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ECOFACT (Eco-Innovative Energy FACTory Management System)

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Description

ECOFACT is a €12 million, Horizon 2020, EU-funded project made up of 20 consortium members from several European countries aiming at developing an ECO-innovative energy factory management platform using improved dynamic LCA/LCCA toward holistic manufacturing sustainability. The platform will be deployed in four different demo sites: a biscuit factory (Gullon), a Stellantis Group automotive factory (Tofas), a Heineken Group brewery (Athenian Brewery) and a multinational household appliances factory (Arçelik). One team is leading the development of the digital twin platform (DTP), based on Autodesk Forge software and composed of production lines, 3D models, and different applications (material-flow simulations, energy simulations, production planning and scheduling, industrial energy disaggregation, and industrial energy flexibility). The DTP will display simulation results and real-time Internet of Things (IoT) data alongside the 3D models maximizing Autodesk Forge functionalities.

Key Learnings

  • Assess the potential of a solution based on the Autodesk Forge platform and its APIs.
  • Learn about how to optimize production—and operation and maintenance—processes and reduce energy consumptions and costs.
  • Learn about validating industrial digital twins in the Autodesk Forge environment, integrating real-time IoT data.
  • Learn about combining energy and resource management systems with a dynamic LCA and LCCA approach.

Speakers

  • Andrea Perego
    Andrea Perego is a Management Engineer graduated with full marks at Politecnico di Milano who firmly believes in the power of ideas supported by sweat and tears. Currently in One Team Andrea leads the business unit dealing with the scouting and management of value-added projects, including Research & Innovation (R&I) projects funded by the European Union (EU), mainly in the field of BIM, Digital Twins and Augmented and Virtual Reality (AR/VR). Andrea and One Team's goal is to create value through innovation, fostering by the way the compliance with current sustainability paradigms. Recently Andrea and his team have been working at three Horizon 2020 (H2020) and three Horizon Europe (HEU) projects: www.bim4eeb-project.eu (7M € budget); www.infinitebuildingrenovation.eu (10.1M € budget); www.ecofact-project.eu (12.3M € budget); www.buildon-project.eu (6.8M € budget); www.crete-valley.eu (25.2M € budget); www.retime-project.eu (5.5M € budget). Furthermore, as a Board Member of the company, Andrea is also in charge of managing strategic alliances with existing partners and scouting new ones. As Marketing Director, Andrea helped his team to manage the rebranding of the company, including the development of One Team's new website (www.oneteam.it). In closing he recently he became CEO of One Team Iberia.
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Transcript

ANDREA PEREGO: Hello, everybody. Thank you for being here with us. We are here for presenting ECOFACT. ECOFACT stands for ECO-innovative FACTory management system. But let's not focus on the acronym and let's see what the project is about.

So let's have a look at the project in a nutshell. In a big nutshell, actually, because it's a huge project. It's a 12 million Euro project. It started in 2020. It lasts 48 months. And it's carried out by a consortium of 20 organizations.

This project has been founded by Horizon 2020 program. So by the European Union. And from our point of view, the core part of this project is the Digital Twin platform based on Forge.

This Digital Twin platform will be the Energy and Resource Management System of the project. And we will see later on what we mean with Energy and Resource Management System.

But first, let's focus on the learning objectives of this class. So first of all, the first learning objective is understanding how to build up a Digital Twin Platform on Forge, exploiting its relevant APIs. Then we will see how to deepen the knowledge on the whole ECOFACT platform. And especially on the part that is composed by the Energy and Resource Management System.

So by the Digital Twin Platform itself, combined with the dynamic LCA and LCC. So lifecycle analysis and lifecycle cost assessment. Assess the potential of the integrated platform and understand how it could support the industrial process optimization and more in general, learn how European Union is contributing to boost sustainability through digitalization thanks to Horizon 2020 program.

But let's start with why. So why have we done this? Why ECOFACT? So basically, we all know that more is inevitable nowadays. So we are producing more and more. We are growing. Population is growing, so we need more cars. We need more streets. We need more buildings. But from the other side, we need to produce more with less, because we have less resources. We can exploit less resources. And furthermore, we have to produce more with less consumption of energy, if possible, and especially with less CO2 emission, because we are all aware of the environmental issues we are all facing.

So there is a huge opportunity, basically. The opportunity of doing better. The opportunity of doing better and do it differently because we cannot solve our problems with the same thinking we used when we created them. And this is basically why the European Union has promoted the Horizon 2020 program.

The Horizon 2020 program, as a matter of fact, is a program financing innovation. So financing innovation with the aim of cutting greenhouse gas emissions. So financing innovation in different sectors, but with the aim of cutting greenhouse gas emissions.

So as I said before, ECOFACT, that is the project we are talking about today is part of this program. But as one thing, we are working also on other projects financed by the European Union with the same aim. So the final aim of those project is to cut the CO2 emission. In this case, you can see other projects as I said. So BIM4EEB, Infinite auto project where our role is to develop always let's call it the BIM platform, so the platform hosting the BIM models, and the BIM models themselves.

And the aim of this project in this case is to foster innovation and make existing buildings more efficient from an energy point of view, exploiting thanks to digitalization, thanks to BIM information modeling.

But today we are focusing, as I said before, on ECOFACT. ECOFACT is a focus on a different sector. So it's focus on manufacturing. It's focused on industry. As a matter of fact, ECOFACT, the goal of ECOFACT is the optimization of processes of industrial processes of factories. Especially for big factories, as we will see later.

And as I said before, in this project, it's necessary a change of paradigm. In this case, in the planning of the industrial processes, thanks to the ECOFACT project, the environmental part will be taking care at the beginning, at the very beginning of the planning, and not at the end of pipe.

So this is the big changes. So taking into consideration as a parameter the environmental issue since the very beginning of the planning of processes.

So let's say that at the end, the final goal is work through sustainability. And as one team-- as One Team is our company, we have always been interested in sustainability issues. As a matter of fact, our logo itself resembled the healthy sets and our motto is transform competencies and technology into value to make a better world, a more sustainable world. That's why we have been working in this industry, in these European-funded projects in [INAUDIBLE].

But let's stop one second before deep diving into who we are and presenting also ourselves. So me, Andrea, and my colleague, Alder. Let's have a look at the overall agenda.

So first we will start with an introduction. So introducing our company, myself, and Mister Alder Moriggi. Then we will talk about the project at a higher level. So we will give you a project overview. Then we will deep dive into the solution.

So we will see what is a digital platform, how is it made. And then we will focus on the next step. So what's next? Which are the future challenges and the call to actions?

OK. As I said before, now let's introduce better the company and ourselves.

So as you can see from the video, we are a consulting company working in different fields. So we are working in building formation modeling. We are working in manufacturing. So we talk about industry 4.0. We are working with the GIS system, so we're talking about [INAUDIBLE] system as a solution. And we have been doing this for over 25 years. And we are also one of the top 10 Autodesk Platinum partners at the EMEA level, and that's why we want to also thank Autodesk for the support and thank you for Autodesk for hosting us today and giving us the opportunity to share with you the results of this interesting, important project.

So these are our key numbers. So we are a staff of more than 100 people. We have 60k users. More than 60k users. Total revenue of about 37 millions, and 11 locations spread all over South Europe.

Last but not least, here we are. I am Andrea Perego. I'm a management engineer. I am the project manager of the ECOFACT project. And I'm here with Alder Moriggi, the leading engineer of the Forge development part. But we are here only as a representation, only as speakers, because we are representing actually a wider team. So we want to thank everybody for the nice work they have done until now.

But now let's focus on the project itself. So let's have a project overview. And starting from the targets, understanding how is the consortium made, et cetera, before deep diving into the solution made on Forge.

So let's start with the overall Gantt. Why I want to start with the Gantt? Not to, let's say, to analyze it in detail or understand each task, because as you see, it would be pretty difficult. But just to say that we are halfway. So the project still has two years. So we are presenting some results of the project. Some of the results will be presented at the end of the project. So stay tuned because maybe you will find us presenting the fact results of the project in the next years.

So let's start from the objectives, the targets of the project itself. There are very specific scientific, technological objectives defined since the very beginning of the project. And I want to focus on the second scientific, technological objective, because it's the solution impacted by the Energy and Resource Management System. So basically, implementing our solution. So the Energy and Resource Management System, the final aim is to cut on the factory energy bill by an average of 25%. So it's an important target.

I would also want to focus on the third scientific, technological objective because it's always linked with the sustainability, because in this case it's thanks to lifecycle analysis and lifecycle cost assessment, reduce environmental footprint of manufacturing processes by an average of four or eight percent.

Alongside these specific objectives, there are other objectives. There are other scientific, technological objectives more high level. So basically, these objectives are saying that we need to deliver something at the end of the project. So we need to deliver a platform as I said before. A platform that should be at technological readiness level seven.

What does it mean? Technological readiness level is a scale from one to nine. And the nine means that the solution is 100% marketable, so it's ready to be marketable. We have to deliver a solution at the technology readiness level seven. It means that the solution should be tested in relevant environment, but still not 100% ready to be scaled to be marketable.

Alongside, there are other non-technological objectives. In general, we could say that we need to exploit the result of the project and disseminate the results of the project. We are doing it also now. We are disseminating the results of the projects.

And now let's focus on who we are. So who is carrying out the work. So the consortium is made up of 20 partners from seven different countries. There are five research institutions, eight large industries, five small-medium enterprises, and two associations across the manufacturing environment.

But let's focus on the real protagonists of this project, so the demo sites. So first of all, we want to underline the fact that we wanted to test the solution on different industrial environments. So from one side, we have a discrete manufacturing demo site. In this case, we have Arcelik, which is a washing machine factory located in Romania. There is Tofas, which is an automotive factory located in Turkey, and is part of the [INAUDIBLE] Group.

While from the continuous manufacturing sector, continuous manufacturing industrial environment, we have Athenian Brewery, which is a brewery. So a factory of beer located in Greece. And it's a brewery from the Heineken group. There is Gullon, which is one of the largest biscuit factories in Europe, and is located in Spain.

Which is the architecture of the solution we are thinking of? Basically, we will start from the factories. So we will start from the data of the factory. So from one side, we will take data-- exploiting existing data, existing systems already in place in the factories. And from the other side, we will put in place, we will implement new sensors. We will implement an internet of things infrastructure in order to collect other datas.

Then this data will be delivered. We will convey to a data broker. And the data broker will harmonize, will clean the data and distribute the data to the application and services layer. In the application services layer, we have our Energy and Resource Management System that is composed by different models. We will see later which are these models.

And from the other side, we have also always in the upper services layer the dynamic LCA and the CCA systems and the supply collaboration system. So this solution integrated will make up the holistic digital sample system, so the ECOFACT platform itself.

So it could sound a little bit complicated. So we wanted just to sum it up focusing on the four milestones. So from one side, we have the local data. The local data, as I said before, local data taken directly from the field, from the factories, leveraging existing systems, existing PLC, existing SCADA, existing sensors from one side, and from the other side, putting in place specific IoT architecture in order to measure and convey other specific data.

Then we have the data broker. The data broker is used, as I said before, to convey data to the different application and services. In this case, the data broker is based on a software platform called Kafka.

Then we have, from our point of view, the most important, let's say, from the user point of view, also part that is the holistic DSS, because it's where the different algorithms, the different application and services are hosted. So from one side, we have the Forge-based Digital Twin Platform that will operate as an Energy and Resource Management System, I said before. And always, I said before, the supply chain collaboration and LCA and LCCA services. In this case, based on, always an existing software solution called SimaPro.

And then we have the last part, that is still very important. That is the user interface which will be used by the final user to interact with the platform and to have access to different data.

So now let's deep dive on the Digital Twin Platform itself. But first, let's clarify, at least from our point of view, let's clarify what the Digital Twin is, because it's a common trend, talking about the Digital Twin. We wanted just to focus on the definition from Dr. Michael Grieves. Dr. Michael Grieves said that a digital twin is a sensor-enabled digital model of a physical object that simulates the object in a live setting.

And that means, basically, that the digital twin is the digital representation of a physical asset, of a physical object, that could receive real time data and then simulate the behavior of the asset in the real setting in a digital environment. So as we can see, also from the schema below, the element scale, we talk about digital twin only when the digital asset, the digital model, we have we can call it, is enabled to receive data directly from the field. So we are referring to level 3 in this case.

Then there are [INAUDIBLE] factor levels that are, let's say, linked to the fact that the digital twin, from the other way around, could control the physical asset itself in a semi-automatic or automatic way. So we're talking about level 4 and level 5.

OK. But now, let's go back to our project. Let's focus on our digital twin and understand how is it made. And to do this, I leave the floor to my colleague, Alder Moriggi. Please, Alder.

ALDER MORIGGI: Thank you, Andrea. And now I'm going to talk about the Digital Twin Platform that is composed by three different components. So the digital twin itself, then the 3D part that will be visualized with the Forge Viewer. And then in data, we have a modulator using in Inventor. And all the relevant documents are related.

Then in the web platform, we have also different tools. Here, we specifically mention OptimiST, because it's one of the tools that we are going to show in this presentation. But there will be other tools linked. There is also the Data Exchange layer, composed by some APIs. And so basically, the platform exchanges data with the other systems. Basically, the data broker that will be used within the holistic decision support systems. OK.

To model old factories lines-- Tofas, [INAUDIBLE], and Athenian Brewery, we used Autodesk Inventor. Machineries and the products are combined into an assembly to create the whole lines. And the after-modeling activity models were uploaded within the Digital Twin Platform in Forge. OK.

And we also used the integration with the Leica technology with the BLK3D a Leica device that realize 3D photos [INAUDIBLE] more photo cameras and the laser that calculated the distance from the machinery.

Measurements are readable directly to on the 3D photos, and the [INAUDIBLE] is easier. And in this case represented in this slide was Athenian Brewery demo site. And we went to Athenian Brewery demo site. And we took measurements. And then we model in line using these measurements. OK.

And this is the result, what we have uploaded in Forge. But we will see it more in detail in the next slides, in next video.

And now I'm going to show you the web application that we built for ECOFACT project. It's a front end to manage factories and all data related to factories. I'm talking about the groups, users, permissions, all those factory lines, documents, et cetera.

And the factories lines we modeled are at Tofas, [INAUDIBLE], Gullon and the Athenian Brewery and the two demo sites. And as I said before, we used Autodesk Inventor. For Tofas, until now we have finished modeling five production lines. And then we have uploaded on [INAUDIBLE] on ECOFACT platform [INAUDIBLE] and [INAUDIBLE]

For [INAUDIBLE], we made the one production line. One production line is completed. And another one is almost ready. For Athenian Brewery, we made two machineries. And for Gullon, we are still working on true assembly and one assembly with the three production lines is almost ready. And for the second one, we started working on.

And in a specific section on the web application, we disposed an interface to upload the [INAUDIBLE] model on Forge Bucket, in order to display them on a Forge view to charge model user as to package all assemblies and parts in Sipa and then upload it on a form that use modeling derivative APIs to upload the source file to OSS and translate the source files. OK.

In the video, after modeling activity for one production line of Tofas factories, in Inventor, we placed the sensors next to machinery like [INAUDIBLE] to monitoring natural gas, tools, to monitoring water consumption, pumps and motors to monitoring electrical energy. And there are also sensors to monitoring hot and chilled water and steam consumption and the compressed air.

We are mapping and codifying the sensor in order to get data coming from database and web services developed in other work packages. We have also prepared connectors to get data from sensors. And finally we prepared, as you can see in the video, a custom Forge viewer to view models. And then we created a custom extension to get all sensors placed in the model, and all components that sensors refers to, as you can see in the right part of the screen.

And we use the GetBulk properties to do better a specific function from Forge APIs. And then we prepared a list of sensors on the right and the user can zoom to the sensors, the related components, and user can also consult daily data graph and also real time data. This is the first integration. But we have to realize ordering integrations like that in the other two here and the other custom extensions. OK.

And here I reported the Forge APIs that we used right now to develop the Autodesk part of the project. We used the authentication APIs to allow the application to use other Forge APIs in a two-legged way. Next, we have to upload the models we produced with the Inventor to the Forget bucket. To manage the directories in the Forge bucket, we use the Model Derivative APIs. And then to build a custom viewers I showed in the previous slides, in the previous video, we used viewer APIs.

We used the GetBulk Properties to extract data required, especially sensors data, and sensors, and the machinery [INAUDIBLE]. We also used some extensions like PDF extension. And finally, we used data visualization APIs to visualize a sensor and data coming from sensor. Andrea, I leave the floor.

ANDREA PEREGO: OK. Thank you, Alder, for the presentation of the Digital Twin Platform and how it's made.

So now let's focus on a specific application integrated within the platform, the OptimiST application. As you can see, it's not the only application integrated within the platform, but it's the more mature one. So we will focus on this specific application.

So how it works, what is the application about? Basically, it's an application that optimizes the minimization of changeover time. So let's say that thanks to the minimization of changeover time, we are able to save time, and not only time, as we will see from the next slide, and redefine the production scheduling accordingly.

As I said before, we save time. But not only, because as we can see from this slide, the time saving corresponds to a CO2 emission saving of about 16%. This is an important result from our point of view, and from the project point of view.

We have applied this technology, this algorithm and this application, for now in different demo site. But the results are now recorded especially for the Athenian Brewery demo site. And as I said before, this application is directly integrated in the ECOFACT platform. As you can see, from the two sessions, you can access the application. You have to input some data about the consumption, about the production, about the cost. Then the application ran. And as you can see, you can have access to the result of the application in an Excel format, if you want, representing the new scheduling, the optimized scheduling of the production. Or also, you can see the representation of the data displayed directly in the Forge platform.

So as I said before, this application has been tested in different demo sites. We have specific results from Athenian Brewery, but also we have been testing this application in Gullon demo site. In this case, the result is always the minimization of the changeover time. And so we will have a different and optimized scheduling of the production for each line.

In the [INAUDIBLE] demo site, we have a different approach. So this application will help the decision maker to minimize the energy bill and redefine the production scheduling accordingly. And so defining also an optimized electrical energy asset management plan. Same we can say for Tofas demo site. So we have the minimization of the energy bill and the optimization of thermal and electrical and energy assets management plan.

Now we gave you a brief overview of the results of the project until now. But let's focus on the next step. What's next?

So as I said before, the OptimiST is not the only application that will be integrated within the platform. There are other four application services that will be integrated in the Digital Twin Platform. And we will focus on each of them very briefly.

So we will integrate a material flow simulation application based on Siemens plant simulation, and a production optimization application based on the Gurobi solver application, Gurobi solver software. Then we will develop an integration with an energy simulation for dynamic operation management and cost optimization. Basically, we will develop and integrate a predictive maintenance model to optimize the operation and maintenance tasks.

Then we will integrate an industrial energy disaggregation component. In this case, we will integrate this component in order to understand the energy consumed for the production of each different product. So it is an energy disaggregation byproduct.

And last but not least, we will integrate the results of our energy resource system. So the results of our application and services developed within the Digital Twin Platform with the dynamic LCA, the [INAUDIBLE] and supply collaboration services developed within SimaPro. And these two components [INAUDIBLE] will be the technological building blocks of the overall holistic digital upper system.

But what's next? What's next from a call to action point of view? Let's say that the ECOFACT project is open to receive feedback, is open to welcome new partners. So for exploitation purposes, we are looking for five other demo sites where to test the solution. So if anyone is interested to test the solution on a specific factory is more than welcome.

We are looking for, also, partners who want to help us develop faster the solution to reach full marketability of the solution by 2028 to 2030. So we are talking about TRL9, technology readiness level 9. And last but not least, if you want to get a deeper knowledge about the project itself, please download our handout. If you want to be updated on the development of the project, follow us. Follow us on LinkedIn, on Twitter, on YouTube. You can subscribe to our mailing list. And if you have any specific questions, please drop us a line to me or to my colleague, Alder.

So I want to close this class with this quote. Because as I said before, as one team, we have been working in different research and development projects. And it's hard. It's not easy. Sometimes you feel lost because it's not easy to start from a white paper and develop something new, and develop following new paradigms.

But it's very challenging and very satisfactory when you manage to do it. And it's the only way for real evolution. And it's the only way to tackle and to face and to solve the challenges we are all facing. So our last sentences is, do not go where the path may lead, go instead where there is no path and leave a trail. Thank you, everybody, for the attention.

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We use Typepad Stats 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. Typepad Stats Privacy Policy
Geo Targetly
We use Geo Targetly to direct website visitors to the most appropriate web page and/or serve tailored content based on their location. Geo Targetly uses the IP address of a website visitor to determine the approximate location of the visitor’s device. This helps ensure that the visitor views content in their (most likely) local language.Geo Targetly Privacy Policy
SpeedCurve
We use SpeedCurve to monitor and measure the performance of your website experience by measuring web page load times as well as the responsiveness of subsequent elements such as images, scripts, and text.SpeedCurve Privacy Policy
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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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

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