An integrated core competence evaluation framework for portfolio management in the oil industry

Drawing upon resource-based theory, this paper presents a core competence evaluation framework for managing the competence portfolio of an oil company. It introduces a network typology to illustrate how to form different types of strategic alliance relations with partnering firms to manage and grow the competence portfolio. A framework is tested using a case study approach involving face-to-face structured interviews. We identified purchasing, refining and sales and marketing as strong candidates to be the core competencies. However, despite the company's core business of refining oil, the core competencies were identified to be their research and development and performance management (PM) capabilities. We further provide a procedure to determine different kinds of physical, intellectual and cultural resources making a dominant impact on company's competence portfolio. In addition, we provide a comprehensive set of guidelines on how to develop core competence further by forging a partnership alliance choosing an appropriate network topology

How reflective is the academic essay?

The purpose of this study was to examine the extent of reflection in academic essays. Forty essays, all previously deemed to be of merit quality, were analysed in terms of three elements of reflection - how the educational issue is conceptualized; what the issue means for practice; how practice might be changed to resolve the problematic. Each element was then assigned one of four levels of reflection - technical, descriptive, dialogical and critical. The main finding was that most of the elements were either at a descriptive level of reflection (which the literature argues is not difficult to achieve) or at a dialogical level (which recognizes that knowledge is not certain but does not tease out the relative merits of differing views). These different levels of reflection are seen as adevelopmental stages (from naïve to sophisticated) in gaining control over the process of co-ordinating extant understanding and new evidenc

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested