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The interaction between clients and consultants during consulting projects is essential for their success and therefore for the long-term survival of consulting companies. The aim of this book is to further advance our understanding of the nature of client-consultant relationships. Building upon a critical discussion of the contributions and shortcoming of existing research, Natalia Nikolova proposes an innovative conceptual model, which provides a critical yet constructive platform for the development of a more differentiated view of professional work. The framework concentrates on the cultural and political relations between clients and consultants during service production. It represents a prolific platform for further research and provides practitioners with an increased awareness of hitherto under-explored issues of client-consultant relationships. This book is aimed at scholars of professional business service firms and those interested in multidisciplinary studies of human relations in general. It will also appeal to students interested in these areas and to clients and consultants involved in consulting projects

Service-learning has gained strong interest among educators as a model of experiential education through community engagement. Its potential to contribute to multiple stakeholders, including students, community partners, faculty, and university, is well recognized. While research has focused on elements of this teaching model that contribute to the realization of student-related benefits, there has been less emphasis on what aspects enable the creation of shared value to other stakeholders. We describe a postgraduate, elective management consulting course based on service-learning pedagogy, which has been running for 10 years at the University of Technology Sydney Business School leading to the completion of 75 community projects to date, and evaluate how it creates shared value to multiple stakeholders. We identify four main elements of the course that enable it to deliver value to multiple stakeholders: a dedicated role of client engagement coordinator, a coaching program involving industry experts, student autonomy, and authentic assessments. The main challenges in continuously providing value to all involved parties are developing focused and realistic project briefs, managing students' commitment and differences in students' skills, and recruiting industry coaches.

This paper observes different alignment conditions in a journal-bearing-supported machine (i.e. propeller bearings within the stern tube of a marine vessel). A test rig is built so that the driven machine has fixed supports, roller bearings in an electrical motor. An alignment tolerance is met at the coupling of the journal, which is positioned within three journal bearings. This research can be applied to any industry with machines of the same characteristic. The journal bearings are designed to simulate the large tolerance seen in a vessel's stern tube only for lubrication control oil is used for the bush bearings. Various sensors are placed on the test rig to record data for later machine analysis. Using proximity probes and accelerometers the vibration of the journals and ball bearings can be visualised using orbital plotting and Fast Fourier Transform (FFT) through both LabVIEW and MATLAB respectively. Four alignment procedures are applied to the coupling using a laser unit. The first two will be so that the offset and angular misalignment is produced in the FFT spectrums as per ISO10816 Mechanical vibration so that a reference point is formed for the test rig. The other two alignment procedures represent current industry practice. These procedures are then compared to see which is the preferred reference point that will optimise the reliability value of a whole system. Finding the procedure with minimal vibration will increase the design life of housing structures for such machines, which will increase the overall profit of the shipping industry. Our developments and procedures will be further utilized in practical classes to improve maritime engineering education.

We present a system for technical diagnostics (TD) that can recognize the actual state of marine equipment. A Bayesian classifier is trained to identify the different classes of a piece of equipment, monitored through multiple pseudo-discrete features. Data learning samples can be acquired with direct experiments for each class. The system is capable of merging subjective expert knowledge and data learning samples using pseudo-Bayesian estimates when the parameters of the conditional likelihood for the classes are identified. In the training process, correction is applied to solve numerical problems arising from zero probabilities. The pseudo-discrete features have hybrid nature and combine probabilistic and fuzzy approaches. They combine the ease of extracting subjective expert knowledge typical for the discrete features with the high precision of using the measured data during recognition typical for the continuous features. The domain of each pseudo discrete feature is divided into several main categories of non-overlapping intervals which are described as words by the expert. If a measured feature falls between two consecutive categories it is treated as a linear combination of those categories. The resubstitution performance of the classifier is assessed using an error matrix. A numerical example of a marine diesel generator demonstrates the proposed algorithm in a classification problem with nine different state classes of the generator, monitored through 23 pseudo-discrete features. Data learning samples are acquired with direct experiments for each class. The created TD system has potential applications in other complex engineering systems and may support improvements in marine engineering education and training.