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Performance-Based Project Management¬ģ - an integrated approach to Principles, Practices, and Processes to increase Probability of Project Success. ab actu ad posse valet illatio
Updated: 14 hours 32 min ago

Quote of the Day

Tue, 07/29/2014 - 17:37

"Great minds discuss ideas; average minds discuss events; small minds discuss people."  - Eleanor Roosevelt 

Categories: Project Management

How To Make Decisions

Tue, 07/29/2014 - 04:22

Decisions are about making Trade Offs for the project. These decisions are about:

  • Evaluating alternatives.
  • Integrating and balancing all the considerations (cost, performance, Producibility, testability, supportability, etc.).
  • Developing and refining the requirements, concepts, capabilities of the product or services produced by the project or product development process.
  • Making trade studies and the resulting trade offs that enables the selection of the best or most balanced solution to fulfill the business need or accomplishment of the mission.

The purpose of this decision making process is to:

  • Identify the trade-offs ‚Äď the decisions to be made ‚Äď among requirements, design, schedule, and cost.
  • Establish the level of assessment commensurate with cost, schedule, performance, and risk impact based on the value at risk for the decision.
    • Low value at risk, low impact, simple decision making ‚Äď possibly even gut feel.
    • High value at risk, high impact, the decision-making process must take into account these impacts.

Trade-offs are essential to strategy. They create the need for choice and purposefully limit what a company offers.

It is only by assessing the impacts of these tradeoffs can the products or solutions of the projects be guided. Otherwise, just producing the requiremenst has no real purpose - no strategic purpose connected to the needed capabilities. ‡

Making decisions about capabilities and resulting requirements is the starting point for discovering what DONE looks like, by:

  • Establishing alternatives for the needed performance and functional requirements.
  • Resolving conflicts between these requirements in terms of the product‚Äôs delivered capabilities.

Decisions about the functional behaviours and their options is next. These decisions:

  • Determine preferred set of requirements for the needed capabilities. This of course is an evolutionary process as requirements emerge, working products are put to use, and feedback is obtained.
  • Determine the customer assesses requirements for lower-level functions as each of the higher-level capabilities are assessed.
  • Evaluate alternatives to each requirement, each capability, and the assessed value of each capability ‚Äď in units of measure meaningful to the decision makers.

Then comes the assessment the cost effectiveness of each decision:

  • Develop the Measures of Effectiveness (MOE) and Measures of Performance (MOP) for each decision.
  • Identify the critical Measures of Effectiveness of each decision in fulfilling the project‚Äôs business goal or mission. These Technical Performance Measures are used to assess the impact of each decision on the produced value of the project.

Each of these steps is reflected in the next diagram.

Screen Shot 2014-07-28 at 8.08.44 AM

Value of This Approach

When we hear that estimates are not needed to make decisions, we need to ask how the following questions can be answered:

  • How can we have a systematized thought process, where the decisions are based on measureable impacts?
  • How can we clarify our options, problem structure, and available trade-offs using units of measure meaningful to the decision makers?
  • How can we improve communication of ideas and professional judgment within our organization through a shared exchange of the impacts of our decisions?
  • How can we improve communication of rationale for each decision to others outside the organization?
  • How can we be assured of our confidence that all available information has been accounted for in a decision?

The decision making process is guided by the identification of alternatives

Decision-making is about deciding between alternatives. These alternatives need to be identified, assessed, and analyzed for their impact on the probability of success of the project.

These impacts include, but are not limited to:

  • Performance
  • Schedule
  • Cost
  • Risk
  • Affordability
  • Producibility
  • And all the other ‚Ķilities associated with the outcomes of the project

The effectiveness of our decision making follows the diagram below:

  Screen Shot 2014-07-28 at 8.05.58 AM

In the End - Have all the Alternatives Been Considered?

Until there is a replacement for the principles of Microeconomics, for each decision made on the project, we will need to know the impact on cost, schedule, technical parameters, and other attributes of that decision. To not know those impacts literally violates the principles of microeconomics and the governance framework of all business processes, where the value at risk is non-trivial.

As Shim says think about it. 

Screen Shot 2014-07-28 at 12.26.24 PM

When you hear planning ahead, by assessing our altenatives is overrated, think again.

‡ What is Strategy? , M. E. Porter, Harvard Business Review, Volume 74, Number 6, pp. 61-68.

Connecting IT and Business Strategy, Glen B. Alleman, 5/12/2007

† Derived from Module J: Trade Study Process, Systems Engineering, Boeing.

Related articles Agile Project Management Concept of Operations First, then Capabilities, then Requirements The Value of Information Critical Thinking Skills Needed for Any Change To Be Effective Why Project Management is a Control System
Categories: Project Management

Quote of Day

Sun, 07/27/2014 - 15:09

"in order to reason well ... it is absolutely necessary to possess ... such virtues as intellectual honesty and sincerity and a real love of the truth." ‚ÄĒ C. S. Pierce

Categories: Project Management

Why Project Management is a Control System

Fri, 07/25/2014 - 21:48

When it is mentioned project management is a control system many in the agile world whince. But in fact project is a control system, a closed loop control system.

Here's how it works.

  • We have a goal, a target, some desired outcome.
  • The desired outcome usually comes with a budget - some expected cost.
  • It also comes with a time frame for achieving that desired outcome.
  • That outcome usually - or should if we're doing it right - has a beneficial outcome.

Each of these elements has some unit of measure:

  • Time - the day we need to deliver value or aa capability to meet the business goals or accomplish a mission. There can of course be incremental delievrables, but those also have a time element.
  • Outcome might be he accomplishments of a mission or fulfillment of the business strategy
  • Cost - there is no way to determine the value of anything without knowing its cost. This is the foundation of microeconomics. This can only happen - not knowing cost - by intentionally ignoring the principles of micro-economics. It's done I know, but Don't Do Stupid Things On Purpose.

Here's a small example of incremental delivery of value in an enterprise domain

Project Maturity Flow is the Incremental Delivery of Business Value

The accomplishment of a mission or fulfillment of a business strategy can be called the value produced by the project. In the picture above the value delivered to the business is incremental, but fully functional on delivery to accomplish the business goal. These goals are defined in Measures of Effectiveness and Measures of Performance and these measures are derived from the business strategy or mission statement. So if I want a fleet of cars for my taxi service, producing a sketboard, then a bicycle, is not likley to accomplishment the business goal.

But the term value alone is nice, but not sufficient. Value needs to have some unit of measure. Revenue, cost reduction, environmental cleanup, education of students, reduction of disease, the process of sales orders at a lower cost, flying the 747 to it's destination with minimal fuel. Something that can be assessed in tangible units of measure.

In exchange for this value, with it's units of measure, we have the cost of producing this value.

To assess the value or the cost, we need to know the other item. We can't know the value of something without knowing its cost. We can't know if the cost is appropriate without knowing the value produced by the cost.

This is one principle of Microeconomics of software development

The process of deciding between choices about cost and value - the trade space between cost and value - starts with information about both cost and value. This information lives in the realm of uncertainty before and during the project's life-cycle. It is only known on the cost side after the project completes. And for the value may never be known in the absence of some uncertainty as to the actual measure. This is also a principle of microeconomics - the measures we use to make decisions are random variables.

To determine the value of the random variable we need to estimate, since of course they are random. With these random variables - cost of producing value and the value exchanged for the cost, the next step in projects is to define what we want the project to do:

  • The desired outcome in the form of capabilities.
  • The desired cost for the desired value.
  • The desired time for the delivery of that desired value for the desired cost.
  • The confidence that we can show up on or before the desired time, at or below the desied cost to delivery the desired value, and deliver the needed capabilities to fulfill the mission.

The actual delivery of this value can be incremental, it can be iterative, evolutionary, linear, big bang, or other ways. Software many times can be iterative or incremental, pouring concrete and welding pipe can as well. Building the Interstate might be incremental, the high rise usually needs to wait for the occupancy permit before the value is delivered to the owners. There is no single approach.

For each of these a control system is needed to assure progress to plan is being made. The two types of control systems are Open Loop and Close Loop. The briefing below speaks to those and their use.

So In The End When we hear about a control loop applied to project management, we'll now know about Open and Closed Loop control. And that there can't be a Closed Loop control process without.
  • A desired outcome - the target budget, date, or some performance parameter.
  • Measures of progress to plan - what has the project been doing to date? This should be measured in units of¬†physical percent complete. Working software is a popular platitude in the agile community. But is it the right working software to fulfill the needed capabilities developed through a¬†Capabilities Based Planning process.
  • Variances between actual and plan - with the target outcomes, capture actuals and calculate variances. These variances are the information needed to make business decisions.
    • These decisions are based not only past performance - the actual's - but future performance - the estimates of future performance given the past performance.
    • This future performance must also be risk adjusted.
    • Both the future performance and the uncertainties that create risks to this performance are statistical processes, producing probabilistic outcomes, that are integrated into the decision making processes.
  • Take Corrective Actions - to keep the project¬†inside the white lines. Using the past performance or cost, schedule, and technical outcomes, the assessment of variances, the role of management is to take corrective action to meet the desired outcomes of the project.
    • The cost goals - we have a target budget that is used for assesing the Return on Investment or target product margin.
    • The schedule goals - we have planned¬†go live or¬†release date¬†that been communicated to the market or the customer.
    • The Needed Capabilities - for the product (internal or external) to¬†earn its keep.¬†
    • Adjustments¬†- to each of these attributes required management action, assessment of this action to actually¬†get back to GREEN, in our parlance, and keep the project headed to success.
  • Monitor these actions against plan - once corrections are taken, management must still monitor the project to assure it¬†stays on plan.
Related articles Elements of Project Success The Value of Information Control Systems - Their Misuse and Abuse Four Critical Elements of Project Success Critical Thinking Skills Needed for Any Change To Be Effective Seven Immutable Activities of Project Success How Not To Make Decisions Using Bad Estimates Why is Statistical Thinking Hard?
Categories: Project Management

How Not To Make Decisions Using Bad Estimates

Thu, 07/24/2014 - 04:54

The presentation Dealing with Estimation, Uncertainty, Risk, and Commitment: An Outside-In Look at Agility and Risk Management has become a popular message for those suggesting we can make decisions about software development in the absence of estimates.

The core issue starts with first chart. It shows the actual completion of a self-selected set of projects versus the ideal estimate. This chart is now in use for the #NoEstimates paradigm as to why estimating is flawed and should be eliminated. How to eliminate estimates while making decisions about spending other peoples money is not actually clear. You'll have to pay ‚ā¨1,300 to find out.¬†

But let's look at this first chart. It shows the self-selected projects, the vast majority completed above the initial estimate. What is this initial estimate? In the original paper, the initial estimate appears to be the estimate made by someone for how long the project would take. No sure how that estimate was arrived at - the basis of estimate - or how was the estimate was derived. We all know that subject matter expertise is the least desired and past performance, calibrated for all the variables is the best.

So Here in Lies the Rub - to Misquote from Shakespeare's Hamlet

The ideal line is not calibrated. There is no assessment if the orginal estimate was credible or bogus. If it was credible, what was the confidence of that credibility and what was the error band on that confidence. 

This is a serious - some might say egregious - error in statistical analysis. We're comparing actuals to a baseline that is not calibrated. This means the initial estimate is meaningless in the analysis of the variances without an assessment of it accuracy and precision. To then construct a probability distribution chart is nice, but measured against what - against bogus data.

This is harsh, but the paper and the presentation provide no description of the credibility of the initial estimates. Without that, any statistical analysis is meaningless. Let's move to another example in the second chart.

Screen Shot 2014-07-23 at 11.22.14 AM

The second chart - below - is from a calibrated  baseline. The calibration comes from a parametric model, where the parameters of the initial estimate are derived from prior projects - the reference class forecasting paradigm. The tool used here is COCOMO. There are other tools based on COCOMO and Larry Putman's and other methods that can be used for similar calibration of the initial estimates. A few we use are QSM, SEER, Price.

One place to start is Validation Method for Calibrating Software Effort Models. But this approach started long ago with An Empirical Validation of Software Cost Estimation Models. All the way to the current approaches of ARIMA and PCA forecasting for cost, schedule, and performance using past performance. And current approaches, derived from past research, of tuning those cost drivers using Bayesian Statistics.

Screen Shot 2014-07-20 at 10.42.05 PMSo What's All The Flap About?

The issue of software management, estimates of software cost, time, and performance abound. We hear about it every day. Our firm works on programs that have gone Over Target Baseline. So we walk the walk every day.

But when there is bad statistics used to sell solutions to complex problems, that's when it becomes a larger problem. To solve this nearly intractable problem of project cost and schedule over run, we need to look to the root cause. Let's start with a book Facts and Fallacies of Estimating Software Cost and Schedule. From there let's look to some more root causes of software project problems. Why Projects Fail is a good place to move to, with their 101 common casues. Like the RAND and IDA Root Cause Analysis reports many are symptoms, rather than root causes, but good infomation all the same.

So in the end when it is suggested that the woo's of project success can be addressed by applying

  • Decision making frameworks for projects that do not require estimates.
  • Investment models for software projects that do not require estimates.
  • Project management (risk management, scope management, progress reporting, etc.) approaches that do not require estimates.

Ask a simple question - is there any tangible, verifiable, externally reviewed evidence for this. Or is this just another self-selected, self-reviewed, self-promoting idea that violates the principles of microeconomics as it is applied to software development, where:

  • Economics is the study of how people make decisions in resource-limited situations. This definition of economics fits the major branches of classical economics very well.¬†

  • Macroeconomics is the study of how people make decisions in resource-limited situations on a national or global scale. It deals with the effects of decisions that national leaders make on such issues as tax rates, interest rates, and foreign and trade policy, in the presence of uncertainty

  • Microeconomics is the study of how people make decisions in resource‚ÄĒlimited situations on a ¬†personal scale. It deals with the decisions that individuals and organizations make on such issues as how much insurance to buy, which word processor to buy, what features to develop in what order, whether to make or buy a capability,¬†or what prices to charge for their products or services, in the presence of uncertainty. Real Options is part of this decision making process as well.

Economic principles underlie the structure of the software development life cycle, and its primary refinements of prototyping, itertaive and incremental development, and emerging requirements. 

If we look at writing software for money, it falls into the microeconomics realm. We have limited resources, limited time, and we need to make decisions in the presence of uncertainty.

In order to decide about the future impact of any one decision - making a choice - we need to know something about the furture which is itself uncertain. The tool to makes these decisions about the future in the presence of uncertainty is call estimating. Lot's of ways to estimate. Lots of tools to help us. Lots of guidance - books, papers, classrooms, advisers. 

But asserting we can in fact make decisions about the future in the presence of uncertainty without estimating is mathematically and practically nonsense. 

So now is the time to learn how to estimate, using your favorite method, because to decide in the absence of knowing the impact of that decision is counter to the stewardship of our customers money. And if we want to keep writing software for money we need to be good stewards first.

Related articles Averages Without Variances are Meaningless - Or Worse Misleading How to "Lie" with Statistics How to Fib With Statistics When Uncertainty is Good No Estimates of Costs and Schedule? The Value of Information COCOMO Model Why is Statistical Thinking Hard? Back To The Future The Failure of Open Loop Thinking
Categories: Project Management

All Decisions Are Based On Mathematics

Thu, 07/24/2014 - 04:25

How Not To Be WrongObvious not every decision we make is based on mathematics, but when we're spending money, especially other people's money, we'd better have so good reason to do so. Some reason other than gut feel for any sigifican value at risk. This is the principle of Microeconomics.

All Things Considered is running a series on how people interprete probability. From capturing a terrortist to the probability it will rain at your house today. The world lives on probabilitic outcomes. These probabilities are driven by underlying statistical process. These statistical processes create uncertainties in our decision making processes.

Both Aleatory and Epistemic uncertainty exist on projects. These two uncertainties create risk. This risk impacts how we make decisions. Minimizing risk, while maximizing reward is a project management process, as well as a microeconomics process. By applying statistical process control we can engage project participants in the decision making process. Making decision in the presence of uncertainty is sporty business and many example of poor forecasts abound. The flaws of statistical thinking are well documented.

When we encounter to notion that decisions can be made in the absence of statistical thinking, there are some questions that need to be answered. Here's one set of questions and answers from the point of view of the mathematics of decision making using probability and statistics.

The book opens with a simple example.

Here's a question. We're designing airplanes - during WWII - in ways that will prevent them getting shot down by enemy fighters, so we provide them  with armour. But armor makes them heavier. Heavier planes are less maneuverable and use more fuel. Armoring planes too much is a proplem. Too little is a problem. Somewhere in between is optimum.

When the planes came back from a mission, the number of bullet holes was recorded. The damage was not uniformly distributed, but followed this pattern

  • Engine - 1.11 bullet holes per square foot (BH/SF)
  • Fueselage - 1.73 BH/SF
  • Fuel System - 1.55 BH/SF
  • Rest of plane - 1.8 BH/SF
The first thought was to provide armour where the need was the highest. But after some thought, the right answer was to provide amour where the bullet holes aren't - on the engines. "where are the missing bullet holes?" The answer was onb the missing planes. The total number of planed leaving minus those returning were the number of planes that were hit in a location that caused them not to return - the engines.

The mathematics here is simple. Start with setting a variable to Zero. This variables is the probability that a plane that takes a hit in the enginer manages to staty in the air and return to base. The result of this analysis (pp. 5-7 of the book) can be applied to our project work.

This is an example of the thought processes needed for project management and the decision making processes needed for spending other peoples money. The mathematician approach is to ask what assumptions are we making? Are they justified? The first assumption - the errenous assumption - was tyhat the planes returning represented were a random sample of all the planes. If so, the conclusions could be drawn.

In The End

Show me the numbers. Numbers talk BS walks is the crude phrase, but true. When we hear some conjecture about the latest fad think about the numbers. But before that read Beyond the Hype: Rediscovedring the Essence of Management, Robert Eccles and Nitin Nohria. This is an important book that lays out the processes for sorting out the hype - and untested and liley untestable conjectures - from the testable processes.

Related articles How To Fix Martin Fowler's Estimating Problem in 3 Easy Steps The World of Probability and Statistics Stationary processes How Not To Be Wrong Why is Statistical Thinking Hard? Selection bias and bombers How Not To Make Decisions Using Bad Estimates
Categories: Project Management

Can There Be Actionable Suggestions Without Evidence of Them Working?

Sat, 07/19/2014 - 01:38

Visiting the Montana State Museum of the Rockies this weekend and came across this sign in an exhibit. 

Now writing software for money is not this kind of science, but it is closely related to engineering and the enablement of engineering processes in our domain - things that fly away, swim away, drive away, and the enterprise IT systems that support those outcomes.


When we hear about some new way to do something around managing projects that spend other peoples money, we do need to ask the questions posed by the sign above.

Is there any evidence that the suggested way - this new alternative of doing something - has the desired outcomes?

No? Then it's going to be difficult for those of us working in a domain that provides mission critical solutions - ERP, embedded software, infrastructure that other systems depend on - to know how to assess those suggestions.

The process of asking and answering a question like that is found in the Governance paradigm. Since our role is to be stewards of our customer's money in the delivery of value in exchange for that money, it's a legitimate question and deserves a legitimate answer. Without an answer, or at least and answer than can be tested outside the personal anecdotal experience of the proposer, it tends to be unsubstantiated opinion. 

Related articles Performance Based Management Positive Deviance Can Agile Be Integrated with Governance Based Development Processes? What Software Domain Do You Work In? The Myth of Incremental Development How to "Lie" with Statistics What Does "Done" Look Like? Why is Statistical Thinking Hard? If We're Going To Make Improvements, We Have To Be Able To Calculate Real Numbers
Categories: Project Management

The Myth of Incremental Development

Thu, 07/17/2014 - 17:07

Screen Shot 2014-07-13 at 6.27.34 PMIn the agile world there is a common notion that incremental delivery is a desired approach. Many taught rapid release, even multiple releases a day.

The question is two fold. Can the customer accept the release into use and the other does the customer have the ability to make use of the incremental capabilities of these releases?

Let's start with the incremental release. I know the picture to the left is  considered a metaphor by some. But as a metaphor it's a weak. Let's look a a few previous posts. Another Bad Agile Analogy, Use, Misuse, and Danger of Metaphor. Each of these presents some issues with using Metaphors.

But let's be crystal clear here. Incremental development in the style of the bottom picture may be a preferred method, once the customer agrees. Much of the reterotic around agile assumes the customer can behave in this way, without looking outside the ancedotal and many times narrow experiences of the those making that suggestion. For agile to succeed in the enterprise and mission critical product and project domain, testing the applicability of both pictures is needed.

Ask the customer if they are willing to use the skateboard while waiting for the car? Otherwise you have a solution looking for a problem to solve.

Now to the bigger issue. In the picture above, the top series is a linear development and the bottom an iterative or incremental depending on where you work. Iterating on the needed capabilities to arrive at the car. Or incrementally delivering a mode of transporatation.

The bottom's increment shows 5 vehicles produced by the project. The core question that is unanswered is does the customer want a skate board, scooter, bicycle, motorcycle, and then a car for transportation. If yes, no problem. But if the customer actually needs a car to conduct business, drive the kids to school, or arrive at the airport for your vacation trip.

The failure of the metaphor and most metaphors is they don't address the reason for writing software for money

Provide capabilities for the business to accomplish something - Capabilities Based Planning

The customer didn't buy requirements, software, hardware, stories, features, or even the agile development process. They bought a capability to do something. Here's how to start that process.

Capabilities Based Planning

Here's the outcome and an insurnace provider network enrollemtn ERP system.

Capabilities Map

Producing skateboards, then scooters, then bicycles and then finally the car isn't going to meet the needs of the customer if they want a car or a fleet of cars. In the figure above the Minimal Viable Features, aren't features they are capabilities. For example this statement is a minimal viable product is likey a good description of a Beta Feature. Could be connected to a business capability, but without a Capabilities Based Plan as in above, can't really tell.

Screen Shot 2014-07-16 at 12.16.26 PM

So How Did We Get In This Situation?

Here's a biased opinion informed by my several decades of experience writing code and managing others who write code - we're missing the systems engineering paradigm in commercial software development. That is for software development of mission critical systems, and Enterprise IT is an example of mission critical systems.

Here's some posts:

The patradigm of Systems Engineering fills 1,000's pages and dozen's of books, but it is boiled down to this.

You need to know what DONE looks like in units of measure meaningful to the decision makers. Those units start with Measures of Effectiveness and Measures of Performance.

Each of those measures is probabilistic, driven by the underlying statistical processes of the system. These mean you must be able to estimate not only cost and schedule, but how that cost and schedule will deliver the needed system capabilities measured in MOE's and MOP's.

SE in One Page

Related articles Do It Right or Do It Twice Why Johnny Can't Estimate or the Dystopia of Poor Estimating Practices Agile vs Waterfall: Introduction Lean Startup, Innovation, #NoEstimates, and Minimal Viable Features
Categories: Project Management

Control Systems - Their Misuse and Abuse

Wed, 07/16/2014 - 03:23

Modern Contrrol EngineeringProject Management is a control system, subject to the theory and practice of control systems. The Project Management Control System provides for the management of systems and processes - cost estimating, work scope structuring and authorization, scheduling, performance measurement, reporting, for assessing the progress of spending other peoples money.

The level of formality for these processes varies according to domain and context. From sticky notes on the wall for a 3 person internal warehouse locator website of a plastic shoe manufacture -  to a full DCMA ANSI-748C validated Earned Value Management System (EVMS) on a $1B software development project and everything in between.

The key here is if we're going to say we have a control system it needs to be a Closed Loop control system, not an Open Loop control system. On Open Loop system is called train watching, we sit by the side of the tracks and count the trains going by and report that number. How many trains should go by, could go by? We don't know. That's what's shown in the first picture. We sample the data, we apply that data to the process and it generates an output. There is no corrective action, it's just a signal based on the past performance of the system. Some examples of Open Loop control implemented in the first picture:

  • A light switch. Turn it on the light goes on. Turn it off the light goes off. Turn it on and the light doesn't go on, don't know why. Could be the switch, could be the¬†blub is burned out, could be the power is out in the neighborhood.
  • Same for a faucet, the burned on the stove, a simple cloths dryer when you use the timer rather than the¬†sense cloths are dry feature.
  • The really cool shade we just installed for the upper deck. Push the button and it lowers to a present position, push it again and it goes back to the storage position.

The key attribute of Open Loop Control 

  • It is a non-feedback system, is a type of continuous control system in which the output has no influence or effect on the control action of the input signal.
  • In an open-loop control system the output is neither measured nor fed back for comparison with the input.
  • An open-loop system is expected to faithfully follow its input command or set point regardless of the final result.
  • An open-loop system has no knowledge of the output condition so cannot self-correct any errors it could make when the preset value drifts, even if this results in large deviations from the preset value.

The key disadvantage of open-loop systems is it is poorly equipped to handle disturbances or changes in the conditions which that reduce its ability to complete the desired task. 

A close loop system behaves differently. Here's some example of controllers used in the second picture

  • Thermostat for the furnace or air conditioner - Set a target temperature and it holds that temperature pretty much constant¬†
  • Refrigerator cold/hot setting - keeps the food in the refrigerator at a preset temperature¬†
  • Same for the temperature setting for oven.

The key attributes of  Close Loop Control, shown in the second picture

  • Closed-loop systems are designed to automatically achieve and maintain the desired output condition by comparing it with the actual condition.
  • This is done by generating an error signal which is the difference between the output and the reference input.
  • A ‚Äúclosed-loop system‚ÄĚ is a fully automatic control system in which its control action being dependent on the output in some way.

Because the closed-loop system has  knowledge of the output condition - in the case of projects the desired cost, schedule, and technical performance, it is  equipped to handle  system disturbances or changes in the conditions which may reduce its ability to complete the desired task.

When we have a target cost - defined on day one by the target budget, a planned need date, and some technical performance target, closed loop control provides the needed feedback to make decisions along the when, when the actual performance is not meeting our planned or needed performance

Open Closed Loop

In the end it comes back to the immutable principle of microeconomics. When we are spending money to produce a value, we need to make decisions about which is the best path to take, which are the best of multiple options to choose. In our to do this we need to know something about the cost, schedule, and performance forecasts from each of the choices. Then we need feedback from the actual performance to compare with our planned performance to create an error signal. With this error signal, we can then DECIDE what corrective actions to take.

Without this error signal, derived from the planned values compared with the actual values there is no information needed to decide. Sure we can measure what happened in the past and decide, just like we can count trains and make some decision. But that decision is not based on a planned outcome, a stated need, or an Estimated Arrival time for example. 

Without that estimated arrival time, we can't tell if the train is late or early, just that it arrived. Same with the project measurements.

  • We need on average 4.5 stories per iteration. How many stories did you need to do to finish the project on the planned day with the planned capabilities.

Open Loop provides no feedback, so you're essentially driving in the rear view mirror, when you should be looking out the windshield deciding where to go next to escape the problem.

Objects are closer3

Related articles Can We Make Decisions Without Feedback? Seven Immutable Activities of Project Success Agile Requires Discipline, In Fact Successful Projects Require Discipline The DCMA 14 Point Schedule Assessment Control system and its classification The Failure of Open Loop Thinking First Comes Theory, Then Comes Practice All Project Numbers are Random Numbers - Act Accordingly
Categories: Project Management

Quote of the Day - Writing SW For Money Is Micro-Economics

Wed, 07/16/2014 - 03:12

We find no sense in talking about something unless we specify how we measure it. A definition by the method of measuring a quantity is the one sure way of avoiding talking nonsense¬†‚ÄĒ Sir Hermann Bondi

So when we hear a suggested approach to solving any problem, what are the units of measure of the discussion elements, the inputs to that discussion, and the outcomes?

Micro-economics is defined as

A branch of economics that studies the behavior of individuals and small impacting players in making decisions on the allocation of limited resources. Typically, it applies to markets where goods or services are bought and sold.

Certainly in the software development business, goods are bought and sold. Software is developed in exchange for money. The resulting product is then put to work to generate some monetized value for the buyer. The value exchanged for the cost of that value is usually assessed as the Return on Investment

ROI = (Value - Cost of the Value) / Cost of the Value

Economics of Iterative DevelopmentLet's start with some basic concepts of writing software for money. I'd suggest these are immutable concepts in a for proft business world. The book on the left is a ggod start, but there are other materials about the economics of software development. The one that comes to mind is Software Engineering Economics, Barry Boehm, Prentice Hall. While some has suggested this book is dated and no longer applicable to our modern software development paradigm, that could only be true if our modern paradigm is not subject to the principles of micro-economics. And that is unlikely, so let's proceed with applying the principles of micro-economics to the problem at hand. That problem is

How can we know the cost, schedule, and performance of a software product with sufficient confidence into order to make business (micro-economic) decisions about how to manage the limited resources of the project?

Those resources are of course the variables we are trying to determine. Cost, Schedule, and Performance. Each of which contains resources. Cost in software development is primarily driven by staff. Schedule is driven by the efficacy of that staff's ability to write code. And the performance of the resulting outcomes are driven by the skills and experience of the staff, who is consuming the funds (cost) provided to the project.

So if we look at the basics of the economics of writing software for money, we'll see some simple principles.

If it's a product development effort, someone in the marketing and sales department has a planned release date. This date and the projected revenue from that release is in the sales plan. These are random numbers of course -  so I won't repeat that, but all numbers in business are random numnbers until they get entered into the General Ledger.

  • These projected revenue numbers are based on releasing the product with the features needed for customers to buy it.
  • The cost to develop that product is subtracted from the revenue - usually in some complex manner - to produce the retained earnings¬†attributed to the product.¬†
  • This of course is the simple formula
    • Earnings = Revenue - Cost¬†
    • Where cost is categorized in many ways, some attributable to the development of the product, some to overhead, benefits, fringe, and other indirect costs (ODC).
    • Revenue recognition is a continuously evolving issue with taxes and performance reporting in actual firms
    • But for the purposes here, the simple formula will do.¬†Managerial Finance, Brigham and Weston is a good place to look for the details.

If it's a service effort, the customer has engaged the firm to perform some work in writing software, doing consulting around that software, integrating existing software or some combination of these and other software related services. Managing the Professional Services Firm, was mandatory reading along wioth other internal company written books when I worked for a large Professional Services (PS) firm. With our small firm now, we still refer to that book.

  • Some type of problem needs to be solved involving a solution that uses software (and maybe hardware), processes, and people.
  • The cost, schedule, and capabilities of the solution need to be worked out in some way in order to know what¬†DONE looks like. Any one subscribing to this Blog know the¬†Knowing What Done Looks Like is a critical success factor for any effort.
  • But in the case of a services solution, this¬†knowing is a bit more difficult than the product solution, since the¬†customer may not know themselves.¬†
  • This is the golden opportunity for incremental and itertaive development.
  • But in the end the PS customer still needs to know the cost, schedule, and what will be delivered, because that person has a similar ROI calculation to do for those funding the PS work.
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Categories: Project Management

The Power of Misattributed and Misquoted Quotes

Tue, 07/15/2014 - 15:30

Warning this is an Opinion Piece.

In a conversation this week the quote Insanity is doing everything the same way and expecting a different outcome. Or some variant of that. Attributed to Einstein. As if attributing it to Einstein makes it somehow more credible, than attributing it to Dagwood Bumstead.

Well it turns out it is not a quote from dear olde Albert. It is also mis-attributed to Ben Franklin, Confucius, and a Chinese proverb.

The first printed record of this quote is in the 1981 Narcotics Anonymous approval version of their handbook. No other printed record is found. 

Why is this Seemingly Trival Point Important

We toss around platitudes, quotes, and similar phrases in the weak and useless attempt to establish credibility of an idea by referencing some other work. Like quoting a 34 year old software report from NATO, when only mainframes and FORTRAN 77 were used, to show the software crisis and try to convince people it's the same today. Or use un-vetted, un-reviewed, charts and graphs from an opinion piece in popular techncial magazine as the basis of statistical analysis of self-selected data

Is it world shaking news? No. Is the balanced of the universe disrupted? Hardly. 

But is shows a lack of mental discipline that leaks into the next level of thought process. It's always the little things that count, get those right and the big things follow. That is a quote from somewhere. But it also shows laziness of thought, use of platitudes in place of the hard work to solve nearly intractable problems, and all around disdain for working on those hard problems. It's a sign of our modern world - look for the fun stuff, the easy stuff, and the stuff we don't really want to be held accountable for if it goes wrong

I will use the Edwin Land quote though, that is properly attributed to him

Don't undertake a project unless it is manifestly important and nearly impossible. 

That doesn't sound like much fun, let's work on small, simple, and easy projects and tell everyone how those successful processes we developed can be scaled to the manifestly important and nearly impossible ones.

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Categories: Project Management

The Failure of Open Loop Thinking

Mon, 07/14/2014 - 14:12

Screen Shot 2014-07-13 at 9.18.12 PMWhen there are charts showing an Ideal line or a chart of samples of past performance - say software delivered - in the absence of a baseline for what the performance of the work effort or duration should have been, was planned to be, or even better could have, this is called Open Loop control.

The issue of forecasting the Should, Will, Must cost problem has been around for a long time. This work continues in DOD, NASA, Heavy Construction, BioPharma, and other high risk, software intensive domains.

When we see graphs where the baseline to which the delays or cost overages are compared and those baselines are labeled Ideal, (like the chart below), it's a prime example of How to LIe With Statistics, Darrell Huff, 1954. This can be over looked in an un-refereed opinion paper in a IEEE magazine, or a self-published presentation, but a bit of homework will reveal that charts like the one below are simply bad statistics.

Screen Shot 2014-07-13 at 9.26.15 PM

This chart is now being used as the basis of several #NoEstimates presentations, which further propagates the misunderstandings of how to do statistics properly.

Todd does have other papers that are useful Context Adaptive Agility is one example from his site. But this often used and misused chart is not an example of how to properly identify problems with estimates,

Here's some core issues:

  • If we want to determine something about a statistical process, we of course need to collect data about that process. This data is¬†empirical - much misused term itself - to show what happened over time. A time series of samples.
  • To computer a trend, we can of course draw a line through population of data, like above.
  • Then we can compare this data with some¬†reference data to determine the¬†variances between the reference data and the data under measurement.

Here's where the process goes in the ditch - literally.

  • The reference data has no basis of reference. It's just labeled¬†ideal.¬†Meaning a number that was established with no¬†basis of estimate. Just¬†this is what was estimated, now let's compare actuals to it and if actuals matched the estimate' let's call it ideal.
  • Was that¬†ideal credible? Was it properly constructed? What's the confidence level of that estimate? What's the allowable variance of that estimate that can still be considered OK (within the upper and lower limites of OK)? Questions and their answers are there. It's just a line.

We can use the ne plus ultra put-down of theoretical physicist Wolfgang Pauli's "This isn't right. It's not even wrong."  As well the projects were self-selected, and like the Standish Report, self-selected statistics can be found in the How to Lie book

It's time to look at these sort of conjectures in the proper light. They are Bad Statisics, and we can't draw any conclusion from any of the data, since the baseline to which the sampled values are compared Aren't right. They're not even wrong."  We have no way of knowing why the sampled data has a variance from the ideal the bogus ideal

  • Was the original estimate simple na√Įve?
  • Was the project poorly managed?
  • Did the project change direction and the¬†ideal estimate never updated?
  • Were the requirements, productivity, risks, funding stability, and all the other project variables held constant, while assessing the completion date? if not the fundamental principles¬†of experiment desgin was violated. These principles are taught in every¬†design of experiments class in every university on the planet.¬†Statistics for Experimenters is still on my shelf. George Box as one of the authors, whose often misused and hugely misunderstood statement¬†all models are wrong, some are useful.

So time to stop using these charts and start looking for the Root Causes for the estimating problem.

  • No reference classes
  • No past performance
  • No parametric models
  • No skills or experience constructing credible estimates
  • No experience with estimating tools, processes, databases (and there are many for both commerical and government software intensive programs).
  • Political pressure to come up with the¬†right number
  • Misunderstanding of the purpose of estimating - provide information to make decisions.

A colleague (former NASA cost director) has three reasons for cost, schedule, and technical shortfalls

  1. They didn't know
  2. They couldn't know
  3. They didn't want to know

Only the 2nd is a credible reason for project shortfalls in performance.

Without a credible, calibrated, statistically sound baseline, the measurements and the decisions based on those measurements are Open Loop.

You're driving your car with no feedback other than knowing you ran off the road after you ran off the road, or you arrived at your destination after you arrived at your destination.

Just like this post Control Systems - Their Misuse and Abuse


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Categories: Project Management

Software Development is Like Play Writing

Sun, 07/13/2014 - 23:23

WaitingForGodotI stole this idea of this blog from Stephen Wilson's post of a similar name. And like all good borrows I've added, subtracted, and made some changes, because everything is a remix.

I don't know Stephen, but his post is provacutative. I'm assigned to a client outside my normal Defense Department and NASA comfort zone. The client needs a Release Management System integrated with a Change Control Board. Both are the basis of our defense and space software world. This client is trying to use agile, but has little in the way of the discipline needed to actually make it work.

The SWDev is like play writing is a beautiful concept that can be applied to the choas of the new client and also connected back to our process driven space and defense, which by the way makes heavy use of agile, but without all the drama of the it's all about me developer community.

Let's start here:

In both software and play writing, structure is almost entirely arbitrary. Because neither obey the laws of physics, the structure of software and plays comes from the act of composition. A good software engineer will know their composition from end to end. But another programmer can always come along and edit the work, inserting their own code as they see fit. It is received wisdom in programming that most bugs arise from imprudent changes made to old code.

It turns out of course neither of those statements is correct in the sense we may think. There is the act of composition, but that composition needs a framework in which to be developed. Otherwise we wouldn't know what we're watching or know what we're developing until it is over. And neither is actually how plays are written or software is written. It may be an act of composition, but it is not an act of spontaneous creation.

Let's start with play writing. It may be that the act of writing a play where the structure is entirely arbitrary is possible, but it's unlikely that would be a play you'd pay money to see. A Harold Pinter play may be unstructured Waiting for Gadot may be unstructured, but that's not really how plays are written. They follow a structured approach - there is a law of physics for play writing.

  • You need characters - a protagonist and the supporting cast
  • You need a setting - where are we and what's going on that supports the story
  • You need some sort of imbalance between the characters, the setting
  • You need some way to restore the imbalance or leaving it hanging
  • You need to engage your audience in some way that will resonant with the personal feelings

That's about the story of the play. To actually write a play, here's a well traveled path to success. These guidelines are for the outcome of the writing effort starting in the beginning.

  • A rough title is needed to anchor the play in the readers mind.
  • An action statement, describing what ¬†the characters are going to do in the play, as a group, as individual. How are they going to change and who changes
  • The form of the play describing the organization of the characters, the situation, the environment. How does the play's action relate to the emotional qualities of the characters and most importantly to the audience.
  • The circumstances of the time and place of the action and other important conditions.
  • The subject as an informational platform.
  • The characters of course, describing what are the forces driving them and their relationships with each other, the circumstance, and the environment.
  • The conflict. It's boring watching a play that is boring. What are the obstacles that have to be overcome by the characters?
  • The meaning for the play. What is the point of view? What are the key thoughts for the play as a whole and the characters in the play?
  • The style of the dialogue, the composition and manner of this dialogue?
  • And finally a schedule for writing the play. When will it be done?

When we talk about writing software there is a similar story line

  • Who are the protagonist? - they're the end users of the software.
  • What is the setting? - there is a stated need for something new.
  • What is the imbalance? - something is not getting done and it needs to be improved.
  • How do we restore the balance? - by closing the gaps between the imbalance and the balance with a software solution.

The story line is the basis of Capabilities Based Planning. With the capabilities, the requirements can be elicited. From those requirements, decisions can be made for what order to deliver them to produce the best value for the business or the mission. 

This process is about decision making. And decision making uses information about the future. This future information comes many times from estimates about the future. 




Related articles Agile Requires Discipline, In Fact Successful Projects Require Discipline People, Process, or Tools
Categories: Project Management

Quote of the Day

Fri, 07/11/2014 - 23:44

Gentlemen, we have run out of money; now we have to think - Winston Churchill

The role of estimating in project and product development is many fold...

  • For product, the cost of development must recouped ¬† over the life cycle of the product. Knowing the sunk cost of the product provides decision making information to the business if the¬†target margin will be achieved and on what day.
  • For projects, the cost of development is part of the ROI equation. ROI = (Value - Cost) / Cost
  • For day to day business operations¬†cash flow is the actual cost of producing outcomes. Budget is not the same as cost. We have define a budget for our work, but some forecast of the cost of that work, gathered from current operations or past performance, let's us know if we have sufficient budget.
  • For products when marginal cost exceeds marginal profit, we're going go out of business if we don't do something about controlling the cost. But our cost forecast and revenue forecast are the¬†steering points to provide feedback for making choices.
  • For projects, the marginal cost and the marginal benefits obey the same rules of microeconomics.

In both cases the future cost and future monetized value are probabilistic numbers.

  • This project or product will cost $257,000 or less with an 80% confidence
  • This project or product will complete on or before May 2015 with a 75% confidence

With both these numbers and their Probability Distribution Function, decisions can be made about options - choices that can be made to influence the probability of project or product success.

Without this information, the microeconomics of writing software for money is not possible and the foundation of business processes abandoned.

In order to make these estimates of cost, schedule, and the technical performance of the project or product, some  model is needed and the underlying uncertainty of the elements of the model. These uncertainties come in two forms

  • Reducible (epistemic uncertainty) - money can be spent to reduce this uncertainty. Testing, prototypes, incremental development.¬†
  • Irreducible (aleatory uncertainty) - this is the normal variance in the process or technical components. The Deming uncertainty. The only action to reduce this uncertainty is¬†margin, Cost margin, schedule, and technical margin. The cost margin is then part of the total project or product budget and the schedule margin part of the total period of performance for the project or the planned release date for the product.

To suggest decisions can be made without knowing this future information violates the principles of microeconomics of business 

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Categories: Project Management

It's Not Bottoms Up or Top Down, It's Capabilities Based Delivery

Tue, 07/08/2014 - 21:36

There is a popular notion that agile is bottoms up and tradititional is top down. Neither is actually effective in deliverying value to the customer based on the needd capabilties, time phased to match the business or mission need. 

The traditional - read PMI and an over generalization - project life cycle is requirements elicitaton based. Go gather the requirements, arrange them in some order that makes sense and start implementing them. The agile approach (this is another over generlaizaiton) is to let the requirements emerge, implement them in the priority the customer says - or discovers.

Both these approaches have serious problems as evidenced by the staistics of software development

  • Traditional approaches take too long
  • Agile approach ignore the underlying architectural impacts of¬†mashing up the requirements
Categories: Project Management

Critical Thinking Skills Needed for Any Change To Be Effective

Tue, 07/08/2014 - 15:40

Why is it hard to think beyond our short term vision? Rapid delivery of incremental value is common sense, no one would object to that - within the ability of the business to absorb this value of course. This is called the Business Rhythm. 

But that rapid redelivery of incremental value is only a means to an end. The end is a set of capabilities of the business that allows that business to accomplish their Mission. To do something as a whole with those incremental features. That is turn the features into a capability.

Think about a voice over IP system, who's feature set was incrementally delivered to 5,000 users at a nation wide firm. This week we can call people, receive calls from people, but we don't have the Hold feature yet. Are you really interested in taking that product and putting it to use? 

How about an insurance enrollment system, where you can sign up, provide your financial and health background, choose between policies, but can't see which doctors in your town take the insurance, because the Provider Network piece isn't complete yet.

These are not notional examples, they're real projects I work on. For these type projects - most projects in the enterprise IT world -  an All In feature set is needed. Not the Minimum Viable Product (MVP). But the set of Required Capabilities to meet the business case goals of providing a service or product to customers. No half baked release with missing market features.

You might say, that incremental release of features could be a market strategy, but looking at actual products or integrated services, it seems there is little room for partial capabilities in anything, let alone Enterprise class products. Either the target market gets the set of needed capabilities to capture market share or provide the business service or it doesn't and someone else does.

An internal system may have different behaviours, I can't say since I don't work in that domain. But we've heard loud and strident voices telling us deliver fast and deliver often when there is no consideration for the Business Rhythm of the market or user community for those incremental - which is a code word for partially working - capabilities.

Of course the big bang, design, code test, paradigm was nonsense to start with. That's not what I talking about here. I'm talking about the lack of critical assessment of what is the value flow of the business and only then applying a specific set of processes to deliver that value. Outcome first, then method.

So Now The Hard Part

The conversation around software delivery seems to be dominated by those writing software, rather than by those paying for the software to be written. Where are the critical thinking skills to ask those hard nosed business questions:

  • When will you be done with all the features I need to implement my business strategy?
  • How much will it cost for all those features I to provide those capabilities that fulfill my business plan?

Questions like that have been replaced with platitudes and simple and many times simple minded phrases.

  • Deliver early and often - without consideration of the business needs
  • Unit testing is a waste - because those tests like the internal documentation that provides a long term maintainability platform, aren't what the customer bought
  • We can decide about all kinds of things in the software business without having to estimate anything - a completion violation of the principle of microeconomics, which requires we know the impact of our choices in some unit of measure meaningful to the decision maker. You know something like¬†Money.
Related articles Is There Such a Thing As Making Decisions Without Knowing the Cost? Capabilities Based Planning and Development Business Rhythm Drives Process Do It Right or Do It Twice What Does It Mean To Be DONE? How To Estimate Almost Any Software Deliverable Alan Kay: The pitfalls of incrementalism Don't Start With Requirements Start With Capabilities How To Create Confusion About Root Causes
Categories: Project Management

Quote of the Day

Mon, 07/07/2014 - 07:02

Movement without direction will create a hole in the ground ‚ÄĒ¬†Sophia Bedford-Pierce

Categories: Project Management

Quote of the Day

Fri, 07/04/2014 - 16:23

It is the mark of an educated mind to rest satisfied with the degree of precision which the nature of the subject admits and not to seek exactness where only an approximation is possible¬†‚ÄĒAristotle (384 B.C - 322 B.C.)

When we hear someone say estimates are guesses, When we estimate we act as if we believe the plan will not change, or similar uninformed nonsense, think of Aristotle. Without the understanding, from education, experience, and skill to realize that all project variables are random variables and vary naturally and vary from external events.

As such in order to determine the future impacts from decisions that involve cost, schedule, and performance, we need to estimate that impact of those random processes on the outcome of our decision.

This is the basis of all decision making in the presence of uncertainty. It's been claimed decisions can be made without estimates, but until someone comes up with the way to make decisions without estimating those impacts, statistical estimating is the way.

Categories: Project Management

The Value of Information †

Thu, 07/03/2014 - 15:18

Since all variables on all projects are random - cost, schedule, and delivered capabilities, in the economics of projects, the chance of being wrong and the Cost of being wrong is the expected opportunity cost. When we write software for money, we are participating in the microeconomics process of decision making based on information about the future:

  • What value will be returned in exchange for the cost to produce that value? This value can be tangible - revenue from the sales of our product, revenue from the efforts produced through our contracting firm to our client, cost savings to the internal IT operation, increased sales from our ability to be faster and better than our competition,¬†or intangible value in some broader sense - a public good for example.
  • What is the cost to produce that value? This cost is almost always tangible. Money spent over some period of time.¬†

Information is needed to assess both the cost and the value in order to DECIDE what to do. The formula for the value of this information can be mathematical as well as intuitive.

We make better decisions when we can reduce uncertainty about those decisions. Knowing the value of the information used to make those decisions is part of the microeconomics of writing software for money. 

If we are uncertain about a business decision, or a decision for the business based on technology, that means we have a chance of making a wrong decision. By wrong it means the consequences of the alternatives cannot be assessed and one chocie that might have been preferable was not choosen. The cost of being wrong is the difference between the wrong choice and the best alternative.

In order to make an informed decision we need information - as mentioned above. This information itself has uncertainty, and therefore most times we need to estimate the actual numbers from the source of the information:

  • How much will it cost? Good question. Can't really tell unless we have a firm fixed price quote from a vendor. Cost is not the same as budget. We might be able to fix the budget, but cost is always varaible in practice. We can stop when cost reaches budget, but then there is a chance we're not done - we have an incomplete deliverable, missing needed features.
  • What value will be produced? Good question. Can't really tell unless we have revenue from the same product or similar products, or have price quotes from our competitors for the same product we are trying to sell.
  • How long will it take to produce a¬†value producing outcome?¬†Good question. Can't really tell since we haven't done this before.

These questions and their answers are critical to the successful operation of any business, whose fundamental principle of operations is to turn expense into revenue. Since the variables involved in our projects are actually random variables, we'll need to estimate the answers, leaving the bigger question unanswered to date...

Can we make decisions without estimating the future impact on cost, schedule, and performance or that decision?

Gather information in support of decision making is decision risk reduction. The desire to reduce risk is good business practice. The decision maker needs information about the behaviour of the random variables involved in the decision making process. These must be estimated before the fact to make a decision about the future. 

To develop the needed estimates we need a Basis of Estimate process, which means building the estimates from Reference Classes, parametric models, or similar cardinal based processes that have calibrated in some way. The Ordinal (relative) estimate are not credible. This removes the ill conceived notion that estimates are guesses.

† Extracted from How To Measure Anything, Douglas W. Hubbard.

Related articles Making Estimates For Your Project Require Discipline, Skill, and Experience The Calculus of Writing Software for Money, Part II Why is Statistical Thinking Hard? How to "Lie" with Statistics All Project Numbers are Random Numbers - Act Accordingly How To Assure Your Project Will Fail Economics of Iterative Software Development Everything is a Random Variable Four Critical Elements of Project Success Why We Must Learn to Estimate
Categories: Project Management

Economics of Iterative Software Development

Tue, 07/01/2014 - 15:41

Economics of Iterative DevelopmentSoftware development is microeconomics. Microeconomics is about making decisions - choices - based on knowing something about cost, schedule, and techncial impacts from that decision. In the microeconomics paradigm, this information is part of the normal business process.

This is why when there is conjecture that you can make decisions in the absence of estimating the impacts of that decision, it ignores the principles of business. Or the notion that when numbers are flying around in an organization they lay the seeds for dysfunction, we need to stop and think about how business actually works. Money is used to produce value which is then exchanged for more money. No business will survivie for long in the absence of knowing about the numbers contained in the balance sheet and general ledger.

This book should be mandatory reading anyone thinking about making improvements in what they see as dysfunctions in their work environment. No need to run off and start inventing new untested ideas, they're right here for the using. With this knowledge comes the understanding about why estimates are needed to make decisions. In the microeconomics paradigm, making a choice is about opportunity cost. What will it cost me to NOT do something. The set of choices that can be acted on given their economic behaviour. Value produced from the invested cost. Opportunities created from the cost of development. And other trade space discussions. 

To make those decisions with any level of confidence, information is needed. This information is almost always about the future - return on investment, opportunity, risk reduction strategies. That information is almost always probabilistically driven by an underlying statistical process. This is the core motivation for learning to estimate - to make decisions about the future that are most advantageous for the invested cost.

That's the purpose of estimates, to support business decisions.

This decision making processes is the basis of Governance which is the structure, oversight, and management process that ensures delivery of the needed benefits of IT in a controlled way to enhance the long term sustainable success of the enterprise.

Related articles Why Johnny Can't Estimate or the Dystopia of Poor Estimating Practices The Calculus of Writing Software for Money, Part II How To Estimate, If You Really Want To How to "Lie" with Statistics How to Deal With Complexity In Software Projects? Why is Statistical Thinking Hard?
Categories: Project Management