Green innovation management in the automotive industry

Online case studies. Managing Innovation is an established, bestselling text for MBA, MSc and advanced undergraduate courses on management of technology, innovation management and entrepreneurship. It is also used widely by managers in both the service and manufacturing sectors. Now in its fourth edition, Managing Innovation has been fully revised and updated based on extensive user feedback to incorporate the latest findings and techniques in innovation management. The authors have included a new and more explicit innovation model, which is used throughout the book and have introduced two new features – Research Notes and Views from the Front Line – to incorporate more real life case material into the book. The strong evidence–based and practical approach makes this a must–read for anyone studying or working within innovation. An extensive website accompanies this text at www.managing–innovation.com. Readers can browse an online database of audio and video clips, as well as case study material, interactive exercises and tools for innovation, whilst lecturers can find additional support material including instructor slides and teaching guides and tips. "Tidd and Bessant's text has become a standard for students and practitioners of innovation. They offer a lively account on innovation management full of interesting and new examples, but one that at the same is rigorously anchored in what we have learned over the last thirty years on how to manage that ultimate business challenge of renewing products, processes, and business models. Those who want to innovate must read this book." — Professor Arnoud De Meyer, Director, Judge Business School, University of Cambridge, UK "Innovation matters and this book by two leaders in the field which is clear and practical as well as rigorous should be essential reading for all seeking to study or to become involved in innovation." — Chris Voss, Professor of Operations and Technology Management, London Business School "...comprehensive and comprehensible compendium on the management of innovation. It is very well organized and very well presented. A pedagogic tool that will work at multiple levels for those wishing to gain deeper insights into some of the most challenging and important management issues of the day." — David J. Teece, Thomas W. Tusher Professor in Global Business, Haas School of Business, University of California, Berkeley, USA "Those of us who teach in the field of Innovation Management were delighted when the first edition of this book appeared 11 years ago. The field had long been in need of such a comprehensive and integrated empirically–based work. The fact that this is now the 4th edition is clear testimony to the value of its contribution. We are deeply indebted to the authors for their dedication and diligence in providing us with this updated and expanded volume." — Thomas J. Allen,Howard W. Johnson Professor of Management, MIT Sloan School of Management, USA.

B2B e-marketplaces in the airline industry:process drivers and performance indicators

Competitive pressures are increasing within and between different strategically oriented groups of airlines. This paper focuses on the level of efficiency improvements gained by using e-Marketplaces in the procurement process. Findings from a survey among 88 international airlines reveal that the use of Business-to-Business (B2B) e-Marketplaces does play different roles across the various airline groupings. Airlines that are involved in strategic alliances show higher joint procurement activities than airlines that are not involved in strategic alliances. However, alliances are probably viewed as loose arrangements and thus airlines may be reluctant to share information on procurement prices and processes with another airline that could also be acting as a competitor. The financial involvement in or initiation of e-Marketplaces by airlines is very low. Low cost airlines show high use of e-Marketplaces, but demonstrate little financial involvement in contrast. Overall, the categories of spares and repairs, office supplies, tools and ground support equipment (GSE) show the greatest potential for reducing costs and increasing procurement process efficiencies. The intense competitive pressures facing carriers will make their search for tools to realise even incremental savings and efficiency gains ever more urgent. There is evidence that e-Marketplaces are one tool to improve such performance indicators.

The role of academic/industry partnerships in the development of supply chain managers

The need for improvement in the logistics and supply chain management capability of companies in Ireland is becoming increasingly recognised. One of the main bottlenecks currently is the shortage of supply chain management professionals. Education and training has a fundamental role to play if the supply of suitably qualified human resource is to be addressed in a meaningful way. Recent research indicates that demand for people with the right knowledge and skills greatly exceed supply. There are numerous techniques and technologies which can facilitate improvement in a company’s supply chain capability. However, experience has shown that these tools alone can not address the weaknesses – any improvement tool is only as good as a company’s ability to utilise it and effective utilisation depends above all on the knowledge and skill of employees. Education and training is essential in developing the requisite knowledge and skills. Consultants can play a role in terms of providing an objective view of a company’s requirements. But the only way to generate a sustainable competitive advantage is to ensure that the necessary knowledge and skills are available in-house. Indeed, the “consultancy culture” which has developed in many companies is a direct result of this lack of in-house expertise. Given the shortage of suitably qualified and experienced people in the job market, the only way that this problem can be addressed is through effective development of existing staff. This paper describes the partnership model adopted by the NITL to achieve its objective of combining academic excellence with real relevance to commercial needs in its supply chain management development programmes. The Executive Development Programme (EDP) is used to illustrate how the model is implemented.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

A micro industry with closed energy and water cycles for sustainable rural development

Sustainable development requires combining economic viability with energy and environment conservation and ensuring social benefits. It is conceptualized that for designing a micro industry for sustainable rural industrialization, all these aspects should be integrated right up front. The concept includes; (a) utilization of local produce for value addition in a cluster of villages and enhancing income of the target population; (b) use of renewable energy and total utilization of energy generated by co and trigeneration (combining electric power production with heat utilization for heating and cooling); (c) conservation of water and complete recycling of effluents; (d) total utilization of all wastes for achieving closure towards a zero waste system. Enhanced economic viability and sustainability is achieved by integration of appropriate technologies into the industrial complex. To prove the concept, a model Micro Industrial Complex (MIC) has been set up in a semi arid desert region in Rajasthan, India at village Malunga in Jodhpur district. A biomass powered boiler and steam turbine system is used to generate 100-200 KVA of electric power and high energy steam for heating and cooling processes downstream. The unique feature of the equipment is a 100-150 kW back-pressure steam turbine, utilizing 3-4 tph (tonnes per hour) steam, developed by M/s IB Turbo. The biomass boiler raises steam at about 20 barg 3 tph, which is passed through a turbine to yield about 150 kW of electrical power. The steam let out at a back pressure of 1-3 barg has high exergy and this is passed on as thermal energy (about 2 MW), for use in various applications depending on the local produce and resources. The biomass fuel requirement for the boiler is 0.5-0.75 tph depending on its calorific value. In the current model, the electricity produced is used for running an oil expeller to extract castor oil and the castor cake is used as fuel in the boiler. The steam is used in a Multi Effect Distillation (MED) unit for drinking water production and in a Vapour Absorption Machine (VAM) for cooling, for banana ripening application. Additional steam is available for extraction of herbs such as mint and processing local vegetables. In this paper, we discuss the financial and economic viability of the system and show how the energy, water and materials are completely recycled and how the benefits are directed to the weaker sections of the community.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

Rotor retaining sleeve design for a 1.12-MW high-speed PM machine

Permanent-magnet (PM) synchronous machines (PMSMs) can provide excellent performance in terms of torque density, energy efficiency, and controllability. However, PMs on the rotor are prone to centrifugal force, which may break their physical integrity, particularly at high-speed operation. Typically, PMs are bound with carbon fiber or retained by alloy sleeves on the rotor surface. This paper is concerned with the design of a rotor retaining sleeve for a 1.12-MW 18-kr/min PM machine; its electromagnetic performance is investigated by the 2-D finite-element method (FEM). Theoretical and numerical analyses of the rotor stress are carried out. For the carbon fiber protective measure, the stresses of three PM configurations and three pole filler materials are compared in terms of operating temperature, rotor speed, retaining sleeve thickness, and interference fit. Then, a new hybrid protective measure is proposed and analyzed by the 2-D FEM for operational speeds up to 22 kr/min (1.2 times the rated speed). The rotor losses and machine temperatures with the carbon fiber retaining sleeve and the hybrid retaining sleeve are compared, and the sleeve design is refined. Two rotors using both designs are prototyped and experimentally tested to validate the effectiveness of the developed techniques for PM machines. The developed retaining sleeve makes it possible to operate megawatt PM machines at high speeds of 22 kr/min. This opens doors for many high-power high-speed applications such as turbo-generator, aerospace, and submarine motor drives.

Influence of cutting parameters and tool wear on martensitic stainless steel surface integrity after a face milling process

Surface finish is one of the most relevant aspects of machining operations, since it is one of the principle methods to assess quality. Also, surface finish influences mechanical properties such as fatigue behavior, wear, corrosion, etc. The feed, the cutting speed, the cutting tool material, the workpiece material and the cutting tool wear are some of the most important factors that affects the surface roughness of the machined surface. Due to the importance of the martensitic 416 stainless steel in the petroleum industry, especially in valve parts and pump shafts, this material was selected to study the influence of the feed per tooth and cutting speed on tool wear and surface integrity. Also the influence of tool wear on surface roughness is analyzed. Results showed that high values of roughness are obtained when using low cutting speed and feed per tooth and by using these conditions tool wear decreases prolonging tool life. Copyright © 2009 by ASME.