External Data: Add a New Dimension to Business Decisions

External Data: Add a New Dimension to Business Decisions

External data can be transformative

Organisations no longer operate as a standalone entity, and instead are part of networks comprising suppliers, resellers, channel partners, regulators, and other stakeholders. Analysing external data can point out the risks, opportunities and trends that firms would miss if they relied on data generated from internal operations, customers, and first-tier suppliers.

Relying solely on internally generated information can leave gaps, and as organisations realise this, they are increasingly moving to incorporate new, non-traditional sources of data that sits outside their systems. The challenge however is analysing the tonnes of data being gathered and stored at an exponential pace. According to a study, the data stored in data centres will grow almost five-fold to reach 1.3 zettabytes globally by 2021.

An MIT Sloan Management Review report found that the firms making the most innovative use of data and analytics were more likely than others to leverage more external data sources, including social, mobile, and publicly available data.

Why external data must be a part of your data strategy

External data gives a bigger picture. Collecting, evaluating, and analysing external data – such as user generated data, public data, competitor data etc – gives business leaders the full view.

It is not expensive. Today several tools, both paid and open source are available, to make sure it does not cost much to source external data. Data from government organisations, the news, social media and other online and broadcast media is even available for free.

Real insights with external data analytics. External data analytics can have a major impact when it comes to making decisions about the future of a business. Organisations can personalise marketing offers, improve HR decisions, build new revenue streams by launching new products or services, improve risk visibility and mitigation, and better anticipate shifts in demand. For instance, investment firms can use third-party data to build models that could predict the best types of customers to target in marketing campaigns. External data can help train the models to identify potential targets that fit profiles similar to the most engaged customers, thus optimise marketing spend. Several start-ups monitor social networking data to predict customer patterns and employee sentiment.

External data helps a business stay competitive. With competition for the customer’s wallet share being at an all-time high, the ability to quickly and regularly keep track of what the competition is doing is invaluable. Organisations can also predict trends and spot patterns that will make them more relevant to customers.

Add real-world context to decision-making. Organisations must gauge and predict the impact of external events – such as shifts in global purchasing trends, pandemics, marketing campaigns and so on – and guide product/ service decisions.

Tap into a data ecosystem

Unfortunately, as studies indicate, most organisations have not yet built in-house capabilities to put third-party data to good use. This would involve identifying, evaluating, procuring, and preparing external data consistently, and designing a continuous process to identify, engage with, and evaluate new external data sources. They would also need to regularly engage with partners and fuse these data sources with analytics processes or product offerings, as well as internal data.
It may help to be a part of a larger data ecosystem which involved multiple entities that directly or indirectly consume, produce, or provide data and other related resources. Organisations can create a cross-functional group as an interface to the wider data ecosystem in order to draw on competencies from multiple areas, such as product management, business analysis, data science, legal, and procurement, to address organizational and technical challenges related to third-party data. They can also create specific roles – termed ‘data curator’ by Gartner – focused on handling third-party data and related requests. Curators can keep data requests and sources up to date, while also ensuring quality and accuracy of data.

Connecting to a data ecosystem

We can categorize data services according to the level of insight they provide, as detailed here:

Simple data services. Data brokers gather data from a variety of sources. The conditioned data they provide serves as an additional input to the decision process, be it for a human user or device.

Smart data services. Analytical rules and calculations are used to enhance the data and present it as scores or tagging of objects.

Adaptive data services. Specific analytical requests from customers are catered to by combining third-party data with data from other sources.

Other ways to segment data services include specialists in domains, such as hedge funds or health care providers.; consulting and systems integration services providers who cater to demands for new insights from publicly available data and other external data, in addition to custom analysis.

The challenges of using external data

Access to external data is getting easier in some ways, but it can still be daunting. Organizations report a wide variety of business and technical challenges in deriving insights from external data. Among the business challenges are the size and complexity of the data-provider market, which can make it hard to identify the right data sources and partners. Negotiating acquisition of data can be arduous, depending on factors such as:

  • Ongoing access to data for refreshing machine learning models
  • Usage restrictions
  • Revenue share demand from the data vendor
  • Liability if the data proves to be inaccurate or tainted

This process can involve lengthy risk and legal reviews of vendor contracts and licensing agreements. The ongoing management of a growing roster of data-sharing relationships and partnerships can be taxing as well.

Third-party data can bring lots of opportunity, but applying it for real results can be challenging.

Even before we consider the technical challenges that hamper the deriving of insights from external data, it can be challenging to identify the right data sources and partners. There are several factors involved such as updating data, usage restrictions, revenue share demands from the vendor, liability of inaccurate or tainted data and so on.

Technical challenges include essentials such as measuring data quality and filtering out inaccuracies. Data pre-processing, such as cleansing and formatting it for analysis, takes a lot of time. Once you have sorted out good quality data, cataloguing it and keeping it secure is the next hard task especially if you have systems originally designed to manage only internal data.

Few organizations have standardized procedures to deal with external data and even fewer utilize external data to its full potential. As internal data analysis teams are less familiar with external data, it might take them a bit longer to understand external data, simply because it’s more complex and quite different from the internal data they are used to compiling and evaluating. Which also means that the data team will have to learn how to package and interpret external data and apply it so that they extract relevant answers to business questions.
Several studies indicate that third-party data is riddled with inaccuracies as well as inconsistencies between external and internal data to resolve before performing an analysis. Cleansing and formatting data before analysis takes a lot of time, with 80% of the analyst’s time being taken by data pre-processing as reports suggest. Organisations may even need to update information management processes and capabilities to securely store and catalogue external data, because these systems have until now only handles internal data.
Nevertheless, the virtues of external data easily outweigh its faults. For instance, in a pandemic relevant data can be drilled down for use in local areas. The real estate industry makes extensive use of external data to draw insights, such as identifying what suburbs and cities to target based on people’s income levels. Meanwhile, logistics companies use geolocation, weather and traffic data, and data about exceptional events – such as natural disasters – to manage their deliveries and avoid disruptions in the supply chain. Generating such data inhouse is a time-consuming and arduous process that can be exhausting unless you are, or own, a data analysis firm. So external data takes away the pressure of producing relevant data themselves from organisations.

Summing up

As organisations increasingly source data from external sources, they need to take consistent steps to extract the most from this data by enhancing their ability to identify, evaluate, and contract for new data through a data ecosystem. With unrelenting pressure on them to improve the efficiency of their operations, organisations must intensify their pursuit for insights that will help them improve business.

By expanding the universe of data outside of traditional organisational boundaries and adding the dimensions of external data, businesses increase the effectiveness of decision making. Moreover, this shift towards external data driven decision making is putting to use the collective wisdom of crowds that can provide faster, better and cost-efficient predictions that reflect the experience and activity of the many, not the few. By applying external data analytics at the right place, businesses can convert standard decisions to strategic decisions. They can combine this contextualised data with internal data to unlock powerful insights for innovation, growth and profitability.

At Tibil, we believe that while dashboards and visualizations are integral parts of data storytelling, there is much more to it. We work closely with our clients to ask the “why” behind the “what”, and turn your statistics into a powerful communication tool. Tibil has the ability to zero in on hidden layers within your business data, and then materialize this information into clear and simple actions for business strategy.

Get in touch to turn data into knowledge.

Edge Computing: The Key to Smart Manufacturing Success

Edge Computing: The Key to Smart Manufacturing Success

Manufacturing firms world over are in the middle of historic development. With the rise of the IoT, we see a rapid increase in the number of data-centric and interconnected smart factories. Emerging technologies such as automation bring the promise of unimagined possibilities. However, smart and connected devices churn out huge amounts of data at the edge which must be processed almost instantly for Industry 4.0 to reach its full potential unobstructed by data-processing issues.

Edge computing is the concept of moving computing processes as close to the source of data as possible. Instead of relying on distant data centers, it uses local infrastructure to process data. It takes the cloud and brings it to the hardware that’s already all around you. Forecasts suggest that there will be 21.5 billion connected IoT devices worldwide by 2025. Imagine if just half of those could run computing tasks for other devices and services. This vast, interconnected computing network would be particularly valuable for smart manufacturing.

Following are a few benefits that manufacturers can gain by powering smart manufacturing with edge computing:

  • More manageable data analytics
    Big data is the foundation of the new industrial revolution. One of the most substantial advantages of the IoT is how it can improve data analytics. But analyzing all of this data requires a considerable amount of storage, bandwidth and computing power. Edge computing alleviates these concerns in two ways. First, it processes data at or near its source, so the overall process is much faster. Second, each data point within the smart factory processes its own information, thus easing the load off any single system while also refining the process. Since it’s segmented by nature, it’s easier to sift through and find the most relevant information.
  • Expanded interoperability
    An IoT network is only as effective as it is interoperable. Finding compatible devices or systems can be a barrier to the expansion of smart manufacturing. Concerns over interoperability are one of the leading barriers to its adoption, since there’s no standard protocol. Moving computing functions to the edge eliminates some of the need for a universal standard. When devices can convert signals themselves, they’ll be able to work with a greater variety of systems. The edge also serves as a connection point between information and operational technology. It breaks down these distinctions, leading to a more cohesive smart factory
  • Predictive maintenance
    Predictive maintenance means that a manufacturer can use data analytics to pre-emptively detect when a machine will fail and prevent this by conducting maintenance before a potential breakdown. By processing data at the edge, it becomes easier to take pre-emptive steps.
  • Reduced latency
    When a data packet is sent to a data centre across the world, any action that depends on the response can get delayed. For mission critical applications this can be disastrous. In the context of manufacturing, if a connected machine detects a malfunction, any delay in transmitting that data and taking appropriate action can be expensive and can even damage the machinery. Cloud computing can thus be limiting. With edge computing, data can be processed right at the location and the appropriate action can be taken.
  • Better cybersecurity
    While the IoT is great for smart manufacturing, more devices in the network also means potentially more entry points vulnerable to cyberattacks. However, if the processing and storage functions were spread throughout the edge and computing took place closer to the data source, a data breach is much more unlikely.
  • Reduced storage costs
    Smart manufacturing produces a lot of data that needs appropriate storage. Legacy local storage options can be complex and cumbersome, and cloud services can be expensive. Storing data locally helps reduce the data that needs to be stored.
  • Edge AI computing
    ‍Edge AI has several applications in the manufacturing domain, such as enabling the widespread implementation of Industry 4.0 initiatives, including predictive analytics, automated factory floors, reconfigurable production lines and optimized logistics.
    Sensors are mounted on machines and equipment and configured to continually stream data on temperature, vibration and current to the Edge AI platform. Instead of sending all data to the cloud, the AI analyses the data locally and constantly to make predictions for when equipment or a particular machine is about to fail. Manufacturers can process data within milliseconds, giving them real-time information and decision-making capabilities for machine learning intelligence.

Edge Computing – the future of Industry 4.0

Industry 4.0 can only get so far without transitioning to the edge, and will fall short of realising its full abilities In many scenarios. Besides, standard IoT analytics collect data from the edge devices and pass them to the cloud for analysis, then back to the device for action, thus increasing both cost and latency. Training a device to process critical data at the network edge via AI can make manufacturing units much more efficient. Manufacturers are now seeing AI’s potential to move beyond observing and reacting to machine behaviour, to taking a predictive approach, including creating a deeper understanding of signs of failure in operator performance, cycle times, equipment, scheduling maintenance runs and material planning through a 360-degree view of operations.
For edge computing to be able to support manufacturers effectively, the data produced by the myriad of sensors, embedded chips, industrial controllers, connected devices wearable computing devices, robots and drones must be analysed.
After bringing the IoT into the cloud, edge computing is the next logical step. Without adopting this technology, Industry 4.0 will be unable to unleash its full abilities. While the transition to the edge will not and cannot happen overnight, in the end it is all but inevitable.

At Tibil, we believe that while dashboards and visualizations are integral parts of data storytelling, there is much more to it. We work closely with our clients to ask the “why” behind the “what”, and turn your statistics into a powerful communication tool. Tibil has the ability to zero in on hidden layers within your business data, and then materialize this information into clear and simple actions for business strategy.

Get in touch to turn data into knowledge.

Augmented Data Management and the impact on Advance Analytics

Augmented Data Management and the impact on Advance Analytics

Data has become a vital business asset for all types and sizes of organizations, which are rapidly realizing the fact that data management is pivotal to realizing the business value and unlocking potential. That’s why, over the past decade, businesses have been investing time and money in building a solid data strategy as well as data capabilities such as data governance, metadata management and data quality. With such strategies in place, much higher use of data at an enterprise level is expected. But this increase in the volume of data, its variety and the compelling need to gather as much data as possible, has made data management that much more complex and time-consuming.

Overloaded with the non-strategic tasks of data cleansing and processing, organizations are struggling to stay on top of their data and are finding it hard to scale their data management practices. They find themselves lagging behind in mining their data for insights, in providing adequate user access to users and in maintaining healthy data quality.

Research shows that data scientists spend 80% of their time in low-value tasks such as data collecting, cleansing and organizing, instead of high-value and more strategic activities such as developing data models, refining algorithms, data interpretation, and so on, that are directed at meeting business objectives.

To reduce this everyday hassle and improve data management, businesses are looking to incorporate AI/ML and analytics. Termed Augmented Data Management, this practice involves the application of AI to enhance and automate data management tasks based on sophisticated and specially designed AI models. Data management consequently takes less time, is more accurate and costs less in the long term. According to Gartner, by the end of 2022, we will see a reduction of 45% in manual data management tasks owing to machine learning and automated service-level management.

Let’s look at some of the challenges that we can expect Augmented Data Management to solve and the subsequent benefits.

Data Management Challenges

Large data volumes
Businesses have data pouring in from multiple sources and the amount of this data is getting too big to handle. They are finding it hard to aggregate, curate, and extract value from data.

Poor data quality
Enterprises typically have to work hard to bring the raw data they receive into a validated form fit for consumption. Their task is a tedious process that involves profiling, cleansing, linking and reconciling data with a master source.

Incongruent sources
Enterprise data is mostly obtained from multiple databases and other sources resulting in inconsistencies and inaccuracies. Be it internal or external data, there is no single source of truth.

Data integration is harder
With multiple data elements, huge data volumes, and disparate sources integrating data can be quite challenging no matter how large or experienced is the team of data scientists.

Augmented Data Management to the Rescue

Augmented Data Management, essentially, uses advanced technologies like AI/ML and automation to optimize and improve data management processes for an organization.

Better data
By applying advanced analytics techniques – such as outlier detection, statistical inference, predictive categorization and time series forecasting – instead of only statistical profiling, organizations can attain a higher quality of data, and do so faster than traditional methods. Augmented data management helps enterprises scan all sorts of data and its sources in real-time and churns up data quality scoring with the ability to track, manage and improve quality over time.

Master Data management
There’s a reason why Gartner discussed augmented data management as a strategic planning topic in its 2019 Magic Quadrant for Data Management Solutions. AI and ML models can be used instead of manual, hard-coded practices to match data and identify authoritative sources to verify data and create a single source of truth. ML-driven data discovery and classification will ensure authentic data tagging as soon as it is ingested and will also allow data scientists to perform duplicate data forensics.

Efficient data integration
Traditional statistical methods can be replaced with automation tools that make the process of analysing the data instances faster, simpler and more accurate, especially in the case of hybrid/multi-cloud data management and multi-variate data fabric designs. It also becomes easier to include new data sources and apply algorithms to build real-time data pipelines and bring all the data together for analysis.

Database management solutions
Database-as-a-service solutions enable automatic management of patching updates, advanced data security, data access, automated backups and disaster recovery, and scalability. Users can easily access and use a cloud-based database system without the organisation having to purchase and set up its own hardware or database software, or managing the database in-house.

Metadata management
Metadata management involves searching, classifying, cataloging and labeling or tagging data (both structured and unstructured) based on rules derived from datasets. Augmented data management AI/ML techniques to convert metadata so it can be used in auditing, lineage and reporting. Data scientists can examine large samples of operational data, including actual queries, performance data and schemas, and use metadata to automate data matching, cleansing, and integration, with the assurance that the data lineage is traceable and accessible by users.

Data fabric
We all know that data is available in a variety of formats and is accessed from multiple locations across the world, be it on-premise or in the cloud. Unfortunately, with several applications involved in the process, the data generated becomes increasingly siloed and inaccessible. Creating a data fabric provides enterprises with a single view of all that data via a single environment for accessing, gathering and analysing the data. Data fabric helps eliminate siloes, and improves data ingestion, quality, and governance, without requiring a whole army of tools.

Closing Thoughts – The Future of Augmented Data Management

Perhaps the main advantage of Augmented Data Management is that it allows enterprises to extract actionable insights without requiring too much time or resources. We believe that augmented data management will help streamline the distribution and sharing of data while mitigating the complexities related to extracting actionable insights from that data.

According to experts, augmented data management will support complete or nearly complete automation, where raw data will be fed into an automated pipeline and organisations will get back cleaned up data that can be applied to improve business. Enterprises can focus on strategic tasks that have a direct impact on the business while offering business recommendations. So in the future, we can expect augmented data management to pave the way for enterprise AI data management, thus democratising data access and use across teams and functions.

The Value of Data in Open Banking

The Value of Data in Open Banking

Open banking is one of the key drivers of the financial revolution today, bringing in higher competition and innovation in the banking sector like never before. Open banking is the practice of securely sharing a customer’s financial data – with consent – between the bank and authorized third parties (including enterprises that may not be active within the financial sector at present). This exchange of data, enabled by Application Programming Interfaces (APIs), makes it easier for new players to offer a larger variety of services giving customers more choices and better control over their financial data.

Open banking has been linked to the Second Payment Services Directive (PSD2) in 2018 in Europe. PSD2 allows financial firms to sell services by categorizing them into two buckets namely, Account Information Service Provider (AISP) or Payment Initiation Service Provider (PISP). AISP certified financial firms can access and view account data via an API with their bank, while a PISP certification allows a bank to make payments on behalf of its customers.

Why does open banking matter?
Customers are hungry for change and are unhappy with their bank’s payments and banking capabilities. Open banking can help set a bank on track for success and present opportunities by putting the customer at the heart of every decision. Instead of being seen as a threat that increases competition, the focus should be on the huge revenue potential that open banking can unleash. Insider Intelligence estimates that in the UK alone, open banking can enable small and medium-sized businesses (SMBs) to reach USD 2 billion by 2024.

Banks can push their APIs beyond regulatory requirements to offer existing customers new services and export data to personal finance managers or small business accounting apps. They can also sell specialized services, such as consumer credit check services to fintechs or identity management tools to smaller banks. This will allow them to engage third-party financial firms to build innovative customer offerings across different avenues.

Incumbent banks fighting about the unfairness of open banking need to understand that all stakeholders including banks, fintechs, third-party aggregators, and regulators, can share their learning and grow the market faster. Instead of defending ownership of data and tools, they should adopt API-driven open banking initiatives for a definite rise in revenue.

Customers, meanwhile, will have better options to decide which financial products they need and will be able to choose products that suit their real needs. And thanks to APIs, customers can aggregate data from multiple accounts, cards and banking products of different entities together in a single app, and manage their finances with greater transparency.

The very valuable banking data
A customer’s transactional data is the most important asset for traditional banks. Banks can leverage data to transform the customer experience and generate new and personalized offers. But this data is locked away in legacy mainframes and applications and is not easily accessible, creating a level of complexity and cost that slows time to market and prevents the implementation of next gen offerings.

By setting up the rules of engagement with PSD2, the EU has changed the game. Advanced analytics can enhance the delivery of financial services to both retail consumers and business customers. Personalised experiences and products powered by AI and advanced analytics are key to improving customer experience, product development, credit assessment and operational performance. Data can also serve as a catalyst for new financial management and business models. Unfortunately, in case of several banks the use of data is patchy and inconsistent due to which the outcomes are underwhelming and without any significant improvement.

Open banking Needs Good Data Analysis
Banks thus need a data architecture that’s agile, scalable, robust and easy to use. Fully understood, this data can reveal what customers are doing with other banks, uncover gaps in service models, point to new competitive threats and suggest appropriate customer strategies. As this data is complex and high in volume, it requires both good analytics as well as top notch data engineering skills to transform the data into actionable insights. Without this capability, banks are missing out on invaluable contextual customer insights that can turn open banking into a competitive advantage.

To be effective, open banking needs stable, coherent, non-federated and organized data. Instead of a data swamp, banks need a digital-first and data-centric approach that will allow them to scale their business and better serve customers. Good data analytics will also help banks better understand the financial environment and make smarter decisions.

Using Data for Deep Customer Focus
As financial offerings become more digital and commoditized, standing out in the crowd requires moving from a product-centric focus to a more customer-centric experience. AI, machine learning, and big data are enabling more personalized customer experiences, allowing fintechs to wow customers and siphon them away from traditional financial providers.

Fintechs have been quick to use technology to become agile and adapt quickly to changing market conditions. They use algorithms to process the vast amount of data that is generated every day, create actionable items and predict and anticipate customer behavior. They can share potential products, upsells and cross-sells with customers. As a result, rethinking customer interaction has become a part of product development whereas only some decades ago the perspective on the product itself dominated the development processes. Thus, banks should aim to provide customers a tailored experience by adapting their digital structure. To make this possible, data will be needed on who the customers are and what they really want. Technologies such as big data as well as distinguished algorithms are potential tools that can help to gain these insights.

Despite initial reservations and scepticism, banks are beginning to see the benefits of open banking, and how it can improve customer experience and help incumbent banks become more agile. Open banking, we believe, is all about a data-driven continuous improvement in customer centricity and leads to increased financial integration with value-added services and overall improvements. Banks are already sitting on a wealth of data, and all they need are the right tools and partners to unlock the potential of that data.

Generative Design – The Power of Cloud Computing and Machine Learning to redefine Engineering

Generative Design – The Power of Cloud Computing and Machine Learning to redefine Engineering

Imagine a technology, that helped Airbus shave off 45% (30kg) of the weight of an interior partition in the A320. That weight decrease resulted in a massive reduction of jet fuel consumption and several thousand tons of carbon dioxide emission when applied across its fleet of planes. It equaled taking 96,000 passenger cars off the road for a year. The technology in question is Generative Design.

So, what is Generative Design? Basically, by using artificial intelligence (AI) software and the computing power of the cloud, the generative design enables engineers to create thousands of design options by simply defining their design problem and then inputting basic parameters such as height, load-bearing capacity required strength and material options. It, therefore, replicates the natural world’s evolutionary approach with cloud computing to provide thousands of solutions to one engineering problem.
With generative design, engineers are no longer limited by their own imagination or past experience. Instead, they can collaborate with technology to create smarter, more cost-effective and environment-friendly options.

Since generative design can handle a level of complexity that is impossible for human engineers to conceive of, it can also consolidate parts. Thus, single parts can be created that replace assemblies of 2, 3, 5, 10, 20, or even more separate parts. Consolidating parts simplifies supply chains, maintenance and thereby reduces overall manufacturing costs. With its ability to explore thousands of valid design solutions, built-in simulation, awareness of manufacturability and part consolidation; generative design impacts far more than just design. It impacts the entire manufacturing process. Thus, the generative design delivers a quantum leap in real-world benefits. It can lead to massive reductions in cost, development time, material consumption and product weight.

As AI becomes a part of all work processes, generative design can become the norm for product design. The order of the day will be products that are better suited to consumer needs, and are manufactured in less time; with less material waste, less fuel waste, and less negative impact on our planet.
However, deploying an algorithm-based design will lead to a paradigm shift in engineering because as Franck Mouriaux, a global expert in aerospace engineering, once stated “Engineers were not trained to formulate the problem. They were trained to find solutions.” But it is formulating the problem, which is the key to generating good geometry.
No doubt, engineers can express a design challenge in natural language. For example: If a suspension part of a car, was to be made significantly lighter, will the car still be secure when traveling at a certain speed? However, formulating such a problem in computable terms – regions targeted for material reduction, regions that must remain unchanged for safety and aesthetics, anticipated stress loads on the part while the object is moving, the direction of the loads, the type of vibrations it is likely to endure, and so on – is a still challenging. The skill to express the design problem as a set of parameters can be found in simulation software users. But this requirement can prove a steep learning curve for people trained in CAD.

Generative design inquiries are usually not typical yes/no questions (will it break or will it hold?); they’re formulated as what questions (under these conditions, what are the best suspension design options for a safe and secure car?). As you add additional constraints or parameters (such as acceptable weight range for the part, preferred manufacturing materials and more), the design options change.

Quite often, what is mathematically optimal – geometry with sufficient material reinforcement to counter the anticipated stress in different regions – is impractical to manufacture or produce, either due to cost concerns or the limitations of the production methods available. Additive manufacturing (AM) now gives the option to 3D print certain complex geometric forms that cannot be machined; however, even with AM, certain limitations persist.
The onset of practical artificial intelligence algorithms has enabled the possibility of mainstream generative design tools. That means engineers can create thousands of design options inherent to their digital design and choose which design meets their needs to the fullest. They can then solve manufacturing constraints and ultimately build better products.

Therefore, many companies are embarking on this path, as they have realised that it is a powerful addition to an engineer’s design arsenal. It results in better ideas and products that are lighter and accomplish their directives better. And so, it comes as no surprise that we see applications beyond the aerospace industry, highlighted by the following use cases:

  1. In the automotive industry, General Motors was one of the first companies to use generative design to reduce the weight of its vehicles. In 2018, the company worked with Autodesk engineers to create 150 new design ideas for a seat bracket and chose a final design that proved 40% lighter and 20% stronger than the original component.
  2. Under Armour has used generative design algorithms to create a shoe with the ideal mix of flexibility and stability for all types of athletic training. It was inspired by the roots of trees. The algorithm came up with a rather unconventional geometry. The prototypes were then 3D printed into a shoe and could be tested by more than 80 athletes in a fraction of the time than it would have taken in the past.

Simply put, Generative design is a tool that uses machine learning to mimic a design approach similar to nature. It interfaces with engineers by allowing them to input design parameters to problem-solving. Thus, the engineer can decide to maintain certain material thicknesses, despite higher costs, due to the higher load-bearing capacity required. All this can be fed into generative design tools.

The algorithms provide generative designs that meet the input criteria. The role of the engineer is then to pick the most suitable design and modify it. In essence, it leads to a digital shortcut to optimizing the perfect design. Thus, it basically kickstarts the entire design process.

In order to build any item, instead of starting with some sketches, creating various designs, and picking the best one; one can start by feeding some constraints into a computer. For instance, input the ballpark cost, the weight it needs to support, and what material it needs to be made out of. Then the computer can deliver thousands of design options. This is what generative design offers to the modern engineer.

True generative design is software that uses the power of cloud computing and true machine learning to provide sets of solutions to the engineer. This is in stark contrast to previous tools, such as topology optimization, latticing, or other similar CAD tools. All of these previous tools improved existing designs, whereas generative design creates a new design.

Generative design is also different from other existing CAD tools in that it can consider manufacturability. The generative design takes into account simulation throughout the entire design process. On the front end, the manufacturing method is considered, and the software will take care of simulating a given design’s feasibility. Thus, only designs that meet the necessary simulated criteria and are manufacturable are generated.

Generative design works best in conjunction with other technologies – 3D printing for instance. 3D printing makes it possible to quickly prototype and test new designs without committing to a costly and time-consuming custom manufacturing run. Also, there are no geometrical boundaries for a 3D printer. This means it can produce extremely complex structures that traditional methods, such as milling, are unable to manufacture.3D printing also facilitates mass-customization, i.e., it can print products tailored to a single specific need.

In addition to saving time, generative design algorithms can also create new products that were not possible before. For example, researchers are using generative design algorithms to analyse a patient’s bone structure and create customized orthopedic hardware on-the-spot using additive manufacturing processes.

Generative design is a fast-evolving field and new stunning applications are created literally every day. However, implementing generative design is not simple. Introducing generative design to a company or engineering department requires readiness and change among multiple stakeholders. It not only creates new products but completely disrupts traditional structures. It is difficult to master the software and a larger learning curve must be considered – it is definitely not a plug-and-play application.

However generative design is a powerful new way to approach engineering design problems. While AI and ML can’t replace humans, they can automate many of the tedious processes that create bottlenecks, ranging from optimization to aesthetics. Many of these capabilities are already present in modern tooling.

In the near future, items that we use every day, the vehicles we travel in, the layout of our daily work environment and more will be created using generative design. Products may take on novel shapes or be made with unique materials as computers aid engineers in creating previously impossible to conceive solutions.

The generative design takes an approach towards engineering that has never been seen before in the digital realm. It replicates an evolutionary approach to design, considering all of the necessary characteristics. Couple this with high-performance computing and the capabilities of the cloud; and the possibilities are limitless.