ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given

Model-driven development of data intensive applications over cloud resources

The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array

Building best practice automotive after sales network:The Volkswagen case

This thesis aims to analyze the service operations and networks in the automotive industry as research into the automotive After Sales service network lacks the necessary fine details and industrial feedback. Its purpose is to present the insights and lessons learned from studying the After Sales service network of Volkswagen, thereby defining a roadmap for further research, and to discuss the needs of the sector. The foremost idea in defining the research question was based on the observation that the automotive After Sales business could be improved by applying and adapting principles and methods used in other industries and in the field of Business Operations research. The initial step thereafter was an extensive external and internal literature research. The key characteristics of the automotive industry in Germany, at the VW Group, at OEMs and at the wholesale level were identified and are described in chapter two. In chapter three the primary After Sales processes are described and analyzed, from the interaction with the customer to the necessary activities at wholesale and OEM level. The proposed research methodology relied on extensive external and internal research and a qualitative and quantitative approach based on structured, in-depth interviews and direct observation. The objective of the interviews was to highlight the most important activities in the service delivery operations within the network and identify the major key factors for success or failure. The best practice dealer model is described in chapter five and was subsequently abstracted and generalized so that it can be applied to other industries too. A Data Envelopment Analysis (DEA), described in chapter six, was undertaken to determine the “efficient frontier” of service operations. Key performance indicators were identified from the important elements discussed and the best practices. In order to achieve an in-depth understanding of their general business models a benchmark analysis of six companies from industrial sectors complementary to the automotive business was then carried out and is described in chapter seven. The thesis highlights the development of a “best practice” network in chapter eight. This network grasps the dynamics of After Sales activities in the light of new technological developments and the experience gained from the benchmark with other industries. The thesis closes with an evaluation of the research work

Key Performance Indicators for Sustainable Freight Transport and Scenario-based Impediments in Pakistan Freight Industry

Freight transport enables economic growth, market connectivity, and access to the global supply chain systems which contribute to the societal progress and inclusive development of a country. However, it may undermine the sustainable operations by incurring external costs, inefficiency, and economic losses due to non-reliability, poor services, and information systems. In this research study, the Key Performance Indicators (KPIs) for Sustainable Freight Transport Systems (SFTS) are discussed based on available literature review and standards of sustainability measurement in freight transport. Then, based on KPIs some of the scenario-based impediments are highlighted which hinder the performance of the freight transport in Pakistan for achieving sustainable development goals. The core impediments included are Strategic Determinants (SD), Information Systems (IS), Infrastructure Management Systems (IMS) and City Logistics (CL). The negative direct impacts of the key identified factors are also highlighted and linked with each scenario-based impediment. This research study would provide an opportunity for the stakeholders to get tangible idea for policy making and upgradation of the freight transport industry in the country. The highlighted implications will also be validated via expert surveys and Delphi-analysis in the future study.

Northeast Asian containerised maritime logistics: supply chain collaboration, collaborative advantage and performance

This thesis aims to develop and validate the dimensions of supply chain collaboration and collaborative advantage in the containerised maritime industry and explores the impact of supply chain collaboration on collaborative advantage and port performance. Additionally, this thesis tests a mediation effect of collaborative advantage on the relationship between supply chain collaboration and port performance. This thesis employs a quantitative method. A theoretical model is built based on thorough literature reviews of supply chain management and maritime studies, in-depth discussions with experts, item review and Q-sorting techniques to signify ambiguity or misunderstanding with the scales and to suggest modifications. The proposed model is empirically tested with survey data using 178 responses from terminal operators, shipping lines, inland transport companies, freight forwarders, ship management companies and third-party logistics providers involved in maritime logistics in the major containers ports of Busan, Gwangyang and Incheon for a comprehensive and balanced view by using structural equation modelling. With regard to the findings of the empirical research, three main constructs were successfully validated as multi-dimensional constructs. The structural paths support hypotheses that supply chain collaboration has a positive influence on collaborative advantage, and collaborative advantage has a strong contribution to port performance. However, the direct impact of supply chain collaboration on port performance is insignificant. A hierarchical approach of the mediation test and bootstrapping test found that the association between supply chain collaboration and port performance is fully mediated by collaborative advantage. In other words, the greater degree of supply chain collaboration between the port and port user enables them to gain a higher degree of collaborative advantage, and, in turn, this collaborative advantage can contribute to augmenting port performance. This thesis synthesises transaction cost theory, resource based theory and a relational view to explain how supply chain collaboration influences collaborative advantage and port performance. Its theoretical contribution expands the concept of supply chain collaboration and collaborative advantage into containerised maritime contexts, capturing the perspective of the ports and port users. Further, despite numerous maritime studies which extol the importance of collaboration between the ports and port users, no systematic approach has previously developed and validated those constructs and relationships. The various maritime logistics organisations would benefit from applying the results of this study to their supply chain collaboration practices when seeking greater collaborative advantage. The results heed practitioners in containerised maritime logistics organisations to focus on balancing the facets of supply chain collaboration to transport flows of containers seamlessly and efficiently from door-to-door, as supply chain management philosophy drives the maritim

Capturing managerial cognition and investigating the impact of scenario planning in the shipping industry

Summary This paper outlines the research aims and some initial findings from two systematic literature reviews that were recently conducted by the authors. Starting with the question 'how do managers think about the future?', this paper engages in a conversation framed within the context of corporate foresight on how stakeholders in shipping industry engage with it in turbulent times. Deriving from the findings of reviews of literature, it sets out the evolution of the application of the scenario technique in the shipping industry as well as the technique's impact on participants in general. The paper further describes the proposed research design to attempt to answer the proposed research questions and articulates some expected outcomes

Automatic performance optimisation of component-based enterprise systems via redundancy

Component technologies, such as J2EE and .NET have been extensively adopted for building complex enterprise applications. These technologies help address complex functionality and flexibility problems and reduce development and maintenance costs. Nonetheless, current component technologies provide little support for predicting and controlling the emerging performance of software systems that are assembled from distinct components. Static component testing and tuning procedures provide insufficient performance guarantees for components deployed and run in diverse assemblies, under unpredictable workloads and on different platforms. Often, there is no single component implementation or deployment configuration that can yield optimal performance in all possible conditions under which a component may run. Manually optimising and adapting complex applications to changes in their running environment is a costly and error-prone management task. The thesis presents a solution for automatically optimising the performance of component-based enterprise systems. The proposed approach is based on the alternate usage of multiple component variants with equivalent functional characteristics, each one optimized for a different execution environment. A management framework automatically administers the available redundant variants and adapts the system to external changes. The framework uses runtime monitoring data to detect performance anomalies and significant variations in the application's execution environment. It automatically adapts the application so as to use the optimal component configuration under the current running conditions. An automatic clustering mechanism analyses monitoring data and infers information on the components' performance characteristics. System administrators use decision policies to state high-level performance goals and configure system management processes. A framework prototype has been implemented and tested for automatically managing a J2EE application. Obtained results prove the framework's capability to successfully manage a software system without human intervention. The management overhead induced during normal system execution and through management operations indicate the framework's feasibility