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"Pich, Michael T"
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Becoming a top manager : tools and lessons in transitioning to general management
Make the move up to senior management with lessons from world-renowned business school experts.
Book
On Uncertainty, Ambiguity, and Complexity in Project Management
This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff.
A key concept is the adequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructurethat is, systems for planning, coordination and incentives, and monitoring.
Journal Article
Managing project uncertainty: From variation to chaos
Uncertainty is an inevitable aspect of most projects, but even the most proficient managers have difficulty handling it. A forward-thinking approach is uncertainty-based management, which derives planning, monitoring and management style from an uncertainty profile comprising 4 uncertainty types - variation, foreseen uncertainty, unforeseen uncertainty and chaos. From variation to chaos, managers move progressively from traditional approaches that are based on a fixed sequence of tasks to approaches that allow for the vision to change, even in the middle of the project.
Journal Article
Becoming A Top Manager
Make the move up to senior management with lessons from world-renowned business school experts
Based on themes from INSEAD's popular Transition to General Management programme, authors Kevin Kaiser, Michael Pich, and I.J. Schecter offer sound advice and practical insights for those looking to move to senior general management roles. By following the stories of three managers making the transition to general management, Becoming A Top Manager highlights not only the most crucial aspects of becoming a successful general manager, but also the necessary mindset changes required—both on a personal and professional level—that will ultimately translate into ongoing success.
* Provides practical insights, clarity and confidence for those looking to move into senior general management roles
* Written by a well-known and experienced international author team
* Outlines key skills and executive tools needed for the transition
* Online resources also available at www.wiley.com/go/topmanager [http://www.wiley.com/go/topmanager]
eBook
On uncertainty, ambiguity, and complexity in project management
This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff.
Journal Article
Two-Moment Analysis of Open Queueing Networks with General Workstation Capabilities
The QNET method for two-moment analysis of multiclass open networks is extended to allow complex workstations of various types. For example, the extension described here allows one to treat stations where several unreliable machines are tended by a small number of repair technicians, or stations where several machines that require setups are tended by a small number of operators. To illustrate the general concepts, a four-station manufacturing example is discussed in detail. In the QNET method, one first replaces the original queueing network by an approximating Brownian system model. The Brownian approximation is motivated by heavy traffic theory, and to achieve a unified treatment of complex workstations within the QNET framework we apply the following principle: For purposes of heavy traffic analysis, a workstation can be characterized by just two parameters, the asymptotic mean and asymptotic variance of its cumulative potential output process. This heavy traffic principle has long been known to researchers in the field, but we show that it has power and utility even in circumstances where the mean and variance parameters cannot be determined analytically. We explain how the heavy traffic principle can be applied successfully under certain conditions, and show by example that those conditions are not always met.
Journal Article
On uncertainty, ambiguity, and complexity in project management
This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff.
Journal Article
On uncertainty, ambiguity, and complexity in project management
This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff.
Journal Article