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1 0 0 0 BUNDLING OR GROUPING PAVEMENT AND BRIDGE PROJECTS: ANALYSIS AND STRATEGIES Yingge Xiong Haas School of Business University of California Berkeley yinggexiong@gmail.com Jon D. Fricker, Ph.D. (Corresponding author) Lyles School of Civil Engineering Purdue University 0 Stadium Mall Drive West Lafayette, IN 0 Tel: -0; fricker@purdue.edu Samuel Labi, Ph.D. Lyles School of Civil Engineering Purdue University 0 Stadium Mall Drive West Lafayette, IN 0 Tel: -; labi@purdue.edu Word count in abstract (), text (0), references (0), figures (0), and tables (00) = words Submission Date: August 0 Submitted for Presentation at the th Annual Meeting of the Transportation Research Board and Publication in the Transportation Research Record

2 Xiong, Fricker and Labi 0 0 ABSTRACT Project grouping is a concept that aims at reducing the overall costs of projects by combining a number of smaller-scale pavement, bridge and other projects under a single contract. To investigate the potential for state agencies to realize the benefits of project grouping, this study accomplished the following:. Documented the economies of scale the decline in unit cost as the size of the project increases for several bridge and pavement work types. Scale economies are true for both single-project contracts and multiple-project contracts.. Investigated economies of competition. Larger contracts lend themselves to economies of scale, but they can discourage all but the largest firms from bidding on the work. As a result, there appears to be a range of contract sizes (measured in this study in terms of bridge deck area or lane-mile of pavement) within which the contract is neither too small nor too big. Based on the work types for which adequate data were available, the size of combined projects that led to the best balance between economies of scale and economies of competition are: About 0,000 sq ft of deck area for bridge projects -0 lane-miles for pavement projects. The single-project and multiple-project unit cost models developed in this project can be used to screen projects for which engineer s estimates are available, to identify single projects that should be considered for grouping into a multiple-project contract. Keywords: Bundling Projects, Grouping Projects, Pavement Projects, Bridge Projects ABBREVIATIONS USED IN THIS PAPER MPC Multiple-Project Contract NB Number of bidders SAC Study Advisory Committee SPC Single-Project Contract SPMS Scheduling Project Management System VIF Variation Inflation Factor

3 Xiong, Fricker and Labi BACKGROUND At the time our research study began in 0, the Indiana Department of Transportation (INDOT) had limited experience with grouping (or bundling) multiple projects into one contract. One noteworthy case was work done along I- on the west side of Indianapolis in years Bids had been solicited for three end-to-end projects, but INDOT also asked for bids on one multiple-project contract, achieved by grouping the three projects. (Where many agencies use the term bundling, INDOT often uses the term grouping.) If the bid for the MPC was lower than the sum of the three SPCs, the work would be carried out under one MPC, which is what happened. The grouped projects made work zone maintenance of traffic easier to coordinate and mobilization/demobilization of personnel and equipment more efficient. These efficiencies and perhaps others led to the successful completion of the projects with significant savings in costs and time. McCarthy et al. () found that nearly half of the surveyed states reported grouping of their construction projects. The combined projects had work types and/or were located near each other. Missouri DOT grouped three separate roadway projects along Interstate 0 in St. Louis. () New York DOT grouped state road segments on the basis of geographic proximity to form one multiple-project contract. () The practice of project grouping has been carried out at agencies such as Minnesota DOT and North Dakota DOT. Also, Caltrans has grouped safety projects and pavement rehabilitation projects. At the Washington DOT, grouping has been carried out for federal-aid local projects. () As our research study progressed, several state DOTs continued or increased their activity in project bundling: Pennsylvania s Rapid Bridge Replacement project () announced it would replace structurally deficient bridges throughout the state under a single, bundled contract. In 0, 00 or percent of the New York State's bridges were rated deficient. () The bridge projects were to be bundled into six statewide Design-Bid-Build contracts and four Design-Build contracts in four geographic areas. Oregon DOT continued to routinely include projects to be bundled on its list of upcoming contracting opportunities. () The recently enacted FAST Act encourages States to save costs and time by bundling multiple bridge projects as one project under one project agreement. () INDOT had been grouping small-scale projects of a routine nature over small geographic areas, but (with a few exceptions) had not grouped a large number and variety of pavement and bridge projects until April 0. Since then, selected projects were bundled so that contractors could submit a single bid to perform similar work on multiple bridges. These projects were let as design-build, which also reduced planning time. () Would a wider use of grouped projects yield significant cost savings to INDOT? If so, which work types would be more conducive to successful grouping? These are the main questions that the study () described in this paper sought to answer and, using available data, provide quantitative documentation. DATA AND PRELIMINARY ANALYSIS Data Sources A reasonable expectation is that, as the size of a project increases, its unit cost decreases. Specific unit costs of work done within each specific single-project contract were analyzed to see if cost savings were apparent within each identified work type. Unit cost vs. project size was

4 Xiong, Fricker and Labi 0 plotted for each work type in completed bridge and pavement projects to ascertain whether a greater quantity of work done could decrease the cost if projects were combined. It was not possible to find meaningful relationships within the cost components studied, given the highly variable bidding strategies employed by different contractors. It was suggested by members of the Study Advisory Committee (SAC) that a more macroscopic approach be taken, by looking at the contract level rather than at the specific work done within each contract. INDOT s Scheduling Project Management System (SPMS) database provides details on each project or contract as to location, work type, county, district, units, cost, and letting date. The data were used to create potential groupings and to identify and quantify any scale economies. Project Cost Trends The SPMS dataset had, useable observations for projects with letting dates between January, and December, 0. The expenditure data were adjusted for inflation (base year 0) using the Construction Price Index from the Bureau of Labor Statistics. After several trials and after consultation with the SAC, the physical dimensions of bridge projects were measured according to the bridge deck area (sq ft) and each bridge was put into an investment size interval, based on the dollar amount spent on the project. The six interval boundaries were: <$0.M, $0.M-$0.M, $0.M-$M, $M-$M, $M-$M, and >$M. Data for pavement projects were organized similarly, but with lane-miles used to measure the physical size of the project. Plots such as Figure indicated a clear presence of economies of scale. Only the bridge projects greater than $M failed to exhibit a distinct concave upward pattern. (a) Unit Price vs. Quantity, $0.M $M INDOT Bridge Projects, -0

5 Xiong, Fricker and Labi 0 (b) Unit Price vs. Quantity, $M $M INDOT Pavement Projects, -0 FIGURE Unit price vs. project size Classification of data for analysis In order to identify the scale economies associated with large project quantities, the data were classified on the basis of factors such as asset type, functional class, and work type. After classifying the expenditure data by functional class and asset type (pavement and bridge), the different project categories were identified within each asset type. For example, for the pavement asset type, the project categories were: Preventive Maintenance HMA Overlay; Functional HMA Overlay; Shoulder Rehabilitation and Repair; Road Rehabilitation (R/R Standards); Partial -R; Wedge and Level; Crack Sealing; and Surface Treatment, and Microsurfacing. Potential criteria for grouping were spatial proximity, timing of letting dates, similarity of work types and simultaneous-completion of work types. These observations serve as a basis for analyzing single-project costs. SINGLE PROJECT COST ESTIMATION In relating Unit Cost Y to Project Size X, several model forms were tried. The power regression form Y = ax b proved to provide the best fit. In Figure, the best fit curve for Bridge Deck Overlay projects is Y =,0*X -0.. The fitted curve demonstrates the concave upward shape associated with economies of scale. Similar curves were fitted for the ten bridge work types that had the largest number of entries in the INDOT database -- from Bridge Widening projects to Bridge Deck Overlay projects. Likewise, curves were fitted for the ten pavement work types with the largest number of entries in the INDOT database -- from Pavement Replacement projects to 0 Pavement Partial R projects.

6 Xiong, Fricker and Labi FIGURE Single-Project Contracts: Bridge Deck Overlay FACTORS THAT MAY AFFECT ECONOMIES OF SCALE Multiple-Project Contract Data In addition to the single-project contract information that is available in the SPMS dataset, it is possible to acquire information regarding projects that have been grouped by INDOT into a multiple-project contract. Unit cost data for the projects that are part of multiple-project contracts were plotted on the same chart as their single-project counterparts with the same work type. This is done for the bridge and pavement work types that had sufficient sample sizes in the SPMS database. Among the plots that were generated, Figure for the Bridge Replacement, Concrete RC Slab work type, is the easiest to understand. For any project size (sq ft of deck area), the SPC unit costs (diamonds) tend to be higher than the MPCs (squares). However, the largest projects for this work type are also SPCs. Perhaps they were too large or too isolated geographically to be combined as part of an MPC.

7 Xiong, Fricker and Labi FIGURE Single-project and Multiple-project Contracts: Bridge Replacement, Concrete RC Slab The plot for the HMA Overlay, PM pavement work type is shown in Figure because it is possible to see four squares (MPCs) that are not hidden by the mass of data points. (If viewed in color, more than a dozen red squares can be seen in the mass in a pattern that reflects economies of scale.) FIGURE Single-project and Multiple-project Contracts: HMA Overlay, PM

8 Xiong, Fricker and Labi 0 0 All the plots suggested, to a greater or lesser extent, that the projects in multiple-project contracts show a downward trend with decreasing slope for bridge and pavement work types. For work types with sufficient samples of projects in multiple-project contracts, an inverse relationship between unit price and quantity is obvious. This indicates that economies of scale also exist in the multiple-project contracts. If we combine several projects into one bigger contract, the resulting unit cost is likely to be lower than if those projects had been done individually. In bridge work types such as Precast -Sided Culvert, Bridge Painting, and in pavement work types such as HMA Overlay PM, Partial -R, the unit price of multiple-project contracts is generally lower than that of a corresponding single-project contract of the same quantity. There is evidence that, not only can larger individual projects have lower unit costs, but also increasing the number of projects in a contract can reduce the unit cost of projects in the grouped contract. However, it was found that the unit costs of projects in multiple-project contracts were not necessarily lower than the unit costs of single-project contracts. This seems to challenge the argument that grouping some projects to form a big contract will always cost less than implementing the projects individually. In addition, the large variability in the cost data indicates that there may be other contributing factors besides the quantity (project size) that influence the unit costs. To the extent that available data allow, factors such as highway functional class, time of year of bid letting, and number of bidders can be examined, in order to provide further insight into these trends and ultimately, to enhance decisions regarding projects grouping. Effects of Functional Class The two work types with the largest sample size from both bridge and pavement were selected for a sample analysis: () Bridge Deck Replacement and Widening, () Bridge Replacement, () HMA Overlay, PM, and () Road Rehabilitation R/R. Projects in both single-project and multiple-project contracts were used and categorized by interstate/non-interstate functional class in order to examine if functional class affects the pattern of scale economies for each work types. Of the four plots, Figure is shown because it is the least cluttered.

9 Xiong, Fricker and Labi FIGURE Interstate and Non-Interstate Projects: Road Rehabilitation R/R Because it is difficult to clearly distinguish between interstate and non-interstate project unit costs, a two-sample t-test for a sample mean comparison and for a sample variance comparison for the unit costs of the two samples was also conducted. The t-test results suggest that one can be.% confident that the interstate/non-interstate classification of projects makes little difference in the sample mean and sample variance of unit cost across all the sampled work types. See the Accept Null test results in Table. Therefore, the functional class has little influence on the mean and variance of the unit cost. TABLE Sample Mean and Sample Variance Tests for Two Samples in Interstate/Non- Interstate Projects ( 0. 0) Work Type Sample Bridge Deck Replacement and Widening Bridge Replacement HMA Overlay, PM Road Rehabilitation R/R t-stats for Sample Mean Comparison P- value Test Result Accept Null Accept Null Accept Null Accept Null F-stats for Sample Variance Comparison P-value e- Test Result Accept Null Accept Null Accept Null Reject Null

10 Xiong, Fricker and Labi 0 0 Effects of Seasonality Does time of year affect the strategy of bidders, which would affect project unit costs? To begin an analysis of the effects of seasonality on project costs, we looked at project data for the four work types with the largest sample size. Plots of project unit cost vs. quantity relationships in terms of four seasons in a calendar year: st Quarter (Jan.-Mar.), nd Quarter (Apr.-Jun.), rd Quarter (Jul.-Sep.), and th Quarter (Oct.-Dec.). The projects were assigned to seasons based on the letting date information given in the SPMS dataset. We used a two-sample K-S test () to examine the empirical distribution difference between datasets across work types. Each quarter was compared to the other quarters. In no case did the pairwise comparison indicate that the null hypothesis could be rejected. The conclusion is that seasonality generally has little effect on the unit cost of highway projects. Effect of Number of Bidders A review of the literature indicates that the cost of highway projects can be affected by the level of bid competition, which is represented by the number of bidders. Regression analyses were applied to all the work types that had sufficient samples: Bridge Replacement ( observations), Bridge Deck Replacement and Widening ( obs.), Bridge Deck Overlay ( obs.), Bridge Rehabilitation or Repair (0 obs.) and Pavement Wedge and Level ( obs.). The first and last of these five work types are presented here. Bridge Replacement Among all the functional forms (linear, polynomial, logarithm, power) we tried, the best goodness-of-fit in terms of r-squared was exhibited by the power regression model: Unit Price = b Qty c a * * NB, 000 where Qty = bridge deck area (sq ft), a, b, and c are estimable parameters, NB = number of bidders, and the error term ԑ is an identically-independently distributed standard normal random variable. The estimation results and the corresponding ANOVA table are presented in Table. TABLE Parameter Estimates for Bridge Replacement % Confident Interval Parameter Estimate Std. Error Lower Upper Bound Bound a b c R squared = 0.. VIF for multicollinearity test = <<, indicating the lack of collinearity between independent variables. All the estimated coefficients in the table fell into the % confident interval, therefore it can be inferred that we are % confident that all the parameters given in the table are statistically significant. This confirms the relationships among unit price, quantity and number of bidders. Additionally, the coefficients b and c were estimated to be negative, which supports the argument that economies exist in both unit price vs. quantity (economy of scale) and unit price vs. the number of bidders (economy of competition). In other words, the bridge ()

11 Xiong, Fricker and Labi 0 0 replacement project unit cost can be lowered by either increasing the project size or getting more contractors to participate in the bidding. Comparing the elasticity of these two variables, the elasticity of the quantity is slightly larger than that of the number of bidders. The trends suggest that, given the available data, increasing the project size by ten percent has a larger effect on unit cost reduction than a ten percent increase in the number of bidders. The ultimate model can explain roughly percent of the total variation in the unit cost, according to the r-squared. To eliminate concerns about the causal relationship between project size and number of bidders, which can cause biased and inconsistent estimates, a Variation Inflation Factor (VIF) test for multicollinearity was carried out. The test results indicate a lack of correlation between the two explanatory variables and support the validity of the estimation results. For comparison purposes, the same data were analyzed using a power regression involving quantity as the only explanatory variable: Unit Price = b Qty a*,000 The results a = 0., b = -0., R squared = suggest that the inclusion of the number of bidders in modeling can explain % more variation in the response variable when studying the unit cost. Pavement Wedge and Level The only pavement work type that had sufficient sample size for statistical modeling was Wedge and Level. The power regression model best explains the inverse relationships in unit price vs. quantity and unit price vs. number of bidders, at the % confidence level. Unlike the results for the bridge work types, the elasticity of the number of bidders was larger than that of project size. The inclusion of the number of bidders in the power regression model was justified because it explains an additional % of the variation in the unit cost data. See Table. TABLE Parameter Estimates for Pavement Wedge and Level (a) With Number of Bidders Parameter Estimate Standard Error % Confidence Interval Lower Bound Upper Bound a b c * R squared = 0.. ** VIF for multicollinearity test = <<, indicating the lack of collinearity between independent variables. (b) Without Number of Bidders Parameter Estimate % Confident Interval Standard Lower Upper Error Bound Bound a b * R squared = 0. ()

12 Xiong, Fricker and Labi Existence of Economies of Competition Overall, the data analysis results indicate that the inclusion of the number of bidders could explain additional variation in the project cost data when using power regression. The inverse relationship between the number of bidders and unit costs demonstrated the presence of economy of competition in the bidding phase. Having more bidders tends to lead to bids with lower unit costs, but larger contracts tend to attract fewer bidders. This tradeoff should be considered when making grouping decisions. GROUPING STRATEGIES SELECTION AND DESIGN Past Grouping Examples Five multiple-project contracts were sampled from INDOT s SPMS database to determine whether there were any cost savings due to grouping projects into one contract. For each individual project that was a part of a multiple-project contract, the single-project cost was estimated using the appropriate single-project contract cost model that was developed in this study. The results for MPC are summarized in Table. TABLE Bridge Multiple-Project Contract CONTRACT # OBSERVED AMOUNT DECK AREA (sq ft) UNIT COST EST UNIT COST EST TOT AMOUNT WORK TYPE 0 $,. $ $ $,0 Bridge Deck Overlay $,. $ $ $,0 Bridge Deck Overlay $,. $ $ $,0 Bridge Deck Overlay $,0. $ $ $, Bridge Deck Overlay Bridge Deck Replacement and $,,0. $ $ $,,0 Widening Bridge Deck Replacement and $,0,. $0 $ $,,0 Widening $,. $ $ $,0,0 Replace Superstructure $,,0. $ $ $,0,0 Replace Superstructure $, 00. $ $ $,, Bridge Deck Replacement $,0 00. $ $ $,, Bridge Deck Replacement $,0, 0. $ $ $,,0 Bridge Deck Replacement $,0, 0. $ $ $,, Bridge Deck Replacement $,,. $0 $0 $,, Bridge Deck Replacement TOTAL COST WITH GROUPING $,00,0 ESTIMATED TOTAL WITHOUT GROUPING $,, COST SAVING FROM % COST SAVINGS GROUPING $,, FROM GROUPING.% Here is a sample calculation from Table : The first Work Type in Table is Bridge Deck Overlay for a bridge with a deck area of. sq ft. (See the Work Type and Deck Area columns in Table.)

13 Xiong, Fricker and Labi Estimate the unit cost of this project in a separate contract, using the equation in Figure. Y =,0 * x -0. = $ with x =.. (See the Est Unit Cost column in Table.) Calculate the estimated total cost of the single-project contract as x*y =.*$ = $,0. (See the Est Tot Amount column in Table.) The actual (observed) total cost of the first Bridge Deck Overlay in Table when part of a multiple-project contract was $,. (See the Observed Amount column in Table.) This is much less than the $,0 estimated for a single-project contract with the deck area indicated. The sum of the estimated single-project costs ($,,) was compared to the cost of the multiple-project contract ($,00,0) in the SPMS database, showing a.% cost reduction. This process was repeated for a number of work types. It was observed that, when four Bridge Deck Overlay projects were grouped, the saving was.% and when five HMA Overlay projects were grouped, the saving was 0.0%. However, when two Part -R pavement projects were grouped, the sum of the single project costs were 0.0% less than the observed grouped cost. Likewise, when three HMA Overlay projects were grouped, the sum of the single project costs was.% lower than the observed grouped cost. Of course, the single project costs in these calculations were based on the SPC equations estimated for each work type, not on engineers estimates for the specific projects in the MPC cases studies, but they illustrate that grouping may not always be wise. Grouping Strategies Several questions must be considered when two or more candidate projects are being considered for grouping.. Are the project work types compatible? Will similar resources (personnel, equipment, etc.) be used in the projects under consideration? Can the projects be undertaken simultaneously or sequentially, so as to minimize the disruption associated with work zones or road closures?. Are the candidate projects in reasonable geographic proximity to each other? The cost and administrative advantages of project grouping may not be realized if the projects are too far apart.. Can projects that are scheduled for letting in different years be rescheduled as a multiproject contract in the same year? Will delaying a project one or more years beyond its originally-scheduled letting date be acceptable in terms of resulting deterioration in asset condition in subsequent years?. What is the best way to identify individual projects that may not be good candidates for grouping? An individual project with an engineer s estimated cost that is lower than the estimated cost that would be assigned to it by the multi-project cost model as part of a multi-project contract should probably be excluded from the grouping process. Making this kind of project part of a group may either (a) lose the economies inherent in that project or (b) lead to a multi-project contract cost estimate that is artificially high. CONCLUSIONS. The economies of scale the decline in unit cost as the size of the project increases have been documented for several bridge and pavement work types. This is true for both singleproject contracts and multiple-project contracts.

14 Xiong, Fricker and Labi In general, project cost can be reduced when a greater number of contractors submit bids. Therefore, an agency needs to identify, for each work type, the target project size that is small enough to attract adequate bidders (thus enjoying economies of competition) yet large enough to realize a lower unit cost (thus enjoying economies of scale).. After looking at numerous unit cost plots for bridge work types, the contract size that seems to reach a compromise between economies of scale and economies of competition is about 0,000 sq ft of deck area. The corresponding unit cost plots for pavement contracts are fewer in number and more difficult to interpret, but initial appearances suggest that between and 0 lane-miles may be the best contract size to capture both scale and competition economies.. Improvements in the SPMS and Bid Tab databases will make possible a stronger statistical analysis of the work types covered in this study and of work types for which data become available. A larger set of data may also make possible the study of factors other than project size, or of other ways to measure project size. ACKNOWLEDGMENTS This work was supported by the Joint Transportation Research Program administered by the Indiana Department of Transportation and Purdue University. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein, and do not necessarily reflect the official views or policies of the Federal Highway Administration and the Indiana Department of Transportation, nor do the contents constitute a standard, specification, or regulation. The authors acknowledge the contributions of Michelle Dojutrek early in the project and of Yu Julie Qiao at the project s conclusion. The Study Advisory Committee members, at various times during the research, were Brad Steckler, Louis Feagans, Tim Wells, Bob Montgomery, Dwane Myers, John Weaver, Anne Rearick, Roy Nunally, Clark Packer, Samy Noureldin, Andrew Fitzgerald, and David Holtz. REFERENCES. McCarthy, L.A., Mensching, and Horgan, A., 0. NCHRP Synthesis : Effective Delivery of Small-Scale Federal-Aid Projects. Transportation Research Board of the National Academies, Washington D.C., p.. AASHTO, 0. Missouri DOT Bundles Three Projects into One to Finish Ahead of Schedule and Under Budget. accessed December, 0.. NYSDOT, 0. Construction Begins on First NY Works Project to Repair Capital District Roads. Accessed December, 0.. USDOT 0. Pennsylvania Rapid Bridge Replacement Project, Statewide, Transportation.gov, US Department of Transportation, Updated: Friday, July, 0.. Cuomo, Andrew M. 0. Governor Cuomo Announces Accelerated Road and Bridge Projects on Long Island as Part of NY Works Program,

15 Xiong, Fricker and Labi April, 0.. ODOT. List of upcoming contracting opportunities, Oregon Department of Transportation, November 0, 0. ACEC%0Liaiso n_dk%0project%0list.pdf. INDOT 0. Collaboration Leads to Massive Statewide Bridge Preservation Effort, posted Aug., 0.. FHWA 0, NATIONAL HIGHWAY PERFORMANCE PROGRAM (NHPP), Bundling of Bridge Projects, February 0, Xiong, Yingge, Michelle Dojutrek, Samuel Labi, and Jon D. Fricker, Strategies for Grouping of Transportation Projects, SPR 0, Purdue University, July 0.. Peacock, J. A.,. Two-dimensional goodness-of-fit testing in astronomy. Monthly Notices, Royal Astronomy Society 0, -.