High accuracy of the project costs prediction is a primary condition of successful bidding. Today costs prediction is still an art by the following reasons.

  1. At the bidding phase the project full engineering data is not available, only major costs adders are usually identified (by costs engineer experience) and used.
  2. As costs prediction is manual and time-consuming, its depth and the cost adders’ scope depend upon the time available for bidding.
  3. There are no standard procedures to build and maintain the costs prediction algorithms database solidifying the costs engineer experience.
  4. The project schedule and scope of outsourcing are not available at the bidding stage, its short time and constrained engineering resources being main reasons.
  5. Project costs indexation is not possible as the costs prediction is partially based on costing engineer assumptions and intuition.
  6. Worst-case scenario does not usually take into account the costs overruns caused by bad management practices, pseudo-concurrent engineering, and the project scope creep. For example, first two points triple the engineering expenses in nearly every project. Errors in ordering and procurement typically add up to 3% to installed equipment costs.
  7. To attain the prediction higher accuracy, higher level of interaction and transparency with the client are needed. They are avoided by bidders for fear of the proprietary information leak.
  8. The subject of the project cancellation charges is not even raised.
desalination megaproject item orders costs

CP allows costs prediction to execute only after Front End Engineering & Design (FEED), PO sequencing mentioned above and the project schedule are completed. These steps produce all the data and metrics needed to perform costs prediction within the “definitive estimate” domain matching the [- 5%, + 10%] accuracy range. The latter is asymmetrical to show that the negative deviation is less probable with CP. For comparison, the highest level of estimate - detailed one (when major purchase orders have been placed) - is expected to produce accuracy within ± 5%.
In addition CP builds the project S-curved expenditures and cancellation charges and the costs adjustment equation.
CP contains a library of cost prediction algorithms directly linked to the properties and parameters of the P&ID items and groups. Properties include such categories as construction materials, design features and standards and even duty type. The interface is smart enough to select the best match based on the algorithm validity range and the age. The obtained costs are subject to auto-indexation.
Besides usual way of the piping cost prediction based on the MTO (material take-off) of piping isometrics (which requires detailed piping layout), CP offers default algorithm that uses neither MTO nor any other input from the designer.
CP uses Project Schedule to calculate workload costs and the project expenditures distribution over the time.
There exists a considerable time lapse between quotation, acceptance of tender and commencement of the project execution. If the bidder is committed to a fixed price at the tender date then all subsequent increases in cost during the project engineering and throughout the construction period, must be borne by the bidder unless a provision is made in the quotation to cover the cost increases of material and labor.
In the past, the water treatment industry has tended to quote on a fixed price basis for the greater part of its turnover. Today, however, with worldwide cost variations, there is a growing tendency for bidders to ask for a contract price adjustment clause to be included and for clients to accept its inclusion and thus ensuring fair distribution of risk and reimbursement for work done between both parties.
CP automatically adjusts prices using internet published price indexes for materials, labor, fuel, electricity, and currency exchange rates. For each specific class of equipment CP establishes key cost drivers based on the historical price data stored in the database. CP produces contract price adjustment equation to be included into commercial offer of the bidder.

As CP reliably shields the project against the worst-case scenarios mentioned above, associated contingency value is removed from the project budget. This contingency may reach 4-6% of the project budget in the EPC companies applying concurrent engineering driven by assumptions. They make up for the broken logic of Project Schedule mentioned above.
CP accelerates transition to the Project Risk Management (PRM) by removing the major component of the project contingency. It is conventionally introduced to compensate for incomplete engineering and scoping done under the pressure of the tender dead line, the limited resources and the offer preparation budget. PRM is based on explicitly defined risk events and the event probability and consequence. This information cannot be fully deduced from the engineering database as it includes such new notions like the vendor credibility, the product history and novelty, the product availability on the market, etc. On the other hand, PRM should detect non-typical solutions that deviate from established standards and procedures implemented by CP. These ideas fuel the CP strategy for PRM development (under development).
Costs prediction by CP is conservative as transition to CP brings up to 8% savings in the project budget.

  1. Less manpower – 3% (due to jump in production rates and decrease in staff training expenses)
  2. No errors in equipment ordering and better prices – 3%
    (much higher chances of the project awarding)
  3. Shorter project schedules – 2% (as FEED and FEM are starting point for the project execution)