Effective Production Rate Estimation Using Construction Daily Work


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EFFECTIVE PRODUCTION RATE ESTIMATION USING CONSTRUCTION DAILY WORK REPORT DATA
FHWA/MT-19-001/9344-504 Final Report
prepared for
THE STATE OF MONTANA DEPARTMENT OF TRANSPORTATION
in cooperation with
THE U.S. DEPARTMENT OF TRANSPORTATION FEDERAL HIGHWAY ADMINISTRATION
January 2019
prepared by H. David Jeong, Ph.D. Chau Le Vijay Devaguptapu Institute for Transportation Iowa State University Ames, IA
RESEARCH PROGRAMS

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Effective Production Rate Estimation Using
Construction Daily Work Report Data
Phase I Report

Effective Production Rate Estimation Using Construction Daily Work Report Data Phase I Report
Principal Investigator Dr. H. David Jeong Professor
Center for Transportation Research and Education, Institute for Transportation Iowa State University
Chau Le, and Vijay Devaguptapu Graduate Research Assistants
Sponsored by Montana Department of Transportation
A report from Institute for Transportation
Iowa State University 2711 South Loop Drive, Suite 4700
Ames, IA 50010-8664 Phone: 515-294-8103
Fax: 515-294-0467 www.intrans.iastate.edu

TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

2. Government Accession No.

FHWA/MT-19-001/9344-504

4. Title and Subtitle

Effective Production Rate Estimation Using Construction Daily Work Report Data

3. Recipient’s Catalog No.
5. Report Date January 2019 6. Performing Organization Code

7. Author(s)

8. Performing Organization Report No.

H. David Jeong, Ph.D. (orcid.org/0000-0003-4074-1869), Chau Le (orcid.org/0000-0002-2582-2671), and Vijay Devaguptapu.

InTrans Project 16-584

9. Performing Organization Name and Address

10. Work Unit No.

Construction Materials and Technology Program Institute for Transportation Iowa State University 2711 South Loop Drive, Suite 4700 Ames, IA 50010-8664

11. Contract or Grant No. 9344-504

12. Sponsoring Organization Name and Address

13. Type of Report and Period Covered

Research Programs Montana Department of Transportation (SPR) 2701 Prospect Avenue PO Box 201001 Helena MT 59620-1001

Phase I - Final Report 14. Sponsoring Agency Code 5401

15. Supplementary Notes

Research performed in cooperation with the Montana Department of Transportation and the US Department of Transportation, Federal Highway Administration. This report can be found at https://www.mdt.mt.gov/research/projects/const/production_rates.shtml.

16. Abstract

Accurate and practical production rate estimates are crucial for an accurate forecast of contract completion time. As costs of highway projects increase with time, the importance of estimating highway construction contract time has increased significantly, thereby emphasizing the need for effective production rates due to the interrelatedness between the two. By reviewing the literature, various aspects of production rate estimation were identified including factors that influence production rates, production rate adjustment factors, and statistical methods, and current practices of the Montana Department of Transportation (MDT). The purpose of this research was to develop historical data-driven estimates of production rates using daily work report (DWR) data in order to enhance current contract time determination practices.

The research team analyzed the MDT’s DWR data along with bid data and GIS data to estimate realistic production rates. Descriptive analysis, regression analysis, and Monte Carlo simulation were deployed to offer insights into historical projects’ characteristics and production rates of 31 controlling activities of MDT. The major findings of the descriptive analysis were statistical measures (i.e., mean, first quartile, median, and third quartile) of 31 controlling activities, which provide more practical, detailed, and updated estimates in comparison with the current published values. In addition, variations of production rates in terms of different seasons of work, districts, area types (urban/rural), and budget types were explored. The study also developed regression equations to estimate production rates of 27 out of 31 controlling activities. For each activity, factors that had a significant effect on production rate were included in the regression model as predictor variables. Besides, a production rate-based method was proposed to evaluate contractor’s performances, and a Microsoft Excel based Production Rate Estimation Tool (PRET) was developed to assist MDT practitioners.

17. Key Words

18. Distribution Statement

Production, Productivity, Output, Production control, Data logging, Data collection, Data analysis, Reliability (Statistics), Contractors, Timetables, On time performance, Turnaround time, Value of time, Progression, Documents, Weather conditions, Production control, Time management, Time studies, Work load, Quality of work, Efficiency.

No restrictions.

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price

Unclassified. Form DOT F 1700.7 (8-72)

Unclassified.

63 Reproduction of completed page authorized

v

Table of Contents

1. Introduction

1

2. Literature review

2

2.1. Production rate estimation

2

2.1.1. Factors influencing production rates of major work items

2

2.1.2. Production rate estimation methods

7

3. Current practices of MDT

9

4. Descriptive analysis of DWR data

11

4.1. Data description

11

4.2. Descriptive analysis

12

4.2.1. Seasonal variation of production rates

14

4.2.2. District level comparison of production rates

14

4.2.3. Urban and rural production rate comparison

16

4.2.4. Budget-based comparison of production rates

17

5. Predictive analysis of DWR data

19

5.1. Production rate database

19

5.2. Data characteristics

22

5.3. Regression models to predict production rates

25

5.3.1. Excavation - unclassified

27

5.3.2. Crushed aggregate course

28

5.3.3. Topsoil-salvaging and placing

30

5.3.4. Special borrow

31

5.3.5. Cold milling

32

5.3.6. Plant mix surfacing

33

5.3.7. Guardrail steel

35

5.3.8. Curb and gutter

37

5.3.9. Farm fence

38

5.3.10. Seeding

39

5.4. Statistical measures of production rates of controlling activities

40

5.5. Evaluation of contractors’ performance

42

5.5.1. Comparisons of production rates of three tiers

44

5.5.2. Production rate comparison among contractors

49

6. Production rate estimation tool (PRET)

50

6.1. The significance of the tool

50

6.2. Development of the tool

50

6.3. Guideline for usage of the tool

50

6.4. Limitations of the tool

50

7. Conclusions

51

8. References

53

vi

List of Figures
Figure 2-1: Factors that significantly impact production rates ....................................................... 3 Figure 2-2: Effect of location on production rates.......................................................................... 4 Figure 2-3: Effect of route types on production rates ..................................................................... 4 Figure 2-4: Effect of seasons on production rates (Jeong and Woldesenbet 2010)........................ 5 Figure 2-5: Effect of quantities on production rates ....................................................................... 6 Figure 3-1: Contract time determination process of MDT ............................................................. 9 Figure 3-2: Sample bar chart developed for rural reconstruction by MDT .................................. 10 Figure 4-1: Key variables selected from DWR data..................................................................... 11 Figure 4-2: District boundaries and project locations in Montana. .............................................. 12 Figure 4-3: Ratios of production rates between construction and winter season ......................... 14 Figure 4-4: Ratios of production rates of districts to those of state average................................. 15 Figure 4-5: Production rate comparisons between rural and urban areas ..................................... 17 Figure 4-6: Production rate comparisons between two budget types ........................................... 18 Figure 5-1: Data sources to form the production rate database .................................................... 19 Figure 5-2: Percentages of each type of projects in the dataset .................................................... 23 Figure 5-3: Percentages of projects undertaken in each district ................................................... 24 Figure 5-4: Percentages of each type of projects in each district ................................................. 25 Figure 5-5: Quantity versus production rate from DWR data (EU) ............................................. 27 Figure 5-6: Available equipment versus production rate from DWR data (EU) .......................... 28 Figure 5-7: Budget versus production rate from DWR data (EU)................................................ 28 Figure 5-8: Quantity versus production rate from DWR data (CAC) .......................................... 29 Figure 5-9: Available equipment versus production rate from DWR data (CAC) ....................... 29 Figure 5-10: Quantity versus production rate from DWR data (TSP).......................................... 31 Figure 5-11: Quantity versus production rate from DWR data (SB)............................................ 32 Figure 5-12: Quantity versus production rate from DWR data (CM) .......................................... 33 Figure 5-13: Quantity versus production rate from DWR data (PMS)......................................... 34 Figure 5-14: Area type versus production rate from DWR data (PMS) ....................................... 35 Figure 5-15: Quantity versus production rate from DWR data (GS)............................................ 36 Figure 5-16: Work type versus production rate from DWR data (GS)......................................... 36 Figure 5-17: Quantity versus production rate from DWR data (C&G) ........................................ 37 Figure 5-18: Quantity versus production rate from DWR data (FF) ............................................ 39 Figure 5-19: District versus production rate from DWR data (FF) .............................................. 39 Figure 5-20: Quantity versus production rate from DWR data (Seeding).................................... 40 Figure 5-21: Production rate distribution of tier 1, CAC.............................................................. 44 Figure 5-22: Production rate distribution of tier 2, CAC.............................................................. 45 Figure 5-23: Production rate distribution of tier 3, CAC.............................................................. 45 Figure 5-24: Production rate distributions of tier 1 and tier 2, CAC ............................................ 46 Figure 5-25: Production rate distributions of tier 2 and tier 3, CAC ............................................ 46 Figure 5-26: Production rate distribution of tier 1, PMS .............................................................. 47 Figure 5-27: Production rate distribution of tier 2, PMS .............................................................. 47 Figure 5-28: Production rate distribution of tier 3, PMS .............................................................. 48 Figure 5-29: Production rate distributions of tier 1 and tier 2, PMS ............................................ 48 Figure 5-30: Production rate distributions of tier 2 and tier 3, PMS ............................................ 49
vii

List of Tables
Table 4-1: Production rates of controlling activities..................................................................... 13 Table 4-2: Districts and their low production rate controlling activities ...................................... 16 Table 5-1: Controlling activities and corresponding item codes .................................................. 20 Table 5-2: Project work types and type codes .............................................................................. 21 Table 5-3: Factors included in the regression models .................................................................. 26 Table 5-4: Statistical measures of production rates of controlling activities................................ 41 Table 5-5: Major factors in evaluation of a contractor (Dye et al. 2014) ..................................... 43
viii

1. Introduction
Contract time for state highway projects is the maximum time allowed in the contract for completion of all work contained in the contract documents (FHWA 2002). An accurate forecast of contract time is crucial to contract administration as the predicted duration and associated cost form a basis for budgeting, planning, monitoring and even litigation purposes (Jeong et al. 2008). Excessive contract time is costly because it extends the construction crew’s exposure to traffic, prolongs the inconvenience to the public (unnecessary increase of road user costs), hinders local businesses, increases the construction costs, and subjects motorists to less than desirable safety conditions for longer periods of time (Chong et al. 2011). Insufficient contract time results in higher bids, overrun of contract time, increased claims, substandard performance, and safety issues. Due to significant importance of contract time determination, Title 23 Code of Federal Regulations (CFR) Section 635.121 requires that States should have adequate written procedures for the determination of contract time and most State Department of Transportations (DOTs), including Montana DOT (MDT) have a written document describing their procedure to determine a project’s contract time. Since a transportation agency maintains numerous ongoing projects under its portfolio, accurate contract time estimation will lead to the timely completion of projects, better success rate and efficient use of funds.
The quantity of production accomplished over a specified period is termed as production rate. Realistic production rates are the key to determining reasonable contract times which are neither excessive nor inadequate (Herbsman and Ellis 1995). Conventionally, the state agencies publish the production rates to be used uniformly across the state. This practice helps to follow the Federal Highway Administration (FHWA) guidelines to implement uniform production rates across the states. However, it has intrinsic constraints – the production rates vary greatly depending upon the quantity to be produced, type of project, geographical location of the project, the budget allocated for the project, seasonal limitations, weather, and contractors’ capacity (Aoun 2013).
This Phase I report summarizes the findings from extensive literature review on production rate estimation and the results of the descriptive and predictive analysis of daily work report (DWR) data. The findings from the literature are discussed in section 2. Current contract time determination procedures of MDT are reviewed in section 3. Section 4 provides an insight into parameters that significantly influence production rates, which are determined by the results of the descriptive analysis of DWR data. Section 5 consists of the development of regression models for production rate estimations, statistical measures of production rates from historical data, and a proposed method to evaluate contractors’ performance using production rates obtained from past projects. A production rate estimation tool was developed based on the results of section 5 and is discussed in section 6.
1

2. Literature review
This section discusses major factors that affect production rates of controlling work activities for highway projects and the range of tools used for production rate estimation. FHWA (2002) recommends that in estimating production rates of work items, an accurate database should be established by using normal historical rates of efficient contractors. The most accurate data can be obtained from reviewing project records (i.e., DWR data and other construction documents) where the contractor’s progress is clearly documented based on work effort, including work crew makeup during a particular time frame (Hildreth 2005).
Conventionally, most state DOTs use a rule of thumb and/or a published list of production rates that were developed years ago. Since highway construction is an outdoor construction operation that involves several types of activities that are heavily affected by a number of operational and environmental conditions, common production rate estimation methods such as expert opinion, engineering judgment, and production rate charts have serious limitations. One of the main limitations is that unique project factors and site conditions are very difficult to be considered quantitatively (FHWA 2002).
2.1. Production rate estimation
The production rates of major construction activities, which fall on the critical path in the project schedule, play an important role in planning project resources and tracking project progress (Jeong and Woldesenbet 2010). Use of static production rates was found in some form across numerous contract time determination manuals. The production rate tables provided by DOTs consisted of highway work items ranging from 20 to over 200 items. Penn DOT has only 20 work items, yet it is used consistently because it goes through multiple reviews from multiple stakeholders. Once the production rate estimates have been modified to the satisfaction of the stakeholders, it is then used to determine the project completion date and project duration. The accuracy of the estimated production rates is very crucial for effective contract administration. Studies suggest that the significant factors that influence production rates are weather and seasonal effects, project location, traffic impacts, project types, etc. (Jeong and Woldesenbet 2010).
2.1.1. Factors influencing production rates of major work items
Establishing factors that influence the production rates in a region is critical for improving accuracy in production rate estimates. Numerous production rate estimation and validation studies clearly show that production rates vary widely depending upon project-specific factors (Jeong and Woldesenbet 2010). Some of the common factors which influence production rates are location, route type, weather, project type, and operating conditions. When those factors are appropriately incorporated into the production rate estimation process, the contract time determination process will be more accurate and become meaningful for contract administration. An advanced and consistent estimation system which accommodates unique project factors can provide production rate estimates with higher accuracy. Common factors found in the literature are portrayed in Figure 2-1.
2

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Effective Production Rate Estimation Using Construction Daily Work