Adaptive management is a structured and systematic process for continually improving decisions, management policies, and practices by learning from the outcomes of decisions previously taken.
In the 1970s this power of adaptation was researched by a group of ecologists that included C.S. Holling and Carl J. Walters. They did this to find the answers to the questions such as how to predict fish stocks when they are dependent on many uncertain factors related to human activities. The scientists came up with the idea of adaptive management or adaptive resource management. Essentially adaptive management is ‘learning bydoing’. It is a structured and systematic process for continually improving decisions, management policies, and practices by learning from the outcomes of decisions previously taken. Since then, adaptive management has become one of the key approaches in environmental engineering. Examples of adaptive management implementation for large-scale natural resource management projects include the Everglades and Grand Canyon National park. The US Department of Defense has been exploring adaptive management concepts for environmental cleanup at Navy facilities. The National Oceanic and Atmospheric Administration has utilized adaptive management for coastal habitat restoration activities.
Many engineers from different fields are using a number of basic principles of adaptive management without actually understanding the work done by Holling and Walters. In 2001, a group of prominent software gurus met in Snowbird resort in Utah and discussed effective software development processes. They came up with what is called “Manifesto for Agile Software Development”. This document offers a number of basic principles, which are similar to the adaptive management concept:
- Regular adaptation to changing circumstances, including changing requirements
- Constant collaboration in project teams and with clients
- Iterative development processes
At the same time, authors of the Agile Manifest suggested a new idea: effective adaptive management is possible only in creative business environments with self-organizing teams and trusted and motivated individuals.
Ideas related to agile project management spread rapidly beyond software development. Many teams and organizations are actively applying the agile approach to complex projects. One of the ‘relatives’ of agile project management is flexible product development. Flexible product development offers an ability to make changes in the product even later in the development cycle.
Agile project management and other similar methods are focused mostly on the organizational aspects of adaptation process. Two principles are the most important:
- Iterative decision-making or making choices based on learning from the outcomes of decisions previously taken.
- Strategic flexibility or avoidance of irreversible decisions
Adaptive management processes originally developed by the ecologists were much broader. In addition to organizational principles, they include quantitative analysis methods, which would help to make better choices based on actual project performance, particularly:
- Multi-model analysis and hypothesis testing
- Actual performance measurement
- Quantitative project risk analysis
Here is the essence of adaptive project management: projects are managed based on learning from actual project performance and these learnings are obtained and analyzed using quantitative methods.
Why Adaptive Methods are not Widely Accepted?
There is only one living species in the world that often actively resists adaptation – humans. In particular, project managers often do not realize that adaptive methods will most likely bring better project results than traditional project management processes, where the project plan is defined upfront.
Many organizations embrace adaptive management methods and techniques. Many software development companies and teams actively use some principles of the agile approach. Nevertheless, traditional project management processes still dominate the field.
Why are project managers so reluctant to embrace adaptive management? The answer lies in human psychology. There are a number of psychological biases that act to prevent people from accepting adaptive principles.
Tendency to be Consistent
Very often politicians blame each other with being inconsistent and this inconsistency is often interpreted as a character flaw, “Three years ago you supported something, now you are against it, are you a flip-flopper?” In reality, inconsistency may be not so bad, if it is based on changing circumstances or new information. For example, if a politician initially did not accept a human role in global warming, but changed his mind after reading new scientific evidence, this is probably a good thing. The world is always changing; new or additional information is revealed and decision-makers must adapt to the new information. In fact, the best politician would be somebody who adapts to changing circumstances rather than sticking to outdated strategies or policies
When people are accused of inconsistency, they tend to become very uncomfortable, something often used by police interrogators and lawyers to uncover information. They try to put people in the position when they make inconsistent statements and then extract necessary information.
The tendency to embrace consistency is very common in project management. When there is new information about the project and it is important to make changes very fast and without any hesitation, the tendency to consistency is often an obstacle to making these crucial decisions. If a device does not work, sometimes it does not make sense to fix it. Building a new device could be a better solution. Project managers have to be willing admit the error and adapt to the new circumstances.
In addition, even if individual project managers are capable of making U-turns, the corporate culture may not support it. Senior management often frowns upon managers who stray from the project plan.
Sunk Cost Effect
In 1996, NASA selected Lockheed Martin to design, build, and fly the X-33 Advanced Technology Demonstrator test space vehicle. The X-33 was to be launched vertically from a specially designed facility and to land on a runway at the end of the mission.
The construction of the X-33 was more than 85 percent complete; however, in 2001 the X-33 project was cancelled. What happened? The composite liquid hydrogen fuel tank failed during testing in November 1999. In response, Lockheed Martin proposed to complete the development of the X-33 by replacing its two composite liquid hydrogen tanks with aluminum tanks. However, NASA concluded the benefits of testing the X-33 in flight did not justify the cost. The X-33 would not be able to reach space with aluminum tanks.
NASA’s investment in the X-33 program totaled $912 million. Despite the huge expenditure, NASA cancelled the program. They essentially resisted the sunk cost effect: The tendency to invest more money in a venture in an attempt to recover previous losses. This psychological effect usually prevents project managers from performing adaptive actions. Instead of stopping work on an ineffective project or course of action, they pump more and more into it with the hope of somehow reviving the project.
One of the examples of sunk cost effect is the Concord aircraft project. French and British government continued to fund this aircraft even when it became apparent that it was no longer economically feasible.
Guilt of Indecisiveness
Organizations expect managers to make decisions, even if the managers do not have the reliable information required to make these decisions. Instead of collecting information and analyzing data, which may give the appearance of indecisiveness, project managers make irreversible decisions intuitively, based on their “gut feel”. This style provides an appearance of decisiveness and leadership, regardless of the quality of the decisions.
In reality, it is very important to analyze when and what additional information is required, how much this additional information will cost, and how waiting for additional information would affect the project’s bottom line. In other words, it is important to use adaptive management.
How Adaptive Management Work?
Traditional project management processes include the phases of project planning, execution, monitoring, and control and evaluation. If, as a result of an evaluation, it was found that something did not go well, this learning may be used in future projects.
Adaptive management processes can be active and passive (Figure 1). The main objective of passive adaptive management is to incorporate the process of learning into existing management approaches. The information obtained from each iteration of the project can be used on subsequent iterations. This way, risk and uncertainties associated with each iteration, can be significantly reduced. Passive adaptive management is used as a part of the agile approach.
Figure 1. Traditional project management process versus active and passive adaptive management processes.
The goal of active adaptive management is to learn by experimentation to determine the best management strategy. The process starts with hypothesis generation, which involves the selection of multiple alternatives for the strategy. The next step is the creation of multiple models. In practical terms, these models are usually project schedules with a set of risks and uncertainties for the particular iteration. All alternative models should be evaluated using quantitative analysis. The outcomes of this analysis are duration, cost, chance of meeting deadlines and other parameters of the iteration that may help to select alternatives for execution.
In most cases, only one alternative model will be selected and executed. However, in cases with significant risks and uncertainties, especially in software development projects, it may be more efficient to execute a number of alternative models at the same time.
Here is an example how active adaptive management can be used:
- Define a project strategy and high-level project plan. Make sure that you provide strategic flexibility: leave room to reverse previous decisions if necessary.
- Split this project plan into multiple phases or iterations.
- Define a more detailed plan for the next phase or iteration. Do not create detailed plans for future iterations, as they may change due to the outcomes of previous iterations. This plan should include a schedule and list of risks. You may choose to create multiple alternative project scenarios (project schedules and risks list) for the same project phase.
- Perform quantitative risk analysis. Different project scenarios may have similar cost and duration, but have a different risk profile. Quantitative project risk analysis will help to determine what will happen with project schedule if certain risks occur. By analyzing this ‘realistic’ project schedule you may choose a scenario to execute.
- Execute one or a couple of project scenarios and continuously measure actual results versus original plan. Then perform quantitative risk analysis again. If the project is partially completed, you may have a better idea of which risks actually occurred, and which ones did not. Also, you should be able to calculate the chance that a risk will occur using the performance data. Figure 2 shows how the results of such analysis may look.
Figure 2. Actual project performance measurement and quantitative risk analysis of partially completed projects.
Currently there are a number of advanced project management and risk analysis tools available to perform quantitative analysis. These tools are easy to use: complex math will be hidden inside the software.
Conclusion and Recommendations
Adaptive management is a structured project management framework. It is not a formalized process that must be strictly followed. This framework can be tailored to different types of space system design and acquisition projects. Principles of adaptive management are strongly endorsed and actively used in many industries, such as information technology and environmental protection.
Rule number one of the adaptive management is simplicity. If adaptive management does not bring tangible benefits and causes extra organizational burdens, ineffective procedures should be abandoned as soon as possible.
Adaptive management includes the basic principles of agile project management, such as iterative processes and creative business environments. In addition, adaptive management involves the active use of quantitative methods to measure project performance and apply learning to improve decisions.
Below are practical recommendations related to the implementation of adaptive management for both hardware and software projects:
- Whenever possible, do not define a detailed project plan upfront; instead, use an iterative project management approach.
- Always identify multiple project alternatives or hypotheses; model these alternatives and, if it is deemed beneficial, start implementing a few alternatives at the same time.
- Use quantitative risk analysis at each phase and iteration of the project.
- Integrate original assumptions and new learning when planning next project iterations.
- Try to minimize the cost of decision reversals; minimize risk by keeping the option to change project direction available.
- Ensure that adaptive management is implemented within a creative business environment characterized by a collaborative structure for stakeholder participation and learning.
Holling, C. S. (ed.) 1978. Adaptive Environmental Assessment and Management. Chichester: Wiley
Kodukula, P., and Papudesu C., 2006. Project Valuation Using Real Options. A Practitioner’s Guide. Fort Lauderdale, FL: J.Ross Publishing
Project Management Institute. 2004. A Guide to the Project Management Body of Knowledge
(PMBOK® Guide), 3rd ed. Newtown Square, PA: Project Management Institute.
Virine L., and Trumper, M. 2007 Project Decisions: The Art and Science. Vienna,VA: Management Concepts.
Walters, C. 1986. Adaptive Management of Renewable Resources. New York: Macmillan.
Lev Virine has more than 20 years experience as a structural engineer, software developer, and project manager. In the past 10 years he has been involved in a number of major projects performed by Fortune 500 companies and government agencies to establish effective decision analysis and risk management processes as well as to conduct risk analyses of complex projects. Lev’s current research interests include the application of decision analysis and risk management to project management. He writes and speaks to conferences around the world on project decision analysis, including the psychology of judgment and decision-making, modeling of business processes, and risk management. Lev received his doctoral degree in engineering and computer science from Moscow State University. You may reach Lev Virine at firstname.lastname@example.org