Improving planning reliability and project performance using the rational commitment model

The paper Improving planning reliability and project performance using the rational commitment model proposes the Rational Commitment Model (RCM), a new decision decisionmaking tool based on lean principles, which uses statistical models to obtain more reliable commitment planning at operational level. RCM allows forecasting commitment planning for short termperiods using information about workers, buffers, and planned progress. Đề tài Hoàn thiện công tác quản trị nhân sự tại Công ty TNHH Mộc Khải Tuyên được nghiên cứu nhằm giúp công ty TNHH Mộc Khải Tuyên làm rõ được thực trạng công tác quản trị nhân sự trong công ty như thế nào từ đó đề ra các giải pháp giúp công ty hoàn thiện công tác quản trị nhân sự tốt hơn trong thời gian tới.

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Journal of Construction Engineering and Management, ASCE (submitted on April, 2009)

Improving Planning Reliability and Project Performance using the Rational Commitment

Model

Abstract

Reliability of commitment planning at an operational level is one of the key factors for improving project

planning and control system based on lean production principles, has improved commitment planning

prevalent pattern in construction, in which decisions are mainly based on experience and intuition This has limited its potential for improving commitment planning reliability To overcome these limitations, this paper proposes the Rational Commitment Model (RCM), which is a new operational decision-making tool based on lean principles RCM uses statistical models to develop more reliable planning commitments at an operational level RCM allows forecasting commitment planning for short termperiods using common site information such as workers, buffers, and planned progress RCM has been tested in several case studies, demonstrating its production forecasting capabilities and its ability to help increase commitment planning reliability, thus improving project performance In addition, it is shown how RCM allows balancing load and capacity Finally, the potential impact on the construction industry

of RCM, as a simple and practical production decision-making tool, is briefly discussed

1 Postdoctoral Fellow, Depto Ing Gestión de la Construcción, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile Assistant Professor, Escuela de Ingeniería de la Construcción, Universidad de Valparaíso, Chile (Corresponding Author) E-Mail: vagonzag@uc.cl

2 Professor, Depto Ing Gestión de la Construcción, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile E-Mail: lalarcon@ing.puc.cl

3 Professor, Depto Ing Industrial y de Sistemas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile E-Mail: smaturan@ing.puc.cl

4 Well Engineer, Integrated Project Management, Schlumberger E-Mail: mundaca@slb.com 5

Planning and Development Engineer, Socovesa, Santiago, Chile E-Mail: jabustamante@socovesa.cl

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As a result, a poor project performance is achieved (Koskela, 2000) Otherwise, construction planning follows a similar behavior leading to a low performance as well (Ballard, 2000)

Among the theoretical issues of construction planning, how planning decisions are made to manage variability in construction projects is one of the most relevant (Laufer et al, 1994) In general, variability

in construction depicts varying production rates, labor productivity, schedule control, cost control, etc

Variability is a well-known problem in construction on which there is much ongoing research (Ballard, 1993; Tommelein et al, 1999; Thomas et al, 2002; among others) Some authors have recognized that traditional project management does not consider the non-linear and dynamic nature of projects (Bertelsen, 2003 and McGray et al 2002) In construction, this yields non-realistic planning outputs (e.g

schedule and budget), since planning process is based on the wrong notion that projects are static

Therefore, construction planning leads to poor management decisions since variability is not explicitly incorporated within planning process, contributing to deteriorate project performance (González, 2008)

Current construction planning not only follow the same pattern of traditional project management, where the effect of variability impacts is systematically neglected, but it also depends on intuition and experience to deal with variability As a result, planning has not been effectively managing projects and

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has not been able to accurately predict how a project should be executed (Laufer et al, 1994), imposing unrealistic expectations on the production process or failing to manage it altogether, increasing system variability (Tommelein et al, 1999)

The Last Planner System (LPS™) (Ballard, 2000) was developed to overcome these limitations in construction planning LPS™ is a production planning and control system based on lean production principles Lean production is a management philosophy focused on adding value from raw materials to finished product It allows avoiding, eliminating and/or decreasing waste from the value stream One of

planning reliability, which provides a stable production environment in projects and reduces the negative impact of variability This creates reliable work plans monitored through the percentage of plan completed (PPC) in a short-term planning horizon (operational level)

Frequently construction projects outsource most of the work to subcontractors, and commitments are arranged between contractors and subcontractors Contractors should strive to obtain reliable commitments from the subcontractors However, many of them assign work to subcontractors based on their intuition and experience, resulting in unreliable commitments (Bustamante, 2007; Sacks and Harel, 2006) Although LPS™ represents a sounder planning framework, it does not deliver an entirely rational planning process mainly at operational level where the work is executed

This paper proposes the Rational Commitment Model (RCM), a new decision decision-making tool based

on lean principles, which uses statistical models to obtain more reliable commitment planning at operational level RCM allows forecasting commitment planning for short term-periods using information about workers, buffers, and planned progress

The following sections in this paper describe the objectives, research methodology, and literature review,

as well as the theoretical and practical foundations of the RCM Then, the validation process for the RCM

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and its effect on planning reliability and project performance, and the load-capacity matching problem, in several case studies are addressed.

2 Research Objectives

The main objective of the research was to develop an operational decision-making tool to improve reliability of commitment planning and project performance, by using statistical models and site information of activities such as workers, buffers and planned progress, which we have called the Rational Commitment Model (RCM)

A secondary objective was to demonstrate how the RCM can help to match work load with on-site capacity, i.e., how it can help to balance the workload involved in work plans with the real labor capacity

of crews, by generating more reliable commitment planning for both contractors and subcontractors

3 Research Strategy and Methodology

This paper uses the case study approach (Yin, 1994), supported with statistical analysis of data obtained from several construction sites This allowed us to validate the research assumptions and the RCM, as well as to determine the impact on project performance of using RCM

The research methodology we use consisted of four stages: 1) Statement of theoretical foundations and practical framework for the RCM; 2) Definition of case studies, where repetitive processes in multifamily residential, multi-story building and industrial projects were studied; 3) RCM development and validation process where multiple linear regression (MLR) techniques were used By using weekly on-site information from case studies such as planned and actual workers, buffer sizes and planned and actual progress at activity level, MLR models were constructed and validated A two-stage approach for the validation process was applied, in which the first stage studied the robustness of mathematical formulation of the RCM, and the second stage principally analyzed the RCM validation and application

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in-action; and 4) Analysis and evaluation of RCM impacts on planning reliability and project performance using several construction samples

4 Last Planner Basics

The traditional management approach for work plans defines activities and schedule work that will be done, in terms of what should be done from a master plan Crews are being committed by management to

do whatever the schedule says should be done, with no real consideration for what they are actually able

unforeseen variations of workflow In construction, workflow is characterized by crews moving from location to location and completing the work that is prerequisite to starting work by the following crew (Tommelein et al, 1999) In general, a reliable workflow depends on the following construction preconditions that should be available whenever they are needed: resource (design, components and materials, workers, equipment, space) and prerequisites (complete work of upstream activities) (Koskela, 2000)

LPS™ overcomes previous limitations providing a predictable production environment in projects, decreasing workflow variability and creating reliable work plans to derive maximum project benefits The

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overarching criterion in the LPS™ is that activities should only be committed if they can be performed (i.e all resources and prerequisites that are needed must be available), transforming what should be done into what can be done, from which a work plan can be formulated Thus, work plans will be based on achievable assignments serving as a commitment to what will actually be done The following criteria are critical for the assignments: 1) they are well defined, 2) the right sequence is selected, 3) the right amount

of work is selected, and 4) the work selected is practical or sound, that is, can be done according to the availability of construction preconditions, which are considered during the lookahead planning

In this paper, the notion of reliability is focused on project planning, so that PPC depicts a project planning reliability index PPC is understood as the ratio between actual completed activities and planned activities A low PPC means unreliable planning and a high PPC near 100%, means the opposite From a Lean Production perspective, PPC is also a measure of workflow variability A low PPC is an indicator of

a highly variable workflow as a result of unfavourable project conditions to execute planned activities A high PPC indicates stable and predictable workflow as a result of favourable project conditions to complete planned activities

4.2 Relationship between Planning Reliability and Project Performance

LPS™ has been applied in numerous projects around the world in the last fifteen years, being reported performance improvements on a wide range of construction projects (Alarcon et al 2005, Liu and Ballard

2008, González et al, 2008a, among others) The main assumption of the system is that an increase in planning reliability, measured through PPC, should improve project performance, and mainly, productivity Recently, several researchers have demonstrated a positive and strong relationship between planning reliability and project performance, where the impact of a better planning reliability has been measured through the improvements over productivity at project level (González et al, 2008a; Liu and Ballard, 2008)

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Due to the limited evidence linking the changes in planning reliability with changes in productivity at the activity level, an in-detail study was carried out by González et al (2008a) Sacks and Harel (2008)´

research addressed a similar issue, but only from a theoretical viewpoint These authors proposed reformulation of the indicator for planning reliability used by LPS™, PPC, to carry out meaningful productivity comparisons at the activity level Therefore, a complementary ‘activity-based’ planning reliability index, called Process Reliability Index (PRI) was developed PRI is defined as (González, et al, 2008):

100 ,

PP

AP

where:

PRI represents a planning reliability index at the activity level PRI does not compare actual to planned cumulative progress because it is based on partial measurements (i.e weekly progress), which can vary from a measurement period to another PRI measures the degree of activity planning effectiveness from a commitment standpoint To measure planning reliability, PRI values range between 0 and 100% When

AP is higher than PP, the PRI value is limited to 100% (González et al, 2008a)

A study of the relation between PRI and productivity at activity level by González et al (2008a) showed that higher PRI levels lead to improved productivity This confirms the assumption that increasing

project level, producing planning reliability improvements not only at that level, but also at activity level

to get improvements in a project as a whole

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4.3 Matching Load and Capacity

Matching load with capacity is critical for productivity of production systems in construction (Ballard, 2000; Hassanein and Mellin, 1997; Thomas and Horman; 2006, among others) According to Ballard (2000), load is the amount of work in a specified time which is assigned through planning to crews In contrast, capacity is the amount of work a crew can do at any point in time with given tools and work methods for actual site conditions The problem in matching load with capacity is that, for instance, actual resource utilization and production rates of crews are production variables many times a-priori unknown, given their changeable behavior caused by wastes in conventional practices (Ballard, 2000), leading to a poor balance between load and capacity and losses of productivity

Ballard (2000) states whatever the precision of load and capacity estimates, the planner must still make some adjustments in which load can be changed to match capacity by delaying or accelerating workflow, capacity can be changed to match load by decreasing or increasing resources, or more commonly, a

match load with capacity by pulling materials and/or information into a production process or activity, only if the activity is able of doing the work, i.e., what activity needs and in the needed amounts are actually available The pulling process is carried out within Look Ahead planning (see Womack and Jones, (1996) or Hopp and Spearman (2000) for more details about pull systems)

work plans This issue can emerge when critical criteria for work assignments are not correctly defined and met during the lookahead planning Several reasons can explain it First, it is difficult to accurately

determine the right amount of work to perform by crews in work plans based only on the experience of

project personnel since it can be not very reliable and subjected to several biases (Spetzler and Von Holstein, 1975, McGray, et al, 2002) In contrast, if historical data is used, it may not be accurate enough, due to changes in current construction practices (Ramírez et al, 2004)

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Second, it is not easy to see if work is practical or sound (i.e all construction preconditions are ready for

crews to perform work) for work plans For instance, the number of necessary site-workers supplied by subcontractors to a specific project depends on business demands, i.e labor requirements from other projects If there are projects with better site conditions where the subcontractor job is more profitable, his preference will be change the labor resource until site conditions are improved in the original project

Thus, labor resource can constantly be changed from one project to another (in a weekly or even daily basis) (Lee et al, 2004; Sacks and Harel, 2006) As a result, labor resources are not ready or available whenever it is required and in the correct amount in a project Another example is when the work of a crew in repetitive processes is dependent on the activity progress of an upstream crew This is also known

as work-in-process buffer, which characterizes work units ahead of the crew that are necessary to perform its own work (González et al, 2009) Many times these buffers are not available whenever the crew needs them, since their own rate of production can be unpredictable (even at a daily level) (González, 2008;

Tommelein et al, 1999) Therefore, this precondition will be not available in the necessary timing

suggesting to change the way in which this process is carried out In this paper we argue that more rational mechanisms could support a better load and capacity matching process, thus improving the quality and reliability of work plans

5 Intuition and Rationality for Making Commitments Planning

Most of the people tend to describe and understand the world around through simplistic models of reality

This may be due to the difficulties that human beings have to manipulate large amount of information, developing in many cases mental twirls (Spetzler and Von Holstein, 1975) In construction, this kind of phenomena is prevalent in its decision-making processes given the complexity and dynamic nature of the projects, which can lead to erroneous and poor decisions (Bertelesen, 2003; McGray et al, 2002) For instance, a common practice for estimating labor productivity, and accordingly, construction schedules

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and budget, is to simply assume that work progress in projects is very stable Fig 1 shows an illustration

of this behavior by using simple linear regression models in a hypothetical construction activity In these

aleatory variable which follows a normal distribution) The regression model in Fig 1.a represents a typical estimation of labor productivity by using an 'average estimated' based on historical data, where the

ideal and non-realistic situation, since it suggests a deterministic behavior for labor productivity on-site

In contrast, Fig 1.b shows the same issue but regarding an intrinsically 'variable estimated' of labor productivity, which is captured using a regression model where site behavior of labor productivity is

relationship between production rates (probabilistically described by an expected value plus a σ), and the number of workers (held constant), although more fitted to a real production situation

a) Production rate v/s Worker-days

y = 2.80x

R 2

= 1

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

σ =1,18 (wo rk units/day)

Fig 1 Labor Productivity Estimates: (a) Deterministic Approach; (b) Probabilistic Approach

To illustrate the idea we will compute the productivity for a given number of workers A closer analysis

of the information provided by the Fig 1.a and Fig 1.b shows that for 5 worker-days a production rate for the crew of 14.0 (work units/day) and 12.8 (work units/day) can be expected respectively Otherwise, Fig

1.b provides probabilistic information for both estimates In this figure, production rates follow a normal distribution with a σ equal to 1.18 (work units/day) Then, cumulative probabilities to achieve an equal or

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higher value to 14.0 (work units/day) and 12.8 (work units/day) are 15.5% and 50% respectively Thus, Fig 1.a overestimates labor productivity in relation to Fig 1.b by 9%, and has nearly three times less chance to achieve an equal or higher estimate than in Fig 1.b (statistical details in Montgomery and Runger, 2007) Therefore, the decision-making process modeled in the Fig 1.a represents an oversimplification to estimate labor productivity with non-reliable results, which could have a cumulative effect during project execution with enormous consequences over its performance Furthermore, many times, in current practice, historical data is neglected when predicting production, relying only on the experience and intuition of decision-makers

most difficult criteria to be met is to define the right amount of work The previous discussion would

suggest that the decision-making process for defining this criterion can be oversimplified leading to

suboptimal definition of work plans Then, this may result in inaccurate amount of work performed by

To overcome the prior issues a methodological framework is proposed that strives to replace the intuition, experience and oversimplification, which is the basis for the current planning practices related to LPS, for

a framework that relies on rational assumptions to obtain more reliable work plans

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6 Rational Commitment Model Framework

RCM is based on lean principles focusing specifically on: 1) reduction of variability in production by improving planning reliability, and 2) promoting a pull production system by matching load with capacity The basic form of the RCM was initially proposed by Mundaca (2006), which was elaborated and improved by Bustamante (2007) and González et al (2008b) We will describe the conceptual and mathematical framework, as well as the RCM process validation and its application methodology

6.1 Conceptual Framework

planned activities that could limit or prevent its execution The most common constraints are design, materials, prerequisite work, space, equipment, labor and planning process (Ballard 2000), which would basically represent construction preconditions RCM regards several of these constraints as prediction variables to estimate the progress of an activity To determine these variables, we used a database with

building), from 12 Chilean construction companies, carried out by the Production Management Center (GEPUC) (Alarcón et al 2005)

remarkable repetition patterns in three RNC: Lack of Labor, Lack of Buffer, and Poor Planning The basic hypothesis for RCM is that the progress of an activity can be predicted, for a short-term planning horizon (typically one week), using a model with at least three variables: labor, buffer, planned progress The following section describes the mathematical elaboration of this relationship

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6.2 Mathematical Framework

RCM uses multiple linear regression (MLR) to formulate the model, which assumes the following form:

corresponding parameters of the dependent variables, and εi is the random error The expression for predicted progress in RCM is:

(2)

Where:

the planning horizon For instance, if the planning horizon is 1 week of 5 days, and there are 5 days, W is 25 workers

For instance, if the planning horizon is one week, the WIPBf for the painting activity, which depends on the wall-stucco activity, is the available work produced by the wall-stucco activity, measured at the

etc

linear-meters, houses, apartments, etc

RCM uses MLR models to estimate the activity progress at the operational level, based on historical data

Only significant variables are selected in the models, since including redundant variables may lead to

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and the P-value, leading to a trade-off between the number of variables, and the R2 and P-values In general, MLR models with the least number of variables, and with the highest R2 and low P-values are preferred

The prediction accuracy of RCM is evaluated using two indicators: Process Reliability Index (PRI), defined earlier and Commitment Confident Level (CCL), which is defined as:

100 ActualPRI

ActualPRI PlannedPRI

/ edicted Pr

1 PlannedCCL /

edicted Pr

j

j j

predicted and planned PRI

in Eq (1) by PRP using the RCM

decision-maker given a planned progress according to his own experience which can be progressively influenced by the RCM outputs Its value can be higher than 0% and lower or equal to 100% (typically a project planner can wish a planned PRI equal to 100%)

using Eq (1)

CCL measures the activity commitment accuracy for the predicted progress which compares the predicted and the actual PRI Similarly, it relates the planned and actual PRI Note that CCL does not measure confidence on the progress of activities When the ratio in Eq (3) is less than 0, its value is set to 0

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6.3 Nomographs for the Rational Commitment Model

RCM was originally implemented through nomographs, which relate mathematical and graphically planned progress with the other production variables (Bustamante, 2007; González et al, 2008a) Then, PRI can be understood as:

PP PRI PRP PP

=

3

2 1

0

PRI

WIPBf W

PP

(5)

Eq (5) is a general expression that relates PP with W, WIPBf, and PRI In Eq (5) PP can be a value either predefined by decision makers or estimated in the RCM Also, PRI can follow a similar criterion (i.e either planned or predicted) Fig 2 illustrates a nomograph for a repetitive housing project, showing the different variables involved The use of nomographs are common in engineering disciplines (e.g., hydrologic engineering) and can be easily applied by construction decision-makers, e.g project managers, which can use it to plan activity progress for a given resource frame Next section will illustrate the main assumptions to use the RCM and its nomographs

7 RCM Methodology Application

Figure 3 depicts a description of the RCM methodology Preliminary, this methodology has been tested in repetitive building projects (González et al, 2008b; González, 2008) The following is a description of its main stages which are recursively performed week to week:

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1) Selection of activities: A set of activities should be selected by managers in order to improve their

planning reliability From a lookahead planning window the activities can be selected or simply the manager can choose them based on different project priorities and requirements

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

: Direction o f growth fo r the WIPBf size (from 700 m 2 to 1000 m 2 ).

ZOOM IN: WIPBf size

PRI =50%

PRI =70%

4 5

Fig 2 General Nomograph to estimate Planned Progress based on the RCM

2) Initial data collection: Gathering of production data for the RCM is weekly-based for each activity

At the beginning of the labor week project managers estimate PP, planned PRI and planned W (necessary as an input for predictions), and is measured the WIP Bf size At the end of labor week,

AP and actual W are measured

• Decision block (i) is deployed to analyze if there is enough data (points) to construct the first

MLR model (see comments in Fig 3)

Tiêu đề	Improving Planning Reliability and Project Performance Using the Rational Commitment Model
Tác giả	Vicente González, Luis Fernando Alarcón, Sergio Maturana, Fernando Mundaca, José Antonio Bustamante
Trường học	Pontificia Universidad Católica de Chile
Chuyên ngành	Construction Management
Thể loại	journal article
Năm xuất bản	2009
Thành phố	Santiago

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Số trang	33
Dung lượng	392,8 KB