Author: Vikas Khaitan

Case for a First Investment Decision (before the Final one)

Vikas KhaitanLeave a comment
3–5 minutes

You are a FOAK project developer and have gone through initial stages of feasibility / conceptual studies (FEL1), pre-FEED level engineering (FEL2) with Class 4 /3 estimates and have also progressed FEED engineering and completed a Class 2 estimate and still waiting to get to Final Investment Decision (FID). To get to FEL3 and finally FID, there are other non-engineering milestones that need to be achieved. Typically, projects need planning permissions, environmental permits and licences, which even if when applied well in advance during FEL2, could take a long time for FOAK projects. Additionally, feedstock contracts, licensor agreements, EPC pricing, offtake agreements and a better understanding of the revenue profile also need to be in place before final investment decisions can be agreed with potential financers.

For FOAK projects, which in many cases seem to be more expensive per unit of the final product, in the initial days, with respect to the current product in the market, find it difficult to reach FID even after completing most of the activities mentioned above. Generally, offtake agreements (at high unit prices) are difficult to obtain. Sometimes, for any new market to develop, there is a stalemate between offtakers, financers and government policymakers and that can take a long time to untangle. Project developers could run out of steam for a delayed FID and because of the standard cycle of project development, the final project could be years later than initially planned or required for capturing the market in a region, or for climate mitigation reasons.

To help smaller FOAK projects accelerate faster, even during the time of stalemate between financers, offtakers and policymakers, I would suggest breaking the FID process into phases. Instead of having a Final Investment Decision gate, we could potentially have multiple First, Second, Third …..Investment Decision gates.

This is illustrated in the diagram below:

Once planning permissions are granted, licensor agreements are in place, a formal EPC pricing and contracting strategy is in place, and there is a theoretically demonstrable revenue stream, perhaps the project could have a First Investment Decision, for a smaller portion of the overall project CAPEX. For example, this could be investing in early detailed engineering. This has the benefit of not going into another FEED re-cycle and de-risking the main EPC contract value when the project finally gets to FID. Other suggested options include early siteworks, which could de-risk any concerns with the selected site having unforeseen underground issues. Yet another potential is an early contract for building works, which are non-process related and are generally sub-contracted out to specialist building contractors anyway.

These activities might look like additional DevEx investment but could be value adder in several ways. First, they de-risk the overall EPC value, thus the project would have to carry much lower contingency at the FID stage. Secondly, the overall project schedule post-FID would be much shorter, thus potentially starting revenue generation earlier. On the other hand, if for some reason the market collapses, and government support does not come about in the region, then the site could be sold to developers of other projects thus not losing the value of any early site work or even the buildings could be re-purposed for something else by another project. Even the early detailed engineering could potentially be sold to a similar a project perhaps in another region.

These additional initial pre-FID investments could be seen as early value-adders from multiple lenses, helping FOAK projects to not only reach FID with less risk, but also help in maintaining schedule in uncertain market conditions.

This suggestion is suitable only if sufficient confidence could be generated in the project for potential initial public or private investors or for any early government investment support applications, to help develop new technologies faster.

Case in Point: The suggestions in this blog are not new, as I have seen some of them being undertaken for major oil & gas projects in a slightly different scenario. Once for a large EPC contract, the governance was taking longer than expected, the project management decided and got agreement to proceed with early detailed engineering works with the selected EPC contractor to protect the overall schedule. In another project I worked on, the project management decided to place early site works contract separate to the main EPC contract to expedite the site works in parallel to the governance process.

This thought-piece is suitable for project developers, financers & investors, policymakers, and all other stakeholders trying to navigate the challenge of scaling up energy transition projects. Hope this is helpful!

Does FOAK Project Size Matter?

Vikas KhaitanLeave a comment
3–5 minutes

I would have really liked to say otherwise, but in my experience in helping various First-of-a-Kind (FOAK) cleantech and energy projects, I have seen that size of a project does matter. Financing these early stage commercial scale projects is really challenging as investors are unwilling to take the risk of uncertainty which is the nature of any FOAK project. Even if technical aspects are resolved, integration of multiple licensors and contracting is generally not resolved, making these FOAK projects difficult to scale-up.

There are several aspects which make the smaller projects look unviable to begin with as they might not give the required return on investment, and the product could be more expensive per unit, thus making it even more difficult to get offtake agreements. Many project developers then end up choosing a larger project to give better return on investment.

But it is not as black and white as that. There are several aspects which could favour much smaller FOAK commercial scale project when a new industry is being developed. The list below shows some of the advantages and disadvantages of large and small FOAK projects.

Given the very early nature of FOAK projects, apart from technology, there are other immature aspects such as supply chains, operating procedures, maintenance cycles, and commercial structures, which all need to be better understood and lessons learnt. When a new technology is scaled, not only the plant capacity is scaled up, but all uncertain aspects need to be scaled as well along with it adding to the overall cost of financing. A large FOAK project concentrates that uncertainty into a single, irreversible capital decision. If assumptions prove wrong, the consequences are expensive.

A small FOAK project on the other hand gives the ability to learn and be flexible, even at a slightly higher unit cost and smaller total returns. But it requires less capital, carries lower investor exposure, and is relatively easier to insure and finance. There is possibility to secure smaller, more realistic offtake agreements, might be able to source feedstock more locally and very importantly can be built faster. Smaller facilities create room for the team to learn from a real commercial scale project. They allow teams to identify bottlenecks, refine processes, optimise designs, improve vendor performance, develop new supply chain, and validate cost assumptions in real conditions. And most importantly if risk is shared, the overall impact of things going wrong is minimised, which is what all stakeholders are concerned about.

In early stages, the first facility not only produces a product, but also reliable performance data, cost validation, bankability benchmarks, operational credibility, investor confidence, which all can be later sold and true economies of scale benefits obtained from subsequent facilities. The improved design and integration aspects, optimised supply chain, better and standardised construction methods can all be shared and sold to third parties if necessary. These could be the real revenue generators for the investors. The additional value generated could allow the initial small project investor to even licence some of these to others building larger projects in various geographies in the future.

I would say a larger project is more appropriate when the technology is mature, supply chains are established and there is demand for the product, but those conditions can only be developed with time for new technologies, even with government mandates, subsidies and revenue guarantees. In emerging cleantech and new energy industries, learning should be more valued than early economies of scale efficiency. I feel starting small allows all stakeholders to help each other and allows learning to happen by distributing the risk across all aspects and parties involved, instead of only the large capital taking all the risk.

Additional Points:

  • I have not specified any technology or industry as these thoughts could be applicable to various FOAK projects. I also have not specified what small and large mean and left that to be discussed on a case by case basis for specific industry, depending on the market and risk appetite.
  • To help with initial investment decisions, larger projects could be designed in such a way that they could be developed in phases or train by train approach (the oil & gas industry has been doing that for decades). Lessons learned in early phases could be incorporated in subsequent phases or trains.
  • The smaller size investment could be more suitable for multiple stakeholders to participate as the overall risk when shared would become small. This is not the case if the overall investment is large to start with.
  • Also, smaller investments could be more palatable for smaller financial assets, funds and portfolio managers, who want to get involved in sustainability and energy transition projects. They will also be perceived as lower risk in large financial portfolios.

This thought-piece is suitable for project developers, technology developers, financers & investors, policy makers, and all other stakeholders trying to navigate the challenge of scaling up energy transition projects. Hope this is helpful!

Risk Analysis in Estimating: do-it-yourself (DIY) Monte-Carlo Simulation

Vikas KhaitanLeave a comment
5–7 minutes

No matter what class of estimate it is, every estimator makes his or her slightly different assumptions based on their own experience. An estimate is finally an opinion of a particular estimator. Some estimators tend to be pessimistic in every assumption, factor, historic data they use, ending up with a very conservative (i.e. high) overall estimate. For the same technical inputs, which might themselves not be perfect, another estimator might make optimistic assumptions for individual elements of the project, which might make the overall estimate very low. In both cases, the estimator is likely to add a contingency to their estimate.

Most often, the assumptions made by individual estimators are not transparent as they are generally not properly recorded or documented. Besides this, the decision-maker is usually given only one estimate from an individual estimator to look at, which does not give them the option to question or critique the possible range of the overall estimate.

If somehow the same estimate could be done by multiple estimators, and all those estimates were tabulated together, we would get a range of estimates, with a maximum and a minimum value, which would allow everyone to understand the possible range of cost outcomes of a future project. But it would be very expensive or nearly impossible to get the project estimated by several estimators at the same time.

But there is a workaround to this! We could instead use MS Excel to do multiple estimates. And not only that, but we could also get Excel to run 5,000 or even 10,000 estimates almost instantaneously (for free!!).

  • This is like predicting 10,000 possible scenarios out of the infinite possibilities (simulating reality by trying to analyse many possibilities).
  • If we could analyse and simply present these 10,000 results, we could in a way say what the likelihood (probability) of success for a particular point estimate would be.
  • This would be better (and more transparent) than trying to present a single number.

Nobody knows the future, so whatever we predict is likely to be wrong; the point is to reduce the amount of error in our predictions. So, instead of trying to produce a correct single number (Point Estimate), I suggest that we generate a reasonable range instead (Range Estimate).

This is called Monte Carlo simulation, and it is a mathematical model of probability analysis. There are various companies selling risk analysis (Monte Carlo simulation) software in the market, but the cost for those tools (toys!) are sometimes not justifiable even within large organisations. But a range estimate would be really useful by allowing decision-makers to understand what contingency would be suitable for a particular estimate and at what risk level are they pitching the overall number.

I have developed something easy and simple to help bring the team together and run these simulations to see what the possible range of the overall estimate could be:

  • Let us say that you come up with a point estimate for the project – say the total is 100
  • And the estimate is built up by adding, say, 20 different items (or more)
  • But each of those different items could have been assigned a different number (within a particular range)
  • The team can come together to understand the assumptions made by the estimators for those individual items, and can together decide an appropriate range for each item

Once those ranges are defined, we could ask Excel to randomly pick any number between those ranges for each of those items (by using the Excel formula “Randbetween”) and add them up to come up with a new estimate, as shown below:

We could do this 10,000 times to come up with 10,000 new estimates. This is how a simple DIY Monte-Carlo simulation model could be made.

Typical results will look like the following:

Interpretation:

  • The first graph plots all the 10,000 estimates (simulations) generated by this method [using a simple scatter graph]
  • Based on these 10,000 simulations, the second graph shows what are the chances of completing a project within the “Point Estimate” value. [this simply plots the Percentiles of all the 10,000 values using a scatter graph]
  • Do note that in a real life situation there is no chance of all possible worst cases happening simultaneously, nor all the best cases can happen for a project. Therefor the total of all the maximums and all the minimums does not make any sense, and the Monte-Carlo results also show that they never occurred within the 10,000 simulations.

The file attached below is the model I have developed as explained here and is a free estimating resource for anybody who wishes to download and use.

20250316_DIY_Monte_Carlo_forEstimating_Rev0 (free resource)Download

This example template file is designed to take up to 50 items in the inputs tab (this is the breakup of the point estimate), with their estimated values and the max-min ranges for each value. The inputs tab is then linked to the Monte-Carlo Results tab where the 10,000 simulations are run by simply pressing “Fn&F9” together on the keyboard. This tab also presents the results in the graphical form presented earlier. If needed, this file can be modified easily to increase the number of items adding up to a total point estimate.

Notes:

  • I have assumed that all the elements are mutually exclusive cost items and there is no interrelation between them.
  • I have used uniform distribution to keep the model simple. This means, once the ranges are defined, all the numbers within that range will have equal probability of occurring. Of course, this is never the case in real life, but it works as a simple model.
  • Here is where discussion and decision-making come into play – the team can sit together and understand the assumptions behind the various elements of the project and see what the likelihood of the overall number is if a different number is chosen within that range for that item.
  • This model will then help the team to understand where the overall point estimate is pitched at, and backup the contingency they want to add to the overall number for any class of estimate.
  • Other tools in the market are more sophisticated with triangular or other probability distributions, interrelated elements etc. which do make them more complicated and difficult to explain, and in my opinion, they may not add that much more value to decision making [as they could be perceived as black-box by decision-makers].
  • This simple DIY tool can be a useful tool for initial estimate reviews and discussions.

ENJOY!

Biomass Power Plant Benchmarking (Open-Source Example)

Vikas KhaitanLeave a comment

In one of my past blogs, I developed and shared an open-source benchmarking example for gas and coal fired power plants. I have now developed another open-source benchmarking graph, using internet-based data, for a typical biomass power plant project. I have mostly used news articles giving the total value of the contract and capacity in Megawatt (MW) of the power plant.

20240622 Biomass Power Plant Benchmarking (Open Source)-Rev0 (free Resource)Download

The data is tabulated (in the attached Excel file) with links to the various websites for cross-checking purposes. Within the file I have also escalated the contract values to the current year using a nominal escalation factor (which will vary depending on the market conditions). This can be modified to any future year when needed. This gives a feel of what the market has paid for a typical biomass power plant. These are not out-turn costs but initial awarded values and thus should be treated as such. This is not exact but gives an idea.

No two projects are similar in scope, but what this does is to give an opportunity to the reviewers to understand any special / specific requirements of a project which might make the current project estimate different (like additional fuel conditioning, remote location, additional transmission lines, piling requirement, etc.). The overall plant cost will also vary based on the technology used, quality of fuel used, efficiency of the plant etc.

Also to be noted is that this is only an EPC cost and any Pre-FEED, FEED, other Owner’s costs, service agreements, operation & maintenance costs are to be added separately as needed.

This can be used in addition to or in the absence of any in-house or any third-party benchmarking data. This can be easily shared with anybody and can be used as a cross-check of the detailed biomass power plant estimate at a $/MW level.

The graph is also suitable for coming up with quick order of magnitude estimates for bid / no-bid analysis, initial project sanctions, bid evaluations, etc.

The graph shows the typical trend of reducing unit cost for larger capacity plants.

I have just done some research to show what can be possible with freely available data and so the graph is not considered comprehensive and can be enhanced with more data points if further research is undertaken. Location / area specific graphs can also be generated if enough data points could be gathered.

Feel free to use this as you see fit with or without modifications.

Special Notes:

Note1: Various studies show that depending on how the estimate was performed, the actual out-turn cost could potentially be much higher. For this reason, note that in this graph, I have also indicated a 75% cost overrun (as an example) to show how the actual out-turn cost might look like. This percentage can be varied within the Excel file to see various sensitivities.

Note2: I have also plotted some additional data points on the graph using cost data published by IRENA (Renewable Power Generation Costs in 2022), suitably escalated to the current year. I have used the high data for North America (see page 181 of the IRENA report) as an example for comparison with my internet based open-source data. I am suggesting that this can be used as another cross-check.

Note3: For some projects, O&M for number of years was included within the award value, which I have subtracted (using a nominal O&M cost per year). This can also be modified as needed within the Excel file.

Economic Challenges in Expanding Renewable Power Deployment – An Essay

Vikas KhaitanLeave a comment

Abstract

To be able to control the climate crisis and limit the global temperature rise, sustainable renewable energy projects will need to be deployed at a much faster and larger scale from where they currently are in the power generation mix. Apart from the technical challenges, this required renewable energy generation growth faces considerable economic challenges like supply chain issues, high initial investment required, global energy poverty, low return on investment, competition for natural resources like land, trained labour shortages, waste management, changing global trade dynamics, requirement for even higher investments in associated infrastructure for transmission and storage, etc., to list a few. These are explained in detail within this essay. A lot of research is currently ongoing to suggest urgent changes in policies, to help mitigate these challenges, to be implemented by governments at a national level as well as collaboratively at an international level. Some of those policy recommendations are highlighted in this essay.

Introduction

Currently the share of renewables is nearly 29% of electricity generation globally. But to achieve the 2050 net-zero goal to limit global warming to 1.5 degrees above pre-industrial level, re-affirmed recently during various COP meetings, several countries agreed that renewable power needs to increase significantly in the next decade to nearly 60% of global generation mix. Even though the science has developed significantly, scaling up renewables have several economic challenges which needs to be understood and suitably mitigated if we want to successfully combat the climate crisis.

Economic Challenges

Supply chain issue for raw material is a major concern. Critical metals like Nickel, Lithium, Cobalt, etc. are important parts of battery technology. Platinum and rare earth metals form the basic components of wind turbines and electrolysers required for the hydrogen economy.  Most of these metals are mined in only handful of countries and most of the refining is currently undertaken by China. This dependence creates a supply chain challenge for the required scale-up of renewables. Geo-political issues, like the currently ongoing Russia-Ukraine war, the recent pandemic related lockdowns, etc., have already substantially increased many raw material prices and could soon counter the past years of reduction in wind and solar project costs.

High Initial Investment required for renewables will increase the cost of supplying electricity in the short term. Increasing renewables in the energy mix also substantially increases the marginal cost per unit of providing peak load electricity as the network operators will then need to pay for maintaining and operating the backup baseload fossil fuel power plants. This also increases the cost of production of other goods and services making them uncompetitive in the global markets.

Energy Poverty is currently emerging as one of the biggest economic challenges for large scale renewable deployment, as some developing economies cannot simply afford such expense. Most of Africa, many developing countries in South America and southeast Asia have this challenge to overcome. After years of disagreement, during the recent COP27 in Egypt, the richer nations have finally agreed for a “Loss and Damage” fund to be created to help developing nations which are most effected by the climate crisis.

Low profit renewable projects are less attractive to large private investors looking to make quick money from their investments. This becomes even more exacerbated when large scale intermittent renewable power sources are added to the grid, which reduces the overall revenue generated by reducing the wholesale electricity price at which the network can sell power to consumers. Whereas the fossil fuel industry, which pays much higher dividends, is generally backed by billionaires, and this is a huge economic issue for the investments required for large scale renewable projects. Some small to medium size renewable project developers have been crowdfunding their projects, which will not be suitable for large scale-up needed in the next decade.

High discount factors are generally used in all economic modelling and analysis for large scale investment decisions, which means assuming high economic growth in terms of increased GDP, which may not be sustainable. Instead, some economists are now suggesting the use of a lower ‘social discount factor’, which considers the investment needed to counter the negative impact of climate change. Using high discount factors increases the LCOE of renewable projects which make them less attractive.

Resource competition is also a challenge in scaling-up of renewable generation. Some renewable projects like solar and onshore wind farms take up a lot of land space, which can compete with agricultural and other industries creating a tension in the local economy.

Another challenge is to grow the corresponding demand for electricity in the other sectors at the same pace, like electrical vehicles and charging points along with large scale investments in suitable grid upgrades to allow for intermittent renewables’ integration. To make large-scale renewable projects economically viable, their curtailment needs to reduce, thus increasing their capacity factors. This means larger investment in connected hydrogen, hydro, battery, and other storage technologies. This is a financial challenge for even the richer economies, with growing cost of living crisis and rising inflation.

Current Global trade dynamics will be impacted by large scale renewable energy development specifically for the fossil fuel exporting countries like Russia and many Middle Eastern countries, whose GDP is heavily dependent on energy trade. Those economies could try to resist this large-scale energy transition.

Trained labour shortage for this fast-growing industry will be another challenge that all economies will have to carefully consider when scaling-up renewable generation. More experienced personnel are reluctant to move out of the fossil fuel industry and new generation seems to be disenchanted with the energy industry in general.

Waste management will become an economic issue with scale-up of the renewable sector in the next decade. The decommissioning costs for old wind turbines and solar panels will need to be understood better, otherwise this is another externality which might conveniently get missed from any economics and the green transition might not remain that green.

Recommendations for Policymakers

To tackle the above indicated economic challenges, the following points are recommended for policymakers.

  • Encourage recycling and investment in the circular economy to tackle the impact of supply chain issues and waste management.
  • Encourage private investments in renewable energy projects by providing tax incentives, risk mitigations measures like government protection for failed projects if trying out new technologies.
  • Governments of richer countries could implement policies to set aside particular percentage of their national GDP to help develop renewable projects in poorer countries, with a realisation that climate change is a global issue, which needs to be funded by people who have. This can happen once governments can start promoting alternative to GDP growth as economic indications of prosperity like the Human Development Index and employ policies like the EU’s ‘Beyond GDP’ initiative.
  • Implement more low carbon Feed-in tariffs, to protect the renewable sector with fixed purchasing pricing even if the wholesale prices fall with increasing renewable penetration.
  • Impose carbon tax on the fossil fuel industry making the renewable projects more attractive.
  • Encourage investments in associated infrastructure like transmission lines, suitable storage, and hydrogen projects.
  • Encourage more collaboration with neighbouring countries, to help build interconnections, thus reducing the cost per unit of renewable power generated.

Conclusion

The predicted global climate crisis is almost upon us, and alternative technologies are being developed to harness natural sustainable energy resources like wind, solar, geothermal, hydro, ocean etc. But a large-scale implementation of renewables faces numerous economic challenges, which can only be resolved by governments of various countries working together and formulating suitable policies. The United Nations 17 sustainable development goals is one such effort which needs to be really converted into national policies.

This article was published in Mar-2024, in the Project Control Professional which is the journal of The Association of Cost Engineers.

This article was awarded the “CaSA Article of the Year 2024” award by Controls & Skills Authority UK (previously known as ACostE UK)

Tank Farm Benchmarking (Open Source Example)

Vikas KhaitanLeave a comment

I have developed an open source benchmarking graph, using internet-based data, for oil storage tank farm projects. I have mostly used news articles giving the total value of the contract and total storage capacity in cubic meters (cu m) of the tank farm.

20210818 Tank Farm Benchmarking (Open Source)-Rev2Download

The data is tabulated (in the attached Excel file) with links to the various websites for cross-checking purposes. Within the file I have also escalated the contract values to the current year using a nominal escalation factor (which will vary depending on the market conditions). This can be modified to any future year when needed. This gives a feel of what the market has paid for a typical tank farm of a particular total capacity. These are not out-turn costs but initial awarded values and thus should be treated as such. This is not exact but gives an idea.

No two projects are similar in scope, but what this does is to give an opportunity to the reviewers to understand any special / specific requirements of a project which might make the current project estimate different (like remote location, additional piping, additional pumping, additional pipelines, piling requirement, etc.).

Also, to be noted is that this is only an EPC cost and any Pre-FEED, FEED, other Owner’s costs, service agreements, operation & maintenance costs are to be added separately as needed.

This can be used in addition to or in the absence of any in-house or any third-party benchmarking data. This can be easily shared with anybody and can be used as a cross-check of the detailed tank farm estimates at a $/cu m level.

The graph is also suitable for coming up with quick order of magnitude estimates for bid / no-bid analysis, initial project sanctions, bid evaluations, etc.

The graph shows the typical trend of reducing unit cost for larger capacity tank farms.

20210818 Tank Farm Benchmarking (Open Source)-Rev2

I have just done some research to show what can be possible with freely available data and so the graph is not considered comprehensive and can be enhanced with more data points if further research is undertaken. Location / area specific graphs can also be generated if enough data points could be gathered.

Feel free to use this as you see fit with or without modifications.

Special Note:

This analysis could be used to benchmark the complete project cost of any site-built tank.

Sometimes, site-built tanks are included as part of larger projects. In those estimates, the site-built tanks are considered as part of the process equipment items and are typically costed as part of the equipment list. In those situations, it would be a mistake to include the total site-built tank project cost (as per this benchmarking analysis) within the equipment cost account. I would suggest including only the steel supply & prefabrication cost for the tank as part of the equipment cost, which would typically be around 25-30% of the overall tank farm cost (as benchmarked here).

Apart from the steel supply & prefabrication, the other costs which make up a complete tank farm project include all civil works including bunds (if required), installation costs, and the costs of supply, fabrication and installation of all the associated piping, electricals, instrumentation and control items. Finally, costs such as shipping, spares, engineering, project management, site mobilisation, construction management, commissioning and any risks and mark-ups are also part of the overall tank farm project cost.

These additional costs are generally added to the estimate of the whole plant and is not separated out for the site-built tanks within the plant.

Print Macro – Automate Generation of Multiple Estimates

Vikas KhaitanLeave a comment

I have seen estimators develop several files or tabs to generate various versions of estimates for the same scope where there are several possible technical and commercial solutions. One example of this is when the client wants to a see what the overall cost of a pipeline project would look like if the diameter is varied and or the material cost is varied based on the supply source. In such a case, the alternative estimates are started as mirror images of the initial estimate but are considered separate estimates. The estimator soon ends up handling multiple estimates in various spreadsheets and tabs for minor updates. There is a high chance of making errors and not updating all the estimates accurately.

Alternatively, there can be only one estimate file, with the detailed estimate changing in response to specific technical and cost inputs. There can be an input table with various options (in separate columns) which is linked to the main detailed estimate (one at a time). In this way, any changes are only made once and would be automatically updated for all options, and the estimator will not have to handle multiple estimates or even separate files.

The only apparent drawback with this approach is that the detailed estimate can only be seen one at a time and the summaries and results of the various options cannot be compared. The results of each option may have to be printed manually into another spreadsheet for analysing. This may seem like a laborious process if there are several options to manage.

Alternatively, a very easy excel macro can help automatically print the results, for the various options, into another tab (or another area of the same spreadsheet) for ready analysis.

The macro will simply run the whole estimate repeatedly for each set of inputs provided. It will then print the required results in pre-assigned cells one after the other. In the above example, if the options are 20 different pipeline diameters with varying material unit cost, the macro would simply run the estimate 20 times, printing the overall results (or any breakups if needed), 20 times at a pre-assigned range of cells in the excel file. These results can then be analysed and presented in a tabular or graphical fashion as required.

A typical example macro looks like below:

Print Macro

In this example, the values from cells A10 to C10 (Range command) from the inputs tab are copied into the estimate tab in cells D14 to F14; the results from the estimate tab in range R49 to U49 are then copied in the output tab in the range G8 to J8. The whole process is repeated 20 times (using x as a counter in conjunction with the “Next” command), with inputs being collected from the next row (Offset command) and the estimate being Run and the results being printed in the output tab in the consecutive rows (using the Offset command again).

Steps:

  • This does not need any prior knowledge of macro writing
  • Go to the “Developer” ribbon in Excel -> click “Macro” -> give a name to your macro such as Macro1 and then press “Create”.

        (If the “Developer” button does not appear in the ribbon, go to File-> click “Options” ->          then go to “Customise Ribbon” and select “Developer”)

  • Then write the command like the one shown in the example box above.
  • Make sure you have the correct ranges for inputs and outputs from your estimate file
  • Note that the input and output ranges can be in a single tab and not necessarily in different tabs.
  • Also, they might not be ranges, but single cell references.
  • Also make sure that the tab names are correctly spelled (I have made that mistake several times in the past)
  • The Offset command moves from one input to the next and is also used to print the outputs in consecutive cells (or sets of cells)
  • Save the macro
  • Then Run the macro

The macro should take few minutes to write, and every time there is an update to the estimate, results of all the options can be printed in seconds by just running this macro and any updated analysis can thus be presented almost immediately. One caution here, during any subsequent updates, make sure that the original cells for the inputs and outputs are not changed, and if they have moved, the macro will not automatically update those, and you will have to change those references by editing the macro with the correct cell references.

I have been using this macro (maybe the only macro I use), for more than 10 years now and this has made the multiple estimate preparation and update process much easier and simpler.

In my view, this is one of the simplest ways to automate the generation of multiple estimates using Excel which does not need any knowledge of macro writing.

The in-house benchmarking graphs for various types of piling as shown in the piling blog were generated using this macro. (I cannot upload a macro-enabled file in WordPress. Try it yourself and see if works, and let me know if you have any questions).

Labour Estimating Norms

Vikas Khaitan4 Comments

It is my opinion that labour is the most important resource for any project and a proper assessment of labour hours and related costs is essential for the accuracy of any estimate. However, assessing labour hours is not straightforward and the labour effort required for various activities vary hugely, and standardised norms for assessing labour needs are not freely available or widely shared.

For any project, the site needs to be prepared, foundations need to be constructed and all material and equipment need to be fabricated and installed. The cost of this fabrication & installation works can be estimated if all the labour hours required for the project can be calculated. During the estimating process, this is generally done by calculating the direct labour hours for the various estimated quantities from material take-offs (MTOs), using Labour Estimating Norms for ideal working conditions.

[The labour norms are subsequently converted to site specific labour hours using productivity factors and then converted to a total construction cost using an all-in labour rate. This method of calculating construction cost is useful as an in-house estimate in the absence of unit fabrication and installation rates from potential contractors.]

The labour norms are standard time taken by a mixed crew to complete a unit of various types of construction works in an ideal location.

For example:

  • Hours per m2 for site preparation
  • Hours per m3 for excavation works
  • Hours per m3 to install concrete foundations
  • Hours per tonne to fabricate and install structural steel
  • Hours per joint to weld pipes (of various diameters, thicknesses and materials)
  • Hours per meter to erect pipes (for various diameters and thicknesses)
  • Hours per meter to install electrical cables (of various sizes)
  • Hours per instrument installed in the field (various kinds of instruments)
  • Hours per equipment installed (various kinds of equipment)

But the question is, where can an estimator get these labour norm hours?

The various labour estimating norms most prevalent in the Oil & Gas industry are the Gulf Coast, DACE or in-house norms.

In-house Labour Norms

Most estimators use their company’s in-house labour norms for all detailed estimating purposes. These norms are not widely shared with the larger estimating community and are considered proprietary by the individual EPC companies. It is not very clear how these in-house norms were developed by the individual companies and for which location. They could be for a specific country / location where that company mostly executes its projects. They have generally been handed down over the years within the estimating departments of the individual companies.

(In the past, I have used the estimating norms of whichever company I have worked for, and they are all slightly different.)

Gulf Coast Labour Norms

This is a general term used by most estimators to refer to labour hours required to complete an activity in a controlled environment in the Gulf of Mexico which is considered an ideal location. But no specific industry standard Gulf Coast labour estimating norms exist for the Oil & Gas industry. Many estimators use their in-house labour norms but call them Gulf Coast norms for all discussion purposes.

My Understanding

Firstly, referring to the in-house norms as Gulf Coast norms is a bit misleading in my opinion. More importantly, the in-house labour norms as used by the various EPC companies are slightly different, thus making it difficult to easily critique or review detailed estimates, as the norms are not shared outside the company. This creates a black-box scenario for any reviewer who is not part of that company. The labour portion of the overall estimate thus becomes only an opinion of that company’s estimator which sometimes is an acceptable situation for many clients. This estimate is then in future compared against any contractor’s pricing for that scope.

If an alternative is required where a company does not have their own in-house labour estimating norms, the following manhour manuals are a good source to be used as Gulf Coast labour norms.

(I have procured the above manuals and have used them for my estimating purposes.)

For estimating civils related works, I have previously used Spons and Richardson for their labour norms. These are UK specific labour norms (not Gulf Coast) to be suitably adjusted for other regions / countries. But for civil works, it is best to use local unit rates from past projects instead of labour norms.

DACE Labour Norms

More recently, the Dutch Association of Cost Engineers (DACE) have compiled labour estimating norms for the process industry. These labour norms can directly be purchased from the DACE website. (I have not used them yet.)

 Note: These are only the norms that I have used in my career as an estimator in the on-shore Oil & Gas industry so far. There are others which I have not used.

In my opinion, if the estimating community wants to make their estimates more transparent, then it would be better to use a more widely available labour estimating norms as a basis. For example, estimators can buy John S. Page’s man-hour manuals and then those estimates can be cross-checked, instead of using in-house norms which are all slightly different and not widely shared.

A Call for Transparency: Sharing Working Estimate Files

Vikas Khaitan3 Comments

Excel is commonly used to prepare estimates of various kinds. Such estimates may be based on detailed MTOs; they may be higher level factored estimates; or conceptual estimates based on capacity curves. Even when proprietary software is employed, the final summary is most often prepared in Excel for any overall adjustments and presentations as required. Once prepared, discussed, reviewed and finalised, many estimators tend to then supply only a PDF print of the estimate summary to their internal or external clients. Even if an Excel version is requested, some send out a value-only copy (without formulae) of the estimate.

In my opinion, this is not a very friendly way of working. The person receiving the estimate cannot check, review or adjust the estimate in any way as he/she will not have the working Excel version. In my career, I have reviewed many estimates, where the estimate was only given to me in the PDF format. In all those cases, I had to re-create the estimate in order to be able to do the checking and then make suitable adjustments based on the latest requirements and or understanding. If the working Excel file was available, a lot of time could have been saved.

Estimates are only opinions and are always subject to adjustments based on who is looking at them and or what it is being used for. The estimator spends a considerable amount of paid time building up a working estimate file for their clients and there is no good reason for not wanting to share the working file.

I have tried to understand the logic for this behaviour:

  • Many estimators think that they will be giving away something proprietary if they share a working Excel file, i.e. a contractual reason
  • Maybe they think that they will violate some kind of “code” if working Excel files are shared; i.e. a professional standard
  • Or they want the client to come back to them every time any adjustment is needed even if it is minor as they do not trust others to make any adjustments to the estimate; i.e. special expertise argument
  • Another explanation could be that they don’t want the reviewer to probe too much into the makeup of their estimates (i.e. simply poorly referenced estimates – I would very much like to think that this is not very common)

If none of the above is true, then the only other explanation could be just bad practice and inertia which has not been challenged. I would recommend and request all estimators to always share working Excel files so that the receiver can quickly review and understand the estimate and if required make suitable adjustments. This would also generate more confidence in the estimate and promote a transparent culture.

I have made it a habit and practice to always share my working excel files whenever I am issuing an estimate and never faced any difficulties due to this.

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Design Optimisation for a Successful Business Case

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As per one school of thought, a conceptual estimate in the early stages of any project, is a very high-level number, and can thus be based on very conservative design assumptions and considerations. This approach invariably makes the initial project cost estimate very high, which in turn could jeopardise the business case altogether as it would not be good value for the client’s money.

Some design teams might think that this gives a more robust technical solution during concept work, but if the project does not go ahead for this reason, then the whole exercise is futile in my opinion.

The obvious alternative could be to base the initial design on slightly more optimistic assumptions and considerations. This could then be followed up with design sensitivities for more onerous requirements and / or considerations. This approach then helps the estimating team to come up with a range of estimates during the concept stage with various degree of confidence in the proposed design solutions. This then gives the management a fighting chance to present the business case for the project with varying technical requirements and constraints.

It is quite understandable that the design team would not want to compromise on robustness of design simply to cut costs. That is not what I am suggesting here. I propose a more collaborative and cost-aware approach so that cost-effective but fully defensible designs are proposed at the concept stage with an offer of add-ons for more robust options.

Some possible places to optimise:

  • Total flowrate/ capacity required (during initial stages of any project, even the client’s team members would not be sure what total capacity would be most appropriate. The requirement could be interpreted differently by different parties, for example this can be due to the aspiration of some team members to build in additional capacity for the future. However, this may not have been discussed with the higher management and might require separate funding or sanctioning. In my opinion, it is best to present various capacity cases during concept)
  •  Efficiency of rotating equipment (newer motors, pumps, turbines and compressors are more efficient than sometimes assumed in design calculations based on old project experience and old technology)
  • Design margins for sizing of individual equipment (sometimes the assumptions are conservative)
  • Understanding of available technology for specialist equipment items (there could be alternative technologies in the market which could be more efficient, requiring less utilities & accessories and cost effective to the overall plant design. Depends on the experience of the individual design team members)
  • Material selection (there is sometimes a range of possible materials that could be suitable for a requirement, and the material selection made could be conservative based on more onerous service envisaged in the future)
  • Different and / or simpler designs for specific areas (like utilities, control & automation, protection philosophy, etc.)
  • Possible multi-use of specific equipment (e.g. combining firewater and wash water into a single tank instead of 2 separate tanks)
  • Design life of equipment and overall plant (the client might not initially require a long design life of individual high-cost specialist equipment and could agree to a complete refurbishment or replacement half way through the plant life to help reduce the initial CAPEX spend)
  • Sparing philosophy requirement (client might not need excessive spares to start with and might want to postpone the purchase of the spares during operations)
  • Any assumed pre-investment for future requirements included in the base design (generally in utilities, additional land clearance areas etc.)

Note that these examples are not actually scope related but more to do with basic design assumption made by specific concept design teams. In these examples, the design team might assume what the client needs, without actually giving the client a chance to really opt for the more onerous design requirements. Depending on the choices made, the above examples could together easily add nearly 20-30% cost to the design or sometimes even more.

If the estimator works closely with the design team and understands the various assumptions and quantifies any alternatives, then they should be able to help the team develop a more optimised estimate and present it as a base case. Add-on numbers could then be presented for the various onerous design requirements. This will better help the decision-making process as everybody would then understand what additional technical features they would get if they choose to spend more money.

The estimator has the right skills to act as an interpreter between the design team and the financial decision-makers and can take on the bigger responsibility during the early stages of any project to work closely with the design development team and present the estimate in a way that is more transparent to help the project being better understood and agreed during any stage gate reviews.

Note: These suggestions are based on my own experience and style of working and could be beneficial to other estimating professionals if they choose. Also note that the examples suggested here are very generic and not specific to any industry, and the fundamental concept discussed should be relevant to any industry and project.

This article was published as an opinion piece in May-2019, in the Project Control Professional which is the journal of The Association of Cost Engineers.