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Creating Sustainability-focused Value through Industrialized Construction: The Role of Organization-related, Process-related, or Product-related Strategies and Technology Interventions

Industrialized construction factory

Project Team

Michael Lepech, Erica Plambeck, Tess Hegarty (Stanford)
Joe Louis (Oregon State University)
Ankur Podder (US DOE National Renewable Energy Laboratory)

Research Overview

Observed Problem: 

Industrialized construction (IC) is proposed to improve construction sustainability. However, it remains unclear whether IC is “more sustainable” because of better manufacturing processes or life cycle thinking during product design?

Primary Research Objective:

This research answers whether IC is more sustainable due to (i) improved organization/process control or (ii) improved product design/analysis.

Potential Value to CIFE Members and Practice:

Construction firms create value throughout the value chain and eliminate non-value-adding activities to engage in modern “Activity-based Management,” or “ABM.” By identifying the sources of sustainability-focused value, this research enables ABM approaches for construction.

Research provides relevant insights for:

Owners, Builders, and Operators/Facility Managers

Research and Theoretical Contributions:

  • Research - (i) understanding sustainability-focused value-creation, (ii) ABM framework for IC.
  • Theoretical - (i) extending the POP framework, (ii) linking POP with ABM theory.

Industry and Academic Partners:

Joe Louis (Oregon State University)
Ankur Podder (US DOE National Renewable Energy Laboratory)

Keywords:

industrialized construction, Activity-based Management POP framework, sustainable construction, probabilistic life cycle assessment (LCA), Monte Carlo simulation, Discrete Event Simulation (DES), digital twin, technology in construction, Building Information Modelling (BIM)

Research Updates & Progress Reports

Summary of Progress Updates

  • Fall 2020-21: identify specific changes relative to the baseline or traditional construction approach, connect two case studies to POP framework; proof of concept of probabilistic LCA from another project; groundwork in thermal performance modelling in COMSOL that will enable the probabilistic LCA approach
  •  Winter 2020-21: complete the probabilistic LCA for both case studies, using DES data to inform input distributions, and integrate quantification into POP framework   
  • Spring 2020-21: connect LCA findings to management theory, creating an activity-based management framework for IC

M1 – Identify two relevant IC case studies with research collaborators

Both case studies have been identified and meetings with research collaborators are underway to further define the specifics. Case study 1 focuses on continuous improvement in IC and involves comparing an improved IC approach (using pre-insulated studs installed in the factory) to a baseline IC approach (installing continuous insulation on site). Case study 2 focuses on identifying advantages of industrialized construction and involves comparing and IC building to an equivalent traditional construction building. Project collaborators have shared a BIM model of a typical Volumetric Building Companies (VBC) volumetric wood-framed module that can be used to determine many initial LCA inputs. For the second case study (IC building compared to equivalent traditional construction building), we requested travel funding to visit a traditional construction site to observe and measure traditional construction baseline. This travel cannot occur under current Stanford COVID restrictions, so we may need to adjust our approach to this case study to exclude travel.

M2 – Identify specific changes relative to the baseline or traditional construction approach, connect two case studies to POP framework, and collect DES data

There are key differences in an industrialized construction approach vs a conventional construction approach that impact a life cycle assessment. Work to identify and categorize these key differences within the POP framework is underway. Some of these changes relate to the process: for example, improved construction quality has been shown in industrialized construction. In particular, a reduction of defects has been observed in quality audits of modular wood framed construction compared to conventional housing (Johnsson & Meiling, 2009). Certain improvements in quality (e.g., reduction in the unventilated gaps around window frames) have potential to improve the thermal performance of IC buildings (and therefore reduce the use phase impacts in an LCA). An organization related change associated with IC is that long term relationships with suppliers are common, which makes it possible to predict (with a high degree of certainty) the distances that building materials are shipped from. Finally, some changes relate specifically to improvements in the product that are enabled by prefabrication (e.g., the use of pre-insulated studs).

M3 – Complete probabilistic LCA of both case studies, using DES data to inform input distributions, and integrate quantification into POP framework   

While the probabilistic LCA results are not complete for this project, research for another project has provided “proof of concept” for the usefulness and practicality of a probabilistic approach to LCA using SIPMath in Excel. In the CarbonCondo project (comparative LCA results shown in Figure 1), the Pedigree matrix approach was used to quantify the uncertainty around all LCA inputs. Notably, the project sponsors (ARPA-e) were particularly interested in learning more about the probabilistic approach to decision making, since they are frequently tasked with making decisions on uncertain information, suggesting that the additional information contained in a probabilistic assessment is considered useful in practice.


Figure 1: Probabilistic LCA results for another project demonstrate that a probabilistic LCA approach can be implemented via SIPMath in Excel, as planned for this project.

For this project, the goal is to take a probabilistic approach to the LCA that uses data collected in the factory to supplement the uncertainty information that can be derived via the Pedigree matrix approach. The key inputs to the probabilistic LCA can be broken down into two phases, embodied impacts and use phase impacts. The inputs needed to determine embodied impacts include material quantities, material waste quantities, transportation distances, material impact factors, transportation impact factors, and construction/fabrication impact factors. The inputs needed to determine use phase impacts include annual energy use and energy impact factors, where the annual energy use is based on thermal performance modelling. For each of the aforementioned LCA inputs, both the average and the uncertainty of each piece of data must be determined and included in the probabilistic analysis.

For material quantities, the averages are derived from the BIM model and data provided by VBC. For material waste quantities, there are typical waste factors for conventional construction approaches have been identified from the literature. The discrete event simulation data across a number of volumetric modules is expected to allow us to identify the mean and standard deviation of material waste associated with the IC approach.  

For material transportation distances, VBC will provide specific manufacturers that their materials are routinely sourced from, which will allow us to determine the average material transportation distances for the IC approach. For the conventional approach, we will likely use approximations for the transportation distances, because in a conventional construction approach, the exact material providers are likely not known at the design phase. Therefore, the uncertainty associated with the material transport is expected to be higher for the conventional building approach. 

For the impact factors associated with materials, transport, and construction activities, the averages for these are mainly from the closest SimaPro’s databases, or from published Environmental Product Declarations (EPDs) or literature. Energy impact factors for a specific location are available via the EPA. The uncertainty for these impact factors will be derived by applying the Pedigree matrix approach.

Determining the energy use related to the industrialized construction approach relative to the conventional construction approach requires a detailed analysis of the small improvements in construction quality, as well as looking at the impacts of any changes that are enabled by IC approach (such as the use of pre-insulated studs in case study 1). Traditional approaches to building energy modeling (such as EnergyPlus) do not capture the level of detail we want to be able to control, so we have been developing models using multi-physics modeling software (COMSOL) to enable prediction of energy use impacts, where the model can be adjusted to capture the quality differences between traditional and industrialized construction.

 Figure 2: COMSOL models of a 2D window frame assembly (left, uninsulated air gap highlighted) and a 3D wall with wooden stud (right).
Figure 2: COMSOL models of a 2D window frame assembly (left, uninsulated air gap highlighted) and a 3D wall with wooden stud (right).

COMSOL models (such as those shown in Figure 2) with key parameters controlled via MATLAB LiveLink can be used to investigate questions such as what impact it has on energy performance if industrialized construction approaches enable a reduction in the average gaps between building elements. If these changes are deemed significant from a LCA perspective, we can determine whether DES data already provides insight into the gaps or tolerances between elements, or work with our research collaborators to determine how to collect this data from VBC. The MATLAB LiveLink approach enables repeated runs of the COMSOL model, which can enable a probabilistic output of predicted thermal performance results, once we have collected data on the actual size of gaps between elements.   


M4 – Connect LCA findings to management theory, creating an activity-based management framework for IC
Not yet started.

M5 – Present findings and host panel at CIFE IC Forum 2022 or host workshop to share research findings
Not yet started.

________________________________________

Detailed Research Overview & Progress Updates

Overview & Observed Problem

Industrialized construction (IC) techniques are being leveraged to improve the efficiency and sustainability of the built environment. IC companies frequently claim improved sustainability relative to traditional approaches as a key value proposition. On the company level, questions about the effectiveness of various pathways toward cost-effective sustainability enhancements should be informed by life-cycle sustainability metrics. The degree to which structural markers of industrialized construction (Lessing, 2006) truly deliver improved sustainability metrics needs to be established so that the effectiveness of continuous improvement efforts (Meiling, Backlund, & Johnsson, 2012) can be maximized.

Figure 1: The pathways between form and function are unknown and unquantified for industrialized construction.

Improved sustainability profile is one of the key value propositions of IC; however, the foundational mechanisms or pathways by which these structural features of IC contribute to improved sustainability and other value propositions (McKinsey, 2017) (Lessing & Brege, 2015) remain unclear, as shown in Figure 1. We want to know if industrialized construction is “more sustainable” due to better process control (i.e., reducing variation and defects of all types) or due to the application of more comprehensive, life-cycle concepts in product design and modeling (i.e., reducing the average building footprint)?

This research looks to answer fundamental questions regarding the sustainability-focused value proposition of industrialized construction: to what extent does industrialized construction (IC) achieve its sustainability value proposition due to (i) improved organization and process control vs (ii) improved product design and analysis techniques?

Figure 2: What is the role of DES in enabling product/process improvements in IC that impact the mean and standard deviation of LCA outputs?

Of the structural aspects above, we are seeking to understand the intersection of off-site prefabrication of building parts with increased use of information and communication technology (ICT), as shown in Figure 2. Strategic ICT integration is thought to enhance value of offsite production systems in industrialized construction (Malmgren, Jensen, & Olofsson, 2011). Specifically, this research will focus on the role of discrete event simulation (DES) of the real-world prefabrication process and visualization of the results via digital twins in virtual reality (VR) simulation to drive either organization/process-related improvements or product-related sustainability improvements in industrialized construction. The application of rigorous sustainability quantification tools and the linking with management techniques will provide a framework for practical application of these findings.

Theoretical & Practical Points of Departure

Prefabrication Process Simulation

Figure 3: Details of test case based on work by collaborators  Figure adapted from Ahrenholz, 2018; Blum, 2014; Tstud, 2020

Simulation allows us to model real systems and gain insights about their performance. It is a “preferred means for studying systems, especially when it is expensive or infeasible to obtain insights from the real system under study through real-world experimentations.” Discrete event simulation (DES) using jStrobe simulation software, can be used to simulate construction operation alternatives and select the effective option (Abiri, Louis, & Riggio, 2019).

Sensors placed in the factory collect data on the environment to inform the DES. These sensors capture vibrations, audio, movements, and video (Louis, 2020). The use of DES has been proven to help quantify savings in lead time, material use, and cost due to a continuous improvement intervention in an industrialized construction process. DES data has been integrated into a digital twin which exists for the production of volumetric wood-framed modules in a factory of an IC company (Volumetric Building Companies) as shown in Figure 3. By digital twin, we are referring to a virtual reality model of a real production line in an industrialized construction factory that integrates data from discrete-event simulation. Sensors in the factory keep the digital twin updated to current factory conditions.

Factory productivity is determined by the interaction of equipment, labor, space, and materials, all factors that the digital twin captures. Digital twins can be leveraged to enable continuous improvements in the production process, by identifying bottlenecks and ensuring that interventions are working as planned. The digital twin can be used to forecast performance and what-if scenarios, enabling the optimization of resource allocation. 

Probabilistic Life Cycle Assessment

Figure 4: Probabilistic LCA increases the likelihood of meeting sustainability targets (Pomponi et al., 2017)

Life Cycle Assessment (LCA) is “a tool for quantifying the environmental performance of products taking into account the complete life cycle” starting from raw material production to the final disposal or recycling of the products (Goedkoop, Oele, Leijting, Ponsioen, & Meijer, 2013). The vast majority of LCA is done deterministically, based on average inputs, which makes the results seem more precise and certain than they are. As shown in Figure 4, this can mislead decisions intended to improve sustainability (Lepech, Geiker, & Stang, 2014; Pomponi, D’Amico, & Moncaster, 2017).Probabilistic LCA can be performed in Excel based on a SIPMath Monte Carlo simulation approach. SIPMath probabilistic modeling performs computations using Stochastic Information Packets (SIPs), in which uncertainty is modeled as an array of possible outcomes (ProbabilityManagement, 2018; Savage & Thibault, 2014). Using SIPMath, uncertainties are represented as thousands of possible outcomes within an array, this preprocessing of uncertain outcomes enables rapid probabilistic analysis of many uncertain variables simultaneously in the native Excel environment.

Process, Organization, Product (POP) Framework

The Product, Organization, Product (POP) framework was developed at CIFE to enable the application of design thinking in building design with the goal of realizing highly valuable buildings (Fischer, Ashcraft, Reed, & Khanzode, 2017). Highly valuable buildings are useable, buildable, operable, and sustainable. Function and form are connected with predicted and observed performance (behavior) across three design levers (product, organization, and process).

Activity-based Management Model

In cost-competitive industries like construction, it is imperative that firms understand the creation of customer value throughout the value chain, and eliminate non-value adding activities that incur costs but provide no value to the end customer (Lanen, Anderson, & Maher, 2020).
Activity-based management (ABM) systems examine processes and activities to determine their effects on costs (Anderson, 1993; Gupta & Galloway, 2003). An ABM system can enable analysis of the “activities in terms of product and process design features, and thereby provides valuable information to the product designers by supplying the cost implications of alternative design choices”. The system can identify factors under the control of design engineers that influence manufacturing costs. Without such systems, companies tend to “design more complex products because the price and market share advantages are perceived to outweigh the additional costs of designing, manufacturing and supporting complex products” (Gupta & Galloway, 2003).

Research Methods & Work Plan

Research Context

A number of proposed industrialized construction technologies have been identified that represent a continuum of industrialization, a few of which are shown in Figure 5. This continuum ranges from very low levels of industrialization (e.g., precast concrete elements) to very high levels of industrialization (e.g., unitary material logic of CARBONHOUSE in which a single material is used to produce all parts of the built environment). Volumetric modular and kit-of part approaches with varying degrees of predefinition fill out the space between these extremes. This research proposal focuses on (i) continuous improvement activities for VBC’s volumetric modular, wood-framed approach, as well as (ii) comparing the industrialized construction approach with a functionally equivalent traditional construction approach.

Case Study 1: improved IC approach vs baseline approach

Figure 5: The proposed research is called out in the boxed portions, shown in the context of the continuum of IC approaches.Figure 6: Existing work connecting LCA to DES (Mawson & Hughes, 2019)

This case study is rooted in work by collaborators Louis and Podder (NREL) and VBC, as explained in 2.1. It is focused on “continuous improvements” in the IC approach. This methodology is also available in visual format in the Appendix.

First, we will identify and quantify the differences between the IC approach vs the baseline approach using integrated DES data. Next, we will quantify sustainability of both approaches. Perform spreadsheet-based probabilistic LCA of a volumetric modular unit (using SIPMath in Excel) informed by DES data and the ecoinvent database in SimaPro (Goedkoop et al., 2013). This part of the methodology will be building off existing work linking DES & LCA, as shown in Figure 6 (Feng, Lu, Chen, & Wang, 2018; Mawson & Hughes, 2019). Then the differences identified will be related to the POP framework, categorizing them as product, organization, or process changes and using sustainability impact metrics to add quantification to the framework. Next, the results will be related to activity-based management, to help answer questions such as “Are the improvements delivering on the sustainability value proposition?” Finally, we will update the digital twin with DES and LCA results, so that digital twin virtually reality environment can be used to identify potential improvements and rapidly update DES/LCA results to explore these continuous improvements.

Case study 2: IC approach vs traditional construction

Case study 2 builds off of case study 1, to look at how the IC approach compares to a traditional construction site build on the building scale. The DES data from case study 1 will provide inputs for the LCA of the IC approach. Our collaborators will set up sensors and the DES on a traditional construction site in order to allow us to identify and quantify differences between IC approach vs traditional construction approach. The methodology will be similar to that explained above (and is available in visual format in the Appendix), but without integrating the information into a digital twin. If no real-world traditional construction project as a baseline for comparison can be identified, DOE reference buildings for use in building energy modelling are available, and can be modified to provide a functionally matched comparison. The probabilistic LCA results coupled with the POP framework will allow us to classify and quantify the life-cycle sustainability advantages of IC approach has in comparison to the traditional construction approach.

Expected Contributions to Practice

Expected practical research contributions include: (i) a deeper understanding of the mechanisms behind creation of sustainability-focused value in the industrialized construction industry, (ii) a framework for engaging in Activity-based Management in the sustainability-focused value chain of industrialized construction, and (iii) refinement of fundamental wholistic sustainability research tools (probabilistic LCA spreadsheets).

The research proposed here is part of our larger goal of elevating the reliability, rigor, uptake, and impact of sustainability quantification within the AEC industry, such that stakeholders are empowered to achieve triple-bottom line sustainability goals (Lepech et al., 2014). Discrete-event simulation of off-site construction processes will inform more detailed probabilistic LCA of finished IC products.

We expect this research to help building owners define sustainability goals for the POP framework and enable the IC industry to meet these goals in a cost-effective manner. Specifically, the framework for engaging in Activity-based Management in the sustainability-focused value chain of industrialized construction will help industry partners predict which sustainability interventions will be simultaneously cost effective and achieve their targeted sustainability goals. We plan to present findings at the CIFE IC Forum in 2022, and expect this research to impact the industry within five years.

Probabilistic LCA spreadsheet models providing rapid feedback on potential changes will be useful to the industry as a foundational wholistic tool to inform management and design decisions. This work will also be of practical use to those IC companies with a software focus (e.g. Project Frog and Prescient), especially as we work in the future to develop these probabilistic LCA spreadsheet tools into versions that can be integrated into BIM/BEM workflows via Grasshopper or Dynamo (Cavalliere, Habert, Dell’Osso, & Hollberg, 2019). We hope that future collaboration with such companies can help foster widespread adoption of probabilistic LCA tools in the IC landscape.

Expected Contributions to Theory

Expected theoretical contributions include: (i) extension of the Product-Organization-Process framework to include quantitative measures of value creation, with a focus on sustainability and (ii) theoretical linking between the Product-Organization-Process framework and the management theory of Activity-based Management.

Publications

References

  • Abiri, B., Louis, J., & Riggio, M. (2019). Decision-making for Cross-Laminated Timber Modular Construction Logistics Using Discrete Event Simulation. Modular and Offsite Construction (MOC) Summit Proceedings, 381–388. https://doi.org/10.29173/mocs117
  • Ahrenholz, T. (2018). Energy code math for commercial walls: How polyiso continuous insulation can help. Retrieved from https://www.constructionspecifier.com/energy-code-math-for-commercial-walls-how-polyiso-continuous-insulation-can-help/
  • Anderson. (1993). Using activity-based costing for efficiency and quality. Government Finance Review.
  • ARPA-E. (2018). OPEN 2018 Project Descriptions.
  • Blokable. (n.d.). Retrieved from https://blokable.com/
  • Blum, J. O. (2014). Impact of Advancements in Model Energy Codes: What’s the effect on insulation? Retrieved from https://www.constructionspecifier.com/impact-of-advancements-in-model-energy-codes-whats-the-effect-on-insulation/
  • Cavalliere, C., Habert, G., Dell’Osso, G. R., & Hollberg, A. (2019). Continuous BIM-based assessment of embodied environmental impacts throughout the design process. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.11.247
  • DPR. (n.d.). Retrieved from https://www.dpr.com/
  • Factory_OS. (n.d.). Retrieved from https://factoryos.com/
  • Feng, K., Lu, W., Chen, S., & Wang, Y. (2018). An integrated environment-cost-time optimisation method for construction contractors considering global warming. Sustainability (Switzerland), 10(11). https://doi.org/10.3390/su10114207
  • Fischer, M., Ashcraft, H. W., Reed, D., & Khanzode, A. (2017). Integrating Project Delivery. John Wiley & Sons.
  • FullStack Modular. (n.d.). Retrieved from https://www.fullstackmodular.com/
  • Gilead. (n.d.). Retrieved from https://www.gilead.com/
  • Goedkoop, M., Oele, M., Leijting, J., Ponsioen, T., & Meijer, E. (2013). Introduction to LCA with SimaPro.
  • GOLDBECK. (n.d.). Retrieved from https://www.goldbeck.de/
  • Gupta, M., & Galloway, K. (2003). Activity-based costing/management and its implications for operations management. Technovation, 23(2), 131–138. https://doi.org/10.1016/S0166-4972(01)00093-1
  • Johnsson, H., & Meiling, J. H. (2009). Defects in offsite construction: timber module  prefabrication. Construction Management and Economics, 27(7), 667–681.  https://doi.org/10.1080/01446190903002797
  • Lanen, W., Anderson, S., & Maher, M. (2020). Fundamentals of Cost Accounting (6th ed.). Boston: McGraw-Hill Irwin.
  • Lepech, M. D., Geiker, M., & Stang, H. (2014). Probabilistic design and management of environmentally sustainable repair and rehabilitation of reinforced concrete structures. Cement and Concrete Composites. https://doi.org/10.1016/j.cemconcomp.2013.10.009
  • Lessing, J. (2006). Industrialised House-Building.
  • Lessing, J., & Brege, S. (2015). Business models for product-oriented house-building companies – experience from two Swedish case studies.
  • Louis, J. (2020). Digital Twins for Analyzing Modular Construction Factories.
  • Malmgren, L., Jensen, P., & Olofsson, T. (2011). Product modeling of configurable building systems - A case study. Electronic Journal of Information Technology in Construction, 16(October), 697–712.
  • Mawson, V. J., & Hughes, B. R. (2019). The development of modelling tools to improve energy efficiency in manufacturing processes and systems. Journal of Manufacturing Systems, 51, 95–105. https://doi.org/10.1016/J.JMSY.2019.04.008
  • McKinsey. (2017). Reinventing Construction: A Route To Higher Productivity. McKinsey & Company, (February), 20.
  • Meiling, J., Backlund, F., & Johnsson, H. (2012). Managing for continuous improvement in off-site construction: Evaluation of lean management principles. Engineering, Construction and Architectural Management, 19(2), 141–158. https://doi.org/10.1108/09699981211206089
  • Mortenson. (n.d.). Retrieved from https://www.mortenson.com/
  • NREL. (2020). Integrating Energy Efficiency Strategies and Distributed Energy Resources into Industrialized Construction. Retrieved from https://www.nrel.gov/buildings/industrialized-construction.html
  • Pomponi, F., D’Amico, B., & Moncaster, A. (2017). A Method to Facilitate Uncertainty Analysis in LCAs of Buildings. Energies, 10(4), 524. https://doi.org/10.3390/en10040524
  • Prescient. (n.d.). Retrieved from https://prescientco.com/
  • ProbabilityManagement. (2018). The Open SIPmathTM 2.0 Standard. Retrieved from https://www.probabilitymanagement.org/20-standard
  • Project Frog. (n.d.). Retrieved from https://www.projectfrog.com/
  • Savage, S. L., & Thibault, J. M. (2014). Towards a Simulation Network or the Medium Is the Monte Carlo (pp. 4126–4133).
  • Skender. (n.d.). Retrieved from https://www.skender.com/
  • Tstud. (2020). Offering a 6-in-1 Solution to Residential and Commercial Building Industries. Retrieved from https://www.tstud.com/
  • Volumetric Building Companies. (n.d.). Retrieved from https://www.vaughanbuckley.com/

Original Proposal

Proposal

Funding Year: 
2021
Stakeholder Categories: 
Owners
Operators/Facility Managers
Builders