Project Management == Herding Cats?
By Glen B. Alleman
Glen's Tip
On Transforming to the Project Based Organization
If you're embarking on a "change process," under the guise of project
management, take a look at Harvard Business Review on Change. This short paperback has all the important HBR articles on change management. We have found this to be indispensable reading in our transformation from level of effort and functional management to project-based management.
I’ve attended several conferences in June and July. Agile Development in Salt Lake City and Balanced Scorecard here in Denver are the most memorable. Both provided new looks at our age old problem of managing information technology and delivering value to the customers of our services.
One thing that was clear from both venues - agility is now a common word in the vocabulary of IT management processes. Agile development is an obvious term used by Extreme Programming and Scrum adherents. Nothing new here. Interestingly though several speakers at ADC came from the "traditionalist"camps. Turner and Boehm (promoting their upcoming book) Balancing Agility and Discipline: A Guide for the Perplexed were one.
The other interesting thing from the ADC was the joining of Earned Value and agile development. Much to my chagrin, my paper was not the only one on this topic! What seems to be emerging is an understanding that agile processes are in fact not radical processes. Here’s a quick summary from the ADC.
| Traditional Methods | Emergent or Agile methods |
| Planning drives results | Results drive planning |
| Delivery is focused on planned results | Delivery is focused on derived results |
| Defined process steps usually using the CMMI framework | Self–organizing process steps using principles of agile alliance or similar statements. |
This is also nothing new from agilest. But to hear Turner speak these terms was breathtaking. During the sidebar discussions, the 'theory of agility' 'came up. Efforts are afoot to describe agility in some more formal manner. This brought to mind thoughts I've had in the past.
Project Management as a Control System
Control systems play an important role in engineering, science, economics, and biological systems. They also play an important role is creating models of other general systems, either as executable models of these systems or as metaphors of the models of these systems.
Early control systems were based on linear feedback models. As the entities being controlled became more complex, the classical control theory, which dealt with single input and single output systems, became less useful. Multiple input and output systems now dominate control systems theory and practice. Recently (ok, in the 1980's) adaptive and optimal control systems have been developed. Applications of modern control theory to non-physical fields are also the norm. Biology, economics, sociology and other dynamic systems are also common practice. Complex Adaptive Systems is a popular topic today.
General Requirements for a Control System
Any useful control system must satisfy the following conditions:
- The first requirement of any control system is stability.
- In addition to absolute stability, the control system must have relative stability, that is the speed of response must be reasonably fast and must show reasonable damping.
- A control system must be capable of reducing errors to zero or to some small tolerance level.
The requirement for relative stability and steady-state accuracy are actually incompatible. The design of a control system becomes a tradeoff between these two requirements.
Adaptive Control Systems == Agile Control Systems?
Adaptation implies the ability to self-adjust or self-modify with unpredictable changes in conditions of environment or structure. In an adaptive control system, the dynamic characteristics must be identified at all times so that the controller parameters can be adjusted in order to maintain optimal performance.
Could adaptive control system theory be the basis of the "theory of agile systems?"
In most feedback systems, small deviations in a parameter's value from its design value will not cause a problem in the normal operations of the system, provided these parameters are inside the control loop. If the process parameters vary widely because of environmental changes, then the control system will exhibit unsatisfactory behaviors. In some cases large variations in process parameters will cause instability in non-adaptive systems.
A simple definition of a adaptive control system is: a control system in which continuous and automatic measurements of the dynamic characteristics of the process are taken, comparisons are made with the desired dynamic characteristics, and differences uses to adjust the system parameters - usually the controller characteristics - or the generation of an actuating signal so as to maintain optimal system performance, regardless of the environmental changes to the process. (OK, not so simple after all)
To be called adaptive, self -organizing features must exist. An adaptive controller consists of the following three functions:
- Identification of the dynamic characteristics of the process.
- Decision making based on the identification of the process.
- Modification or actuation based on the decisions made.
By performing these functions continuously, self-organization can take place to compensate for unpredictable changes in the process.
Identification - The dynamic characteristics of the process must be measured and identified continuously. This measure should be accomplished with effecting the normal operation of the system. Identification may be made from normal operating data or by the injection of test signals. Identification with normal data is possible only when this data has adequate signal characteristics (bandwidth, amplitude, etc.) for proper identification.
Decision Making - Decisions are made on the basis of the process characteristics, which have identified and on the computed performance index. Once the process has been identified, it is compared with the optimal characteristics (or optimal performance), and then a decision made as to how the adjustable controller characteristics should be varied in order to maintain optimal performance.
Modification - refers to the changes of the control signal according to the results of the identification and decision processes. There are two approaches to modifying controls signals:
- Controller parameter modification -in which the controller parameters are adjusted, to compensate fro changes in the process dynamics.
- Control signal synthesis -in which optimal control signals are synthesized based on the transfer function, performance index, and desired transient response of the process.
Control is the guiding a set of variables toward a common goal. Management Control Theory can be characterized as after-the-fact control or before-the-fact control. Control theory, suggests that where consequences are easily monitored, after-the-fact controls are more effective. Where consequences are unique and hard to monitor, before–the–fact control is appropriate.
Agile Control Theory
This last sentence is the basis of discussion of agile processes in project management and software development. How to build this conjecture -that adaptive control theory is useful in describing agile systems -will be next month’s discussion, along with the Balanced Scorecard adaptive control processes.
About the Author
Glen
Alleman is Vice President, Program Management for the Information and Network
Services organization of CH2M HILL's Communication Group. Prior to this
position Glen was the Principal Consultant for Niwot Ridge Consulting, where
he specialized in the management enterprise application integration projects.
At CH2M HILL, Glen provides services to Rocky Flats Environmental Technology Site. This work involves program management services for software development, server and network operations, infrastructure installation and removal, as well as telecommunications and wireless devices. Glen B. Alleman < galleman@niwotridge.com>