High Lead Time Research Articles

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.

Virtual Manufacturing concepts reducing the Flow Time in Aircraft Manufacturing

The high cost and long lead times of traditional manufacturing makes it difficult for manufactures to efficiently meet eternally changing market demands. In order to determine the manufacturability of the component and low cost production of aircraft manufacturing, Digital Virtual Manufacturing plays a vital role. By the advancement in technologies like Virtual Manufacturing tools used through Product Lifecycle Management (PLM), are competent to access and reuse the best practices, as well as evaluate 3-D manufacturing scenarios. By implementing advance 3-D CAD systems with the CNC machines enables the success of the determinate assembly approach to significantly reduce the lead time aircraft manufacturing. This paper encompasses the virtual manufacturing concept to enable complex manufacturing set-up analysis, exchanging expertise at collaborative work. Virtual Manufacturing is using the manufacturing knowledge at PLM environment for defining the assembly process of an aircraft parts.

Optimally configuring a two-stage serial line supply chain under the guaranteed service model

Design-and-development buyers sourcing new supply chains and strategic sourcing analysts making outsourcing decisions for existing supply chains are in different organizations but share a common problem. Both determine whether an existing part should be replaced. Relative to the existing part, new candidates could be cheaper with longer leadtime, or more expensive with shorter leadtime. Furthermore, a longer leadtime part could be buffered with inventory and this could be cheaper than paying more for a shorter leadtime part.To derive analytical insights into the nature of this problem, we restrict our scope to a two-stage serial line supply chain. This restriction is consistent with sourcing analysts that consider sourcing a single part from different vendors and different transportation alternatives. The resulting two-stage supply chain configuration model jointly determines the chosen option and inventory stocking level at each stage to minimize cost of goods sold, pipeline stock cost and safety stock cost.We prove it is preferable to synchronize the supply chain by employing the same type of option, either low cost long leadtime or high cost short leadtime, at both stages. We prove that the selection threshold for high cost short lead time options is lowest at just the downstream stage, highest for just the upstream stage, and between these extremes if such a candidate is selected for both stages. If a part’s cost-time relationship follows a functional form, we establish conditions when it is optimal to choose the lowest cost, longest leadtime, option available.

Sobre o Uso da Persistência de Previsões Determinísticas de Vazão para a Tomada de Decisão

Resumo Previsões de vazão de médio prazo, que são geradas utilizando um modelo chuva-vazão forçado por previsões numéricas de precipitação, são muito úteis para a antecipação de eventos hidrológicos. Tradicionalmente estas previsões eram geradas de forma determinística, porém na última década o movimento por geração de previsões de vazão por conjunto vem ganhando força. No meio termo entre estas duas técnicas (determinísticas e por conjunto) o uso da informação de persistência de previsões determinísticas de vazão para a tomada de decisões, usando ela como uma medida do grau de incerteza dos resultados aparenta ser uma estratégia interessante. Esta pesquisa investiga justamente estes possíveis benefícios do uso da persistência, das previsões como informação de incerteza. Os resultados sugerem que o uso da previsão com base na persistência apresenta vantagens sobre a previsão simples determinística nos horizontes de previsão mais distantes.

Exploring Leadership Styles, Flexibility, Postponement and Integration Levels of Mass Customizations Firms

In earlier times, pure customization and mass production were the two prime production techniques in industry management. Pure customization helps manufacturers to produce unique products to satisfy specific customer needs. These practice leads to high product price, high lead time and complicated production line. On the other hand, mass production leads to lower product variety and lower customer satisfaction. Mass Customization is a production process to attract customer and deliver them the products according to their choice at a very low cost. This paper presents an ongoing research on the leadership style of the companies that follows mass customization. Several hypotheses exploring personality styles and leadership styles of leaders of mass customization firms has been proposed.

The Lead Time Syndrome of Manufacturing Control: Comparison of Two Independent Research Approaches

The aim of production planning and control is to ensure the achievement of the logistic targets of high due date reliability, low lead times, high capacity utilization, and low WIP levels, while maintaining productivity and quality targets. If order due dates are missed, a common intuitive reaction of production planners is to adjust planned lead times. How often and to what extent updates are reasonable has previously been unclear because, while trying to improve the logistic target achievement, planned lead time adjustments may actually cause an opposite effect, which is known as the Lead Time Syndrome (LTS) of Manufacturing Control [1]. Previous research on the LTS interactions has shown that the line of argumentation of the LTS is valid [2]. Knollmann et al. showed by means of mathematical modeling, control-theoretic simulation and case study research that planned lead time adjustments lead to a short-term increase in lead time variation, thus to an increase in lateness variation and to a decrease in due date reliability [2–5]. The authors suggest to choose update frequency depending on the ratio of latency period and the update frequency (the period between two consecutive adjustments) as the misbalance of these two parameters turns out to be the main trigger of the LTS. Selçuk investigated the LTS by means of queuing theory in an independent approach [6–8]. The authors concluded that planned lead time adjustments lead to an increase in process variability, thus to high WIP levels and long lead times. However, they suggest to reduce update frequency, to decrease process variability and thus to avoid LTS. This conclusion is not in line with the conclusions drawn from the research presented by Knollmann et al.. Therefore, this paper compares the different research approaches methodologies and discusses how the different research methodologies impact the conclusions drawn for practice application. This comparison provides further insights into LTS research and indicates further research fields.

Prediction of daily rainfall by a hybrid wavelet-season-neuro technique

Accurate daily rainfall prediction is required for accurate streamflow prediction, flooding risk analysis, constructing a reliable flood control and early warning system. However, because of its nonlinearity, prediction of daily rainfall with high accuracy and long prediction lead time is difficult. There are many daily rainfall prediction methods in the literature, but they are known to yield inaccurate predictions with short lead time, require many physical parameters and involve complicated mathematical equations with huge computational burden. Recently, artificial neural network has been used for predicting rainfall with the objective of addressing the above mentioned problems. But still, the accuracy has not been satisfactory and predictions are with short lead time. In this study, two methods called combined season-multilayer perceptron (SAS-MP) and hybrid wavelet-season-multilayer perceptron (W-SAS-MP) were developed to enhance prediction accuracy and extend prediction lead time of daily rainfall up to 5 days by using data from two stations in Turkey. These two models were compared with the stand-alone multilayer perceptron and another most commonly used method called combined wavelet-multilayer perceptron (W-MP). The performances of the models were evaluated by using coefficient of determination, coefficient of efficiency and root mean squared error. The SAS-MP model was found to be better than W-MP in most cases, except lead time day 1, where W-MP performed better. Throughout all the lead times, however, the hybrid W-SAS-MP model performed best with CE values of 0.911 and 0.909, respectively, for prediction lead time of 1 day and 0.588 and 0.570, respectively, for prediction lead time of 5 days at Stations 17836 and 17837, respectively, at the model testing (validation) phase. Therefore, W-SAS-MP can be an appropriate tool for enhancing daily rainfall prediction accuracy and extend prediction lead time.

Forecasting of Time Series Significant Wave Height Using Wavelet Decomposed Neural Network

In this current study, a hybrid model of wavelet and Artificial Neural Network (WLNN) has been developed to forecast time series significant wave height for lead times up to 48h. The data used in the hybrid model are significant wave heights (Hs) belongs to two stations, one near to New Mangalore port, Indian ocean and another near to west of Eureka, Canada in North Pacific ocean. The three hourly significant wave height data for a period of one year was first decomposed through discrete wavelet transformation in order to obtain frequencies of different bands in the form of wavelet coefficients. Later these coefficients are used as inputs into Levenberg Marquardt artificial neural network models to forecast time series significant wave heights at multistep lead time. Two different methods WLNN-1 &WLNN-2 employed for the first station data to forecast significant wave heights at higher lead times. From the result it is found that the second method (WLNN-2) in wavelet-ANN model performed better than first method (WLNN-1).Model results obtained for two stations showed good predictions at lower lead times but slight deviation observed at higher lead times. As compared to first station results, the second station results are slightly poor because of more statistical variations in the data set.

Role of Tendering Process on Performance of Public Institutions: A Case Study of Nakuru County Government

The purpose of the study was to determine the role of tendering process on performance of public institutions. The study was guided by the following specific objectives. To assess the role of transparency resulting from tendering process on the performance of public institutions, to establish the role of accountability resulting from tendering process on the performance of public institutions, to find out the role of quality resulting from tendering process on the performance of public institutions, to determine the role of lead time resulting from tendering process on the performance of public institution. A case study design was adopted for the study. The study targeted 43 procurement officers from 10 ministries in Nakuru County Headquarters. Census technique was applied. Questionnaires were used to collect data. Data analysis was done using descriptive and inferential statistics. The findings revealed that the level of transparency in the county government was good. They further revealed that transparency reduced corruption during tendering process hence resulting to enhanced performance in public institutions. Further the researcher found out that Public administration on tendering process leads to accountability during the tendering process and hence positive performance of public institutions. The research findings also revealed that high lead time variability due to tendering was a major reason for the institutions inability to achieve inventory goals and hence affecting the performance of public institutions negatively. Based on the findings, the researcher recommended that the organization employees to be more transparent in the tendering process, the organization puts in place measures to enhance transparency during the process and also to minimize lead time to enhance the speedy delivery in the organization and thus enhance overall organization performance of the organization. The researcher recommended a further research to be carried out on the role played of other alternative procurement methods on performance of public institutions. Further studies should be conducted on the critical success factors of tendering in public institutions.

Benefits of CPFR Collaboration Strategy under Different Inventory Holding and Backorder Penalty Costs

This study uses discrete event simulation to investigate the benefits of Collaborative Planning, Forecasting andReplenishment (CPFR) strategy over Traditional Supply Chain (TSC) under different inventory holding costsand backorder penalty costs for both the manufacturer and retailer. A two-echelon production-inventory systemwith a retailer and manufacturer in a variable demand environment is considered for this study. No information isshared in TSC, while sales, forecast and inventory level information is shared in CPFR between a retailer and amanufacturer. The order quantity for retailer and the production quantity for manufacturer during each period aredetermined using periodic review order-up-to inventory policy. Lot-for-lot production policy is used by themanufacturer with a lead time of one period. The results suggest that CPFR performs better than TSC for boththe manufacturer and retailer under all inventory holding costs. As inventory holding cost per unit per periodincreases, benefits of CPFR also increases for both the manufacturer and retailer. Also, when inventory holdingcost is high, maximum benefits of CPFR is achieved for both the manufacturer and the retailerin an environmentwithhigh demand variability, high backorder penalty cost and long delivery lead time.

Modelling the future of the Canadian cardiac surgery workforce using system dynamics

Due to the high costs and lengthy lead times involved with training health human resources such as physicians and surgeons, combined with the serious burden borne by the general population when health care provider shortages occur, advance planning of resource requirements is critical. This is particularly true in light of current demographic trends and Canada’s ageing population, which will potentially increase demand for health care providers in the future while also leading to the retirement of many of the providers currently practicing. The purpose of this research was to develop a model simulating the workforce within a single specialty at a national level, which includes students training to enter the profession, providing a tool that would help to inform future resource planning. We present the details of this model, developed using system dynamics modelling, and demonstrate it using the example of cardiac surgeons in Canada.

Concurrent Engineering for Composites

Faced with increasing global competitiveness, the issues of high costs, product robustness, and long lead times associated with product development are emerging in forefront of the problems facing American industry. It is increasingly clear that a significant (70–80%) percentage of system life cycle costs are committed during the phase of concept development and this is of special significance in composites were design decisions are highly coupled. The continued growth of composites is thus dependant on both development of low–cost fabrication technologies and adoption of the philosophy of concurrent engineering. It is essential that the early decisions are made based on the integration of all phases of product development. In this paper we discuss the need for concurrent engineering methodology for composites, and present framework for such an approach.

River flow forecasting through nonlinear local approximation in a fuzzy model

This study investigates the potential of nonlinear local function approximation in a Takagi---Sugeno (TS) fuzzy model for river flow forecasting. Generally, in a TS framework, the local approximation is performed by a linear model, while in this approach, linear function approximation is substituted using a nonlinear function approximation. The primary hypothesis herein is that the process being modeled (rainfall---runoff in this study) is highly nonlinear, and a linear approximation at the local domain might still leave a lot of unexplained variance by the model. In this study, subtractive clustering technique is used for domain partition, and neural network is used for function approximation. The modeling approach has been tested on two case studies: Kolar basin in India and Kentucky basin in USA. The results of fuzzy nonlinear local approximation (FNLLA) model are highly promising. The performance of the FNLLA is compared with that of a pure fuzzy inference system (FIS), and it is observed that both the models perform similar at 1-step-ahead forecasts. However, the FNLLA performs much better than FIS at higher lead times. It is also observed that FNLLA forecasts the river flow with lesser error compared to FIS. In the case of Kolar River, more than 40 % of the total data are forecasted with <2 % error by FNLLA at 1 h ahead, while the corresponding value for FIS is only 20 %. In the case of 3-h-ahead forecasts, these values are 25 % for FNLLA and 15 % for FIS. Performance of FNLLA in the case of Kentucky River basin was also better compared to FIS. It is also found that FNLLA simulates the peak flow better than FIS, which is certainly an improvement over the existing models.

Improved higher lead time river flow forecasts using sequential neural network with error updating

Abstract This paper presents a novel framework to use artificial neural network (ANN) for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN), is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (&gt;94% efficiency) was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead). The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.

Analysis of production and inventory systems when orders may cross over

This paper is concerned with the analysis of periodic-review order-up-to inventory systems with backorders in which stochastic replenishment order crossovers may be observed, i.e., where it is possible that orders arrive in a different sequence to that in which they were issued due to high stochastic lead time fluctuations. This may be the case whenever sequentially placed orders are processed on parallel replenishment systems with independent stochastic processing times, and multiple orders may be open at the same time. Following the widespread tendency in the manufacturing industry to reduce order sizes and thus increase order frequency while parts and materials are replenished from distant sources, these conditions are nowadays met in many real-world processes. Such systems typically use regular order intervals, e.g. to enable joint replenishment of various item positions. In this paper, the concept of effective lead times is elaborated as a methodological basis for analyzing periodic replenishment processes that may exhibit order crossover. It is compared with the existing concept of outstanding orders. Based on this, formulae are developed to give an exact analysis of three essential performance indicators of a periodic-review order-up-to inventory system with independent stochastic lead times.

Implementation of Lean Service with Value Stream Mapping at Directorate Airworthiness and Aircraft Operation, Ministry of Transportation Republic of Indonesia

The purpose of this paper is to apply value stream mapping (VSM) for enabling leanness in Directorate Airworthiness and Aircraft Operation, Directorate General of Air Transportation, Ministry of Transportation Republic of Indonesia. A current state mapping is made to determine the activities that provide value-added and non-value-added activities or waste. Weaknesses in the current state process mapping will be analyzed and improved in the future state mapping to obtain a better flow, faster and more efficiently. The reduction of total lead time, makes total cycle time decrease significantly, i.e. 69%, or from 94.5 to 29.5 days, while the process time is constant, i.e. 18 days. Future state mapping can reduce lead time and increase cycle time efficiency. Current State Mapping shows weaknesses/waste and is non value added. Current state shows quite a high lead time value, so that cycle time efficiency is low. The efficiency value is 0.112 and after future state mapping becomes 0.70. Lean service research in government organizations has been the first time done in Indonesia.

Linkages between common wheat yields and climate in Morocco (1982–2008)

In Morocco, wheat production shows a high inter-annual variability due to uncertain rainfall. In view of the importance of this resource to the country's economy, it is important to gain a better understanding of the natural large-scale climate oscillation governing this variability. In this study, we analyzed de-trended (1) time series of common wheat yields (1983-2008) from 11 agricultural provinces that account for 80% of national wheat production; (2) monthly rainfall and 10-day temperature from ten meteorological stations; (3) 10-day normalized difference vegetation index (NDVI) from the AVHRR sensor; (4) monthly atmospheric climate indices [North Atlantic Oscillation (NAO) and Scandinavian Pattern (SCA)] and monthly 500 hPa geopotentials fields; and (5) monthly sea surface temperature (SST) fields and indices (NIÑO3, Tropical North Atlantic and Tropical South Atlantic). The relationship between rainfall and temperature during tillering in early winter and grain filling in early spring and wheat yields already observed at the plot scale was also found to be significant at the provincial scale. The linkages between wheat yields and large scale climate have been analyzed for the first time over Morocco. In agreement with previous studies, results show a complex and competing influence of different climate phenomena. The NAO is found to be significantly related to yields during the early stage of wheat growth in December, whereas the SCA correlates with yields later in the season, in January and February. Interesting lagged correlations with higher lead time are also highlighted, with the leading modes of SST variability in the equatorial Atlantic during October (the "Atlantic Niño" mode) and in the North Atlantic (the "Atlantic tripole" mode) in February. Our conclusion is that regional climate indices and variables represent valuable information with which to increase lead time and skill regarding wheat yield predictions in Morocco.

Critical spare parts ordering decisions using conditional reliability and stochastic lead time

Asset-intensive companies face great pressure to reduce operation costs and increase utilization. This scenario often leads to over-stress on critical equipment and its spare parts associated, affecting availability, reliability, and system performance. As these resources impact considerably on financial and operational structures, the opportunity is given by demand for decision-making methods for the management of spare parts processes. We proposed an ordering decision-aid technique which uses a measurement of spare performance, based on the stress–strength interference theory; which we have called Condition-Based Service Level (CBSL). We focus on Condition Managed Critical Spares (CMS), namely, spares which are expensive, highly reliable, with higher lead times, and are not available in store. As a mitigation measure, CMS are under condition monitoring. The aim of the paper is orienting the decision time for CMS ordering or just continuing the operation. The paper presents a graphic technique which considers a rule for decision based on both condition-based reliability function and a stochastic/fixed lead time. For the stochastic lead time case, results show that technique is effective to determine the time when the system operation is reliable and can withstand the lead time variability, satisfying a desired service level. Additionally, for the constant lead time case, the technique helps to define insurance spares. In conclusion, presented ordering decision rule is useful to asset managers for enhancing the operational continuity affected by spare parts.

The roles of ICT in driverless, automated railway operations

Transportation plays an important role in building supply chains; several emerging theories and practices of supply chain management rely heavily on transportation. In recent years, ICT has been favourably employed in engineering, operations and business sectors. However applications and opportunities of ICT are still evolving; with reference to ICT, today’s norm is obsolete tomorrow. Rail transport too is becoming increasingly dependent on ICT, not only for commercial purposes such as asset management, passenger ticketing and information broadcasting etc., but also for mission/safety-critical functions, particularly in infrastructure development in the era of high speed and automated railways. A critical issue is that railway infrastructure is capital intensive and has a long gestation period. The initial effort and cost of developing ICT for safety/mission critical railway applications are high and detailed planning is necessary to study requirements and product specifications. The ever evolving ICT standards contribute to high cost over-runs and long lead time. Whilst the rail industry has embraced the technology of the day, several ICT advancements remain unexplored by railways; financial and technical mishaps tend to make one cautious. This paper analyses automated rail operational technology in-depth and the role of ICT in a broader overview of enhancing transportation capacity.

Evaluation of Dynamic Behavior of Machine Tools for Sculptured Surface Manufacturing

The production sector of molds and dies is under a continuous pressure for process optimization due the high lead time required for the production of complex tools. In recent years, to improve this sector, numerous studies have been aimed at optimization of the CAD/CAM/CNC chain. However, despite recent technological innovations such as development of cutting tools, machine tools, and process simulation systems, the machining step still represents a major bottleneck in this process. The current generation of Numerical Control (NC) programs that represents the tool path do not consider the dynamic characteristics of the machine tool in the decision-making process to choose the most appropriate cutting strategies, resulting in NC programs that do not reach levels of excellence. The main objective of this paper is to present the development of a reference index based on the dynamic characteristics of the Machine Tool to support the NC programming with a commercial CAM system. This index considers the Machine Response Time (MRT) of NC controller, the acceleration and deceleration on the axes of the machine tool and the tool path segment (size and inclination). The index makes possible an experimental evaluation of relationships between the programmed feed rate for the tool path and dynamic performance due to movements of the machine axes, thus becoming an important decision-making factor in choosing the best machining strategy and consequently a better plan of the production process in die and mold industries.