Feedback from cost management firms is that there is a dearth of software to support the cash flow forecasting requirements for developers and consultants. One of the reasons for this is the complexity in generating an algorithm that can handle with accuracy the permutations of projects that are run in the construction industry. One of our clients has 12 construction industry sectors (e.g. Health, Education, Commercial, Leisure etc). Across all sectors there are over 30 project types or sub sectors (e.g. oil and gas, solar for energy sector, defence and justice for government, Office short, medium and tall plus fitout for commercial etc) and against 30 project types there are literally hundreds of asset types (Halls, libraries, pools, stadia etc etc). Then you need to consider the work types for these projects (i.e. new build, refurb, extension etc). And finally size and complexity all need to be considered.
How can a software developer generate an accurate profile of how an estimate will be spent over time? Many project managers build up work breakdown structures with both effort and material allocated. This is then sequenced and durations determined for each piece of work. Then via the Gantt profile, scheduling software can spit out a cash profile. However, this is an enormous amount of work to complete for an investment that may only be at feasibility stage or even right up to detailed design. What if there was a better way?
We believe that a simpler way is to leverage the past to predict the future. As projects are completed, an actual earned profile is developed for that project. Categorising that project with a selection for each of the meta data described below means that we can use this information on any future projects that match the meta data classification. So over time, your consultancy, development company or construction company can use past projects to predict the spend profile of future ones.
Read our blog post on the release of this cash flow algorithm here:
http://uniphi.com.au/blog/view.aspx/uniphi-12-s-curve-phasing-algorithm-based-off-benchmark-past-621551636789556201
Or watch a demonstration of it in use on YouTube below:
Now this database of old projects can be an inhibitor to the success of such an algorithm, however, the expectation is that most organisations have strategic plans that result in them focused on only a small subset of the sectors, project types and asset types listed above. Therefore, it does not take long for cost data to exist in their key strategic niche to. Cost consultancies could look to sell actual phasing profiles of past jobs to companies in niche areas of work (so long as that company has UniPhi to take advantage of the data!). With a small amount of strategic activity, even a startup construction company or developer could obtain rich benchmark data for very little when compared to the risk of poor cash flow forecasting leading to funding issues and other financial stresses.
How can a software developer generate an accurate profile of how an estimate will be spent over time? Many project managers build up work breakdown structures with both effort and material allocated. This is then sequenced and durations determined for each piece of work. Then via the Gantt profile, scheduling software can spit out a cash profile. However, this is an enormous amount of work to complete for an investment that may only be at feasibility stage or even right up to detailed design. What if there was a better way?
We believe that a simpler way is to leverage the past to predict the future. As projects are completed, an actual earned profile is developed for that project. Categorising that project with a selection for each of the meta data described below means that we can use this information on any future projects that match the meta data classification. So over time, your consultancy, development company or construction company can use past projects to predict the spend profile of future ones.
Read our blog post on the release of this cash flow algorithm here:
http://uniphi.com.au/blog/view.aspx/uniphi-12-s-curve-phasing-algorithm-based-off-benchmark-past-621551636789556201
Or watch a demonstration of it in use on YouTube below:
Now this database of old projects can be an inhibitor to the success of such an algorithm, however, the expectation is that most organisations have strategic plans that result in them focused on only a small subset of the sectors, project types and asset types listed above. Therefore, it does not take long for cost data to exist in their key strategic niche to. Cost consultancies could look to sell actual phasing profiles of past jobs to companies in niche areas of work (so long as that company has UniPhi to take advantage of the data!). With a small amount of strategic activity, even a startup construction company or developer could obtain rich benchmark data for very little when compared to the risk of poor cash flow forecasting leading to funding issues and other financial stresses.
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