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Ascertaining of the success and applicability of any claims made that are outside the accepted practices of business, engineering, or governance processes require careful testing of ideas through tangible evidence they are actually going to do what it is conjectured they're suppose to do.
The structure of this checklist is taken directly fromĀ Scientific American'sĀ essay on scientific baloney, but sure feels right for many of theĀ outrageousĀ claims found in today's software development community about approaches to estimating the cost, schedule, and likely outcomes.
How reliable is theĀ
source of the claim?
Self-pronounced experts often appear credible at first glance, but when examined more closely, the facts and figures they cite are distorted, taken out of context, long out of date, mathematically wrong, missing critical domain and context basis, or occasionally even fabricated.
In many instances the data used to support the claims are weak or poorly formed. Relying on surveys of friends or hearsay, small population samples, classroom experiments, or worse anecdotal evidence where the expert extends personal experience to a larger population.
Does this source often make similar claims?
Self pronounced experts have a habit of going well beyond the facts and generalizing their claims to a larger population of problems or domains. Many proponents of ideas make claims that cannot be substantiated within a testable framework. This is the nature ofĀ early development Ā in the engineering world. Of course, some great thinkers do frequently go beyond the data in their creative speculations.
But when those creative thinkers are used to support the new claims it's more suspect the hard work of testing the claim outside of personal experience hasn't been performed.Ā
They said agile wouldn't work, so my conjecture is getting the same criticism and I'll be considered just like those guys when I'm proven right.
Have the claims been verified by another source?
Typically self pronounced experts make statements that are unverified or verified only by a source within their own private circle, or who's conclusions are based primarily on anecdotal information.
We must ask, who is checking the claims, and even who is checking the checkers? Outside verification is crucial to good business decisions as it is crucial to good methodology development.
How does the claim fit with what we
know about how the world works?Ā
Any specific claim must be placed into a larger context to see how it fits. When people claim that a specific method, approach, or technique results in significant benefits, dramatic changes in an outcome, etc. they are usually not presenting the specific context for the application of their idea.
Such a claim is typically not supported by quantitative statistics as well. There may be qualitative data, but this is likely to be biased by the experimental method as well as the underlying population of the sample statistics.
In most cases to date, the sample size is minuscule compared to that needed to draw correlations and causation's to the conjectured outcomes.
Has anyone gone out
of the way to disprove the claim, or has only supportive evidence
This is the confirmation bias, or the tendency to seek confirmatory evidence and to reject or ignore disāconfirmatory evidence. The confirmation bias is powerful, pervasive and almost impossible to avoid.
It is why the methods that emphasize checking and rechecking, verification and replication, and especially attempts to falsify a claim, are critical.
When self-selected communities see external criticism as Ā harassment orĀ you're simply not getting it, or Ā those people are just like talking to a box of rocks, the confirmation bias is in full force.
Does the preponderance of evidence point to the claimant's conclusion or to a different one?
Evidence is the basis of all confirmation processes. The problem is having evidence alone is necessary but not sufficient. The evidence must somehow be "predicted" by the process, fit the process model, or somehow participate in the process in a supportive manner.
Is the claimant employing the
accepted rules of reason and tools of research, or have
these been abandoned in favor of others that lead to the desired conclusion?Ā
Unique and innovative ways of conducting research, process data, and "conjecturing" about the results are not statistically sound. In almost every discipline there are accepted mechanisms for conducting research. One of the first courses taken in graduate school isĀ quantitative methods Ā for experiments. This course sets the ground rules for conducting research in the field.
Is the claimant providing an explanation for the observed phenomena or merely denying the existing explanation?Ā
This is a classic debate strategyācriticize your opponent and never affirm what you believe to avoid criticism.
Show us your data, is that starting point for engaging in a conversation about a speculative idea.
If the claimant proffers a new explanation, does it account for as many phenomena as the old explanation did?Ā
This concept is usually lost on "innovative" claims. The need to explain previous results is mandatory. Without this bridge to past results, a new suggested approach has no foundation for acceptance.
Do the claimant's personal beliefs and biases drive the conclusions, or vice versa?
All claimants hold social, political and ideological beliefs that could potentially slant their interpretations of the data, but how do those biases and beliefs affect their research in practice?
Usually during some peer-review system, such biases and beliefs are rooted out, or the paper or book is rejected.
In the absence of peer review - self publishing is popular these days - there is no external assessment of the ideas and therefore the author reinforces of the confirmation bias.
Ā So the next time you hear a suggestion that appears to violate a principles of Ā business, economics, or even physics, think of these questions. So let's move to the #NoEstimates suggestion that we can make decisions in the absence of estimate, that is we can make decisions about a future outcome in absence of estimating the cost to acheive that outcome and the impact of that outcome.
The core question isĀ how can this conjecture be tested beyond the personal anecdotes of those proffering the notion that decisions can be made in the absence of estimates? Certainly those making the claim have no interest in performing that test. It's incumbant on those attempting to apply the notion to first test if for validity, applicability, and simple credibility.Ā
A final recommendation is Ken Schwaber's talk and slidesĀ to think aboutĀ evidence basedĀ discussions around improving the business of software development. And the book he gave away at the end of the talkĀ Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based ManagementRelated articles Falsifiability and Junk Science
If a man will begin with certainties, he shall end in doubts, but if he will be content to begin with doubts, he shall end in certainties. - Francis Bacon
Everything in project work isĀ uncertainty. Estimating is required to discover the extent of the range of these uncertainties, the impacts of the uncertainties on cost, schedule, and the performance of the products or services.
To suggest decisions can be made in the presence of these uncertainties without knowledge of the future outcomes and the cost of achieving these outcomes, or deciding between alternatives with future outcomes ignores completely the notion of uncertainty and microeconomics of decision making in the presence of these uncertainties.
In a recent postĀ there are 5 suggestions of how decisions about software development can be made in the absence of estimating the cost, duration, and impact of these decisions. Before looking at each in more detail,Ā let's see what the basis is of these suggestions from the post.
A decision-making strategy is a model, or an approach that helps you make allocation decisions (where to put more effort, or spend more time and/or money). However I would add one more characteristic: a decision-making strategy that helps you chose which software project to start must help you achieve business goals that you define for your business. More specifically, a decision-making strategy is an approach to making decisions that follows your existing business strategy.
Decision making in the presence of theĀ allocationĀ ofĀ limited resourcesĀ is called Microeconomics. These decision - in the presence of limited resources - involvesĀ opportunity costs. That isĀ what is the cost of NOT choosing one of the alternatives - the allocations?Ā To know these means we need to know something about the outcome ofĀ NOT choosing. We can't wait to do the work, we need to know what happens - to some level of confidence - if weĀ DON'T Do something. How can we do this? We need estimate what happens if we don't choose one of the possible allocations, since all the outcomes are in the future.
But first, the post started with suggesting the five approaches are part of Strategy. I'm familiar with strategy making in the domain of software development, having beenĀ schooled by two Balanced Scorecard leaders while working as a program manager for a large Department of Energy site, where we pioneered the use of agile development in the presence of highly formal nuclear safety and safeguards applications.
What is Strategy?
Before proceeding with the 5 suggestions, let's look at what strategy is, since it is common to confuse strategy with tactics.
Strategy is creating fit among a firm's activities. The success of a strategy depends on doing many things well ā not just a few. The things that are done well must operate within a close nit system. If there is no fit among the activities, there is no distinctive strategy and little to sustain the strategic deployment process. Management then reverts to the simpler task of overseeing independent functions. When this occurs, operational effectiveness determines the relative performance of the firm.Ā
Improving operational effectiveness is a necessary part of management, but it is not strategy. In confusing the two, managers will be unintentionally backed into a way of thinking about competition that drives the business processes (IT) away from the strategic support and toward the tactical improvement of operational effectiveness.
Managers must be able to clearly distinguish operational effectiveness from strategy. Both are essential, but the two agendas are different. The operational effectiveness agenda involves continual improvement business processes that have no tradeāoffs associated with them. The operational effectiveness agenda is the proper place for constant change, flexibility, and relentless efforts to achieve best practices.
In contrast, the strategic agenda is the place for making clear trade offs and tightening the fit between the participating business components. Strategy involves the continual search for ways to reinforce and extend the companyās position in the market place.Ā
āWhat is Strategy,ā M. E. Porter, Harvard Business Review, Volume 74, Number 6, pp. 61ā78.
Using Porter's notion of strategy in a business context, the post seems more about tactics. But ignoring that for the moment, let's look further into the ideas presented in the post.
I'm going to suggest that each of the five decision process described in the post are the proper ones - ones with many approaches - but each has ignored the underlying principles of Microeconomics. This principle is that decisions about future outcomes are informed by theĀ opportunity cost and that cost requires - mandated actually since they're in the future - an estimate. This is the basis of Real Options, forecasting, and the very core of business decision making in the presence of uncertainty.
The post then asks
The 1st question needs another question to be answered.Ā What are our business goals and what are the units of measure of these goals.Ā In order to answer the 1st question we need aĀ steering target to know how we are proceeding toward that goal.
The second question is about risk. All risk comes from uncertainty. Two types of uncertainty exist on projects:
Reducible (Epistemic) and Irreducible (Aleatory). Epistemic uncertainty comes fromĀ lack of knowledge. EpistemologyĀ is the study of the acquisition of knowledge. We can pay money to buy down this lack of knowledge. That is Epistemic uncertainty can be reduced with work. Risk reduction work. But this leaves open how much time, budget, and performance margin is needed?
ANSWER: We need an Estimate of the Probability of the Risk Coming True. Estimating the Epistemic risk probability of occurrence, the cost and schedule for the reduction efforts, and the probability of the residual risk is done with probabilistic model. There are several and many tools. But estimating all three components: occurrence, impact, effort to mitigate, and residual risk is required.
Aleatory uncertainty comes from the naturally occurring variances of the underlying processes. The only way to reduce the risk arising from Aleatory uncertainty is withĀ margin. Cost Margin, Schedule Margin, Performance Margin. But this leaves open how do we know how margin?
ANSWER: We need to estimate the needed margin from the Probability Distribution Function of the Underlying Statistical Process. Estimating the needed aleatory margin (cost, schedule, and performance)Ā can be done with Monte Carlo SimulationĀ or Method of Moments.
All decisions have inherent risks, and we must consider risks before elaborating on the different possible decision-making strategies. If you decide to invest in a new and shiny technology for your product, how will that affect your risk profile?
All risk is probabilistic, based on underlying statistical processes. Either the process ofĀ lack of knowledge (Epistemic) or the process ofĀ natural variability (Aleatory). In the consideration of risk we must incorporate these probability and statistical behaviours in our decision making activities. Since the outcomes of these processes occur in the future, we need to estimate them based on Ā knowledge - or lack of knowledge - of their probability of occurrence. For the naturally occurring variances that have occurred in the past we need to know how they might occur in the future. To answer these questions, we need a probabilistic model. This model based on the underlying statistical processes. And since the Ā underlying model is statistical, we need to estimate the impact of this behaviour.
Let's Look At The Five Decision Making Processes
1. Do the most important work first -Ā If you are starting to implement a new strategy, you should allocate enough teams, and resources to the work that helps you validate and fine tune the selected strategy. This might take the form of prioritizing work that helps you enter a new segment, or find a more valuable niche in your current segment, etc. The focus in this decision-making approach is: validating the new strategy. Note that the goal is not "implement new strategy", but rather "validate new strategy". The difference is fundamental: when trying to validate a strategy you will want to create short-term experiments that are designed to validate your decision, instead of planning and executing a large project from start to end. The best way to run your strategy validation work is to the short-term experiments and re-prioritize your backlog of experiments based on the results of each experiment.
2. Do the Highest Techncial Risk First -Ā When you want to transition to a new architecture or adopt a new technology, you may want to start by doing the work that validates that technical decision. For example, if you are adopting a new technology to help you increase scalability of your platform, you can start by implementing the bottleneck functionality of your platform with the new technology. Then test if the gains in scalability are in line with your needs and/or expectations. Once you prove that the new technology fulfills your scalability needs, you should start to migrate all functionality to the new technology step by step in order of importance. This should be done using short-term implementation cycles that you can easily validate by releasing or testing the new implementation.
3. Do the Easiest Work First -Ā Suppose you just expanded your team and want to make sure they get to know each other and learn to work together. This may be due to a strategic decision to start a new site in a new location. Selecting the easiest work first will give the new teams an opportunity to get to know each other, establish the processes they need to be effective, but still deliver concrete, valuable working software in a safe way.
4. Do the legal Requirements First -Ā In medical software there are regulations that must be met. Those regulations affect certain parts of the work/architecture. By delivering those parts first you can start the legal certification for your product before the product is fully implemented, and later - if needed - certify the changes you may still need to make to the original implementation. This allows you to improve significantly the time-to-market for your product. A medical organization that successfully adopted agile, used this project decision-making strategy with a considerable business advantage as they were able to start selling their product many months ahead of the scheduled release. They were able to go to market earlier because they successfully isolated and completed the work necessary to certify the key functionality of their product. Rather then trying to predict how long the whole project would take, they implemented the key legal requirements first, then started to collect feedback about the product from the market - gaining a significant advantage over their direct competitors.
5. Liability Driven Investment -Ā This approach is borrowed from a stock exchange investment strategy that aims to tackle a problem similar to what every bootstrapped business faces: what work should we do now, so that we can fund the business in the near future? In this approach we make decisions with the aim of generating the cash flows needed to fund future liabilities.
General Principles of SoftwareĀ Validation; Final Guidance forĀ Industry and FDA Staff, US Food and Drug Administration.
To achieve great things, two things are needed; a plan, and not quite enough time.
ā Leonard Bernstein
The notion thatĀ planning is a waste is common in domains where mission critical, high risk - high reward,Ā must work, type projects do not exist.
Notice the Plan and the Planned delivery date. The notion that Ā deadlines Ā are somehow evil, goes along with the lack on understanding that business needs a set of capabilities to be in place on a date in order to startĀ booking the value in the general ledger.
Plans are strategies. Strategies are a hypothesis. The Hypothesis is tested with Ā experiments. Experiments show from actual data what the outcome is of the work. These outcomes are used as feedback to take corrective actions at the strategic and tactical level of the project.
This is called Closed Loop Control. Set the strategy, define the units of measure for the desired outcome - Measures of Effectiveness and Measures of Performance. Perform work as assess these measures. Determine theĀ variance between the planned outcomes and the needed outcomes. Take corrective action by adjusting the plan to keep the project moving toward the strategic goals. For Closed Loop Control, we need
Control systems from Glen Alleman Related articles Project Risk Management, PMBOK, DoD PMBOK and Edmund Conrow's Book
If we were setting out to build a home, we would first lay out the floor plans, grouping each room by function and placing structural items within each room according to their best utility. This is not an arbitrary process ā it is architecture. Moving from home design to IT system design does not change the process. Grouping data and processes into information systems creates the rooms of the system architecture. Arranging the data and processes for the best utility is the result of deploying an architecture. Many of the attributes of building architecture are applicable to system architecture. Form, function, best use of resources and materials, human interaction, reuse of design, longevity of the design decisions, robustness of the resulting entities are all attributes of well designed buildings and well designed computer systems.Ā 
In general, an architecture is a set of rules that defines a unified and coherent structure consisting of constituent parts and connections that establish how those parts fit and work together. An architecture may be conceptualized from a specific perspective focusing on an aspect or view of its subject. These architectural perspectives themselves can become components in a higherālevel architecture serving to integrate and unify them into a higher level structure.
The architecture must define the rules, guidelines, or constraints for creating conformant implementations of the system. While this architecture does not specify the details on any implementation, it does establish guidelines that must be observed in making implementation choices. These conditions are particularly important for component architectures that embody extensibility features to allow additional capabilities to be added to previously specified parts.Ā Ā This is the case where Data Management is the initial deployment activity followed by more complex system components.
By adopting a system architecture motivation as the basis for the IT Strategy, several benefits result:
 āHow Architecture Wins Technology Wars,ā C. Morris and C. Ferguson, Harvard Business Review, MarchāApril 1993, pp. 86ā96.
We gave a recent College of Performance Management webinar on using techncial progress to inform Earned Value. Here's the annotated charts.
In a recent post to āWho Is Ed Conrow?ā a responder asked about the differences between the PMBOKĀ® Risk approach and the DoD PMBOK risk approaches as well as a summary of the book Effective Risk Management: Some Keys to Success, Edmund Conrow. Ed worked the risk management processes for a NASA proposal I was on. I was the IMP/IMS lead, so integrating Risk with the Integrated Master Plan / Integrtaed Master Schedule in the mannder he prescribed was a live changing experience. I was naive before, but no longer after that proposal won Ė$7B for the client.
Let me start with a few positioning statements:
With all my biases out of the way, letās look at the DoD PMBOKĀ®
Page 124 of DoD PMBOKĀ® summarizes the principles of Risk Management as developed in two seminal sources.
Now all these pedantic references are here for a purpose. This is how people who manage risk for a living, manage risk. By risk, I mean technical risk that results in loss of mission, loss of life. Programmatic risk that results in loss of Billions of Tax Payer dollars. They are serious enough about risk management to not let the individual project or program manager interpret the vague notions in PMI PMBOKĀ®. These may appear to be harsh words, but the road to the management of enterprise class projects is littered with disasters. You can read every day of IT projects that are 100% over budget, 100% behind schedule. From private firms to the US Government, the trail of destruction is front page news.
A Slight Diversion ā Why are Enterprise Projects So Risky?
There are many reasons for failure ā too many to mention ā but one is the inability to identify and mitigate risk. The words āindentifyā and āmitigate,ā sound simple. They are listed in the PMI PMBOKĀ® and the DoD PMBOKĀ®. However, here is where the problem starts:
Using Conrow as a Guide
Here is one problem. When you use the complete phrase āProject Risk Managementā with Google, you get ~642,000 hits. There are so many books, academic papers, and commercial articles on Risk Management, where do we start? Ed Conrowās book is probably not the starting point for learning how to practice risk management on your project. However, it might be the ending point. If you are in the software development business, a good starting point is ā Managing Risk: Methods for Software Systems Development, Elaine M. Hall, Addison Wesley, 1998. Another broader approach is Continuous Risk Management Guidebook, Software Engineering Institute, August 1996. While these two sources focus on software, they provide the foundation for the discussion of risk management as a discipline.
There are public sources as well:
However, care needs the be taken once you go outside the government boundaries. Here are many voices plying the waters of ārisk management,ā as well as other voices with āaxes to grindā regarding project management methods and risk management processes. The result is many times a confusing message full of anecdotes, analogies, and alternative approaches to the topic of Risk Management.
Conrow in his Full Glory
Before starting into the survey of the Conrow book, let me state a few observations:
From the introduction:
The purpose of this book is two-fold: first, to provide key lessons learned that I have documented from performing risk management on a wide variety of programs, and second, to assist you, the reader, in developing and implementing an effective risk management process on your program.
A couple of things here. One is the practical experience in risk management. Many in the risk management ātalkingā community have limited experience with risk management in the way Ed does. I first met Ed on a proposal for a $8B Manned Spaceflight program. He was responsible for the risk strategy and the conveying of that strategy in the proposal. The proposal resulted in an award and now our firm provides Program Planning and Controls for a major subsystem of the program. In this role programmatic and technical risk management is part of the Statement of Work flowed down from the prime contractor.
Second Ed is a technical advisor to the US Arms Control and Disarmament Agency as well as a consultant industry and government on risk management. These āresumeā items are meant to show that the practice of risk management is just that ā a practice. Speaking about risk management and doing risk management on high risk programs are two different things.
One of Edās principle contributions to the discipline was the development of a micro-economic framework of risk management in which the design feasibility (or technical performance) is traded against cost and schedule.
In the end, this is a reference text for the process of managing the risk of projects, written by a highly respected practitioner.
What does the Conrow Book have to offer over the Standard approach?
Edās book contains the current ābest practicesā for managing technical and programmatic risk. These practices are used on high risk, high value programs. The guidelines in Edās book are generally applicable to many other classes of projects as well. But there are several critical elements that differentiate this approach from the pedestrian approach to risk management.
The ordinal approach works like this. Ed describes some classes of risk scales which include: maturity, sufficiency, complexity, uncertainty, estimative probability, and probability based scales.
A maturity risk scale would be:
Basic principles observed
Concept design analyzed for performance
Breadboard or brassboard validation in relevant environment
Prototype passes performance tests
Item deployed and operational
Ā The critical concept is to relate the risk ordinal value to an objective measure. For a maturity risk assessment, some ācalibrationā of what it means to have the ābasic principles observedā must be developed. This approach can be applied to the other classes ā sufficiency, complexity, uncertainty, estimative probability and probability based scales.
Itās the estimative probability that is important to cost and schedule people in our PP&C practice. The estimative probability scale attempts to relate a word to a probability value. āHighā to 80%. An ordinal estimative probability scale using point estimates derived from a statistical analysis of survey data might look like.
Median probability value
Almost no chance
Calibrating these risk scales is the primary analysis task of building a risk management system. What does it mean to have a āmediumā risk, in the specific problem domain?
These two concepts are the ones that changed the way I perform risk management on the programs Iām involved with and how we advise our clients. They are paradigm changing concepts. No more simple mined arithmetic with probabilities and consequences. No more uncalibrated risk scales. No more tolerating those who claim PERT, Critical Path, and Monte Carlo are unproven, obsolete, or āwrong headedā approaches.
Get Edās book. Itāll cost way too much when compared to the āpaperbackā approach to risk. But for those tasked with āmanaging risk,ā this is the starting point.
Scott Adams provides cartons ofĀ what not to do for most things technical. Software and Hardware. I actually saw him once, when he worked for PacBell in Pleasanton, CA. I was on a job at major oil company deploying document management systems for OSHA 1910.119 - process safety management and integrating CAD systems for control of safety critical documents.
The most popular use of Dilbert cartoon lately has been with the #NoEstimates community in support of the notion that estimates are somehow evil, used to make commitments that can't be met, and generally should be avoided when spending other people's money.
The cartoon below resonated with me for several reasons. What's happening here is classic misguided, intentionally ignoring the established processes of Reference Class Forecasting. As well, in typical Dilbert fashion,Ā doing stupid things on purpose.
Reference Class Forecasting is a well developed estimating process used across a broad range of technical, business, and finance domains. The characters above seem not to know anything about RCF. As a result they are DSTOP.Ā
Here's how not to DSTOP for cost and schedule estimates and the associated risks and the technical risk that the product you're building can't do what it's supposed to do on or before the date it needs to do it, at or below the cost you need it to do Ā it in order to stay in business.
The approach below may be complete overkill for your domain. So start by asking what's theĀ Value at Risk. How much of our customers money are we willing to right off, if we don't have a sense of what DONE looks like in units of measure meaningful to the decision makers.Ā Don't know that? then it's likely you've already put that money at risk, you're likely late, and don't really know what capabiltiies will be produced when you run out of time and money.
Don't end up a cartoon character in Dilbert strip. Learn how to properly manage your efforts, the efforts of others, using your customers money.
Managing in the presence of uncertainty from Glen Alleman
It seems lately there is an intentional disregard of the core principles of business development of software intensive systems. The #Noestimates community does, but other collections of developers do as well.Ā
These notions of course are seriously misinformed on how probability and statistics work in the estimating paradigm. I've written about this in the past. But there are a few new books we're putting to work in ouyr Software Intensive Systems (SIS) work that may be of interest to those wanted to learn more.
These are foundation texts for the profession of estimating. The continued disregard - ignoring possibly - of these principles has become all to clear. Not just in the sole contributor software development domain,. But all the way to Multi-Billion dolalr programs in defense, space, infrastructure, and other high risk domains.Ā
Which brings me back to a core conjecture - there is no longer any engineering discipline in the software development domain. At least outside the embedded systems like flight controls, process control, telecommunications equipment, and the like. There was a conjecture awhile back that the Computer Science discipline at the university level should be split -Ā software engineering andĀ coding.
Here's a sample of the Software Intensive System paradigm, where theĀ engineering of the systems is a critical success factor. AndĀ Yes Virginia, the Discipline of Agile is applied in the Software Intensive Systems world - emphasis on the term DISCIPLINE.
The principles of management, project management, software development and its management, product development management are immutable.
What does done look like, what's our plan to reach done, what resources will we need along the way to done, what impediments will we encounter and how will we overcome them, and how are we going to measure our progress toward done in units meaningful to the decision makers?
This are immutable principles. These immutable principles can then be used to test practices and process by asking what is the evidence that the practice or process enables the principle to be applied and how do we know that the principle is being fulfilled.
In recent discussion (of sorts) about estimating - Not Estimating actually - I realized something that should have been obvious. I travel in a world not shared by the staunch advocates of #NoEstimates. They appear to be sole contributors. I came top this after reading Peter Kretzman's 3rd installment, where he re-quoted a statement byĀ Ron Jeffries,
Even with clear requirements ā and it seems that they never are ā it is still almost impossible to know how long something will take, because weāve never done it before.
This is a sole contributor or small team paradigm.
So let's pretend we work at Price/Waterhouse/Cooper and weāre playing our roles Ā -Ā Peter as CIO advisor, me as Program Performance Management adviser.Ā We've been asked by our new customer to develop a product from scratch, estimate the cost and schedule, and provide some confidence level that the needed business capabilities will be available on or before a date and at or below a cost. Why you ask, because that's in the business plan for this new product and if they're late or overrun the planned cost that will be a balance sheet problem.
What would we do? Well, we'd start with PWC resource management database ā held by HR ā and ask for āpast performance experienceā people in the business domain and the problem domain. Our new customer did not āinventā a new business domain, so it's likely we'll find people who know what our new customer does for money. Weād look to see where in the 195,433 people in the database that work for PWC world wide there is someone, somewhere, that knows what the customer does for money and what kinds of business capabilities this new system needs to provide. If there is no one, then we'd look in our 10,000 or so partner relationships database to find someone.
If we found no one who knows the business and the needed capabilities, weād no bid.
This notion of āI've been asked to do something thatās never been done before, so how can I possibly estimate itā really means āI'm doing something Iāve never done before.ā And since āIām a sole contributor, the population of experience in doing this new thing for the new customer is ONE ā me." So since I don't know how the problem has been solved in the past, I can't possibly know how to estimate the cost, schedule, and needed capabilities. And of course I'm absolutely correct to say -Ā new development with unknown requirements can't be estimated. Because those unknown requirements are actuallyĀ Unknown to me, but may be known to another. But in the population of 195,000 other people in our firm, I'm no longer alone in my quest to come up with an estimate.
So the answer to the question, āwhat if we encounter new and unknown needs, how can we estimate?ā is actually a core problem for the sole contributor, or small team. It'd be rare that the sole contributor or small team would have encountered the broad spectrum of domains and technologies needed to establish the necessary Reference Classes to address this open ended question. This is not the fault of the sole contributor. It is simply the situation ofĀ small numbers versusĀ large numbers.
This is the reason the PWCās of the world exist. They get asked to do things the sole contributors never have an opportunity to see.Related articles Software Requirements Are Vague
Peter Kretzman has a nice post in his series on #NoEstimates. Peter and I share a skepticism of "making decisions in the absence of estimating the cost and impact" of those decisions. In Peter's current post there is a quote that is telling.
Letās useĀ Ron Jeffriesā statementĀ as an example of this stance:
āEstimates are difficult. When requirements are vague ā and it seems that they always are ā then the best conceivable estimates would also be very vague. Accurate estimation becomes essentially impossible. Even with clear requirements ā and it seems that they never are ā it is still almost impossible to know how long something will take, because weāve never done it before. ā
One of my 3 half time jobs is working in the space and defense program performance management domain, both embedded systems and enterprise IT systems. DOD is the largest buyer of ERP on the planet. In this domain we have a formal process for determiningĀ what went wrong. The department looking after this is calledĀ Performance Assessment and Root Cause Analysis (PARCA). PARCA provides Root Cause Analysis for programs that haveĀ gone Nunn McCurdyĀ as we would say.Ā
When you read the reports from Rand and Institute for Defense Analyses on N-M breaches, requirements instability is in the top 5 asĀ root causes.Ā
It seems to me - in my narrow minded program performance management view of the world - that unstable requirements being used as the reason for vague estimates is so obvious a problem that has been completely ignored by the #NoEstimates advocates. It's like the olde saw
Doctor, Doctor it hurts when I do this (make estimates in the presence of vague requirements). Then stop doing that!
The notion of Capabilities Based Planning is missing in many software organizations. So having vague requirements is a natural outcome of not having definitive understanding of what Capabilities the system must provide, in units of measure meanigful to the decision makers. These units are:
Without these requirements have not home, are vague, and therefore create theĀ root cause of bad estimates.
So what would a logical person do when working on a project that spends other peoples money, sometimes lots of other peoples money? Not Estimate? Does that sound like the corrective action to theĀ root causeĀ of the problems with software project success shortfall?
Not to me. It's theĀ doctor, doctor this hurts paradigm. So until the root cause is determined, the corrective actions identified and applied, there can be no credible solution to theĀ estimating problem. And there is a huge estimating problem in our domain, just read the N-M reports at RAND and IDA (Goggle nunn-mcurdy Rand or IDA to find them). Similar assessments ofĀ root causes can be found for enterprise IT from many sources.Ā
The #NoEstimates advocates are attempting to solve the wrong problem with the wrong approach. They've yet to connect with the core process of writing software for money - MicroEconomics of software development. Here's a starting pointĀ to address the root casue rather than the symptom. Fixing the symptoms does nothing in the end. It just spends money, with no actonable outcomes. And that woudl be very counter to the principles of Agile.
Capabilities based planning (v2) from Glen Alleman
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There is a popular quote used by many in the #NoEstimates community, that is sadly misinformed.
Those who have knowledge, donāt predict. Those who predict, donāt have knowledge. ā Lao Tseu
This of course was from a 6th Century BC Chinese philosopher, who was not likely familiar with the notion of probability and statistics developed some 900 years later. The quoting and re-quoting of Lao Tseu as an example of why estimates can't be made brings to light one of the more troublesome aspects of our modern age.
The lack of understanding of basic probability and statistics when applied to human endeavors.
Or possibly the intentional ignorance of probability and statistics as it is applied to the development of software systems. I can't really say if it is for lack of understanding, lack of exposure, or just a simple intent to ignore.Ā
But for any of those reasons and more, here's a starting point on how to actually become a member of the modern of statistical estimating community, once it is decided that is better than ignoring the basic knowledge needed to be a steward of other peoples money.
Here's some starting points in no particular order, other than that's how they came off the office book shelf.
These are just a small sample of the information readily available at your local book store or through the mail. If you google "software cost estimating," (all in quotes) there will be 100's of more articles, papers, and web sites. As well tools for estimating software are used every single day in a variety of domains.Ā
TheĀ Value at Risk is a starting point as well. Low value - this is defined by those providing the money, not by those doing the work, and low risk - this usually defined by those doing the work, not by those providing the money - at least in the domains we work. ThisĀ Value at Risk, sets the tone. Low Value, Low Risk - and this is in absolutely no way an assessment of the relative value and risk - usually doesn't need much estimating.
Got a 6 week, 2 person database update project. Just do it. Got a 38 month, 400 personĀ National Asset sofwtare project, probably so. Everything and anything in between needs to ask and answer thatĀ value at riskĀ question before deciding.Ā
So poor Mr. Tzu was sadly informed when he made his quote. As are those repeating it. In the 21 century
Those who have knowledge of probability, statistics, and the processes described by them can predict their future behaviour. Those without this knowledge, skills, or experience cannot.Related articles How to Estimate Software Development
The unassisted hand, and the understanding left to itself, posses but little power. Effects are produced by the means of instruments and helps, which the understanding requires no less than the hand. And as instruments either promote or regulate the motion of the hand, so those that are applied to the mind prompt or protect the understanding - Novum Organum Sciantiarium (Aphorisms concerning the Interpretation of Nature and the Kingdom of Man), Francis Bacon (1561 - 1626).
When we hear we can make decisions in the absence of estimating the impacts of those decisions, the cost when complete, or theĀ lost opportunity cost for make alternative decisions, think of Bacon.
He essentially saysĀ show me the money.
Control systems from Glen Alleman
Nothing is too wonderful to be true, if it is consistent with the laws of nature, and in Ā such things as these, experiment is the best test of such consistency ā Michael Faraday's Diary, March 19, 1849
When we hear wonderful concepts conjectured that are untested outside personal anecdote, ask where has this worked, in what domain, in what governance framework, and what was theĀ value at risk to applying the idea.
From the Introduction of the book to the left.
Good estimates are key to project (and product) success. Estimates provide information to make decisions, define feasible performance objectives, and plans. Measurements provide data to gauge adherence to performance specifications and plans, make decisions, revise designs and plans, and improve future estimates and processes.
Engineers use estimates and measurements to evaluate the feasibility and affordability of proposed products, choose amoung alternatives designs, assess risk, and support business decisions. Engineers and planners estimate the resources needed to develop, maintain, enhance, and deploy a product. Project planners use the estimated staffing level to identify needed facilities.
Planners and managers use the resource estimates to compute project cost and schedule, and prepare budgets and plans. Estimates of product, project and process characteristics provide "baselines"to assess progress the execution of the project. Managers compare compare estimates and actual values to identify deviations from the project plan and to understand the causes of the variation.
For products, engineers compare estimates of the technical baseline to observed performance to decide if the product meets its functional and operational requirements. Process capability baselines establish norms for process performance. Managers use these norms to control the process and detect compliance problems. Process engineers use capability baselines to improve the production process.
Bad estimates affect everyone associated with the project - the engineers and managers, the customer who buys the product, and sometimes even the stockholders of the company responsible for delivering the software. Incomplete or inaccurate resource estimates for a project mean that the project may not have enough time and money to complete the required work.
If you work in a domain where none of these conditions are in place, then by all means don't estimate.
If you do recognize some or all of these conditions, then here's a summary of the reasons to estimate and measure, from the book.
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Complex and unstable environments encountered in project work - especially software development project work - calls for critical thinking by all participants. Complexity comes in part from technical uncertainties, starting with requirements for software capabilities. If there is uncertainty in what capabilities are needed, the project is starting off on the path to failure on day one. Certainly the functional and operational requirements have emergent properties that create uncertainty. As do staffing, productivity and risks created by reducible and irreducible uncertainty.
To deal with these complexities,Ā critical thinkingĀ is needed on behalf of the project leaders and project participants alike.
The first responsibility of the project staff as well as management is to think. To think what is being asked of them. To consider that they are being paid to produce value by someone other than themselves. To think about whyĀ they are there,Ā whatĀ they are being asked to do, andĀ howĀ they can go about being stewards of the money provided by those paying for their work.Ā To be true professionals applying their education, training, and experience through analysis and creative, informed thought to make daily decisions.
Failure to apply critical thinking creates a Ā disconnect between those providing the value and those paying for the value. This is best illustrated in the notion that business decisions can be made in the absence of knowing the cost and outcomes of those decisions. The first gap in critical thinking occurs when the decsion making process ignores the principles of MicroEconomics. This gap is future reinforced when the probabilistic nature of all project work is also ignored.Ā
The three core elements of all projects are the delivered capabilities, the cost of producing those capabilities, and the time frame over which those capabilities are delivered for the cost. Each of these acts as a random variable, interacting with the other two in statistically complex ways.
ToĀ manage in the presence of these random variables, means making decisions in the presence of uncertainties - and resulting risks - created by theĀ randomness of the variables. These uncertainties are reducible - we can pay more to find out more information. Or they are irreducible, we can only manage in their presence withĀ margin for cost, schedule, and technical performance.
To make decisions in the presence of this paradigm we need to Estimate. Making decisions in the absence of estimating first violates the principles of microeconomics, and secondly ignores the underlying statistical and probabilistic nature of all project work.
When we hear that decisions be made in the absence of estimates, ask how Microeconomics and statistical uncertainty are handled.Ā
We shall not cease from exploration We shall not cease from exploration and the end of all our exploring will be to arrive where we started and be to arrive where we started and know the place for the first time. -T.S. Eliot
Quoting 29 year old reports or referencing 30 year old books it not likely the best way to reveal the problems of the day. These problems have existed of 30 year, but new more effective, and much more transparent solutions are available today.
For all the words written and posted around estimating or not estimating - and I've contributed my share - the basis of estimates has yet to be addressed outside of a few people. @PeterKretzmanĀ @aritanninenĀ @kalapaistos@fscavoĀ come to mind.
The gap here is simple. No one seems to ask - or even want to ask - Who are the estimates for? They are not likely for developers, who rightly, so in some cases see estimating as taking away from their valuable development duties.
Who Are Estimates For?
Estimates are for business managers providing the money that appears in the developers paycheck. Estimates are for those same business managers accountable for the Profit & Loss statement of the firm employing the developers writing the code. Those estimates forecast confidence intervals of profit or loss on a project or service before that profit or loss arrives and is irrevocable.Ā
Estimates are for the business marketing staff in a product firm, who are forecasting the "break even" plan for the sunk cost of developing Ā software that will be sold in the market. Whose revenue will pay back the short term loan (line of credit) used to pay the salaries of the developers. Without this forecast, decisions about spending or further spending have to be made in the dark.
Estimates are for the business development staff in a professional services and development firm to forecast the confidence in the assure that the contractual obligations to provide working software will not cost more - including management reserve and contingency - than they quoted the customer during the early phases of the project. Since all forecasting are probabilistic, this confidence is - or should be - discussed as theĀ probability of cost at of below orĀ completing on or before. The dysfunction of using estimates as commitments, is recognized as just that - dysfuntion. But as a dysfunction, it's classified as Bad Management. Don't Do Stop Things on Purpose is good advice for any business.
Estimates are for the internal business finance staff accountable for managing and forecasting costs for internal software development or procurement used to run the business - and likely used to generate revenue - and assure the senior finance people that the "value" produced by this software measured in monetized units of "money" will exceed the cost to achieve that value when the project completes. And some sense of when the date will be, so those monetized benefits can start to appear on the balance sheet using FASB 86 accounting rules.
The estimates are not for the developers
Those talking aboutĀ #NoEstimatesĀ from the developers point of view are talking to the wrong people. They appeat to be talking to their own self-selected group and not the group that provides the money for their work. As my former NASA Cost Director colleague reminds me "follow the money." So follow the money. Unless the developers are providing the money themselves, the question of estimating or not estimating is a self-referencing conversation in the absence of these people. Because of that, those best to say if estimates are of value or not are not in the conversation.Ā
So Back To The Original Question
Ignoring for the moment the observed or perceived dysfunctions found in low maturity software development organizations of the misuse of estimates. Ignore for the moment the preception that making estimates of the future cost, duration, and probabilistic outcomes of development work is part of normal engineering processes. Ignore the emotional rhetoric of the Dilbert approach to management.Ā
The core principle of Microeconomics of software development requires we Ā have some approximation of the future to make decisions about alternatives. The opportunity cost, the trade-space of decision making, requires we approximate the cost and outcomes of our decisions.Ā
Now add the core business process of managing expenditures against a planned and targeted Return on Investment, which has both Value and Cost in it's equation.Ā
Then ask those conjecturing there are:
ToĀ connect the dots to those conjectures with Microeconomics of software development and ROI assessments of standard business processes.
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The Lean Aerospace Initiative and the Lean Aerospace Initiative Consortium define processes applicable in many domains for applying lean. At first glance there is no natural connection between Lean and System Engineering. The ideas below are from a paper Igave at a Lean conference.
Core Concepts of Systems Engineering
Typical System Engineering Activities
Steps to Lean ThinkingĀ 
Differences and Similarities between Lean and Systems Engineering
Despite these differences and similarities both Lean and Systems Engineering are focused on the same objectives ā delivering products or lifecycle value to the stakeholders.
It is the lifecycle value that drives both paradigms and must drive any other process paradigm associated with Lean and Systems Engineering. Paradigm like software development, the management of any form of a project and the very notion of agile. A critical understanding often missed is that Lifecycle Value includes the cost of delivering that value.
Value can't be determined in the absence of knowing the cost. ROI and Microeconomics of decision making require both variables to be used to make decisions.
What do we mean by lifecycle?
Generally lifecycle is a combination of product performance, quality, cost and fulfillment of the buyers needed capabilities.
Lean and Systems Engineering share this common goal. The more complex the system, the more contribution there from Lean and SE.
Putting Lean and Systems Engineering Together on Real Projects
First some success factors on complex projects 
This last success factor is core to any complex environment, no matter what the process is called. In the absence of stability of requirements and funding, improvements to the flow of work is constrained.
The notion of adapting to changing requirements is not the same as having the requirements ā and the associated funding ā be unstable.
Mapping of the Value Stream to the work process requires some level of stability. It is the search for this stability where Systems Engineering ā as a paradigm ā adds measureable value to any Lean initiative.
The standardization and commonality of processes across complex systems is the basis for this value.Ā 
Ā āThe Lean Enterprise ā A Management Philosophy at Lockheed Martin,ā Joyce and Schechter,Ā Defense Acquisition Review Journal, 2004.
Ā Lean Thinking, Womack and Jones, Simon and Schuster, 1996
Ā Lean Enterprise Value: Insights from MITās Lean Aerospace Initiative, Murman, et al, Palgrave 2002.
Ā āLean Systems Engineering: Research Initiatives in Support of a New Paradigm,ā Rebentisch, Rhodes, and Murman,Ā Conference on Systems Engineering, April 2004.
Ā LM21 Best Practices, Jack Hugus, National Security Studies, Louis A. Bantle Symposium, Syracuse University Maxwell School, October 1999
 āEnterprise Transition to Lean Roadmap,ā MIT Lean Aerospace Initiative, 2004 Plenary Conference.Why Projects Fail, No Matter the Domain When We Say Risk What Do We Really Mean? How to Deal With Complexity In Software Projects? Big Systems Acquisitions - Lessons for ACA Web Site