a continuous improvement approach: closing the loop in an engineering, procurement, and construction...

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Performance Improvement, vol. 49, no. 7, August 2010©2010 International Society for Performance Improvement

Published online in Wiley InterScience (www.interscience.wiley.com) • DOI: 10.1002/pfi.20163

A CONTINUOUS IMPROVEMENT APPROACH: CLOSING THE LOOP IN AN ENGINEERING, PROCUREMENT, AND CONSTRUCTION MANAGEMENT ENVIRONMENT

Roberto Santana, BSc(Eng), MBA

Continuous improvement initiatives are of increasing importance due to the high operating risks

of engineering, procurement, and construction management (EPCM) firms in the oil and gas

sector ($188 billion worth of oil and gas projects in 2008). This article describes a continuous

improvement framework that translates performance problems into an action plan and helps to

prevent their recurrence.

IN THEIR Quality Handbook, Juran and Godfrey (1999) predicted that “the 20th century will be remembered as the Century of Productivity, whereas the 21st century will come to be known as the Century of Quality”(p. 14.2). Maybe the world needs another decade for this prediction to come true.

Engineering companies in the oil and gas industry have an enormous challenge ahead in their effort to achieve success in a dynamic and competitive environ-ment. As they work to become results driven, they often implement key performance indicators (KPIs) for core work processes. Beginning in the 1920s, Shewhart (1939) stressed the importance of controlling a process. He used the term special for assignable causes as opposed to chance or common causes to distinguish between a process that is stable and in control, with a variation due to random (chance) causes only, from a process that is out of con-trol, with a variation due to some nonchance or special (assignable) factors (Montgomery, 1996).

In today’s business world, many performance improve-ment professionals react to a natural variation as though it were a special cause, when an out-of-specification situ-ation is not generally a special cause unless the variable is out of control (Breyfogle, 2008). In addition, many improvement initiatives lack a holistic view of continuous

improvement that addresses the necessary alignment, linkage, and value-added replication around the business strategy (Juran & Godfrey, 1999).

CONTINUOUS IMPROVEMENT APPROACHAlmost all engineering firms in North America hold ISO 9001 certification, However, it seems that only a few have successfully implemented continuous improve-ment principles due to the complexity of managing con-tinuous improvement project activities. Figure 1 shows an example of project management activities modeled for a complex environment in the oil and gas sector.

Plan, Do, Study, and Act CyclesThe challenges that engineering firms face with this approach are immense. Each activity bears on the respon-sibilities and demands on the client side and requires coordination and integration of knowledge and informa-tion flow in three dimensions: vertically, horizontally, and longitudinally (see Figure 2).

Plan: EPCM Continuous Improvement ApproachThe project scope of work (SOW) and contract are common inputs for the planning phase of a continuous

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26 www.ispi.org • DOI: 10.1002/pfi • AUGUST 2010

improvement approach. A suggested first step is devel-oping a project policy deployment, a strategic planning methodology developed by Yoji Akao (1991).

Engineering companies will find that the greatest strength of policy deployment is its ability to translate qualitative, executive-level project goals into quantitative, achievable actions (see Table 1).

In this article, effective policy deployment starts with the client requirements of the specific oil and gas project being designed. It includes mission and vision statements, a developed quality policy, critical success factors, clear objectives, and defined metrics.

Performance Feedback: Individual Control Charts. This step creates the foundation for managing, measuring, and monitoring performance. The use of Individual control charts that identifies when there is a special cause variation helps avoid the typical firefighting and point finger culture, a reactive culture instead of the prevention that organiza-tions need to assure accountability and value added cre-ation across engineering disciplines and project phases.

The next step (“Feedback” block) is the improve-ment driver where all the project’s initiatives begin. The

FIGURE 1. CONTINUOUS IMPROVEMENT APPROACH: CLOSING THE LOOP IN AN EPCM ENVIRONMENT

FIGURE 2. EPC PROJECTS: THREE-DIMENSIONAL OVERVIEW


Performance Improvement • Volume 49 • Number 7 • DOI: 10.1002/pfi 27

objective here is to identify when a metric is out of con-trol and then conduct a detailed analysis of the variation to determine the root cause.

Most of the metrics collected in an engineering, procure-ment, and construction management (EPCM) engineering firm are variables instead of attributes. The individual con-trol chart (XmR), which conforms to ANSI/ASQC (1996), is suitable for this kind of observation. The XmR uses Three Sigma control limits and indicates an out-of-control signal if a single point falls beyond the control limits. The development of the equations for computing the control chart and its limits can be found in Juran and Godfrey (1999).

Problem Definition and Description. The best problem statements make no assumptions; they simply document the current state. Cochran (2006) stated that crafting a problem statement is one of the most important steps in problem solving.

Do: EPCM Continuous Improvement ApproachThe root cause analysis by means of an Ishikawa diagram can generate several possible solutions. The best solution could be selected after considering the factors set out in Table 2.

In Table 2, there could be i improvement alternatives. However, which is the best alternative? To answer that question, this table weights those i or n alternatives in nine evaluation factors.

This phase ends with the improvement action plan, which will be followed until the projcet is finished.

Study and Act: EPCM Continuous Improvement Approach“Study” implies understanding the sources of variation in the process (common versus special causes). Therefore, it requires acting, that is, executing the nested plan-do-study-act cycles. During this stage, it is necessary to compare the actual benefits of the solution to the benefits expected, as defined in the improvement action plan.

Lessons LearnedIf expectations have been met, solutions are generally stan-dardized and a lessons-learned database is updated with the solution (Wieneke, 2008). The organization is now practic-ing knowledge management and can potentially transform its own performance history with a list of best practices.

CASE EXAMPLE: THE SMART PROJECTThe sample definition of one KPI for this case example is as follows:

Schedule performance index (SPI): A measure of • schedule efficiency on a project.

An SPI equal to or less than 1 indicates a favorable con-• dition: the project is on schedule or ahead of schedule.

An SPI greater than 1 indicates an unfavorable condi-• tion: The project is behind of schedule.

SPI • � Actual days/baseline days.

Table 3 shows a scale with the possible values and a definition of the KPI SPI:

TABLE 1PROJECT POLICY DEPLOYMENT FOR THE BASIC DESIGN PHASE OF PROJECT SMART

QUALITY POLICY: SMART PROJECT OBJECTIVES

CRITICAL SUCCESS FACTORS

KEY PERFORMANCE INDICATORS

TARGET (X% BY DEC. 5, 2010)

Is committed to customer success

1. Objective 1 1: Meeting key milestones, deliverables

2: Avoiding design change

1. Overdue regulatory commitment index

2. Milestones achieved

3. Satisfaction index

0

�95%

�85%

Provides a service of excellence

2. Objective 2 4. Design engineering changes

5. Audit findings resolution

�10%

�95%

Delivered on time

3. Objective 3

6. Float

7. Schedule performance index

�30%

�1

Delivered on budget 8. Cost performance index �1



Green:• A common cause of variation that means great performance.

Yellow:• A common cause of variation that means to stay alert.

Red: • The value is out of specifications, meaning per-formance is poor.

If the SPI is out of statistical control or is unpre-dictable, it is characterized as a special cause of varia-tion and requires a root cause analysis and corrective action.

Using this color-coding in a dashboard creates a finger-pointing culture among the project members (Breyfogle, 2008). It should be replaced by a professional quality tool: the individual control chart.

Individual Control Chart Using this technique requires certain crucial assumptions:

The process is in statistical control.•

The distribution of the process considered is normal.•

If these assumptions are not met, the resulting statistics may be highly unreliable.

Table 4 shows the SPI performance during the first 8 months of the basic design phase of Project SMART. These data will be used in the analysis.

Figure 3 shows the individual control chart in Excel 2003, and Figure 4 shows the individual control chart made in Minitab 15.1 (referred to as I chart in Minitab). The results in Excel are similar to a professional statistical software: The variable SPI is in statistical control.

TABLE 2 PONDER AND SELECTION OF THE BEST IMPROVEMENT ALTERNATIVE

NUMBER EVALUATION FACTOR WEIGHTALTERNATIVE 1

(IMPROVEMENT PLAN 1) ALTERNATIVE i REMARKS

1 Productivity 15 (1)a

2 Quality 20

3 Investment 5

4 Safety 15

5 Required skill level 10

6 Time to implement 5

7 Technical feasibility 5

8 Ergonomics 15

9 Ecology 10

Total 100

aThe value of (1) is the result of multiplying weight by the score that follows, that the experts will give to each factor or criteria: 4—excellent, 3—good, 2—fair, 1—poor.Source: Zandin (2001).

CATEGORY KPI DESCRIPTIONSOURCE OF KPI MEASURE TARGET

KPI RANGE

ACTUALGREEN YELLOW RED

Schedule Schedule performance index

Measures schedule efficiency

Project control report

Actual days/base-line days

1 �0.99 1–1.059 �1.06

TABLE 3 SCHEDULE PERFORMANCE INDEX PROFILE



TABLE 4 SPI MONTHLY PERFORMANCE

MONTHJAN.2010

FEB.2010

MAR.2010

APR.2010

MAY2010

JUN.2010

JUL.2010

AUG.2010

SEP.2010

OCT.2010

SPI 0.91 1.02 0.84 1.01 0.91 1.03 1.11 1.04

FIGURE 3. SPI CONTROL CHART

FIGURE 4. METRIC SPI



FIGURE 5. SPI PROBABILITY PLOT

Note. AD � Anderson-Darling Test.

Note. LSL � lower specifi cation limits; USL � upper specifi cation limits; Z.Bench � benchmark of Z level, this statistic is computed by fi nding the Z value using standard normal (0,1) distribution for the corresponding statistics; Z.LSL � Z level of lower specifi cation level; Z.USL � Z level of upper specifi cation level.

FIGURE 6. SPI PROCESS CAPABILITY



Note. AD � Anderson-Darling Test; Cp � the ratio of the engineering tolerance (USL - LSL) to the natural tolerance (6s); Cpk � long-term process capability; LSL � lower specifi cation limits; Pp � long-term performance index; Ppk � short-term performance index; USL � upper specifi cation limits.

FIGURE 7. SPI PROCESS CAPABILITY SIX-PACK

To prove that the variable SPI follows a normal distri-bution, the hypothesis is:

H0: The SPI sample follows a normal distribution.

Ha: The SPI sample does not follow a normal distribution.

Figure 5 shows the results of the Anderson-Darling normality test. As the computed p-value (0.317) is greater than the significance level alpha � 0.05, one should accept the null hypothesis H0. The risk of rejecting the null hypothesis H0 while it is true is 31.7%. The variable follows a normal distribution.

The metric is stable. Therefore, the process capability index (Cpk) can be determined (see Figure 6).

Minimum Accepted CapabilityProcess capability attempts to answer the question: Can we consistently meet customer requirements?

In a capable process, Cpk is 1 or greater. It will be higher only when the variable is meeting the target con-sistently, with minimum variation. However, considering the characteristics of the EPCM industry, it is recom-mended to accept Cpk � 1 as a minimum capability.

After the organization has applied this approach to an entire project, it is ready to replicate the knowledge, reducing variation and increasing the minimum capabil-ity target.

Figure 6 shows all the results in one graph.The value of Cpk (0.08) in both Figures 6 and 7

means that there is a great deal of variation, and prob-ably the organization could not consistently meet its customer requirements in the future. At 0.96, the lower specification limit for the SPI metric, it is obvious that performance has been behind schedule in five of the past eight months (SPI � 1). However, this is just part of the natural variation. Therefore, it means the pro-cess or system being used is incapable of being within specification limits; it has to be redesigned to reduce variation.

Analysis of SPI Variation The Project Management Institute reports that mature companies have an SPI variation of 0.08 and a cost performance index variation of 0.11. Less mature com-panies have corresponding values of 0.16 for both indexes.



Juran, J.M., & Godfrey, A.B. (1999). Juran’s quality handbook (5th ed.). New York: McGraw-Hill.

Montgomery, D.C. (1996). Statistical quality control (3rd ed.). Hoboken, NJ: Wiley.

Shewhart, W.A. (1939). Statistical method from the viewpoint of quality control. New York: Dover.

Wieneke, S. (2008, April). Replacing a lessons learned database with a visible learning process. Presented at World Congress by National Contract Management Association, Cincinnati, OH.

Zandin, K.B. (Ed.). (2001). Maynard’s industrial engineering handbook (5th ed.). New York: McGraw-Hill.

Related Readings

Construction Sector Council. (2006–2008). Alberta construc-tion workforce supply/demand forecast. Retrieved September 2009, from http://www.coaa.ab.ca/LinkClick.aspx?link�pdfs/forecast1.pdf&tabid�67.

Crosby, P.B. (1979). Quality is free. New York: McGraw-Hill.

Deming, W.E. (1986). Out of the crisis. Cambridge, MA: MIT Press.

ISO 9001. 2008—Quality management systems: Requirements.

The SPI variation in the SMART Project is 0.08753 (see Figure 5). The SPI is close to the industry average, and the variation is due to common causes.

CONCLUSIONThe proposed framework closed the improvement loop, integrating lessons learned and replicating it throughout the organization, while achieving a desired alignment with the project’s strategic deployment. If applied properly, this continuous improvement approach can solve critical performance problems, increase com-munication among project team members, and increase the linkage between disciplines and project phases.

References

Akao, Y. (1991). Hoshin kanri: Policy deployment for successful TQM. New York: Productivity Press.

ANSI/ASQC. (1996). B1-B3–1996: Quality control chart meth-odologies.

Breyfogle, F.W. (2008). Integrated enterprise excellence, Vol. 3: Improvement project execution: A management and black belt guide for going beyond lean six sigma and balance scorecard. Austin, TX: Bridgeway Books.

Cochran, C. (2006). Becoming a customer focused organization. Chico, CA: Paton Press.

ROBERTO SANTANA, BSc(Eng), MBA, is a quality management and performance improvement analyst who has worked in several production and service industries. In the last 5 five years, he has worked for engineering firms as an information technology project manager and project quality manager. He most recently worked for SNC-Lavalin, Chemical and Petroleum Business Unit. His professional interests include Lean Six Sigma and strategic management. He holds an industrial engi-neering degree and an MBA and is an ISO 9001 certified lead auditor (British Standards Institution and RABQSA). He may be reached at [email protected].


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