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on Project, Program and Portfolio Management |
| By: | Jiri Kriz (Brno University of Technology); Lenka Smolikova (Brno University of Technology); Vladena Stepankova (Brno University of Technology) |
| Abstract: | Project management is characterize like the broader concept of a comprehensive set of management processes and activities that are limited in time and whose aim is to implement something specific, whether the introduction, change, etc. In project management, which aims to ensure effective management of a comprehensive package of activities to a greater or lesser extent, concerns virtually all organizations and from internal changes or activities, supply of products, the introduction of ICT technologies to large investment projects. Project management involves the application of knowledge, experience, skills, activities, tools and techniques so that the final project met its requirements and achieves its goals in a limited time interval. Between the initial and final state the project goes through several phases, including project risk. To eliminate these risks is determined by the risk management as an area focusing on analysis and risk reduction using various tools and techniques. If we seek to answer the question what is the risk, then in terms of project management it can be understood as the likelihood that an event occurs that is contrary to the assumption. The first stage is to identify risks. This is based on the areas covered by the project and cannot be generalized for different types of projects. For example, a project for the implementation of data warehouse will have different areas of risk than new product development. The next stage is risk analysis. At this stage, we try to find the level of risk and its impact on the completion of the project. We are looking for those risks which are important and have a significant influence on the project (priority risks). Following the planning and risk management, which proposes procedures to minimize risk, responsibility for the procedures and time frames in which the procedures are being implemented. The last phase is monitoring, which leads to elimination of risks, which are no longer relevant and to re-identify new risks. This entire process is appropriate to support software tool that allows us to their effective management. We can use Business Intelligence tools as one of the software tools, especially in the phase of risk identification and analysis. Identifying risks putting together a basic set of potential risks when the input use various available sources of information such as the previously identified risks files or lists the usual risks in managing similar projects. In the analysis phase, then we can make risk assessment of the potential risks, including the determination of their probabilities to create a catalogue of potential risks of the project, which must be addressed at the planning stage and management, Business Intelligence tools are with justification used for the suggestions to minimize risks. The article discusses the Business Intelligence tools and their application in the field of project’s risk management. This is an opportunity to create panels, tables, graphs and matrices, including analyses of data cubes and to a certain extent and use of prediction algorithms for determining the probability of the risk and its impact on the implementation of the project. |
| Keywords: | risk management, Business Intelligence, Data Cube, Prediction Algorithms |
| JEL: | O22 G32 D81 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:pes:wpaper:2015:no141&r=ppm |
| By: | Alta Van der Merwe (University of Pretoria); Aurona Gerber (University of Pretoria); Otto De Vries (University of Pretoria) |
| Abstract: | Project management (PM) is the skill of planning and organisational effort set about to accomplish a task or goal (Chai, Zhou, & Wang, 2007). PM is perceived as being one of the main ways in assuring the quality of a project and is critical in a collaborative environment, which is typically more difficult to control and manage (Wong, B., & Bhatti, 2009). Modern project management started between 1900 and 1950 – during this time the advance was specifically on the shortening of the project schedule caused by technology advancement. After 1958 the rapid advancement of technology changed the way that PM was done until we are now in a period where the internet changed business practices in the mid 1990s and ‘provided a fast, interactive, and customized new medium that allowed people to browse, purchase, and track products and services online instantly. Between 1995 and 2000, the project management community adopted internet technology to become more efficient in controlling and managing various aspects of projects.’ (Kwak, 2005:7). Being connected created many opportunities and also opened new opportunities for collaboration over boundaries. Individuals are now able to do more for themselves, be able to do more with others and improves the capacity to do more in formal organizations. Miles & Trott (2008) summarize three lessons learned from several data collection activities, as being having a common purpose, sharing power and that if you do it right, it is worth it.Collaborative research projects over countries introduce new challenges to the project manager of the project. For this paper we give feedback on the first phase of a collaborative systems development project between 5 countries and the lessons learned from the collaborative effort from a project management perspective. |
| Keywords: | Project Management, Collaborative Projects, Collaborative systems development |
| JEL: | O32 M15 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:1003381&r=ppm |
| By: | Patrick Legros; MOHAMED MOUNIR Sraieb |
| Keywords: | aid modalities; pooling contract; targeted infrastructure |
| JEL: | D86 F35 O12 O19 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/199533&r=ppm |
| By: | Szekeres, Szabolcs |
| Abstract: | The Weitzman-Gollier Puzzle was observed in a setting of risk neutrality. This paper extends its analysis to cases of constant proportional risk aversion and finds that the phenomenon of the puzzle is not confined to the case of risk neutrality. Weitzman discounting produces declining discount rates for risk aversion values below one, but increasing ones for higher degrees of risk aversion. The finding that Weitzman’s discounting is actually time reversed negative compounding is confirmed. As Weitzman certainty equivalent rates (CERs) pertain to the cost of storing resources, rather than to interest earned from investing them productively, they should not be used in the evaluation of investment projects. Discounting project net benefits with declining discount rates (DDR) is never justified. |
| Keywords: | Discount rate, uncertainty, declining discount rate, negative compounding. |
| JEL: | D61 H43 |
| Date: | 2015–05–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:64286&r=ppm |
| By: | Pandey, Rita (National Institute of Public Finance and Policy); Mehra, Meeta Keswani (Jawaharlal Nehru University) |
| Abstract: | Many of the most promising low-carbon technologies currently have higher costs than the fossil-fuel based technologies. It is only through incremental learning from research, development and deployment that these costs can be reduced. Government intervention in the innovation process through fiscal policy instruments can be useful to accelerate this process, and catalyse early adoption. This paper reviews the best practices associated with the choice and design of such instruments and identifies the main lessons learned of their implementation in the case of renewable energy. The paper outlines an analytical framework which identifies the characteristics of drivers and barriers in innovation of RETs; sequencing of various steps involved in promoting innovation; and various policy tools in the context of each barrier that will help accelerate the process and enhance the outcomes. The paper notes that the issue of design and implementation of fiscal policy measures for RE technologies is complex and requires a nuanced, case by case approach, however, some useful broad conclusions can be drawn on the lessons learnt from these programs for future policy design and implementation. |
| Keywords: | Fiscal instruments ; Low-carbon technology continuum ; Renewable energy policy framework ; Price and quantity based instruments ; Market failures and barriers in RE |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:npf:wpaper:15/147&r=ppm |
| By: | Tom Broekel; Dirk Fornahl; Andrea Morrison |
| Abstract: | This paper investigates the allocation of R&D subsidies with a focus on the granting success of firms located in clusters. On this basis it is evaluated whether firms in these clusters are differently embedded into networks of subsidized R&D collaboration than firms located elsewhere. The theoretical arguments are empirically tested using the example of the German biotechnology firms’ participation in the 6th EU-Framework Programmes and national R&D subsidization schemes in the early 2000s. We show that clusters grant firms another premium to their location, as they are more likely to receive funds from the EU-Framework Programmes and hold more favourable positions in national knowledge networks based on subsidies for joint R&D. |
| Keywords: | Innovation policy, R&D subsidy, collaboration networks, embeddedness, technology cluster |
| JEL: | R11 O33 R58 D85 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:1514&r=ppm |
| By: | Upasena, W. J. J.; Brewer, J. D.; Haq, K. A. |
| Keywords: | Irrigation management; Rehabilitation; Design; Planning; Farmer participation; Monitoring; Farmers organizations |
| URL: | http://d.repec.org/n?u=RePEc:iwt:rerpts:h019771&r=ppm |