Martin Fischer, Calvin Kam, Jung In Kim, Nirupama Kotcharlakota, Jacqueline Lo, Bochen Zhang
VALUE - “What’s in it for me?”
This dashboard will report indicator data collected anonymously and inform trends for the data. This will provide you with a point of reference for performance indicators that can be easily adopted to track and improve your project performance. By helping us collect data and converge on a proven set that is most effective in informing a project’s progress in cost and schedule, you can optimize your resources to track the most impactful and informative KPIs, enhance your project performance and thereby also enhance your enterprise performance.
Come partner with CIFE and be a part of leading change in the Architecture, Engineering, Construction, Owners and Operators (AECOO) industry! Our rich pool of previous industry collaborators include - CIFE members including industry leaders - construction companies, design firms, owners and government authorities. For more information on who our current CIFE members are please follow link: http://cife.stanford.edu/membership .Collaborating with you will be our pleasure and we believe that we can bring great value to your projects with our new dashboard.
PARTNER- “What should I do?”
You will be paired with a researcher from our CIFE team. With his/her help, you can identify projects to track selected indicators. Participation in our research may only require approximately 10 hours of your time every month. Our team will enable you to report data for these indicators in a smooth and efficient manner. Our proposed interface will not only bring your attention to aspects of your projects that are essential to track but also help you report them efficiently.
Performance measurement is important for every industry in its strife to become more efficient and productive. Many industries understand the importance of monitoring and tracking metrics, as the famous saying from Peter Drucker said what is measured gets done. Production and manufacturing industry has various key performance indicators (KPIs) such as rejection rate, overall equipment effectiveness (OEE) and takt time. Other industries such as the textile industry use KPIs efficiently by analyzing correlations and providing insights for decision-making (Zevenbergen et al., 2006). Fields such as management accounting also felt the need to improve the planning, control, and performance measurement functions and use performance indicators such as the balanced scorecard (BSC) (Stan Davis, Tom Albright, 2003). Evidently, in every industry the need to have KPIs in order to evaluate performance and provide a yardstick for improvement is undeniable.
In our current Architectural, Engineering, Construction, Owners and Operators (AECOO) industry, many valuable performance indicators have been developed to monitor the cost and schedule progress in a project’s lifecycle. With the abundance of performance indicators, however, selecting an optimal subset of indicators that effectively characterizes a project’s progress becomes challenging, especially in respect to the limited number of performance indicators a project could reasonably track. Our research propose to develop a solution to report standardized and informative performance indicators for the AECOO industry - the CIFE performance dashboard. Through prioritizing which metrics have the largest impact on what kind of project, and have benchmarks that aid short and long term decision-making, AECOO industry can determine which KPI to measure and benefit from it.
Performance indicators that reflect a project’s progress allows team members to discover anomalies and problems in the project, so that they may address and contain issues in a timely manner. However, the return of tracking additional performance indicators eventually levels off and falls as the cost of tracking and analyzing more performance indicators overweigh the added information the new performance indicators provide. Given a project’s resource constraints, one would want to maximize the information obtained about the project’s progress given a limited number of performance indicators the project could track, so that the project can be well-monitored and potential issues can be addressed promptly. We propose to take stock of the metrics that are tracked across the CIFE community so that we can understand how far towards achieving a buildable structure the CIFE community has become.
This seed research aims to help the AECOO industry to prioritize which performance indicators need to be tracked that will benefit the project the most by providing a dynamic dashboard with usable, widely applicable and clear KPIs. This will provide project stakeholders a reference for ‘key’ indicators to track and help save valuable time and effort lost in tracking too many PIs.
This dashboard aspires to put forth a set of more simplified, easy to track performance indicators to accurately and effectively portray the changes in performance tracked over a period of time. The dashboard will be a dynamic report of how these performance indicators and project performance are related with the most up-to-date data collection. This dashboard will be a dynamic reference and will be updated regularly. The objective of the research is not just traditionally publishing journals but to keep this dashboard active with ongoing research. Changes in the AECOO industry’s needs and limitations will lead to continuously adapting and enriching this dashboard.
There are a few resources that reflects the current practices in our industry. These researches or databases combined can provide us with a large set of candidate performance indicators that are accepted by our industry and have applied in industry projects. In this session, we will mainly focus on the Construction Industry Institute Performance Management System and the technical note written based on projects from the CIFE certificate program.
CII (Construction Industry Institute) Performance Management System
Since 1996, the CII (Construction Industry Institute) has created performance indicators database from 1000 projects and has been collecting data from its partner companies. These indicators are categorized into five perspectives: Safety, Cost, Change, Schedule and Rework. These indicators are either chosen from a pool of performance indicators summarized by academia or supplied by construction companies. However, those indicators are beyond the number that a company could track. The CII database did not prioritize those performance indicators in each category to a reasonable number of trackable indicators. This database of performance indicators serves to cross- validate the important performance indicators chosen from previous VDC research with industry practices outside of CIFE.
Performance Metrics Report on Previous CIFE VDC Certificate Program
This report explored what and how performance metrics were tracked in the reported construction projects in the CIFE Certificate Program. Popular performance metrics are distilled from the reported indicators by counting their occurrence and indirectly representing the group of performance indicators that are commonly accepted and recognized by the industry.
This repository of performance indicators collected from CIFE members act as a basis for the proven set of performance indicators that are useful in the industry and can be further analyzed in the research to streamline into “key” performance indicators.
The objectives of the proposed dashboard are listed as follow:
- Provide a set of KPIs to the industry
- Recommend a few combinations of KPIs that will be the most informative given certain limitations
- Benchmark the provided set of KPIs for the industry
- Allow our industry partners to search for KPIs and compare their result with the industry
The research methodology requires first to collect the existing performance indicators in both industry and academia. We started with proven existing indicators utilized by companies in the CIFE community and found by VDC Scorecard research, as well as the public CII performance indicator studies. Building on these previous research and data collection, a total of around 800 performance indicators have been analyzed and archived to form the primary candidates for the Key Performance Indicator. In addition to these proven indicators, our research assistants also come up with new performance indicators based experience and other research in order to measure the project performance from as much as possible aspects.
KPI Selection Process
With an active mind to provide the most representative and to reduce the resource that tracking one performance indicator takes to keep track of the Key Performance Indicators. The list of the candidate performance indicator is maintained to be holding around 20 to 30 candidate performance indicators throughout the time. The popularity and the level of measurement complexity are determined for each of the 800 candidate indicators to filter out the top 20 to 30 candidates. While filtering and archiving the candidate performance indicators, categorization schemes are created to fit in different project goals and to meet different needs.
After the categorization schemes created, and the candidate indicators settled in each category, an organized performance indicator candidate pool is established. After finalized the pool, candidate performance indicator under each category is given a rank by comparing their popularity, ease of measurement, amount of information provided and category relevance. After cross-comparing all the criteria for each candidate performance indicator, a distilled down set of 18 Key Performance Indicators is proposed to the AECOO industry.
Data Collection Phase (Current Phase)
Key Performance indicator data will be collected from online forms that industry collaborators can fill out within minutes. Data will be collected from the industry partners on a regular basis. Members of the research team will also be in continuous correspondence with the collaborators to survey what, if any, additional information or insights they gained from measuring each indicator. Partnering with various industry professionals from AGC, bimforum, AIA research and BCA will help us collect more data from a greater variety of projects to strengthen our results.
The class CEE 212B is a research-based course with curriculum objectives that align closely to our research. Students in this class will help collect results from companies and populating the dashboard. We will also receive data by engaging the participants in the CIFE certification program who are involved with CIFE in enhancing their VDC knowledge.
Data Analysis Phase
The importance of a performance indicator is determined by the information it provides the project team. To select the most important and effective indicators, we record the frequency and degree of information team members gain from tracking each performance indicator. We will distill the performance indicators that provide significantly more infroamtion than others by carrying out principal component analysis (PCA) on normalized dataset.
Result Presentation - Dashboard
The criteria we envisage for the design of the dashboard are simple and dynamic. We plan to present the information pertinent to each KPI in a clear, concise format. For all KPIs within the dashboard, trends for tracking each of them will be plotted using the data collected (i.e. all data trends will be plotted on the same graph with each trend corresponding to a particular project that used this KPI. All data trends will be plotted anonymously). Within the page for each KPI, general information of the KPI will be listed, including step-by-step guidelines to track the KPI. The website for tracking the recommended KPIs will be accessible to all CIFE member companies and industry collaborators that contributed to creating the dashboard.
Current Pool of Key Performance Indicators
We sifted through a large set of candidate performance indicators developed from previous industry research and used by current industry projects. We now have distilled down set of 18 performance indicators.
Current bucket of KPIs
Categorization Scheme 1:
- Quality - Represent the overall quality of the work being done, indicated by the ratio between errors detected and work completed.
- Efficiency - Represent the efficiency or productivity of the workers/officers in a certain time period
- Reliability - Represent the general reliability of parties to parties or between individuals
- Satisfaction - The overall satisfaction of the people in terms of a variety of aspects
- Latency - Indicates the delaying time between something being requested to something is received
Categorization Scheme 2-4: