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The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios.

SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model.

Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy.

The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world.

By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%.

The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.

As the global economy continues to boom, there remains a significant need for more efficient transportation and effective management in corporate logistics. In this regard, railways have been considered one of the most efficient modes for long distance transportation. In Asia, there are several extensive and direct railroads such as the TSR (Trans-Siberian-Railroad), the TCR (Trans-Chinese-Railroad) and the TMR (Trans-Mongolian-Railroad) which could connect Asia to Europe. If these railroad networks such as the TKR (Trans-Korean-Railroad) were fully operational, it is expected that they would replace a major portion of the current global trade transportation with is sent through other shipping methods. Therefore, the development of railroad networks is one of the most important steps toward an integrated international transportation system. However, in reality, it is difficult to achieve this vision because of the political and economic problems surrounding multiple countries that this network must cut across. Moreover, it is difficult to ensure the railways’ economic competitiveness when it is compared with other logistics options. In this study, we aim to discover the status quo about railway networks by focusing on the TCR and TSR. Through in-depth interviews and surveys with actual users of these networks, current issues and problems are analysed in order to make suggestions for improvements. This research also provides meaningful insights which the TKR-TSR and TKR-TCR railway networks should consider if they want to continue to be successful in the future.

This paper seeks to present a decision‐making model for manufacturers to maximize their profits in reverse logistics operations.

A system dynamic model has been developed to complement with prior models and is validated using data collected from a computer company manufacturer handling returns with volumes transacted over a period of two years.

The results from the model indicate that part replacements from suppliers are more profitable than refurbished computer parts. In addition, transportation delay and supplier delay in processing returns have a significant impact on the viability of reverse logistics regardless of return volumes.

The current model is not designed for third‐party logistics (3PL) offering reverse logistics services. However, this can be accomplished by resetting some of the parameters in the model. The other limitations are exchange rate fluctuation and product depreciation which are not incorporated in the model. This is important in Asia where each country has its own currency which fluctuates with time.

This dynamic model will assist decision‐makers to test new policies related to reverse logistics, for example, liberal versus conservative return policy from supplier, shipment consolidation (longer delays) versus direct shipment, batch (longer delays) versus JIT remanufacturing, pricing of new parts versus re‐condition parts, as well as to examine its long‐term viability.

Using system dynamics to understand the profitability of reverse logistics for both replacement parts to suppliers and refurbished parts to manufacturers.

This paper constructs the uncertainty analysis model of prefabricated building supply chain risk. The model is designed to study the formation path of prefabricated building supply chain risk and is expected to be used by industry stakeholders for supply chain risk management.

Based on the uncertainty circle model, construct a configuration analysis framework for supply chain risks in prefabricated buildings. The fuzzy set qualitative comparative analysis (fsQCA) is used to study the configuration influence of five uncertain factors, including environment, plan-control, demand-supply, manufacturing and assembly-transportation, on the risk of the prefabricated building supply chain.

There are three paths to promote the high-risk generation of the prefabricated building supply chain: assembly-transportation-oriented, plan-control-oriented and manufacturing-oriented. There is a specific equivalent substitution relationship among the five causal conditions. Under specific conditions, different combinations of conditions have the same effect on promoting supply chain high-risk generation through equivalent substitution.

The multiple concurrent causal relationships of risk conditions in the assembly construction supply chain are studied under the grouping perspective, which helps to expand the research perspective of assembly construction supply chain risk and provides theoretical guidance for supply chain risk management of construction enterprises.

Roger Osayende, a former management consultant, must advise the Ministry of Health of Ektu, a fictional country in Central Africa, on how to implement a new point-of-care diagnostic test for infants with HIV. In Ektu, mothers often transmitted HIV infection to infants during pregnancy, delivery, or breastfeeding due to inadequate resources to invest in prevention efforts. The existing procedure to diagnose infants with HIV required collecting dried blood samples at more than two hundred healthcare facilities around the country and transporting them to a central laboratory in the capital for testing. This process was characterized by significant delays due to long transportation times, batching of samples in transportation and processing in the lab, and concomitant congestion in the lab. This delay resulted in loss to follow-up, that is, lost patients due to mothers not collecting their infants' results. A new point-of-care device was about to be introduced, which would obviate the need for this centralized processing and the resulting diagnostic delay. The key decision under consideration is where to place the devices to maximize their effectiveness.

Understand the importance of making public health decisions based on a data-driven, logical framework   Uncover the link between operational performance of the healthcare system and health outcomes at the population level   Appreciate the relevance of operational decisions in enhancing or diminishing the effectiveness of a medical technology   Use process analysis concepts to characterize various components of delays

Risk factors related delay hinder the schedule performance of most construction projects in the world. It is a critical challenge to realize the advantages of prefabricated construction projects (PCPs) under the negative effect of schedule delay. This paper aims to propose an exhaustive list of risk factors impeding the progress of PCPs and evaluate the collected risk factors based on the cause–effect relations. The ultimate goal is to improve the understanding of the complex relations among various risk factors related delay in PCPs, and also offer managers a reference on aspect of schedule risk management.

This paper proposes a hybrid method of GT–DEMATEL–ISM, that is combing grounded theory, DEMATEL (decision-making trial and evaluation laboratory) and ISM (Interpretative Structural Modeling), to collect, evaluate and structure risk factors related delay for PCPs. The research procedure of this methodology is divided into three stages systematically involving qualitative and quantitative analysis. In the first stage, GT is utilized to implement qualitative analysis to collect the risk factors leading to schedule delay in PCPs. While, the quantitative analysis is to analyze and evaluate the collected risk factors based on the cause–effect relations in the next two stages evaluation by the DEMATEL focuses on quantifying the priority and intensity of the relations between factors. Additionally, ISM is employed to construct the hierarchical structure and graphically represent the pairwise relations between factors.

The outcome of qualitative investigation by grounded theory proposes a theoretical framework of risk factors related delay for PCPs. The framework contains three levels of category, namely, core category, main category and initial category and provides a list of risk factors related delay. Following this finding, evaluation results by the DEMATEL classify factors into cause and effect groups and determine 11 critical delay risk factors. Meanwhile, the findings show that risks referring to organizational management issue foremost impact the progress of PCPs. Furthermore, a systemic multilevel hierarchical structure model is visually constructed by ISM to present the pairwise linkages of critical factors. The model provides the risk transmission chains to map the spread path of delay impact in the system.

The contribution of the study involves twofold issues. Methodologically, this research proposes a hybrid method GT–DEMATEL–ISM used to identify and analyze factors for a complex system. It is also applicable to other fields facing similar problems that require collecting, evaluating and structuring certain elements as a whole in a comprehensive perspective. The theoretical contribution is to fill the relevant research gap of the existing body of knowledge. To the best knowledge of the authors, this paper is the first attempt to integrate qualitative and quantitative research for risk analysis related delay and take the insight into the whole process of PCPs covering off-site manufacture and on-site construction. Furthermore, the analysis of findings provided both a micro view focusing on individual risk factor and a managerial view from a systematic level. The findings also contribute the effective information to improve the risk management related schedule delay in PCPs.

Research work describes in‐transit distribution strategy by determining and analyzing key principles of it as well as by illustrating its application in practice. Emphasis on in‐transit distribution strategy is to turn transportation pipeline as a mobile inventory holding place, and actively dispatching goods to a destination, where there is a predicted demand before any customer orders are actually received. The use of this strategy is supported by current trade flows: emerging market trade has increased considerably, but simultaneously Swedish export prices, for example, have significantly decreased. The paper aims to address this issue.

In‐transit strategy is examined through a multiple case study from industrial companies having main factory operations in Sweden as well as using a system dynamics simulation model, and Monte Carlo analysis. These are supported by the second hand data of trade flows between Sweden, and India and China.

In order to be successful with in‐transit strategy, the case studies show that excellent planning, working closely with customers, first‐class market knowledge, and an enterprise resource planning (ERP) system that is able to support the process sufficiently are required. Other highlighted requirements of this strategy are low variation in demand, and predictable distribution lead‐time. Simulation study of one hypothetical product group verified case study findings, but the authors find it interesting that manufacturing output variance especially is very sensitive regarding to the overall results. If variation increases, then in‐transit strategy is not able to deliver for customers with the necessary accuracy. Also increasing average customer demand, and longer transportation delays lead to undesired outcomes (e.g. too much inventory or out of stock situations).

The case study and second hand analysis is limited to one country, and further evidence is needed from other European, and possibly North American companies, to verify these findings.

There has been a rather limited amount of research works completed from the use of in‐transit strategy, even if increased trade activity and lower price of exported items is that of the old west in their exports to emerging markets, and continues to be so in the future (was even strong to China during credit crunch year 2009). Our research is seminal in terms of a developed system dynamics simulation model.

The aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.

Two mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.

The results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.

Although integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

This paper explores the interaction and impact of political disruptions on textile supply chain performance in Pakistan. A qualitative approach is adopted to explore the linkages and relationships between political disruptions and supply chain disruptions and performance. Semi-structured interviews were conducted at 25 different textile manufacturing firms. This study confirmed the prevalence of severe and variegated impacts of political disruptions on the textile supply chain. Supply chain disruption is found to be a key mediating factor that directly and indirectly affect supply chain performance through an increased production and delivery lead-time, transportation delays, interruptions of raw material supplies to plants and distributors and the restricted access to workplaces for suppliers and workers. The linkages are represented through vicious circles that illustrate the interactions and inter-relationships between disrupted supply chain and performance. This study provides empirical evidence to help government to formulate pertinent labour laws and industrial policy to mitigate political disruptions and minimise deleterious effects of supply chain disruption on production and distribution networks whilst respecting and protecting the democratic rights of people.