Project Management Journal

Within the field of project management, research in the subject of conflict analysis and resolution has received considerable attention. This paper presents the application of cognitive analysis, based on the workings of “human judgment theorists,” to the resolution of representative conflict situations. The employed conflict-resolution approach presents cognitive differences between parties as a primary source of conflict. However, it also presents feedback that provides analysis of each individual's judgment and comparisons with his or her counterpart's judgment. This feedback, termed “cognitive feedback,” is used as a way to give insight to people in conflict, providing them an opportunity to resolve their conflicts acceptably. The objective of this paper is to establish a systematic methodology for analyzing and resolving conflict. An actual case study of conflict resolution between union and management personnel at a petrochemical plant in Kuwait is used to illustrate the methodology. Both self-understanding and the understanding of one's counterpart were found to be generally poor before receiving cognitive feedback. The use of cognitive feedback for both groups proved feasible and helped reduce conflict.

The objective of this research was to schedule project activities based on maximum net present value (NPV) and minimum duration. A new hybrid heuristic based on the combination of minimum late start (MLS) and shortest processing time (SPT) priority-rules heuristics was developed. The new late start and shortest processing time (LSSPT) heuristic was tested using 60 problems of the Patterson set, and results compared with 4 other heuristics. To test all 5 previous heuristics, 5 computer programs were developed. Statistical analysis was performed. Results indicate that the new heuristic produced better and encouraging results.

This paper describes the Dynamic Baseline Model^© (DBM) as a framework for analysis of the project management learning process and an indicator of the expected success of a project. By matching project complexity with the appropriate project management approach, the DBM identifies individual learning needs and the appropriate response to the challenges of today's projects. As project management tools and techniques are more and more applied as a one-size-fits-all solution, there is a need to explore beyond these tools and techniques. The DBM suggests that our ability to create solutions is bounded by our current learning horizon, which may be too restrictive for the needs of a project. The model helps us find suitable solutions by enabling us to ask the right questions.

This paper describes the development and application of a Statistical Project Control Tool (SPCT) for engineering project managers. This tool consists of a set of modified Shewart statistical process control (SPC) charts. The fundamental purpose of these charts is to determine whether or not a project process is under statistical control. The SPCT includes charts that monitor time, cost, and technical performance-related project parameters. With the SPCT, project managers can be alerted to developing situations and take corrective action before project failure occurs. Chart validation indicates that the SPCT chart methodology possesses the capability to detect out-of-control situations.

This paper presents a risk management matrix for effective planning of industrial projects. A schematic view of the project environment has been suggested to systematically identify the effects of various project environments, and thereby take a holistic view of risks arising out of immediate and external environments. Various subfactors within each risk element are presented. A process to quantify the immediate and external risks for industrial projects using a weighted probability method is proposed. To derive a useful interpretation, a risk management matrix is also proposed. The results of empirical validation of the model in two recently concluded large industrial projects are also presented. The risk management matrix model can be used to identify a number of policy alternatives at the planning stage.