The Cone of Uncertainty is a framing assumption used to model the needed reduction in some parameter of interest in domains ranging from software development to hurricane forecasting.
This extended post covers
- The framing assumptions of the Cone of Uncertainty.
- The Cone of Uncertainty as a Technical Performance Measure.
- Closed Loop Stochastic Adaptive control in the presence of Evolving Uncertainty.
These topics are connected in a simple manner.
All project work is uncertain (probabilistic and statistical). Uncertainty comes in two forms - Epistemic and Aleatory. Uncertainty creates Risk. Risk management requires active reduction of risk. Active reduction requires we have a desired reduction goal, perform the work needed to move the parameter toward the goal - inside the control limits, and measure progress toward the rduction goal. Management of this reduction work and measurement of the progress toward the goals is a Close Loop Control System paradigm. Closed Loop Control, has a goal, an action, a measurement, and a corrective action. These activities, their releationships and values are defined in a PLAN for increasing the probability of success for the project. The creation and management of the Plan is usually performed by the Program Planning and Controls group where I work.
Framing Assumptions for the Cone of Uncertainty
Of late, Cone of Uncertainty has become the mantra of No Estimates advocates claiming that data is needed BEFORE the Cone is of any use. This is course is NOT the correct use of the Cone. And without this data, the Cone has no value in the management of the work. This fallacy comes from a collection of data that did not follow the needed and planned reduction of uncertainty for the cost estimates of a set of software development projects.
The next fallacy of this conjecture is that the root cause for why those projects did not have their uncertainty reduced in some defined and desirable manner was never provided. The result of the observation is a symptom of something. But the Cause was not sought in any attributable way. This is a common problem in low maturity development organizations. We blame something for our problem, without finding out the cause of the something or even if that something is the actual cause.
In our domain, Root Cause Analysis is mandated before ANY suggested change for improvement, prevention, or corrective actions are taken. Our RCA method is the Apollo Method. Apollo is distinctly different from other root cause approaches in that each effect requires a Condition and Action. Please read the book in the previous sentence to see why this is critical and how it is done. Then every time you hear some statement about an observed problem, you can ask what's the cause (both condition and action)?
So when you hear I have data that shows that the Cone (or for that matter anything) does not follow the expected and desired outcomes and there is no Root Cause to explain that unexpected outcome - ignore that conjecture, until the reason for the observed outcome is found and corrective actions have been identified and those corrective actions have been confirmed to actually fix the observed problem.
So let's recap what the Cone of Uncertainty is all about from those who have created the concept. Redefining the meaning and then arguing my data doesn't match that is a poor start to improving the probability of project success.
The Cone of Uncertanty describes the uncertainty in the behaviors or measurement of a project parameter and how that uncertanty needs to be reduced as the project proceeds to increase the Probability of Project Success.
If that parameter is NOT being reduced at the planned rate, then the Probability of Success is not increased in the planned needed manner.If the parameter of interest is not being reduced as needed, go find out why and fix it, or you'll be late, over budget, and the technical outcome unacceptable. The Cone of Uncertanty does NOT need data to validate it is the correct paradigm. The Cone of Uncertainty is the framework for improving the needed performance of the project. It's a Principle.
Here's some background on the topic of the Cone of Uncertainty. Each of these can be found with Google.
- Misinterpretations of the “Cone of Uncertainty” in Florida during the 2004 Hurricane Season, Kenneth Broad, Anthony Leiserowitz, Jessica Weinkle, and Marissa Steketee, American Meteorological Society, May 2007
- Reducing Estimation Uncertainty with Continuous Assessment: Tracking the “Cone of Uncertainty”, Pongtip Aroonvatanaporn, Chatchai Sinthop, Barry Boehm
- “Shrinking The Cone Of Uncertainty With Continuous Assessment For Software Team Dynamics In Design And Development,” Pongtip Aroonvatanaporn,” Ph.D. Thesis, University of Southern California, August 2012.
- "Improving Software Development Tracking and Estimation Inside the Cone of Uncertainty, "Pongtip Aroonvatanaporn, Thanida Hongsongkiat, and Barry Boehm.
The Cone of Uncertainty as a Technical Performance Measure
Technical Performance Measures are one of four measures describing how a project is making progress to plan. These measures - combined - provide insight into the probability of program success.
- Measure of Effectiveness
- Measure of Performance
- Technical Performance Measure
- Key Performance Parameters
Here's a workshop that we give twice a year at the College of Performance Management's conference. The definition of a TPM is on page 14.
This approach is the same for any other TPM - Risk, Weight, Throughput, and even Earned Value Management parameters. This is the basis of the closed loop control system used to manage the program with empirical data from past performance compared to the planned performance at specific points in time.
This Closed Loop Program Control Process provides the decision makers with actionable information. The steering target - the MOE, MOP, TPM, KPP - is defined upfront and evolves as the programs progress with new information. Same for the Uncertainties in the program measures. This approach is also the basis of Analysis of Alternatives and other trades when it is determined that the desired measures cannot be achieved. Decisions are made to adjust the work, adjust the requirements, or adjust the measures.
Close Loop Control Systems in the Presence of Emerging Stochastic Behaviours
Risk management is how adults manage projects - Tim Lister. All risk comes from uncertainty. Uncertainty comes in two forms - Epistemic (reducible) and Aleatory (Irreducible). Here's an overview of how to manage in the presence of uncertainty
Let's look at the Closed Loop Control System paradigm for managing projects. Control systems exist in many domains. From simple fixed setpoint systems like your thermostat controlling the HVAC system, or a PID controller running a cracking column at a refinery, to multi-dimensional evolving target control systems guiding an AIM-9L missile (which I wrote SW for), to multi-target acquisition systems needed to steer midcourse interceptors to their targets, to cloud-based enterprise ERP systems, to autonomous flight vehicles operating in mixed environments, with mission planning while in flight and collision avoidance with manned vehicles - the current generation of swarm UAVs in the battlefield.
A concept that spans all these systems - and is shared with project management control systems (we work in Program Planning and Controls as a working title) is the idea of a PLAN.
- What is the Plan to keep the temperature in the room at 76 degrees? It's a simple plan, run the A/C or Heater, measure the current temperature, compare that to the desired temperature, adjust appropriately, all the way up to
- An evolving battle management system where the control loop for individual vehicles interacts with control loops of other vehicles (manned and unmanned) as the emerging mission, terrain, weather, and mission.
- What are the units of measure for this Plan? What are the probabilistic and statistical behaviors of these units measure? How can we measure the progress of the project toward compliance with these measures in the presence of uncertainty?
- How can the variance analysis of these measures be used to take corrective actions to keep the program GREEN?
All these control systems share the same framing assumptions - there is a goal, the needed capabilities to accomplish that goal are known, I have PLAN by which that goal can be accomplsihed, that PLAN may evolve - with manual intervention or with autonomous intervention - as the PLAN evolves, the Mission evolves, and the situation evolves.
Project work is a complex, adaptive, stochastic process with evolving Goals, resources, and situation, all operating in the presence of uncertainty. Here are the moving parts for any non-trivial project.
In the control system for project management, like the control system for those non-trivial examples above - the PLAN for the behavior of the parameter of interest is the starting point. The feedback (closed loop feedback) of the variance between the desired Value and the Actual Value at the time of the measurement is connected with other information on the program to define the corrective actions needed to Keep the Program Green.
Like control systems in automation with closed loop control, a project control system must be implemented that controls cost, ensures technical performance, and manages schedules. All successful control systems require measurements. All closed loop control systems have a plan to steer toward. A temperature for a simple thermostate all the way to a target cost, schedule, and techncial performance for a complex software project. In the project management world those measurements (inputs/calculated values) are called metrics and the greater the frequency of measurement (up to a point - the Nyquist sample rate), the more accurate the control. All measurements need to be compared to an expectation - the Set Point. When deviations are noticed action is required to modify the processes that produce the output - the product or service produced by the project.
Here are some resources of closed loop control systems for project management based on following the plan for the performance of the Parameter of Interest
- "Feedback Control in Project-Based Management," L. Scibile, ST Division – Monitoring and Communication Group (ST/MC) CERN, Geneva, Switzerland
The notion of the Cone of Uncertainty has been around for awhile. Barry Boehm's work in “Software Engineering Economics”. Prentice-Hall, 1981.
But first, let's establish a framing assumption. When you hear of projects where uncertainty is not reduced as the project progresses, ask a simple question. Why is this the case? Why, as the project progresses with new information, delivered products, reduced risk, is the overall uncertainty not being reduced? Go find the root cause of this, before claiming uncertainty doesn't reduce. Uncertainty as a principle for all projects, should be reducing thorugh the direct action of Project Management. If uncertainty is not reducing the case may be - bad management, an out of control project, or you're working in pure research world where things like that happen.
As well never measure any project parameter as a ratio. Relative numbers - ordinal - are meaningless when making decisions. Relative uncertainty is one. Relative to what? Cardinal numbers are measures used to make decisions.
So a quick review again. What is the Cone of Uncertainty?
- The Cone is a project management framework describing the uncertainty aspects of estimates (cost and schedule) and other project attributes (cost, schedule, and technical performance parameters). Estimates of cost, schedule, technical performance on the left side of the cone have a lower probability of being precise and accurate than estimates on the right side of the cone. This is due to many reasons. One is levels of uncertainty early in the project. Aleatory and Epistemic uncertainties, which create the risk to the success of the project. Other uncertainties that create risk include:
- Unrealistic performance expectation with missing Measures of Effectiveness and Measures of Performance
- Inadequate assessment of risks and unmitigated exposure to these risks with proper handling plans.
- Unanticipated technical issues with alternative plans and solutions to maintain effectiveness
- Since all project work contains uncertainty, reducing this uncertainty - which reduces risk - is the role of the project team and their management. Either the team itself, the Project or Program Manager, or on larger programs the Risk Management owner.
Here's another simple definition of the Cone of Uncertainty:
The Cone of Uncertainty describes the evolution of the measure of uncertainty during a project. For project success, uncertainty not only must decrease over time, but must also diminishe its impact on the project's outcome. This is done by active risk management, through probabalistic decision-making. At the beginning of a project, there is usually little known about the product or work results. Estimates are needed but are subject to large level of uncertainty. As more research and development is done, more information is learned about the project, and the uncertainty then decreases, reaching 0% when all risk has been mitigated or transferred. This usually happens by the end of the project.
So the question is? - How much variance reduction needs to take place in the project attributes (risk, effectiveness, performance, cost, schedule - shown below) at what points in time, to increase the probability of project success? This is the basis of Closed Loop Project Control Estimates of the needed reduction of uncertanty, estimates of the possisble reduction of uncertainty, and estimates of the effectiveness of these reduction efforts are the basis of the Close Loop Project Control System.
This is the paradigm of the Cone of Uncertainty - it's a planned development compliance engineering tool, not an after the fact data collection tool
The Cone is NOT the result of the project's past performance. The Cone IS the Planned boundaries (upper and lower limits) of the needed reduction in uncertainty (or other performance metrics) as the project proceeds. When actual measures of cost, schedule, and technical performance are outside the planned cone of uncertainty, corrective actions must be taken to move those uncertanties inside the cone, if the project is going to meet it's cost, schedule, and technical performance goals.
If your project's uncertanties are outside the Planned boundaries at the time when they should be inside the planned boundaries, then you are reducing the proabbility of project success
The Measures that are modeled in the Cone of Uncertainty are the Quantitative basis of a control process that establishes the goal for the performance measures. Capturing the actual performance, comparing it to the planned performance, and compliance with the upper and lower control limits provides guidance for making adjustments to maintain the variables perform inside their acceptable limits.
The Benefits of the Use of the Cone of Uncertainty
The planned value, the upper and lower control limits, the measures of actual values from a Close Loop Control System - a measurement based feedback process to improve the effectiveness and efficiency of the project management processes.
- Analyzing trends that help focus on problem areas at the earliest point in time - when the variable under control starts misbehaving, intervention can be taken. No need to wait till the end to find out you're not going to make it.
- Providing early insight into error-prone products that can then be corrected earlier and thereby at lower cost - when the trends are headed to the UCL and LCL, intervention can take place.
- Avoiding or minimizing cost overruns and schedule slips by detecting them early - by observing trends to breaches of the UCL and LCL.
enough in the project to implement corrective actions - Performing better technical planning, and making adjustments to resources based on discrepancies between planned and actual progress.
A critical success factor for all project work is Risk Management. And risk management includes the management of all kinds of risks. Risks from all kinds of sources of uncertainty, including technical risk, cost risk, schedule, management risk. Each of these uncertainties and the risks they produce can take on a range of values described by probability and statistical distribution functions. Knowing what ranges are possible and knowing what ranges are acceptable is a critical project success factor.
We need to know the Upper Control Limits (UCL) and Lower Control Limit (LCL) of the ranges of all the variables that will impact the success of our project. We need to know these ranges as a function of time With this paradigm we have logically connected project management processes with Control System processesIf the variances, created by uncertainty going outside the UCL and LCL. Here's a work in progress paper "Is there an underlying Theory of Project Management," that addresses some of the issues with control of project activities.
This is the critical concept in successful project management
We must have a Plan for the critical attributes - Mission Effectiveness, Technical Performance, Key Performance Parameters - for the items. If these are not compliant, the project has become subject to one of the Root Causes of program performance shortfall. We must have a burndown or burnup plan for producing the end item deliverables for the program that match those parameters over the course of the program. Of course, we have a wide range of possible outcomes for each item in the beginning. And as the program proceeds the variances measures on those items move toward compliance of the target number in this case Weight.
Here's another example of the Cone of Uncertainty, in this case, the uncertainty in weight of a vehicle as designed by an engineering team. The UCL and LCL are defined BEFORE the project starts. These are the allowable ranges of the weight values for the object at specific points in time. When the actual weight or the projected weight goes outside that range Houston We Have A Problem. These are used to inform the designer of the progress of the project as it proceeds. Staying inside the control limits is the Planned progress path to the final goal - in this case, temperature.
Wrap Up of this Essay
The Cone of Uncertanty, is a signaling boundary of a Closed Loop Control system used to manage the project to success with feedback from the comparison of the desired uncertainty in some parameter to the actual uncertainty in that parameter.
This boundary is defined BEFORE work start to serve as the PLANNED target to steer toward for a specific parameter. In simplier closed loop control systems, this is called the Set Point†
† The setpoint is the desired or target value for an essential variable of a system, often used to describe a standard configuration or norm for the system. In project management or engineering development, the set point is a stochastic variable that is evolving as the program progresses and may be connected in non-linear ways with other set points for other parameters.
