aace cost engineering journal ce15-01

www.aacei.orgENGINEERINGENGINEERING

January/February 2015

THE JOURNAL OF AACE® INTERNATIONAL -THE AUTHORITY FOR TOTAL COST MANAGEMENT®

COSTCOST

FORENSIC SCHEDULE ANALYSIS METHODS:RECONCILIATION OF DIFFERENT RESULTS

RISK ANALYSISAT THE EDGE OF CHAOS

INTEGRATED PROJECT REPORTING

USING DASHBOARDS:HARNESSING THE POWER OF PRIMAVERA P6

INTEGRATED PROJECT REPORTING

USING DASHBOARDS:HARNESSING THE POWER OF PRIMAVERA P6

1COST ENGINEERING JANUARY/FEBRUARY 2015

4 Integrated Project Reporting Using Dashboards: Harnessing the Power of Primavera P6 John W. Blodgett and Brian Criss, PSP

13 Forensic Schedule Analysis Methods: Reconciliation of Different Results

John C. Livengood, CFCC PSP and Patrick M. Kelly, PE PSP

28 Risk Analysis at the Edge of ChaosJohn K. Hollmann, PE CEP DRMP

CONTENTS

TECHNICAL ARTICLES

COST ENGINEERING

2 AACE International Board of Directors 2 Cost Engineering Journal Information 27 Sam Griggs Awarded ICEC Fellow 38 SOMA Beirut Residential Project 41 Professional Services Directory

41 Index to Advertisers 42 AACE International Online Store 44 Calendar of Events

ALSO FEATURED

2 COST ENGINEERING JANUARY/FEBRUARY 2015

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Vol. 57, No.1/January/February 2015

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CONTENTS

O rganizations striving foreffective project controlsare consistently lookingfor better ways to

communicate among the variousstakeholder groups. Typically, withinorganizations, one finds that theproject controls umbrella actuallyentails the involvement of severalgroups within the organization, eachwith their own specific agenda (orcriteria for success by which they aremeasured), as well as, softwareapplications used to meet these goals.The challenge for executives and

project controls managers alike is theinherent desire to consolidate theinformation captured and maintainedby these various groups within theorganization, (See figure 0). The common reference heard inreference to organizational structuresis the term “silos.” This often has anegative connotation; however, thisdynamic may often be seen as anecessity in order for the organizationto function properly as a whole.Charles Duhigg, in his book “ThePower of Habit,” examines themakeup of large companies [1]. In his

book, he discusses the makeup ofcompanies as less than a single whole,but rather as a fiefdom of varioussmaller components of theorganization. These differingcomponents of the company haveevolved independently over time asare the rules (both written andotherwise) by which these differentgroups interact with each other.Further, Duhigg maintains that suchseparation of these groups is essentialto the overall efficiency of theorganization and allows for the day-to-day work to get done without elevatedlevels of bureaucracy to governdecisions. For project controls professionals,this dynamic is often the central areaof concern which needs to beovercome. Namely, how best toachieve interoperability among toolsused by the various internal interestgroups to arrive at a consolidation ofinformation by which decisions maybe made in a timely manner? Often,very manual and disjointed reportinghas been developed and leads toconflicting “your spreadsheet vs. myspreadsheet” dynamics. In addition, project controls oftenseeks to integrate various toolstogether so that they can seeconsistent data across the varioustools. There is the concept of the“system of record” for certain types of


TECHNICAL ARTICLE

Integrated Project ReportingUsing Dashboards:Harnessing the Power ofPrimavera P6John W. Blodgett and Brian Criss, PSP

Abstract:"Integrated reporting" can be accomplished by using reporting anddashboard tools that support accessing multiple data sources directly orthrough a data warehouse. The data sources can include Primavera P6 forplanning and scheduling; a financial tool such as SAP; and/or internal home-grown databases. The data warehouse is the "central" database containingdata with differing structures used in producing reports that incorporate thedata directly from their auditable source databases. The report/dashboardsolution is supported by programming to align the data based on commonelements to provide enterprise level information, supported by individualsystem audit reports. A further objective is to move beyond siloed spread-sheet data by incorporating various sources into a single source of consoli-dated information. The data warehouse can also be used to store snapshotsof datasets in order to perform trending and analytics. This article will discussthe business case for developing integrated reporting dashboards; an exam-ple case-study of an existing dashboard developed and used by a utility com-pany; and alternative approaches, such as off-the shelf analytics offerings.This article was first presented as OWN.1670 at the 2014 AACE InternationalAnnual Meeting in New Orleans, LA.

Key Words: Analytics, integrated reporting dashboards, data, databases, Pri-mavera P6, planning and scheduling, SAP, and trends.

data (i.e., schedule or financial data),whereby people trust the data comingout of a particular system, and theyoften look for how that data “ties” todata from other systems. This can bedifficult because of differing datastructures in different systems. Forexample: a department’s budget maybe seen in SAP and a home-growndatabase, but if the report parametersand data structures are not consistent,the data will not tie-out. Thus, peoplelose confidence in project controlsability to provide accurate data. One method by which this aspectof effective project controls may beaddressed is via integrated reporting.This method seeks to harvestinformation from various systems forpurposes of reporting; however, thissolution does not attempt to insertdata from one system into another. Inshort, data can (and will) continue toexist within its existing application,maintained by the system owners,only now it will be combined withother data related to the same project. For purposes of this discussion,examination and examples will bepresented based on the use ofPrimavera P6 as the application usedfor project execution, SAP as thesolution for financial information, anda “home grown” internal database ofinformation related to management ofthe projects. These uniqueapplications are deployed andmanaged by various individuals andgroups within the organization andrepresent the “systems of record” forproject controls information.

The Problem Organizations seeking efficientand effective project controls arealways searching for new ways tomake information available to usergroups and decision makers. Thisinformation is often held in differentsoftware applications and the ability toallow executives to see the “fullpicture” is often a challenging task.More often than not, an integratedsolution is desired by which systeminformation is passed to anothersolution via automated routines andcustom programming. Evidence of thisfact is found in the preponderance ofintegration tools on the market whichseek to provide organizations with“plug and play” utilities which connectunique systems in an effort to provideintegrated software tools. Systems of record are animportant concept for integratedreporting for the simple fact that allinformation disseminated in the formof reports or interactive dashboardsneed to “tie back” to a single source ofinformation. For those who seek tochallenge the project controlsleadership and objectives, this will beone of the primary methods ofvalidation. To produce a projectcontrols solution which effectivelydrives decisions and sharesinformation, confidence in the datawhich is being reviewed is vital to longterm success. When informationpresented in reports or shared inmeetings cannot or does not align withthe system of record, the end resultmay be further division within theorganization, leading to slow ormisinformed decisions, inefficientlabor intensive data crunching, andultimately, ineffective project controls. Many organizations use PrimaveraP6 as the execution arm of the overallproject controls environment.Primavera provides a databaseplatform by which projects schedulesare maintained. Fundamental to theseproject schedules is the use ofactivities and logic to drive keymilestones for execution of the work;and these schedules, in turn, areupdated on a routine basis (i.e.,monthly, weekly, or daily depending

on the direction of management).Additionally, Primavera P6 providesthe ability to code projects to reflectunique elements such as where thejob is scheduled to take place, andwho is managing the work. Theseproject codes provide an excellentsource of information in theestablishment of portfolios of projects,which allow project controlsorganizations to break down projectsand information into meaningfulgroups for reporting and analysis. Onechallenge is that Primavera hasevolved from a single project localenvironment, to an enterprisedatabase environment. As a result, theP6 database was configured to clearlydistinguish between project, activity,and resource types of data. The netresult of this evolution is that reportsgenerated from within the applicationcannot easily traverse these uniquetypes of data. For example,management wants to see reportswith a single line per project. Thesingle line combines project level data,such as project name, project managername, Location, etc.; with activity leveldata, such as construction start dateand construction finish date. Noproject or activity level layouts nativeto Primavera are capable of this.Oftentimes, Primavera experts arecalled on to develop macro work-arounds in Excel reports to bringactivity milestone dates up to theproject level. Cost information is primarily thecriteria by which most organizationsjudge the effectiveness or inadequacyof their project controls solutions.Financial information related to aproject has many unique aspectsbeyond just a simple budgetedamount. Rather, organizations oftenhave a desire to track much morediscreet information such as a life ofproject budget (multi-year), FY annualbudget, amount funded, multiplefunding mechanisms, committedcosts, contingency, and amountauthorized to spend. This informationis often included within the system ofrecord for the simple fact thatsomeone within the organizationbelieves this data to be important and


Figure 0 — Example of “Silos”Organizational Structure

those responsible for establishingeffective project controls solutionsmust, in turn, find a way to addressthis in the overall reporting ofprojects. In large organizations, thefinancial system of record is often alarge Enterprise Resource Planning(ERP) application such as SAP orOracle. Most of these ERP systems arebuilt well to handle companyfinancials, logistics, purchasing, andoperations; but they are not leading inthe area of capital project controls. Integration of information fromsystems of record entails that thedesired information be exchangedbetween two unique systems. That isto say, there is a desire on the part ofthe organization that when data inone system is updated or changed thatthis, in turn updates fields in anotherproject controls software application.

This interoperability is a technicalreality given today’s project controlssolutions and open architecture(integration API and web servicesfunctionality) found in many of thesoftware products, includingPrimavera P6. In order to achieveinteroperability between two systems,there needs to be an effort to mapdata between the two systems. Theprocess of data mapping entails thatthe receiving application provides aplace for the integrated data to reside.For example, when looking atintegration of financial informationinto P6, this information needs topopulate within the P6 database aseither a cost value on a resource, anexpense item at the activity level, or auser defined field (UDF). These wouldrepresent the typical places which a

cost value would be representedwithin the application (there may alsobe options to explore for acceptingcost data at the project level). The key question often ponderedduring the data mapping process iswhether or not the integration istransforming the receiving applicationfrom its intended purpose? PrimaveraP6 is an excellent tool for planning andforecasting of costs and unitinformation from activity data. But isthere value in attempting to transformP6 into a tool with the same robustaccounting information as thefinancial tools from which it may bereceiving information? Is the productbeing migrated from its intended use?Again, the key driver for this decisionis the organizational desire to achieveconsolidated sources of information


Figure 1 — Portfolio Level Data Sample (1)

Figure 2 — Portfolio Level Data Sample (2)

with reduced effort in handling theinformation. A key example of this is the use of“Store Financial Period Performance”in Primavera P6, which must be done

project-by-project, and cannot bedone en masse for the enterprise atthe end of each month. This poses achallenge of getting hundreds orthousands of projects all synced up

with actual costs, and having multiplepeople trying to do Store PeriodPerformance all on the same date withthe same accuracy as that which isalready produced by the controlled


Figure 5 — Project Page View (2)

Figure 4 — Project Page View (1)

Figure 3 — Project List View

financial close of the company’s ERPsystem. Another consideration is whetheror not the organization will requiremore resources to handle theincreased data which needs to bemaintained within a system like P6 tofacilitate the integration. Often,integration routines are predicated oncoding information (project, activity,or resource coding). Depending on thescope of the integration pondered,there is a strong likelihood that the keyintegration codes will need to bemaintained by P6 users on the front-

end of the application. This will place agreater emphasis on quality controlmaintained within a system like P6 toprevent the integration from failing atroutine intervals. After all, integrationsolutions essentially replace whatfront-end application users would doto align the information, andautomate the process; however, theroutines do not usee judgment. Theroutine is established to executespecific tasks at triggered intervals,and as such, relies on the structure ofthe information found in eachapplication to perform properly.

Recommended Solution Integrated reporting provides amethod for delivery of the corerequirements to provide centralizedreporting for the project controlsorganization. The idea is similar to theconcept of integrated applicationdata; however, the difference is thatthe data does not actually transfer toapplications outside of the system ofrecord. In this method, the financialdata is entered and continues toreside in SAP, and schedule(milestone) data continues to residewithin Primavera P6. This method


Figure 6 — Primavera Notebook Topics

Figure 7 — Data Quality View


allows for unique softwareapplications to continue supportingthe lines of business, theirconstituents, and the desired use casewithout having to compromise theway they are used in order to facilitatesoftware interoperability. The use of custom programmingis still a requirement in thedevelopment of an integratedreporting solution. Namely, theobjective is to create automatedroutines to mine the desiredinformation from the unique systemsof record. In the case of Primavera P6,web services are used to generate anightly ETL (extract, transfer, and load)into a data warehouse. A similarprocess will be required for datacoming from other systems with theobjective being to create automatedroutines which allow for data to berefreshed on a regular basis.Additionally, the use of a “flat file”transfer to a secure FTP site may alsosubstitute for the use of a webservices extract of financial data(Note: direct access to financialsystems if often tightly controlled andthe preference is a file based transferof information as requested by theproject controls organization). Further,the owners of the data within the ERPare often very nervous and resistant toproviding data outside the system. Theproject controls organization will needto go through a process to explainwhat they are doing with the data andwhy it’s needed. There will often be apush to bring data into the ERP systemand use the ERP system’s datawarehouse and reporting tools. Thiscan be something to consider,however oftentimes, there is a muchlarger volume of project data thatresides outside of the ERP (scheduledata, text status fields, and riskmanagement information), so it doesnot seem efficient to push a largevolume of data into the ERP system. Inaddition, there are often tightercontrols around report developmentand modification within the ERPsystem. In order to facilitate an integratedreporting platform, a unique project

identifier must be present to make thesolution viable. Ideally, this should bea consideration when setting up thePrimavera P6 database andspecifically, how the projects will beidentified in P6. When the informationis compiled within the datawarehouse, there will need to be acommon method of aligning the P6project data along with the financialinformation. To best facilitate thisoperation, it is useful to evaluate fieldswithin the software applications asneeding to be unique. In the case ofPrimavera P6, the project ID field is arequirement for all projects andcannot have duplicates within thedatabase. Once the methods of transferringdata from the systems of record intothe reporting data warehouse areestablished, the next step is thedevelopment of the actual reportinginterface. The actual delivery of thereporting solution is one area whereorganizations have a great deal offlexibility. The organization should, atthis point, be prepared to addressquestions related to the overall designof the interface including usabilityoptions and data display. Depending on the size of theorganization, and the various roles ofthose using the integrated reportingsolution, the interface should bedesigned with the idea that it willneed to provide flexibility and use forall members of the organization.Executive level personnel will oftenhave a desire to consume informationat a summary level with graphicaldisplays which provide actionableitems and KPI’s for the organization.However, in order to achieve themaximum benefit for the organization,the integrated reporting solutionshould also possess the ability toprovide detailed information relatedto specifics of the projects such asdetailed cost data, schedule andmilestone deliverables, as well aswritten text information from thoseresponsible for the project delivery. To address these variable levels ofreporting detail, design of an interfaceto allow users of all levels within the

organization to navigate to the mostbeneficial level of information isessential. A quality integratedreporting solution should allow forhigh level information to be traverseddown to the lower levels of meta datato perform root cause analysis. Thiscapability to “drill down” from highlevel metric information to lowerlevels of detailed data should be anoverall requirement for any integratedreporting solution. For the solution developed as partof this example, the overall solution isarchitected to provide 3 levels ofinformation:

1. Portfolio Level—This is theprimary landing page for users. Theobjective is to display aggregateproject level information related toschedule and costs. This level of detailis controlled via filter parameters toallow for projects to be displayed inmeaningful groups. These filterparameters are based on the projectcode assignments in P6. The portfoliolevel is primarily graphical andintended to provide intuitive dataacross multiple projects.

2. Project List View—This levelrepresents the second level of data fordisplay to users. The projectsrepresented are based on theportfolio level selections and theproject list view is intended to providegreater levels of detail when selectinga portfolio graphic. This view providesmuch greater levels of detail viacolumn data which can be customizedby the user at run time. The ability tosort and show/hide selected columnsare available, as well as the ability toexport the information into excel.

3. Project Page View—Theproject page view displays bothgraphical, as well as narrative data(based on P6 notebook topics) for asingle project. The ability to traverse projectinformation within the application isan essential element to achieve thedesired adoption. By building thesolution to accommodate the


intended use case for executive level,as well as PM level consumption, theintegrated reporting solution willprovide significant return oninvestment. The use of project level notebooktopics in P6 is a good way to getdifferent categories of textual statusfor projects. Different notebook topicscan give status of project elementssuch as environmental, permitting,variance explanations, etc. Projectmanagers like to keep a history ofstatus updates, so in order to preventa long historical narrative fromprinting in your current status report,a different notebook topic can be usedto store the archive status. Themovement of current status report toarchive can be a simple copy and pasteby the project manager during eachupdate. This process could also beautomated through the dashboardfront end. A further functionality that wasdesired in this dashboard solution wastrending data. Managers wanted toknow what changed from the last timethey saw the data. The dashboardemployed a data warehouse capabilitywhere snapshots of the dataset weretaken every night. This allowedmanagement to easily comparecurrent data to any past period downto the day, in order to see whatchanged (see right side graphic paneon figure 1 for an example of thistrend data). All of the trend graphicsoffered drill-downs to lists thatshowed the old data details on top ofthe new data, so it could be easilyseen what was different at any givenreporting period. There are many differenttechnology alternatives available toproduce integrated reporting anddashboards. The technologyemployed on this particulardashboard solution is just one of theoptions for integrated reporting. Thisparticular dashboard was built usingRuby on Rails JavaScript typeprogramming with a SQL database.Highcharts JS and Ext JS were used forgraphics and tables. Web services wasused to integrate pulls from various

data sources. Future-proofing wasaccomplished through the use ofHTML5 and CSS3. Other programminglanguages and technology availableinclude Python, Tableau, and Pentaho. Most ERPs, including SAP andOracle also employ native reportingand analytics systems: SAP BusinessWarehouse and Business Objects 4.0and Oracle Business Intelligence (BI)11g. Primavera P6 Analytics 3.0 is builton Oracle BI 11g. These systems offergreat graphics and analytics capability,but did not allow for the sameflexibility and control that was desiredfor this particular dashboardapplication. None of the project costdata was being integrated intoPrimavera P6, so that also limited theuse of Primavera P6 Analytics.Historical data snapshots were alsonot available in Primavera P6Analytics. SAP Business Objects wasnot used because most of the projectdata resided outside of SAP, and itwould have been a much larger datatransfer to get data into SAP, ratherthan the current solution thattransfers data out of SAP.

Lessons Learned In the development of theintegrated reporting solution, the ideaof “horizontal” traceability is animportant factor. Horizontal refers tothe idea that a unique project iscommonly identified across all of thedifferent software applications fromwhich data will be used in thereporting solution. In this specificinstance, the SAP generated ordernumber was used as the project ID inP6 and is also referenced within thehomegrown project database system.What this provided was a singleunique identifier across all systems,even if they were used differently inother applications (i.e., not necessarilythe project ID). The key considerationin deciding what identifier to use iswhether the system will allowduplicates. Only the project ID inPrimavera P6 will not allow duplicates.When developing the output forreports and consolidating the data,programmers may more easily align

the SAP, project database, and P6 databy referencing this field. Should there be a lack oftraceability across the other systems,the net result would have been amuch slower development time alongwith additional costs for developmentof translation tables to align dataprogrammatically. When establishingproject controls software applicationswithin any organization, it is a goodstrategy to evaluate how financialsystems identify projects and makecertain that a project found in othersystems (such as P6), may be easilytied back to the data for that project inthe financial system. Another key lesson learned isaround data quality in Primavera P6.The group was very new to PrimaveraP6, and data quality issues werepresent and hard to detect. Thedashboard exposed these issues in avery public way, which led toimprovements because people did notwant to be embarrassed with blanksor incorrect data. Managers used thedashboard on their tablets inmeetings, which helped encouragethe proper use of Primavera P6.However, good CPM schedulingpractices were not a strong skill set inthe group; therefore some quick andeasy data quality metrics weredeveloped to expose incorrect CPMpractices. A dashboard page wasdeveloped that grouped individuals bythe number of activities “riding” thedata date, and the number of out-of-sequence activities. Claim digger,schedule logs, and reports aresometimes used for this, but thedashboard gave the capability toquickly access this information acrossthe entire portfolio, grouped andranked by individual. This allowed thedata quality analysts to focus theirattention on acute problem areas andpeople who needed additionaltraining and support.

One key aspect of the overallreporting solution is to clearly identifywho is the target audience for theinformation. For example, datarequired by executives within the


organization to make informeddecisions may require high levelsummary information which may bequickly consumed and graphical innature. On the other hand, projectmanagers may desire far greater levelsof detail in the tracking and managingthe day-to-day operations of theproject. The key point here is toconsider the audience for each of theelements represented in theintegrated reporting solution. Theseconsiderations may be seen as part ofthe overall scoping exercise whenorganizations are developing thecontent and delivery methods ofreports (as well as data required forinclusion in the data warehouse). If the chosen method of deliveryis to have the reporting solutionaccessible via a web browser, then it isimportant to know what thesupported web browser is for theoverall organization. If the reportingsolution has developed customgraphics and views, it is important thatthose be developed for use with theproper browser. Failure to realize thisafter the fact may cause delays withthe production deployment asmodifications to the programmingcode used in the development of thereporting solution may requiremodification for earlier browserversions. For large organizations, it isnot uncommon to see older browserversions deployed since the testing ofnewer versions by the organizations ITstaff may be time consuming. Inaddition, users are unlikely to haveadministrative rights on theirmachines to download or use newerbrowser versions so the lesson here isto make sure the development of thereporting solution is in alignment withthe supported browser which willoperate it. Another key consideration is tohave a browser-neutral orobsolescence considerations. Thesolution should have the ability toeasily work on tablet computers, aswell as traditional desktops. Whilecompanies are allowing bring yourown (BYO) devices for tablets andsmart phones; often the access to a

company’s intranet is restricted orinvolves an extra login step via aremote access solution. An externallyhosted reporting system can helpmake this access easier.

Conclusion Integrated reporting through theuse of dashboards and analytics toolsis quickly becoming as important asimplementing the tools themselves.The employer of one of this article’sauthors recently presented on the factthat their last three employee hireshave been programmers, as thedemand for integrated reportingsolutions increases. As tools, such asPrimavera P6 and Primavera Unifierbecome more powerful andintegrated, so too will the demand tointegrate them in other ways that thesoftware developer has not yetconceived of. In a large enterprise,integrated reporting plays aparticularly important role because ofvast, and often siloed groups usingdifferent tools and the need formanagement to see “one view of thework.” As technology improves, andvarious software enhancements arereleased, there will be more tools thattry to do more project managementfunctions. However, there are often“tool wars” within organizationswhere it is difficult to get groups to allagree on a one size fits all solution.Integrated reporting helps solve thatissue because reports and graphicscan consolidate information frommultiple, auditable data sources. Thenthe project controls group can becomemore focused on data quality andanalytics of the results, rather thanspending time manually consolidatingand scrubbing data into reports. This article presented just oneexample of how integrated reportingcan be done through a dashboard.There are many other optionsavailable. Future considerations couldbe given to building user-friendlyfront-ends and automations to toolsthat were once the realm of thehighly-experienced professional user.The use of large databases and newtechnology is making reporting and

graphical options available that arereally only limited by the imagination.With the right programming orsolution, companies can achieveintegrated reporting success that willlead to efficiencies and improvedemployee engagement with the tools.◆

REFERENCE1. Duhigg, Charles, 2012, Chapter 6,

The Power of a Crisis, The Powerof Habit, First Edition, Pages 223-226, Random House PublishingGroup, New York

ABOUT THE AUTHORS

John W. Blodgett iswith the Pacific Gas& Electric Co. Hecan be contacted bysending e-mail to:[email protected]

Brian Criss, PSP, iswith DR McNatty &Associates Inc. Hecan be contacted bysending e-mail to:

[email protected]

FOR OTHER RESOURCESTo view additional resources onthis subject, go to:www.aacei.org/resources/vl/

Do an “advanced search” by “au-thor name” for an abstract listing ofall other technical articles this authorhas published with AACE. Or, searchby any total cost management sub-ject area and retrieve a listing of allavailable AACE articles on your areaof interest. AACE also offers pre-recorded webinars, an Online Learn-ing Center and other educationalresources. Check out all of the avail-able AACE resources.

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Forensic Schedule Analysis Methods

F orensic Schedule Analysis(FSA) is the applied use ofscientific and mathematicalprinciples, within a context

of practical knowledge aboutengineering, contracting, andconstruction means and methods, inthe study and investigation of eventsthat occurred during the design andconstruction of various structures,using Critical Path Method (CPM) orother recognized schedule calculationmethods [5]. An analyst begins an FSAwith a review and analysis of the

planned construction sequencing inthe baseline model, calculation andanalysis of activity duration (withrespect to planned quantities,estimated resources, and productivitylevels), activity sequencing, resourcescheduling, and evaluation of thetrade-offs between cost and time. Theanalyst then, either by using theexisting model (CPM schedule) or bycreating mathematical or statisticalmodel, in order to analyze, in averifiable and repeatable manner, howactual events interacted with thebaseline model and its updates inorder to determine the significance of

a specific deviation or series ofdeviations from the baseline modeland their role in determining theultimate sequence and duration oftasks within the complex network [4].The form that the mathematical orstatistical model takes defines theanalysis “method.” AACE’s Recommended Practiceon Forensic Schedule Analysis, (RP29R-03), is a unifying technicalreference developed with thecooperation of dozens of experiencedFSA experts, and the analysesperformed for this article wereconducted in keeping with theprinciples and methodimplementation protocols (MIPs)described therein. There are nine MIPsoverall; however, the RP breaks themethods into four major families: theAs-Planned versus As-Built (APAB/MIP3.2), the Contemporaneous PeriodAnalysis (CPA/MIP 3.5, sometimescommonly called the “Windows”method), the Retrospective TimeImpact Analysis (RTIA/MIP 3.7), andthe Collapsed As-Built (CAB/MIP 3.9).The further breakdown of the familiesinto the nine methods – the MIPs – isdefined by factors such as timing ofthe analysis, whether the model relieson active CPM calculations or not,whether the model adds or subtractsfragmentary networks (“fragnets”) tosimulate the effects of delays, or

TECHNICAL ARTICLE

Forensic Schedule AnalysisMethods: Reconciliation ofDifferent ResultsJohn C. Livengood, CFCC PSP andPatrick M. Kelly, PE PSP

Abstract: Perceived wisdom within the construction industry is that differentForensic Schedule Analysis (FSA) methods produce different results on thesame set of facts. Although there are many potential variables that couldcause this, such as bias of the analyst or the quality of the implementationof a method, some experts have expressed concern that the methods them-selves generate different results, and therefore some may be potentially de-fective. But, do the different methods actually generate different answerswhen applied properly to the same set of facts, or are the observed differ-ences natural aspects of the methods that can be documented and quanti-fied? This article will explore that question by examining a specific set offacts and applying each of the four major FSA methods – the As-Planned vs.As-Built, Contemporaneous Period Analysis, Retrospective TIA, and CollapsedAs-Built – to those facts. Further, if the methods do generate different results,the article will explain how and why that occurs, how to quantify and recon-cile the differences, and what conclusions a FSA expert should draw fromthose differences. This article was first presented as CDR-1593 at the 2014AACE International Annual Meeting in New Orleans, LA.

Key Words: Forensic Schedule Analysis, construction, as-planned, as-built,contemporaneous period analysis, retrospective TIA, and collapsed as-built


whether the analysis is performedglobally or in periodic steps [6]. A definition of the term,“window,” is necessary to avoidconfusion between the many uses ofthe term. Although the term“Windows” is sometimes used as aterm to describe a specific analysismethod, it is important to understandthat a window is a slice of time in thelife of a project, within which theanalyst will use the selected method toexamine that window’s events. Mostof the methods can be implemented ina way that subdivides the projectduration into windows. The choice anddefinition of the periods of time usedto form the windows will bedependent on the circumstances.However, it is common practice totime the start and finish of thewindows to coincide with the monthlyprogress update and pay application.Occasionally, the start and finishpoints for windows are identified tocorrespond with specific delay eventswhich are of interest to the analyst.Although this is potentially valuable, itis inadvisable to have analysiswindows which are wider than theperiod encompassed by the progressupdates. The monthly update (or pay

application date, if no updates exist)should be the maximum width of awindow. In a CPA, wide windows (greaterthan a month) are undesirable. One ofthe major benefits of a CPA is to trackthe movement of the critical path,which is known to be variable basedon progress and evolving means andmethods. Using wide windows opensthe possibility that the critical path willundergo multiple shifts during thewindow and will not be cataloged bythe analyst. This would allow delays tobe misallocated to specific events andparties. A month-long window isusually the maximum width of awindow because of the fact that thepay applications—a useful back-checkon the state of progress to date—aregenerally submitted on a monthlybasis. One of the more importantdifferences between the forensicmethods relates to how it treats theproject management team’sunderstanding of the critical pathwork, and whether the contractor andthe owner used the schedules duringthe project to establish their beliefsregarding which work was drivingproject completion and then used that

knowledge to plan the upcomingperiod’s work. What the projectmanagement team knew is called its“contemporaneous understanding ofcriticality.” From the perspective of theproject management team that isproperly using their prospectiveschedules for planning and executingthe next period of work, theirknowledge of what was critical toproject completion (and therefore theexplanation of their actions at thetime) is related to the status of thecritical path at the time in question.Even in the case where future eventsshift the final as-built critical pathaway from an activity that wasconsidered critical at the time, theunderstanding of the projectmanagement team’s actions ispossible only by understanding whatthey thought was critical at the time. Amajor difference in the analysismethods involves whether (and how)they incorporate thecontemporaneous understanding ofcriticality. Some methods rely heavilyon the contemporaneous view ofcriticality, while others determinecriticality in a different way (such asthe determination or calculation of an“as-built critical path” which may or

Table 1—The Role of the Contemporaneous View of Criticality in FSA Methods


may not have a relationship to thecontemporaneous critical path). Theauthors and many commentatorsbelieve FSA methodologies thatreflect the contemporaneousunderstanding of criticality ispreferred [1] [20]. Table 1 provides ageneral overview of the role of thecontemporaneous view of criticality inFSA methods; however, the specificsof that role will be discussed in moredetail later. However, a contemporaneousunderstanding of criticality can onlyexist on projects that have a validcontemporaneous schedule series.The fact is that some projects haveschedule series that do not representthe contemporaneous planning.Sometimes such schedule series stemfrom an adversarial relationshipbetween the parties that developsduring performance of the work.These schedules are generallyunsuitable for use in a forensicanalysis [19]. Other projects do nothave schedules at all— even,sometimes, despite the fact that thecontract mandated their use [14]. Aswill be discussed, the perspective onthe contemporaneous understandingof criticality, selected by the analyst,will have a significant impact on theresults of the analysis. Regarding the need to avoidadversarial interests in the use ofschedules developed under anadversarial relationship, the author’sstate: “In Nello L. Teer Co., the Boardfound that the usefulness of a CPMschedule tends to become suspectwhen the contractor and the ownerhave developed adversarial interests.The Board noted that there are toomany variable subject to manipulationto permit acceptance of theconclusions of CPM consultants insuch circumstances. The Board alsonoted that this is not to say that theCPM analyses are not to be used inconnection with contract claims. Onthe contrary, they often are the mostfeasible way to determinecomplicated delay issues. However,the Board must have confidence in thecredibility of the consultants and the

cogency of their presentations. Inconnection with the testimony ofNello Teer’s scheduling expert, theBoard noted that the expertcontinually expressed conclusions asto construction management thatwere beyond any expertise that theBoard considered the expert to havedemonstrated.” The fact of whether or not acontemporaneous understanding ofcriticality was reflected in a scheduleshould be a factor in determiningwhich method is best for analyzing agiven project’s delays. For instance, ifa project had update schedulescreated by a scheduler off-site thatwere never reviewed by the projectmanagement team, it is probably notappropriate for a forensic analyst tochoose a method like the CPA/MIP 3.3to analyze that project. Conversely, ananalyst would likely be in error inselecting the APAB/MIP 3.2, whichdoes not inherently consider thecontemporaneous understanding ofcriticality, to analyze a project that hada good series of schedules used by theproject manager and superintendentto plan and execute the project. Inaddition to the other factors toconsider in selecting an appropriateFSA method, the analyst should alsoconsider how the schedules wereused and whether they influenced thedecision-making process duringexecution.

The Differences in Results A common criticism of the fourmajor methods of examining is thatdifferent methods applied to the sameset of facts yield different results.Several practitioners have previouslyexamined these criticisms [24].Although there have been variedresults from the studies, it is generallyaccepted wisdom in the industry thatthe four major methods returndifferent results when applied to thesame set of facts. This has created aperception in some that some or all ofthe methods are invalid. Furtherexacerbating the problem is the factthat professional practitioners of FSAseem apparently incapable of

explaining the differences andreconciling them, which can result inthe analysts engaging in a “battle ofthe scheduling experts” that doeslittle to efficiently resolve disputes. First, many of the problems withreconciling the results of competinganalyses stem from other, non-mathematical sources. Theseproblems include, but are not limited,to the incorrect selection of a method,the poor implementation of a MIP, orthe use of a schedule series that isunreliable, unverifiable, or otherwisenot capable of supporting a forensicanalysis. These factors continue tocause problems with disputeresolution where competing delayanalyses are involved; however, themethods proposed in this article arenot expressly designed to correct forthese factors. Instead, the authorsanticipate these methods beingchiefly used when two competentlyprepared analyses are in conflict as tothe existence, quantum, andresponsibility of delays. That being thecase, we do also anticipate thataspects of these methods could beemployed to identify a poor analysisand to highlight its deficiencies. An aspect of the FSA methodsthat is often disregarded in thisdiscussion, however, is that themethods tend to analyze the schedulemodel in different ways. The APAB, forinstance, measures “what actuallyhappened” by using hindsight tocalculate the As-Built Critical Path(ABCP) and measuring delays alongthis path. In contrast to this, the CPAmeasures what the project teambelieved to be critical as of a givenschedule’s data date, and the impactthat events had on thecontemporaneous CP. The shiftingnature of the CP is well documentedand understood, and the ABCP andthe contemporaneous CP may not bethe same. The CP shifts over time—sometimes between updates—until itultimately comes to rest on the finalday of the project. Therefore, ananalyst performing an APAB maydetermine that, for a given window,the project lost, for instance, 23 CD as


a result of activities on the APCP,whereas the opposing analystperforming a CPA would determinethat during the same window, theproject lost 30 CD as a result of anactivity on the contemporaneous CPthat does not ultimately appear on theABCP. This fundamental disagreementbetween methods is common, but notinsurmountable. In order to overcome theproblems caused by the differences inthe methods, we recommend acommon communication format: thecumulative delay graph. ”Cumulativedelay” is the number of days of delaythat have accrued through a givenpoint in time. In order to generate acumulative delay graph, one must plotthe number of days of delay that ananalysis shows the project to havesuffered as a function of each dateduring the project. The source and thefrequency of the data points for thecumulative delay graphs will varyslightly between methods. Mostnotably, the cumulative delay graphfor the APAB should be plotted as theDaily Delay Measure (DDM) graph[16]. For the CPA, RTIA, and CAB, thedays of predicted delay should beplotted as of the data date of theschedule at which the delay days areshown to have accrued. As will bediscussed further, the resulting graphcan assist in identifying reasons fordifferences in specific windows of theproject, thereby facilitating resolution. We see the cumulative delaygraph as part of a larger reconciliationprocess between methodologies. Forour comparison of the number andtiming of delay days generated foreach methodology, we haveundertaken the following seven steps:

1. The source data is validated as aprerequisite to method selection.

2. As part of the method selectionprocess, [7] the project recordsare examined to determinewhether the contemporaneousview of criticality should be aprimary determining factor indeciding which method to use. Aswith all parts of the method

selection process, this decisionshould be supported withevidence.

3. The causal activity for a windowmust be identified. The causalactivity should be determined onas frequent a periodicity as theanalysis method will allow.

4. The DDM line should be plotted.This line will serve as a baselinefor comparison of all the otheranalyses. The DDM will serve asthe cumulative delay graph forthe APAB analysis.

5. Each of the analyses is thenplotted on a cumulative delaygraphs. Each data point should bethe predicted completion date ofthe schedule as a function of thatschedule’s data date. We overlaidall the lines onto a single graph foreasy comparison.

6. Each window of the projectduration is reviewed, and thecausal activities identified by eachanalysis, and the amount of delaydetermined to have accumulatedas a result of that causal activityare noted. Similarities in thecausal activity and the quantumof delay allow for agreementbetween the parties andresolution of delay related to thatspecific window.

7. Differences in either causalidentification or in quantum wereidentified and explained. Thedifferences should be able to beexplained as resulting from thedifferences in the perspectives ofthe analysis methods.

The purpose of this procedure isto first and foremost underline thefact that there are documentable andquantifiable reasons why twocompetent analysts of the sameproject could return different results.This will not, of course, resolvedifferences in opinion about theunderlying reason why a causalactivity was delayed. If both partiesidentify the same activity and similarquanta, but have different opinionsabout why that specific activity wasdelayed and therefore apportion

responsibility differently, thisreconciliation process will not helpresolve that issue. However, if that isthe case, then the dispute is no longerabout the schedule analyses and isinstead properly concerned with thefacts of the case.

Creation of the Test Schedule Series The ability to reconcile the resultsof different methods hinges in part onan understanding of the normaldifferences that will be exhibited bythe cumulative delay graphs of eachmethod. In order to establish andanalyze these differences, the authorscreated a test schedule seriesconsisting of a baseline schedule, 37updates, an as-built schedule, and acollapsible as-built schedule. We didthis, rather than use an existingschedule series from a past project, toavoid as many of the problemsassociated with poor schedulingpractices as possible. Additionally, itallowed us to control the updateschedules and eliminate logicrevisions between the updates. The baseline schedule was basedon a hypothetical bridge constructionproject, wherein an existing bridgewith two separate spans was beingreplaced, one span at a time, withactive traffic shifted to the other span.The proposed maintenance of trafficplan mandated that a single span beopen to two-way traffic during theconstruction; therefore, the generalprocess for construction involvedswitching all traffic to the existingspan, demolishing the abandonedspan, construction of the new span,and switching all traffic to the newspan. The second existing span wouldthen be demolished and the secondnew span constructed in its place. Theoriginal baseline schedule containedover 432 activities, and had a Noticeto Proceed date of 1-Mar-2010, and apredicted completion date of 7-Jun-2012. In order to create the test seriesof schedules for use in this analysis,the authors took a copy of thebaseline schedule and created newdurations which would represent the


ultimate actual durations of theactivities. These durations werecreated based on a series oftheoretical productivity problems thata bridge project encountered. Thenew durations were input into thecopy of the baseline schedule, and thisschedule was recalculated as of theoriginal Data Date of 01-Mar-2010.The authors then created a total of 17activities that represented delays thatoccurred during this project. Five ofthese activities representedcontractor-caused delays (such asstart delays or rework issues) whilethe remaining 12 activitiesrepresented owner delays. These 17activities were tied into the networkof this schedule, with appropriatepredecessors and successors for theissue described by the delay activity.The schedule was recalculated, againas of the original data date of 1-Mar-2010. The new predicted completiondate of the schedule was 19-Apr-2013,or 316 calendar days (CD) after thebaseline predicted completion date.This schedule contained no datesassigned to the actual start or finishcolumns, and as a result the networkcalculations were driving all the datesand float calculations; however, it did

represent the actual progress of theproject. This schedule therefore wascapable of serving as the “CollapsibleAs-Built” schedule [9]. The Collapsible As-Built schedulewas used to calculate the As-BuiltCritical Path (ABCP) of the project, andwas also used in the performance ofthe Collapsed As-Built analysis. Tocreate the fully actualized As-Builtschedule, the authors appliedprogress across the entire project,thereby making the start and finishdates in the Collapsible As-Builtschedule into actual start and finishdates. This As-Built schedule had adata date of 1-May-2013. To create the test series of 37update schedules necessary forportions of this analysis, the authorsextracted the actual start and finishdates, and the actual durations, fromthe As-Built schedule, and input theminto a de-progression spreadsheet.This spreadsheet was designed toallow the user to estimate a remainingduration of an activity at a given pointin time. Therefore, we were able toenter the desired data date of the firstupdate schedule (in this case, 1-Apr-2010) and the spreadsheet wouldreturn a list of activities that would

have started and finished, as well as alist of activities that only would havestarted. For these activities, thespreadsheet also gave a remainingduration, based on an assumption ofstraight-line progress between actualstart and actual finish. The authorsthen copied the baseline schedule andimported the “actual starts, actualfinishes,” and remaining durations forthe activities that would have seenprogress during the update window.The schedule was then recalculated asof the new data date, and thepredicted completion date wasrecorded. This process was repeatedfor each of the 37 months for whichthe project was in progress. The schedule series was alsocreated with a “weather exclusionperiod” that was simply a non-workperiod in the calendar assigned toasphalt work. According to thecalendar, no asphalt work could occurbetween the start of the third week inDecember and the end of the secondweek in March. Any asphalt activitiesthat were pushed into this non-workperiod would immediately jumpforward three months, when theweather would presumably be warmenough to place asphalt. This is a

Figure 1—Combined Cumulative Delay Graphs


common technique in constructionschedules to represent periods duringwhich no work can be performed on atype of work for a specified period,and it has a magnifying effect ondelays. For instance, assume that in thetest schedule update for June, anasphalt activity is shown ascompleting in early December. Lack ofprogress in the window (June to July)creates a three week delay thatpushes that asphalt activity into theweather exclusion period. Because thecalendar with the weather exclusionperiod will not allow the asphalt workto start until mid-March, the threeweek delay that occurred in June hasnow become a three month delay. Thisis also a common source of dispute inapportionment of delays in a forensic

analysis, since in many cases there aremultiple parties responsible for thedelays leading up to the point wherethe weather exclusion period isaffecting the predicted completiondate. As that is the case, disputesoften arise over who is assigned themagnified delay that occurs when theschedule’s predicted completion datejumps across the wide non-workperiod. The 39 test series schedules thatwere originally created representedthe contemporaneous updates thatthe analyst would receive as theproject record schedules. Theseschedules were then copied (asnecessary) and used to implement thefour analyses. Clearly, the fourmethods require different schedulesfor performance: the APAB requires

only the baseline schedule and the as-built; the CAB requires the collapsibleas-built schedule; the CPA requires allthe schedules as they existed duringthe project; and the RTIA requires allthe schedules, as well as the fragnetsfor insertion into the schedules.

Creation of the Cumulative Delay The combined cumulative delaygraph is shown in figure 1. The blackline represents the DDM line,generated from the comparison of theas-planned dates in the baseline to theactual dates in the as-built. Thecumulative delay graph for eachmethod was developed by calculatingthe predicted completion date foreach schedule in the analysismethod’s series of schedules, andplotting that predicted completion

Table 2—Delay Totals by Method

Figure 2—Daily Delay Measure Graph


date as of the data date of theschedule within which it wascalculated. Generally, it is clear that thecumulative delay graph for the CAB(MIP 3.9) (in green) diverges the mostfrom the other three analyses. TheAPAB (MIP 3.2) DDM line (in black), theCPA (MIP 3.3) line (in blue), and theRTIA (MIP 3.7) line (in orange) runalong a largely similar path betweenMarch 2010 and December 2011; afterthis point, the CPA/MIP 3.3 line andthe RTIA/MIP 3.7 line both dropprecipitously, whereas the APAB/MIP3.2 DDM line continues along roughlythe same slope as before this point.Analysts seeking to reconcile thedifferences between methods mustunderstand the causes andimplications of these differences, andhow it relates to the specific way themethod analyzes the CPM scheduleand measures delay. Note that the authors havecalculated the slope of the cumulativedelay lines in units of calendar days permonth (CD/Mo). Since a projectcannot experience more delay in amonth than the duration of thatmonth (in absence of an insertedfragnet) the maximum natural slope of

an unedited network will not exceedroughly 30 CD/Mo. Any time periodswith slopes greater than the maximumnatural slope result from editednetworks. Table 2 shows the sum of delaydays attributable to each party, bymethod. Recall that in thishypothetical, responsibility for aparticular delay has been assigned to aparty, only the timing of the delayduring the course of the project is ofconcern. For example, the contractorwas assigned delay days for“Contractor Delay” activities and forproduction delays. The owner wasassigned delay days for “Owner Delay”activities. One window within CPA/MIP3.3 had two concurrently criticalactivities, one belonging to each party.These 6 CD were therefore designatedas concurrent delay. One very notable difference in theresults of the four methods stems fromthe weather exclusion period. Notethat in CPA/MIP 3.3 analysis, theweather exclusion period becomes aprimary driver of the predictedcompletion date in January 2012,whereas in RTIA/MIP 3.7, thepredicted completion date is driven bythe weather exclusion period starting

in December 2011. APAB/MIP 3.2 isnot affected by the weather exclusionperiod, which is due to theobservational nature of the method.CAB/MIP 3.9 is a modeled method,and such methods could potentiallyshow effects of such large non-workperiods; however, the test series as itwas organized did not ultimately allowthe CAB/MIP 3.9 analysis to do so. Theimplications of this will be discussedfurther below; however, for thepurposes of table 2, the delay daysattributable to the effects of theweather exclusion period is kept in aseparate column withoutapportionment to one party. As-Planned vs. As-Built analysescompare a baseline schedule plan,consisting of one set of network logic,to the as-built state of the samenetwork [11]. The schedules can becompared globally, or can be brokeninto smaller windows that can increasethe granularity and precision of delaydetermination. Additionalmathematical analyses (such asproductivity analysis, earned valueanalysis, or measured mile analysis)help establish the as-built critical pathand apportion responsibility forspecific periods of delay to specific

Figure 3—CPA/3.3 as Compared to APAB/3.2 DDM


parties—so that the analysis does notdescend into the rightly rejected “totaltime” analysis [2]. In its simplest implementationthat borders on a “total time”methodology, the APAB/MIP 3.2 doesnot consider contemporaneousunderstanding of criticality; however,more sophisticated implementationsattempt to identify the as-built criticalpath through a careful examination ofthe record. Identification of the as-built critical path can take into accounta contemporaneous under-standing ofcriticality, although this is not essentialto the method [8]. As a result, theDDM line on the cumulative delaygraph does also not consider thecontemporaneous understanding ofcriticality. It is not a projection of howmany days ahead or behind schedulethe project management teambelieved themselves to be at a givenpoint in time —it is a mathematicalcalculation of the actual number ofdays of delay at the point ofmeasurement. The calculations for the DDMvalues were performed on a weeklybasis for the duration of the project,and plotted on the graph in figure 2.The slope of the DDM line does not

exceed the maximum natural slope.Given that the APAB does notrecognize delays until they actuallyoccur (no project forward delay), this isexpected. As measured by the DDM,the delay accumulated during awindow will not exceed the duration ofthat window. In other words, the slopeof the DDM line will not exceed themaximum natural slope. The DDM line in figure 3 serves asthe basis of comparison for thecumulative delay lines of the othermethods. Since it measures the actualdelay as it occurred, it provides auseful reference point from anobservational perspective againstwhich analysts can compare themodeled methods.

MIP 3.3: Contemporaneous PeriodAnalysis The CPA uses the updateschedules created during constructionto reconstruct the events of theproject, and thereby demonstratingthe changing nature of the critical paththrough each of the successiveupdates [8, 18]. As project events, suchas progress and unforeseen conditions,unfold and are reflected in thecontemporaneous schedules, the

effects of progress and subsequentnetwork revisions (hopefully linked tothe contractor’s revisions to intendedmeans and methods) will cause gainsand losses to each schedule’spredicted completion date.Additionally, subsequent schedules inthe contemporaneous series will showwhen the critical path of the projectshifts from one area to another. Thesize of the window to be analyzed isvariable: month-to-month is common,but it is possible to make the windowsmore narrow (such as week-to-week)or define windows by alleged delayevents. The CPA and its more compleximplementation, the Bifurcated CPA,rely heavily upon thecontemporaneous understanding ofcriticality because they are using theexisting schedule series to determinewhat the project team though wascritical at the time [12, 13, 18]. Notethat there is a third type ofContemporaneous Period Analysis—the Recreated ContemporaneousPeriod Analysis—which uses schedulesrecreated by the analyst, presumablybecause adequate schedules were notcreated contemporaneously [10].Since the schedules used in this

Figure 4—RTIA/MIP 3.7 as Compared to APAB/MIP 3.2 DDM


analysis did not exist on the project,they could not have influencedexecution. The RecreatedContemporaneous Period Analysisdoes not, therefore, use acontemporaneous understanding ofcriticality. MIP 3.5 allows for a widerange of after-the-fact reconstruction.If only minor adjustments to thecontemporaneous schedule updatesare made, they may actually reflect thecontemporaneous understanding ofcriticality. The update schedules created forthe test series were used to create thecumulative delay graph. When ownerdelay activities start during the updateperiod, they were shown with theiractual start date and a remainingduration proportional to the originalduration, assuming straight-lineprogress across the activity. They werenot used to forward-project theentirety of the delay, as is the case withRTIA/MIP 3.7. Figure 3 shows this. The most notable feature of theCPA/MIP 3.3 line is that between NTPand 1-Jan-2012, it generally follows asimilar path to the APAB/MIP 3.2 DDMline; however, it is also clear that theCPA/MIP 3.3 line tends to lead theDDM line by some amount.

Specifically, the CPA/MIP 3.3 lineaccrues delay between two and 30 CDfaster than the APAB/MIP 3.2 DDMline. On average, the CPA/MIP 3.3 lineleads the DDM by approximately 5 CD. This is consistent withexpectations: CPA/MIP 3.3 predicts theupcoming window’s delay as of thatschedule update’s data date, whereasthe APAB/MIP 3.2 DDM line tracksactual delay as it occurs. For instance,in figure 4, on 1-Sep-2010 thecumulative delay values for theAPAB/MIP 3.2 DDM show that theproject had accumulated 51 CD ofdelay. On the other hand, CPA/MIP 3.3determines that the project hadaccumulated 82 CD of delay as of thesame date. This difference of 31 CD ofdelay is superficially a significantdifference in the results of the twoanalyses; however, as of 1-Oct-2010,the APAB/MIP 3.2 DDM line showsroughly 77 CD of delay, and theCPA/MIP 3.3 CPA line continues toshow 82 CD of delay. The CPA simplylooked forward and predicted that inthe upcoming window, there would be82 CD of delay. The DDM does not lookforward, and therefore the delayaccrued over the window until the

point where the two analyses arelargely in agreement. In this test series, the two analysesidentify the same activities as thecause of delay during this window. Thecumulative delay graphic thereforeprimarily assists in this window withquantification of delay associated withthe specific events. However, in theevent that the APAB/MIP 3.2 DDM andthe CPA/MIP 3.3 determined that theABCP was different than thecontemporaneous critical path duringthis window, the discussion betweenthe parties should shift to whether theCPA/MIP 3.3 is an appropriate methodfor the analysis. Assume that a CPA/MIP 3.3 showsthat a given activity was, as of the datadate of a particular update, predictedto cause 15 CD of delay, and that theanalyst performing the CPA/MIP 3.3asserts that the predicted delay isproof of entitlement to an excusableand compensable time extension.Meanwhile, the APAB/MIP 3.2 DDMfor that same window shows that adifferent activity drives the ABCP forthat same window and caused 17 CDof delay. The quanta are roughly inline; however, the cause of delay is indispute. The issue again becomes

Figure 5—RTIA/MIP 3.7 as Compared to CPA/MIP 3.3


whether the schedule series affectedthe contemporaneous understandingof criticality. The analyst performingthe APAB has the benefit ofdemonstrating what actually delayedthe project; however, as previouslydiscussed, the contemporaneous viewof criticality is preferred, if it can beproven. The analyst performing theCPA/MIP 3.3 cannot simply state thatthe prediction showed a delay wouldoccur; he or she should also show thatthe prediction affected the projectmanagement team’s actions in someway (such as shifting resources to theactivity perceived to be critical, orplanning for accelerated work in thefuture). If the predicted delay existedonly on the scheduler’s software andnever influenced the projectmanagement team’s actions, then thecontemporaneous understanding ofcriticality was not affected and thepredicted delay is meaningless. In thiscase, the authors believe that theAPAB/MIP 3.2 DDM line and itsassociated as built critical path causalactivity are more appropriate todetermine the delay for the window. In January 2012 the CPA/MIP 3.3line drops from a predicted delay of185 CD to 300 CD of delay. This is as aresult of the effects of the previouslydiscussed weather exclusion period.This sudden drop of 115 CD is, again, apredicted delay resulting from theeffects of the weather period. Note,however, that the APAB/MIP3.2 DDMcontinues to trend steadily downward

at an average slope of approximately 8CD/ Mo. In practical terms, the analystperforming the APAB/MIP 3.2 andrelying on the DDM would say that theeffects of the weather exclusionperiod were irrelevant: the contractorhad been incapable of maintainingschedule prior to 1-Jan-2012, and thework excluded by the non-workperiod would not have been availablefor execution any earlier. In theAPAB/MIP 3.2 analysis, then, the 115CD were a result of the contractor’spoor progress. In contrast, the analystperforming the CPA/MIP 3.3 wouldargue that owner delay activities(including differing site conditions,design changes, etc.) pushed the workinto the weather exclusion period andthat therefore the 115 CD were theresponsibility of the owner. In answer to the other party’scharges that poor progress was thecause, the contractor could mount adefense of “pacing” of work. In otherwords, the contractor would allegethat given his or her knowledge of thefuture delay brought about by theweather exclusion (linked with hiscontemporaneous analysis thatattributed this delay to the owner),the contractor deliberately slowedproduction on available work so that itwould be complete only just in timefor the early start of the weather-affected work. Once again, however,this is an argument that rests heavilywith the contractor’scontemporaneous understanding of

criticality. In order for this pacingargument to be legitimate, thecontractor would need to show thathe had this understanding of theweather delay as of 1-Jan-2012, andthat he or she took actions to slow theproduction. Without thisdemonstration, it will be difficult forthe owner to accept that theproduction delays before 1-Jan-2012,were not the result of the contractor’spoor productivity, whereas theproduction delays after 1- Jan-2012,were the result of deliberate pacing.The cumulative delay graph highlightsthe need for proof of thiscontemporaneous understanding ofcriticality. Therefore, for the purposes ofestablishing that the CPA/MIP 3.3graph is the appropriatemeasurement tool and that it shouldsupersede the other method’s graphfor a given period, the analystperforming the CPA/MIP 3.3 shouldestablish the following:

• The analyst must confirm that themeans and methods wereaccurately represented in thecontemporaneous update.

• The analyst must confirm that theschedule was used to plan andexecute the project, and that theresults of the CPM calculationinfluenced the contemporaneousunderstanding of criticality.

Figure 6—RTIA/MIP 3.7 as Compared to CPA/MIP 3.3 for November 2010 to April 2011


The analyst would conceivably accomplish this through review ofproject documentation such asmeeting minutes, daily reports, andcorrespondence. This backupinformation would be essential,however, to justifying the use of aspecific method’s cumulative delaygraph and associated causal activities. These recommendations aremade to be performed in concert withthe recommendations of AACERecommended Practice 29R-03’sSection 2 on source validation.

MIP 3.7: Retrospective Time ImpactAnalysis The TIA is one of the mostcommon and widely acceptedmethods to analyze project delays. ATIA compares two schedules with thesame data date—one schedule (the“unimpacted schedule”) thatrepresents the status of constructionand the critical path just before thediscovery of an event, and a secondschedule (the “impacted schedule”)that represents what happens to thecritical path and the predictedcompletion date once the delay eventoccurs. The event, administrativeresolution time, and added work

necessary to return to originalcontract work are represented in theimpacted schedule through theaddition of a fragnet consisting ofrepresentative activities and logic. Thecomparison of the predictedcompletion dates of these twoschedules (before and after thefragnet insertion) determineswhether there is entitlement to a timeextension. Though widely popular andcommonly used, one importantaspect of the TIA is also widelyoverlooked: the timing of the analysis.If a TIA is conducted before the addedwork is performed, it is a ProspectiveTIA [3]. A Prospective TIA is anessential tool for the projectscheduler to determine the likelyimpacts of changed conditions on aproject and is often included as arequirement in the contract as aprerequisite for granting a timeextension. When the changemanagement plan on a project isworking properly, a Prospective TIA isassociated with a bilateralmodification that adds the time (andmoney) to the contract necessary tocompensate the contractor for thechange [15].

However, as discussed, theforensic analyst is constrained by thefact that he or she joins the projectafter project completion. Therefore,any TIA that is performed is done afterthe added work has been completed,and is therefore a Retrospective TIA.There is some controversy about theuse of Retrospective TIAs because ofthe potential for manipulation, andthe fact that modeling eventsretrospectively allows selectivemodeling of only one party’s allegeddelays while excluding others [17]. ARetrospective TIA that only modelsowner delays will tend to concludethat only the owner was responsiblefor the delays, whereas one whichonly models contractor delays willshow the opposite. This can lead to animbalanced view of responsibility ofdelays. Despite this, it is conceivablypossible to perform an effectiveRetrospective TIA. In selecting thismethod, however, the analyst isabandoning the contemporaneousunderstating of criticality, because thistechnique is creating new schedules,not used on the project, whilemodeling actual eventsretrospectively.

Figure 7—CAB/MIP 3.9 as Compared to APAB/MIP 3.2 (DDM)

The RTIA/MIP 3.7 line tends tolead the CPA/MIP 3.2 DDM line in amanner similar, yet more pronounced,than did the CPA/MIP 3.3 line. TheRTIA/MIP 3.7 RTIA/MIP 3.7 line leadsby an average of approximately 13 CD.Again, this lead is related to the factthat the RTIA/MIP 3.7 is predictingdelay rather than measuring actualdelay; however, in contrast toCPA/MIP 3.3, the RTIA/MIP 3.7 ispredicting delay in inserted fragnets,as well as in the original CPM network.To better understand the differencesbetween the two, refer to figure 5. The RTIA leads the CPA/MIP 3.3line by an average of roughly 7 CD. Inaddition, note that in Figure 6, thenumber of days assigned to thecontractor (40 CD of delay) is muchlower than in the other methods. Inthe CPA/MIP 3.3 analysis, theapportioned delay between ownerand contractor was 51% to 49%; inRTIA/MIP 3.7 RTIA/MIP 3.7, theapportioned delay split was 71% to17%. The contractor tends to receive alower apportionment of delay days inmethods that forward-project delaysassociated only with the owner. Inother words, if the fragnets insertedinto an RTIA are always representativeof the other party’s alleged delays,then the analysis will tend to showthat the other party is responsible formost of the delays. For this reason, itis not good practice to only model oneparty’s delays. However, there areconceivably occasions when such ananalysis could be appropriate, andthose would be times when theinserted fragnets were representativeof the contemporaneousunderstanding of criticality. CPA/MIP 3.3 (including therelated bifurcated CPA/MIP 3.4) andRTIA/MIP 3.7, each propose aforward-looking modeled analysiswherein the contemporaneousunderstanding of criticality isassumed, but must be proven.However, CPA/MIP 3.3 only assumesthat the unimpacted CPM networkinfluenced this understanding, whileRTIA/MIP 3.7 assumes that both thefragnet and the CPM network were

influential. Of course, this is notalways accurate. If the fragnet wascontemporaneously proposed andestablished, it is likely that the use ofthis fragnet in a RTIA/MIP 3.7 iscorrect in its assumption that theparty inserting the fragnet had acontemporaneous understanding ofcriticality as projected by the fragnetand schedule recalculation. Thisassertion could of course be disputedor refuted by the other party.However, if the fragnets are createdafter the fact and were neverconsidered by the projectmanagement team during projectexecution, then it is unlikely that theRTIA/MIP 3.7 in this case isrepresenting any contemporaneousunderstanding of criticality. In otherwords, it pretends that the on-sitemanagement would see future eventsas the re-calculated after-the-factschedule depicts them. The impact is seen in thecumulative delay graphs in the waythat more delay accrues earlier in theRTIA/MIP 3.7 graph. Figure 6 showsthe MIP 3.3 and the MIP 3.7 graph forthe period between November 2010to April 2011. Both cumulative graphs begin atthe same point of delay, eachcalculating that the project was 76 CDbehind schedule as of 1-Nov-2010.However, at the start of December2010, RTIA/MIP 3.7 calculates that theproject is 100 CD behind schedule,compared to only 84 CD for CPA/MIP3.3. The RTIA/MIP 3.7 cumulativedelay graph stays flat from 1-Dec-2010to 1-Feb-2011, at which point it beginsto accumulate delay again. Theauthors reviewed the test schedulesto determine what the drivingactivities were during this window,and determined that in the 1-Nov-2010 update schedule, the RTIA/MIP3.7 included a fragnet representing adiffering site condition. The insertionof the fragnet caused the sudden lossof 24 CD during the month ofNovember. In comparison, theCPA/MIP 3.3 line identifies only an 8CD delay during the same month,related to poor contractor production.

This dichotomy reveals the heartof many disputes. One party uses amodeled technique that “proves” thatthe critical path ran through an owner-caused differing site condition, whilethe other party’s modeled technique“proves” that the problem wasactually sustained poor production.Particularly if the contractor is usingthe RTIA/MIP 3.7 and the owner isusing the CPA/MPI 3.3, this argumentcan go on without resolution.However, the cumulative delay graphhighlights the timing of the delayaccrual, which relates directly to thecontemporaneous understanding ofcriticality. The RTIA/MIP 3.7 effectivelyalleges that, as of 1-Nov-2010 (orreasonably close to that date) thecontractor had identified the differingsite condition, had estimated theduration of time necessary toovercome the change in order toreturn to contract work, and hadperceived that the predictedcompletion date was delayed by 24 CDas a result. These are the facts thatmust be proven to establish thepropriety of the RTIA/MIP 3.7’sconclusions; without this, it is veryeasy to foresee scenarios when oneparty’s analyst simply forward-impactsa CPM model with fragnets of theother party’s delays until the analyst’sclient apparently bears noresponsibility for any delay. TheRTIA/MIP 3.7 line will simply stair-stepdown through the project duration,claiming that delay accrued earlierthan it actually did and was always theresponsibility of the other party. Therefore, for the purposes ofestablishing that the RTIA/MIP 3.7graph is the appropriate measurementtool and that it should supersede theother method’s graph for a givenperiod, the analyst performing theRTIA/MIP 3.7 should establish thefollowing: [25]

• The analyst must confirm that themeans and methods wereaccurately represented in thecontemporaneous update.

• The analyst must confirm that theschedule was used to plan and


execute the project, and that theresults of the CPM calculationinfluenced the contemporaneousunderstanding of criticality.

• The analyst must also confirmthat as of the Data Date of theschedule (or reasonably soonthereafter) the projectmanagement team becameaware of the issue modeled in thefragnet, that they impacted theschedule with the fragnet, andthat the resulting shift in the CPand later predicted completiondate influenced the projectmanagement team’scontemporaneous understandingof criticality.

• The analyst should also beprepared to discuss whetherthere was contemporaneouspacing.

MIP 3.9: Collapsed As-Built The Collapsed As-Built methodrecreates a CPM model of the as-builtschedule by creating logic anddurations that reflect the apparentlogic that drove the work and theactual dates on which the work wasperformed. The analyst then dissolvesselected delay activities recalculatesthe schedule in order to show whatwould have happened had a certainevent not taken place. The CollapsedAs-Built method can either beperformed in a single step (deleting allalleged delay activities at once) or inmultiple steps (removing one activityat a time and recalculating after eachdeletion). A conceptual advantage tothe Collapsed As-Built method is thatthe as-built schedule contains bothparties’ delays, so if the analystremoves only one party’s delays fromthe schedule, the other party’s delaysare still present. In other words, theCollapsed As-Built naturally considersboth parties’ delays. Because thistechnique involves creating a series ofCPM schedules which were not usedon the project, it does not rely uponthe contemporaneous understandingof criticality. For this analysis, the authorsstarted with the test series’

Collapsible As-Built Schedule, anddissolved each owner delay activity inturn, beginning with the activity withthe latest finish date and movingbackwards. After each dissolution, theschedule is recalculated and thechange in the predicted completiondate was recorded. As shown in figure7, after all the owner delay activitieswere dissolved, the predictedcompletion date had shifted 31 CDearlier than the actual finish. As such,these 31 CD were assigned to theowner, while the remaining 285 CDwere assigned to the contractor. The cumulative delay graph forCAB/MIP 3.9’s analysis is clearly themost divergent from the APAB/MIP3.2 DDM line. This is understandablein terms of the fact that the method isattempting to account for bothparties’ delays by deleting only oneparty’s and leaving the others in theschedule series. This runs counter toRTIA/MIP 3.7, in that with the RTIAthe additive modeling of, for instance,the owner’s delays has a tendency tomask the contractor’s. In the CAB, thecontractor’s delays remain after thestepped deletion of the owner’s delayactivities. The cumulative delay graphfor RTIA/MIP 3.7 therefore includesdelays by both parties, while CAB/MIP3.9 depicts only one party’s delays. The conclusion that could bedrawn from the CAB analysis is that,but for the delays of the owner, thecontractor would only have finished31 CD earlier. Note that the weatherexclusion period was not regainedduring the dissolution of the ownerdelay activities; therefore, it ispossible to conclude that regardless ofthe owner’s delays, the contractorwould have encountered the weatherexclusion period’s jump in predictedcompletion date on its own. Thisexplains why the weather exclusionperiod days are assigned to thecontractor in table 2. The CAB measures delay in asignificantly different manner thanthe other three methods. First, it doesnot attempt to start at the NTP date,where there were zero days of delayaccrued, and work forward through

each window. Instead, it analyzes theproject in reverse, starting with theactual number of delay days accrued.Second, the method is designed tospecifically leave behind one party’sdelays. As a result, the MIP 3.9 CABline will never return to zero. Theauthors have concluded that at thispoint the only technique forreconciling the CAB with the othermethods would be to perform theCAB/MIP 3.9 twice, once excludingthe owner’s delays and then excludingthe contractor’s delays.

Conclusions The use of the cumulative delaygraph can be a useful tool inreconciling the apparently differentresults of methods. It is particularlyuseful when used as part of a largerprocess of putting the results of themethods into a common format and acollaborative effort between theparties to establish periods ofsimilarity and differences. Thecumulative delay graph will aid inestablishing when delays accrued; itwill not, however, resolve disputeswhere the causal activity is agreedupon but the underlying reason fordelay is at issue. Generally speaking, theAPAB/MIP 3.2 DDM line establisheswhen the delay actually occurred. TheCPA/MIP 3.3 line tends to show thatdelay accrues slightly earlier than theAPAB/MIP 3.2 DDM line, because theCPA/MIP 3.2 is calculating the delay tothe predicted completion date basedon the unedited CPM network alone.The RTIA/MIP 3.7 tends to show thatdelay accrues earlier than theCPA/MIP 3.3, because the RTIA/MIP3.7 is calculating delay to thepredicted completion date based onthe CPM network as impacted byfragnets. A longer fragnet will tend toclaim more delay earlier. The cumulative delay graphhighlights when delay either actuallyoccurred, as in the APAB/CIP 3.2 DDMline, or when it was perceived by theparties to have occurred, as with theCPA/MIP 3.2 and the RTIA/MIP 3.7. Inorder to prove that this perception



represents a valid means for viewingproject delay, analysts shouldestablish whether thecontemporaneous understanding ofcriticality can or cannot be assumed.However, it is still possible to comparean analysis which does assume andone which does not (such as MIP 3.2to MIP 3.3, which is particularlyeffective). But in a situation where thecontemporaneous understanding ofcriticality cannot be established, itmay be necessary to eliminate certainmethods from consideration. Finally, analyses developedoutside of standards of good practicewill likely show radically differentresults on this chart. Therefore, theuse of this technique can help refutethe technical implementation of theopposing expert’s analysis. ◆

REFERENCES1. Bruner, P. and J. O’Connor. 2007, §

15.130 p. 348, Bruner &O’Connor Construction Law,Volume 2, West ThompsonReuters, New York, The authorsstate: “Proof of what activitieswere ‘critical’ to timelycompletion at any point in time isno easy task because the criticalpath is dynamic andaccommodates and adjusts toever-changing job conditions.”

2. Bruner, P. and J. O’Connor. 2007,Section 15.133, p. 352, Bruner &O’Connor Construction Law,Volume 2, West ThompsonReuters, New York.

3. Calvey, T. and R. Winter,. 2006,AACE Recommended Practice52R-06, Time Impact Analysis - AsApplied in Construction, AACEInternational, Morgantown, WV,

4. Hoshino, Kenji P., John Livengood,and Chris Carson. 2011, AACERecommended Practice 29R-03,Forensic Schedule Analysis, Page1, AACE International,Morgantown, WV.

5. Hoshino, Kenji P., John Livengood,and Chris Carson, Chris. 2011,AACE Recommended Practice29R-03, Forensic Schedule

Analysis, Page 1, AACEInternational, Morgantown, WV

6. Hoshino, Kenji P., John Livengood,and Chris Carson. 2011, AACERecommended Practice 29R-03,Forensic Schedule Analysis, Page11-16, AACE International,Morgantown, WV, Section 1.4defines the functional taxonomy.



9. Hoshino, Kenji P., John Livengood,and Chris Carson. 2011, AACERecommended Practice 29R-03,Forensic Schedule Analysis, Pages125-131, AACE International,Morgantown, WV.

10. Hoshino, Kenji P., John Livengood,and Chris Carson. 2011, AACERecommended Practice 29R-03,Forensic Schedule Analysis, Pages38-50, AACE International,Morgantown, WV, See MethodImplementation Protocol 3.5.

11. Hoshino, Kenji P., John Livengood,and Chris Carson. 2011, AACERecommended Practice 29R-03,Forensic Schedule Analysis, Pages38-50, AACE International,Morgantown, WV, See MethodImplementation Protocols 3.1 and3.2.



14. Kelly, Patrick M. and WilliamFranczek. Fall 2013, “Clearing theSmoke: Forensic Schedule AnalysisMethod Selection for ConstructionAttorneys,” p. 30, TheConstruction Lawyer, This articlefurther discusses the role of thecontemporaneous understandingof criticality in method selection.

15. Kelly, Patrick M., 2012, CDR.923:Recommended ContractualMethods for Resolving DelayEvents Prospectively orRetrospectively, 2012 AACEInternational Annual MeetingProceedings, AACE International,Morgantown, WV.

16. Livengood, John, 2003, DailyDelay Measure: A New Techniqueto Precisely Identify Delay, 2003AACE International AnnualMeeting Proceedings, AACEInternational, Morgantown, WV.

17. Livengood, John, 2008, CDR.08:Retrospective TIAs: Time to LayThem to Rest, 2008 AACEInternational Annual MeetingProceedings, AACE International,Morgantown, WV

18. Schumacher, Lee, 1991, SeattleDaily Journal of Commerce,25/26 December, These twoarticles are the basis for theContemporaneous PeriodAnalysis method.

19. Wickwire, J., T. Driscoll, R.Hurlbut, and R. Hillman. 2010, §15.06 p. 651, ConstructionScheduling: Preparation, Liabilityand Claims, 3rd edition , AspenPublishers.

20. Wickwire, J., T. Driscoll, R.Hurlbut, and R. Hillman. 2010, §9.05 p. 268, ConstructionScheduling: Preparation, Liabilityand Claims, 3rd edition, AspenPublishers, The seminal textexplains the primacy ofunderstanding thecontemporaneous understandingof criticality within a forensicdelay analysis: “Delays are bestevaluated on a chronological andcumulative basis, taking intoaccount the status (and criticalpath[s]) of the project at the time


of the delay in question. With thismethod and protocol, all partieson the project live with theevents, actions, and ‘sins’ of thepast.”

21. Robert D’Onofrio andAnthony Meager, “What is aSchedule Good For? A Study ofIssues Posed by Schedules onComplex Projects, ”TheConstruction Lawyer, Winter2013, p6.; Mark Sanders, “ForensicSchedule Analysis: ExampleImplementation,”2008,”AACEInternational Transactions,(Morgantown, WV: AACEInternational, 2008; MarkSanders, “Forensic ScheduleAnalysis: ExampleImplementation Part 2,” ”AACEInternational Transactions,

(Morgantown, WV: AACEInternational, 2011; MarkSanders, “Forensic ScheduleAnalysis: ExampleImplementation Part 3,” ”AACEInternational Transactions,(Morgantown, WV: AACEInternational, 2012.

ABOUT THE AUTHOR

John C. Livengood,CFCC PSP, is withNavigant. He can becontacted bysending e-mail to:

[email protected]

Patrick M. Kelly, PE PSP, is withNavigant. He can be contacted bysending e-mail to: [email protected]


Do an “advanced search” by “au-thor name” for an abstract listing ofall other technical articles this authorhas published with AACE. Or, searchby any total cost management sub-ject area and retrieve a listing of allavailable AACE articles on your areaof interest. AACE also offers pre-recorded webinars, an Online Learn-ing Center and other educationalresources. Check out all of the avail-able AACE resources.

At the 9th ICEC (International Cost EngineeringCouncil) World Congress 2014 in Milan, Italy, SamGriggs was awarded the ICEC Distinguished Interna-tional Fellow (DIF) award.

The DIF award recognizes individuals who havemade significant contributions to ICEC and to the asso-ciation or institute of which they are a member for aperiod of years. The recipients must be nominated toreceive the award by an ICEC member association orinstitute. They must:

• Have held a significant volunteer position in bothICEC and a member association or institute;

• Have had significant involvement over a period ofseveral years in both ICEC and a member associa-tion or institute; and

• Must have made a significant contribution to thedevelopment of the cost management profession.

The AACE Board of Directors and the AACE Certifi-cation Board are very proud of Sam and his contribu-tions to both AACE International and ICEC.

After joining AACE International in 1977 andachieving his CCP certification in 1981, Sam has beenactively involved as a member of the AACE CertificationBoard where he has served for over 10 years. ◆

Sam Griggs is Awarded ICECDistinguished International Fellow


A ccuracy is a measure ofhow a cost estimate willdiffer from the final actualoutcome. Empirical

estimate accuracy data has beenresearched for over 50 years.However, as published by the authorin 2012, the level of industryunderstanding of the reality ofaccuracy and our ability to predict thisreality is very poor [15]. That articlepointed out that using AACERecommended Practices to quantifysystemic risks (i.e., parametricmethods) supported betterforecasting. However, none of theAACE Recommended Practices (RPs)are able to predict the reality that 10

percent of large projects overrun theirbudgets by 70 percent or more.Overruns of that scale for a megaproject can cause significant financialdamage to a company, projectfinanced or not [11]. Falling back on the Rumsfeldian“unknown-unknowns” construct toexcuse our predictive failure (i.e.,ignore the reality one sees.) is a cop-out; it does not help one make betterproject decisions or improve practices.The author has learned frombenchmarking and post-mortemanalysis experience that project costdisasters generally result from a stewof systemic weaknesses, risk events,and poor practices of all kinds. This

mix of mundane risks, in the presenceof complexity and stress, can push aproject’s behavior over the edge ofchaos; (i.e., the tipping point). Chaoticproject behavior is unpredictableexcept to say that the cost outcomewill likely be a disaster. What can bepredicted is the approaching edge ofchaos and there are things that can bedone to avoid it. This article summarizes theauthor’s learnings from industryregarding chaos and complex systemstheory in respect to project costbehavior. It then presents a set ofpractical risk quantification methods,building on AACE RPs in place, thatallow warning of management ofpending chaos and how to head it off.

Project Cost Behavior This article concentrates onengineering and construction projectsin the process (e.g., oil, gas, chemical,mining, metals, utility, etc.) andinfrastructure (often associated withprocess projects) industries. They arecharacterized by complexity, uniquework scopes, design change andsometimes new technology. For theseindustries, cost behavior, expressed interms of estimate accuracy (i.e., actualoutcome/funding estimate), is welldocumented and rather grim. Figure 1from a paper by the author shows theheavily skewed actual distribution ofaccuracy versus the much less skewed

Risk Analysis at theEdge of ChaosJohn K. Hollmann, PE CEP DRMP

Abstract: Empirical studies show that the distribution of actual/estimatecost data has a very long, bimodal tail on the high side. Actual p90 valuesare often triple the values one is estimating; traditional risk analyses is failingto predict the tail. The author hypothesizes that the bimodal tail reflects thecost outcome of project chaos. Borrowing from chaos and complex systemstheory, the author developed a practical method to warn management whena project’s risks threaten to push project behavior over the edge into chaosand cost disaster. Complex systems theory is a maturing project managementtopic (e.g., as in lean construction, etc.); however, it has not found muchpractical application in risk quantification. This article reviews chaos andcomplex systems theory and how they relate to project cost uncertainty, andpresents a method that brings the understanding of chaos and complexityinto a practical risk quantification toolset. This article was first presented asRISK.1584 at the 2014 AACE International Annual Meeting in New Orleans,LA.

Key Words: Risk analysis, construction, cost, estimate, and project manage-ment

TECHNICAL ARTICLE


distributions that we are forecastingfor funding estimates [15]. A parallel AACE paper byAlexander Ogilvie and others, basedon the more robust and statisticallysolid data from benchmarking firmIPA, Inc., adds insight into the actualdistribution of accuracy for estimatesat different project scopedevelopment phases as representedin figure 2 [26]. For the respectiveestimate class (industry tends toauthorize projects based on estimatescloser to Class 4 than Class 3) theseempirically-based sources agree;there is no question as to the reality ofthe very long tail. The long tails in figures 1 and 2have been smoothed by curve fittingby the author (log-normaldistributions are a good fit.) However,the actual data distributions are notsmooth (i.e., not orderly.) As EdwardMerrow, the founder and CEO of IPAInc., said in a recent podcast on mega-projects: “the distribution of successor failure is highly bimodal” [24]. Thisbi-modality is evident in the IPAhistograms in figure 3 [26]. Theauthor’s hypothesis is that the mode

on the high end is dominated byprojects that crossed the edge ofchaos; an alternate, but notuncommon, project reality. Anotherhypothesis, as suggested by Dr. BentFlyvbjerg, is that these projects wereintentionally underestimated [12].However, the author’s experienceagrees with Edward Merrow whostated that, “…no Machiavellianexplanation is required” to explainthese dismal outcomes [23]. The projects in or near chaos defyour current risk quantificationmethods, but they are too prevalentto ignore and write off as unknowable.If one can understand where the edgeof chaos is; (i.e., the tipping point interms of combinations of complexityand risks), one can at least warnmanagement of impending disaster,and, at best, provide/recommendactions to pull the project back fromthe precipice. As it is, every industryrisk analysis the author sees today ispresented to management as if theproject were a well behaved, orderlysystem (albeit skewed), even forprojects with extreme risks; this is notthe whole story. The story is one of

chaos and complexity as discussed inthe next section; this will set the stagefor presenting a tipping point warningindicator.

Systems, Chaos and Complexity In searching for practical methodsto address the true distribution ofproject cost uncertainty, the author’slearning path went from systemdynamics, through chaos theory tocomplex system theory. It is a logicalprogression of thought and the authorhopes the following summary inlayman’s terms will adequatelyexplain the basis of the methods thisauthor arrived at. First, the author learned to lookat projects as systems (somethingwith parts that interact to form anintegrated functioning whole). Thisview has been around a long time;(e.g., an author in a 1971 AACEpublication said, “Cost engineeringmust be viewed as an integral part ofsystems engineering” [31]). It doesnot take much experience for one tosee that project systems are alsodynamic; they change over time.These realizations lead one to systems

Figure 1—As Estimated and Target Accuracy vs. Empirical Accuracy and Funding

dynamics (SD) which studies howcomplex systems behave over time.SD has evolved fascinating modelsusing feedback loops (e.g., rework).Unlike Critical Path Method (CPM)based risk models that do not addressrework, SD models demonstratenonlinear behavior which looks morelike reality than traditional riskanalyses. Work by Kenneth Cooperand many others, since the 1970s, hasdemonstrated SD application onprojects, particularly in claims analysis[10]. Unfortunately, current SDmodels are too difficult for everydayuse and they are based on thepremise of complex, but orderly, (i.e.,reductionist) systems when disorder isthe observed reality of most of ourbimodal projects. The search for non-linear/disorderly models leads one to

chaos theory. The work of Lorenz inthe 1960s on weather forecasting (andthe advent of powerful computers)kicked off this field [20]. In simpleterms, researcher Sven Bertelsen tellsus that, “chaos may be defined as astate of the project system where thefuture development of the system isnot predictable or only poorlypredictable” [8]. Chaos is a disorderedand unpredictable state; one that isout of control. Chaotic systems arealso non-linear which for projectsmeans that progress is notproportional to the work effort; (i.e.,the project seemingly goes in alldirections, racking up huge bills anddelays and getting nowhere). JohnHackney, a founder of AACE, gave usan excellent case description of such aproject in the “Chaos” chapter of hisseminal book on capital project

management [14]. The “edge ofchaos” then is where a project teetersbetween order and chaos. If one couldknow the key attributes of projects atand over the edge of chaos, one mighthave the start of a risk analysismethod. That key attribute in systemsdynamics and chaos theory is systemcomplexity. This takes one to complexsystems theory. Rigorous study ofcomplexity is fairly new; the definitionof complexity is still being debated [6].It is generally agreed that complexityis more than complication.Complication implies lots of parts orsize (e.g., a complicated WBS) whilecomplexity implies lots ofinterrelationship and interaction ofthe parts. Complicated, non-complexsystems are likely to be orderly, linear,and responsive to traditional control


Figure 2—Accuracy for Estimates at Different Phases of Scope Development

Figure 3—Bimodal Tendency in Estimate Accuracy Data (used with permission [26])


while complex systems are more likelyto be disorderly, non-linear and atworst, chaotic. Aggravating thecomplexity are stresses put on asystem by management, the market orenvironment (e.g., pushing foraccelerated schedules). SvenBertelsen does a good jobsummarizing the relationship betweencomplexity (dynamics in his terms),stress and the edge of chaos [8].Figure 4 borrows from his concept; theperson losing their balance representsa project on a system playing field withthe cliff representing the edge ofchaos toward which complexity andstress are pushing the project. Stress (or pressures) can bepositive. The power of stakeholders,owners, and management, to takemitigating action and make changescan be a positive force (Bertelsencalled this “decision power” [8]).However, reactive decisions often addmore negative stress. For example,accelerating a lagging, disorderedproject can be deadly; as Merrow putit: “speed kills” [24]. Lastly, risk eventsor unexpected conditions cancompound negative stress, add tocomplexity (e.g., added risk treatmentscope) and confound change efforts.Figure 5 illustrates the addedstressors. A final element of complexity isuncertainty. An analogy for generaluncertainty is a fog obfuscating orconfusing the system. If one canmeasure these stresses (positive andnegative), the complexity (ordynamics) and their interaction withrisk events, allowing for uncertainty,one may have a way of measuring howclose our project is to the edge ofchaos. The next step is then to identifythe attributes of complexity andstress, and how to measure them;when are they too much? But before leaving this topic, it isimportant to note that in theory and inmy experience, traditional “control”cannot restore order to a chaoticproject. Diabolically, “change” isrequired to restore order [29]. This is aproblem because change during theproject execution phase is anathemato most project management systems.

In theory, complex adaptive systemscan address how ongoing projectorganizations, living on the edge ofchaos, can change to restore order,but adaptation requires more timethan a lone project usually has.Control versus change will be revisitedin later discussion about risktreatment.

Measuring Complexity and Stress As mentioned, the definitions ofcomplexity are still maturing. An earlypaper by David Baccarini that reviewscomplexity definitions is oftenreferenced [7]. A more recent paper

by Saleem Gul and Shahnawaz Khanpulled together a complexity modelreferencing many other researchersthat with a few changes fit mypurposes for a risk analysis tool [13].Figure 6, based conceptually on theGul model, shows the elements ofcomplexity with their typicalattributes (some attributes apply tomultiple elements). Figure 7 illustrates thecomponents of stress on a project.Arguably, these are just moreelements of system complexity, butthese reflect elements that tend to acton a system once it is in place. This is

Figure 4—Complexity and Stress Can Drive a Project Over the Edge of Chaos

Figure 5—Adding Stressors at the Edge of Chaos


conceptually based in part onBertelsen’s construct of stress anddecision power with the author’saddition of risk events [8]. Given these elements ofcomplexity and stress, how does onemeasure them? Fortunately, thefundamentals are already in handwithin the AACE RecommendedPractices (RPs) for risk quantificationand the research they are based on.One can leverage the AACE RPs in atipping point warning model.

AACE Recommended Practices andTipping Point Measures Starting in 2007, the AACEDecision and Risk Managementcommittee developed a robust set ofrisk quantification RPs based onagreed principles [16, 17]. Oneprinciple was that risks differ in howthey impact project costs andtherefore methods will vary in how toquantify these risks. AACE defines thismethodological risk breakdown as:

• Systemic Risk: Artifacts orinherent attributes of the projectand enterprise system.

• Project-Specific Risk: Risk eventsand conditions affecting thespecific project and plan.

• Escalation Risk: Driven by thegeneral economy.

Analogies for these risk typessuggested by others include: strategic(enterprise), operational (project), andcontextual (global) risks respectively[28].

Figure 6—Breakdown Model of the Elements of Project Complexity

Figure 7—Breakdown Model of the Elements of Project Stressors


The AACE RPs for methods forquantifying these risk types include:

• Systemic: AACE RPs 42R-08 and43R-08 cover parametric methods[3 and 4].

Note: These AACE RPs are basedin large part on research by RANDInstitute [25]. Since the RANDresearch, IPA, Inc., has added a“Team Development Index (TDI)”factor as a major systemic riskdriver [22].

• Project-Specific: AACE RP 65R-11covers expected value withMonte Carlo Simulation (MCS)[2].

• Escalation: AACE RP 68R-11covers escalation methods usingindices and MCS [1].

Figure 8 takes the complexity andstress indicators, based on theory, andmatch them up with the risksidentified in the AACE RPs andunderlying research. In each case thecomplexity/stress factors areaddressed in existing methods, albeitlinearly. All that has not been dealtwith is the non-linear interaction ofthese risks at the edge of chaos.

Addressing Nonlinearity The parametric and expectedvalue methods in the AACE RPs uselinear approaches which only apply to

projects in an ordered mode. They donot predict the long tail, let alonebimodality. Complex systems theoryand observation suggests that theserisks can interact in a non-linear way(i.e., the output is not proportional tothe inputs). In other words, the impactof two risks is not the sum of theimpacts but something like a powerfunction of them. As stated by F.Ackermann, “the impact of the wholeis greater than the sum of its parts”[5]. A way to look at thismathematically is that the risk impactsare x and y; then the total impact asone approaches the edge of chaos isnot (x+y) but (x+y)e where e is greaterthan 1. Readers have all seen thisproverbial effect as, “the straw that

Figure 8—Complexity/Stress Elements and AACE RPs that Address Them

broke the camel’s back” (an analogythat fits the swaybacked bimodaldistributions in figure 3.) So, what in this complexitymeasurement roster is e? Theauthor’s hypothesis is that the driversof non-linearity and disorder are thestress factors. A complex project, evenwith uncertainties, will tend to stay inthe realm of linearity and order if thenet stress (as e) is near 1; the morethat the net stress exceeds 1, thecloser the project will be to chaos.One can summarize the stress factorsin figure 7 as follows:

• Aggressiveness of requirements.• Team/stakeholder management.• Quality of decision making

(recognizing authority andresponsibility).

• Risk events and conditions thatoccur (focus on critical risks).

Each stress can be a positive ornegative influence on e depending onwhether it is aggravating thecomplexity and uncertainty (e.g.,pushing for a faster schedule or hittingrock in the soil) or mitigating it(responsive decision making orexperiencing perfect weather). Thefirst three factors may be attributes of

a defined project system andorganization and it is tempting toequate a “well-defined” system as aninherently positive actor. However, asdiscussed by Nassim Taleb, legacysystems nurtured in an orderedenvironment may be fragile at theedge of chaos [30]. Care must betaken to measure how these stressorsbehave when faced with disorder.

Putting the Parts Together in theTipping Point Indicator Based on the theoreticalgrounding of complex systems theory,and the practical grounding of AACERP-based risk analysis tools andempirical research findings, theauthor enhanced the risk analysistoolset he uses to support his client’smajor projects [16,17,18]. His existingtoolset is a hybrid method employingthree integrated tools:

• Systemic: A parametric model,based on RAND and otherpublished empirical researchfindings, (e.g., IPA’s teamdevelopment and project controlfindings).

• Project-Specific: An expected-value model with MCS for riskevents and conditions (i.e.,probability times impact) thatintegrates with the systemic riskmodel (i.e., uses the systemic tooloutcome as the first project risk),and…

• Escalation: A price index modelwith indices from economistswith MCS applied; it incorporatesthe probabilistic cost andschedule output distributionsfrom the other tools.


Figure 9—A Simple Tipping Point Warning Indicator

Table 1—Tipping Point Indicator Criteria and Risk Treatment


This toolset also produces a“universal” capital cost and schedulerisk output distribution. For anordered regime, it provides the mostcomplete and empirically validoutcome the author knows how toproduce; however, it is not enough.Even though it may tell an owner thattheir p50 value contingency is say 25%(rather than 10% from traditionalmethods), 25% does notcommunicate the risk story; in fact, itmay not even raise an eyebrow. In histext on project financing, JohnFinnerty indicates that financierswould view overruns of 25% as“modest” [11]. What one needs issomething to clearly warnmanagement that the modestcontingency is actually sitting at theedge of a much larger blow-out. Thewarning should come with tips forbacking away from the edge. In sum, as shown in figure 9, theauthor added a simple tipping pointwarning sign to his integrated toolset.This is in addition to the usual costand schedule outcome distributionsand risk tornado diagrams. Note thatthe category of “risk events andconditions” flags the presence ofcritical risks; (i.e., those shown tocontribute to disorder such as ashortage of skilled labor and/or wherethe risk response would likely result inmajor increases in project workersand/or the control base (e.g., workpackages, budgets, schedule logic,etc.). This indicator is similar to theconcept of the systemic “risk filter”reported by Ackermann in 2006 [5].The risk filter (a rating based on asystemic risk questionnaire) results inone of three outcomes that insimplified terms are: cancel/recycle,treat, or go. In 2006, Peter Maidmentand Martin Gough reported on a“Project Stability Index,” which is aratio of positive over negativestressors (i.e., the product of projectsystem integrity and teameffectiveness divided by a measure ofrisk severity [21]). The CanadianTreasury also presented a “complexityand risk assessment” tool in 2012,that they use to filter proposed

projects to higher approval authoritylevels depending on the sponsordepartment’s rated capacity to handleprojects of a given risk level [19]. Forthe later tools, one must take care torate system “integrity” and “capacity”in respect to their robustness in theface of disorder (i.e., fragility). In allthese tools, the indicator serves as akind of filter to assure that the projectrisks gets the appropriate seniormanagement attention. Thesestressor risks, other than minor riskevents, generally belong to seniormanagement to take action on. For the author’s tipping pointindicator, the general criteria for eachtipping point indicator and typical risktreatment advice for management areshown in table 1. Behind the green /yellow / blue stress factor indicatorsare quantitative ratings of theexpected norm for each factor, as wellas the project being reviewed.

Applying the Tipping Point Indicatorin Quantitative Risk Analysis As mentioned, in the author’sconsulting practice, he uses a hybridparametric and expected value riskanalysis toolset consistent with AACERPs 42R and 65R. It was the mostempirically validated toolset he coulddevise. However, it was unable togenerate the 70% cost growth at p90(for Class 4) and bimodality as seen infigures 1 to 3. Using the stress factorrating behind the tipping pointindicator, this author developed aversion of his model that couldgenerate bimodal output. Thisrequired adding an alternate riskimpact distribution that reflected achaotic regime and incorporating apercentage of iterations in a MonteCarlo simulation crossing over theedge into chaos based on the stressfactor (e). A random number

*Previously known as the Certified Cost Consultant/Certified Cost Engineer

Figure 10—Tipping Point Model Versus Actual GrowthOutcomes


generator was used to develop themerged, bimodal distribution [27].Figure 10 compares the chaos model’sMonte-Carlo simulation output tothat of the empirical IPA data fromfigure 3; the similarity is remarkable(both reflect Class 4/FEL 2 estimates). The tipping point model outcomein figure 10 reflects the following twokey input assumptions:

• The alternate chaos distributionis the same as the base (non-chaos) distribution except shiftedto the right by about 5 to 6 timesthe base contingency set at p50.One might call this factor the“chaos penalty.” For example, ifthe base contingency was $100Mat p50, then the chaotic regimedistribution would be shifted by$500M (5X) to the right.

2. The impact of the stress factor (e)resulted in 15 percent of theMonte Carlo iterations using thealternate chaos distribution. Ifthis is what is really happening, itmeans that 1 in every 6 or 7major projects experience chaos(the hypothesis being that thisproportion was highly complexand/or stressed).

The author is reluctant to use thismodel version in practice because it isreductionist; implying that theoutcome of chaos is predictable andthat his assumptions reflect reality.More empirical research is needed. Inany case, figure 10 does present adramatic picture and it is useful forillustrating the tipping point concept.

Tipping Point Indicator Applicationand Risk Treatment Unfortunately, in weak projectsystems, the initial tendency is to hideproblems; optimism bias and/or fearof punishment prevail. PMs or keymembers of the team may hideproblems or upper management mayignore the PM’s demand for change.John Hackney even suggested that anignored PM might “ride the rapids”into chaos to convince (i.e., scare)upper management of the need tomake changes [14]. A subtle tipping

point indicator would not help such anorganization; what is required is a“risk-aware” organization that hasalready learned its lessons and is opento early warning signals. For such an organization, thetipping point indicator tool should beapplied at decision gates, and also atkey milestones during projectexecution, or whenever problemsbecome apparent. When the “yellow”light comes on during execution,actions that reduce stress, such asslowing a schedule or reducing workon overtime may be called for. Whenthe “red light” goes on (or if thewarning was not heeded and chaos isalready happening), it may call forimmediate risk responses that onemajor chemical company client called“containment”. No theoreticalunderstanding was needed by thiscompany; they knew that when aproject was “going off the rails” theyneeded to take action. As JohnHackney stated in his book, “actionmust be swift and decisive” [14].Seasoned veterans know that onelarge project blowout can destroy thecapital effectiveness of a wholecompany portfolio (and ruin the creditratings for smaller companies). A common risk response noted byHackney and also employed by thecompany above is to assign a “swatteam” of experts who can step in tohelp beleaguered projects.Rebaselining the control system fromscratch is a common response whendisorder is prevailing. Replacing thePM, ineffective contractors, and/orvendors may be called for. In any case,business as usual will not suffice.These responses are stressful; but inall cases they are directed towardvigorously restoring order. Theorysuggests that reductive control onlyworks in an ordered, linear regime;recovery from chaos requires timely,decisive and appropriate change.

Conclusion This article went a long waythrough theory to arrive at a simpletool; however, decision makers shouldappreciate the theoretical andempirical research grounding of the

method. Complex systems theory isevolving and fairly new to mostpeople and companies. The riskanalysis approach described here is astarting point; many variations ofdealing with potential chaos arepossible and it is hoped that othermethods will be developed andreported. In summary, this article reviewsthe studies of actual project costgrowth and the bimodal nature ofreality. It hypothesizes that the costoutcomes we are seeing are the mixedresult of linear/ordered projects andd i s o rd e re d / n o n - l i n e a r /c h a o t i cprojects. It points out thatreductionist control only applies tothe former. The article then takes alogical walk through the evolvingtopics of systems dynamics, chaostheory and complex systems theory toexplain the disaster projects. Theauthor believes that these areas ofknowledge are where the nextgeneration of practical risk analysistools will come from. This article alsoexplains that one cannot reliablypredict the outcome of chaoticprojects; one can only point out whena project is nearing the edge of chaosand disaster. This article then presents amethod to apply learnings aboutcomplexity and chaos into a practicalrisk analysis tool; a tipping pointwarning indicator. The good news isthat AACE RPs provide many of thebuilding blocks in terms of riskidentification and rating (albeit in alinear/ordered way.) This articlehypothesizes that the concept of“stress” on complexity is a main driverof non-linearity; compounding theeffects of complexity and pushingtoward the edge of chaos. Somesimilar tools by others are offered forcomparison. Finally, this articlesuggests advice to managementabout ways to help them pull theproject back from the edge or containa blowout. It is hoped by the author that thisarticle will motivate others to delveinto the non-linear world and comeup with practical ideas that get theknowledge of SD, chaos and


complexity out of its academicpurgatory. Practitioners need to takerisk analysis practices to the next level(after all, putting theory to work in apractical way is the role of engineers).◆

REFERENCES1. AACE International, “Escalation

Estimating Using Indices andMonte Carlo Simulation,”Recommended Practice (draft)68R-11, AACE International,Morgantown, WV, (latestrevision).

2. AACE International, “IntegratedCost and Schedule Risk Analysisand Contingency DeterminationUsing Expected Value,”Recommended Practice (draft)65R-11, AACE International,Morgantown, WV, (latestrevision).

3. AACE International, “Risk Analysisand Contingency DeterminationUsing Parametric Estimating,”Recommended Practice 42R-08,AACE International, Morgantown,WV, (latest revision).

4. AACE International, “Risk Analysisand Contingency DeterminationUsing Parametric Estimating –Example Models as Applied forthe Process Industries,”Recommended Practice 43R-08,AACE International, Morgantown,WV, (latest revision).

5. Ackermann, F, C. Eden, T. Williamsand S. Howick, “Systemic RiskAssessment: A Case Study,”Journal of the OperationalResearch Society, January 2006.

6. Ameen, Massod and Mini Jacob,“Complexity in Projects: A Studyof Practitioners’ Understanding ofComplexity in Relation to ExistingTheoretical Models,” MastersThesis, Umea School of Business,Sweden, 2009.

7. Baccarini, David, “The Concept ofProject Complexity: A Review,”International Journal of ProjectManagement, Vol 14, 1996.

8. Bertelsen, Sven and Lauri Koskela,“Avoiding and Managing Chaos inProjects,” 11th Annual

Conference on Lean Construction,Blacksburg, VA, July 2003.

9. Bertelsen, Sven, "Construction asa Complex System," 11th AnnualConference on Lean Construction,Blacksburg, VA, July 2003.

10. Cooper, Kenneth G., “Naval ShipProduction: A Claim Settled and aFramework Built,” Interfaces,Informs, Catonsville, MD,December, 1980

11. Finnerty, John D., ProjectFinancing-Asset Based FinancialEngineering, 3rd edition, JohnWiley & Sons, Hoboken, NJ, 2013.

12. Flyvbjerg, Bent, Mette SkamrisHolm and Søren Buhl,“Underestimating Costs in PublicWorks Projects; Error or Lie?,”APA Journal, 68:3, 279-295, 2002.

13. Gul, Saleem and ShahnawazKhan, “Revisiting ProjectComplexity: Towards aComprehensive Model of ProjectComplexity,” Proceedings, 2ndInternational Conference onConstruction and ProjectManagement, ACSIT PressSingapore, 2011.

14. Hackney, John W., Control andManagement of Capital Projects,2nd Edition (Chapter 56), CD,AACE International, MorgantownWV, 2002.

15. Hollmann, John K., “EstimateAccuracy; Dealing with Reality,”Cost Engineering journal, AACEInternational, Nov/Dec 2012.

16. Hollmann, John K.,“Recommended Practices for RiskAnalysis and Cost ContingencyEstimating,” 2009 AACEInternational Transactions, AACEInternational, Morgantown, WV,2009.

17. Hollmann, John K., “The Monte-Carlo Challenge: A BetterApproach,” 2007 AACEInternational Transactions, AACEInternational, Morgantown, WV,(latest revision).

18. Hollmann, John K. and Larry R.Dysert, “Escalation Estimation:Working With EconomicsConsultants,” AACE InternationalTransactions, 2007.

19. Kenney, Greg, “Assessing ProjectComplexity and Risk and theCapacity to Manage It: TheGovernment of Canada’sApproach,” Treasury Board ofCanada Secretariat, PMIMarketplace, ProjectManagement Institute, Inc.,2012.

20. Kuhfittig, Peter, and ThomasDavis, “Predicting theUnpredictable,” Cost Engineeringjournal, AACE International,February, 1990.

21. Maidment, Peter and MartinGough, “The Stability ModelMethod of Risk Management andEarly Prediction of ProjectPerformance,” The Revay Report,Revay & Assoc. Ltd., February2006.

22. Merrow, Edward W., IndustrialMegaprojects, John Wiley &Sons, Inc., New York, NY, 2011.

23. Merrow, Edward, W., “Why LargeProjects Fail More Often;Megaproject Failures:Understanding the Effects ofSize,” Presentation to the JointMeeting of the AACE NationalCapital Section and AmericanSociety of Mechanical EngineersSection, April 20, 2011.

24. Merrow, Edward W., “4 out of 5Oil and Gas Mega Projects Fail,But Why,” Podcast, Oil and GasJournal IQ(www.oilandgasiq.com), July 1,2013.

25. Merrow, Edward W., Kenneth E.Phillips and Christopher W.Myers, “Understanding CostGrowth and PerformanceShortfalls in Pioneer ProcessPlants,” R-2569-DOE, p48, TheRAND Corporation, 1981.

26. Ogilvie, Alexander, Robert A.Brown Jr., Fredrick P. Biery andPaul Barshop, “QuantifyingEstimate Accuracy and Precisionfor the Process Industries: AReview of Industry Data,” CostEngineering journal, AACEInternational, Nov/Dec 2012.

27. Palisades Knowledge Basis,Techniques and Tips, @RISK


Distributions, 3.31 BimodalDistribution,

http://kb.palisade.com/index.php, 3.31. Bimodal Distribution

28. Rolstadås, Asbjørn, Per WillyHetland, George Farage Jergeasand Richard E. Westney, RiskNavigation Strategies for MajorCapital Projects: Beyond theMyth of Predictability, Springer-Verlag London Limited, 2011.

29. Singh, Harvir and Dr. AmarjitSingh, “Principles of Complexityand Chaos Theory in ProjectExecution: A New Approach toManagement,” Cost Engineeringjournal, AACE International,December 2002.

30. Taleb, Nassim Nicholas,Antifragile: Things That Gain

from Disorder, Random House,New York, NY, 2012.

31. Uriegas, Carlos T., “Towards aScientific Approach to CostEstimating,” 1971 AACETransactions, AACE International,Morgantown, WV, 1971.

ABOUT THE AUTHOR

John K. Hollmann,PE CEP DRMP, iswith ValidationEstimating, LLC. Hecan be contactedby sending e-mail

to: [email protected]


Do an “advanced search” by “au-thor name” for an abstract listing ofall other technical articles this authorhas published with AACE. Or, searchby any total cost management subjectarea and retrieve a listing of all avail-able AACE articles on your area of in-terest. AACE also offers pre-recordedwebinars, an Online Learning Centerand other educational resources.Check out all of the available AACE re-sources.

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Cost EngineeringThe international journalof cost estimation,cost/schedule control,project management,and total costmanagement.Subscriptions areaccepted on an annualbasis. An automaticbenefit of AACEInternationalmembership, also

available to nonmembers. 5060-07 - US$72.00 (US) - US$90.00 (other countries) Please add US$8.00 for airmail - US$61 electronic subscription

Cost Engineers’ Notebook This CD-ROM is an important reference for anyproject or cost professional. It includes data andprocedures related to basic skills and knowledgethat all cost engineers should possess, extensivematerial on capital and operating cost estimation,and papers in four subject areas: cost control,planning and scheduling, project management, andeconomic analysis and business planning. 4060-28zip - Download

- US$65.00 member/US$80.00 nonmember

AACE International Recommended PracticesCost Engineering Terminology; Cost EstimateClassification System; Estimate Preparation Costs inthe Process Industries; Project Code of Accounts;Required Skills and Knowledge of a Cost Engineer;Roles and Duties of a Planning and SchedulingEngineer; Profitability Methods; plus many more. 4060-05zip - Download - US$70.00 member/US$110.00 nonmember

The Total Cost Management FrameworkJohn K. Hollmann, PE CCP, Editor, 2012 4060-20zip - Download - US$50.00 member/US$80.00 nonmember

Paper Version available through Amazon.com

2014 AACE International Transactions

5220-14 zip -Download

- US$90.00 member

- US$115.00nonmember

For CD-ROM version please contact AACE International Head-quarters

AACE INTERNATIONAL ONLINE STOREAACE INTERNATIONAL ONLINE STOREmore online at www.aacei.org

More AACE Publications at the Online Store - www.aacei.org

PPG#1: Contracts and Claims, 4th Ed.James G. Zack Jr., Editor, 2008Covers: Contract Administration; Management ofConstruction Schedules; Schedule Control; ScheduleFloat Ownership; Cost Control; Management ofChange; Cost Impacts; Productivity Impacts; Manage-ment and Analysis of Delay; Concurrent Delay Issues;Pricing of Delay; and more.

PPG#2: Risk, 3rd Ed.David C. Brady, P.Eng., Editor, 2012Covers: Dictionary; Capital Investments; Cash Flow;Competitive Bidding; Contingency Analysis; Contracts;Cost Engineering; Currency Rates; Decision Trees; Eco-nomic Analysis; Escalation; Human Factors; Manufac-turing; Research & Development; Safety & Health;Schedule; Technological Risk; and Value Engineering.

PPG#3: Cost Engineering in Aerospace and Avi-ation Sarwar A. Samad, Editor, 1998 Covers: Aerospace and Aviation.

PPG#4: Planning and Scheduling, 3rd Ed.Trevor X. Crawford, CCP, Editor, 2011Covers: Planning; Schedule Development; ScheduleManagement/Control; and Classics.

PPG#5: Earned Value, 2nd Ed.Robert A. Marshall, Editor, 2007Covers: Why Use Earned Value?; Basics of EarnedValue; Cost/Schedule Control System Criteria; ActualPhysical Percent Complete; Productivity and Earned

Value; Earned Value Reporting; Applications of EarnedValue Project Management; and more.

PPG#6: Construction Cost Estimating, 3rd Ed.Dr. Douglas D. Gransberg, PE CCP, and Carla Lopezdel Puerto, CCP, Editors, 2011Covers: Recommended Practices; Estimating Theory;Conceptual, Parametric, and Range Estimating; Esti-mating Factors and Indices; Estimating Material Costsand Quantity Surveying; Estimating Labor Costs; Esti-mating Equipment Costs; Subcontracting Costs; Esti-mating Overhead and Indirect Costs; Profit,Contingencies, and Mark-Ups; Estimating Interna-tional Construction Costs; and more.

PPG#7: Cost Engineering in the Utility Industries, 2nd Ed.Dennis M. Thompson, Editor, 2007Covers: Auditing; Cost Estimating; Cost Modeling;Cost/Schedule Control; Generation Power Plant; Nat-ural Gas Industry; Nuclear Power Plant; Other EnergyRelated Topics; Planning and Scheduling; ProjectManagement; Utility Rates; and Utility Property Valu-ation.

PPG#8: Contingency, 3rd Ed.Kul B. Uppal, PE CEP, Editor, 2010Covers: General Topics On Contingency; Cost Estimat-ing and Contingency; Risk Analysis and Contingency;and Other Related Topics.

PPG #10: Project Delivery Methods, 2nd Ed. Dr. Douglas D. Gransberg, PE CCP, Tammy L. Mc-Cuen, and Keith Molenaar, Editors, 2008 Covers: Design-Bid-Build (DBB) – DBB Estimating, DBBScheduling, DBB Project Management; ConstructionManagement (CM) – CM Estimating, CM Scheduling,CM Project Management; Design-Build (DB) – DB Es-timating, DB Scheduling, DB Project Management; In-ternational Project Delivery; Constructability; andPartnering.

PPG #11: Environmental Remediation & Decommissioning, 2nd Ed.Richard A. Selg, CCP, Editor, 2009 Covers: Environmental Remediation Planning andScheduling Methodology; Cost Estimating, Project Con-trols, Cost Modeling, and Reporting; Contingency Man-agement, Risk Analysis, and Environmental Regulations;Benchmarking and Lessons Learned; Economics of En-vironmental and Waste Management; Cost-EffectiveWaste Minimization and Pollution Prevention; Design,Construction Practices, and Other Related Topics.

PPG #12: Construction Project Controls, 2nd Ed.Dr. Douglas D. Gransberg, PE CCP, and Eric Scheepbouwer, Editors, 2010 Covers: Introduction to Construction project Controls;Cost Control; Schedule Control; Quality Control; Doc-ument Control; Computer Applications; and Interna-tional Project Controls

PPG #13: Parametric and Conceptual Estimating, 3rd Ed.Larry R. Dysert, CCP CEP, and Todd W. Pickett, CCPCEP, Editors, 2012Covers: Parametric/Conceptual Estimating; Classifica-tion; Methodology; Capacity Factoring; Process andNon-Process Industries; and Systems

PPG #14: Portfolio and Program Management,2nd Ed. Randy R. Rapp, PE CCP, Editor, 2007 Covers: Enterprise Management: General Imperatives andConcerns; Asset Requirements Elicitation and Analysis;Asset Planning and Investment Decision-Making; Asset Per-formance Assessment and Change Management; and Pro-gram Management.

PPG #15: Life-Cycle Cost AnalysisDr. Carla Lopez del Puerto, CCP, and Dr. Douglas D.Gransberg, PE CCP, Editors, 2012 Covers: Life-Cycle Cost Theory; Life-Cycle Cost Methods,Determining Discount Rate; Estimating Capital Cost ofDesign and Construction; Estimating Operating Costs;Estimating Salvage/Residual Value; Estimating Sustain-ability; Life-Cycle Cost Risk Analysis; Life -Cycle CostCase Studies; Life-Cycle Cost Analysis in the Interna-tional Context

PPG #16: Cost Engineering in the Global Environment, 2nd Ed.Kul B. Uppal, PE, Editor, 2011 Covers: General Topics on International Projects; Appli-cable AACE International Recommended Practices; CostEstimating Methodology; Risk and Contingency; andMiscellaneous Topics

PPG #17: Public Sector EstimatingJoseph L. Macaluso, CCP, Editor, 2007 Covers: Basis of Estimates; Labor Costs; Overhead andProfit; Soft Costs; Bid/Estimate Reconciliation; andChange Orders

PPG #18: Green Building, 2nd Ed.Joseph L. Macaluso, CCP, Editor, 2012 Covers: Recognition of Affects and Economic Costs onthe Environment; Formulating Ways of AddressingGreen Building Strategies and Associated EconomicCosts; Specific Green Building Strategies and ProjectCosts; Budgeting and Justifying the Cost of SustainablePractices; Evaluating Competing Sustainable Strategies:Using Value Engineering; Evaluating Competing Sustain-able Strategies: Other Techniques

PPG #19: Leadership and Management of PeopleJohn J. Hannon, CEP, Editor, 2008 Covers: Leadership; Teams; Leadership Roles; Motiva-tion; and Ethics.

PPG #20: Forensic Schedule AnalysisJames G. Zack, Jr., CFCC, Editor, 2008 Covers: Recommended Practice No. 29R-03 ForensicSchedule Analysis; Synopsis of Recommended Practice;Basics of Schedule Delay Analysis; MIP-ObservationalStatic Gross; MIP-Observational Static Periodic; MIP-Ob-servational Dynamic Contemporaneous As-Is; MIP-Ob-servational Dynamic Contemporaneous Split;MIP-Observational Dynamic Modified or Recreated; MIP-Modeled Additive Single Base; MIP-Modeled AdditiveMultiple Base; MIP-Modeled Subtractive Single Simula-tion; Non-CPM Schedule Delay Analysis Techniques;General Schedule Analysis Articles

PPG#21: Cost Engineering in the Process Industries Kul B. Uppal, PE CEP, Editor, 2009Covers: General Topics on Process Industries; Cost Es-timating Methodology; Project Management; Inter-national Projects; Scheduling; Construction Activities;Risk Management; Project Controls; and ApplicableAACE International Recommended Practices.

The AACE International Professional Practice Guides (PPGs)The AACE International Professional Practice Guides (PPGs)

(PPGs) are a series of reference s thatconsists of selected Cost Engin eering articles, AACE Inter national Transactionpapers, and other previously publisheddocuments to which AACE has rights.

Price per PPG: Download Member Price US$50.00

Download Non-Member Price US$70.00

Price for the PPG Package includes all 21 PPGs:

Download Member Price US$874.00Download Non-Member Price

US$1223.00


JANUARY 201529 Owners’ Night,Southern California Chapter ofConstruction Management Associ-ation of America (CMAA)The Grand Conference CenterLong Beach, CA

Contact: www.cmaasc.org

FEBRUARY 20155 2015 Western Winter Workshop,San Francisco Bay Area Sectionand Southern California Section of AACE InternationalHyatt Regency Lake TahoeIncline Village, NV

Contact: www.aaceisf.org

19 Change Orders: Best Practices,Southern California Chapter ofConstruction Management Associ-ation of America (CMAA)The Grand Conference CenterLong Beach, CA

Contact: www.cmaasc.org

MARCH 20156 2-Day Winter Seminar,Chinook-Calgary Section of AACE InternationalThe International HotelCalgary, Canada

Contact: www.aaceicalgary.org

MAY 20156 Cost Estimating and ProjectControl - Closing the Loop,Cost EngineeringHotel AraZwijndrecht, the Netherlands

Contact: www.costengineering.eu

JUNE 201528-July 1 AACE International’s2015 Annual Meeting, AACE InternationalMGM GrandLas Vegas, NV

Contact: phone 1-800-858-COSTfax (304) [email protected]

AACE International, 1265 Suncrest Towne Centre Dr,Morgantown, WV 26505-1876USA phone: 304-296-8444 fax: 304-291-5728 e-mail: [email protected]: www.aacei.org

Please submit items for futurecalendar listings at least 60 daysin advance of desired publication.

CALENDAR OF EVENTS

Recruiting qualified professionals has never been easier.

AACE CAREER CENTER

The AACE Career Center is themost effective way to find leadingpractitioners in the total costmanagement profession. Unlikegeneric job posting services, AACEInternational commits to not onlyhelping you hire the best personfor your position, but also helpsyou develop that individual totheir fullest professional potentialby offering a complimentaryAACE International membershipfor the balance of the year theperson is hired or a $150 discounttoward registering for an AACE In-ternational credential such asCCP, CEP, CFCC, EVP, or CCT.*

About AACE InternationalSince 1956, AACE Internationalhas been the leading-edgeprofessional society for projectmanagers, schedulers, costestimators, cost engineers, andproject control specialists. AACEInternational is the authority fortotal cost management.Promoting the planning andmanagement of projects,programs, and portfolios, AACEInternational is the largestorganization serving the entirespectrum of project managementprofessionals. AACE Internationalis industry independent , andhas members in over 80countries.

*In order to qualify for this incentive, your companymust advertise an employment position with AACE International’sCareer Center for at least two months. Once you hire a person forthat position, regardless of the source, AACE International will giveyou the option of either having that new person’s membership paidfor the balance of the year or a $150 credit toward the new hire earn-ing his or her AACE International credential. This is non-transferable.Should the person you hire already be a member in the current year,we will extend their membership for another full year. New hiresmade after October 1 will receive membership benefits for the bal-ance of the current year plus the entire next year. If you are not fa-miliar with the many benefits of being an AACE Internationalmember, we invite you to review our online membership presenta-tion at www.aacei.org/mbr/presentation/

The AACE Career Center helps stream-line your hiring process with unmatchedexposure for job listing and, higher qual-ity candidates. Because AACE membersare among the most skilled and besttrained total cost management profes-sionals in the world, the AACE CareerCenter offers a highly targeted pool ofexceptional talent, which is an asset toyour business.

AACE Career Center offers:

• Quick and easy job posting

• Quality candidates

• Online reports provide you with job activity statistics

• Simple pricing options

START LOOKING FOR THE BEST...

1265 Suncrest Towne Centre DriveMorgantown, WV 26505-1876800.858.COSTwww.aacei.org/career

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