorrelation is associated with larger BWE. This new knowledge on the associations between CFB and BWE with firm attributes can help inform important decisions, including those made at the firm, industry, or government policy level.

Technology Focus: Offshore Drilling and Completion (October 2023)

Optimizing and continually increasing efficiencies at every stage of the drilling and completion process are central themes in the papers showcased in this feature. Innovating our way through difficult times is what the oil and gas industry excels at, especially during challenging periods. During the COVID-19 pandemic, the offshore energy industry faced immense struggles with high upfront capital investment and long lead times in uncertain circumstances with extremely low oil prices. The challenges brought about by the pandemic served as a backdrop to many of these innovations. Companies were compelled to discover creative solutions to maintain efficiency, reduce costs, and ensure safety during an era of unprecedented disruption. Innovation in advanced analytics and machine-learning algorithms is used to optimize drilling-center locations, leading to cost reduction. This development became even more critical during the pandemic as companies aimed to maximize efficiency and minimize expenses. The execution of large-scale projects such as the Johan Sverdrup development highlights innovation in project management, enabling timely and cost-effective operations in a challenging environment. The emphasis on digital solutions and fiber-optic technology in Guyana’s offshore operations illustrates the industry’s adaptation to the pandemic by enabling remote operations and real-time data collection while maintaining efficiency in a new discovery. Innovations in riserless light well intervention systems are particularly relevant during the pandemic. These systems improved efficiency and safety for offshore interventions, enabling the industry to operate with minimal personnel onboard. The focus on well-planning strategies to overcome challenges in deep depleted reservoirs includes approaches that optimize drilling in complex conditions, which is crucial when efficiency is paramount. Retrofitting autonomous inflow control devices proved an effective approach in implementing state-of-the-art technology to improve reservoir sweep and productivity. Moving forward, this innovative spirit in our industry as seen in these papers is a testament to our resilience during challenging times. In the coming years, this ability to sustainably increase recovery efficiencies to ensure energy security is crucial to shaping the future of energy production. Recommended additional reading at OnePetro: www.onepetro.org. SPE 212499 4D Radar Imaging of Wellbore Geometry While Drilling by Lars Øy, Well ID, et al. OTC 30979 Guyana Operations and First Oil by Mike F. Ryan, ExxonMobil, et al. SPE 212915 Enhancing Riserless Light Well Intervention Operability From Operator Perspective by Michael Long Ge, BP, et al. SPE 214057 Challenges of Drilling Deep Wells in a Complex Overburden With Severe Depletion and Experiences From Caspian Sea by Anwar Juma Al Saadi, Dragon Oil, et al.

B-038 Applying Robotic Process Automation Technology to Automate the Release of Pathology Results and Reduce Costs, Time and Labor in a Clinical Laboratory

Abstract Background The need for pathological anatomy tests has increased considerably in recent years, especially in one of our regional branches, where the total operational time to release the report to the patient is crucial to obtain competitive advantages in the market. As a result, the branch´s technical team observed the high lead time of the process, the inefficiency of the use of internal resources and the impact of the various fully manual activities in order to deliver the service offered to the customer and, therefore, sought out the Production Planning and Control (PPC) team with the objective of implementing improvements, in order to optimize the total operation time and automate the activities involved, adding more value to the patient and highlighting the laboratory's brand against competitors. Methods In order to implement continuous improvements in the operation of anatomical pathology samples, the involved teams held several brainstorming sessions and mapped out the supply chain for these types of tests. Firstly, the samples were received and outsourced to a partner laboratory and as the results were ready, the reports were made available in their Laboratory Information System (LIS). These reports were then retrieved manually by a capacitated employee whose only job was to download the reports from the partner´s website, revise it and upload it to our LIS so that our patients had access to the result report. Through the application of the principles of the Value Stream Map (VSM) tool, it was possible to determine the time needed for each stage of the process and identify the use of resource in exclusive functions that negatively impact productivity such as the manual activity of retrieving and uploading files. Considering the analysis carried out, it was observed that the best way to automate the process and reduce lead time would be the development of an RPA (Robotic Process Automation) that would login in the partner laboratory´s LIS, search for the patients´ reports, download it, and upload it into our LIS. Once the report was available in our system, the analyst had only to revised it briefly for matching information and release the results. Results With the creation of RPA, the process was redesigned along with the teams involved, manual activities were automated and the need for resource exclusivity was mitigated. Thus, it was possible to obtain a 50% optimization of the designated employee's time, a 75% reduction in the total lead time of the internal operation of the laboratory team and a gain of approximately 38% in the productivity of the technical area, making the process faster and more efficient. Conclusion With the implementation and validation of RPA in the external unit, the manual process of using the systems was eliminated and the new automated process obtained excellent results when compared to the previous scenario, while now we have greater agility and speed in activities and a continuous flow of information that includes the efficient release of pathological anatomy exam results, strengthening the laboratory's competitiveness in the market and ensuring customer loyalty.

Lean improvement for pantograph jack production process using Value Stream Mapping

The objectives of this research are to study the current process and improve the production process of pantograph jack by using Lean manufacturing techniques. The Lean tool used is Value stream mapping (VSM). The study starts with collecting data from the upstream to downstream to create a Current-state VSM to identify wastes and problems. The first problem was the low efficiency of the production process due to the high lead time of 7 days 8 hours. The ratio of value-added time to the lead time was only 0.026%. The next problem was overproduction because the cycle time is 69.54 seconds/piece while the takt time is 140.15 seconds/piece, which is 46.62% of the takt time, resulting in the early stoppage of the production line to prevent over inventory of finished products. After identifying the problems, the next step is to define production process improvement approaches by creating a Future-state VSM. The tools for the improvement included the Kanban system, Supermarket, Line balancing, and Cellular manufacturing then a simulation model of both current and future states was created to compare the results before and after the process improvement. The result from the simulation shows that the total lead time was reduced to 4 hours 39 minutes or a decrease of 97.54% from the current state. The ratio of value-added time to the lead time was increased to 1.304%, which is 49.39 times more than before the improvements, and the cycle time was increased to 133.02 seconds/piece, which is 94.91% of the takt time.

Homology-independent targeted insertion (HITI) enables guided CAR knock-in and efficient clinical scale CAR-T cell manufacturing

BackgroundChimeric Antigen Receptor (CAR) T cells are now standard of care (SOC) for some patients with B cell and plasma cell malignancies and could disrupt the therapeutic landscape of solid tumors. However, access to CAR-T cells is not adequate to meet clinical needs, in part due to high cost and long lead times for manufacturing clinical grade virus. Non-viral site directed CAR integration can be accomplished using CRISPR/Cas9 and double-stranded DNA (dsDNA) or single-stranded DNA (ssDNA) via homology-directed repair (HDR), however yields with this approach have been limiting for clinical application (dsDNA) or access to large yields sufficient to meet the manufacturing demands outside early phase clinical trials is limited (ssDNA).MethodsWe applied homology-independent targeted insertion (HITI) or HDR using CRISPR/Cas9 and nanoplasmid DNA to insert an anti-GD2 CAR into the T cell receptor alpha constant (TRAC) locus and compared both targeted insertion strategies in our system. Next, we optimized post-HITI CRISPR EnrichMENT (CEMENT) to seamlessly integrate it into a 14-day process and compared our knock-in with viral transduced anti-GD2 CAR-T cells. Finally, we explored the off-target genomic toxicity of our genomic engineering approach.ResultsHere, we show that site directed CAR integration utilizing nanoplasmid DNA delivered via HITI provides high cell yields and highly functional cells. CEMENT enriched CAR T cells to approximately 80% purity, resulting in therapeutically relevant dose ranges of 5.5 × 108–3.6 × 109 CAR + T cells. CRISPR knock-in CAR-T cells were functionally comparable with viral transduced anti-GD2 CAR-T cells and did not show any evidence of off-target genomic toxicity.ConclusionsOur work provides a novel platform to perform guided CAR insertion into primary human T-cells using nanoplasmid DNA and holds the potential to increase access to CAR-T cell therapies.

Reduction of Manufacturing Lead Time by Value Stream Mapping of a Selected RMG Factory in Bangladesh

Bangladesh is a fast-growing economy powered by the readymade garments (RMG) industry which has promoted the country in the world through the motto ‘Made in Bangladesh’. The RMG industry has become the lifeline of the economy of Bangladesh. However, this industry faces several significant obstacles to its continued, sustainable expansion. The trading climate has become very competitive because of the phase-out of the export quota system. Additionally, with today’s rapid fashion, consumers want items with short lead times and affordable prices. Adopting the lean methodology across the board in the garment business is essential for long-term sustainability in this cutthroat context. Lean manufacturing is a philosophy to deliver the finest goods on time with minimum endeavor and less price. Lean manufacturing aims to cut lead times and remove wastes that don’t impose value on products. The purpose of this research is to identify wastes and to reduce manufacturing lead time by eliminating or reducing the excess tasks that cause higher lead time by implementing lean concepts. But implementing lean philosophy is a difficult task. During the visit to the selected RMG factory, the Short Sleeve T-shirt, one of their products, was selected for conducting this research. All the processes were observed required for the transformation of raw materials into a finished T-shirt. During the observations, the methodology of time study was applied to collect the data. Necessary information was gathered by observing the processes and interacting with the executives. Then, the current state of the value stream mapping was drawn. Wastes, responsible for a longer time, were ranked by taking the help of an 80-20 rules diagram. After this, root cause analysis was performed to find the reasons for the unwanted time consumption. Improvement strategies were introduced to reduce waste. Lean tools like Kanban to reduce WIP, Single Minute Exchange of Die to reduce changeover time, and Total Productive Maintenance to reduce machine breakdown were used to minimize the waste. Based on improvement strategies, The value stream mapping’s future state was illustrated, showing the refinement where total manufacturing lead time was reduced by 61.20%. Finally, this research has suggested some recommendations to the management of the selected RMG factory to minimize waste and increase efficiency and reduce waste in the operations. This research illustrates the usual conditions of the RMG industry of Bangladesh by presenting the current state of the value stream. The Bangladeshi clothing sector must completely embrace lean principles and embark on the direction of sustainable growth.

Research on digital energy carbon management platform and services for energy-using companies under the “3060” target

This study focuses on serving governments, enterprises and individuals related to digital energy carbon management. The National Grid provides digital carbon management services to electricity-using enterprises, solving the problems of high cost, difficulty and long lead time in managing carbon assets of electricity-using enterprises. We have innovated daily real-time carbon emission management services, carbon emission reduction services, carbon decision-making services, value-added carbon asset services and carbon trading services. Xinjiang Autonomous Region is a water-scarce and arid region which water resources are particularly important, so we choose the wastewater treatment industry in Xinjiang as a case study. The daily electricity consumption of Xinjiang Wastewater Treatment Plant A is 4063.75 kWh and the daily electricity consumption carbon emissions are 2.36 tonnes CO2eq, which is estimated to be about 2.78 tonnes CO2eq per day. the daily carbon emissions of the daily electricity consumption Wi of Xinjiang Wastewater Treatment Plant B is 3469.22 kWh and the daily electricity consumption carbon emission is 2.02 tonnes CO2eq, which is estimated to be 2.38 tonnes CO2eq. The carbon account value-added service provides carbon market users with transaction-assisted decision-making services, realizes accurate and efficient services, and helps society achieve the “3060” strategic goal of low-carbon green transformation.

Developing a Conceptual Framework Model for Effective Perishable Food Cold-Supply-Chain Management Based on Structured Literature Review

This paper reviews the literature on perishable food cold-chain management (FCCM) in order to assess its current state, pinpoint its knowledge gaps, and suggest a framework for addressing the issues faced by this industry. This work examines 103 academic articles on the topic of the perishable food supply chain published in various journals between 2001 and 2022. Research publications were selected from two reputed databases—Scopus and Web of Science. The study finds that the current trend in FCCM is toward sustainable FCCM, which offers financial, ecological, and social benefits. However, sustainable FCC practices are more common in wealthy nations but are still lacking in developing countries. High lead times, costs, waste, order returns, complaints, and dissatisfied consumers are the results of a fragmented market and the associated proliferation of chain intermediates. The authors have also developed a conceptual framework based on the findings that illustrates the interconnected nature of the food cold-chain facilities, collaboration among food cold-chain (FCC) stakeholders, concern among FCC stakeholders, economic enhancement, fulfilment of FCC stakeholders’ responsibilities, and overall functioning of the FCC. This study may be helpful to FCC professionals, food regulators, government authorities, and researchers because it gives a concise picture of the state of research in the field.

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

AbstractOccurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time.

Innovative transdermal drug delivery system based on amoxicillin-loaded gelatin methacryloyl microneedles obtained by 3D printing

The microneedle (MN) technology platform has a significant potential for enhancing the efficacy of transdermal drug delivery, by using micron-scale arrays with restricted geometries that pierce the skin non-invasively. In this paper, we developed gelatin methacryloyl (GelMA) MNs containing amoxicillin (AMX) as a novel transdermal delivery system using digital light processing (DLP) based 3D printing technology. With the DLP approach, MNs were obtained with high resolution and minimal lead times without geometric limitations. The 3D-printed AMX-loaded GelMA MNs were examined for their morphological, mechanical, swelling and antimicrobial properties as well as the drug release profile. The morphological analysis demonstrated the successful fabrication of the MNs with regular structure and sharp tips. The fabricated MNs displayed good mechanical properties consistent with 0.1 N/needle without breaking. Moreover, the results of the in vitro antibacterial activity assays indicated that AMX-loaded GelMA MNs have significantly inhibited the Staphylococcus aureus and Escherichia coli growth. These findings suggest that the 3D-printed drug-loaded GelMA MNs are a useful platform for transdermal therapeutic delivery and can be developed as a next-generation medical device for managing bacterial infections.

On the verification of ensemble precipitation forecasts over the Godavari River basin

The ensemble forecasts from Numerical Weather Prediction (NWP) models are considered as essential elements for operational hydrologic forecasting. However, these forecasts are burdened with significant inherent biases and cannot be used directly for hydrologic forecasting. Therefore, the present study examines the capability of ensemble precipitation forecasts obtained from two NWP models i.e., a 20-member ensemble from the National Center for Environmental Prediction (NCEP) and a 50-member ensemble from the European center for Medium-term Weather Forecasts (ECMWF). The skill of 70-member multi-model grand ensemble (MME) generated from the NCEP and ECMWF forecasts is also verified in this study. Furthermore, post-processing of the raw ensemble members was carried out using two methods (Quantile Mapping (QM) and Quantile Regression Forests (QRF)) in order to minimize the inherent biases. The analysis was performed over subbasins of an Indian river basin namely the Godavari River Basin. Here, a set of deterministic and probabilistic verification measures (forecast error box plots, correlation coefficient, Relative Mean Error, mean Continuous Ranked Probability Score, spread-skill relationship, rank histograms, reliability diagram, Area under Relative Operating Characteristic Curve) were employed to assess various quality attributes of the forecasts. The findings from the study suggests that QRF post-processed NCEP and MME forecasts are comparatively better than other forecasts in terms of all employed verification measures over all subbasins. The QRF post-processed forecasts were found to be superior than QM post-processed and raw forecasts over all subbasins. The adopted post-processing methods resulted in improved forecasts at shorter lead times. However, the skill of both raw and post-processed forecasts declines at higher lead times over all subbasins.

Axiomatic design for lean autonomous maintenance system: an application from textile industry

PurposeAutonomous maintenance (AM), one of the pillars of total productive maintenance (TPM), aims to achieve performance toward zero defects and zero breakdowns. AM system equipped with comprehensive lean tools provides continuous improvement during the AM activities. Despite its long duration, establishing a lean AM system with a robust guideline would provide significant benefits such as high quality and short lead time. Therefore, AM design approach should be provided in a holistic and detailed manner. This study aims to develop a framework for AM design, including preliminary, reactive, preventive and proactive steps using the axiomatic design (AD).Design/methodology/approachRequirements and technical parameters of the AM system are explored with AD. An extensive literature review and a real-life application are presented.FindingsThe proposed design was validated by adapting the proposed roadmap to a textile manufacturing system in Turkey. The application results justify the established AM system design with an average downtime improvement of 69.2% and the average elapsed time between two failures improvement of 65.1% for apparel department.Originality/valueThis study has the novelty of establishing an overall AM system design with all of its stages stepwise. It presents a comprehensive guideline in terms of integration of lean philosophy into AM design by generating maintenance-related use cases for lean tools. The developed approach facilitates creating and analyzing complex systems to improve maintenance implementations while reducing nonvalue-added operations.

Early prediction of hypothermia in pediatric intensive care units using machine learning.

Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.

Assessment of enhanced Kohonen self-organizing map, quantile mapping and copula-based bias-correction approaches for constructing basin-scale rainfall forecasts

ABSTRACT This study evaluates the performance of an enhanced Kohonen self-organizing map (eKSOM)-based bias-correction technique and compares the results with a copula-based bias-correction approach and the traditional quantile mapping (QM) to correct the daily multi-model ensemble short- to medium-range rainfall forecast products of the India Meteorological Department (IMD-MME) in the Hirakud Reservoir catchment in India. The copula and eKSOM outperformed the raw IMD-MME and QM rainfall across all lead times. While both the copula and eKSOM-based bias-corrected forecasts could satisfactorily provide the temporal pattern of the observed rainfall, the corresponding eKSOM-based forecasts presented better skills in capturing seasonality in the observed rainfall. The streamflow at one- to five-day lead times are simulated by forcing a suitable hydrological model, MIKE11 NAM (Nedbør Afstrømnings Model)-HD (Hydrodynamic) with the raw and bias-corrected rainfall inputs. The overall performance evaluation reveals significant improvement in both the rainfall and streamflow forecasts by copula and eKSOM, with the latter performing most accurately at higher lead times.

Post-COVID-19 strategic sourcing decisions for escorting stakeholders’ expectations and supplier performance in construction project works

PurposeThe occurrence of COVID-19 has impacted the wide-reaching dimensions of manufacturing, materials, procurement, management, etc., and has loaded disruptions in the wide range of supply chain (SC) activities. The impact of COVID-19 has twisted supplier performance and influenced stakeholders’ thinking towards selecting supplier sources and making strategic sourcing decision for convinced arrangement of construction management (CM) resources. Nowadays, suppliers are intently evaluated by stakeholders in post-COVID-19 phase to induce agile availability of CM resources. Accordingly, this paper aims to demonstrate competent CM dimensions under post COVID-19 scenario for ease managing construction projects by the stakeholders.Design/methodology/approachThe authors have implicated Grey Sets Theory along with decision-making trial and evaluation laboratory (DEMATEL) technique for understanding significant outcomes. Varieties of diverse decision aspects responsible for strategically influencing supplier sourcing decision is projected under post COVID-19 scenario for handling construction projects by the stakeholders.FindingsThis study investigated sustainable construction management dimensions (SCMD) at the stage of resource deliveries and client aspirations under post COVID-19 situation. The study demonstrated “Lead time” as the most crucial, “Product Range” as the second and “Customers dealings and relationship” as the third crucial aspect considering by the stakeholders for selecting supplier sources based on the attainment of performance score of 0.1338, 0.1273 and 0.1268, respectively. It is found that high lead time stimulates the stakeholders to divert their orders to other competent supplier sources holding a low degree of lead time as compared.Research limitations/implicationsThe present study rollovers its existence by serving critical thinking, conceptual modelling, criteria identification and evaluation under CM domain for drafting effectual strategies by the suppliers. The study investigated the impact of COVID-19 on stakeholders’ decision-making and enlisted SCMD that strategically stimulated them in choosing supplier sourcing decision.Originality/valueThe present study realizes the insights of stakeholders in the post COVID-19 scenario related to the supplier sources based on performance score. The study quantified sustainable supplier attribute for construction work and practices. The study analysed the expectations of the stakeholders purchasing different varieties of construction materials from supplier sources for civil works in the post COVID-19 scenario.

Modeling the enablers for blockchain technology adoption in renewable energy supply chain

The literature illustrates Blockchain Technology (BT) to offer various advantages resulting in progress in the renewable energy supply chains (RESC). It is predicted that BT will bring a novel paradigm in the approach of transactions implemented in the RESC by decreasing the number of intermediaries, delayed payments, and high transaction lead times. As a developing economy, Iran provides the energy needs of an ever-growing population and confronts many issues affecting RESC. As a result, it is, therefore, crucial to accept BT in the RESC to leverage the different advantages. In the current study, we recognize and implement the relationships between the enablers of BT adoption in RESC. Twelve enablers were identified from the literature, and verified by the experts before using a Fuzzy Interpretive Structural Modelling (FISM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) methodology. The results propose that the comparison among the identified enablers shows the most superior of secure database for BT implementation in RESC tracked by immutability and decentralized database. The results will provide better situations for the practitioners to design the strategies for BT implemented in renewable energy within a real-time data-driven RESC.