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Edinburgh Research ExplorerCorrelating the chemical engineering plant cost index withmacro-economic indicatorsCitation for published version:Mignard, D 2014, 'Correlating the chemical engineering plant cost index with macro-economic indicators',Chemical Engineering Research and Design, vol. 92, no. 2, pp. 2Digital Object Identifier (DOI):10.1016/j.cherd.2013.07.022Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublished In:Chemical Engineering Research and DesignGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 15. Mar. 2021

12Correlating the Chemical Engineering Plant Cost Index withmacro-economic indicators34Dimitri Mignard5School of Engineering, University of Edinburgh,67Edinburgh EH9 3JK, Scotland, UKD.Mignard@ed.ac.uk, Tel. 44 131 651 The Chemical Engineering Plant Cost Index (CEPCI) is widely used forupdating the capital costs of process engineering projects. Typically,forecasting it requires twenty or so parameters. As an alternative, we suggesta correlation for predicting the index as a function of readily available andforecast macro-economic indicators:n CEPCI (n) 0.135 CEPCI (k o ) exp A ik B Poil C , k ko with ko the first year of the period under consideration, ik the interest rate onUS bank prime loans in year k, and Poil the US domestic oil price in year n.Best fit was obtained when choosing distinct sets of values of the constantsA, B and C for each of the three periods 1958 to 1980; 1981 to 1999; and2000 to 2011. These changes could have resulted from the impact of the oilshocks in the 1970’s and very high interest rates in the 1980’s, whichperhaps heralded changes to the index formula in 1982 and 2002. The errorwas within 3% in any year from 1958 to 2011, and within 1% from 2004 to2011 after readjusting the weighting of the price of oil. The correlation wasapplied to forecast the CEPCI under different scenarios modeled by theEnergy Information Administration or predicted from oil futures contracts.282930Keywords: Chemical Engineering Plant Cost Index, capital costestimates, price of oil, interest rates, inflation31321. Introduction33341.1 The Chemical Engineering Plant Cost Index3536Process engineers often require to forecast or update the capital cost of newplants as a function of historical data on plants that were previously built.1/24

1234Cost indices are available for estimating the escalation of costs over theyears, from a year m where the known or estimated cost is Cm and the indextakes the value Im, to a year n where it is Cn and the index takes value In. theprojected cost in year n is then56Cn ( In / Im ) x CmEqn (1)78910111213141516Several indices are available to the process engineer; for example theNelson-Farrar Refinery Cost Index published in the Oil&Gas Journal is widelyused in the oil and gas industry; the Marshall and Swift equipment cost index,which was published monthly in Chemical Engineering until April 2012 and isnow made available online (Marshall & Swift/Boeckh, LLC, 2013) is intendedfor the wider process and allied industries (chemicals, minerals, glass, power,refrigeration etc.); and the Process Engineering Plant Cost Index publishedby the UK monthly Process Engineering provides data not just for the UK butalso for 16 other OECD countries.1718192021222324252627However, it seems that the best known process plant cost index worldwide isthe Chemical Engineering Plant Cost Index (CEPCI), which has appearedevery month in the publication Chemical Engineering since 1963. Although itis primarily based on US cost data, the relative lack of local and specialisedcost indices for the process industries amongst the countries in the world(according to The Institution of Chemical Engineers, 2000) might explain itswidespread adoption. The dominance of the US dollar as an internationalcurrency has also favoured the use of an index based in the US. Often, theCEPCI is used alongside a location factor to transpose the estimate from onecountry to another.28293031The CEPCI is a composite index, made up from the weighted average of foursub-indices, and currently calculated from the following equation:CEPCI 0.50675 E 0.04575 B 0.1575 ES 0.290 CLEqn. (2)323334where E is the Equipment index, B is the Buildings index, ES is theEngineering and Supervision index, and CL is the Construction Labour index(Vatavuk, 2002).35363738The Equipment index E itself is in fact a weighted average of sevencomponents, including: Heat exchangers and tanks; process machinery;pipes, valves and fittings; process instruments; pumps and compressors;electrical equipment; structural supports and miscellaneous.3940414243In turn, each sub-index is the weighted average of sub-indices, derived frommonthly Producer Price Indices (PPIs, that are compiled by the USDepartment of Labor’s Bureau of Labor’s Statistics (BLS) from about 100,000price quotations issued by about a quarter as many domestically producingcompanies. Sub-indices or components for which labour costs have a2/24

123456789significant influence are discounted by multiplying their labour costcomponent by a productivity factor (calculated from an average yearlyincrease of 2.2% in productivity since 2002). Baselines are taken as valuesof 100 in 1957-1959 for the composite CEPCI and all four sub-indices(Vatavuk, 2002). Finally, although the CEPCI underwent overhauls in 1982and 2002 which affected the selection of PPIs, the productivity factor and theweighting coefficients in equation (2), it remained unchanged in its basic formand adjustments were made to provide revised indices in years prior to thechanges (Vatavuk, 2002).10111.2 Forecasting the Chemical Engineering Plant Cost Index12131.2.1. Micro-economic approach14151617181920212223The composite make-up of the CEPCI suggests that forecasting it requires apiecemeal approach to each of its four components as per Eqn. (2), giventhat each component is likely to respond differently to factors such asinflation on raw materials, productivity gains, labour costs, etc. In turn, eachcomponent could be disaggregated into the relevant sub-indices from whichit is made. However, when taken too far, this disaggregation can becomedifficult. All 53 PPI inputs would require tracking and forecasting, not tomention the added inconvenience that at times some of the PPI componentscan be modified or even discontinued by the BLS.242526272829303132333435363738394041These difficulties would suggest using a reduced number of sub-indices asproxies for the whole set. This ‘micro-economic’ approach was firstadvocated by Caldwell and Ortego (1975), who proposed a surrogate indexthat could track the CEPCI by using only five BLS indices: four wholesaleprice indices (metal tanks; general purpose machinery and equipment;electrical machinery and equipment; and processing materials andcomponents for construction), and one chemical engineering labour index.Earl (1977) found that Caldwell and Ortego’s index failed to keep up well withhistorical data after 1974, and advocated a more disaggregated approach.He kept the main sub-indices and their respective weightings in the CEPCIbut substituted 24 variables for the 70 or so that the CEPCI was then using.Importantly, he selected the 24 proxy variables from those amongst theBLS’s PPIs for which both historical records and forecasts were available.This basic approach appears to have been retained in modern practice: forexample Hollmann and Dysert (2007) quoted that in their experience, nomore than 20 or so relevant proxies are applicable to estimating costescalation of a process plant.42431.2.2. Macro-economic approach443/24

1234567891011121314151617181920As an alternative to the disaggregation method, straightforward prediction ofthe CEPCI from more general economic indicators on the cost of materialsand labour could also be attempted. Cran (1976) suggested two componentindices as effective proxies for major construction engineering indices,including the CEPCI. The two indices that he proposed tracked the costsassociated with steel and labour respectively, with the proxy index a weightedaverage of the two. He found that the resulting index was following theCEPCI pretty closely. However, these correlations may then become toosimplistic to withstand major changes in technology, productivity, market orother macroeconomic factors. In the same year as Cran’s paper, StyhrPetersen and Bundgaard-Nielsen (1976) observed that his two-componentindex could not account for productivity gains in assembling plantcomponents, leading to an overestimate for the capital cost of plants inWestern Germany between 1973 and 1975. In spite of its flaws, Cran’sapproach was followed by the PEI index, which was published by the journalProcess Economics International for 36 countries, and formerly called theEngineering and Process Economics (EPE) index. Styhr Petersen andBundgaard-Nielsen also suggested that to a lesser extent other multicomponent indices would be affected in a similar manner, including 142434445464748Nevertheless, the idea that wider macro-economic data can be the sole inputparameters is attractive because of the wide availability of data and forecastsfor these. In fact, the wider economic activity is not just indicated by the costof materials and labour as in Cran’s model, but can be linked with moregeneral indicators. This type of approach seems to have been initiallyadvocated by Caldwell and Ortego (1975), as an alternative to their ownmicro-economic approach. They found that simple linear correlations heldbetween the CEPCI and any of the following: the Gross National Productdeflator; the Consumer Price Index; the Wholesale Price Index; and otherprice indices. In all cases the slope of the correlation was close to 1.However, they observed that the actual values of the CEPCI significantlyswung cyclically above and below the values predicted by those simple linearcorrelations. Since then, literature on the topic of correlating the CEPCI withmacro-economic indicators appears extremely scarce. A more recentexample that we found regarded the Nelson-Farrar refinery cost index ratherthan the CEPCI, but it evidenced again the type of difficulty Caldwell andOrtego faced when trying this type of approach: Parker (2008) presented agraph where he plotted the fuel cost index against the construction cost indexof the Nelson-Farrar refinery cost index from 1930 to 2007. While on alogarithmic scale the construction cost index seemed to be a broadly linearfunction of the fuel cost index with a slope of 1.00, there were wide swingsaway from this parity ratio, with vertical and horizontal segments indicatingperiods of rapid surges and drops of one factor apparently independentlyfrom the other. The two indices were correlated to some extent, but theywere visibly subject to different influences too. As we shall see later, this maybe explained by the fact that correlations are not immediately apparentunless at least two parameters are considered, and the right selection of4/24

12these parameters is made, including careful appraisal of their degree ofmutual 5262728In fact, econometric methods have been developed since the 1970’s outsidethe field of engineering that more generally model economic variables. Agood introduction to these methods for the non-specialist can be found in(Koop, 2000). Of critical importance to these methods is a rigorous handlingof time series, in particular with respect to the autocorrelation of thevariables, which is the influence that the past values of a variable have on itscurrent value. Another critical aspect of these methods lies in the avoidanceof spurious correlations of trended variables (i.e. variables that tend to eitherincrease or decrease monotonically with time), which will inexorably occur asthe sample size of the series increases (if only as the ratio of the averagerates of change with time of the series). In fact, Caldwell and Ortego’s as wellas Parker’s seemingly good correlations (op. cit.) may have been affected bythis flaw. Spurious correlation can often be resolved by differencing thevariables, however testing for its presence (denoted by the existence of a‘unit root’, i.e. the observed variable being correlated with its lagged valuewith a slope of 1) requires appropriate statistical testing which is not alwaysconclusive if root values are close to 1. In the end, models may be obtainedthat predict the observed variable as a function of its past values as well ascurrent and past values of the explaining variables, each variable and itslagged values being tested for statistical significance and retained ifappropriate. From a practical viewpoint, building and testing these modelsrequire specialised software (e.g. Microfit or Stata ). They may also requireas many adjustable parameters as there are variables, including laggedvalues, thus potentially being as cumbersome as the models derived fromthe micro-economic approach.293031323334Therefore, it is the aim of this paper is to present a simpler approach that canbe readily used by engineers without the requirement for specialist tools;takes into account the influence of past values but with a very small set ofadjustable parameters; and still allows effective modelling and prediction ofthe CEPCI.352. Methodology363738Values for the CEPCI from 1958 to 2010 were taken from Vatuvuk (2002)and Chemical Engineering, (2009) and (2012).394041424344From consideration of the process of constructing a plant, we first determinethe likely macro-economic factors that seem to impact directly on the capitalcost of chemical plants: firstly, finance costs when paying for the project;secondly, market forces such as the balance of supply and demand ofmaterials, equipment, and even labour during design and procurement,5/24

123456contracting and construction; and thirdly, labour productivity and costs duringdesign, construction and commissioning. (We chose to neglect other factorssuch as taxation and subsidies as they are more site-specific, particular to agiven state or region of the world.) The interdependence of these factorsmeans that care is required in selecting macroeconomic indicators that willact as independent parameters in a model for the CEPCI.782.1 Financial costs91011121314Finance costs play a critical role in the construction of process plants. Prior tothe decision on whether or not to invest in the plant and build it, they aretypically factored in as “cost of capital” for the purpose of calculating a NetPresent Value (NPV). In order for a project to be viable, the NPV must be ashigh as possible, highlighting the prime importance of financing costs to theindustry.1516171819202122The considerable extent to which financial costs have an impact on costescalation has been known for a while. Often ‘real’ interest rates in whichinflation has been discounted are used in NPV calculations when inflation isnot explicitly applied to the data. However in this paper we are seeking tocorrelate an inflation indicator (the CEPCI) with interest rates, and thereforewe wish to exploit this relationship, rather than nullify it through the use of a‘real’ interest rate. For this reason, we only consider uncorrected interestrates.232425262728293031The question is then, what is the observed relationship between inflation andinterest rates in historical data? Back in 1981, Remer and Gastineauremarked that interest rates (taken as US AAA corporate bonds) and inflation(taken as the rate of increase of the EPE index) tended to cancel each otherout for the purpose of calculating NPV on engineering projects, due to acertain degree of correlation between the two. We found that this still appliedto some extent throughout the period from 1958 to 2011, for example wefound a linear regression coefficient R2 0.19 between the CEPCI inflationrate and the yearly averaged rate on prime loans, as shown in Figure 1.323334353637383940414243This result is not surprising. It can be expected that any rise in the cost offinancing will affect the CEPCI at several levels, from the costs to thecompany commissioning the plant to the cost of contractors and equipment,with everyone passing on their financing costs to their customers unlesscompetition is significant and margins are wide enough to cushion any rise ininterest rates. Conversely, market forces will also influence the cost offinancing: depending on inflation figures, Central Banks like the FederalReserve in the US will sell or buy back securities on the open market and incompetition with private investors. While their mandated aim in doing so is toachieve an interest rate that they have set, ultimately the intendedconsequence is to keep the economy within a safe and fairly narrow windowof inflation by controlling the availability of money.444546When looking for a suitable indicator for finance costs, we considered boththe rates on US AAA corporate bonds (long term) and the rates on US primeloans (short term), the data being collated by the Federal Reserve Bank of6/24

1234St. Louis and found on their website (Federal Reserve Bank of St. Louis.2012a) and b)). Both rates are expected to be representative of the rangethat would be available to industry. In this work, we tested both asparameters, and settled for the one that gave the best fit correlations.5678910111213141516171819202122232.2 Market forces and the price of oilAlongside financing costs, market forces like the balance of supply anddemand for raw materials, for plant components and for labour are expectedto affect prices significantly. While we have just seen that finance costs areconnected to some extent to market forces, we found that the yearly changein the price of oil seems to bear no apparent correlation with interest rates(for example, R2 0.0004 with rates on prime loans from 1958 to 2011, as2shown in Figure 2; and still R 0.014 when replacing the yearly change inthe price of oil by the yearly % change in the price of oil). Therefore, wechose the price of the barrel of oil as our second indicator, as a major drivingforce for inflation that will gauge the state of the market fairly independentlyfrom interest rates. The historical data for US domestic crude oil prices wastaken from (Illinois Oil and Gas Association, 2012), however otherbenchmarks could also be used (e.g. Brent or WTI). For the same type ofreason that we chose to use raw interest rates rather than real interest rates,the yearly averages for the price of oil were taken without discountinginflation, i.e. we believe that the CEPCI being an escalation index it may aswell be accounted for by inflation in the price of its contributing factors.24252.3 Productivity and the cost of labour2627282930313233343536Finally, we also attempted to consider productivity and the cost of labour as afactor influencing the CEPCI. The relevant index that combine these twoelements is the unit labour cost (in US labour cost per US output)published by BLS. However, we found some degree of linear correlationbetween the % change over a year of the unit labour cost and the interestrates (R2 0.45 over the perio

11 which was published monthly in Chemical Engineering until April 2012 and is 12 now made available online (Marshall & Swift/Boeckh, LLC, 2013) is intended 13 for the wider process and allied industries (chemicals, minerals, glass, power, 14 refrigeration etc.); and the Process Engineering Plant Cost Index published 15 by the UK monthly Process Engineering provides data not just for the UK .

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