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Hello and welcome to another Take Five with Automation World . Im David Miller, Senior Technical Writer for Automation World.

Today, Im going to be talking about a very interesting development relating to digital twin technology. Now, as our viewers certainly know, digital twins are simulations that provide end-users with a virtual copy of plant assets and production systems, and are used in industry for a multitude of tasks such as remote asset monitoring, remote training, and virtual commissioning of new facilities.

However, what were starting to see now is that their functionality is actually being extended being these production-oriented uses and into other business departments such as sales and marketing. And whats even more interesting is that in order to enable this shift, technologies and methods from the field of video game development are being used to enhance the usability, aesthetic presentation, and degree of realism these simulations are able to achieve.

In essence, what you have with current industrial digital twins is, its true, a visual representation of plants, but the representation is often highly technical and may be difficult for someone without an engineering background to fully comprehend or make sense of. But the engines that drive video games are capable of generating highly sophisticated and realistic visual imagery that is aimed not at engineers and specialists, but frankly your average video game playera consumer.

And on top of that they actually boast quite sophisticated physics engines that are able to replicate all kinds of interactions between objectsif our viewers have ever played or seen a modern video game played they know what Im talking aboutexplosions, objects colliding and tumbling around, bodies strewn through the air, etc.? Thats all driven by physics simulations.

So, I was able to speak recently with Brad Hart, Chief Technology officer at Perforce Software, which works with companies on implementing these types of digital twin technologies, and he was able to share with me some specific examples of more precisely how this technology is actually being put to use and how its enhancing not only the digital twin experience for industrial users, but helping the digital twin to find new uses, like I said, in departments such as sales and marketing.

So lets talk about some use cases. Heres one from the automotive manufacturer Audi. Audi uses the Unreal Engine, which is what drives the popular Unreal series of video games to bring data from its production pipeline into a highly realistic, physically responsive simulation of new vehicle designs. And they do two things with these simulations.

On the one hand, they use them to test the performance of vehicles in a virtual environmentso thats not so different from virtual factory commissioning, though it does involve some more sophisticated physics. The second thing they do is they are using the technology to exhibit their new vehicle designs to corporate executives in a highly realistic and interactive manner before even a single unit has been produced. And so the key here is theyre not just looking to create a representation that engineers will understandtheyre looking to make a very glossy, splashy sort of model that someone on the business and marketing side can see and get familiar with.

Another exampleaerospace companies that build private jets for high-class clientele or companies that build custom yachtsthese are very expensive, luxury pieces of equipment and those purchasing them want to know what theyre getting. So in these situations, these more realistic digital twins can be used as a marketing tool for these very, very upper-echelon types of consumers. And thats helping them to make these frankly massive sales.

And the final thing, which is more general, is just that the previous capacities that industrial digital twins already possess for rapid prototyping and virtual commissioning are simply enhanced as the simulation technology becomes more robust. So, tests that previously involved complex calculationseven with the aid of a digital twincan be iterated more quickly through the use of a more powerful simulation.

So, thats all I have for you for today, but keep your eye on this space for more videos to come in the days ahead.

Excerpt from:

Perforce Software Industrial Digital Twin Take Five with Automation World - Automation World

As the world of business continues to change, pressures from investors continue to influence industry trends. As a result of increased fees and lower margins, improving operational efficiencies becomes even more necessary.

Does this have anything to do with hedge fund management and automation? Quite a lot as the industry is finding out.

In the competitive world for hedge funds and other forms of asset management, automating standard processes is an essential step for managers of hedge funds to differentiate themselves from the crowd.

The impact and transformative power of technology on business and client relationships make automation a must for asset management firms of all sizes and types - whether family offices, private equity funds, hedge funds, or fund administrators.

Managers are specifically investing in technology in the middle and back office to improve the speed and quality of data reporting and management. Ernst and Young reported that of hedge fund managers interviewed in a survey, 57% said their organization is investing or will invest in operational efficiency. In addition, 28% of investors said they would like to see their hedge funds become more innovative regarding their operational efficiency.

Business Spectrum benefits

Over the years, fund accounting and middle office have developed to become the most advanced functions where managers invest in accurate and timely reports. On the other hand, the marketing and investor relations department is the least automated. This can be deduced from the EY 2020 Global Alternative Fund Survey, where nearly 65% of investors believe an investment in this area would benefit their future relationships.

By using technology to automate operations in the marketing and investor relations area, managers will enhance the investor experience and meet the needs of their clients.

One of the most significant benefits of automation in hedge funds is the power it gives managers to do more with equal or lesser resources. Good workflow automation optimizes operations at every stage, allowing managers to do more with the same or less staff, improve investor relations, improve reporting and regulatory requirements, making the manager more profitable.

Why now?

It is imperative that managers prioritize investor experience to stay competitive in the market. An asset owner told a recent EY global survey on alternative investing that managers should value people and their infrastructure more than costs. Hence, hedge funds need to free up time and use the right tools to deepen client relationships.

Automation and integration of communication and collaboration tools can provide hedge fund managers with a more comprehensive understanding of their investor journey, allowing them to identify better ways of attracting and retaining investors.

Multifaceted solutions for diverse needs

The smooth operation of a fund is dependent on multiple systems communicating on the back end, which is why fund managers require a centralized, automated terminal that enables them to create a fund swiftly, manage personnel and resources, and optimize processes to ensure flexibility and scalability.

Hedge funds can do this either through internal development or by partnering with a service provider like MetaQuotes Ltd., which provides a comprehensive set of solutions, including automated tools for hedge funds.

An administrative terminal on MetaTrader 5 for hedge funds, for example, permits managers to create funds and financial instruments in less than five minutes, automate fund and fee calculations, and select shareholders and investors to the fund and amount of shares they hold.

While investors may be satisfied with their managers technology implementation, there is certainly room for improvement in the automation of various other functions. Automating hedge fund operations makes sense because investors and other constituents expect that manual processes can and should be phased out.

See more here:

Automation: Manage Hedge Fund Effortlessly and Stay ahead of the Markets - Benzinga

Its pretty ironic to consider how automation is transforming the fintech sector when fintech transformed the financial sector.

Perhaps its testimony to the speed and pervasiveness of technological advances that the disruptors are being disrupted.

Transformation today is coming from hyper-automation, intelligent automation, or extreme automation. These terms mean that automation tools are very sophisticated and are being used in combination with each other.

Lets look at how they are being used for fintech applications.

Fintech companies have used technology to cut costs and time and replace traditional institutions. However, they must also provide quality, customer trust, and regulation.

Advanced automation tools provide the solution.

RPA has been with us for a while. These are the bots that automate routine and rules-driven tasks. They are efficient, tend not to make human mistakes, and significantly cut the time and cost of repetitive tasks.

However, these bots cant deal with complex, dynamic processes and workflows.

This is where Artificial Intelligence, Machine Learning, and Natural Language Programming come into play.

In these technologies, the software learns from itself and uses guiding principles to solve new problems.

AI is the science of using computers to mimic human intelligence. It is a form of advanced information processing.

For example, in the financial field,

Machine Learning is a subset of AI.

Systems learn from data, identify patterns, and use algorithms to predict outcomes. It is particularly useful for data on a large scale.

For fintech,

NLP is a subset of ML.

Computers now understand, analyze and even generate human language.

We know how Google can find relevant search results, translate pages from one language to another, or auto-correct spelling. NLP has now moved to recognizing speech and answering questions. It generates text from images or videos and detects sentiment.

All these functions are relevant to fintech.

A blockchain is particularly well suited to fintech.

It is a virtual ledger of transactions, tracing the transfer of ownership of assets. Records are authenticated by computers spread across the world and cannot be altered. Everybody has access to the same data in real-time.

Smart contracts can be attached to transactions, automatically triggering actions when pre-defined conditions have been met.

Automation is even better when techniques are combined.

The ultimate combination is blockchain and AI. AI deals with large data sets, and blockchain provides security, trustworthiness, and quality to that data.

Ultimately, there will be trading on blockchain as the tokenization of assets becomes more prevalent and datasets grow. AI and ML will be used for pattern detection and predictive algorithms.

Advanced automation tools such as artificial intelligence, machine learning, natural language processing, blockchain, and smart contracts are transforming the fintech sector.

They bring both convenience and security to fintech applications and open the door for new products and services.

Read more:

How Automation is Transforming the Fintech Sector - FinSMEs

Twitter today is introducing a new feature that will allow accounts to self-identify as bots by adding a label to their profile. This feature is designed to help people better differentiate between automated accounts like bots that retweet the news, public service announcements or other updates from those operated by humans. Its not, however, designed to help users identify the bad bots, which are those that pose as people, often to spread misinformation or spam.

The company has been contemplating labeling bots for years.

In 2018, Twitter CEO Jack Dorsey was asked during a Senate Intelligence Committee hearing whether he believed users had a right to know if they were speaking to a bot or a human on Twitters platform. He agreed that Twitter should add more context to tweets and was considering identifying bots, to the extent that it could. However, Dorsey also pointed out it would be more difficult to identify bots that were using scripting to give the appearance of being a human, compared with those that were leveraging Twitters API.

Last year, the company finally solidified those plans, saying it would later introduce new features that would allow users to be able to distinguish between human-run accounts and those that were automated. When Twitter launched its account verification system in May, it reminded users that it would soon offer other ways to identify different types of accounts beyond the long-coveted blue badge such as labels for bots.

Image Credits: Twitter

Today, Twitter says its new Automated Account label that identifies good bots will be made available to over 500 Developer Accounts. This group will test the feature and provide feedback before its opened up more broadly to all Twitter developers. As its still a test for the time being, the label wont be required.

However, when Twitter updated its Developer Policy last year, it did ask developers to indicate in their account profile or bio whether the account was a bot, what the account is and whos behind it. These account labels would allow developers an easier way to comply with that policy rather than having to handwrite this information in their bio.

Twitter tells TechCrunch that based on what it learns during this experiment, it may decide to make adopting the label a requirement for all developers who run automated accounts in the future, once it becomes broadly available.

Image Credits: Twitter

To be clear, Twitter doesnt have any problem with those who run good bots, as it understands how automation can allow accounts to update people with helpful, relevant or, sometimes, just fun information. The company even celebrated a few of its favorite bots when announcing todays developer news, including the public service account @earthquakesSF; a bot offering COVID-19 updates, @vax_progress; a bot that offers an ongoing breakdown of the last 100 bills introduced in Congress, @last100bills; an accessibility-focused bot, @AltTxtReminder; and others that just add value in their own way, like @met_drawings, which shares public domain works from The Mets Drawings & Prints department, or the goofy @EmojiMashupBot, among others.

All these will be a part of the initial test group.

Twitter is also less concerned with how consumers may use automation to update their own accounts, perhaps by using third-party tools like IFTTT to post links or other content.

You are ultimately responsible for the actions taken with your account, or by applications associated with your account, Twitters policy advises Twitter users. Before authorizing a third-party application to access or use your account, make sure youve thoroughly investigated the application and understand what it will do. It also adds that Twitter users that adopt automation will still need to adhere to Twitters guidelines.

The company has been on a tear lately in terms of rolling out new features. Just this week, it has launched Communities, tests of emoji reactions, support for full-width photos and videos and a way to soft block followers, among other things.

Twitter has not said how long the test would run before the Automated Account labels are rolled out more broadly.

Read this article:

Twitter introduces a new label that allows the good bots to identify themselves - TechCrunch

Like their cloud-native counterparts, many large or long-standing enterprises aspire to automate as much of their operations as possible. As a result, many of them get overly ambitious with their process automation goals, and attempt to roll out sweeping, company-wide digital transformation initiatives. While ambition is a good thing, many of these initiatives take years to complete, and often require ripping and replacing legacy systems.

Few organizations consider that end-to-end process automation takes a change in mindset that spans people, processes and technology. Lets take a look at three of the most common process automation mistakes, and how organizations can work together to fix them.

Rolling out strategic automation initiatives too fast is the first. While theres nothing wrong with being strategic, thinking on too large of a scale is a common pitfall of overly ambitious automation projects.

Taking on too much strategic work too early runs a high risk that the organization doesnt see any business value for a long time. As a result, developers will most likely get completely stuck in shaping a complex platform without understanding its use case.

Instead, try to break down larger strategic initiatives into component parts, starting with the most urgent or important projects first. Heres one way to approach it:

Start with Pilot Project: The goal of this project is to define and validate both architecture and stack. Very often, this pilot project is set up as a proof-of-concept (PoC). However, it is important to go-live with that pilot to really learn about all aspects of the workflow solution throughout the full software development life cycle (SDLC).

Accelerate to a Lighthouse Project: Soon after running a successful pilot, you should tackle a lighthouse project. This project should have a broader, but still realistic scope which can be better leveraged to show off architecture, tooling, and value of workflow automation to other people and teams within your organization.

Progress to Broad-Scale Transformation: Leverage the lessons learned from the lighthouse project, empowering the people on that project team to run a Centre of Excellence (CoE) to break down silos across teams and drive organization-wide change.

I call this approach the art of gradual transformation.Ideally, before approaching a large-scale automation project, try to map out the entire ecosystem of processes including the people, systems and devices at work in the background. Start by modernizing high impact processes that affect customers the most. Then design a transformation approach that fits the business or customers needs, rather than your technology stacks requirements.

Handling Automation Projects in Silos

Even though a gradual transformation approach is recommended, it does not mean siloed or without structure. If each team chooses its own tools, it can be hard to effect organization-wide change, or end-to-end process automation. Technology decisions are a commitment for years and sometimes even decades. These decisions and the resulting maintenance affect more than just the current team in the trenches.

As mentioned above, a CoE approach can help break down organizational silos and share best-practices on what has worked or not worked in previous automation projects. Ideally, this group does not dictate arbitrary standards, but maintains a list of approved tools and frameworks that can be reused across the entire company.

Beyond tooling alone, a CoE can also maintain start guides, project templates, and reusable open source components/libraries for teams to leverage. In addition, they can serve as advocates for automation, by running a community to raise awareness for new automation initiatives within the company. Within this framework, more teams can get inspired by the potential for automation within their departments.

Failing to Embrace Microservices Architectures

One important factor to address is the way software is built within the company. Embracing a microservices architecture in a legacy company is easier said than done. Often, there are legacy systems in place that are difficult to unseat.

By one estimate, there are still more than 200 billion lines ofcode written in COBOL, a decades-old programming language. A wholesale replacement of these systems could cost upwards of $US4 to $US8 trillion (or more).

Thats where the gradual transformation approach comes into play. For example, many companies have surface-level automations in place with RPA implementations sitting on top of legacy systems (like those written in COBOL). A good approach for these scenarios would be to go through a modernization in three main stages:

The advantage of a microservices-based automation workflow is that it allows for a decentralized architecture where each team owns its own isolated processes. In the event something goes wrong with a single process, it can be easily controlled and fixed. From there, a process engine can drive these microservices-based processes across the organization, and unify them where it makes sense.

To sum up, end-to-end process orchestration cant happen in a vacuum. Stakeholders from across the organization should be involved, and projects should start small. Without a clear pilot project, large-scale, strategic automation efforts can easily fail to prove their business value. By working together to define priorities, create best-practices, and roll out the technological changes needed, organizations can ensure that end-to-end process automation happens successfully.

To learn much more about process automation, sign up for the virtualCamundaCon 2021event on September 22-23. Jakob Freund is CEO at Camunda, a business process management solution provider.

See original here:

3 common process automation mistakes (and how to fix them) - IDM.net.au