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The Good, the Bad, and the Ugly: A Case Study on Vault Professional Integrated with Autodesk Fusion Manage

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说明

In this session, we'll present a study showing the process for implementing Vault Professional software with Autodesk Fusion Manage software with bidirectional integration. See what went well, and what didn't go well and why. Discover what lessons we learned. Explore how much can be implemented at one time for users to absorb, understand, and gain ROI from.

主要学习内容

  • Learn what the failures are.
  • Learn about the wins.
  • See what we learned.

讲师

  • Taylor Alberts
    Taylor Alberts Engineering Manager, Bradford White Taylor Alberts has been involved in the engineering department at Bradford White Water Heaters for nearly 20 years. Starting as an engineering drafter in 2004, she now leads the Engineering Services Team, overseeing engineers, designers and technical writers. She and her team maintain Bradford White's CAD, PDM, and PLM software, along with Bills of Material (BOMs) and technical documentation for the products. She leads a cross functional team, encompassing new product development, design, current production, and product support. She has led the department through a tremendous period of growth. Specializing in streamlining engineering processes and procedures which has led to existing product improvements, streamlined documentation, and increased error free work.
  • Kimberley Hendrix 的头像
    Kimberley Hendrix
    Based in Tulsa, Oklahoma, Kimberley Hendrix provides custom solutions for lean engineering using Autodesk, Inc., products and industry knowledge to streamline design and engineering departments. Hendrix has worked in the manufacturing industry for over 30 years and she specialized in automated solutions for the heat exchanger industry. She has worked with Autodesk products since 1984. Hendrix is associated with D3 Technologies as the Manager of Data Management, focusing on data management, plant, automation, and mechanical issues
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      Transcript

      KIMBERLY HENDRIX: Thanks for joining our class. The Good, the Bad, the Ugly. It's a Case Study on Vault Professional Integrated with Fusion Managing along with our friends from Bradford White. So Taylor Alberts and I will take you through the process of joining Vault and Fusion Manage together and the whole two year process as we go, along with the good and the bad.

      So who are we? I'm Kimberly Hendrix. I'm the VP of professional services for Team D3. I'm based out of Oklahoma. I have four wonderful kids, and that is actually me playing the saxophone. And if you are at AU last year I left because this little man was born right in the middle of AU. And then I'd like to introduce our first time speaker this year is Taylor Alberts from Bradford White. Taylor, would you like to introduce yourself?

      TAYLOR ALBERTS: Sure. I'm Taylor Alberts. I'm the engineering Services Manager at Bradford White. I work at our manufacturing facility located in Michigan. I'm where all the engineering changes meet. New product development, production support. That's what I do outside of work. I have three beautiful daughters, and when we are not going to wrestling tournaments, we are traveling to car shows around the country.

      KIMBERLY HENDRIX: Awesome. All right, let's get started. So what we're going to cover today is the pain or what we are trying to solve for Bradford White, way back in '21 when we first proposed this project, and then we'll talk about what the original project was from the original statement of work. We're going to talk about what went well and what quite didn't go as well as we wanted. So the ugly. And then where we're at now and what will we do differently if we had this to start all over now with the lessons that we've learned. So with that, Taylor, can you explain the original ask, the pain that drove Bradford White to explore this PDM/PLM solution?

      TAYLOR ALBERTS: Yeah, we were looking for something to help us track who has what, how long they've had it, for our drawings, for our prints. A system that would help us so we wouldn't duplicate work and stop writing up our changes in Excel and then somebody manually putting them into our ERP system. We had people working on the same print at the same time, basically, doing different changes.

      And we just needed more constraint on our changes. Our engineering changes would make it to the production floor sometimes just because we were so slow at it, we'd have to implement it into production and then the paperwork would follow and our system would be updated. And that's not how anyone wants to work. We still PDF our prints to send them via email to our supplier, and didn't feel like that's a very safe way to send out our work, not knowing did they receive it, how did they receive it. And there's no checks and balances there either to even say that we sent it.

      And then lastly, something that plays nice with what we already had. We didn't want a clunky trying to integrate multiple different softwares. Like we just want something that works well.

      KIMBERLY HENDRIX: So you said that you had multiple people working on the same prints at the same time. I assume that caused some problems on the shop floor occasionally.

      TAYLOR ALBERTS: Yeah, that absolutely causes downtime. It was like a race. Whoever got their change released first, that's what the production floor would see. And the other person's work never made it to production. And then it would cause confusion and they stop and look at it. And every time we're down, every minute we're down is thousands of dollars lost.

      KIMBERLY HENDRIX: Wow, OK, so then the decision. You want to take us through that?

      TAYLOR ALBERTS: Yeah so quite the extensive amount of research went into it. But we identified the need. We needed a PLM system. We needed a PDM system to help solve both of our pain points. So we could keep growing because we're only adding more products, expanding, as we move. So then we decided like, yep, this is what we need. We need a PLM. F3M. Fusion 360 manages what we want to what we want to get. PDM Vault software. These play nicely because we are Autodesk, we already have Autodesk products. So it would help with that.

      And just help us manage all of our pain. Just help us manage the new product introduction, consistency, manufacturing, making sure our bill of materials match and match the print. Nice collaboration with our suppliers. No more emailing PDFs and just have that overall improved quality and efficiency for our process.

      KIMBERLY HENDRIX: So that brings us to the proposal. So the original project way back in November of '21, that the team at Team D3, along with our partners at Autodesk, proposed a solution that included Vault Professional and implementation, a data load and some training, along with Fusion 360 Manage and about seven or eight workspaces. Customer success, engineering management, change management, change order, NPI supplier management and quality management. And then along with that, connecting those two, so that where one needed to talk to the other, we did that automatically using our partners at coolOrange and powerPLM to put those two together.

      So when a change order happens, it affects the file and Vault. And when the Vault file gets released, it should affect what's in the change order in F3. So that was the original plan, the original project. So what went well? The Vault Professional. That first implementation of Vault Professional, it's a mature product, it's a mature team that puts it in. The data was all there. There were a few things that we missed-- we'll talk about that-- we put in a Vault Professional foundation. We did Vault lifecycles. We then took that foundation plan and converted it to an item centric Vault, because we were going to do items and BOMs in F3M. So we needed the items in Vault.

      And then we did a bunch of property mapping. We did miss something in the assessment, though. We went, we missed that. So we're just being transparent here. So we're going to tell you what went good and what didn't go good. So we missed the fact that at the time Bradford White was predominantly 2D based in AutoCAD. That's a different animal than Inventor and 3D based with properties and building materials and converting to items. So we missed that in the assessment. So property mapping became somewhat of an issue.

      But we cleaned it up and we've got that with some automation in the background, trying to help clean that up. They're in the process, I believe, of going 2D to 3D. It's a process though, isn't it, Taylor?

      TAYLOR ALBERTS: It is absolutely a process. We were 82% AutoCAD, by the way, when we did this.

      KIMBERLY HENDRIX: OK that's a lot. AutoCAD still prevalent out there. It's still there. We added a job processor again, powering with our friends at coolOrange for the powerJobs and doing some automation. You heard Taylor say that they created PDFs for their suppliers. We still create PDFs for them. We just try to automate that feature. So a drafter or a salesperson or a technical person is not going in and open that drawing and creating a PDF. We try to do that automatically on the life cycle changes.

      We also did a full data load of all their historical data. I'm not sure that's always necessary for everybody, but in their world, it was. They wanted all their historical data in the Vault and we put it in a file structure that literally says something to the effect of old data or historical data or something like that. And then there's a line in the sand and everything new gets put in a proper folder structure with the proper folder, property mappings and file property mappings, and no duplicate file names.

      We did then take all the historical data that we could and automatically using some scripting created items for all of that, so that they were up at ground zero. Best we could be with the data that they had. There were some issues with the item creation. Anytime you're doing something automatically, it's that 80-20 rule. And then duplicate file names still are a problem today. But we have a plan for that in the newer releases of Vault in '24 and '25. You can set a folder and says from here down, duplicate file names are allowed. So we can allow them in that historical data and not allow them in that new stuff. And that'll clean that up.

      But then after going live in the Vault and realizing that there were some issues, team D3 went in and met with Bradford White and we were like, demo back to us. The customer, as in Bradford White. Demo back to us your entire process. And I'm not going to lie. I'm sitting there at that table in the middle of Taylor's office and I'm going, wow, we should automate this. And they shouldn't have to manually do this. And I was trying to be quiet and let them finish. I didn't make it.

      But after all of that, we did go back and add some more automation to their prior job. So that some of that PDF creation is done at the right time so that the checks and balances can be done. And they're not going back. We're sending emails automatically. We're doing some little things to help with that automation so that they don't have so many points and clicks. So there was some back and forth and some cleaning up of that as we went through.

      And then next we put in Fusion Manage. And there was about seven main workspaces that were done. Product engineering requests, engineering change request, and change orders, advanced information release, items and BOMs, control documents. And then there's a slew of misleading supporting miscellaneous-- I'm sorry-- supporting workspaces that help manage all of that, like a to-do list and work list and things that Taylor needs to get done every day are on one screen. So that she can see that in one dashboard. So those kind of things are done as well.

      And then also, in this environment, we use the Vault Job Processor and our friends at coolOrange used powerPLM to do that connection between them. And then there was some initial data load of the items and BOMs, but Bradford White handled that themselves as needed.

      So what is connected between Vault and F3 is primarily around the change request and change orders. So when a change order is implemented in F3, it goes and finds the linked file in Vault and changes its life cycle state. So that you remember when she talked about that first in, first out thing. And people working on the same thing. We don't want a change order sitting out here and that file is still being set at released or in work in progress. It's going to put it in that change order state so that people know where it's at, and keep those two things in sync.

      The opposite is also true going back. When that file is released in Vault, it's going to notify F3. And it does that. And this is just a little snippet of a script, and it's part of powerPLM from coolOrange. It does that by pulling Fusion 360 Manage every so many minutes and anything marked as pending, it will move the corresponding Vault. Item and file to the right life cycle states.

      And that's an ongoing-- it's called a Cron job-- and it's an ongoing job that runs every, I don't remember how many minutes. Taylor, you might remember, but every few minutes and checks for those. How many?

      TAYLOR ALBERTS: Five.

      KIMBERLY HENDRIX: Five minutes. So it runs every five minutes and checks for any changes and then takes care of those changes in the Vault as well. So what didn't go well? So when you start talking about a multifaceted project, taking somebody from a chaotic environment, which we'll talk about in a minute, and network drives and siloed information and PDFs, communication is huge. And I'll be honest, we didn't do it well at the beginning.

      So you're talking about PDM, PLM, integrations, migrations, all at the same time. One, it's a lot. Two, there's a lot of people on both sides that are involved, and the communication across these teams are vital. And during the initial phases, we failed. We failed to communicate that properly and that allowed for a repeat of work and meetings that were repeating the same stuff over and over again and stepping on each other's progress.

      We cleaned that up with an overall team and an Oversight Committee that cleaned that up, and since then it's been going much better. But communication is vital, wouldn't you say, Taylor, in these projects?

      TAYLOR ALBERTS: Absolutely.

      KIMBERLY HENDRIX: Yeah. It can make it go bad quickly. And we were going down that path. Let's talk about the next thing. I'm going to call it the ugly, the consumption model. So if you look at the image in the background, it's kind of-- and if you've been around the Autodesk world, you've probably seen this image before. It's like the chaos model. All these siloed things and data going back and forth and email everywhere and PDF you got Excel here and you might have some Power BI here.

      And Bradford White was no different. They had a lot of different systems that worked independently well, but they didn't work together. And so CRM, ERP, network drives, Excel, email, PDF, all that was and we tried to go from this chaos to this beautifully stacked, organized everything talking to each other in one step. There was a lot.

      And honestly, if you try to do all that in one step, you're going crater somewhere. Something's got to give somewhere. And what was given was production and the quality of the work. And so that comes down to our lesson of what did we learn from this? And what we learned is, we cannot go from chaos to calm in one step. The teams have got to still work in production, and too much consumption at one time leads to frustration, miscommunication and slowdowns. Taylor, you want to talk about how it affected your production line and the work in you're trying to do all this at once?

      TAYLOR ALBERTS: It got to a point, we were pretty much at a screeching halt. We could not keep up with any ongoing changes for production that they needed. We had new product development, chomping at the bit. We got government deadlines where we need to release new product and we are just spinning our heads in circles at this point. It was frustrating, to say the least. And yeah, absolutely. Just it delayed us significantly. I think we had one project that got delayed by over three months and it was just because we could not get our stuff together to make it move forward.

      KIMBERLY HENDRIX: So from that, what would we do differently. And I think and Team D3 is really working towards this and we've talked with Taylor and some of the newer workspaces and stuff that we're doing at F3 we're doing in this work, doing in this way. And we should start foundationally. You know when you build a house, you don't start with the shingles, right? Or even the roof decking. You don't even start with the walls. You got to start with the foundation at the very bottom and build it up. You can't just throw it all in there all at once. Even if you're prefab, right? They still start at the bottom and work their way up.

      So we've taken a step back, learned our lessons from this project. And what do we do different. We would start with the data. To me, that's the foundation, that's the PDM package, this. Implement it, load it, train it, rework it, automate what we can with the powerJobs, with the job processor and the PDFs and what works to that point. Get all the data in, get it all loaded, get it item centric, get it all cleaned, help them go from 2D to 3D if that's what they want to do. Get to your items, take a breath, then let's add PLM.

      Maybe there's some overlap if the teams can work simultaneously while we're implementing the PLM. We're going to implement, we're going to load, we're going to train, we're going to learn that process of what we do. Before we start automating things and connecting things, we have to know how we do it manually. What is that process that gets us from A to B to C all the way to Z in a manual workflow before we start automating it and everybody's not understanding what's going on in the background?

      So lastly, we'll connect it, integrate it, test it, train it, and then we'll stop and we'll do extended packages on top of that. What's next, Taylor? It was like we're connecting to your ERP system now, I think is the next step. And we're starting to grow the PLM system and we're adding some workspaces around that. Because now that we've gone through that the hard way, they know what the product's doing and what's missing. And what easy buttons they need. And we can do it efficiently rather than doing and redoing and doing and redoing.

      So we want to drive that maturity and consumable bites. And I don't know, Taylor. I think if we had to step back in '21 and done it this way, we might be further along than we are now. What are your thoughts on that?

      TAYLOR ALBERTS: Absolutely. I think we'd be done. We'd already have additional packages and everything on top of it. It definitely would have been beneficial. We've changed our process, I think about three times now, and how we do things because we just didn't understand the full capacity of what our PDM system did, how it worked. We didn't understand it because at the same time, we're also trying to get the PLM, and we're still learning that and what it can do, and now we're expanding. But we should have-- yeah, we should have already that had we taken smaller bites.

      KIMBERLY HENDRIX: Yeah, so to wrap that up, there was a lot of good things that we learned both, I think, in Bradford White situation and ours on how this it's a very common project to go from network drives and siloed information to PDM and PLM. And think if we do it in a consumable bites, in packages that are fixed in range, and everybody knows what we're doing, and you can get into it, it's a rapid time to ROI. You can use your ROI from the last one to fund the next one. You can learn what the project is doing. So we're not doing these big bespoke spiderweb projects at the beginning, when we don't even know what the processes are as we're changing those.

      So that's kind of our lessons learned. Anything else you want to add, Taylor, to that.

      TAYLOR ALBERTS: No, I think you hit all the points.

      KIMBERLY HENDRIX: Awesome. So if you want to talk more about this process and the maturity roadmap and how Taylor and I identified what was good and what was bad and what was ugly and what we learned from it, feel free to connect with us on our LinkedIn here with those QR codes and our LinkedIn deals. And we sure appreciate your time. Thanks so much.

      TAYLOR ALBERTS: Thanks, everyone.

      ______
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      我们通过 CJ Affiliates 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. CJ Affiliates 隐私政策
      Commission Factory
      我们通过 Commission Factory 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Commission Factory 隐私政策
      Google Analytics (Strictly Necessary)
      我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
      Typepad Stats
      我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
      Geo Targetly
      我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
      SpeedCurve
      我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
      Qualified
      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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      改善您的体验 – 使我们能够为您展示与您相关的内容

      Google Optimize
      我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
      ClickTale
      我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
      OneSignal
      我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
      Optimizely
      我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
      Amplitude
      我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
      Snowplow
      我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
      UserVoice
      我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
      Clearbit
      Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
      YouTube
      YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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      定制您的广告 – 允许我们为您提供针对性的广告

      Adobe Analytics
      我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
      Google Analytics (Web Analytics)
      我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
      AdWords
      我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
      Marketo
      我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
      Doubleclick
      我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
      HubSpot
      我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
      Twitter
      我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
      Facebook
      我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
      LinkedIn
      我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
      Yahoo! Japan
      我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
      Naver
      我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
      Quantcast
      我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
      Call Tracking
      我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
      Wunderkind
      我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
      ADC Media
      我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
      AgrantSEM
      我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
      Bidtellect
      我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
      Bing
      我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
      G2Crowd
      我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
      NMPI Display
      我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
      VK
      我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
      Adobe Target
      我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
      Google Analytics (Advertising)
      我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
      Trendkite
      我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
      Hotjar
      我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
      6 Sense
      我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
      Terminus
      我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
      StackAdapt
      我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
      The Trade Desk
      我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
      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

      是否确定要简化联机体验?

      我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

      个性化您的体验,选择由您来做。

      我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

      我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

      通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。