Estimate Software Costs

The IEEE published “Top 11 Technologies of the Decade” in the  January 2011 editions of the IEEE Spectrum magazine.  It should come to a surprise to no one that the Smartphone was number 1 on this list.  The answer to the author’s question “Is your phone smarter than a fifth grader” was a resounding YES![1] 

 In 1983 Motorola introduced the first hand hell cellular phone.  It weighed in at two and a half pounds, had memory capacity for 30 phone numbers, took 10 hours to recharge and had a selling price of $4000 ($8045 in 2006).  The phone was the size of a man’s head and would sustain an hour of conversation before a recharge was required.  In June of 2007 Apple announced that it was launching the iPhone which would basically integrate all of the electronic gadgets teenagers carried around in their pockets into a complete package – phone, web browser, iPod and camera.  The iPhone 5 which should be available by Fall 2011 incorporates NFC technology, an upgraded operating system with cloud integration, music streaming, voice interface, 4G connectivity and an embedded social networking tool. NFC technology makes it possible to use the phone effectively as a credit card – making payments by swiping the phone near a device that can read its information. [2][3][4][5]

The proliferation of smartphones and tablets has led (and will continue to lead) to the proliferation of mobile applications.  And it seems as there are no bounds to the kinds of applications that are being developed for smartphones.  A sampling of some popular applications is listed below:

* Chase Mobile – which allows users to check account balances and review transactions
* Angry Birds – very popular gaming software
* Facebook - allows users to report their status from anywhere
* Yelp – allows users to locate places to eat, shop , etc. along with reviews from local patrons of said establishments

The list goes on but clearly if you can dream it – there’s an app for that (or at least there could be an app for that).  As practitioners in the art of estimation, all this mobile application development leads to the inevitable question of what does it cost to develop mobile apps and how is mobile application development different from more traditional forms of development? 
Mobiles apps can be categorized as either native applications – which run entirely on the device or web applications - with small clients resident on the device which interact with applications running on a remote server.  It appears that in general the web applications are less complex than the native ones, and thus less effort is required to build, test and deploy them.
While mobile application development is still in its infancy so a lot of what is going on in the industry now has a bit of the Wild West feel to it.  This stage of any technology is impacted by learning curve issues which may dampen productivity but at the same time there is the newness factor – where smart people are excited about the promise of new technologies and are willing to work extra hard to make things happen.  So while there is some effort/cost data available for mobiles apps, we must temper our enthusiasm.
Another concern when developing mobile applications is which platform(s) it is being developed for.  If an application is being developed for iPhone, Android and Blackberry the effort is significantly increased.  Although there are elements of the design that can certainly transcend platforms, each of these platforms has its own operating system and development environment. There are additional potential compatibility issues in cases such as Android where there are multiple companies manufacturing devices. Additionally, application developers need to determine which versions of OS for each of the platforms the application will work with.  They also need to be aware of and respect the user interface guidelines developed for the device(s) for which they are building apps.
Mobile applications may need to respond to various forms of external data from sensors, a real or virtual keypad, a GPS, microphones, etc.  They also may need to respond to movements of the actual device as well - so the screen adjusts when the user changes the orientation of the device.  There are also many instances where mobile apps will need to interact with other applications on the device.  Often the mobile application will need to share elements of the user interface with other applications. 
Mobile applications need to be developed in such a way as to limit the consumption of resources.  No matter how good your killer app is, if it drains the phone battery in half an hour no one is going to use it.  Along with all other applications that have access to the Internet, they should be built with a focus on security so that the users data is protected from malicious intrusions.
All of the issues listed above are likely to impact the complexity of the development effort of the mobile application – thus they have the potential to be an important part of the cost equation as well.  In many cases mobile applications are smaller than traditional applications so increased complexity may be offset because the projects and corresponding project teams are small.  Still this increased complexity is important to consider.
Another area where mobile apps are dramatically different than traditional apps is in the testing of the application.  Simulators and emulators exist and can be helpful in some circumstances but they are not always easy to use, effective or efficient.  There are also issues, with some platforms around the maturity of the technology that allows applications to be transferred from the development environment to the mobile device – further complicating the testing process.  Finally there is just the sheer magnitude of making sure that the application functions correctly on all the combinations of hardware, operating system and carriers on which it will be expected to perform.  The cost and effort associated with testing may present significant differences when being evaluated for mobile applications.
Smartphones are here to stay and more and more businesses will want (or need) to develop apps for them in order to remain competitive.  With the technology still relatively immature there is limited data that will help us develop cost models but there is feedback from the field on what issues are most likely to impact costs.  Though the technology is still emergent – there is some data available from commercially based mobile app developments – so at least we have a place to start.
Share your experiences with mobile application development by leaving a comment for this post.

[1] Ross, Philip E., “Top 11 Technologies of the Decade”, IEEE Spectrum, January 2011
[2]  http://www.motorola.com
[3] http://www.apple.com
[4] http://en.wikipedia.org/wiki/Mobile_phone
[5]http://www.ibtimes.com/articles/170418/20110628/quick-look-at-iphone-5-features-as-september-release-date-looks-probable-nfc-camera-8mp-lte-4g-displ.htm

There is a ton of code out there and we’re constantly adding more.  Gartner reported in 2009 that there were more than 310 billion lines of code in use worldwide. MS Word has grown from 27,000 lines of code in the first version to about 2 million in 2010.  The Windows Operating System grew from 3 million lines of code in 1992 to 20 Million in 1998.  You get the point – there’s lots of code out there doing lots of the same things that we may want our software to do.

One of the challenges software project leaders have in estimation and planning is successfully quantifying the functionality they can reuse and understanding and communicating the effort and cost associated with that reuse.  In projects where significant reuse is anticipated, reuse is really going to shape the project.  Reuse comes in many forms.  A firm grasp of the types of reuse and the potential pitfalls of such reuse facilities a thorough analysis of the impacts that reuse has on the project.  It is ill advised to expect to get a one to one benefit for reuse.  It is even more problematic to plan on reuse as a quick fix to schedule pressure.  

Some of the things you want to consider:

* Reusing legacy code can be a great productivity booster if the code is stable, well written and some of the original developers are on hand.  On the other hand, if you’re working with legacy code that has become spaghetti code which is only supported by a mildly deranged programmer in the corner cube – you might be better off starting from scratch
* The auto translation of existing code to newer technologies can also boost project productivity but one cannot expect the process to be transparent or without hiccups.  It is important to understand the nature of the translation tool, the amount of preparation required to use it and the expected amount of human intervention to make the translation complete
* The use of Commercial Off the Shelf Software (COTS) can significantly impact the schedule for a software project but there needs to be a thorough evaluation of the capabilities of the COTS against the requirements of the project.  There also needs to be acknowledgement with the consumer of the software system that using COTS software limits the amount of customization possible resulting in the risk that the finished product is not exactly what they expected.

Also it is often more complicated to integrate reused (not developed here) solutions since there is a steeper learning curve during the integration phase.  Code reuse can be a great way to achieve increased productivity but it is important not to assess this improvement over optimistically but rather through a careful appraisal of what the reuse activity entails.

Have a reuse story (success of horror) you want to share?  Post it as a comment to this blog.

At the 2011 ISPA Conference, I conducted a ½ day workshop How To Develop Data-Driven Cost Estimating Relationships  in TruePlanning. The attendees at the workshop learned how to import their own data into TruePlanning and develop custom Cost Estimating Relationships. We covered three case studies:

·         In the UCAS case study we demonstrated how we can build CERs at a higher level to provide a test of reasonableness to the CAPE.

·         In the SRDR case study we demonstrated how we develop a CER to estimate SLOC based on historical data and use the results to within the new code parameter of the UCAS Reconnaissance software estimate. 

·         In the Aircraft case study we demonstrated how we can build higher level trend lines across various aircraft system platforms and classes of aircraft to compare as “ball-park” estimates.

In each case, the attendees learned how to work with real data and develop CERs traceable back to the original data points that generated them. This was a “hands-on” workshop and everyone had a chance at generating a number of CERs. Overall, we had a very successful session and provided a new and exciting capability to our TruePlanning framework.

I also wanted to share with you a few takeaways from this workshop.

• By setting up the trend lines equations in the Equation Cost Object, estimators can create libraries of data-driven CERs for future projects
  • All data used to develop the equation can be attached to the CER to identify datapoints and methodology used.
• The power of this approach is that the CERs are integrated into the TruePlanning framework and can be used with other cost objects (H/W, SW, IT, etc)
• Libraries of CERs can be shared among projects.
• CERs are “white box” and auditable both in terms of the trend line equation and underlying data points.
• CERs represent an “estimating database” in addition to TruePlanning Cost Objects.

If you are interested in obtaining a copy of the workshop presentation including the data files and results, please e-mail me at Zachary.Jasnoff@pricesystems.com .

I’m on my way home from the ISPA/SCEA (International Society of Parametric Analysts, Society of Cost Estimating and Analysis) Conference held in Albuquerque this week.  Attendance was very good (2nd best in the conferences history) and as the content seemed especially good this week.  I attended lots of good talks on topics ranging from SEPM (System Engineering, Project Management) cost estimating, Joint Confidence Levels, Software Estimating, Affordability,  Agile software development and estimating for Enterprise Resource Planning Systems.   Of course, just because the topics are good and well presented doesn’t mean I have to agree with everything that gets said.

One particular talk entitled “Function Points, One Size Fits All” got me a little worked up.  It seems as though there is an on-going and completely unnecessary amount of energy devoted by some to convince the software community that we need to settle on a single method of measurement for software.  The author makes some really good, credible points.  Function Points are a much better method of measuring software if your intent is to compare productivity across or within specific industries.  They help eliminate development technologies, programmer inconsistencies and software architecture decisions from an analysis of how productive an organization is at delivering business value to their customers.

Having said this, I firmly believe that sometimes SLOC (Source Lines of Code) are a better tool to help organizations estimate the costs of the software development projects they are planning.  Knowing how your productivity stacks against the industry is great information when you are looking for process improvement or identifying best practices.  But if you want to know how much the next project is going to costor how many months it will  be before you can deliver content, your own history is much more relevant than the industries.  Not that your history can’t be in Function Points – it just doesn’t have to be.  Many of the weaknesses sighted with SLOC – while they certainly can be considered weaknesses) -aren’t really noticeable when working within a certain context.  Within an organization, software development projects are often similar to previous projects, targeted at similar markets and are being developed by similar teams with similar (on average) coding styles. And SLOC Counting processes can be institutionalized within an organization. When this is the case, SLOC counts are just as good, if not better, than Function Point Counts and probably much easier to count since their count can be automated and does not require certified specialists.

The author contends that SLOC is a bad option because it is hard to estimate early on.  I contend the same is true with Function Points - which if the Counting Practices Manual is followed to the letter – one is required to make an assessment of not only all transaction but also all the data element types and record element types.  The author contends that SLOC is a bad option because there is no standard for what a SLOC is.  I contend that the same can be said for Function Points.  Perusal of the International Software Benchmark Standards Groups’ (ISBSG) database shows that there are several different ‘standards’ for counting Function Points – because one standard was not deemed sufficient by many in the community to meet all software measurement needs.

Don’t get me wrong – I do not disagree with many of the assertions the author made, only the notion that one size fits all.  Let’s be real – software measurement is not easy and there are many different ways to approach it depending on the reasons for the measurement and the circumstances around those needs.  Function Points are a good tool to have in your software measurement tool box, so are Source Lines of Code, Use Case Points and user Stories.  It’s true that if all you have is a hammer every problem is a nail.  Fortunately we have many different tools and should feel empowered to choose the right tool depending on the current need.

Going to ISPA SCEA in New Mexico?  If so, join us for a workshop on data driven cost estimating.  Register: http://www.pricesystems.com/services/predictive_modeling_schedule.asp#/?i=3

Description:  Building transparency and traceability into your estimating process leads to more defendable estimates.  This hands-on workshop demonstrates how historical data is transformed into predictive models.   You will learn how your organization’s data can be synthesized into custom models that can be employed in support of third party models within a single analytical framework. 

Participants will learn:  (1)   To develop system level estimating relationships to provide a test of reasonableness and historical cross-check to proposed estimates. (2) To develop sub-system level relationships that estimate Software Lines of Code based on requirements. (3)  To develop historically-based relationships across classes of aircraft systems in support of “ball-park” estimates based on specific program actuals.

Location:  2011 ISPA/SCEA Joint Annual Conference /  Hyatt Regency Albuquerque - Fiesta 1-2 /  June 7^th, 2011 /  9:00 am – 1:00 pm (includes complimentary lunch).  Hope to see you there!  To register go to: http://www.pricesystems.com/services/predictive_modeling_schedule.asp#/?i=3

Agile software development practices are predicated on the following tenets as introduced in 2001 in the Agile Manifesto [1]

•  Individuals and interactions over processes and tools
•  Working software over comprehensive documentation
•  Customer collaboration over contract negotiation
•  Responding to change over following a plan

Agile development processes rely on experienced, highly skilled people communicating with clients and each other to deliver software that provides the clients with the most value for the money they spend.  This requires both developers and consumers of software to accept the reality that things will change over the course of the project and that the software that is eventually delivered may not be the same as that was envisioned when the project was first kicked off.

At any given time, the agile software development team is only working on the feature that the customer currently feels is the most valuable.  Estimation is performed by the team (including developers, BAs, QAs and customer representatives) and is only focused on the feature that is currently on deck.  At the end of an iteration, the customer has an opportunity to review the implementation to date and can reprioritize remaining features based on changes in their requirements, market or expectations.  So estimating beyond the current feature doesn’t really make agile sense.

Unfortunately, the fact that estimation doesn’t make sense for the agile team does not mean it doesn’t make sense for the business.  The business needs to see the forest not just the trees.  Customers need an idea when their software will be delivered, businesses need to prep the market for new products and features, businesses need to be able to optimize resource allocations across many projects, etc.   This of course begs the question of how to perform an estimate for software when you really don’t know exactly what software you’re going to build.  As an industry, we’ve been pretty unsuccessful estimating software projects even when we think we have a handle on the end product – how can we be successful with so much uncertainty.

First we accept uncertainty and commit to incorporating uncertainty into our estimates.  Once we’ve made that commitment we are then free to pursue one of many top level estimating techniques to help us get our head around a likely range of values for cost and schedule based on what we currently know about the software project.  Parametric techniques are particularly suitable for this task especially for an organization that has some historical data from previous agile projects.  Parametric estimating models like TruePlanning for Software provide a repeatable framework through which an organization can study their past performances on similar projects (similar number of features, similar market, similar customer, etc) and use what they learn to perform estimates on the project at hand.   The amount and nature of the similarities should guide the amount of uncertainty in the estimate.  Organizational history can be used to map Story Points or User Stories to a size measurement such as source lines of code, function points or user points.  Analysis of performance on past projects can inform decisions about productivity and other project drivers.  This information can be used to drive the parametric algorithms to develop estimates for cost, effort and schedule.

Since we have already acknowledged that we didn’t get the question right, it’s unreasonable to assume that the answer will be ‘the right answer’.  What we do come away with is a range for cost, effort and schedule which will give decision makers realistic data to work with. If we have properly studied our history and thoughtfully assessed uncertainty, we can present these ranges with a quantifiable degree of certainty. 

What process does your organization employ to plan around agile induced uncertainties?

[1] Agile Alliance, “Agile Software Development Manifesto”, Feb 2001, available at www.agilemanifesto.org (retrieved January 2010)

1. A round table peer review, where we describe and defend our parametric modeling approach as well as consistency across similar objects/ systems/ alternatives. 
2. Introduction of an independent 3^rd party, not part of the estimating team, who is an experienced modeler. This resource can take the client perspective in reviewing, say, procurement versus cost-of-ownership as well as comparables from similar program experiences.  
3. Take an agile approach where your project leader is the “product owner” who reviews all interim deliverables top-down.  
4. Another option is incorporating a client representative into your pre-release reviews for similar validation testing. 
5. Finally, we pair off internally to verifying each other’s work against the real-time documentation we keep per common templates.  (Available from PRICE Systems, for both hardware and software modeling inputs). They are great visualization tools for internal parameter-tracking, plus external pre/ post model review with customers.

...wear the worst shoes. The cobbler was a master at his craft; he was just too tired to practice it when he got home from the shop.  Sound familiar?

A disciplined approach to understanding (functional) requirements as well as analogous projects (with actuals) is our not-so-secret sauce. Why run the risk of creeping back up our career learning curve? There’s already enough scope creep to keep us busy. Plus, for you management types charged with prospecting, a consistent approach towards estimation is a great way to connect with people who've felt the pain of being the cobbler's kids.

I recently reconnected with a colleague who found himself immersed in an early-stage, large-scale federal software project. Discussion points ranged from parametrics utility to sizing to Earned Value Management. Not leaving my toolset at the shop translated into good conversation with followup request. A bad example is yours truly, a year ago, so immersed in a highly-featured personal project, that he neglected to find agreement on scope and cost with his team. Bad idea! Now my “shoes” (a one-off custom hot-rod) are way behind schedule and way over budget. Keeping on my professional cost estimation consulting hat would have saved on both… bigtime! 

I consistently run into this idea of data driven estimating.  Yet, there is no clear explanation of this concept.  I am not trying to provide one here, however, I am interested in is what is at the root of this growing movement.  My take is that it is an attempt to scratch an itch.  But what’s the itch?

I believe it is related to my early post (Accuracy is Risky Business).  In the struggle to answer the accuracy question people have decided that understanding the data used in the estimating process is key to understanding its accuracy.  To a certain degree this makes sense.  It is useful to gain insight into the information upon which a cost estimation model was based.

• How much data was available?
• How current was the data?
• Was it relevant to the project being estimated?
• What statistical techniques were employed to analyze the data?

In terms of data, there are two general areas considered relevant: data quality and fit. Understanding how much data was used; how old it was; where the data came from sheds additional light on the results. Once the amount, age and source of the information is established, the next concern is how the information was analyzed. 

Descriptive statistics are often used to describe a collection of data in quantitative terms. They aim to quantitatively summarize a data set. Descriptive statistics include many measures including central tendency (mean, median, mode), dispersion (standard deviation, range, quartile) and association (correlation). Additionally, there is statistical inference, which makes propositions about populations, using data drawn from the population of interest via some form of random sampling. 

The industry is fraught with estimates backed by reams of data surrounded by all the necessary statistics. But does understanding descriptive statistics and statistical inference alone improve our understanding and confidence of an estimate?  If so, why then is expert opinion one of the most widely used forms of estimating?  Why do programs based on these data driven estimates continue to perform poorly?  Is data driven the answer?  What do you think?

The concept of the fuel cell was first published in 1938 by Christian Friedrich Schonbein.  Based on this publication Sir William Grove invented the precursor of the fuel cell in 1839. The Grove Cell created current by applying two acids to zinc and platinum electrodes separated by a porous ceramic pot.  In 1842 Grove developed the first actual fuel cell which produced electricity with hydrogen and oxygen, much like many fuel cells in use today.

Fuel cells remained an intellectual curiosity until the 1960’s when the US space program identified a requirement for extended life batteries for which fuel cells seem to offer a promising solution.  The focus on green technologies has increased interest in consumer uses of fuel cells for transportation, residential and commercial power supply, emergency backup power and portable power supplies for consumer and battlefield applications.  Increased usage of any technology begs the question of how to address the costs associated with that technology. 

A fuel cell is an electrochemical cell which converts some fuel, usually hydrogen, into electric current.  It does this through a reaction between the fuel and an oxidant in the presence of an electrolyte.  The waste product of this chemical process is water and heat.  Fuel cells, unlike conventional batteries, consume reactant from an external source rather than one stored in the battery. They do require a continuous supply of fuel, but given that this supply is available, they will not run out of charge like a conventional battery. 

Because fuel cells require neither flame nor combustion to convert fuel to electricity, there is much hope that they will become a viable power source of the future as we try to reduce our carbon footprint.  Fuel cells are very reliable and less likely to be effected by the environment as some more conventional power delivery systems are.  Because of this they are being adopted in industries such as the telecommunications where outages are particularly problematic.  They are often considered for power generation in remote areas where energy from the grid is expensive and outages are frequent.  Because heat is a waste product of the fuel cell electricity generation process, micro combined heat and power systems are gaining popularity for residential and small business needs.  Other interesting uses of fuel cell power include material handling, backup power systems and uninterruptable power supplies.

Despite increases in the use of fuel cells, they continue to evade wide spread use because they are expensive.  Certainly significant progress has been made through increases in efficiency and improvements in manufacturing processes, but it is still more expensive, in most domains, to get electricity from fuel cells than from more conventional methods. According to a report from the Department of Energy in May 2010, high volume automotive fuel cell stack cost has been reduced from $275/KW in 2002 to $61/KW in 2009 and appear to be on track to reach the $30/KW goal by 2015.   The same report indicates a 24% increase in system power density for stationary fuel cells making it possible to reduce the fuel stack volume, weight and cost.

PRICE recently conducted a research effort using publically available data to develop cost estimating relationships for various types of power systems utilizing fuel cell technology. For a white paper describing this project and the resulting cost estimating models email info@pricesystems.com with the code word FUEL in the subject line.

As is often the case, there is a difference between definition and connotation. The intended meaning of consumerism at these events was the phenomena of the consumer goods infiltrating and influencing government IT policy.  The IPad was all too visible in the audience as were the comments from CIO staff about their user base wanting to integrate IPad-like products into the work domain.

Like it or not we are all consumers; and consumers are getting more tech savvy.  As the adoption of these products or services increase in our daily lives, the question becomes unavoidable – Why can’t I use these for work? It was clear that for the CIOs attending these conferences this question was top of mind.  While most of the U.S. government is attempting to identify, inventory, consolidate and remove redundancies of IT infrastructure in response to Mr. Kundra’s IT modernization plan, CIOs are also feeling pressure (and criticism) from users about their ability to support seemingly everyday applications.

I’m sure there are many reasons why these aren’t supported; my intention is not to address them here.  Instead I’d like to posit an opinion.  There are  tools supporting new paradigms in how we communicate, at work and home, which are becoming the norm.  They are no longer ‘toys’ for the younger crowd.  For those waiting for this social media fad to pass, I’d suggest another look.  Recent events in the Middle East are real illustrations of their significant impact.  The U.S. State Department is beginning to use a nation’s access to internet and social media as a barometer for civil liberties.  Yet, there is still much resistance in adoption or more fundamentally understanding and embracing these venues. 

I’m encountering resistance and sometimes denial of these forces on a daily basis.  I don’t think you need to have 500 Facebook friends or Tweet until thumbs bleed.  Nor do I think being a LinkedIn Pro replaces human interaction.  It is important though to recognize the shift in communication.  Take just a few minutes to understand the different tools.   You’ll find it’s not unlike a conversation you may have in a coffee room or a hallway if you still have a coffee room or a hallway.  Many don’t.  For those who are time, money or resource constrained, you can access industry experts, thought leaders and practical advice on your own time at no cost.  There are plenty of free introductory videos or articles on line.  You can decide which mediums work best for you. 

At PRICE, we are raising discussions, sharing product capabilities and answering questions about cost estimation models through our social media.  Because people have different preferences for certain outlets or certain restrictions at work, we repeat similar material across our social media networks.  You can check us out on Facebook, LinkedIn, Twitter or follow our blog.

My June blog entry suggested the use of parametrics in real-options valuation. This month, I’d like to offer the generalized use of our type of modeling in valuing tangible assets. 

Typically, fundamental analysis evaluates the intrinsic value of securities. I won’t attempt to compete with Warren Buffet here. But it is certainly the case that a company, or portfolio of securities reflecting many companies, is based in part on the market value of its product assets and their potential for future earnings, as well as other objective and subjective considerations.

In parametric estimation, we take a top-down approach to developing models of estimating ultimate acquisition and ownership costs. In the case of the former, I’d argue that we can readily… and uniquely… estimate operating costs for companies developing and selling, say, technology-based products.  Marketing can determine pricing, based on their competitive landscape analyses and market-demand for unmet needs. 

We are in an excellent position to complete the forecasting of earnings (and hence, discounted cash flow) using our parametric methods to evaluate a product-system’s should/will costs. Anyone who does detailed bottoms-up budgeting for “go to market” expense, as well as production delivery, would find value in our modeling.  

And again, once estimated costs are subtracted from revenue forecasts, the netted estimate of earnings and cash flows will allow for traditional market value assessment of this asset or entity. For example, your organization needs to compare economic potentials of emerging new software products. There are no balance sheets. There are no income statements. Yet. 

Now your sales and marketing function has a good independent sense of revenue opportunity. So it’s left to you to estimate the costs of developing and delivering. Independent parametric estimation is the answer. No idea on sizing, either SLOC or function points? No problem. 

Data-driven True Planning has years of data modeling that allow you to run application-specific calculators. In addition, you can take advantage of calibrating actual data from similar projects. Likewise, this process of valuing assets holds true for IT infrastructure products as well. Cloud computing, SAAS and ERP are all further examples of where new IT/software products are emerging… and need estimating as well as financing via valuation.

I think parametric estimating has significant value commercially in the estimation of value of future product assets, and consequently their companies and associated securities.  What do you think?

TruePlanning 2010 SR1 estimation software is now available as an upgrade for existing PRICE customers. The most significant update to this version of TruePlanning is the capability to use both parametric estimating models as well as analogous data to produce estimates. This capability validates and increases the defensibility of estimates.

TruePlanning provides a framework that allows content driven parametric models to be estimated in one system. Most notably, hardware, software, IT and Systems of Systems (SoS). No other commercially available estimating tool can make that claim. However, whereas in previous versions estimates relied on parametric models which are based on industry benchmarks, now users can also incorporate their own data for analysis. Customers can import organizational data to TruePlanning, run regression analysis on that data, and further validate their estimates. So now estimates can be backed by internal organizational data as well as external benchmark data, which is a powerful way to defend an estimate.

To view complete details of this new feature as well as other enhancements visit our New Release Info page. 

Today, PRICE Systems, Senior Research Analyst, Bob Koury, will be presenting on Will Cost/Should Cost management.

The presentation will focus on two main requirements mandated in the Ash Carter memo (mentioned here several times): Developing Should Cost/Will Cost targets and establishing Affordability as a requirement.  An example will be provided of how parametric estimating models were used to establish “Should Cost” targets and how they can be used by a budget authority (government or Industry) to be an informed consumer of contractor or sub-contractor bids. The demonstration portion of this webinar will focus on a process for using a parametric estimating model to produce the reasonable affordability targets.

To sign up for the webinar go here: https://www2gotomeeting.com/register/142598427