A Bayesian Network Estimation of the Service-Profit Chain for Transport Service Satisfaction

Bayesian network methodology is used to model key linkages of the service-profit chain within the context of transportation service satisfaction. Bayesian networks offer some advantages for implementing managerially focused models over other statistical techniques designed primarily for evaluating theoretical models. These advantages are (1) providing a causal explanation using observable variables within a single multivariate model, (2) analysis of nonlinear relationships contained in ordinal measurements, (3) accommodation of branching patterns that occur in data collection, and (4) the ability to conduct probabilistic inference for prediction and diagnostics with an output metric that can be understood by managers and academics. Sample data from 1,101 recent transport service customers are utilized to select and validate a Bayesian network and conduct probabilistic inference

A study on decision-making of food supply chain based on big data

As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value from big data

Multifaceted modelling of complex business enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control

Assessing Relationship between Personal Value and Customer Satisfaction:Evidence from Nigerian Banking Industry

Results implicated self-transcendence as having positive relationship with customer satisfaction while selfenhancement has negative relationship with customer satisfaction. It was also established that the overall personal value has significant effect on customer satisfaction. The research measures showed encouraging psychometric values. These findings were discussed and situated within the Nigerian banking industry. It was recommended that the banking industry should place more emphasis on target marketing practices thereby enhancing the quality delivery of services to customers. Areas of further studies were also suggested

Quantifying economic benefits for rail infrastructure projects

Investment in rail infrastructure is necessary to maintain existing service and to cater for future growth in freight and passenger services. Many communities have realized the importance of investment in rail infrastructure projects and set up goals and visions to achieve economic development through investing in such projects. Due to limited funds available, communities have to select a single or very few projects from a variety of projects. It is very critical that right projects must be selected at the right time for a community to realize economic development. The limited methods for quantifying the economic benefits to the stakeholders often cause a problem in the selection process. Most of the conventional methods focus mainly on the economic impact of the project and ignore the metrics that convey the economic impacts in meaningful ways to the key stakeholders involved. This leads to uncertainty in the project selection and planning process and often leads to failure in achieving the goals of the project. This study aims to provide a mathematical framework that quantifies economic benefits of investment in rail infrastructure projects in meaningful ways to the key stakeholders through three different approaches, namely, Leontief-based approach, Bayesian approach and system dynamics approach. The Leontief-based approach is the easiest of all the three approaches provided that historical data is available. Bayesian approach is also very beneficial as it can be used by coupling small data with surveys and interviews. Also, system dynamics model is very useful to conduct qualitative analysis, but the quantitative analysis part can become very complex --Abstract, page iii

A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport

We formalise and present an innovative general approach for developing complex system models from survey data by applying Bayesian Networks. The challenges and approaches to converting survey data into usable probability forms are explained and a general approach for integrating expert knowledge (judgements) into Bayesian complex system models is presented. The structural complexities of the Bayesian complex system modelling process, based on various decision contexts, are also explained along with a solution. A novel application of Bayesian complex system models as a management tool for decision making is demonstrated using a railway transport case study. Customer satisfaction, which is a Key Performance Indicator in public transport management, is modelled using data from customer surveys conducted by Queensland Rail, Australia

Quantifying Economic Benefits for Rail Infrastructure Projects

This project identifies metrics for measuring the benefit of rail infrastructure projects for key stakeholders. It is important that stakeholders with an interest in community economic development play an active role in the development of the rail network. Economic development activities in both rural and urban settings are essential if a nation is to realize growth and prosperity. Many communities have developed goals and visions to establish an economic development program, but they often fail to achieve their goals due to uncertainties during the project selection and planning process. Communities often select a project from a vast pool of ideas with only limited capital available for investment. Selecting the right project at the right time becomes imperative for economic and community development. This process is significantly hampered by limited methods for quantifying the economic benefit to key stakeholders. Four methodologies are used in this project to determine the most useful tools for quantifying benefit given the availability of data, relevant expertise, and other information

Uncertainty analysis in product service system: Bayesian network modelling for availability contract

There is an emerging trend of manufacturing companies offering combined products and services to customers as integrated solutions. Availability contracts are an apt instance of such offerings, where product use is guaranteed to customer and is enforced by incentive-penalty schemes. Uncertainties in such an industry setting, where all stakeholders are striving to achieve their respective performance goals and at the same time collaborating intensively, is increased. Understanding through-life uncertainties and their impact on cost is critical to ensure sustainability and profitability of the industries offering such solutions. In an effort to address this challenge, the aim of this research study is to provide an approach for the analysis of uncertainties in Product Service System (PSS) delivered in business-to-business application by specifying a procedure to identify, characterise and model uncertainties with an emphasis to provide decision support and prioritisation of key uncertainties affecting the performance outcomes. The thesis presents a literature review in research areas which are at the interface of topics such as uncertainty, PSS and availability contracts. From this seven requirements that are vital to enhance the understanding and quantification of uncertainties in Product Service System are drawn. These requirements are synthesised into a conceptual uncertainty framework. The framework prescribes four elements, which include identifying a set of uncertainties, discerning the relationships between uncertainties, tools and techniques to treat uncertainties and finally, results that could ease uncertainty management and analysis efforts. The conceptual uncertainty framework was applied to an industry case study in availability contracts, where each of the four elements was realised. This application phase of the research included the identification of uncertainties in PSS, development of a multi-layer uncertainty classification, deriving the structure of Bayesian Network and finally, evaluation and validation of the Bayesian Network. The findings suggest that understanding uncertainties from a system perspective is essential to capture the network aspect of PSS. This network comprises of several stakeholders, where there is increased flux of information and material flows and this could be effectively represented using Bayesian Networks