Monday, March 14, 2016

UniPhi 12 - Project Custom Fields

Business are constantly faced with two contradicting problems: 1) having too much information to analyse or 2) not having enough information to make a firm decision. The internet of things is rapidly removing problem number 2 and this is leading to problem number 1 being the main problem to solve.

In the first round of document and content management systems, meta data was tagged upon data capture. This has proven to be a cumbersome and time consuming exercise that has led to miscoding and hence the same end result of too much information and being unable to analyse it.

UniPhi's solution is to capture meta data at the project level once and then every time a new piece of information is added to this project, the meta data is automatically applied to it. The types of meta data that each business wants to capture is many and varied. To allow for this, UniPhi comes with the ability to categorise and create an unlimited number of project custom fields.

Project custom fields are classified as either being relevant for the entire duration of the project, or per financial period. The per financial period information is of most use for ongoing reporting purposes. An example of how this feature can be utilised is for including data in recurring reports such as project status reports, Project Control Group (PCG) reports, or statistical summary information. One of the nice things about the per financial period option is that UniPhi allows you to copy your information form the previous period. In effect this means that for a status or PCG report, the amount of effort required to update can be reduced through reuse of  information from the previous month. In addition, you will constantly have a reference to your historic data which is available via the periodic drop down list. If you want to know the status of your project in March of last year, simply select that month from the list, and you will find its status.

The flexibility that UniPhi offers when it comes to configuring your own deployment means the ways that you can use periodic custom fields is practically limitless. If your project involves building a section of road, your custom field can be structured to capture the progress made within that period. If your project needs to track safety incidents which have occurred, you can configure it to capture this information. You can even select a significant photo image and attach it to your project summary.

More importantly, once your valuable data has been captured within the custom fields, you will more than likely want to present it to your stakeholders. The way UniPhi enables you to do this is via documents, and more specifically via document template controls. There are numerous document controls that you can use to present UniPhi data in attractive document formats. In this case though we would be using the Periodic Custom Fields control. This control allows the document creator to display the periodic information in an output document. If its a text field, then it will appear as such in your document, if however your custom filed is a numeric value it will present as a table and display the number for this period, and a cumulative total amount.

The other type of project custom field is for data which is required for the "whole project". As you manage more projects over time, your goal will no doubt be to get better at managing them, and learning from prior experience is often the best way to do so. In the construction industry for example it should be entirely possible to know with a high degree of certainty how much a building will cost, and how many widgets will be required, and when they will need to be purchased.

As per this recent blog, we have enabled you to draw on your cost data from closed projects in order to create an accurate benchmarked cash flow. In that blog I explained the concept of applying additional filters to the benchmark data that was displayed, and as a follow up, I'll describe below how you can actually do that.

Remaining in the construction industry for a moment, let's suppose that across your entire portfolio of projects, you know and want to capture specific information against each build. A common category of information that would assist in understanding your project specifics is Building Areas. Typical metrics captured within building areas are things like FECA (Fully Enclosed Covered Area), GFA (Gross Floor Area), NLA (Net Lettable Area), etc. Another example of the category of information that will be required is Functional Units. The type of information captured within Functional Units will be distinct units which have a numerical value such as number of floors, number of units, or number of lifts. Typically these fields retain a constant value throughout the life of the project.

This unique feature, and the way that you would leverage the information captured within your projects is for comparative and benchmark purposes. Relating back to our cash flow benchmarking post; having this data available across your portfolio, means that each time you kick off a new project, you can conduct benchmark comparisons to validate the assumptions, and financial information that has been used.  UniPhi unlocks this information for you via several Benchmark reports. In the example below, we can observe a line of best fit for all of the projects that match our specific requirements. And, those requirements have been defined and configured by the administrator within the organisation. Once again, capturing the whole of project meta data once then allows you to aggregate any other numerical piece of information by this whole of project field across your portfolio.

Friday, March 11, 2016

Benchmark Series III - Utilising Data Captured

So far in this series I've spent most of the time describing the process and critical success factors to capturing good benchmarking data. The end of the last blog however commenced the discussion on how to use this information. Using information stored in databases is often harder than you'd think. Some systems are great at designing efficient ways to consume data but then spend very little time on ways to view what has been captured. When the goal is benchmarking data, the outputs are the most important functional units.

Outputs vary dramatically depending on the users perspective of benchmarking and what they want to use it for. Below is a list of examples that we have encountered over the past four years (how many could you lay your hands on in less than 5 minutes):

  1. Show me the average elemental rates for particular sectors, project types, asset types and work types for a particular location or across locations
  2. Using the same function, show me the average trade rates over the past 12 months in a specific location to update a rate library in a cost planning tool
  3. Display average USD/SF GFA rates as a bar chart for particular sectors, project types, asset types and work types for a particular location or across locations
  4. Map the range of wall to floor ratios for selected filters
  5. List architects from most expensive to cheapest for average new builds in commercial office towers in London using a £/SF rate of GFA for construction costs.
  6. List the top 10 most expensive museums on a USD/SF of GFA basis across the US in the past 10 years. Show me their design characteristics and functional units (e.g. excavation required, area per floor, no. of levels, no. of rooms etc)
  7. Show on a map where my projects have been and change the size of the circle to reflect the cost of the project
  8. What's the average time to reach final account on a commercial new build project costing more than $20m and lasting longer the 6 months?
  9. Using past projects, price a new project for me in this location for a new build industrial factory with 10,000 sqm of floor space.
  10. Utilise past projects to phase my new one

And the list goes on...

One of the difficulties in developing systems and processes to cater for all these types of questions is in the design of the report engine. Some of the questions are impossible to answer via a generic report interface (e.g. the parametric pricing model used for number 9 above). But most can be answered via the design of a simple to use data analytic tool and a well designed business intelligence cube. We've used the following interface options to present data captured into our BI cube:

A possible alternative to the bespoke web applications, which can be expensive to build, maintain and expand on would be the use of SharePoint's integration with Analysis Services (which is what our BI cube is built on).

I guess the biggest lesson learnt from this space is that you should get as long a list of desired outputs as possible as soon as possible to assist in preparing an overarching architecture that can support what will ultimately be a variety of interfaces into the data.

If you're interested in how UniPhi's software can help you benchmark and get the outputs you want contact us

Monday, March 07, 2016

UniPhi 12 - S-Curve Phasing Algorithm Based Off Benchmark Past Projects

Among the problems faced by cost managers when attempting to produce a reliable cash flow is the disparate nature of the information they rely on (estimation tools, excel spreadsheets, emails, document versions, etc.). Once a cash flow is created, it requires further analysis to ensure data accuracy and validity, all of which takes yet more time. Up until recently UniPhi allowed an estimate to be phased in a linear manner over a period of time, or manually to fit the Gantt. While working closely with a number of our clients we realised that there was no recent software system that allows for automated 'S' curve phasing.  An 'S' curve is the shape a cash flow typically takes when profiled on an XY diagram due to the fact that projects generally start slow, get busy in the middle, and tail off during practical completion.

At UniPhi we are always looking at ways to improve our product, and thus differentiate ourselves from other software applications in the marketplace. With the goal of improving cash flow phasing in mind, we took a step back and analysed the available data that was stored in UniPhi. The core design structure of UniPhi means that data entered once is available in many places. One example of this data that is stored within UniPhi is the progressive sequence of payments made against a contract. This information tells us the actual dates that payments were made, and their individual values. This means that our clients build up 'S' curve profiles on actual projects as time goes by.

Our development team realised they could use this "Actual" phasing to create an automated benchmark algorithm. The most impressive thing about the new benchmark feature and its underlying algorithm is that your UniPhi users are automatically generating the benchmark data via the contract admin function that they've been using for years.

We completed a preliminary implementation of the "S" curve functionality to one of our clients, and found that when compared with their old process of manual calculations, UniPhi's cash flow phasing and associated "S" curve were produced to great satisfaction, and accuracy. Not to mention being much faster than the old method. Through this process of collaboration and review, we discovered that there were additional levels of nuance that were observed and could also be leveraged. Therefore we added a productivity column. The purpose of the productivity column is to factor in periods of low productivity, e.g. Christmas, New Year, and public holidays. The concept of factoring in these periods of lower productivity is powerful, but our design makes the configuration very simple to configure and update.
Adding your organisations productivity calculation is simple

I recently demonstrated the ability to phase costs using benchmark actual data to a client, and his initial response was that because each project type is subtly different, the benchmark data would also be subtly different (e.g. building a high school would be different to building a hospital). The good news is that UniPhi also allows you to select the distinct set of data you need, according to your own criteria. UniPhi has always empowered our clients to perform  administration and configuration tasks through flexible design interfaces, and this capability is extended to allow you to create your own project custom fields. If for example you have previously used UniPhi to manage several constructions projects of varying size, UniPhi allows you to create a project field and specify the "number of floors" that were built for the project. Then, when you are tasked with managing another smaller sized single floor project, you can selectively filter your benchmark data so that only smaller sized 1 to 2 floors projects were displayed. No other system has this level of functionality.

With this new benchmark cost phasing, your own data becomes your most valuable asset. By simply referencing the progress payments which have been made in your previously completed projects, you can get a reliable forecast of costs for your current project.

As UniPhi also features integrated contract and document management modules, our clients have an end to end solution for managing projects. Estimates can be produced in UniPhi, or imported from a third party product, costs can be phased according to your organisations own specific benchmark data, and then once awarded the costs can be managed with accuracy ad transparency. Because your project has relied on benchmark data, you will have confidence that the phasing is correct. In fact you can take it to the bank!

Thursday, March 03, 2016

Benchmark Series II - Control Quantities

The solution

See how UniPhi can capture benchmark ratios from elemental quantities in this 1min 30sec video

Calculation and use

Control quantities are ratios between one element of a building or construction project and another. They can be used to measure a whole variety of things including design efficiencies (for example the wall to floor ratio), cost drivers (preliminaries:construction costs), Density (floor area ratios) and thermal performance (windows to floor ratio).

Graphical display of relationship between floor area and wall to floor ratio

The good thing about ratios and control quantities is that they can be compared on a global basis without worrying about inflation and currency differences. When benchmarking globally, the ability to compare design parameters globally for similar types of projects can greatly assist multi-national investors and perhaps even drive change in various countries to align to what might be better practice. Without a global benchmark of control quantity information, discovering these discrepancies or variations is impossible.

But the use of control quantities doesn't have to be limited just to global benchmarking. Typically, these ratios are known by cost managers and calculating them for each estimate provides a sense check as to the validity of the elements in the estimate. The issue here is the time taken to do this important task. Key items that can take time to be able to complete sense checks using control quantities are:

Problems and solutions to obtaining and using control quantities

In our experience, there are four problems an organisation has to resolve to be able to obtain and use control quantities in their cost planning:

  1. Estimating to a standard elemental level (The NPWC coding structure for example)
  2. Aggregating quantities to the elemental level
  3. Consistently applying the range of formulas to the model
  4. Aggregating multiple similar projects to compare the current estimate to the benchmark

Standard elemental structures

Standards are the drivers for many things. Many tech hardware and software devices would cease to exist without industry standards being used and relied upon in their build. The same goes for cost benchmarking. Without standards it becomes impossible to benchmark and without benchmarks, control quantities have no comparator to give them a use. So key to generating a successful benchmarking database is to get estimators to compile budgets against a standard elemental structure and for those estimates to have both elemental quantities as well as monetary totals.

Part of the NPWC industry standard elemental structure for buildings
Inhibitors to creating a standard are clients of estimating services wanting the estimate to fit their own bespoke template, recalcitrant estimators who only see the value in their single estimate, not in the flow on benefits of capturing this and comparing it against others and organisations actually agreeing to what the standard should be.

Solutions to each of these problems are selling the benefits of benchmarking and control quantities analysis to these clients (see 5D BIM discussion below), transparently displaying information about who is generating good benchmarking information and rewarding staff on this type of performance and adopting an industry standard as your standard for estimating (e.g. NPWC, POMI, NRM etc).

Elemental quantities

Problem number 2 can be easily solved using many estimating software products that exist in the marketplace that have key functionality to allow for quantity inheritance. Two that come to mind are CostX by Exactal and Cato by Causeway. Both systems allow you to pick which detailed line item to inherit when rolling up costs from the detailed to the elemental level. For example, the wall area qty would be including underneath the wall element "external walls (excl windows)" many times (perhaps as a subset) at the detailed level with different rates applied, by marking one line or a combination of line items totalling the wall area to inherit when summing the total external wall cost, our aggregated quantity problem has disappeared.

Elements with Quantities

Time taken to calculate the ratios

Once our estimates are being compiled at a standard format and quantities are being aggregated correctly to the elemental level, the next step is to make sure we calculate each ratio correctly and present this information to reviewers of cost estimates in an integrated fashion. Many organisations use pen, paper and a calculator to complete the sense check calculations of an estimate. This is obviously prone to error and does not provide the reviewer with any transparency that the calculation has been completed correctly.

Key metrics dynamically captured and classified via cost plan import
UniPhi solves this through its calculated metrics functionality. Here, a system administrator can use the codes in the elemental structure to define formulas that get calculated in the UniPhi cost module. The documents module is then used to integrate the display of estimate, control quantities, benchmark ratios and design documentation to a reviewer.

See how we do this end to end via our you tube videos capturing calculated metrics.

Setting up the key metrics

Using these metrics to calculate benchmark ratios

Finding similar estimates to aggregate and compare

The final and most important issue that basically makes all the other solutions moot is the disparate storage of these estimates. Many organisations will email colleagues asking for excel spreadsheets of similar estimates waiting hours or days for the data to be forthcoming and when received having to then resolve the three issues above before being able to manually aggregate them and use the averages.

The portfolio aspects of UniPhi can aggregate an average of all projects across the portfolio that meet the characteristics of this particular project. This provides the necessary benchmark number to compare the current estimate to. This comparison provides for a wealth of information for the cost manager to bring to the table for any value management or cost saving exercise that might ensue.

Graphical display of relationship between floor area and wall to floor ratio for similar projects

Cost estimators response to the threat of 5D BIM

It is this last piece of the puzzle that opens a door of opportunity to cost estimators be they internal estimators for contractors or external consultants for clients of construction services. The time taken to manually measure drawings to derive estimates has been reduced through the development of BIM software that can allow a user to measure through clicks on a screen and then adjust this when a drawing is adjusted by an Architect. However, by capturing the data in a structured manner and using the competitive advantage of having pools of information on previous projects to provide insights to the current design, an estimator can actively participate in the design conversation.

This value add removes the commodity type nature of the industries work and lifts cost consultancy up into the realms of management consultancy as the integrators of design and engineering parameters to make sure a cost effective solution that meets the functional and service outcomes of the clients is achieved.

Please feel free to give us your feedback on the complexities and issues faced providing and using benchmark data at your organisation.