Short Lead Times Research Articles

Reference Model to support decision-making in the planning and evaluation of automated order picking systems of heavy flatpacks in retail and wholesale fulfilment units

With increasing customer expectations in close shopping possibilities and shorter lead time in e-commerce, the home furnisher companies have a higher interest in automated order picking systems. Although technologies for automated order picking processes are developing rapidly, picking of the unergonomically large and heavy flatpacks is mainly conducted manually. This is due to the flexibility of manual processes, but also due to the long planning and evaluation time as the overall system is more complex with automation. This paper presents the conceptual architecture of a Reference Model to support the planning and evaluation of automated order picking systems of heavy flatpacks for retail and wholesale fulfilment units. The aim of the Reference Model is to provide a systematic approach in the project management, mainly focusing on the planning and evaluation process of automated order picking systems by supporting the decision-making and enabling the earlier integration of simulation in the planning phase.

Optimization of a hybrid multi-item fabricating-shipping integrated system considering scrap, adjustable-rate, and postponement

This study aims to optimize a hybrid multi-item fabricating-shipping integrated system incorporating scrap, adjustable rate, and postponement. In present-day competitive market environments, there is a clear client demand trend for various goods, shorter lead time, and expected quality. To satisfy the client’s needs, the management of manufacturing firms requires an effective and efficient plan to fabricate various high-quality goods in an expedited period, under limited capacity, and with minimal operating expenses. Inspired by facilitating production management to determine the best fabricating scheme/plan to achieve their operational goals, this work proposes an exploratory postponement model with quality assurance and uptime reduction strategies for their decision-making. By employing a two-phase making scheme, the required standard components are first made in the 1st phase, and multiple finished merchandise is fabricated in the 2nd phase. The study suggests strategies of contracting out a part of the common parts’ batch and adopting an adjusted/expedited making rate in the 2nd phase to considerably reduce both phases’ production uptimes. During both fabricating processes, the screening tasks identify/remove scrapped/faulty goods to ensure each finished batch’s quality. Equal-amount multiple shipments of end merchandise are transported to the clients in fixed time-interval. Optimization methodology and mathematical analyses support us in deriving the model’s expected annual operating cost and deciding the optimal production-transportation policy. A numerical illustration helps verify our model’s applicability and reveals important managerial insights into the studied problem to facilitate management in decision-making.

ИСПОЛЬЗОВАНИЕ ТОРФОПРОДУКЦИИ В СЕЛЬСКОМ ХОЗЯЙСТВЕ

Peat products play an important role in agriculture. An analysis of the use of peat products in agriculture was carried out. It was noted that in livestock farming, peat is used as bedding for livestock to produce peat manure, peatponics, the production of protein and feed additives, silos, and the production of drugs from peat in veterinary medicine. Peat is used to improve soil fertility, create substrates, for growing seedlings, for mulching the soil and protecting plants from frost and weeds in crop production. The use of peat briquettes as fuel for heating greenhouses and livestock buildings is possible. After burning them, peat ash remains - a good fertilizer. The organic and chemical composition of peat is shown. For the effective processing of peat and the use of peat products, it is proposed to create an agricultural consumer cooperative (ACC). A model of the interaction of structure-forming elements of ACC for peat processing is presented. The creation of an industrial complex for the processing and integrated use of peat products is promising, since it realizes the simplicity of technical solutions, the possibility of scaling processes, short lead times and the environmental friendliness of the resulting products. The integrated use of peat products in agriculture allows not only to improve soil fertility and increase crop yields, but also to ensure the effective use of peat as fuel, feed additives, animal bedding, oil product sorbent, etc.

Online Learning for Dual-Index Policies in Dual-Sourcing Systems

Problem definition: We consider a periodic-review dual-sourcing inventory system with a regular source (lower unit cost but longer lead time) and an expedited source (shorter lead time but higher unit cost) under carried-over supply and backlogged demand. Unlike existing literature, we assume that the firm does not have access to the demand distribution a priori and relies solely on past demand realizations. Even with complete information on the demand distribution, it is well known in the literature that the optimal inventory replenishment policy is complex and state dependent. Therefore, we focus our attention on a class of popular, easy-to-implement, and near-optimal heuristic policies called the dual-index policy. Methodology/results: The performance measure is the regret, defined as the cost difference of any feasible learning algorithm against the full-information optimal dual-index policy. We develop a nonparametric online learning algorithm that admits a regret upper bound of [Formula: see text], which matches the regret lower bound for any feasible learning algorithms up to a logarithmic factor. Our algorithm integrates stochastic bandits and sample average approximation techniques in an innovative way. As part of our regret analysis, we explicitly prove that the underlying Markov chain is ergodic and converges to its steady state exponentially fast via coupling arguments, which could be of independent interest. Managerial implications: Our work provides practitioners with an easy-to-implement, robust, and provably good online decision support system for managing a dual-sourcing inventory system. Funding: This work was supported by the Amazon Research Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0323 .

Adrenocorticotropic hormone combined with magnesium sulfate therapy for infantile epileptic spasms syndrome: a real-world study.

Infantile epileptic spasms syndrome (IESS) is a serious disease in infants, and it usually evolves to other epilepsy types or syndromes, especially refractory or super-refractory focal epilepsies. Although adrenocorticotropic hormone (ACTH) is one of the first-line and effective treatment plans for IESS, it has serious side effects and is not sufficiently effective. A retrospective study of the clinical outcomes of ACTH combined with magnesium sulfate (MgSO4) therapy for IESS in two hospital centers was conducted. The major outcome of the single and combined treatment was evaluated by changes in seizure frequency and improvements in hypsarrhythmia electroencephalography (EEG). To reduce the confounding bias between the two groups, we used SPSS for the propensity score matching (PSM) analysis. We initially recruited 1205 IESS patients from two Chinese hospitals and treated them with ACTH combined with MgSO4 and ACTH alone. Only 1005 patients were enrolled in the treatment (ACTH combined with MgSO4: 744, ACTH: 261), and both treatment plans had a more than 55% response rate. However, compared to patients treated with ACTH alone, those patients treated with ACTH combined with MgSO4 had better performance in terms of the seizure frequency and hypsarrhythmia EEG. After PSM, the two groups also showed significant differences in responder rate [70.8% (95% confidence interval, CI) = 66.7%-74.8%) vs. 53.8% (95% CI = 47.4%-60.2%), P < 0.001], seizure frequency (P < 0.001) and hypsarrhythmia EEG resolution (P < 0.001). Notably, multivariate analysis revealed that the lead time to treatment and the number of antiseizure medications taken before treatment were two factors that may affect the clinical outcome. Patients with less than 3months of lead time responded to the treatment much better than those with > 3months (P < 0.05). In addition, the overall incidence of adverse reactions in the ACTH combined with MgSO4 group was much lower than that in the ACTH group (31.4% vs. 63.1%, P < 0.001). During the treatment, only infection (P = 0.045) and hypertension (P = 0.025) were significantly different between the two groups, and no baby died. Our findings support that ACTH combined with MgSO4 is a more effective short-term treatment protocol for patients with IESS than ACTH alone, especially for those patients with short lead times to treatment. Video Abstract (MP4 533623 KB).

USAGE OF ADDITIVE MANUFACTURING IN THE AUTOMOTIVE INDUSTRY: A REVIEW

Additive manufacturing (AM), commonly known as 3D printing, is an emerging technology with immense potential across various industries, particularly in the automotive sector. It involves the layer-by-layer fabrication of parts by gradually depositing material, distinguishing itself from traditional manufacturing through various ways, including design flexibility, reduced material wastage, shorter lead time, and the absence of tooling requirements. It is still in its nascent phase, and several drawbacks, such as a lack of mechanical strength, limited material availability, and poor surface finish, still hinder its integration into the mainstream production workflow. As a result, further exploration is required to harness its potential along with traditional methods. The automotive sector is long known for its adoption of cutting-edge technologies. Due to the vast potential of AM, the automotive industry is actively trying to incorporate additive manufacturing into mainstream production processes. Consequently, the automotive industry continuously explores ways to enhance this process for seamless integration into product development. However, research elucidating different additive manufacturing processes used within the automotive industry, such as stereolithography (SLA), fused deposition modeling (FDM), and selective laser sintering (SLS), is limited. This paper aims to illuminate various additive manufacturing processes used by automotive companies worldwide. In addition to exploring different processes, the study delves into the advantages and limitations of additive manufacturing, contributing to a comprehensive understanding of its application in the automotive manufacturing landscape.

Using ultrasonic atomization to recycle aluminium bronze chips for additive laser directed energy deposition

In the post-processing of large maritime components, a considerable amount of waste in the form of milling and grinding chips is produced. At the same time, additive manufacturing technologies have shown great potential in producing high-volume parts for maritime applications, allowing novel design approaches and short lead times. In this context, this study presents a sustainable approach to recycle and use aluminium bronze waste material, generated during post-processing of large cast ship propellers, as feedstock for laser-powder directed energy deposition. The recycling technology used to produce powder batches is inductive re-melting in combination with ultrasonic atomization. The derived metal powders are characterized using digital image analysis, powder flowability tests, scanning electron microscopy as well as energy dispersive X-ray spectroscopy. Compared to conventional metal powders produced by gas atomization, the recycled material shows excellent sphericity and a powder size distribution with a higher content of finer and coarser particles. Metallographic sections of deposited additively produced specimens show an increased hardness and reduced ductility, but also competitive densities and higher yield and ultimate tensile strength compared to cast material. The process chain shows high potential for the maritime sector to enable circular and sustainable manufacturing.

Laser welding of additively manufactured parts - A review

Additive manufacturing (AM) is a process in which parts are built up layer by layer, introducing novel approaches to how parts can be manufactured with less material waste, shorter lead times and lower costs than traditional manufacturing. One of the key advantages of AM over conventional manufacturing is its design flexibility, which enables for manufacturing of parts with highly detailed geometries in one go, leaving out the need for molding, casting, etc. However, due to the chamber size of the machines, the size of AM parts is limited. To overcome this limitation, joining AM parts together or to wrought or cast material has been proposed. Among the various welding technologies, laser welding is considered a suitable candidate for joining AM parts because of its low heat input, resulting in low deformation, high welding speed, and full automation capability. This study will provide a fundamental understanding of laser welding of AM parts by reviewing current research in the field. The possibility of joining most commonly used AM parts such as AlSi10Mg, AISI 316L, Ti6Al4V and Nickel alloy 718 by laser welding are investigated. Furthermore, the effect of laser welding parameters on mechanical and microstructural properties of joined AM parts are discussed.

Effect of Vibration on Mechanical Properties in Sand Casting Process using Organic Binder

Casting setups must create the components with a short lead time in today's highly competitive world market. This essential industry now demands defect-free castings with low production costs. Rejection of casted products is caused due to defective components which are influenced by various factors which includes process parameters, all of which can be optimized to improve the quality of the casted products. In this study, an experimental approach was made using vibrated assisted techniques to assess the applicability of the method and improvement of the quality of the casted parts using organic binders in terms of mechanical properties especially the tensile strength, hardness and micro-structure of the products. After investigations, it has been observed that with the influence of vibration frequency at 40 Hz with amplitude 2 V the tensile strength has been improved from 176.60 MPa to 180.81 MPa, hardness improvement from HB33 to HB36 and uniformity in the micro-structure has been observed.

A statistical–dynamical approach for probabilistic prediction of sub-seasonal precipitation anomalies over 17 hydroclimatic regions in China

Abstract. In this study, we develop a spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method to improve probabilistic sub-seasonal precipitation forecast skill over 17 hydroclimatic regions in China. The calibration model is established by post-processing ECMWF raw forecasts using the Bayesian joint probability (BJP) approach. The bridging models are built using large-scale atmospheric intraseasonal predictors, including zonal wind at 200 hPa (U200) and 850 hPa (U850); an outgoing longwave radiation anomaly (OLRA); and geopotential height at 200 hPa (H200), 500 hPa (H500), and 850 hPa (H850) defined by the STP method. The calibration model and the bridging models are then merged through the Bayesian modelling averaging (BMA) method. Our results indicate that the forecast skill of the calibration model is higher compared to bridging models when the lead time is within 5–10 d. The U200- and OLRA-based bridging models outperform the calibration model in certain months and certain regions. The BMA-merged forecasts take advantage of both calibration models and bridging models. Meanwhile, the BMA-merged forecasts also show high reliability at longer lead times. However, some improvements to reliability are still needed at shorter lead times. These findings demonstrate the great potential to combine dynamical models and statistical models in improving sub-seasonal precipitation forecasts.

FuXi: a cascade machine learning forecasting system for 15-day global weather forecast

Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)’s high-resolution forecast (HRES), which is widely considered as the world’s best physics-based weather forecasting system. Specifically, ML models have outperformed HRES in 10-day forecasts with a spatial resolution of 0.25∘. However, the challenge remains in mitigating the accumulation of forecast errors for longer effective forecasts, such as achieving comparable performance to the ECMWF ensemble in 15-day forecasts. Despite various efforts to reduce accumulation errors, such as implementing autoregressive multi-time step loss, relying on a single model has been found to be insufficient for achieving optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts at a temporal resolution of 6 hours and a spatial resolution of 0.25∘. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation demonstrates that FuXi has forecast performance comparable to ECMWF ensemble mean (EM) in 15-day forecasts. FuXi surpasses the skillful forecast lead time achieved by ECMWF HRES by extending the lead time for Z500 from 9.25 to 10.5 days and for T2M from 10 to 14.5 days. Moreover, the FuXi ensemble is created by perturbing initial conditions and model parameters, enabling it to provide forecast uncertainty and demonstrating promising results when compared to the ECMWF ensemble.

Investigation of Key Parameters for Syringe-printing of Nano-silver Paste

Additive manufacturing (AM) techniques, also known as 3D printing, have been embedded into electronic components used for power modules as they can meet complex design requirements with less material waste and shorter lead-time. However, the corresponding manufactural considerations need to be investigated to ensure good printing quality. This paper is a parametric study of previous research [1], specifically focusing on the key parameters for the syringe-printing method that has been used to print a single-layer planar transformer winding implemented on a 10kW DC-DC power converter. A series of key parameters such as kick, trim length, feed rate, and pass spacing are defined, followed by inquiring into the printable regarding the width of a single trace and the gap/printing spacing between two traces. The results indicate that the targeted trace width and gap are subjected to certain errors, resulting in an imprecise actual print, that cannot be employed when designing a CAD model. Instead, a set of recommended values corresponding to the targeted values are acquired. Linear relations between the actual values and targeted values are obtained, which provides convenience for further design when considering using syringe-printing as an AM technique to print nano-silver traces.

Supply Chain Management Process of Textile Industry: A Case Study on Bangladesh

This research examines a descriptive analysis of Bangladesh's textile industry's process of export, import, and logistic system for yarn dyeing. Bangladesh's industrial sector, which includes the textile industry, is vital to the country's economic growth. In addition to meeting a significant portion of Bangladesh's foreign demand for clothes and apparel, the textile industry provides for about 100% of the country's internal demand. The textile industry is a complex supply chain that manufactures garments, complements, raw materials, and other items. As a traditional management tool, SCM enables manufacturers to work toward increasing the quality of their products, lowering the cost of their products and services, and speeding up product delivery and reaction times in a highly competitive market. Based on primary data of an organization which is Union Limited, a well-reputed and big textile base organization as well as secondary data from internet databases, journals, review articles, etc., this study was constructed. reduced stocks, reduced costs, increased productivity, more agility, shorter lead times, higher profitability, and increased customer loyalty are all aspects of the textile industry's successful supply chain management. This study discusses the overall processing of how a textile industry imports yarn, and how they deliver the final product to the consumer. This research paper will give the concept of the overall processing of the textile industry of Bangladesh.

Supplier Evaluation Management Practice and Supply Chain Performance of Kenyan Selected County Governments in Nyanza Region

Adequate supplier evaluations reduce the cost of projects. Therefore, inadequate supplier monitoring can lead to high costs, non-performance risks, and poor quality of the deliverables. The study examined the influence of supplier evaluation management practices on the supply chain performance of selected Kenyan county governments in Nyanza Region. The study used a number of methods, first utilising a descriptive and correlational research design. The study targeted 112 staff in the procurement department as well as nine lead prequalified suppliers. Data was collected using closed-ended questionnaires and an interview schedule. For data analysis, descriptive and inferential statistics were used. The study utilised both descriptive and inferential statistics. Supplier evaluation management practice had a positive and significant effect on supply chain performance (t = 5.789, p&lt;0.05). The study recommends that devolved units in Kenya should evaluate suppliers to ensure that effectiveness, efficiency, customer satisfaction, and a shortened lead time are realized. This can be achieved through quality commitments, distributions, and delivery strategies and evaluating the cost, thus leading to improved performance of the supply chain of devolved units.

Metal Additive Manufacturing in Automotive Industry: A Review of Applications, Advantages, and Limitations

Metal additive manufacturing (AM) technology is getting more interest and developing continuously in recent years due to its potential to revolutionize production processes in a variety of industries completely. The automotive industry is one of the most important industries where metal AM has shown great potential in the production of complex parts with high precision and shortened lead times. In this paper, current applications, advantages and limitations of metal AM for the automotive industry are analyzed by providing a comprehensive review. The paper examines the potential of metal AM for automotive applications, compares it to conventional manufacturing processes to determine its benefits, and identifies the significant drawbacks and difficulties. The review emphasizes how metal AM has the potential to transform the automobile sector by enabling producers to produce highly customized parts with enhanced performance properties, at lower costs, and with shorter lead times. However, currently, this technology is in its early stages and has several limitations such as limited material availabilities, high cost of equipment and materials, limited printing sizes, and need for several post-processings to get better results. To sum up, metal additive manufacturing technology has great adoption potential in the automotive industry, but further research and development are required to overcome its current limitations. Researchers and professionals in the industry seeking to comprehend the potential effects of metal AM on the automotive industry will benefit greatly from the findings of this study.

Seasonal climate models for national wheat yield forecasts in Brazil

National wheat yield depends on climate conditions and usually remains unknown until harvest. In-season knowledge can be provided by wheat yield forecast systems, supporting the decision-making of farmers, food traders, or policymakers. In this study, we improved a previously developed statistical wheat yield model to forecast trend-corrected wheat yield in Brazil with monthly temperature and precipitation data from seasonal climate models (SCM) from the last three months before harvest. We chose SCM from the European Center for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the UK-based Met-Office (UKMO). A multi-model ensembles (MME) approach from the three individual models as well as a climatology (CLIMATE) approach were also tested. Wheat yield forecasts were issued at the beginning of each month from planting in April to harvest in November. Each month, features from future months are forecasted by SCM, and past features are supplemented with observations from weather stations. Our approach shows a 12% RMSE in forecasting yield early in the season, from April to June. Forecasts start to improve from July onwards, with shorter lead times and including observed features from September onwards. At the beginning of October, about two months before harvest is completed, wheat yield can be forecasted with 7.6%, 7.9%, 7.9%, 9.1%, and 9.3% RMSE using climate data from UKMO, ECMWF, MME, NCEP, and CLIMATE respectively. Seasonal climate models can be useful tools to forecast national wheat yield, even shortly before harvest to prepare for possible food shortages. Our approach could be applied to other staple crops and regions.

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

Retrofitted Autonomous Inflow Control Devices Optimize Completions Design

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31301, “Optimized Completions Design Using Retrofit Autonomous Inflow Control Devices,” by Ezinne A. Nnebocha, Schlumberger; Akinola Akinbola, NPDC; and Omagbemi G. Kayakor, First Hydrocarbon Nigeria, et al. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. _ Discovered in 1964, the Beta Field in the Niger Delta sedimentary basin consists of 25 stacked hydrocarbon-bearing reservoirs between 5,500 and 12,000 ft true vertical depth subsea (TVDSS). Oil production peaked at 8,900 STB/D shortly after field startup and has been on the decline. Two horizontal wells, Beta 7 and Beta 8, were planned in the field, but a production forecast of the planned wells showed potential early water breakthrough and high water cut. Autonomous inflow control devices (AICDs) were deployed successfully in the Beta Field for the purpose of minimizing water production and maximizing oil recovery. Introduction After history matching, the two new horizontal wells were simulated to target the sweet spots in the reservoir using constraints of oil saturation, porosity and permeability limits, and distance to the oil/water contact (OWC). The history-matched model was then used in forecasting the production profile for the new wells using a target liquid production rate of 2,000 STB/D. The production forecast without inflow control showed water breakthrough in the new wells within the first month of production, reaching to 45% within 3 months of production. Cyclonic AICD completions then were simulated for Beta-7 and Beta-8 horizontal wells, which showed improved oil production and delayed water production. AICD Retrofit Design Cyclonic AICDs were selected for application in the Beta Field to improve production performance. However, the short lead time required for the job execution and border closures caused by the COVID-19 pandemic created restrictions in mobilizing personnel and equipment from outside the country. It did not appear that the equipment could arrive in time for the job. The conventional nozzle-based passive ICD joints were available in country. Therefore, leveraging engineering skills and operational knowledge, a solution to retrofit AICD nozzles in existing ICD screen joints was conceived. The engineering team developed a design of machined components that could be quickly manufactured at the facility of the original equipment manufacturer (OEM) and shipped to the location. Failure-mode and effects-analysis assessment, and subsequent risk-management procedures, became useful to manage the process locally with the help of the remote OEM team. A step-by-step procedure on the retrofit was devised. Fig. 1 shows a flow chart used to guide the retrofitting process and the retrofitted AICD joints after modification. Upon completion of the screen retrofit, the joints were quality-checked and inspected to verify their preparedness for well completion.

587P Short lead time and high rates of concomitant radiographic recurrences for ctDNA-based minimal residual disease assays colorectal cancer (CRC) during surveillance: Results from the MD Anderson INTERCEPT program

587P Short lead time and high rates of concomitant radiographic recurrences for ctDNA-based minimal residual disease assays colorectal cancer (CRC) during surveillance: Results from the MD Anderson INTERCEPT program

Fast fashion versus circular economy: an exciting match?

Fast Fashion is an extremely successful business model that brings apparel and home textiles to the market in ever shorter lead times at ever lower prices. On the one hand, this can be seen as extremely consumer-friendly, as customers can afford top-styled clothing that is always in line with the latest trends, even on a small budget. The opportunity to dress fashionably therefore does not remain a privilege of high earners. On the other hand, the production of fast fashion takes place without compliance with social and environmental standards. It's all about producing as cheaply and quickly as possible. In the EU, the linear economic model is currently being transformed into a circular one. This development does not stop at the textile sector. The textile industry is obliged to bring more durable products onto the market, to comply with social and environmental standards, and to recycle a high proportion of the textiles at the end of their life cycle. In this communication it is shown that fast fashion leads to an uncontrolled growth of textile production and that recycling alone cannot be the solution. The only way is to tackle the problem at its roots and not to regard waste prevention as just an empty phrase. It is obvious that fast fashion and the circular economy are not compatible and that there can only be one winner.