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How Analytics Is Bringing Insights to Toyota's Factory Projects

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Description

This case study will cover portfolio creation on business intelligence developed by Toyota Motor North America and Autodesk Consulting. We will discuss Toyota's business requirements, KPIs, and metrics. We'll evaluate Autodesk sources—including Data Connector and the Autodesk Platform Services Token Flex API—against Toyota's requirements. The presentation will teach BI development from raw data extracted from Autodesk products. It will list technical aspects of the ELT process for data extracting, loading, and transforming. An area of focus will be the data engineering processes required to produce meaningful consolidated tables, where data are sliced and diced to target KPIs. The final products include modules' BI dashboards for Executive Overview, RFIs, Issues, Submittals, Forms, Assets, and token consumption, which transform raw data into actionable insights that inform tactical business decisions. The visuals generated from BI equip Toyota with detailed intelligence about the state of the business.

Key Learnings

  • Gain an overview of business initiatives and pain points, KPIs for projects and users, and metrics developed and their measurements.
  • See evaluations of the Autodesk Data Connector, Autodesk Platform Services Token Flex API, and Autodesk Construction Cloud Connect for customized workflows.
  • Learn about metrics and products, including modules like Executive Overview, RFIs, Issues, Submittals, Assets, and tokens consumption.
  • Learn about value added to customers, and get recommendations on products' strategies for diagnostic and predictive analytics.

Speakers

  • Tomotoshi Jo
    Tomotoshi Jo, MBA Subject Matter Expert – PE Toyota Motor North America Tomotoshi Jo has a background in information systems, BIM and project management. He supports Toyota Production Engineering (PE) team members who use Autodesk products such as ACC, AutoCAD, Inventor, Navisworks, Vault, and more. He also helps different PE departments build integration factory models for their new equipment installation project and new plant construction project. Tomotoshi graduated with his bachelor's degree in automation engineering from Shanghai University. He also has an MBA focusing on project management from the University of California, Riverside.
  • Avatar for Liang Gong
    Liang Gong
    He is a structural engineer by training (PE) with a background in preconstruction/estimating, construction management, BIM/VDC and data science. He helps customers leverage the data they produce through the design and build process to generate actionable insights including forecasting and scalability. He also automates customized workflows with ACC Connect and Autodesk Platform Services. After graduating from Duke University, Liang is currently working on his second master's degree in Applied Data Science at University of Chicago, focusing on AI/ML as a part-time student.
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      Transcript

      TOMOTOSHI JO: Hello, everyone. Our topic is "How Analytics is Bringing Insights to Toyota's Factory Projects." So this is the safe harbor statement. And my name is Tomotoshi Jo. I have a background in information system, BIM, and project management. I support Toyota Production Engineering team members to use Autodesk products such as Autodesk Construction Cloud, AutoCAD, Inventor, Navisworks, Vault, and many more.

      I also help different PE departments build integrated factory models for their new equipment installation and plant construction project. About one year ago, I made a promise to my colleagues to attend AU as a speaker. Here I am. It's my first time to attend AU and be a speaker. I would like to say thank you to all people who helped me so far. Next, I would like to hand over to my co-speaker Liang and let him introduce himself.

      LIANG GONG: Hello, everyone. This is Leon Gong from Autodesk Consulting. I am a consultant specialized in analytics and automations. Meanwhile, I'm currently an MS candidate at University of Chicago specializing in AI and machine learning. Thanks. Next slide, please?

      TOMOTOSHI JO: So in this class, I will talk about the Toyota Way, which drives me to partner with Autodesk consulting team to bring analytics to Toyota Production Engineering, explain the challenge that Toyota Production Engineering is facing, and lastly, discuss how analytics helps Toyota team understand the current situation and make decisions on the next step.

      So here are the four key learning objects of the session. I will mainly speak from the business perspective, while Liang will cover the technical side. So I believe many people know about Toyota company, but you may not know or even heard about Toyota Way. It's the core values of Toyota. To me, the number one core value is to drive curiosity. At Toyota, we ask to discover the mechanic behind phenomena. This mindset generates new ideas.

      A second, the number 2 core value is to continue the quest for improvement. In Japanese, it is also called as Kaizen. At Toyota, we believe in the nature ability of people to change things for the better. Even improvement regardless of size is valuable. Encouraging both incremental and breakthrough innovative thinking, we seek to evolve with Kaizen, never accepting the status quo.

      And the number 3 value is to create room to grow. At Toyota, focusing on what is essential, we eliminate waste and manage our resources carefully to create room to grow. This is the foundation for agility and cultivation of the new ideas for the future. These three core values are the backbone of this case study.

      So every organization has its own challenge. Toyota is not an exception. Since 2020, Toyota has had an EBA contract with Autodesk. As you may know, the EBA is a partnership with Autodesk that can include more products and services than the traditional contracts. But there are three major benefits of EBA. First, token flags, second, the enterprise priority support, and third, advisory and implementation service.

      With the EBA, it opens the door for all the members at Toyota Motor North America to use any Autodesk product. If Toyota members have ideas to improve the current process or build new workflow, they can request implementation service from Autodesk. And a dedicated consulting team will help to map the process and deploy new product or features.

      While everything has two sides, challenge also comes with EBA. Toyota Production Engineering is joint organization with 10 plants across the nation and over 2,000 engineers working inside plants. From the high level, we always need to consider how to maximize the benefits of EBA, how to capture the value from EBA, and how to adjust by the return of investment of EBA.

      So at the working level, we also face the challenges to transition to Autodesk Construction Cloud Build. PlanGrid was used to share information, manage the sheets, joins in the Toyota's factory projects. As you know, PlanGrid was acquired by Autodesk in 2018. And later, Autodesk launched ACC Build, which is the next generation solution for field and project management. Because ACC Build is covered under Toyota's EBA, the management team made a decision to generate a move to ACC Build.

      However, switching to ACC Build is not easy. First of all, we have to train not only the users inside Toyota, but also the external users, such as general contracts and subcontracts. Different users have different purpose to use ACC Build. For example, project lead needs to set up tools for the project members in ACC Build, while our project members may just need to upload or download files, create issues, RFIs, and submittals.

      Second, we have to update the existing Shikumi. Shikumi is a Japanese word which means "operation procedure." PlanGrid is embedded into many existing Shikumis to move to ACC Build. We must fully understand the whole operation procedure and the function difference between ACC Build and PlanGrid so that we can revise the process to match the purpose of each Shikumi and make it useful to the end user.

      And lastly, we have to upgrade the existing dashboard. The data sets of existing Power BI dashboard are imported from Excel files. Those Excel files, sheets contains many formulas. And to switch to ACC Build, we must study the current data set and develop a new one leveraging the data set imported directly from ACC Build.

      So here is a glance at current ACC Build usage. We started to use ACC Build in 2021. Currently, there are over 500 active users, 150 projects managed inside ACC. And some are small projects like installing the charging station. Some are large projects like the new Battery Plant Project. There are over 1,000 issues, 3,000 RFIs, and 4,000 submittals created and managed in ACC Build.

      So another challenge is to optimize token usage at Toyota Production Engineering. Token flag is a type of licensing model provided by Autodesk and the EBA. It lets Toyota pre-purchase tokens to access any product via daily rate. For example, a user will be charged a fixed rate even just using AutoCAD for 10 minutes in a day. But Toyota has a limited token to use for the whole organization.

      Every token comes with a price. We have to watch the token usage carefully, understand how our users use the Autodesk products, and guide them to effectively use those tools. Maybe even suggest them to use some free Autodesk tools so that we could save some tokens and create room for the future growing usage. So under Toyota's EBA, the token usage is categorized into three types-- desktop product, cloud system, and adjustments.

      Here, I just want to explain a little bit about adjustments. Adjustments refers to the Autodesk products that are not valuable as token flags. Basically, they charge monthly at a fixed rate. And since May this year, the Autodesk desktop product consumed over 300,000 tokens. The most used products are AutoCAD Inventor and VRED. And the cloud system consumed over 100,000 tokens.

      The most used products are ACC Build, being Collaboration and Docs. And for the adjustment, it consumed over 70,000 tokens. And the most used products at Toyota are ACC Connect, ProEst and Pype. To obtain insights of those construction projects and token usage managed in ACC, the DB analyst is required. And I would like to let Liang talk about his strategy and the way to map the analyst's process. Thank you. Liang?

      LIANG GONG: Thanks, Tommy. Before we go into the deep sea of the technologies associated with the methodology that Tommy was talking about, I'd like to give the overview picture of where we are, where here the zone is at in perspective, the data strategy approach. So this is a normal evolution of the data strategies for the AEC industry. You could see we start with descriptive analytics, and it goes into diagnostic, which means here, if you're trying to benchmark or scoring the different entities like the projects under your BIM 360 or ACC hub, that's a diagnostic analytics example.

      And then we evolve into predictive and prescriptive. And the later two phases, they are more like associated with machine learning and AI, which are hot topics these days. But in order to evolve to the latter two phases, it's always better to build the foundation of the "how solid," which is the descriptive and diagnostic, which is also associated with the database foundation, which I'm going to talk about in the next page. Next page, please?

      As you can see here, when we are mentioning the CDE, the connected data environment or common data environments, what exactly they are. In the previous slides, he was talking about the different kinds of analytics during the evolution process. And the foundation of that is really the database. As you can see on this slide on the left side and the right side-- so on the left side, it's more like mimicking the database for all the normalized tables.

      On the very left side, the very left column like cost, operations, sketches, design, all those data are siloed data which contain a lot of the normalized table. And on the right side, very right, like the prediction, forecasting, correlations, training, diagnosing, these are the visualizations that are ready to be consumed by the end users. For the end users, they do not need to understand the back end, which on the left side, how the data engineering process is looking like.

      They just need to get ready to consume the data, interpret the data for their business purpose. So that's why this slide is divided into two big parts. The first part is data storage environment, which contains all the raw data in silos. And on the right side, the data analytics environment, which is more on the front end like Power BI or Tableau, which are ready to consume the data for the end users.

      As you can see here, the biggest problem is that in order to build this data pipeline, we need to perform a lot of the data engineering work because inevitably, there are a lot of silos in our business today. And we need to do a lot of data engineering work to consolidate data, to consume the data in order for them ready to be used for the end users. So basically, using an analogy, you want to build the foundation of the house very solid before you're building the upper structures and the facade of the house. So that's the analogy here.

      And next page, I'm going to talk about the data connector. So remember, the left side of this page, consolidation process, is more where the data storage environment is. And in our example-- next page, please. In the ACC in the Autodesk example, the data storage environment is the data connector, which the full name is ACC Autodesk Construction Cloud data connector. If you use network product, if you go to the insight module of ACC or BIM 360, there is a sector called Data Connector.

      I put the link here for your reference if you're interested in knowing more about it, those two links, and read the articles. So what it basically does is that all the data you put onto our user interface, UI, onto BIM 360 or ACC, let's say you put a lot of data on our issues module, all those data are going to be organized and put under this data connector ready for you to download and ready for you to consume.

      And on the right side, this is how the data connector looks like. It consumes all the normalized table. By normalized table, this is what I mean-- all the different CSVs for the siloed modules on the right side. So this is our use case for the data storage environment under the Autodesk ACC's perspective under this structure.

      Next page, I will have Tommy talking about the data analytics environment because I just talked about the data storage environment, which is kind of the backend of this design workflow. And next, Tommy is going to talk about the data analytics environment, which is going to show you the videos that are ready to be consumed by the business partners. Tommy, please take it over for the data analytics environment. Thanks.

      TOMOTOSHI JO: So we use the data connector to import ACC Build project data into Power BI dashboard. And here, I just want to discuss about what dashboard we develop and how those dashboard, our analysts benefit toward the production engineering. So first, executive overview. This dashboard shows a summary of the audit projects in the Toyota ACC hub.

      The management team could easily view the project start date, project location, number of companies, and members for each project and understand what's happening and going on with inside organization, Toyota PE organization. Second, the Issue Analysis dashboard. So the Issue Analysis dashboard contains the performance metrics such as the average days to close, number of open issues, status of issues in each project. It could quickly help senior managers identify the road block for each project and common issues among all the projects.

      So the third is the Forms Analysis dashboard. So at Toyota, compound is used to confirm the quality of all aspects of construction projects. So compound literally means signal. In ACC Build, we use forms to implement this concept. The form dashboard helps the management know the progress and the lead time to complete quality confirmation for all projects. That's really helpful, especially safety is the biggest concern for the manufacturing plant.

      Next, the fourth one is the Assets Analysis dashboard. The assets dashboard summarizes all the assets for each project. It helps the operation team understand what equipment will be handed over to them after the project ends and what the status of each equipment is so that they can plan the maintenance in the future. So that's all the dashboard analysis we develop with the data connector.

      Next, so here are-- previously, I mentioned the challenge to optimize the token usage at Toyota. Next, I will let Liang talk about ACC Connect and how he developed the dashboard to analyze token usage for us.

      LIANG GONG: Thanks, Tommy. So in order to analyze the tokens usage, the first step is to really get the data, the tokens consumption data first before we analyze them, before we visualize them, right? So the first step is how we actually get the data. That really relies on our APS APIs. So two parts-- what is APS? Autodesk Platform Services. It's a cloud service which contains a lot of APIs.

      And then what is the API? API is an application programming interface. It is a way for more computer programs to communicate with each other. It is a type of software interface offering a service to other pieces of software. And what is API documentation? It is a document or standard that describes how to build or use such a connection or interface. That is what? An API specification.

      So together, this is APS API Autodesk Platform Services that provides an application programming interface for different softwares to talk with each other, including for the software that Autodesk provides to talk to third-party software, external software like shown on the screen like SharePoint, Google Sheets, or DocuSign. On the left side, these are on the slide, which Tom is presenting. On the left side, these are the products that Autodesk Construction Cloud provide, like the Autodesk Build, Autodesk Takeoff, Autodesk Docs.

      If I wanted this software, this platform to talk with external software like SharePoint, if you want to have any interactions or automation workflows set up, you need to rely on our APIs, which is the bottom right side pop-up, Autodesk Platform Services, which includes the APIs for the different modules, like for issues, RFIs, et cetera.

      And to give a little bit more about what is ACC Connect, ACC Connect is kind of similar to Power Automate, but it's different because it is designed specifically for the Autodesk ecosystem. If you go to next slide, please? So how do we use those APS APIs? We leverage ACC Connect to write those APIs. And regarding ACC Connect, previously-- its parent company is called Workato, but Autodesk rebranded and add our own customized connections and give it a new name, ACC Connect.

      What are the use cases for ACC Connect? We see a lot of this usage areas like document management, between DocuSign, between Box, between SharePoint. We're also seeing a growing area for project management systems like Excel, Smartsheet, Google Sheets, how you analyze this. These are more associated with analytics. And another biggest area we see lies under accounting. If you want, for example, want your ACC cost module to talk with the external accounting system, like QuickBooks, how we can automate that workflow, it's going to utilize the APS APIs and ACC Connect.

      And in our case here at Toyota, Tommy wants to analyze the token's consumption, we use ACC Connect and APS APIs to extract the data to set up a data pipeline. Next slide, please? So I'd like to give you another example of the application of ACC Connect because it's not only restricted to extracting the tokens's data. Here's another example I like to illustrate.

      Everyone's like a lot of us, who work on the construction side here, and we have a lot of QA/QC work to do. In this real example that Toyota wants to create QC and commissioning their equipment on the side in the factory, in order to do that, they wanted to use the ACC Build app. But how do you scan each equipment? So here brings up the concept of barcode. So ideally, we want to put a barcode on each equipment and scan the barcode with the app to bring up all the associated asset and associated Kanban forms with that specific asset.

      If we print out the barcode for each asset equipment, it's going to be very time consuming because there could be more than 500, more than 1,000 assets. So in order to automate this workflow in order to save time, we use ACC Connect to automatically generate a barcode column for each asset. And the barcodes are all unique. Meanwhile, we'll print out a PNG file as you can see on the lower right side of the slide and put it under the DOCX file.

      So in this way, after the automating process, we could print out a barcode for each asset and stick it to each equipment in the factory. So if you're a QC commissioner, you could just open up your ACC Build app and scan the barcode. The corresponding asset is going to pop up. You can see its associated its own asset information, associated Kanban forms information, associated issues. It's all digitalized. So that's the benefit of automating this workflow with ACC Connect.

      Coming back to the topic of token assumptions after we're automatically extracting the tokens data. And now Tom is ready to consume them to visualize these tokens consumption data for his business case. So next page, Tommy is going to talking about the visualization and data analytics for the tokens consumption data.

      TOMOTOSHI JO: OK, thank you, Liang. So here, I want to show two examples of dashboard we developed for token usage. The first one is the User Token Analysis dashboard. So this dashboard helps management know the trade of a user account. And based on the user's ID, we could understand which department they belong to and which area they may focus on. For example, the plant they are designed, or the tooling design, or the simulation side.

      Next is just another-- the other dashboard is called Product Token Analysis. So this dashboard helps management understand the token consumed and hour used per product. For example, on the right side, you can see the most used product at Toyota is AutoCAD. And since May this year, Toyota engineers already spent over 90,000 hours on the Autodesk CAD product only. So it may help us to consider why and how we could help our users maybe use AutoCAD.

      So that's the two examples of token usage analyst dashboard. And in the next here in the end, we would like to conclude this case study and give the recommendation. First for the conclusion-- so by analyzing the data obtained while the data connector and ACC Connect, we can learn project overall performance, individual project members workload, and token usage at Toyota. So it leads us to make the improvement to project planning and management of balanced workload for each project members, and target the people to provide them the right tools and training. So next, Liang will give his recommendation.

      LIANG GONG: Thanks, Tommy. So a very important thing, as you probably have already noticed earlier in this presentation that we want to build a solid foundation for the data pipeline for the different kinds of analytics. So right now, as you could see in the chart, we bring the data directly from ACC Construction Cloud into Power BI directly for analytics.

      However, when the data are growing more and more, Power BI is going to lose its efficiency because Power BI is not really a data storage tool. That's why we're adding a semantic layer, which is the data house or data warehouse or data storage like a SQL database or Snowflake between Power BI and our ACC Construction Cloud.

      The benefit of doing that is listed below for the seven points. I'm not going to read one by one. But overall, you could perform the data engineering, writing the queries instead of the data warehouse before you bring the consolidated, ready-to-consume data sets into Power BI for visualization purpose only. So this going to save tons of time to write the queries in Power BI because that's only going to slow down the performance of Power BI when we're having more and more projects data. So adding the semantic layer of data warehouse here is very important. That's a recommendation to all the audience here.

      And the next slide is for predictive analytics. For the first two parts, we're talking about the descriptive and diagnostic analytics. And now I like to talk about predictive analytics because this is more advanced. What can we do with this AEC data for AI machine learning? I gave some examples here on the slide. For example, if you want to predict the issues-- because I believe a lot of the audience who are using issues model in ACC-- you want to know when you're putting, let's say, like 100 issues on a project, you want to know which issue to solve first.

      So this really relies on the issues priority level. Most of the cases, the superintendent on the construction side probably based on his or her experience, subjectively choose the issue to solve because he thinks or she thinks this is more important. But in order to put in a more objective way or using an algorithm, so we're using the different parameters, like business unit, issue type root cause if it has impact on schedule or cost, which company the issue is from, and what's the trade the issue is liaising?

      Based on these eight parameters, we're predicting the label, which is the priority of the issue. So in this way, systematically, it's going to tell you, this issue is at a high priority. That issue is a lower priority. So you could objectively choose the issue to solve first based on the priority level. And the other use cases like time series analytics, this is more associated with, for example, if you want to predict the tokens consumption in the next year, if you want to predict the labors, these in the next year for your factory, et cetera, it is based on the timeline.

      There's more involved with the statistics like the exponential models like triple extension model, double exponential model, or robust remote model, this lies in this area for the time series analytics. Another example is NLP and LLM. It stands for natural language processing and large language model. For example, you put a lot of descriptions of the issues on the construction side. Based on those descriptions, I want to see which issue contains more risk, right?

      This is another perspective to analyze the issues priority level by using the NLP and LLM modules, and the description, the text words you put in there associated with the issues' descriptions. There are a lot of different possibilities speaking of AI and ML's application in the AEC industry. If you're interested in those areas, we could talk more. And you could use our consulting services to tackle those areas. That really wraps up the technology part that are associated in this presentation. I will pass it over to Tommy for the conclusion.

      TOMOTOSHI JO: OK. Thank you, Liang. That's all for the session. And next, we will go to the Q&A.

      LIANG GONG: Thanks, everyone.

      TOMOTOSHI JO: Thank you.

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      Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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      Improve your experience – allows us to show you what is relevant to you

      Google Optimize
      We use Google Optimize to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Google Optimize Privacy Policy
      ClickTale
      We use ClickTale to better understand where you may encounter difficulties with our sites. We use session recording to help us see how you interact with our sites, including any elements on our pages. Your Personally Identifiable Information is masked and is not collected. ClickTale Privacy Policy
      OneSignal
      We use OneSignal to deploy digital advertising on sites supported by OneSignal. Ads are based on both OneSignal data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that OneSignal has collected from you. We use the data that we provide to OneSignal to better customize your digital advertising experience and present you with more relevant ads. OneSignal Privacy Policy
      Optimizely
      We use Optimizely to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Optimizely Privacy Policy
      Amplitude
      We use Amplitude to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Amplitude Privacy Policy
      Snowplow
      We use Snowplow to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Snowplow Privacy Policy
      UserVoice
      We use UserVoice to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. UserVoice Privacy Policy
      Clearbit
      Clearbit allows real-time data enrichment to provide a personalized and relevant experience to our customers. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID.Clearbit Privacy Policy
      YouTube
      YouTube is a video sharing platform which allows users to view and share embedded videos on our websites. YouTube provides viewership metrics on video performance. YouTube Privacy Policy

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      Customize your advertising – permits us to offer targeted advertising to you

      Adobe Analytics
      We use Adobe Analytics to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Adobe Analytics Privacy Policy
      Google Analytics (Web Analytics)
      We use Google Analytics (Web Analytics) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Web Analytics) Privacy Policy
      AdWords
      We use AdWords to deploy digital advertising on sites supported by AdWords. Ads are based on both AdWords data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AdWords has collected from you. We use the data that we provide to AdWords to better customize your digital advertising experience and present you with more relevant ads. AdWords Privacy Policy
      Marketo
      We use Marketo to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. We may combine this data with data collected from other sources to offer you improved sales or customer service experiences, as well as more relevant content based on advanced analytics processing. Marketo Privacy Policy
      Doubleclick
      We use Doubleclick to deploy digital advertising on sites supported by Doubleclick. Ads are based on both Doubleclick data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Doubleclick has collected from you. We use the data that we provide to Doubleclick to better customize your digital advertising experience and present you with more relevant ads. Doubleclick Privacy Policy
      HubSpot
      We use HubSpot to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. HubSpot Privacy Policy
      Twitter
      We use Twitter to deploy digital advertising on sites supported by Twitter. Ads are based on both Twitter data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Twitter has collected from you. We use the data that we provide to Twitter to better customize your digital advertising experience and present you with more relevant ads. Twitter Privacy Policy
      Facebook
      We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
      LinkedIn
      We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
      Yahoo! Japan
      We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
      Naver
      We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
      Quantcast
      We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
      Call Tracking
      We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
      Wunderkind
      We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
      ADC Media
      We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
      AgrantSEM
      We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
      Bidtellect
      We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
      Bing
      We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
      G2Crowd
      We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
      NMPI Display
      We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
      VK
      We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
      Adobe Target
      We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
      Google Analytics (Advertising)
      We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
      Trendkite
      We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
      Hotjar
      We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
      6 Sense
      We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
      Terminus
      We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
      StackAdapt
      We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
      The Trade Desk
      We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
      RollWorks
      We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

      Are you sure you want a less customized experience?

      We can access your data only if you select "yes" for the categories on the previous screen. This lets us tailor our marketing so that it's more relevant for you. You can change your settings at any time by visiting our privacy statement

      Your experience. Your choice.

      We care about your privacy. The data we collect helps us understand how you use our products, what information you might be interested in, and what we can improve to make your engagement with Autodesk more rewarding.

      May we collect and use your data to tailor your experience?

      Explore the benefits of a customized experience by managing your privacy settings for this site or visit our Privacy Statement to learn more about your options.