Construction industry analysts understand that performance standards and measures are not practiced, but that they are necessary if the industry intends to produce innovative, cost-effective, high performance buildings. The four-fold aim of this study is to help the reader understand that:
- The construction industry does not have production or performance measures beyond commodity level costs, and although unintended, commodity-based standards lead to high cost and low value across the whole building.
- Construction must adopt whole building (and major compound, building systems) performance-based standards and measures in order to drive improvement, quality, innovation, productivity and performance.
- Because of construction's complexity, systems-thinking and computational sciences have not yet been applied to the industry. Commodity-based standards still prevail.
- Through consolidation of metric standards and computational modeling systems, whole building (and major compound building systems) may now be applied. This will be achieved by: (1) Establishing Standards and Baselines (norms and objectives), (2) Establishing Performance Indices, (3) Defining and Normalizing Data Measures, (4) Developing Technology Enabling Standards and Measures, (5) Establishing a national Authority for Performance Standards and Measures.
It is a truth commonly acknowledged in economics and psychology that the adoption of standards and measures is directly related to increased performance. This truth is based on an apparently fundamental trait of human behavior: if an individual can measure an activity against a standard, he or she will want to beat that standard. Tell a runner to run a marathon, but place no bets. Tell a runner that in 2007 the average finish time for men was 4.29, and give him a systematic training regimen with incremental goals, and bet on his beating 4.29. Likewise, tell construction to "be green" and to achieve "net zero carbon foot print," and the vague vision will dissolve into good intentions. Establish performance standards and give construction a way to measure performance, and watch the industry train itself to beat those standards.
In the forthcoming study by the National Research Council entitled Advancing Competitiveness and Efficiency in the U.S. Construction Industry, the establishment and practice of "effective performance measurement to drive efficiency and support innovation" is cited as one of five principle actions recommended for industry advancement.1 "Performance measures," continues the report, "are enablers of innovation and of corrective actions throughout a project's life cycle." Analysts agree that performance standards and measures are critical for improvement and innovation, but no one has yet detailed how to establish them. This paper describes what such an establishment might look like, and proposes principles, methods, technologies and organizational structures by which performance measures can be developed and implemented throughout the building life-cycle.
The analysis is divided into four sections: (1) The Case for Performance Standards and Measures describes the problems inherent to the current practice of commodity standards, and the importance of transitioning to performance standards. It discusses the logic behind standards and measures, which is based in the work of W. Edwards Deming, and concludes with EMR, the early example of performance standards and measures in the safety sector; (2) Performance Measures defines the parameters of performance, including the baseline standards, proposed values for measurements, performance indices, normalization and definition of measured data, and the proper use of measures; (3) the Technology Necessary to Enable Performance Standards and Measures describes the computational science and systemic thinking behind function-based BIM, the technology that makes prediction and validation possible, and (4) Standards and Measures Authority discusses the structure and role of organizations responsible for the collection, archiving, evaluation and use of actual data and information needed to validate and calibrate standards modeling systems.
THE PERFORMANCE PARADIGM
This is one in a series of white papers presenting a comprehensive solution for construction's productivity and innovation problems. The solution begins with understanding how construction still operates according to an industrial paradigm; the solution ends with an industry-wide shift to the emerging performance paradigm. While the industrial paradigm is characterized by linear logic and fragmentation, the performance paradigm uses computational science and systemic thinking to produce high performance. With new technology systems, building performance is no longer a vague marketing slogan, but a quantifiable quality.
Building performance is the quality of a building’s operation when measured against a standard. Consequently, without a standard, performance is guesswork. Because standards and measures are currently non-existent for whole buildings, building producers and building processes, no one can know precisely what the industry's performance is, much less how and where to fix it. Why don't standards and measures exist? Construction is massively complex. It has so far been impossible to collect, organize and standardize sufficient building data for a comprehensive analysis. However, new technologies are emerging that use computational modeling to convert construction's complexity into useful data models.
For the sake of the economy, the environment and energy independence, the industrial paradigm is no longer viable for construction. Conventional buildings tax the environment, and conventional "Green Buildings" tax the economy. As political, social and marketplace forces begin to demand high performance buildings; the low performance industry must change. Construction must shift in order to perform.
So, the performance paradigm shift revolves around five key transitions in the industry's structures and practices:
Performance Standards and Measures —The organization and analysis of the performance of the building product, the building project team, and the building delivery process. It requires collection and standardization of construction data.
Function-based Computing — The sophisticated computational modeling system necessary for processing the complex data. This cyber modeling system simulates standards for projects, and also validates and calibrates performance measures.
Operating Building Focus — Project management focused on the life-cycle of completed building — functional, operational and environmental (shift focus away from services, documents and production in accordance with those documents.)
Integrated Innovation — Performance standards and measures promote new interdisciplinary and integrated practices that naturally encourage innovation. Standards-driven innovation begins with manufacturers, but will soon spread to the building community generally.
Integrated Optimization — Rather than optimizing a fragmented system, construction first needs to establish an integrated system, and then optimize that. There are, in fact, three main building systems that need to be established and optimized: the building organization, building process, and the operating building.
When these transitions have been made, then the construction industry can be said to have entered the performance paradigm. The benefits of such a shift are many, and include high performance "green" buildings produced by a high performance industry, as well as the national and global recognition — and hopefully emulation — that will attend successful examples of innovation and optimization in such a complex industry.
THE CASE FOR PERFORMANCE STANDARDS AND MEASURES
Improving the industry is going to take an extended program of study, testing and implementing. This paper's premise is that performance standards and measures + computational systems thinking is the silver bullet to be studied, tested and implemented. Therefore, the paper's goal is to convince readers of the value of performance standards and measures so that such a program can be launched. In its most basic form, which is elaborated below, the case runs thus: performance standards and measures re-organize an industry around performance-based values that are the essential units of a high performance building and industry.
Statement of the Problem
The industrial-paradigm problem, in short: when value is based on a commodity, not function (performance), all energy goes toward producing a given commodity at the lowest cost, instead of producing a given function or performance at the lowest cost. So, if a document specifies a commodity (e.g., a certain fan), whatever supplier can provide the lowest-cost fan within that specification will get the contract. Two problematic consequences: (1) over time, exclusive focus on cost reduction instead of quality leads to decreased quality, defects and rework, decreased productivity, and, ultimately, increased costs, and (2) working within a commodity-based specification means that innovative alternatives outside the specification are neither procured nor produced (see the functional chart below for a spatial version of this).
These two charts show the logic of commodity-based value: the intentions and the consequences. The top chart shows Why (left to right) and How (right to left) commodity-based value was intended to produce value. The bottom chart shows What Happened (left to right) and How (right to left) in the typical commodity-based value situation: reduced quality and performance that causes increased costs and decreased value.
When production and procurement revolve around total building performance (instead of component commodities), all purchases, designs, contracts, etc. are chosen for how well they contribute to the overall performance of the building system. The benefits of this practice are twofold: (1) over time, exclusive focus on performance leads to increased quality, which leads to increased productivity, value and reduced costs (per Deming: less re-work, better use of the system, etc.), and (2) working within a performance-based specification means that innovative alternatives are encouraged because whatever system or combinations of systems best perform that function wins. In this way, performance-based value opens up procurement and production to all sorts of innovations that commodity-based procurement discourages. Moreover, once a performance data archive is established, building teams will be able to access market performance averages and know, for example, the standard heating/cooling performance of like buildings, which will become the performance standard for the team to beat.
The following chart shows Why (left to right) performance standards and measures lead to reduced costs, and How (right to left) reduced costs, increased value and productivity follow from performance standards and measures. The process for improving quality and innovation resembles Deming's cycle, although much of the "Act" and "Test" stages can be done virtually with today's technology.
In sum, for any area that needs improvement, the first step is to replace commodity-based standards with performance-based standards. Again, "performance" here always relates to "total building performance," so that, even though you can talk about the performance of a component part or sub-system, it must be measured in relation to the performance of the total building system.
The Deming Standard
The logic behind performance standards and measures was developed by W. Edwards Deming who repeatedly taught both the importance of measurement and the importance of a system. The psychology behind measurement can be summed up: "train people to measure things and they will keep pushing their own standards higher to beat themselves."2 In the case of construction, there has not been a rigorous system of performance measures with which to measure the performance of the various construction components. In the absence of performance measures, construction components have mainly been measured by commodity cost, which is only one aspect of true value. In Deming's argument, until people are trained to measure performance, they will not be able to push performance standards higher. The importance of systems thinking was developed in his theory of "profound knowledge." According to Deming, a system is a "network of interdependent components that work together to try to accomplish the aim of the system” (ibid., 50). The aim of the system is its most important feature: "A system must have an aim. Without an aim, there is no system." Management's role, continues Deming, "requires knowledge of the interrelationships between all components within the system and of the people that work in it." For construction, this means understanding all component parts as a network of interdependent disciplines, sub-disciplines, process tiers, etc. working together to try to accomplish the aim of the system: total building performance. Aiming for total building performance is the most important feature of the construction system. Management's role, therefore, requires knowledge of the interrelationships between all the various disciplines, process tiers, planning stages, etc. However, total building performance is too complex for manual analysis, which is why systems thinking needs to be paired with computational science, the computer-aided analysis capable of processing construction's complexities.
Actually, performance standards and measures have already successfully transformed construction safety. The Experience Modification Rate (EMR) is a relatively straightforward computation that compares a company's annual losses in insurance claims against its policy premiums over a three-year period. The EMR standard was set at the average rate among contractors, and then normalized to a ratio equal to 1.00. This practice of measuring safety performance has proven much more effective than prior practices of rules and regulations. There was no need to establish a safety goal ("Reduce site accidents by 50% by 2020"). Simply by measuring safety performance and understanding performance averages, contractors are motivated to drive their EMR as low as possible by whatever means best work for them.
Obstacles to the Solution
Construction's adoption of standards and measures has had two main obstacles: (1) the claim that building performance is too complex for standards and measures, and (2) the claim that both the general industry and individual projects are too fragmented among the various disciplines and process tiers to accommodate systematic standards and measures.
Regarding the first, it's true: buildings are very complex and have way too many variables and combinations of variables to be measured and standardized without a sophisticated method of collecting, archiving, computing and modeling the data. For example, consider the variables necessary for establishing a standard for the consumption of electric energy: solar-degree days, insulation and shading characteristics of the building materials, orientation of the building, people and equipment loads, building control systems, etc. Where possible, various, isolated standards have been established through energy codes, ASHRAE standards, LEED certification, etc., but these have not been applied in the context of total building performance, and cannot be said to represent either a norm or realistic objective. The second claim, that the industry and specific projects are too fragmented, is also valid. However, instead of abandoning standards and measures because the industry and projects are too fragmented, the industry needs to integrate and consolidate to accommodate performance standards and measures. Strategies are proposed here for industry and project integration and consolidation. Note that strategies for establishing industry-wide and project-wide standards are gaining momentum: the National Institute of Building Sciences' development of the National Building Information Modeling Standards is an important example of the adoption of standards for complex technologies and processes.
In sum, standards and measures for total building and industry performance will not be very easy, but they will be very worthwhile. If performance standards and measures have failed in the past, it is because the industry lacked the computational science that is now being developed through function-based data modeling technologies. The industry should look to the simplicity and effectiveness of EMR for a successful example of performance standards and measures, and prepare to implement them throughout the building community.
A comprehensive system of standards and measures is one that ranges from the most general (total building criteria: e.g., gross building area and project capital costs, building operating costs, etc.) to the minutely particular (point-of-production: e.g., number of defects in the drywall per unit area of finished surface, or the average length of ¾” conduit installed per man-hour).
The two broadest indices proposed are: (1) the Capital Expense Effectiveness Index (CEI), which rates a project’s first cost effectiveness, and (2) the Building Performance Index (BPI), which rates a completed building’s operating performance (including the capital cost). Also proposed is a series of second tier indices to guide the process of achieving high productivity and performance. Third and fourth tiers indices will be applied at the material production and sub-discipline or trade (point-of-production) levels, respectively. These indices were chosen because they best correspond with the objectives of productivity and sustainability. Others will be created as needed.
Standards as Baselines (Norm and Objectives)
Creating and using these indices depends on baseline or benchmark control models. Any given measured value has up to three standard baselines:
Market Average Baseline (MAB): The MAB represents the current market norm, i.e., the average value in the current industry market in a given baseline year (Note: The Department of Commerce uses 1964 as its labor productivity base, but it may make the most sense to establish 2010 as the base year for the CEI, BPI and other measures).
Industry Objective Baseline (IOB): The IOB represents a standard set by a given organization (an owner user group, market sector group, or other authority). This baseline can be optional, particularly for projects not represented by some collective market sector resource. This should be an informed and realistic target value and not an arbitrary value or one that is used to establish a quota or ranking of employees.
Project Objective Baseline (POB): The POB represents a baseline objective set by either the owner or the project team based on the state of integration, innovation and optimization possible for that project.
The "Statistically Establishing Performance Standards" graphic shows the relationship between the three baselines. The market average may begin as a simulated model, but will gain accuracy as more COMPS (Actual Project Comparables) are compiled and normalized to the prospective project. The starting point for a project will be the Market Average Baseline (MAB) which resembles the “appraised value” common in the real estate business.
The appraised value for the whole real estate asset is based on comparables (COMPS) of like real estate. Residential real estate is a more straightforward process because: (1) the current and proposed uses are considered the same, and typically do not require a value consideration for adaptive re-use capital investment, and (2) there is typically a sufficient number of like homes from which to derive a one-to-one comparable. Commercial real estate appraisals are more complicated. However, in both residential and commercial, the purpose of the appraisal (MAB) is to determine value under the current market conditions.
What to Measure?
The following measures are proposed as a point-of-departure for further consideration by leaders and specialists. There should be at least four tiers ranging from the total project level to the point-of-production, point-of-delivery, point-of-installation level, etc. As presented here, the first two tiers encompass the total project, including the following as a starting point:
- Capital Expense: Total project first cost. Note that portions of the project such as site work, property acquisition, and furnishings, will be analyzed and reported separately from the building costs. (see below)
- Total TruEx (true expense): The net present value of the total project cost plus the facilities related operating cost (utilities, service, maintenance, repairs, moves/adds/changes, etc.), less salvage value at end of life cycle term.
- Hours of Effort to Produce: Direct and indirect value (throughout the project hierarchy including subcontractors in certain cases). (Note: There will be a general hours-of-effort-to-produce value as well as specific values broken down by discipline or system, and also according to specific products.)
- Energy Usage/Expense: Project construction process as well as the operation of completed facility/infrastructure.
- Waste Removal Volume/Weight/Expense
- Selected/Participating Products and Systems Expense
- Other variable operation cost of completed facility/infrastructure
- Building spaces (gross and net areas): values organized by space use and classes, as well as by other key parameters such as number of floors.
Each of these values should be normalized according to the type of project/spaces, location, climate, time, quality, hours of facility operation, scope or massing, etc. Although the task of normalization will require some initial effort, this will be a fraction of the effort that normalization will save. With the cooperation of contractors and vendors, all of the above data would be available and needs only a well-organized process of recording and collection. The organization of this data will produce the market standards for performance, conservation and sustainability against which future projects will be measured, evaluated and improved.
In effect, the modeling process for a given project will have two preliminary stages: first, the various values of the Market Average Baseline (MAB) will be established based on modeling, and then the Performance Objective Baselines (IOB and/or POB) will be determined by the owner and/or delivery team's estimation. Again, as more actual project and facility data is collected, modeling will become more accurate.
The Performance Index
In many cases, measures will be expressed by an index — a single number that gauges performance against a standard. The index numerator is the value associated with the prospective project or system within a project. The denominator is the normalized standard against which the prospective project is measured (either the market average (MAB) or the project objective (POB)). See below for further explanation and illustrations. The various performance measures span the whole gamut of building: from total project costs, to promises made and kept, to instances of drywall defects per square foot. The master index, the Building Performance Index (BPI), will be the number that tells all, as it is the basket that contains both cost effectiveness and productivity of the project development. The master index, (BPI), will measure the operating performance of the completed facility or infrastructure.
The proposed primary and secondary indices could include, but are not limited to:
- Primary Indices:
- Capital Cost Effectiveness Index (CEI): Capital Cost/Baseline Cost Project Productivity Index.
- Building Performance Index (BPI): Total TruEx of Project /Baseline TruEx.
- Secondary/Selected Indices:
- Project Development:
- Capital Productivity Index (CPI): Capital Cost Effectiveness Index/Labor Productivity Index
- Labor Productivity Index (LPI): (Capital Cost/Total Number of Man-hours)/(1964 Dept of Commerce Control Value)
- Space Use Effectiveness Index: Building Area/Baseline Functional Building Area
- Energy Consumption Index: Projected Cost of Energy/Baseline Cost of Energy (to be broken down by electrical power and other fuels as well, see below example)
- Indirect Labor Productivity Index: Capital Cost/Total Number of Indirect Man-hours
- Direct Labor Productivity Index: Capital Cost/Total Number of Direct Man-hours
- Project Material Waste Index (also ties to LEED points): Cost of Rubbish and Waste Removal/ Project Cost (This should be measured for recycled, land-filled, as well as the total)
- Manufactured and Prefabricated Products and Systems:
- Product Productivity Index (PPI): (Cost of Product/ Number of Man-hours to Place)/(1964 Dept of Commerce Control Value)
- Material Waste Index (MWI): Volume or Weight of Material and Packaging Delivered to Site/Actual in-place Volume or Weight produced by BIM schedule
- Completed Facility Operations:
- Energy Performance Index: (also ties to LEED points) (EPI): Cost of Energy/Baseline Cost of Energy
- Facility Performance Index (FPI): Facility Variable Costs/Baseline Facility Variable Cost
Each of these indices will tell building producers where the current project stands against the market average and their own performance objectives; this information allows the team to track their progress and understand what must be done to beat the objective. For a given performance index, the denominator will be expressed as a subscript, depending on the standard (MAB, IOB, POB). For example, the Capital Cost Efficiency Index would be identified as CEIM, CEII, and CEIP for Market Average Baseline, Industry Objective Baseline and Project Objective Baseline, respectively. See below for an illustration.
Each discipline, sub-discipline (trade) and process tier (design, building production, and product production) will also establish its own performance standards, measures and indices. These will be woven into the various “index baskets” representing quality, safety, productivity, schedule/milestone adherence, change management, and other building process aspects. So, for example, the total project would have a MAB and POB for, say un recyclable waste removal, but so would select trades where waste is an area needing improvement, such as drywall.
Defining and Normalizing Data Measures
Performance definitions and norms do not appear in nature, and therefore must be constructed, which is an important task. When a performance index is being defined, there must be a commensurate ("apples-to-apples") relationship between subject project unit and the standard unit against which it is measured. In an obvious example: it would be pointless to compare a university building with an HVAC system tied to a central chilling plant on campus to the market average baseline building with an integral chilled water system, without normalizing the difference in water system sources. In a less obvious example which demonstrates the importance of the task, consider the Capital Cost Effectiveness Index (CEI = Capital Cost/Baseline Cost Project Productivity Index). Although by normal definition the Capital Cost includes sitework costs, these should be segregated out in performance definitions because the variations that occur in sitework are extreme, and if included with a sampling of like building types, would skew the accuracy of the building measures. Likewise, although Capital Cost does not include construction interest, that value should be aggregated in because it is directly affected by productivity, and consequently the duration of the building construction. Sitework segregation and construction interest aggregation are foreign to conventional budget and tracking standards, but they nevertheless provide a more accurate assessment of project performance. Performance definitions like these will require careful attention at the beginning, but once established, will need only occasional review.
Besides performance definition (which delimits a performance measure) performance norms must be established. Performance norms bring all data and measures onto a common scale so that they can be accurately compared. Data will be normalized to include:
- Space(s) types and use: The indices will reflect the type of space(s) at an appropriate level. So, for example, medical spaces will be indexed differently for surgical vs. medical office spaces. Likewise, open office space will be indexed differently than enclosed office spaces.
- Location: Market cost Index (such as RS Means CCI)
- Time: Cost inflation index
- Climate characteristics
- Scope and massing
- Quality level or rating
- For operating cost, the hours of operation
These are just a few of the variables that affect a project’s cost and energy consumption. Information technology systems to address these must be as comprehensive as possible in establishing pre-defined variations to be normalized, but also nimble enough to allow the building team to incorporate the infrequent sorts of variations.
Two illustrations will help here, although they will use only the “Mean Values” (that is, for illustration purposes, the low and high ranges of the predicted standards are not included, although they typically would be). First a simple example: say that the market average baseline for electrical power consumption (PPCM) for a prospective building is 10 million kwh. If the building team produces a building that consumes 7 million kwh in its first year of operation, it will have achieved a Power Consumption Index (PCIM) of .70 (30% reduction). This would be a big victory — especially if the team’s Project Objective Baseline (PPCP) was greater than or equal to 7 million kwh. At say, $.10/kwh, the annual power bill savings would be $300,000 ((10 million – 7 million kwh) x $.10 = $300,000).
Now for a more complicated example where the total indirect labor is measured against two standards: the market average (what the industry expects) and the performance objective baselines (what the building team is shooting for).
- Capital Expense Scenario:
- The Market Average Baseline (MAB) for the Capital Expense (PCEM) of a prospective project has been set at $18,000,000.
- The building team has established the performance objective (POB) for the Capital Expense (PCEP) to not exceed $16,800,000 ($1,200,000 below market average)
- If the final actual Capital Expense (PCEA) came in at $16,900,000 ($1,100,000 below the market average and $100,000) above the project objective, then:
- The resulting Capital Effectiveness Indices: CEIM = .94 ($16,900,000 / $18,000,000 or 6% below the Market Average); CEIP = 1.006 ($16,900,000 / 16,800,000 or .6% above the Project Objective).
- Indirect Labor Productivity Analysis:
- The Market Average Baseline (MAB) for Indirect Labor (PILM) for a project is 40,000 hours (including all design, construction management and subcontracting).
- The Indirect Labor Productivity Ratio (ILPRM) would be 450 ($18,000,000 / 40,000hrs = 450 dollars of contract per hour of indirect labor).
- If the goal was to increase indirect labor efficiency by just 15%, the resulting POB (ILPRP) would be 518 (450 x (1 + 15%)).
- This, in turn would generate an Indirect Labor POB (goal) of 32,432 hours (16,800,000 / 518 = 32,432). This is in comparison to the 40,000 hours expected by the average building team.
- Now, let's say that project is completed and the actual indirect labor hours amounted to 34,000 hours. The results:
- The indirect labor productivity (ILPRA) of 497 ($16,900,000/34,000hrs), ended up 10.4% (1 - 497 / 450) above the Market Average) and 4.1% (1 – 497 / 518), below the Project Objective.
- If the average total cost for those engaged in indirect labor was $50/hr, the total project savings (just from indirect labor) would be $250,000 (40,000 – 35,000) x 50 or 20.8% ($250,000/$1,200,000) of the total targeted Capital Expense savings.
With regard to the use of the indices: in one case (cost effectiveness) the lower the index, the better the performance, and in the other (labor productivity) the higher the index, the better the performance. Of course, this is all subject to the industry standards authority establishment (see below). That is, it may make sense to try to get every index normalized to 1.00 as the MAB, and work either up or down (as in the case of the EMR).
The Proper Use of Measures
In sum, the proper use of standards and measures is the technique that connects performance goals and performance reality. Performance standards and measures turn vague qualities like "performance goals" and "savings" into concrete, quantifiable data that can be collected, organized, evaluated and predicted, as illustrated above. Like all performance tools, there's a bad (fragmented) way and a good (systemic) way to use standards and measures. The bad way is making the various trades and disciplines compete against each other for better performance indices. Internal competition, even with standards and measures, leads to the imbalanced success of some disciplines and failures of others at the expense of the overall building goal. The good way is establishing an informed building performance objective and understanding how all the component indices will work together to achieve that system-wide goal. At the level of data collection and analysis, it's important to establish market averages from past project data history as a basis for a proposed project's performance objectives. A project is not an isolated affair, but is the latest in a series of projects. Collecting and organizing the past projects' experience in the form of defined and normalized data will enable building producers to understand and improve future projects. As historians say, we must study our past in order not to repeat its mistakes. Building producers, too, need to be building historians: by archiving and studying past building experience, they can learn from their past, avoid past mistakes, and make better, more accurate plans for future buildings.
TECHNOLOGY NECESSARY TO ENABLE PEFORMANCE STANDARDS
Maybe construction is more complex than anything Deming ever had to deal with, but our technology is more complex than his, and, complexity being even, Deming's theory of "profound knowledge" is as valid for construction as it was for manufacturing. In Deming's theory, "knowledge" is a prediction that comes true. If the vagaries of construction confounded past producers, today's producers can submit those vagaries to a sophisticated modeling system good enough to produce accurate predictions. That is, today's building producers, with functional modeling technologies in hand, are capable of profoundly knowing construction -- if the data is collected and available.
Prediction and Validation Science
Buildings are really complex. Most commercial building projects have dozens of attributes (function, scope and quality, physical and market-related constraints) that could easily skew a prediction if not accounted for. Therefore, it's necessary to have a modeling system with a broad range of data as well as accommodation to aberrations that lack historical comparables. The full scale of the functional modeling technology incorporates all these needs. This schematic summarizes how the technology works. It is also detailed in the white paper, “Function-based BIM.”
Clarification: "bottom up" modeling vs. "top down" modeling. Top down modeling begins with a functional or performance overview of the building (tiers 1 and 2) and then formulates data models for the physical systems. In other words, top down modeling converts project performance criteria (a building's function, program scope and quality definitions, as well as a project's physical and market constraints) into physical building characteristics: program spaces, systems scope, cost, schedule, etc. These conversions are based on pre-established relationships between criteria and physical characteristics (e.g., a building with function x will have generate spaces ranging from 'y' to 'z'). Bottom up modeling pieces together the smaller tier 3 and 4 sub-systems that eventually give rise to systems, and ultimately the whole-building system (tiers 1 and 2). That is, bottom up modeling begins with the manual or semi-automated production of "empty or developing" geometric forms that are developed into physical characteristics, intended by the designer to meet the actual functional needs of the building.
Standards and Prediction for the Whole Building
Currently commodity standards exist only in tiers 3 and 4, where the commodity averages of sub-systems are simple enough to determine manually and use for predictions. In a tier 4 example, an electrical contractor is trying to determine the number of work hours for a project that requires 20,000-lf of ¾” conduit. The contractor knows from his past 20 projects that the average productivity for installation of ¾” conduit is 40 ft/hr. Based on this performance standard, the contractor determines the number of hours for the proposed project would be 500 hours (20,000/40 = 500).
Determining the production performance standard for conduit installation is fairly straightforward (the rate for laying conduit doesn't vary much), but determining the production performance standard for the entire electrical system or an entire building requires many identical projects that, of course, do not exist. Clearly performance standards for tiers 1 and 2 are impossible for the vast majority of projects: with the exception of identical prototypical buildings (like a McDonald's restaurant), tiers 1 and 2 are too complex for manual standardization. However, getting performance standards for tiers 1 and 2 is essential for planning, designing and producing a high performance building.
Why? Again, per Deming: measure things and people will improve them. If you can measure whole building performance against a standard, building performance will improve — highly. Top down planning begins planning with performance standards and measures for the whole building and for the compound systems (building enclosure, mechanical system, etc.). If project teams have access to standards and measures customized for their particular project early in the planning process, they can identify standard problems for their project and pursue measures for improvement. As with the above example, a project manager with access to averages for total indirect labor hours in like projects can anticipate ways to reduce those hours in an informed way and right away. Similarly, the project superintendent with access to the average total number of direct man-hours (for the whole project, and for specific trades) can better understand the time needs of the respective trades, and strategize the best way to coordinate them.
The dilemma: performance standards and measures are essential for the whole building and compound systems, yet for the vast majority of projects it's impossible to establish standards for measure, whether it is gross building area, capital expense, power consumption, labor hours, etc.
Function-based BIM resolves the dilemma by simulating the performance standards: taking real data and information from actual projects and then predicting the standards for the prospective project’s data and information. The effect is an estimate (a prediction of the standard) of what the prospective project should be as if there were 20 prior near-identical actual/control projects available as comps. The theory and science is this: The real data and information for the 20 actual projects are used to validate and calibrate the functional computational system that is modeling prospective project. If that modeling system is validated and adjusted based on the strength of actual projects, it will be valid and will adjust to establish the standard from the prospective project. This is particularly true if some portion of the actual 20 projects have similar functions (space and systems) as the prospective project, even though the scope, location, climate, are significantly different. This is how it works:
1. The functional model includes catalogs of composites of functional spaces, building systems, unit measures (costs, hours, energy consumption, etc.). Algorithms then relate the function, scope and constraint inputs to spaces and systems. It also includes adjustment factors (for location, climate, mass, soil conditions, etc.) This information is composed by specialists in the respective fields who establish both standards and statistical variations to the standards. The user inputs the functions, scope and constraints from which the functional model, using the catalogs and algorithms, calculates the parametrics: spatial program, scope and quality definition, cost, schedule, etc.
2. The first stage is to model the actual historical projects (the below illustrates five of the twenty) and then establish the calibration factor by taking the actual data (see "Capital Expense" in the "Illustration of Function-based Modeling" below), and dividing the data by the like modeled data.
3. After the 20 actual "model" or "control" projects are measured, the next step is the calculation of the average (and the variations) of ratios of actual/control to model.
4. Finally, the proposed project is processed through the functional model in the same way that the actual/control projects were. It is vital that this project run through the identical model as the actual/control projects. The results from the model are then adjusted by the above ratio (actual/control to the proposed project model data). This calculation then produces the standard measure—and also the variation to the standard.
Let’s use an example of gross building area as the measure: taking the first of the actual/control projects, the functional model calculates the mean gross building area (GBA) to be 65,000-sf. The actual GBA turns out to be 62,400-sf. The ratio of actual to model for this first project is .96 (62,400/65,000 = .96). This calculation is performed on the other 19 projects, with the results: Mean = 1.03, 1 Std Deviation = .02. Again, this is the validation and calibration of the functional model by actual projects.
Next, the proposed project is processed through the functional model, and it produces a mean GBA equal to 92,000 sf. Based on the 50 actual/control projects, the estimated mean GBA — prediction of the standard would be 94,760-sf (92,000 x 1.03 = 94,760) — the market average baseline. A statistical expression of the standard based on 1 std deviation: between 92,865-sf and 96,566-sf (94,760 +/- .02 x 94,760).
This example shows a tight result from the 20 actual projects. In such an instance, the team can quickly establish the market average baseline (MAB) and proceed to the project objective baseline (POB). In other cases where the standard deviation is much higher, a further analysis into both the comp projects and the accuracy of the information fed into the functional model will be necessary.
The principle here: predictive knowledge of standards for whole-building projects and compound systems can be accurately established using available market information. Without a representative sampling from actual projects, a specific project has only the data supplied by the designer and whatever personal experience the project team brings (the current system).
The white paper, "Function-based BIM," describes this technological bridge between "profound," predictive knowledge and construction. Without functional modeling, there is no other known way to (1) convert actual whole-building project data into performance standards and measures, and (2) apply the standards to create market average baselines (standards) for future projects.
With the computational system in place, the next step is the collection, archiving, evaluating and retrieval of actual historical market data and information.
STANDARDS AND MEASURES AUTHORITY
For standards and measures to be useful to anyone, they will have to be the same for everyone; that is, performance units and methods need to be the same across market sectors, disciplines, and process tiers. The National Institute of Building Sciences (NIBS), working with the Construction Specifications Institute (CSI), developed a series of formatting conventions and standards that include The National Building Information Modeling Standards (NBIMS), MasterFormat and OmniClass. These are strategic initiatives that will enable the free exchange of information between users and technology systems.
For the construction project archive, each building producer (team) should have a system conforming to established national standards for managing data from past and current projects. Ideally, there will be archives at the market sector level or, even better, at the national level. The larger the archive, the greater the sampling and control basis for benchmarking. New or existing organizations (or a group of market sector organizations) must form to establish the archive(s). Using "Institute of Building Standards and Measures," or "IBSM," as a placeholder name for such an organization, a list of its functions includes:
- Defining Standards and Measures
- Adopting standards for classifying and categorizing data fields to be collected. These should align with the NIBS and CSI standards.
- Developing and publishing productivity, performance and value measures and indices and obtaining consensus on these with various industry groups (see below for initial recommendations for the higher level indices).
- Collecting, Archiving and Accessing Data
- Providing web-based input templates for the collection of data together with database structures for archiving and retrieval based on a wide range of queries and reporting requirements.
- Providing analysis and reporting of the historical project data for use by owners, practitioners, manufacturers and innovators.
- Developing and Using Baselines and Improvement Objectives
- Developing and/or reporting industry performance standards based on market average baselines, together with objectives that may be established by industry users and leaders.
- Tracking and reporting information, trends, cause and effect results, and other findings that will emerge from such an initiative. Like with the EMR, watching the market average change (effect) as measurement of actual against the norm (cause) are being reported. Hopefully, one of the "findings" will be that legal disputes are significantly reduced--and may inspire a measurement on this as well.
- Promoting, Equipping and Implementing in the Continuous Improvement Cycle
- Providing training and education on systems thinking and related measurement based (Deming) industry improvement.
- Equipping owners and agencies with the information and capacity to adopt Performance Standards and Measures approaches to projects
- Retrieving and validating historical data and information from actual projects and using this information in the continuous improvement cycle.
Because the performance paradigm shift depends on the availability of a project data archive to inform performance and measures, the IBSM, or equivalent organization, will be instrumental. Without a specialized authority or institution to oversee data management, building producers (teams) will be obliged to do data collection individually or not at all.
Every industry has standards and measures, and every industry organizes itself around its basic units of measure. In the industrial paradigm, the unit is cost of parts and services (which, in the end, is only one aspect of true value, which includes qualities like sustainability, functionality, efficiency, etc). As long as buildings are planned and built according to cost units, they will function at the lowest value within a specification. If, however, performance becomes the fundamental unit of building, then all planning and construction will re-organize itself to produce the highest building performance for the lowest cost.
Performance standards and measures + function-based modeling becomes a fantastically powerful information processing engine that brings measurement and statistical analysis to all levels and dimensions of a project, from planning, to production to facility operation. This is the engine that replaces the horse, the packet-switching network that replaces circuit-switching, the satellite that replaces the land line, the search engine that replaces the card catalog. It is the system of temperature measurement that replaced "hot" and "cold". Like all important transitions, the transition will meet resistance. The necessary collection and organization of performance data will be a pain, but it is a fraction of the pain involved in continuing to labor under the diseased cost-based system. Moreover, the pain of performance data collection will be short -- the break even will be within the first couple pilot projects.
1From the National Academies Press (www.nap.edu)
2Taking the Mystery out of TQM, Capexio and Morehouse, 1993 page 68