Data Strategy to Achieve Digital Daiwa House with Autodesk Consulting

AKIRA YOSHIKAWA: Good afternoon, everyone. Thank you for attending our session, "Data Strategy to Achieve Daiwa House with Autodesk Consulting." We are not fluent in English, but we do our best to share our ideas and approaches we are working on to accelerate digital transformation in the company.

Let's introduce ourselves first. My name is Akira Yoshikawa. I joined Daiwa House in 2007 and worked too as a architectural designer in general construction building division for 11 years. I have studied [INAUDIBLE] in the projects since 2010.

After moving to BIM Promotion Department in 2018, I supported architecture design team closely and developed BIM standard nationwide in Daiwa House. Currently as the head of planning office in digital transformation promotion department, I am leading the team to identify the medium and long-term measures for the growth areas. And also, to adapt established measures across the business units both in design and construction.

TAKUMA OGAWA: My name is Takuma Ogawa. I started to study BIM at university. To apply it into practice, I joined the Daiwa House in 2019. My first project was to have ISO 19650 certified as a first company in Japan. I have worked with a team to establish templates for [INAUDIBLE], authorization settings, as well as standard rules for operation and deployed them to building division nationwide.

We also have conducted many trainings for all engineers to improve the maturity level of end users. Currently to utilize ACC corporate world, we are accelerating development [INAUDIBLE] with ERP and other internal systems using APS. I want to move forward with further transformation by utilizing AI or other advanced technologies. By the way, sashimi, chihiro, and miso soup in the photo is not my current responsibilities at work. But I enjoy cooking Japanese dishes like this.

AKIRA YOSHIKAWA: Let us briefly introduce our company, Daiwa House, about its 70 years of history. We have delivered about 2 million residences over 47,000 commercial facilities and 10,000 medical and nursing care facilities in Japan. We are the largest construction company in Japan with total sales of the group companies exceeded five trillion yen, which is around $35 billion in the [INAUDIBLE] of 150 yens per dollar. Recently, we are expanding our business in the US, Asia, and the ASEAN region, Oceania and Europe.

This very old photo is the first prefab construction in Japan we have developed called pipe house, a prefabricated warehouse for railway facilities in 1955. The founder developed a new concept at that time to utilize the standardized components and expanded the business.

In the process of deepening and developing our own industrialized construction, we have tried to bring information between the departments in various ways. See, we are facing additional challenges, such as increasing number of custom made components and products or the growing complexity of systems and rules.

In response to the changing times and technology, we are aiming to renew industrialized construction where utilizing BIM foundation and applying DfMA. In other words, it is a convergence of digital technology based on BIM and the building technology of industrialized construction we have cultivated. In this session, we talk about the Digital Daiwa House, our concept of evolving industrialization.

Now, let me give you an overview of the concept in the short video.

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- A revolutionary innovation in the Japanese construction industry occurred about 10 years ago. Daiwa House began developing BIM standards in 2017. We are presently in the establishment phase of BIM and moving into the utilization phase.

This is part of the evolution of our new Digital Daiwa House concept. Daiwa House's processes revolve around digital data. The data generated in this phase will further create new value. That future is Digital Daiwa House.

There are two important elements in realizing a Digital Daiwa House. The first is to further improve the maturity of BIM, so that data can be linked together without being tied to a file. This will help you be more standardized and collaborative with your processes going beyond BIM.

The second is to mature the data that is linked to BIM. We will develop data that is easy to use and of high quality, so that it can be useful to many in the organization. For realization of maximum value from data, we will develop a common data environment or CDE connected to other digital business initiatives to further facilitate data utilization throughout the company.

Through business process change that will better connect your business units and customers through business process change or BPC, increasing the maturity of BIM and data is also valuable. But it is not enough. Therefore, through connecting BIM with additional data, we will build a configurator to be a mechanism that maximizes business value by first connecting design and cost data real-time to connect and improve estimates and development of design.

It is very important to build an environment to accumulate reliable data and to use it efficiently in order to realize Digital Daiwa House. Data obtained from thousands of projects per year cannot be utilized in the future if it is simply stored on paper or in separate files for each application. By properly recording engineering data in Autodesk Construction Cloud ACC, the data becomes an asset that can be used and reused to higher quality buildings and service.

According to the UK definition of BIM levels, level 3 is a world where BIM data is integrated with various data and web services to streamline the entire lifecycle. To date, BPC has focused on achieving inter-departmental collaboration at Daiwa House. Our next goal is to realize BIM level 3, which enables data centered collaboration in projects beyond the boundaries of our own company.

To move towards this goal, Daiwa House is building a configurator as a mechanism to create business value by connecting BIM and data. Our goal is not just a mechanism to automate the generation of building forms or designs, but helps present business rules from data across the life cycle to make informed decisions.

As the maturity of the data that drives the configurator improves, the performance of the configurator itself will also improve, expanding the scope of what it can handle and further increasing its business value. In this way, the configurator continues to evolve with the maturity of the data.

The Digital Daiwa House concept is a data strategy that goes beyond BIM and enables enterprise level transformation and adaptability to new business opportunities through Digital Daiwa House. While looking at the impacts of data on the construction industry as a whole, we are also raising the company's own digital maturity level. The Digital Daiwa House concept will continue.

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As mentioned in the video, Digital Daiwa House is an innovation. The concept could have an impact to transform the entire construction industry. By achieving Digital Daiwa House, we will provide services to enrich the activities not only for customers, but also for employees, stakeholders, and many others, creating a new value.

Now, I'd like to talk about how we are planning to achieving Digital Daiwa House. Simply speaking, the purpose of Digital Daiwa House is to create new values. At first, we thought about what kind of process we should apply to achieve it. We constructed our execution process, referring to DIKW model.

As some of you may know, I will briefly explain DIKW model. DIKW model is a framework for effectively implementing knowledge management. It consists of four layers-- data, information, knowledge, and wisdom.

The bottom layer is data. Data alone has no value. And even if correct as it is, it cannot be utilized. By classifying, analyzing, considering, and verifying data and giving it meanings, it will become information.

When integrating multiple pieces of information, systemising it with regularity and adding some insight, it will become knowledge. This is where the value comes into play. Then, based on the knowledge, if we think and act to create new value, it will become wisdom.

Based on DIKW model, we saw about the execution process that achieved Digital Daiwa House. We define it as data maturity process. We also considered classifying data into two types-- BIM data and [INAUDIBLE] data and improving the maturity of each type.

To improve data maturity, we first wanted to understand where we are now. We define the data maturity indicator as data maturity level and divided them into four levels. Level 0 consists of data generated by conventional business process, for example, data printed on paper or stored in PDF files.

Level 1 is a level of maturity where each department can utilize data respectively. Level 2 refers to stage where data is integrated across the company and can be linked and utilized across multiple departments. Level 3 will be the stage where data [INAUDIBLE] can generate new valuable data based on cutting edge technologies such as AI.

Recognizing our service as level 1, we conducted several analyses with Autodesk Consulting. We found our weak area in data quality and data scalability. We need to strengthen those areas to reach to level 2.

We have organized the necessary action to enhance data quality and scalability into three steps. We named it as 3C cycle-- take the initial letter of those three steps. The first is to create. The second is to collect. The third is to consume. To overcome our weakness, it is important to create the right data and correct and accumulate those data properly.

[INAUDIBLE] start with a step of creating data, structured and standardized as a key. Even if we create our data, it cannot be retrieved or handled. We cannot utilize it with other data. In other words, the system should be able to freely input and output data independent from individuals or environment. We define the indicator as structured.

In addition, if the quality of the retrieved data is not ensured, the expected roots cannot be obtained even if the data is [INAUDIBLE]. The data should be utilized without any restriction from certain functions, operations, or departments. We define the indicator as standardized. We conducted four initiatives as described here. Establishing structured and standardized data improves data quality.

The next step is collecting data. Data-centric approach is a key. Conventional data processing methods, such as bucket relays, not only slow down the speed of process execution but also deteriorates the quality and connectivity of the data itself. In contrast, the data center oriented idea places the data at the center, creating an environment where all processes, technologies, and stakeholders can always retrieve and use the most up-to-date and necessary granularity of information. The data-centric protocol improves the scalability of data.

Daiwa House has been [INAUDIBLE] so far a vision of a mobius loop, whereas data is seamlessly passed between processes and [INAUDIBLE]. However, only with this idea, the data transform may depend on individuals and correct feedback might not be reached to the next process or future projects.

Therefore, we have added a new vision, data-centric and advancing the data strategy to have grown our data maturity from level 1 to level 2. Not all of our data is currently at level 2 maturity, but with this new vision, we achieve level 2 maturity and tends to lead to level 3.

Let's take a look at Daiwa House's common data strategy, which incorporates data-centric philosophy. The important key for the common data strategy is a CDE. We define CDE as a common data environment necessary to facilitate the seamless execution of the construction lifecycle for all projects.

We believe Autodesk Construction Cloud is a perfect tool for CDE. It manages rapid data in ACC and leverages stocks and BIM to execute the process, making the construction [INAUDIBLE] run effectively and smoothly.

Why leveraging this to achieve our vision of Digital Daiwa House, we need a data-centric foundation. It can effectively execute the entire business lifecycle. We include it in our common data strategy.

The key here is how we implement the concept of centralized management. Centralized management doesn't mean uploading all data to ACC for management. We believe that the centralized management is an environment where designated people can access and retrieve the necessary data when and where they need with the proper access control.

This slide shows the overall picture of common data strategy. We consolidate building information around the BIM model. By managing the BIM model with CDE, we can manage construction project from design to construction and maintenance.

At the same time, we build a database environment that manages multiple construction projects across the board. We call it the building database. We need it to manage a variety of specialized knowledge data required to conduct projects as a knowledge database. For instance, material data, structure data, procurement data, estimated data, products data are a part of the database.

Each data is maintained in its own master database. Each master database grows while repeating 3C cycle of create, correct, [INAUDIBLE] data. As it grows, it forms a larger master database, interacting with each other. We will achieve our common data strategy by utilizing Revit as a DB2, ACC as a CDE, and APS as a technology to connect to data.

AKIRA YOSHIKAWA: In the previous chapter, Ogawa talks about the first two steps of 3C cycle to improve data maturity as a part of our data strategy to realize Digital Daiwa House. The first step is create data. And the second step is correct data. In this chapter, I'm going to share with you our approach on the third steps, consume data, or how to leverage data.

We are continuing to work on improving the maturity of BIM and data in order to achieve Digital Daiwa House. Having suffered from Autodesk Consulting, we started to develop a mechanism to link both and create value as a form of utilizing data.

This is a configurator as a solution to create business value. Our goal is to create a next generation configurator, not only generating 3D models, but also optimizing the entire construction life cycle by integrating digital technology and construction technology.

The configurator is driven by data such as rules, components, product info, and accumulated knowledge. As the maturity of the data improves, the configurator performance can be improved. And its scope coverage will also expand.

The configurator maturity level 1 focuses on the design phase. Main target user is architecture design. Based on the [INAUDIBLE] engine, it manipulates parameters to generate building information. It also can visualize metrics by linking information such as cost, carbon emissions, or a schedule.

This is the outline of the prototype we are currently working on as level 1. We have analyzed data from accumulated 3,000 projects and created a master based on building use, loom use, and [INAUDIBLE] configuration. With this approach, even with the low LOD model, we can interactively obtain more detailed and accurate costs at early stage of design phase. This is a short video for the first prototype.

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- The configurator is launched showing an interactive dialog box, allowing the user to define the characteristics and the performance of a specific room and wall. This will then suggest potential core and skin values that the user can either select or change based on the desired needs of the application, applying these then to the new wall and creating a new wall family.

So once the changes have been made, we can validate the changes by looking at the wall. Once we see that that has been changed, we can then go and change the characteristics, the core, and the skin again and see what these changes do to the price of that wall. Notice the price changing down in the lower right corner of the dialog box. Then we can apply those settings to the wall and double check the wall again to determine if those changes are what we ultimately want.

So once we've gone and applied all of the different characteristics, cores, and skin values to all the different pieces, we can switch over to the dashboard. The dashboard allows us to look at each of the different versions from a cost perspective, as well as look at what materials have been used the most and where they've been used from a room perspective. In the future, we will also have industry standards for carbon calculations that will be able to be displayed and compared.

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AKIRA YOSHIKAWA: As mentioned in the movie, one of the important capability we want to incorporate into level 1, maturity configurators. We are currently looking into the calculation of embodied carbon. With the help of Autodesk Consulting Team, we are developing a tool to calculate the carbon of structure members in conjunction with ACC.

At this moment, the development of the two is independent. But we would like to integrate the system into the configurator in near future. It will allow us to update our workflow for all designers to perform carbon analysis as a part of their design work every day. Although not yet integrated into the configurator, we are rolling out the functionality of carbon calculus as a tool next [INAUDIBLE]. And I'd like to share a short demonstration.

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- Data governance and security have been central to the design of the ICT. The tool links directly with the ACC project and inherits roles that inform the access for the assessment process. Designers create a single source of truth by selecting the project models. The automated permissions for administrators, editors or reviewers control access rights and data.

It was important for the Daiwa team to form a complete assessment profile up front, as the structure can contribute to between 40% to 60% of the total emissions for the building. This is typically made difficult when the components, such as rebar or bolts, are not modeled until late in the project. Using the tool, the team are able to assess the carbon impact from the structural elements in the Revit model alongside supplemental assumptions.

The information from the Revit model is displayed in a data tree representing all of the model elements which can interact dynamically with the Model Viewer. The assumptions for rebar and other non-modeled elements, which can be automated based on the category type or CAD material, can be set and viewed also in the model tree.

Building on the theme of data governance, it was important for the Daiwa team to be able to centrally control the carbon database. The Japan standard data set of emission factors has been loaded into the system, which then can be managed by the administrators for all users.

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AKIRA YOSHIKAWA: As a level 1 configurator, we get up ideas considering various scenes, phrases, and personas. With [INAUDIBLE] shows those ideas plotted on [INAUDIBLE] as a vertical axis AND the construction life cycle as the horizontal axis. With this process, we noticed that the configurator would be used to accelerate the decision making by stakeholders.

[INAUDIBLE] function will be developed in parallel. And they are integrated into core configurator. This integration will allow the architectural designers to make the right decision.

At the earliest stage, having the backup of the specialties and expertise coming from individual configurator before the integration, we believe it would be the goal of the maturity level 1 configurator. Also, we can accelerate to organize data with data-centric approach by strategically prioritizing the data.

Next, let's talk about what to consider as the level 2 configurator. At this stage, building models are generated by the user input, applying [INAUDIBLE] products and materials info. Those information were prepared, taking into account money, manufacturing, construction, and other factors.

Create data can be used for actual manufacturing and construction and can be smoothly linked to subsequent processes. It is necessary to standardize product information defined the necessary information for each product and managed them throughout the life cycle.

By integrating a configurator with DfMA capabilities, the configurator evolved from level 1 to level 2, which include manufacturing, construction, and supply chain elements. The construction technology component will support the design phase to produce a more feasible output. Target user will be expand to include not only designers, engineers, but also clients and sales. It will further accelerate decision making, resulting in shorter overall lead times.

The configurator at maturity level 3 has a potential for further breakthrough with technological innovation. We will continue to leverage the data accumulated in past projects, achieving even more advanced forecasting, analysis, and optimization. Applying AI will accelerate the movement.

Let me summarize what we have discussed so far about configurator. We build this configurator as a solution connecting BIM and data. It creates value and will continue to evolve. The source of the configurator is definitely BIM and data. By enhancing these elements, we will achieve Digital Daiwa House.

Now, I would like to close today's session with two more slides. The construction industry faces many challenges-- shrinking workforce, changes in materials for the configuration of environment, or changes in demand, a wide range of issues. We believe utilizing BIM and other types of data is one of the solution.

However, the construction industry is still generally paper-based and file-based work. This way of working does not allow us to utilizing data. The most important thing is to store and accumulate data in the correct form to be able to utilize properly.

There are so many stakeholders in the construction industry. If more and more companies handled data based on the idea we have discussed today, the potential for data utilization in the industry as a whole will expand. I believe it is a responsibility of leading company to expand such efforts.

It is a dream of our chief technical officer to have the Japan quality construction to a global export industry through such efforts. Today, we are lucky to share these ideas. [INAUDIBLE] from all over the world. We look forward to connecting with you and tributing to the transformation of the industry. That's all. Thank you very much for your kind listening.