Showing posts with label industry 4.0. Show all posts
Showing posts with label industry 4.0. Show all posts

Saturday 14 August 2021

Industrialized Building and the Failure of Katerra

Why Modern Methods of Construction Don't Work


Offsite manufacturing, modular and prefabricated building have been transforming construction like nuclear fusion has been transforming energy: they have both been twenty years away from working at scale for the last 60 years. These ‘modern methods of construction’ have a dismal track record. The brutal economies of scale and scope in a project-based, geographically dispersed industry subject to extreme swings in demand have always bought previous periods of their growth and development to an end. 

 

While the history of prefabrication features major projects like the Great Exhibition in 1855 and more recently the Oresund Bridge in 2000, the reality is that prefabrication has only been successful in specific niche markets such as institutional buildings, or house manufacturers like the Japanese and Scandinavian firms Sekisui and Ikea. Failures like Katerra in mid-2021 and the mail order houses sold by Sears Roebuck a hundred years ago in the US are common. In the UK 2017 Industrial Strategy Construction was one of the four Sector Deals along with AI, the car industry and life sciences, with the aim to change the way buildings are created with a manufacturing hub for offsite and modular construction. By 2021 the focus had moved on, to the energy efficiency of buildings and new design standards. 

 

The up-front capital requirements of prefabrication make it a capital-intensive form of production, which brings high fixed costs in a cyclic industry characterised by demand volatility over the cycle. This means macroeconomic events often determine the success or failure of the underpinning business model and the success or the eventual failure of the investment. A batch of new US prefab housing firms failed during the GFC after 2007, for example, demonstrating the importance of the relationship between economic and business conditions and the viability of the business model for industrialised building.

 

Manufactured housing in the US also provides an insight into the institutional barriers to industrialisation in construction that exist in many countries and cities. Although the Department of Housing and Urban Development hasa national code, US cities discriminate against manufactured housing as local and county governments use a variety of land use planning devices to restrict or ban their use, and often place them in locations far from amenities such as schools, transportation, doctors and jobs. Despite these barriers, in 2021 there were 33 firms with 136 factories that produced nearly 95,000 homes. 

 

An ambitious attempt at offsite manufacturing (OSM) and industrialized building was made by Katerra, a US firm that was reinventing construction but has now gone into receivership. The manufacture of building elements and components somewhere other than the construction site has been variously called prefabrication, pre-cast and pre-assembly construction. Types of offsite construction are panelised systems erected onsite, volumetric systems that involve partial assembly of units or pods offsite, and factory built modular components or pods. The degree of OSM and preassembly varies from basic sub-assemblies to entire modules. Katerra manufactured prefabricated cross laminated timber (CLT) structures.  

 

 

Katerra

 

Katerra was a Californian start-up, founded in 2015. In 2017 it reached a $1 billion valuation, The company’s goal was complete vertical integration of design and construction, from concept sketches of a building to installing CLT panels and the bolting it together. On their projects the company wanted to be architect, offsite manufacturer and onsite contractor. This led to issues with the developers and contractors the company dealt with most of whom, it turned out, didn’t want the complete end-to-end service Katerra offered. 

 

The company started by developing software to manage an extensive supply chain for fixtures and fittings from around the world, but particularly China, and then added a US factory making roof trusses, cabinets, wall panels, and other elements. In 2016 the business model changed because architects weren’t specifying Katerra’s products. Katerra would design its own buildings and specify its own products. In 2017 it built a CLT factory that increased US output by 50 percent. The factory shut in 2019. Dissatisfied with design software that didn’t meet its needs, it developed a custom suite called Apollo. This was to be a platform for project development and delivery, well beyond the document control and communication of then available software from Oracle Aconex, Trimble Connect, Procore and SAP Connect. Apollo integrated six functions: 

1.      Report: use an address to find site information, zoning, and crime rates etc.; 

2.      Insight: design with the two building platforms; 

3.      Direct: a library of components used in the building; 

4.      Compose: for coordination between the different groups working on a project;

5.      Construct: for construction management (similar to Procore and Bluebeam):

6.      Connect: for managing the workforce on a project, with a database of subcontractors.

 

One of the company’s three founders was a property developer, and his projects provided the initial pipeline of work that made the company viable. Initially, buildings were designed by outside architects, but in 2016 the company started a design division. A second founder had a tech venture capital fund, the third and CEO did a stint at Tesla. Their ambition was to leverage new technologies to transform building by linking design and production through software, designing buildings in Revit and converting the files to a different format for machines in the factory. 

 

In 2018, after raising $865 million in venture capital led by SoftBank’s Vision Fund, Katerra acquired Michael Green Architecture, a leading advocate of CLT, and over a dozen other architects and contractors. In 2020 the business model changed again, by taking equity stakes in developments to boost demand. Katerra struggled to complete the projects. Accumulating losses and cost overruns during the Covid pandemic overwhelmed the company and in June 2021 Katerra Construction filed for Chapter 11 bankruptcy. 

 

In six years Katerra had grown to a 7,500 person company. That growth cost both money and focus, of the total US$2.2bn raised, SoftBank invested $2bn between 2018 and 2020. Without a clear focus, Katerra didn’t have a target customer base and got distracted by software and developing internet-of-things technology. The executive team was dominated by industry outsiders, but Katerra hired architects and engineers from traditional firms. Tension was inevitable. The fatal problem was execution, Katerra didn’t vertically integrate acquisitions into a company that did everything. It was fragmented and didn’t have a product platform or Apollo ready in time.   

 

With Apollo, Katerra was actually behind other companies developing platforms that manage design and construction in various ways. These platforms are at the technological frontier, a fourth industrial revolution technology for OSM with automated production of components. Other firms have developed different approaches to digital manufacturing and restructuring of firm boundaries to Katerra, integrating design and construction through development of digital platforms that provide design, component specification and manufacturing, delivery and on-site assembly. 

 

For example, in 2018 Project Frog released KitConnect, bringing together a decade of development into prefabrication and component design, and integrating BIM with DfMa and logistics. US start-ups in the wake of Katerra like Junoand Generate also don’t build factories but outsource assembly. Outfit offers homeowners a DIY renovation from its website, then orders and ships the materials and provides step-by-step instructions for completing the work (the Sears model again). Also in 2021, the IPO for PM software company Procore raised $635 at a valuation near $10bn, a record for construction tech. Rival Aconex was bought by Oracle in 2017 for $1.2bn. Platforms are in the process of becoming a basic part of construction tech. In the UK Pagabo launched a procurement platform in 2021, mainly for the public sector, using framework agreements for building work valued between £250k to £10m. Australian 2021 procurement IPO Felix had local start-ups Buildxact, SiteMate, Mastt, Portt and VenderPanel with competing platforms.  

 

 

Conclusion

 

The idea of construction as production was based on OSM, but after decades of development has yet to become a viable business model. There have been successes in manufactured housing, but often macroeconomic factors undermined their viability. Niche markets exist in institutional building, or wherever it is the most effective or efficient piece of technology available. This manufacturing-centric view of progress in construction, endorsed by numerous government and industry reports, is the end point of the development trajectory from the first to the third industrial revolutions.

 

The technological base of OSM is a mix of those from the first industrial revolution, like concrete, with second and third revolution technologies like factories and lean production. Despite all efforts this has not become a system of production because OSM does not deliver a decisive advantage over onsite production for the great majority of projects. Instead, construction has a deep, diverse and specialised value chain that resists integration because it is flexible and adapted to economic variability. Policy makers may neither like nor appreciate this brute fact, but economies of scale are the economic equivalent of gravity and OSM has not delivered. 


The constraints of OSM have outweighed the drivers and benefits. At this stage the market share of OSM remains small and niche, estimates are low single digits of total construction work in the UK, US and Australia. Success elsewhere is restricted to a few specific markets and project types. The problem is not the technology, which can be made to work, but the expected economies of scale are difficult to achieve because of a range of factors. Some of these factors are internal to construction, but others are external. In particular, macroeconomic events like financial crises or energy and commodity price changes can quickly undermine a business model. 


Norman Foster said in an interview ‘A building is only as good as its client’. With industrialized building the client is the producer, which is not necessarily a bad thing, however this has restricted its use to niche markets. How to apply the technologies of the fourth industrial revolution so they work with the economies of scale for onsite production in construction, beyond the OSM paradigm that has been followed for years without success, is the challenge

 

 

 

 

Friday 15 January 2021

Digitization and advanced business technologies in industry

Data on the technology frontier at the industry level

 

 

There is a commonly held view that construction is a digital laggard.  A widely cited McKinsey report in 2016 argued: “the construction sector has been slow to adopt process and technology innovations …. The industry has not yet embraced new digital technologies that need up-front investment, even if the long-term benefits are significant. R&D spending in construction runs well behind that of other industries: less than 1 percent of revenues, versus 3.5 to 4.5 percent for the auto and aerospace sectors. This is also true for spending on information technology, which accounts for less than 1 percent of revenues for construction, even though a number of new software solutions have been developed for the industry.

 

Technology use and diffusion is an important dynamic in industry development, but good data is rare. Most surveys are of specific industries or selected firms (typically large ones), and surveys with large samples that would be more representative of firms across the economy by including small and medium size firms are scarce. This particularly affects built environment industries like construction and professional services because of the large number of small firms in those industries. 

 

The United States Census Bureau conducts an Annual Business Survey (ABS), and in 2018 the ABS included a technology module with three questions about the extent of technology use between 2015 and 2017: the availability of information in digital format (digitization), expenditure on cloud computing services, and use of a range of advanced business technologies. The survey was a partnership between the Census Bureau and the National Center for Science and Engineering Statistics, and the first results have been released in a working paper from the National Bureau of Economic Research. The results are summarized below.

 

The survey data is at a high level of generality, to make the questions relevant across the variety of firms and industries included. There are also issues around how the data has been modelled and analysed, and the adjustments for high counts for firms that minimally use or are not using advanced business technologies at all. Importantly, the survey shows construction is not significantly lagging other industries in the US in digitization and use of cloud services, however it is doing less testing and development of advanced business technologies. 

 

The main finding of the survey was “Despite increasingly widespread discussion in the press of machine learning, robotics, automated vehicles, natural language processing, machine vision, voice recognition and other advanced technologies, we find that their adoption rates are relatively low. Furthermore, adoption is quite skewed, with heaviest concentration among a small subset of older and larger firms. We also find that technology adoption exhibits a hierarchical pattern, with the most sophisticated technologies being present most often only when more-basic applications are as well.” 

 

There were 583.000 responses to the survey. Two thirds of the firms employed under 10 people and were less than 20 years old, making this the most comprehensive survey of diffusion of advanced business technologies done so far. The industry breakdown of firms is in Table 1, with the built environment industries of construction, real estate and professional services well represented. This makes the data particularly interesting. 

 

Table 1. Industry breakdown of firms

Sector 

Distribution %

Agriculture, Mining, Utilities

2

Construction

10

Education

1

Finance, Insurance, Real Estate

10

Health Care

9

Information

2

Management & Administrative

5

Manufacturing

8

Other (Arts, Food, Other Services)

14

Professional Services

17

Retail Trade

13

Transportation & Warehousing

4

Wholesale Trade

5

 

Following are extracts from the NBER paper on the results of the survey on the three technology questions: the availability of information in digital format, expenditure on cloud computing services, and use of advanced business technologies with a focus on the use of AI. The next post will discuss the survey and its implications for construction. 

 

 

Digital Share of Information by Business Activity 

 

The first question in the 2018 ABS technology module queried firms on the type of information stored digitally. In all sectors, financial and personnel information are the most likely to be digitized, followed by customer feedback and marketing. This is the case for Construction, with financial, personnel and marketing digitized. The lowest rates of adoption are in production and supply chain activities.

 

Figure 2 is a butterfly chart of adoption and use rates for digital information by sector, where the ranking of sectors by adoption and intensity of use rates parallel each other. The right panel of the chart represents, by sector, the adoption rates of digital information across all surveyed information types. The segments within each bar in the chart capture adoption rates by the number of information types in digital format. In all sectors, a large share of adopters report having three or more types of information digitized. 

 

The left panel of Figure 2 represents intense use of digitization. Most firms report digitizing at least two types of information, regardless of sector, the fraction of firms digitizing only one type of information intensively is relatively small in each sector. Overall, digitization appears to be highly prevalent across sectors. Manufacturing, Information and Professional Services are among the highest adopters of digitization, with size being a primary correlate of adoption. 


 

Figure 2: Extensive and Intensive Margin Measures of Digitized Information by Sector



 


Cloud Service Purchases by IT Function 

 

This section describes the adoption patterns for cloud service purchases across size, age and sector. Like digitization, the highest adoption and intensive-use rates are in Information, followed closely by Professional Services and Education. The lowest rates are in Agriculture, Mining, Utilities, Retail Trade, and Transportation and Warehousing, and the Other category. Figure 4 reveals that cloud services purchases have much lower diffusion rates compared to those for digital information in any given sector. 

 

Billing and Security are the most common IT functions for most sectors, with certain sectors, including Construction, predominantly relying on the cloud to perform collaborative or synchronized tasks. The Data Analysis function has the lowest number of firms reporting some cloud purchase, Billing and Account Management has the highest number of firms, closely followed by Security or Firewall and Collaboration and Synchronization functions. 

 

Although the adoption rates for business IT functions in the cloud is significantly lower than the adoption rates of storing information digitally, this technology is widespread across various applications, with nearly a third of each different type of IT function being performed in the cloud and being used intensively. 


 

Figure 4: Extensive and Intensive Margin Measures of Use Rates for Cloud Service Purchases by Sector - Conditional 



Advanced Business Technologies 

 

In this section we analyze firm responses to the business technologies question. Due to their wide technological scope, we link the responses here with the previous technology adoption questions and perform a deeper set of analyses assessing the range of response categories. Very few firms use the business technologies included in the module, and many answered, “Don’t know”. Based on our tabulation weights, only 10.3% (8.5% non-imputed) of firms adopt at least one of the listed advanced business technologies. 

 

The highest use frequencies are in touchscreens and machine learning. For touchscreens the adoption rate is 6.1% of firms. Machine learning comes second but the rate is low at 2.9%. Voice Recognition and Machine Vision, which are can be considered examples of Machine Learning applications, have the next two highest use rates. 

 

The overall diffusion of robotics is very low across firms in the U.S. The use rate is only 1.3%, concentrated in large, manufacturing firms. The distribution of robots among firms is highly skewed toward larger firms. The least-used technologies are RFID (1.1%), Augmented Reality (0.8%), and Automated Vehicles (0.8%). 

 

Looking at the most common types of business technologies adopted by sector in Table 2 there is substantial variation. All sectors (except Manufacturing adopt Touchscreens followed by Machine Learning or Voice Recognition. Manufacturing is most likely to adopt Machine Learning followed by Touchscreens and Robotics. RFID technology is most commonly used in the Retail, Wholesale, and Transportation and Warehousing sectors, consistent with these industries tracking physical goods through supply chains. 

 

 Table 2. Top Use Sub-Categories for Business Technologies by Sector (p. 60).                                     

Sector

1st

2nd

3rd

Agriculture, Mining, Utilities

Touchscreens

Machine Learning

Automated vehicles

Construction

Touchscreens

Machine Learning

Voice Recognition

Education

Touchscreens

Machine Learning

Voice Recognition

Finance, Insurance, Real Estate

Touchscreens

Voice Recognition

Machine Learning

Health Care

Touchscreens

Voice Recognition

Machine Learning

Information

Touchscreens

Machine Learning

Voice Recognition

Management & Administrative

Touchscreens

Machine Learning

Voice Recognition

Manufacturing

Machine Learning

Robotics

Touchscreens

Other Arts, Food, Other Services

Touchscreens

Machine Learning

Machine Vision

Professional Services

Touchscreens

Voice Recognition

Machine Learning

Retail Trade

Touchscreens

Machine Learning

RFID

Transportation & Warehousing

Touchscreens

Machine Learning

RFID

Wholesale Trade

Touchscreens

Machine Learning

RFID

 

The testing-versus-use rates across different technologies are used to assess which technologies are in earlier phase of diffusion, that is, where testing is high relative to use. In Figure 6, the vertical axis represents the ratio of the fraction of firms testing to the fraction of firms using. The technologies are represented by the circles. The size of each circle corresponds to the use rate for that technology with larger circles representing higher rates of use. Technologies are ordered in the figure by usage rate, low to high. 


As shown in panel a, the technology with the highest testing-to-use ratio is Augmented Reality, where nearly half as many firms as those using the technology report testing it. The next highest ratios are observed in RFID and Natural Language Processing and the lowest ratios are in technologies that are relatively more diffused (and hence, used), such as Touchscreens, Machine Learning and Machine Vision. For Touchscreens, for instance, only about 15 firms report testing the technology for every 100 that use it. It is notable that most testing-to-use ratios are below 0.3, indicating that there are fewer than 30 firms testing the technology for every 100 using it. 


The remaining panels of Figure 6 plot the testing-to-use ratio for technologies by firm size, age, and manufacturing status. Panel b displays ratios by firm size, where small firms are defined as those with 1-9 employees and large firms are those with at least 250 employees. The blue circles capture usage among large firms and the orange circles represent usage among small firms. The sizes of the circles are smaller for small firms for each technology, consistent with the earlier finding that larger firms tend to use the business technologies at a higher rate, in general. 

 

Figure 6: Testing-to-Use Ratios 

a. Testing-to-Use Ratios for all Business Technologies (All Firms) 

 

b. Testing-to-Use Ratios for all Business Technologies (By Size) 

 

c. Testing-to-Use Ratios for all Business Technologies (By Age) 

 

d. Testing-to-Use Ratios for all Business Technologies (By Manufacturing Status)

 


The butterfly chart in Figure 7 provides sectoral diffusion rates for all business technologies considered together. Manufacturing leads with about 15% of firms indicating use of at least one business technology, followed by Health Care (14%), Information (12%), Education (11%) and Professional Services (10%). The lowest diffusion rates for the technologies are in Construction, Agriculture, Mining and Utilities, Management and Administrative, and Finance, Insurance and Real Estate sectors. 


 

Figure 7: Extensive and Intensive Margin Measures of Use and Testing Rates for Business Technologies by Sector 

 

The third question asked directly about the use of advanced “business technologies,” including those typically categorized as “AI.” These technologies include automated guided vehicles, machine learning, machine vision, natural language processing, and voice recognition software. Respondents are presented with a list that covers robotics (i.e., “automatically controlled, reprogrammable, and multipurpose machines”), various cognitive technologies (i.e., applications that help machines to “perceive, analyze, determine response and act appropriately in [their] environment”, a standard definition of AI), radio frequency identification, touchscreens/kiosks for customer interface, automated storage and retrieval systems, and automated guided vehicles. 

 

Across all AI-related technologies, the aggregate adoption rate for all firms in the economy is 6.6% meaning that approximately 1 in 16 firms in the US are utilizing some form of AI in the workplace. This adoption rate is significantly lower than the adoption rate highlighted in the AI survey by the European Commission and other private surveys by McKinsey, Deloitte, and PwC. However, it is important to consider the sampling methods of those surveys. None of the other surveys claim to be nationally representative and tend to focus on larger, publicly traded companies. In contrast, the ABS sample includes many small firms where AI adoption is very low. This is important because AI adoption rate varies greatly by firm size. Adoption rates (defined as usage or testing) increase from 5.3% for the group of firms with the smallest number of employees to 62.5% for firms with 10,000+ employees. 

 

In other words, scale appears to be a primary correlate of AI usage, likely due to both the large quantities of data and computing power required to fully realize the most popular types of AI currently available. This may potentially have far-reaching implications on topics such as inequality, competition and the rise of “superstar” firms, especially if AI is shown to have widespread productivity benefits. If only a select group of firms are able to fully realize the benefits of AI, we can expect further divergence for the “frontier” and most productive set of firms. 

 

Our data and explanatory variables are simply too crude to provide a reliable predictor for the precise types of firms that adopt certain technologies and those that do not. While we can claim that size is a reliable predictor of adoption, even amongst large firms, we see heterogeneous patterns of adoption depending on the technology type. In other words, there are simply too many unknown factors that cannot be measured by traditional metrics (such as firm size, age and industry) that appear to drive technology adoption. 

 

 

Conclusion 

 

We have provided an introduction to the technology module in the 2018 ABS and placed it in the larger context of related work at the Census Bureau to collect comprehensive data on technology adoption and use by U.S. firms in order to provide a more accurate picture of the state of advanced technology use in the U.S. economy. Because of the large pool of respondents (about 850,000 firms) in the 2018 ABS, the module represents a unique opportunity to offer insights on technology adoption and use across all sectors of the economy and across a variety of key firm characteristics. The same technology module is expected to be a part of the 2021 Annual Business Survey.

 

A primary contribution for the paper is to develop a nationally representative set of technology adoption and use measures based on the survey results, which in public use tabulations report aggregate response counts for each technology question (see public use tabulations at: https://www.census.gov/data/tables/2018/econ/abs/2018-abs-digital-technology-module.html ). 

 



Advanced Technologies Adoption And Use By U.S. Firms: Evidence From The Annual Business Survey, by  Nikolas Zolas, Zachary Kroff, Erik Brynjolfsson, Kristina McElheran, David N. Beede, Cathy Buffington, Nathan Goldschlag, Lucia Foster and Emin Dinlersoz. 2020. National Bureau of Economic Research, Cambridge, MA Working Paper 28290 http://www.nber.org/papers/w28290