Tech & Biz – NeuroSYS: AI & Custom Software Development

What is user research? Overview of types and methods

p.kozlowski@dev.neurosys.com — Tue, 06 Sep 2022 11:26:36 +0000

Knowing your users inside out is the best starting point for building physical and digital products. Before you think about its awe-inspiring features and dazzling looks, it’s good to learn if your customers actually need them. User research will help you discover that by gathering insights into their motivations and behaviour. Only with this knowledge will you be able to build desirable products and services, and guide users best via app design and copy.

User research is popular in UI design, UX design, and UX writing. When planning your study, always remember to set clear objectives and determine available resources, so you don’t bite more than you can chew.

After a short introduction, we’re good to go!

What is user research?

User research is a meticulous study of customers held to understand their needs, problems, and motivations. The study aims to create the best products, in terms of design and usability, which in our case applies to web and mobile applications.

The tool is particularly helpful when product owners, together with their teams, have to make tough decisions. After the research is done, they can do it based on insight and information rather than a personal conviction or lucky guess.

What is key in user research?

What is important to mention here is that user research is a methodological and structured approach that has to follow certain research principles. Therefore, asking your friends and colleagues how they like your app, by far, can’t be called user research!

Questions to ask yourself

Before we dive deeper into specific types and methods, and before you decide on taking a particular approach, we recommend you to think over the following issues:

What do I want to find out? So you won’t spend time and money on research that doesn’t bring you any closer to your goals. A good example would be: What do users need first and foremost in a smart home app?

Do I have the capacity to conduct research? Your study will undoubtedly result in vast amounts of data, documents, and various kinds of files. Thus you will need enough resources not only to conduct the research but also to organise data and analyse it. Since it is a structured task, you’ll also need a person responsible for the project.

Do I know how to tackle legal matters? Taking into account GDPR and other regulations, user data is extremely sensitive. You can’t take the issue lightly when working with real people, gathering their personal information. No matter whether you analyse data collectively or record interviews with particular users, you have to obtain their consent. You might also need to sign NDAs with interviewees if they’re testing your prototypes. To do it by the book, consult a lawyer specialising in these particular issues in a given country.

Do I have relevant experience? Educate yourself, involve researchers from your team, you might also consider a collaboration with a research company or freelance researchers that will take the project off your shoulders or at least support you in particular areas, such as choosing the most appropriate method.

Types of user research

There are different user research categorizations regarding data types (what), the way something is done (how), and when it fits in the project’s timeline.

What?

One of the most well-known categorizations focuses on the type of data that is collected:

Qualitative research
You collect and analyse non-numerical data to understand opinions, experiences, and broader concepts.
Quantitative research
You collect and analyse numerical data to discover correlations and formulate hypotheses.

How?

The second categorization refers to the way data is collected:

Primary research
You gather information on your own.
Secondary research
You analyse data collected by others, such as statistics, books, and articles.

When?

The third categorization we wanted to specify focuses on when the research is conducted:

Exploratory research
You carry it out at the beginning of the process before building a product or a feature.
Validating research
You assess if what you’ve built actually works.

Five user research methods

You may come across a number of user research methods, but we will only focus on those we exercise ourselves.

Competitor analysis

Competitor analysis is one of the most common, simple, and inexpensive (in comparison to other) research methods. We can’t imagine considering entirely new products or adding significant features without identifying and analysing companies that sell similar solutions. Knowing what the competition offers and how they present their products and services, you can decide in an informed way how to position yourself in the market.

As we’ve mentioned before, a competitor analysis has to be conducted in a structured way. You set identical variables according to which an analysis of each company is conducted. Thus, the best way to do this is through a table, be it a Google Spreadsheet, Microsoft Excel, or any other tool of your choice.

When analysing data gathered, you can highlight similarities and differences between your competition and the things that seem to stand out from the crowd. Watch out, though! The method poses a risk of copying others, which is never a good choice because why would customers choose you in this respect?

Target group observation

This method involves observing your current or potential customers in their natural environment and real-life situations. You watch what they do and say, either incognito or overtly. This way, you can follow them using your application: what they click first, how they proceed, what problems they have, how they solve them, etc. Apart from their behaviour (user experience research), you observe what they say when interacting with your product or how they act in a general situation.

To structure your study correctly, you need to prepare an observation guide, covering all crucial aspects of your customer’s journey. Watch out for legal issues here, especially if you observe people being in disguise or conducting observations in the public spaces.

Focus groups

Focus groups are small assemblies, usually no more than 12 people, gathered in one place. The study aims to stimulate a discussion between the participants. A structured debate, to be precise: with questions prepared beforehand and space where everyone has a voice.

This fantastic user research tool allows you to ask pertinent questions, see how people react to your product, figure out their needs, and gather ideas for new features. Unfortunately, since a lot happens during focus group sessions, it’s easy to dive into it and not see the wood for the trees. Thus, you have to focus on what you want to find out at all times. A user research plan will help you with that.

User interviews

During user interviews, you have a chance to converse with your current and potential customers and ask them relevant questions. These one-to-one sessions have the potential to provide you with the deepest insights. For a vast majority of people, it is easier to talk about their habits, values, needs and dreams but also ask questions about the product in an informal, private discussion where they don’t feel judged by others.

Not to distract your partners by taking notes, request their permission (in writing) to record the sessions. And as always, remember about the interview structure, so after multiple sessions, you can gather results, compare them, and draw conclusions. Lastly, pick your interviewees wisely, so they represent your customers as adequately as possible regarding their gender, age, income, etc.

Surveys

Surveys are a poorer version of user interviews but are also less costly and quicker to carry out. In addition, they allow you to ask personal questions similar to those asked during interviews. However, there is little or no room (online surveys) to respond to the answers and deepen the knowledge.

On the one hand, your surveyees don’t feel judged and can respond more sincerely, but on the other, they might feel less motivated to spend time filling your forms and might do it by halves. Thus, try to keep your surveys brief and the questions clear.

User research examples

When working on a smart home application, one of our tasks was redesigning its interface. The key aim was to improve its usability.

Research objectives

Having redesigned the IoT app, we wanted to verify if the new design met our and the client’s expectations (validating research), including:

Learnability. Is the new interface user-friendly? How long does it take to complete simple tasks when users encounter the app for the first time?
Effectiveness. How do the users deal with the application?
Memorability. Is the application designed in such a way that the users can easily handle the navigation flow?
Mistakes. How many mistakes do the users make? How often does it take place? How can they put them right?
Satisfaction. Is it gratifying to use the app? Are there any areas that can be improved to make it more user-friendly?

Test types

We run thorough UX tests that included:

Individual In-depth Interviews (IDI)
Tasks performed by users in real-time

Interviewees were to complete tasks using interactive app mockups. Thanks to this particular research method, we were able to observe their decision-making process, the difficulties they ran into, and the way they reacted to task solving.

Areas verified

We have examined the following areas:

App navigation: whether the new app is easy to navigate,
Information architecture: whether the information structure is logical and it’s easy to find what’s needed,
The influence of the app design on its usability: whether users feel comfortable and confident interacting with the app,
Copy: whether the names and instructions are understandable and helpful,
Abstraction level: whether the icons and other visual elements are clear and users know what they mean,
App overload: whether users aren’t lost in the multitude of widgets,
UX design: whether all the elements have been designed following the golden User Experience rules.

In-depth user research means combining methods

When you want to fully understand your target group and draw the most accurate conclusions, the best idea is to combine the above methods. Gather both numerical and non-numerical data, analyse what your competition does, and ask real users what they think, especially when arriving at irreversible decisions based on the research findings. And one more thing, consider the incentives, small prizes for your research group participants, adequate to the time they devoted.

How to invest in tech startups?

Jowita Kessler — Tue, 16 Aug 2022 12:28:38 +0000

How to tell if your tech startup of choice is legit?

We’re living in the glory days of startups.

There’s a startup for nearly every problem that needs to be solved. These small but fierce companies play a big role in the business ecosystem. One of the startups’ greatest strengths is inducing market competition and stimulating innovation, leading to economic development. No wonder there are countless venture capitals and investors looking for emerging companies with high growth potential to devote their money to.

But let’s put aside inspirational pitches, and instead of extolling virtues – get back to business. The key question is – how to invest in startups? It’s not an easy one, so we’ll take it slow.

The good, the bad, and the wisely advertised

The three above-mentioned don’t necessarily need to be mutually exclusive, but let’s not get ahead.

Are the concerns about startup credibility even justified? Or are stories about unfortunate investments just urban legends?

Most probably you’ve heard about some breakthrough companies, offering complex blood testing from a single drop despite not having the actual technology for it, providing innovative home appliances to squeeze juices from premade packets in a revolutionary way, that could be prepared by hand and not cost $400, or smart cups so smart they could recognize the liquid inside them or count the times they were refilled.

The above-mentioned visionaries are just some of the most flagrant cases, but there are many more examples of products that just weren’t worth it. There’s nothing bad with someone trying their luck with a product or service, unless it involves deceiving the investors and/or the public.

How to recognize the real unicorn? Or: Which startup is best to invest in?

Aside from the question depicting an oxymoron of course. We don’t believe in unicorns when it comes to business. We believe in integrity and engaging in trusted, proven undertakings.

So, you’re serious about investing in tech startups and looking for an IT startup to add to your investor portfolio? Or do you already keep an eye on something? Assuming the emerging company operates in areas you’re familiar with, your risk is smaller. If the startup you’d like to invest in is from the medical field, its assessment requires experts with a medical background. The same goes for other areas, typically entailing at least some general understanding.

When you’re looking for a startup to invest in, don’t follow the hype or a temporary fashion. Sure, if it’s digital solutions that you fancy, the field is dynamically changing. Still, your hard-earned money should rather be put to good use after some analysis and consideration.

Hard questions need to be asked. A startup pitch is fun and catchy, crafted to allure and stun. What we’re looking for is a down-to-earth, merit, and rational evaluation of what really is going to happen and what are the facts behind the idea.

If the startup of your interest plans to conquer the IT scope, your envoy should be someone familiar with digital technology. IT suffers from a lot of hype and buzzwords, but when you take a closer look at the actual stack and capacity – not everything is as it is advertised.

Not all that glitters is gold

One of the common exaggerations is calling everything artificial intelligence. You might think that AI is everywhere. From your fridge and car through municipal bins and vending machines to all sorts of business processes. The trick is, often it isn’t AI at all.

Many service providers use ordinary statistics and data analysis – if it’s sufficient and works for their product, good for them. However, labeling their offering with the most buzzing names, calling it BIG DATA and ARTIFICIAL INTELLIGENCE, when there’s no evidence of any advanced algorithms, is no different from false advertising of miracle diets or rejuvenating cosmetics with mysterious ingredients that in the end turn out to be ordinary vaseline. Maybe not so ordinary, since it’s packed in a fancy wrapper and advertised by a popular celebrity. Still, it’s a shell product – there’s not much behind all that glitter and great promises. Someone purchasing it for the promised spectacular results and extraordinary effectiveness would feel highly disappointed in the end, after discovering it’s not what they paid for. Marketing, promotion, storytelling, and all other bells and whistles did their job right, but for the wrong cause.

What we’re saying is that overpaying e.g. a cosmetic product by 20$ can be a letdown but misinvesting in a shell startup can be – you guessed it – a major disenchantment. When you’re an investor and on the lookout for a company to entrust your funds to, there must be actual technology and know-how following the marketing magic.

Aren’t you much of a tech expert yourself? Consider a technical audit. Before splurging out on that new, innovative, disruptive technology send your emissary to ask around and verify the facts.

Time to say: Check!

Or: objection! We’ll leave the choice to individual auditors. The thing is that a technical assessment is vital for a tech startup investment. Don’t let anyone put wool over your eyes saying “it’s too complicated”, “you wouldn’t get it”, “we’ll explain later, now we need the money to develop the solution”, etc. Technologies too complex to understand don’t emerge suddenly; most probably you’ve already heard about something similar and comprehend at least the general idea.

While fireworks can work wonders in marketing, when it comes to spending large amounts, we need the startup to lay their cards on the table. It’s not that uncommon for the loudest, most attention-grabbing advertisements to cover the weakest ideas. Some good ideas, products, and services are quiet. The best way to invest in startups is to know what’s working under the hood. Startups investment opportunities require some time and consideration before you decide to go all in.

The things to verify:

A few topics to address before investing in startup companies.

Feasibility of the idea

Checking feasibility requires determining the viability, profitability, and practicality of the breakthrough idea. Has the startup analyzed all available data, conducted market research, and prepared projected income statements? In short, do they know where they stand? Sustainable development of business ideas calls for proper preparation and delivering tangible data for assessment. How to check it? Ask for Proof of Concept (PoC) and/or Minimum Viable Product (MVP) or subsequent “M’s” – MMP, MMF, MMR, MSP, etc. Delving deeper into preliminary product versions allows investors to see through the honeyed words. When you’re about to invest millions, it better really be artificial intelligence as promised. And not a bunch of apprentices working in the back, pretending to be the advertised algorithms.

There’s even been a startup that hired actors and rented a lab to set up a believable show for investors’ visit at “their site”. After all, maybe those apprentices aren’t the worst that could happen? Still, that’s not what investors sign up for when spending their money.

A working code

There are plenty of tutorials for startup founders and serial entrepreneurs that advise not to learn to code when building a startup. While this may work for non-technical founders and new companies aiming for other market fields, when it comes to tech startups – code is king. Can the startup handle the technical risk of their idea? Can the architecture be built and work as meant to? Is the code behind their project adequate to the advertised potential?

A common sin of startups is, again, those ill-fated apprentices or students assigned to write code. Code, that once the project is about to be commercialized, requires immediate rewriting to present any value in terms of further development, maintenance, or just ensuring stability and responsiveness for users.

Potential for delivering the promised results

Do the startup founders have a growth strategy for their product or service? Is the idea developed well enough to work in real-life conditions? Can their product handle an increasing workload or is it sufficient only for test purposes? Investing in an idea that only looks good when the business model assumes an extensive user base is a risky move. Startup assessment requires checking all the things that could go wrong and not being able to deliver promises is a major sin to eliminate.

In case you’d wonder, why should you choose us?

We’re a software company with over 12 years of experience and an extensive portfolio of executed projects. You’re here not to read our bragging, so if you’d like to learn more about our expertise, check the case studies tab. We may not be a startup ourselves, but having two of our own (Nsflow and Samelane), we know the tech field inside and out, meaning our auditors can recognize shams on the spot and help you with a business startup investment you won’t regret. We may not tell you outright where to invest in startups, but we’re positive about recognizing tech companies to invest in safely.

The takeaway

Don’t leave your business’s future to chance. Sure, honest mistakes happen even in the most proven and reliable cooperations. But a stitch in time saves nine, so if you have even the slightest doubt about whether a startup you’d like to invest in seems a tad off – an audit won’t hurt.

Scrum vs Kanban, what’s the difference? Which one to choose?

Rusana Holen — Fri, 12 Aug 2022 12:13:00 +0000

Scrum and Kanban are the most popular frameworks that belong to the same Agile family. Whereas Scrum likes rituals, clear roles and rules, Kanban is more of a free spirit, known for its pretty face (and effectiveness, too!). Which style feels closer to your heart? Which would you like to form close bonds with? If you are on the fence between the two, we’re here to give you a hand.

What is Agile?

Scrum and Kanban are both Agile frameworks, so they share a lot of features. Thus, before we can dig deeper into them one at a time, we need to stop for a while to discuss what Agile really is.

The answer will depend on whom you talk to. Product owners, developers, business analysts, or CEOs might perceive it in a different way. They might refer to Agile as a philosophy, mindset, way of thinking, or, more down to earth, a methodology.

Four Agile values

Agile followers live by four, let’s call them, commandments. Here they are:

Individuals and interactions over processes and tools, so that you invest more time and effort in face-to-face communication.
Working software over comprehensive documentation, so that you focus on project deliverables instead of creating lengthy papers.
Customer collaboration over contract negotiation, so that customer success is your guideline along the way and you’re not blindly following the initial deals.
Responding to change over following a plan, so that you are ready (and willing) to adjust to whatever happens.

The chief reason why Agile was created and became so popular is that the traditional methodologies, such as Waterfall, deliver value at the end of the project. Taking into consideration that it takes months or years to build digital products, waiting till the end of the way is definitely too long. Therefore, Agile focuses on delivering value faster, in smaller increments. This way you can test solutions, adjust, improve, deliver MVPs, get user feedback, start earning, gain funding, and so on. And on top of that, Agile welcomes changes with open arms, because they typically lead to improvements.

Waterfall approach

Agile is often contrasted with the traditional Waterfall methodology. The latter, linear approach means that you and your team can’t move to the next project phase unless you have completed the tasks from the previous one. It’s also difficult to go back, once something is done.

In Waterfall, you have to identify most of the requirements, analyse them, design, develop, implement a solution, and finally test if it all works. If you proceed step-by-step, you deliver value and get customer feedback really late. The problem is, that if you decide to make some changes, while already being in the last two phases of the project, it will take a lot of time and work. Basically, you need to go back to square one. Another thing, which may happen is that requirements have been understood differently by the client and the contractor/development team. Due to the nature of this linear methodology, you can make this discovery only at the end of the project. Waterfall doesn’t like changes.

The Waterfall approach

Scrum methodology

Scrum is out and away the most popular Agile framework. In fact, when companies say they work in Agile, in most cases they mean Scrum.

Scrum cherishes roles and ceremonies, of which sprints come first: time-boxes wherein other events take place. What makes it highly effective is the/its transparency. All roles, responsibilities, and meetings are clearly defined, and everyone knows what other team members are working on at a given moment. If any disagreement arises, the team discusses the problem and resolves it TOGETHER.

Scrum roles

Roles and their responsibilities in Scrum are clearly defined:

Product Owner: is responsible for product vision. S/he gets in touch with a client, understands their needs and project challenges. Based on this knowledge, Product Owner creates user stories and identifies priorities for the team.
Scrum Master: assists the team, helps in their daily work and Scrum ceremonies, and removes project roadblocks. S/he is neither a project manager nor a team member.
Scrum Team: is responsible for technical aspects and project execution. Everyone that works on the project belongs to this category, which means not only developers but also business analysts or UI designers, etc.

Ceremonies and events

Sprints are the essence of Scrum. A single sprint takes from 1 to 4 weeks. It consists of a variety of Scrum ceremonies and events which include:

Daily standup meetings. Dailies are the 15-minute meetings that take place every day at the same time and place. At the meeting, every team member answers three questions: what they did yesterday, what they’re going to do today, and if there are any obstacles in fulfilling their tasks.
Sprint planning meetings. The team thoroughly plans what they can deliver to the client in a given timeframe, that includes not only development but also testing, so the feature is ready to go live.
Sprint review meetings. These take place at the end of every sprint when the team, product owner, and client discuss the progress and other issues to consider during the next sprint. It’s rather an informal meeting.
Sprint retrospectives. During retros, the team discusses the last sprint in terms of what they did and didn’t do well, and what should be improved in the future based on lessons learned. The meeting should be treated as a safe space for everyone to share their thoughts, so there’s no room for blaming or criticising anyone.
Backlog refinement. This meeting resembles workshops. Its aim is to add more details to the backlog once a sprint is underway.

On top of that, there are other terms that you will come across in Scrum: user stories, team velocity, Scrum poker, product backlog, product increment, the definition of ready, and the definition of done. But we won’t delve deeper into the terminology, as we can refer you to some of our more detailed articles in the subject:

Sprints in Scrum

Kanban methodology

Kanban is the next, after Scrum, most popular Agile framework. It is known best for its visual aspect, a Kanban board, which helps to understand workflows easily.

Kanban is a continuous process, there are no time-boxes or fixed events. Of course, you can have daily stand-ups and retros but you don’t have to, it depends entirely on you. The key metrics in Kanban are time-based: lead time and cycle time.

Roles and tasks in Kanban

Roles in Kanban aren’t defined, team members comply with their organisational roles. Also, they aren’t assigned the tasks, they simply pick the cards from the board depending on their skills, talents, or what they feel like doing at the moment.

In Kanban, there’s much room for companies and teams to lay down their own rules and policies on how to manage things. The key is to make the policies explicit and known to everyone concerned.

Kanban board

The project board shows the status of work in progress, so one look at it should give you an idea of how everything is going. Kanban cards contain information on tasks and they are grouped into three areas: to do, doing, and done. Usually, their hierarchy is set from top to bottom, beginning with the highest priority. Team members pick their tasks and as time goes by, they move them between three sections of the board.

Kanban boards can be physical, arranged with sticky notes, but online ones have become more popular. The reason for digital Kanban boards preference is the hybrid and remote work, requiring the dispersed teams to collaborate closely. Online Kanban boards can be created with a variety of well-known apps, such as Trello, Jira, or YouTrack (Agile Boards function), which we use in NeuroSYS.

WIP limits

Kanban concentrates on task completion. Too many tasks marked as in progress might indicate that the work is not proceeding or the tasks were put on hold. That is why Kanban limits the WIP, work in progress. A good practice to keep focus and get things done is to set WIP limits in your online Kanban board.

Kanban board

What is the difference between Scrum and Kanban?

Being Agile frameworks, Scrum and Kanban have a lot in common, such as task estimation and focus on delivering value in no time. But now it’s time to get your arms around their disparity.

The difference between Scrum and Kanban lies in a variety of aspects, more or less fundamental:

Structure: Scrum is highly structured. All roles and meetings, including their duration, are clearly defined. Kanban is fluid and way less structured. There are no set roles, sprints, and meetings (if you don’t need them).
Time-boxes: In Scrum, your work is divided into 1-4-week sprints. That’s the essence of the framework. With Kanban, the work cycles are fluid, you move from the to do tasks to the done section without clear breaks.
Retrospectives: In Scrum, the discussion on what worked, what didn’t, and why, takes place after every sprint and constitutes its inherent part. In Kanban, you organise a meeting whenever you feel it would be good to talk things through.
Tasks: Scrum tasks are assigned to the specific team members. In Kanban, it is team members that pick tasks for themselves and take ownership of the assignements.
Roles: In Scrum, you’ll find team members with set roles. In Kanban there are no specific roles defined. Team members comply with their organisational roles.
Teams: Scrum teams are cross-functional, they have all the competencies needed to carry out their tasks. In contrast, Kanban teams can be specialised, such as teams of testers or engineers.
Metrics: The key metric in Scrum is velocity. It reflects the number of story points that are delivered in each sprint. In Kanban, the key metric is cycle time, which is the time that passes between the beginning of the task and its completion.

AREA	SCRUM	KANBAN
Structure	Structured	Less structured
Time-boxes	Sprints	Fluid cycles, no set breaks
Retrospectives	After every sprint	When it makes sense
Tasks	Assigned to the team	Picked by the team
Roles	Specified	Non-specified
Teams	Cross-functional	Cross-functional or Specialised
Metrics	Velocity	Cycle time

The difference between Scrum and Kanban

Is Kanban better than Scrum?

And is an apple better than a pear? This is a similar type of question.

Or, as our Managing Director would answer, IT DEPENDS. It depends on your organisation, team composition and its members’ experience. Naturally, personal preferences play an important role as well.

The fact is that organisations which have already started their Agile journey, in most cases have begun with Scrum. The framework offers structure and a set of rules that are helpful, especially at the beginning. Starting straight away with Kanban might feel more like throwing yourself in at the deep end. But it doesn’t have to be this way. Kanban can be a made-to-measure approach, particularly when increment of work isn’t linear and the project has to pick up speed first, before it will be monitored and managed.

Generally, if matters like cyclical delivery of increments, tools for work planning, customer engagement, transparency, and retrospectives are important to you, you should go for Scrum. Meanwhile, Kanban works perfectly during maintenance periods when the system goes through end-to-end tests or is streamlined, or technical debt is being paid off. In situations where work is hard to plan, Kanban is a perfect match.

To give you food for thought, Kanban doesn’t have built-in retrospective mechanisms. Thus sometimes it is difficult to give a sense of purpose and success, to the team and clients. Scrum secures that thanks to cyclical events and clear sprint goals.

For those who are still undecided or like both options equally, there is something in between, a framework called Scrumban. It is a blend of Scrum and Kanban, taking the best practices out of each. For example, in Scrumban you use the Kanban board but also have mandatory daily meetings.

As you can see, it isn’t a black-and-white choice to make. We can’t categorise the projects as Scrum- and Kanban-prone just that easily. What we can suggest here, is to use Scrum/Kanban as logic dictates, taking into account the above-mentioned benefits but also limitations.

Scrum vs Kanban wrap-up

We can’t praise Agile methodology enough. No matter whether you choose Scrum or Kanban, Agile’s focus will be put on software quality, effectiveness, constant improvement, great results, and trust in people. Simple as that but most importantly working.

Bob’s your uncle, we’ve reached our destination. But what a journey it was, right? At the end of the day, the choice of the framework is yours, though we hope we’ve managed to help you out. If you’re still in two minds about it, let us know. We can give you a helping hand during free consultations.

Digital transformation in the automotive industry

p.kozlowski@dev.neurosys.com — Tue, 02 Aug 2022 14:01:22 +0000

The automotive industry is one of the most dynamically moving (You see what we did there, don’t you?) market fields. It’s also the second most data-driven sector. Each decade brings further enhancements, spectacular changes, and new solutions, some of which are quickly discarded while others continue to shape the cars of the future.

Is the future already here?

When thinking about futuristic cars, pop culture made us yearn for the incredible KITT known from the Knight Rider series, vehicles spiked with useful gadgets used by James Bond, and the unforgettable DMC DeLorean from the Back to The Future movies.

How has technology changed cars?

For many petrolheads, applying new technology in the automotive industry is unnecessary, as, in their opinion, car design and performance reached their peak in the 80s and 90s. Many drivers however admit that the vehicle evolution shouldn’t stop at headlight wipers.

So, Hollywood magic aside, how does today’s technology change cars and what is there to come for the industry? Are we already cruising in cars of tomorrow?

Probably the most visible technologies shaping the industry include the shift towards electric vehicles. Since hybrid and electric cars are becoming more and more competitive, their market share will continue to grow. Since we’re over the fact that we won’t be driving flying cars or get the chance to befriend KITT anytime soon, let’s break down what the automotive transformation has already changed – for the better.

Autonomous vehicles

Just a few decades ago driverless cars seemed like pure sci-fi yet here we are, driving around hands-free and minding our own business, while the autopilot keeps its eyes on the road. Since autonomous cars are capable of sensing the environment and responding immediately to encountered obstacles and occurring events, a human driver is not necessary anymore. The human doesn’t even need to be in the car! Or – that’s how the manufacturers wish it would work like, but we’re still not there. Yet. What have we come to, passenger-less cars, who would have guessed?

Since most probably the future of the automotive industry is electric, self-driving cars, more and more manufacturers enter the race. It was Tesla that stirred the general public imagination, but the Texas-based innovative manufacturer is not a lonely driver anymore. The biggest European and Asian car moguls decided to integrate driving assistants to enable their clients to drive without holding the wheel as well, enhancing competition.

Self-parking systems

Autonomous cars’ relative, the parking assistant, makes vehicles more attractive by simplifying drivers’ lives. While still enjoying full control over the automobile on the road, drivers can choose not to park on their own. It’s not just placing the vehicle in any random free spot in the lot – the system will remember drivers’ preferences and notify them once they absent-mindedly pass by their favorite place.

Each manufacturer’s system has a different name, but the working principle is similar. Self-parking cars use integrated cameras and sensors to prevent collisions and properly maneuver, keeping the vehicle on the right track. Parallel or perpendicular parking? Not a problem. A sneaky curb or other cars standing in the way? The self-parking system will find a way.

Biometry

Biometrics have the power to enhance car users’ experience. No more manually adjusting the seat and steering wheel after each switch between drivers, the vehicle will remember preferences. Once seeing the familiar face, the system automatically adjusts each regulated piece inside the car, including temperature and map settings.

Applying digital assessment to biological features is not only about convenience. With the growth of automotive biometrics, vehicle security can be increased. Employing a system remembering unique physical traits (facial recognition, fingerprints) allows drivers to fully embrace the possibilities of keyless ignition, keyless door opening, and surveillance. Digital solutions focused on the user monitor their health. Whether it’s fever, excessive driver fatigue, sleepiness, or drowsiness, the vehicle recognizes potential hazards in road traffic. Using automotive biometrics contributes to better alertness during driving and improved security.

Digital twins

Digital models, representing physical assets in 3D, allow designers to try out assumptions prior to/instead of using traditional measures. Advanced software gathers sensor and inspection data, configuration details, and other bits of information. Digital twins mirror the appearance and behavior of the entire car or its components.

Industrial companies, including car manufacturers, value the potential digital twins carry. 3D representations streamline the design and production process, contributing to better performance of the vehicle and reducing costs on the manufacturers’ side. From car design to predictive maintenance to boosting sales with digitally created models, the twin technology is becoming one of the most popular software solutions in modern car manufacturing.

Generative Adversarial Networks (GAN)

GAN, neural networks, are a class of machine learning algorithms used to create images based on provided picture sets. The automotive industry uses GAN in generative design to boost additive manufacturing with AI. Employing GAN and coupling it with in-depth data analysis and 3D printing, car manufacturers can achieve results previously impossible to obtain using traditional methods. One of the opportunities is injection molds, allowing producers to create unusual shapes and construction, opening new ways for the more and more desired customization.

Quality assurance

Car manufacturing giants like BMW employ artificial intelligence in their production lines. Companies entrust AR with quality control, as even the most meticulous workers are prone to fatigue that can result in errors. On the contrary, algorithms can work error-free, 24/7. In Bavaria-originating manufacturer’s plants, the car assembly process takes on average 30 hours. From the floor plate to a complete vehicle, production generates extensive data sets, useful in improving the cycle.

For instance, the plant marks all metal sheets with lasers. The engraved codes allow processing stage tracking, aggregating details and parameters. As a result, the factory can cut the necessary inspections down, as algorithms signal the need for part replacement, unburdening staff from constantly monitoring machinery condition. The manufacturing plant employs digital tools to supervise over dusting levels in the paint shop, test car keys calibration, and perform other tasks.

New technology in the automotive industry doesn’t end with digital solutions applied to vehicles per se. Answering the changing users’ needs (and manufacturers too) calls for employing top-notch tools.

Shared mobility

Digital transformation in the automotive industry includes software solutions aimed at service improvement. More and more people living in big cities depart from owning cars in favor of alternative options. When a car is necessary, shared mobility companies give a helping hand, providing ready-to-drive vehicles to be used only when they’re needed. Repairs, check-ups, car insurance? Users don’t need to bother with these aspects, as the service provider takes care of it.

Shared mobility solutions entail managing extensive data sets to understand their customers’ behavior, forecast the demand for vehicles, plan their distribution across desired areas, and, as a result, enhance customer experience and satisfaction. Digital tools are there to analyze data, visualize it, and put it to good use, for example, parking shared vehicles at the right time and in the right place once such demand is recognized.

Knowledge retention & management

Employee training and knowledge retention in case of generational change is a vital matter across numerous industrial sectors. What is special about the automotive industry is the sudden need to train not only employees replacing the retiring generations, but also a new workforce specializing in electric vehicles (EV).

Even in the most advanced manufacturing plants processes can be mundane or troublesome, burdening staff with excessive workload. On-site employee skill-building can be streamlined by adopting augmented reality solutions. As a result, staff undergoes standardized training, the process can be shortened, and use fewer resources, e.g. trainers’ time. AR allows precisely guided, step-by-step courses, overseeing results, and gathering data for future reference.

Will the top technology trends in the automotive industry grow?

There are no indications for the car industry to return from the once chosen path. The sector faces various challenges, and the automotive digital transformation is most probably the best shot companies can take at future-proofing their operations.

Pain points in the industry for technology to solve:

Knowledge retention, mitigating the generational change effects
Training a whole new generation of employees such as EV technicians
Improving the manufacturing process
Changes in customer behavior, preferences, and expectations
Restrictive regulations
High competition
Availability of materials and components

While not every challenge can be addressed directly with digital solutions, modern technology drives the automotive industry. From autonomous vehicles, through digital twins and predictive maintenance, to customized services, new technology in the automotive industry will continue to deliver futuristic cars we get to ride aside from watching on the silver screen.

The undeniable impact of the newest technologies on cars has already reached our homes.

With over 50 countries manufacturing and assembling vehicles and millions of cars getting into the market annually, the range of possibilities to improve with digital technology will only grow.

Are you looking for a digital transformation partner for your automotive company? Let’s have a chat and see where we can get together with the help of technology.

Artificial intelligence does the trick in digital transformation

Marta Dunajko — Tue, 26 Jul 2022 09:25:24 +0000

Digital transformation, our old chestnut, huh?* You might be thinking about leaving the page right now, but hold your horses as today we’ll present it to you from a brand new perspective. The perspective is, yes you’ve guessed it right, artificial intelligence. The apple of our eye and something that we have full confidence in.

* If it is not yet a broken record for you, it’s time to catch up!. Below you’ll find our other articles on digital transformation that will lay solid foundations for today’s topic.

Why is AI in digital transformation important?

Just to clarify things, digital solutions don’t equal artificial intelligence by default. They can but they don’t have to. Saying it flat out, there is no need to look for AI solutions just for the sake of it. Sometimes simply switching widely-used tools to e-tools will do the trick. However, in a lot of cases, artificial intelligence is the way to push the envelope and expand your business.

Examples of artificial intelligence solutions in digital transformation

Before we get the bit between our teeth let’s spell one thing out:

How to tell AI-powered solutions from the rest?

The easiest way to find out is to determine whether they aim to mimic intelligent human behaviour and solve the problems unsolvable for traditional algorithms, the way people would. Also, through data processing and analysis, AI algorithms should be able to learn in time and get better in what they do.

Now it’s time to put all the above into practice and show you AI-based digital transformation in action. To organise it neatly, we’ve divided the topic into five areas.

Computer vision

We use artificial intelligence to detect, recognize, and identify the contents of photos and videos. Depending on the business needs and areas to be digitalized, AI focuses on:

people and faces, as in case of entrance authentication, identifying workplace bottlenecks, determining whether employees wear protective equipment
places, e.g. localising your workers, creating self-driving industrial vehicles, locating parcels in logistics, improving workstation ergonomics (you can delve into the topic in visual place recognition and VPR Part 2)
objects – machinery automation (machines gaining sight), healthcare (disease diagnosis based on X-rays), pharma process automation (see our project on bacterial colony identification and counting), advanced quality control, e.g. elimination of impurities in the production processes, soil and crop monitoring for more adequate watering or fertilisation
text – invoice and contract automation, including optical character recognition (OCR); digitalization of all documentation and other sources (paperless factory being a thing nowadays)

We’d like to point out that computer vision is widely used in manufacturing quality control, in algorithms that don’t use AI at all. Computer vision with AI is needed in cases where conventional CV can’t figure it out, such as telling air bubbles from bacteria colonies grown on Petri dishes.

Natural language processing

With natural language processing (NLP) algorithms, digital systems can identify, understand, and analyse human language. We would like to flag up the fact that it is still one of the most challenging areas of AI and the systems don’t work perfectly. However, the new Generative Pre-trained Transformer 3 (GPT-3) seems to do the trick.

With NLP, we can speed up a lot of tasks, such as:

customer service – AI-powered chatbots answering the most common inquiries, while detection of the most sensitive cases that need an immediate reaction is possible thanks to sentiment analysis
customer profiling offering tailored solutions automatically (increasing the chances for your offer to be accepted)
semantic search helping employees to look for information in company files
classification of documents and client/patient/contractor data

Data science

Every day, your business gathers a mass of data: on your customers and their journey, operations, employee effectiveness, etc. Data science aims at uncovering intricate patterns that can help businesses to improve their processes, and eventually grow. The areas worth mentioning are:

forecasting – route planning in logistics, management of orders, forecasting the interest in particular products at a given time e.g. at Christmas, during the holiday season
risk reduction – risk analysis, predictive maintenance in manufacturing
operation efficiency improvement – bottleneck identification, resource management, waste reduction
recommender systems in e-commerce and well-targeted, more effective marketing

Similarly to the case of computer vision, we need to emphasise that not all data science mechanisms use artificial intelligence by definition. DS involves a lot of conventional statistics before it needs to reach for AI-based algorithms.

Predictive modelling

You can use predictive modelling to forecast events, customer behaviour, or market changes. Instead of analysing historical and current internal/external data manually, algorithms can do that effectively, speedily, and, most importantly, in real-time. A couple of usage examples:

sales volume prediction – for more effective production or store/hotel/restaurant service demand planning
risk calculation – commonly used in banking (among others in fraud detection), the insurance industry, manufacturing (predictive maintenance), or health care for analysing patients’ medical records

Sound recognition

Sound identification algorithms might seem less spectacular and their use limited compared to the above examples. Still, you can use them successfully in the process digitalization:

surveillance and monitoring – systems immediately detect the sound of glass breaking or any other unusual sounds, also identifying faulty machinery
voice-controlled devices and machines in manufacturing, pharma, and healthcare, which do not require taking the gloves off
automatic transcription and voice dictation converting your calls and meetings into text
assisting employees and customers with disabilities such as vision impairment

As proven with the numerous examples above, artificial intelligence plays a significant role in digital transformation. It takes operations, customer support, and daily work on a whole new level and makes businesses immune, or at least prepared, to the unexpected events. Want to try AI for yourself? We’ll be happy to help (so, make sure to contact us, we’ll take you for a test drive!).

How to improve process effectiveness with digital transformation

p.kozlowski@dev.neurosys.com — Wed, 20 Jul 2022 09:27:21 +0000

Digital transformation serves particular purposes. What could these purposes be? Since the process of employing the newest technologies in organizations is aimed at reimagining business in the digital era, the goal of said transformation can’t be something trivial.

Digital transformation and how should it be done

On many occasions before we’ve mentioned that – in the perfect world – the transformation…

The transformation should be a process

There’s no time to waste in the global market – grab your processes and get in the car, we’re going to transform.

We’ve already focused our attention on reducing costs thanks to including digital solutions and this time, we’d like to show you how to improve process effectiveness. With digital transformation, of course.

How to decide whether an industrial or business process needs improvement?

It may require a bit more than just a hunch to identify the right area for improvement. Among tools helpful in assessing them we can list:

Analysis

Business operations generate a lot of data. Applying statistical and/or logical techniques allows us to evaluate the bigger picture emerging from it. Tools like operational surveys, process mapping, and cause analyses enable the precise identification of bottlenecks and trouble spots.

Audits

The examination of a company’s reports and books can give unambiguous answers on areas for improvement, potential pain points, and risks. Audit results should allow for preparing a strategy on the necessary process improvement steps and prioritization of particular stages.

Key Performance Indicators (KPI)

The business approach shouldn’t change things for the sake of changing, that’s why indicators are necessary. Said meters measure the performance of investigated processes and help assess the results of actions.

Benchmarks

Benchmarks are reference points against which the taken measures and their results are compared. Depending on needs and particular processes, benchmarks can apply to the competition, industry standards, and trends. Setting reference points helps assess the performance and identify further deficiencies to address.

Which processes can be suspected to lack effectiveness in the first place?

These that worked well when the company was ¼ of its present size
These that were sufficient when the telex was the latest fashion
These that require way too much attention or energy compared to the value they add
These that are unnecessarily done manually

Inefficient processes are often rooted in similar causes, including fear of innovation, the force of habit, and the consequent attachment to outdated solutions.

How does digital transformation improve process efficiency?

Faulty processes are an Achilles’ heel of the organization. While temporary setbacks may happen everywhere, once a problem persists, permanent damage to the company’s operational efficiency may occur.

Improving efficiency with digital solutions has many faces. Sometimes, it may mean as much as introducing better channels of communication. In an organization where employees spend too much time writing emails, making phone calls, or sitting in meetings, the process can be streamlined with digital communication tools.

In another case, process effectiveness can be improved with automation. When operations require staff to perform repetitive tasks, ceding work to technology can free the workforce to focus on other, more demanding activities. This does not apply only to fields like industrial manufacturing, more associated with the newest technologies. Administrative and office tasks can equally well be automated. Printing, scanning, archiving extensive binders full of files? That’s not just a waste of paper and storage space, but the most valuable resource – time.

As the office case shows, improving processes doesn’t need to mean full-blown digitization from Day 1. Digital transformation can be successfully handled in stages. Let’s take as an example invoice processing.

Simple and more complex automation

This way, effectiveness can be measured, personnel has time to adapt to new processes, and the structure functions smoothly without disruptions.

How does it look from a digital service provider perspective?

Usually, there are a few scenarios possible. Either the company has traditional processes and requires digitalization, or some operational areas are already digital (or partially digital), but are unsuited to the needs. Or, the process on the clients’ side needs both digitalization and optimization. While each situation requires different solutions, the cooperation has similar conduct in all of them.

Starting with the overarching question: How could you improve a process with digital transformation?, we begin with the analysis of the company’s operations. Only after getting to know its needs and requirements, together we choose processes for digitalization. While drafting a strategy, we agree on subsequent stages to follow during its implementation. As digital transformation is not an all-or-nothing undertaking, we decide on MVPs and the criteria for their assessment.

Before we introduce improvements into the company’s structure, it’s time to engage its personnel. Changes need not only to be announced but also the staff should – and in many cases, can – be involved in the process. While data gives valuable insights into core operations, asking daily users for their feedback and ideas on what to improve can shorten the time needed to come up with a working solution.

Without knowing where we’re heading it would be hard to decide if we’ve made it there, thus measuring the digital transformation requires adequate meters. This is also the time to consider A/B testing. Split testing of two or more variants helps us to assess which version performs better. If the existing method falls short compared to available alternatives, it’s time to consider improvements. Each improvement can undergo A/B testing again until the right solution is in place.

Digitalization doesn’t end with taking the nearest technology and throwing it in the middle of a working structure. Changes resulting from the transformation can take more time and thus will require proper management. We say this to emphasize that most technologies can’t be treated as miracle cures and be left to do wonders unsupervised. It’s the other way around. Once the digital enhancements aimed at improving process effectiveness are ready for operation, it’s time to observe, measure, and, if necessary – improve the solution. The strategy for improving processes can change in the course of action and thus, should be observed.

The first pancake is always spoiled

Not necessarily, no. Beginnings may be challenging, especially for organizations that have been functioning traditionally. It does however get better with time. What we wholeheartedly advise is to engage in the transformation process with moderation and avoid a hype-driven revolution.

Digitalizing a single, standalone area within the enterprise is a good starting point. When we turn the whole structure upside down in most cases it will cause chaos. Instead, minor changes can bring significant improvements – when carried out properly. In addition, going for the low-hanging fruit can be a great start to the transformation. Identifying processes that are easily digitalized and produce excellent results will encourage the company to move to the next stages.

Some distinguish digital transformation from digital improvement, but when it comes to tangible results, we won’t argue about semantics. That’s true, some changes may not seem spectacular when looking from the outside, yet they bring satisfactory results. Even if the digital enhancement may seem too isolated from a full-blown transformation, it’s the result that matters.

Heart and soul of digital transformation

Risk of poor process improvement

Change is always burdened with risk, and so is the implementation of new solutions. Proper preparation, not only in terms of the sole process but also of the staff is necessary. What needs to be emphasized is that introducing technology into operations shouldn’t be feared by personnel. Actions ceded to digital solutions will unburden the workforce and allow to relocate the resources within the company. As a result, staff can take on more demanding tasks instead of continuing repetitive work.

The takeaway

Digital transformation is not an extreme home makeover. It doesn’t happen overnight, giving instant and spectacular results. But let us tell you something – it’s better this way. Improving process effectiveness with digital transformation requires attention, analysis, and expertise, and as such – brings tangible, lasting results.

A well-thought-out and well-informed change is certainly worth a shot in the dynamically changing world. Do you want to learn more? You can drop us a line and book your one-hour free consultation, where we’ll dwell on your needs and what our team can do to be the digital transformation partner you need. Up for some more reads? See what we have in store for you and find out more about whether your company is ready for digital transformation, what digital transformation actually is, and how does it turn out in particular industries, like the healthcare field.

Is my company ready for digital transformation?

Marta Dunajko — Tue, 12 Jul 2022 08:12:17 +0000

The real question here should be: is my company ready not to take part in digital transformation and face the consequences of such a stance? It’s hard to overlook the fact that firms, regardless of industry, become increasingly dependent on technology. According to Statista, by 2023 digitally transformed companies will contribute to more than 50% of global GDP. And that’s the natural (sic) way of things we have no other choice but to accept… and adapt to.

Do I need digital transformation?

But first things first, you have to ask yourself a question: do I need digital transformation at all? Since the aim of DT is to deliver greater value to customers, it’s hard to think of a business that wouldn’t benefit from digitalization. No matter how you operate – B2C or B2B, locally or internationally, traditionally or as an e-business. Neither is it relevant whether you belong to the healthcare, banking or food production industry, or hire 100 or 100k employees. Whatever company size or sector you’re in, digital transformation will benefit your business. However, it’s important to understand that the need of DT doesn’t always equal to being ready for it.

Factors to consider before deciding on digital transformation

There is no “one fits all” answer when it comes to any company shift. We’ve picked the most important aspects to take into consideration, when thinking about digital transformation. Based on the subjects expanded below you will be able to say yes to digital transformation (or run away).

Number of employees

We believe that no matter how many people you hire, you can always benefit from introducing digital tools and methods. However, if you have just a few employees, the game might not be worth the candle. At least for the time being, because the cost of digital transformation solutions holistically is still relatively high. Indeed, DT pays off quickly, but the quicker the more people and processes it pertains to. That’s why the benefits become particularly visible in large corporations, manufacturing companies, etc.

Costs

You shouldn’t carry out your transformational project at any cost, that’s for sure. In the first place, you need to ensure your business continuity. Thus, even in large organizations, you don’t have to start off with a bang. You might consider the digitalization of one process at a time. For starters. Then, the group of employees it applied to can become the apostles of change and which would make the job easier to carry on.

This approach works for both large and smaller companies. In the former, you could focus on downtime reduction that takes place in the effect of machinery failure. When it comes to the latter, tackle a core process, digitalization of which would result in great financial benefits.

Type of processes

Next, you might want to assess your business processes and identify those, which can be improved and optimized. Raise some vital questions, like: Are your workflows easily disturbed and do silos hinder company development? What do your clients say about your products and services, and the delivery process? How about your employees, are they satisfied with your onboarding process and career paths? What are the most mundane tasks they have to deal with and frequently complain about? Which tasks are most error-prone?

When you know the answers, it’s easier to decide where to start the course of digitalization. Bear in mind that it doesn’t always have to be one of your vital processes. Sometimes it’s better to pick low-hanging fruit which is more likely to succeed and will pave the way for further transformations. To provide an example, we’ve developed a system that detects whether workers wear masks or hard hats for one of our automotive industry clients. This relatively simple undertaking has opened doors for more sophisticated digital transformation projects involving production.

On the other hand, if you sell handmade products, the production process probably shouldn’t be automated. As the handcraft seems to be the core of your business and the unique value of your goods. However, you can still think about automating other processes – orders, logistics, waste minimization, etc.

Employee churn

Does a high employee churn trouble your business? Do you spend too much time training the new ones? Do your employees perform their tasks in a complete opposite way because they were trained by several different experts? Does your HR department have problems keeping up with recruitment needs? If you answered yes to these questions, digital transformation can bring the solution and work magic in your organization.

HR process digitalization can offer solutions to all the above issues – improve employee retention, training new hires faster and more effectively, and standardizing knowledge that is shared. How is it feasible? First, it is worth it to take care of your employees, so they can develop, feel valued and important. It can be done with tools designed for employee learning and development ( have a look at our learning management system Samelane) or platforms that provide support in the daily tasks, such as step-by-step instructions in Nsflow.

On a side note, we observe that human resources is a great area to start your digital transformation journey. Your employees become familiar with digitalization and see that it can be okay. At the same time, you don’t touch on critical areas which could be risky.

Competitors’ moves

When you run a company, you surely keep a weather eye on your competition and adjust your business strategy accordingly. How does their offer change? What tools do they use? How do they communicate with their customers? Is their management structure changing? How do they promote their products and services? And finally, are they transforming digitally? If they are, there’s no time to waste.

The reason is simple. As soon as your business rivals complete the transformation, it will be really hard for you to get up to speed and remain competitive in the market. They will produce faster thanks to process optimization, take care of the customers better, offer more attractive prices due to automation, and provide more flexibility in terms of their offer.

Another thing is that there are back-office, production processes that you aren’t able to sneak a peek at. Then you have to focus on the outcomes, such as same-day delivery, and work on operations that can give similar results or improve your services in a way (make them cheaper, of higher quality, etc.).

Your attitude

Last but not least, no transformational project can be successful without a management board believing in its positive results. You have to be convinced it is the right path and share your enthusiasm about what is going to happen. This way your employees will be more open to the idea and will trust that you know what you’re doing. If you didn’t want to transform, why would you do it then?

Things to consider before deciding on digital transformation

How to prepare for digital transformation?

Strategy

Digital transformation won’t happen in a day. The process should involve all crucial areas – customers, employees, culture, operations, and technology. Only then the shift is complete. Since it is so complex and multistep, make sure you’ve developed a digital transformation strategy. Ad hoc activities can be risky and, needless to say, costly. And above all, DT should fit nicely into your overall business strategy.

Measurement metrics

To plan digital transformation and evaluate if it was successfully executed, you will need to assess the current state, taking into account:

business model and income structure
technical/IT environment
data gathering and usage
digital tools already in use
effectiveness of the production processes
management processes
process bottlenecks
back-office work
employee productivity
HR processes – recruitment, onboarding, learning & development, offboarding

All the above operations need to be well described and evaluated. Through comparing before and after process measurement metrics you’ll know if your digital transformation was successful.

Goals

You have to know your long- and short-term goals, the problems you encounter, and possible solutions based on information technology. If you’re not sure how to respond to those questions, we recommend checking out our free of charge consultations.

PS We invite you to visit our blog regularly, as there is an article on DT strategy coming soon.

Communication

Before you start transforming your operations, you need all your employees to be on the same page with what’s going to happen. They have to know why the company is shifting, how it will happen, what’s the timeline for that. Your team will also want to know what is expected from them, and how it will affect them. You need to communicate progress regularly, so that they can feel they’re a part of the change. Let your employees know about small successes, which are as important as the spectacular end result.

How to communicate digital transformation to your employees?

How to communicate digital transformation to your employees: why, how, when, what

A common fear concerning digital transformation is that the company wants to downsize. Thus, your employees need to understand that digital solutions are there for their benefit – make the work easier, help them avoid mistakes, and free them from the most mundane tasks. So that they can focus on self-development and the company can grow to the sky.

How can digital transformation reduce costs? 5 examples of cost saving opportunities

p.kozlowski@dev.neurosys.com — Thu, 07 Jul 2022 16:55:27 +0000

The shift towards process digitization is fueled by business determinants. Introducing changes is necessary to meet the new objectives, hire the desired staff, and achieve new levels of efficiency. It enables us to deliver new products and services, and last but not least, remain competitive.

The digital transformation and its effects

Digital transformation, the process of employing digital solutions to improve the existing procedures or create new ones, design products, and services.

So much for theory.

In practice, it’s a journey companies embark on to prepare for the challenges in the modern world. Issues like generational shifts, lack of employees, entering new, more demanding markets, adjusting to changes in policies and regulations, and the strive to deliver better products. All of them require proper care, enforcing enterprises to introduce new solutions.

Since the shift takes place in the business world, reimagining processes should deliver tangible results. Can digital transformation reduce costs? In a nutshell, yes. Though, let’s try to elaborate on the subject a bit further.

What generates costs in business?

What generates costs in business? Lease/mortgage, staffing, utility bills, licenses, patents, equipment and supplies, technology costs, back office processes.

Can all the above factors be reduced? Most of them, yes. And the good news is that a significant share of the costs can actually be tackled by employing digital solutions. While real estate and managing properties are rather far from our expertise, we’ll focus on topics we have a solid background in.

Streamlining business processes with digital solutions

Digital technologies can be employed in nearly every area of business to bring the enterprise closer to desired results. Tech solutions are most commonly used in the following fields:

Management

From human resources (onboarding, training automation, familiarizing with procedures, monitoring progress), to performance and facility administration

Operations

Broadly understood automation of processes – document digitization, cloud services, transformation and optimization of procedures

Strategy

Utilizing insights from available data, analytics, automation

Bearing in mind the above, processes in most organizational areas can be automated. Ceding tasks to machines is not a miracle solution to all troubles, but the popularity of optimization stems from the genuine relief it provides. But let’s not get ahead of ourselves!

What are the cost-saving opportunities of digital transformation?

Reducing human workload

The workforce is relatively costly, taking into consideration wages, training, insurance, holidays, roughly all that adds up to hiring costs. Wasting human potential on repetitive, mundane tasks is inefficient, therefore, depending on the type of operations carried out, technology can take over activities traditionally carried out by employees. A programmed machine will perform tasks error-free, 24/7, freeing the professionals to focus on more challenging tasks.

What’s crucial – reducing human workload doesn’t equal redundancies. Aside from processes easy to automate, most structures require the human touch. Digital solutions allow to entrust employees with other, more demanding tasks.

Examples of ceding work to automation include, but are not limited to:

laboratory automation
lean production systems
automated process monitoring
robotic process automation
assembling parts on conveyor belts
filling out forms
sorting documents
responding to messages (e.g. chatbots)
report generation
email dispatch, marketing automation

Reducing errors

Eliminating errors and their consequences can be costly, especially when it comes to healthcare, automotive, and aviation – or any other area where a single fault can cause damage to machinery or endanger human life.

While human approach is still necessary to supervise the work carried out by machines, even in the most hi-tech environments, it can be successfully reduced in areas prone to errors. Automating tasks that do not require in-person attention is the easiest way to reduce mistakes. One who never failed at filling Excel spreadsheets should cast the stone first!). This also counts for seemingly more challenging work, like laboratory research. For instance, it takes highly trained personnel to recognize and count microbial organisms, many hours spent at the microscope affect attentiveness. Automated solutions can work efficiently 24/7 and assess thousands of samples error-free, hence, creating new opportunities for various industries.

One of the risk reduction opportunities is to include big data in the company’s operations. Enterprises conducting business in e.g. the medical field, generate and handle extensive data sets, processing of which not only gives answers to crucial viable questions but helps solve issues here and now. Patients’ diagnoses, medication dosing, disease spreading – all the information can be assessed more precisely with the help of algorithms, instead of relying solely on human analysis. The outcome? Improved overall patient satisfaction, more precise diagnoses, which results in a reduced number of faults that could lead to costly lawsuits.

Employing technology to mitigate the risk of errors can help not only optimize costs but also build a trustworthy, reliable image, allowing the company to maintain a steady market position.

Easier prototyping

The manufacturing process is preceded by several pre-production stages. Before a new product gains its physical form, the design process continues from the initial idea through CAD models to prototypes. Adding digital technology to the process, more precisely, augmented reality and digital twins, allows for significant shortening of the whole process.

Digital models of a newly created item or a machine allow designers to assess its properties in a more realistic way. Digital twins allow for not only visualization of the object, but also analysis of its future behavior and performance. A better feel of the object, more immersive experience of the properties, and, perhaps the most important from the cost reduction point of view – once changes to the model are sealed, amendments can be made immediately. Physical models, e.g. solid clay pieces used while designing new car models and versions, take more time and work than just adjusting the 3D model.

Predictive maintenance

Better safe than sorry, as the old saying goes. One of the ways digital transformation solutions can help with is securing machinery against malfunctions and downtimes. Digital solutions allow for responding to the service and maintenance needs before the actual necessity occurs. How come? AI algorithms and augmented reality solutions enable monitoring of equipment performance with the use of sensors, providing supervisors with predictions on the likelihood of malfunctions. Decreasing downtimes contributes to production continuity, reducing losses from halted processes.

Omitting operational costs

It’s safe to say that every company needs to develop, maintain, and secure its know-how to remain competitive. Onboarding and training, when conducted the traditional way, tend to be time and cost-consuming. Instructors, paper manuals, rented or maintained classrooms, oftentimes business travel – to name a few training-related costs. To an extent, these can be reduced or eliminated completely by implementing digital solutions.

Depending on the needs, it can be either a Learning Management System (LMS), allowing personnel to access courses anytime and anywhere they feel like learning, without the need to sit in the classroom. Augmented reality solutions display digital tutorials right before trainees’ eyes and automatically generate reports on the progress. They also introduce interactive tests and gamification models into companies’ competency-building processes. The possibilities are nearly endless but they all have a common denominator, which is: improving the efficiency of learning, while at the same time reducing costs and ensuring consistency of knowledge passed on.

Another solution for cost reduction no matter the industry is to switch from maintaining own servers to cloud services. Moving to virtual servers is nowadays not only a secure option but a highly adjustable one. Rates differ depending on the cloud service provider, but most often, you pay as you go, without the need of supporting extensive, unused resources. When your business (and computational needs) grow, your storage space can be easily adjusted. It works also the other way around – should you need to downscale your cloud (and the bills!) you may shrink it accordingly.

Is it worth it in the long run? Are digital transformation cost savings worth the hassle?

Transforming operations digitally has its challenges, obstacles, and costs. Introducing technology to the enterprise may come across initial resistance due to concerns about employment reduction or daily work becoming too complicated. Convincing staff to lean towards modern solutions requires attention from the management and facilitation of a modern, balanced environment benefiting from digital transformation.

While the introduction of new technologies requires certain investments (software, licenses, hardware, maintenance, operational costs covering e.g. communication and change management), the transformation costs are a long-time investment, the benefits of which will be paying off in the years to come.

Digital transformation, aside from costs, carries numerous advantages we’ve outlined above. But to fully benefit from its potential, companies need to define their goals. If you don’t know where you’re heading, any road can get you there, at least according to Lewis Caroll. The digital world slightly resembles Wonderland, yet still, we advise better preparation for the journey than Alice took. Before changing business processes and procedures, the organization should know what objectives to achieve and how to assess progress.

Can digital transformation reduce costs?

It’s important to remember that digital transformation is not a one-off action. The cost-saving operation starts with a well-thought-out strategy. It’s a process, and oftentimes – a longstanding one. Starting with recognizing the need, assessing risks and opportunities, finding a trustworthy transformation partner, implementing changes, and ending up measuring the effectiveness.

A majority of leaders across companies that transform digitally, declare their organization’s profitability improvement as a result of the well-executed implementation of technology into performed operations. Since digital transformation isn’t carried out just for the sake of it, companies need to calculate the efforts and their results. How to recognize the success of the digitization processes? Evaluating the impact of technology adoption goes way beyond the feeling of we made it. To learn more about assessing the business goals and performance indicators go to our article on measuring digital transformation.

Got questions about the digitalization opportunities of your industry? Let us know! We’ll go deeper into your processes and decide collectively which ones can be improved with technology. See how digital solutions can help you carry out your operations more productively and cost-effectively.

The future of manufacturing

admin — Tue, 21 Jun 2022 07:15:08 +0000

It’s not about buzzwords or temporary crazes, it’s all about solutions that work. This time we’ll delve deeper into digital transformation in industrial manufacturing, helping the working people worldwide.

Manufacturing, one of the main pillars of the global economy, is heavily dependent on technology use. Even before the common electrification, production plants adopted the most modern solutions to streamline production, achieving the highest possible efficiency.

The Industry 4.0 era led the transformation far beyond manufacturing machinery. The Fourth Industrial Revolution takes place right before our eyes. The times of production plants relying on error-prone manual labor, extensive paperwork, repetitive tasks, and dispersed expert knowledge goes out of date.

Now it’s the time to shine for digital technology in manufacturing.

What are the 4 main areas of digital transformation?

Digitization of manufacturing operations to create new processes, new quality, and new products occurs in the below areas:

Business model transformation

When the current offer does not suffice the market demand anymore, companies switch to a different, digital business model.

Domain transformation

Some market fields become a thing of the past or turn out unprofitable. The obvious solution is moving the business to a different domain, redefining offered products and services.

Process transformation

Remaining competitive in the market requires a change in the execution of those company processes which have become inefficient or obsolete. Whether the amendments are small or comprehensive, modernization allows for achieving new goals.

Organizational transformation

Manufacturing does not solely focus on machines and processes, and there is also a human aspect of transformation that needs to be addressed.

Why digital transformation is important in manufacturing?

Those who don’t adapt stay in place. While small family businesses and local workshops often don’t feel the need of technological improvements, global enterprises set a direction for the industry. Digitalization of manufacturing is brought down to satisfying customer needs, responding to market changes, achieving optimal efficiency, and making profits. The more extensive the operational scope, the greater the range of possibilities.

How is digitization transforming the manufacturing industry?

Digital in the industry – a broad and diverse range of applications. Below you’ll find some of the by far, most remarkable examples of digital transformation in manufacturing.

Internet of Things

Solutions like condition monitoring sensors become more and more popular in the manufacturing sector. The reason behind it is the convenience the devices offer in tracking the health statuses of equipment, machinery, and the facilities as a whole in real-time. Since the staff needs to intervene only if the sensors detect abnormalities (or respond according to preloaded preferences), less human attention is needed to monitor the production plant condition.

Artificial intelligence

More and more manufacturing plants carry out their processes under the watchful eye of AI. The use of computers capable of performing tasks usually associated with humans, spans various fields. Manufacturing processes generate huge amounts of data, analyzing which helps to determine the variables crucial for production operations.

There are many applications of AI in manufacturing companies, such as Generative Adversarial Networks (GAN) used in generative design, predictive maintenance, and analysis, used to forecast occurrences (malfunctions and downtimes) potentially threatening operational continuity, and algorithm-powered quality assurance processes.

Augmented and virtual reality

Supplementing (or substituting) the physical world with digital enhancements is widely used by industrial companies. Technicians equipped with dedicated wearable devices are able to execute tasks ranging from hands-on training, and maintenance operations, to remote factory acceptance testing, at the same time, remaining in touch with experts located anywhere in the world. AR and VR shorten the processes requiring on-site presence when carried out the traditional way.

Assisted reality solutions contribute to strengthening internal knowledge management by aggregating expertise in a standardized, easily accessible way. Using digital tools in the competency-building process allows for delivering more engaging content. It is also possible to reduce the costs of training (less workload of trainers, no classrooms or paper tutorials). Digital learning platforms help retain knowledge, by protecting internal know-how from outflowing when experts leave the organization. Solutions like the Nsflow AR platform, designed with the industry in mind, improve industrial operations, helping address the problems faster and more efficiently.

Big Data

Using Big Data technology manufacturers can conveniently track and analyze their process data. Understanding the data behind operations helps to solve issues previously unnoticeable or difficult to detect using traditional methods. Finding patterns and relationships enables providing better solutions and carrying out processes in a modern environment. Big Data is successfully used in executing quality checks, streamlining supply chains, detecting areas for optimization, and improving overall efficiency.

Data analysis is the foundation of another practice crucial for modern production plants: manufacturing analytics. As manufacturing machinery creates streams of data, companies turn to using them for forecasting demand, maintenance optimization, order optimization, and risk management. The process of acquiring, arranging, and analyzing data is vital for preventing breakdowns, identifying areas for enhancement, and predicting future applications – in a more comprehensive manner than an individual would do.

Automation

Ceding tasks to technology reduces the need for human input. The manufacturing industry and its processes are one of the most grateful fields for automation. The term covers several types of automation: flexible (with programmable computerized systems controlling the machines, great for batch production and assembly lines), fixed (the so-called hard automation, combining various sequences in the production of large quantities e.g. material conveyor systems), and programmable (good for machines used for various tasks, e.g. industrial robots, where reprogramming allows the system to execute different sequences).

Digital twins

Using a digital representation of physical objects allows for applying more immersive and life-like examples than traditional CAD models. With the dedicated software, manufacturers can shorten processes, reduce the need for building physical prototypes, which results in executing design and production processes faster. Industries successfully adopted the digital twin concept as a part of the smart manufacturing approach, utilizing extensive datasets to visualize their products.

Why is digital transformation a challenge for manufacturers?

Transforming digitally is no small feat for manufacturers and requires overcoming several obstacles on the way. Some of these challenges include:

Skills

Retaining expert knowledge and preserving internal know-how. Bearing in mind phenomena like the Great Resignation and the ongoing generational change, companies need to protect their expertise and ensure its efficient transfer to the newly incoming employees. Filling in the generational gap will involve training approximately 2 mln new employees globally, many of which represent the younger generations. As such, new hires may require a different approach to learning to remain motivated and efficient.

Costs

Extensive investments may seem like a significant cost, but missed opportunities, knowledge outflow, recurring downtimes, and losing to the market leaders is the cost companies pay when not adapting to the changing environment.

The right solutions

it’s not about using something digital just for the sake of it or because everyone does it. To make it even harder, there’s no such thing as a universal path to follow. Benefitting from digital transformation services requires an in-depth understanding of ongoing processes and ways technology can improve them. (The good news is we’re here to help and assist you in the strive to transform your operations digitally.)

The impact of digital transformation on manufacturing

Does the manufacturing industry need to transform digitally? Taking into account all the above examples, it is clear that revamping the processes and implementing the latest technologies is essential for remaining competitive and delivering value.

Benefits of digital transformation in manufacturing

Digital transformation in the manufacturing industry needs to bring tangible results, as enterprises won’t turn their processes upside down just for the sake of digital advancement. The business has its own rules according to which, the efforts put into the transformation should bring profits. Therefore, it’s worth pointing out some of the advantages arising from the digitalization of manufacturing?

Improved efficiency

Less time spent on repetitive, mundane tasks, better allocation of resources, and improved workforce management.

Enhanced safety

Advanced recognition of hazards, reduced need for workforce performing potentially dangerous tasks, and more precise execution of tasks.

Greater competitiveness

Strengthened market position through introduced innovation, future-proofed processes, and greater value delivered to customers and stakeholders.

Savings

Reduced labor costs, effective avoidance of human error and machinery downtimes, and reduced spending on business travels and training expenses.

Digital technologies enter production processes and power the industry. The changes may not be occurring dynamically, as extensive processes and production plants require some time to adjust to new conditions. The business is certainly adapting to the new reality of Industry 4.0. The accelerating change is rather unlikely to fluctuate anywhere in the foreseeable future. The traditional industry we used to know, is becoming a thing of the past. It’s time for digitally enhanced manufacturing, benefiting from technological advancements and maximizing its potential.

Are you considering a digital transformation for your company? Did any of the top digital transformation trends in manufacturing catch your attention? Seek no more – book your one-hour free consultation and find out how technology can streamline your operations.

Digital transformation in banking and financial services

admin — Wed, 15 Jun 2022 09:54:23 +0000

Money makes the world go round, as the song goes. The financial sector provides services to commercial and business customers all over the world. This dynamic industry consists of entities including retail banks, commercial banks, neobanks, credit card companies, savings associations, and credit unions.

Ever since the Mesopotamians created the first known form of currency approximately 3,000 years before our era, the money’s circulating endlessly.

The ever-moving cash flow enforces keeping abreast with the times. The financial sector is always constantly alert to emerging novelties and employs modern solutions in its processes. Despite major crises in the past years, the industry is growing strong, and its digital field is expected to expand significantly in the coming years.

Is the future of banking digital?

The rising power of digital solutions couldn’t have gone unnoticed by the financial sector. Digital transformation, the process of adopting the newest technologies to boost efficiency, competitiveness, and innovation, is a path banks and other financial entities embark on. Under the umbrella term fintech (financial technology) the transformation takes place right before customers’ eyes.

Why do banks need digital transformation?

To improve security
To simplify processes
To ensure regulatory compliance
To increase productivity
To address customer needs
To become immune to contemporary threats

All roads lead to Rome (if by Rome we mean digital banking/financial services)

There’s no one right way for banking and financial institutions to transform digitally. Companies follow various digitization models, of which the two most common are digital as a new line of business (devoting a part of operations to be transformed, yet still maintaining traditional banking/financial services) and digital native – remaining up-to-date with the newest technologies since the very beginning of operations.

What slows down the progressing digitalization in the financial sector?

Despite the undeniable need for implementing newer and newer technologies, there are still obstacles to overcome. What hampers the digital transformation most are obsolete and inflexible IT infrastructures, too centralized to scale and handle the speed of incoming changes, so desired by modern banks and financial institutions.

Issues caused by obsolete IT infrastructure

Why do banks need digital transformations? The times are a-changin’ and what used to work well a decade or two ago, now turns out to be obsolete. Not paying enough attention to staying up-to-date with the changing world results in:

Obstructed cooperation between dispersed teams and weak communication affect global processes and suppress innovation.

Inability to gather and process data on clients in real-time. Older systems lock data in silos, shutting it from efficient analysis allowing a deeper understanding of clients’ needs.

Low quality of digital experience delivered to clients, as refraining from modern solutions causes issues with multichannel and direct service available immediately.

Challenges in implementing digital solutions obstruct the efficient generation of new products, sources of revenue, and new ways of engaging clients.

Potential threats to customer data safety, and security breaches lowering users’ trust.

Excessive costs resulting from e.g. maintaining out-of-date systems, eliminating consequences of the mentioned above issues, and the overall systems’ insufficiency.

7 examples of digital transformation in banking/financial services

In the third decade of the XXI century going digital is not about having a website or a banking app anymore. The list below presents some of the most remarkable, future-proof examples of digital banking.

Blockchain

The decentralized, distributed, immutable ledger of individual records (blocks) records transactions and allows tracking assets (both tangible and intangible).

In banking, blockchain plays various roles including:

Streamlined payments – with blockchain, banks can reduce the need for third-party verification, speeding up the processing of transactions. As a result, decentralization contributes to faster payments with lower fees. The same works for loans and credits, as processing loan applications involves assessment of debt-to-income ratio, credit scores, and ownership status, and blockchain backs the credibility of necessary data, speeding up the process.

Neobanking

Neobanks, known also as virtual, digital, online, or internet-only banks, operate exclusively online, without brick-and-mortar branches. This way of doing business appeals to tech enthusiasts’ taste, eliminating the need for handling any issues on-site. Neobanks offer mobile-focused services, using apps and dedicated platforms to satisfy customers’ needs. Some of the entities operating in this model sprouted from established traditional banks and function under their parent companies’ bank license, while many others emerge as standalone brands with built-from-scratch systems and platforms.

The advantage of neobanks is largely their flexibility, the speed of processes, and addressing market needs in a more agile way compared to traditional banks. As a result, online banks have a remarkable potential for narrow specialization focusing on specific demands, yet the market is still to verify whether there will be enough niches and take-up for numerous new players in the banking industry.

Artificial intelligence

The adoption of AI in banking is rapidly increasing. There’s no wonder, as machines are capable of performing human-like operations oftentimes not worse than the workforce does – and doing it 24/7 without holidays and sick leaves. One of the most common applications is chatbots, where AI answers inquiries on the current balance, upcoming payments, and other frequent queries. Employing algorithms to serve customers at the first line allows better allocation of expert personnel to handle more challenging tasks requiring the human touch.

Another instance of AI in banking is the role in credit scoring. Assessing borrowers’ creditworthiness by hand can (and usually is) be mundane and time-consuming. Algorithms perform the analysis of data from various sources, allowing for the creation of scoring models even in the case of individuals or companies with limited credit history.

Other fields in banking where artificial intelligence is used successfully are e.g. cybersecurity (improving financial systems based on data from previous breaches, learning from past mistakes, and analyzing patterns) and high-end advisory (suggesting steps beneficial to the customer based on their credit history, analysis of their finance, and the current offer of services).

Internet of Things

IoT combines the broad field of physical objects containing sensors and access to communication networks. The environment of interconnected devices is always on the lookout for useful information, gathering, and transferring data for future use. Moreover, IoT contributes to improving customer service and analytics, and can be used for real-time monitoring.

From interconnected devices mounted in financial facilities supporting on-site security with smart alarm systems, to mobile devices requiring biometric authentication and supporting fraud prevention, to leveraging data from everyday technology and wireless payments, the financial sector owes much to IoT.

Advanced data analysis

The key to offering tailor-made products and services with a superb user experience is understanding customers’ needs. Data-driven banking requires data on customer behavior, needs, and abilities. Remarkably, the financial sector is well-known for its early adoption of analytics, as the sector was always on the lookout for changes and disruptions, securing operations by examining historical data. In times of digital transformation, banking applies big data processes to squeeze even more valuable information from the available datasets.

Data is the greatest ally companies could imagine in delivering unique customer-centered solutions. Aside from building well-thought-out products, in-depth data analysis allows for more efficient resource allocation through process optimization, strengthening customer relations, mitigating risks, reducing operational costs, and creating successful strategies resulting in better performance.

Authentication

The process of verifying user identity and their clearance to access particular resources. Two-factor authentication (2FA), requiring an additional step aside from users’ names and passwords, adds another layer of security. The price for safety is convenience, as making the 2nd factor an obstacle for clients can result in abandoned carts, causing revenue loss and satisfaction decline.

A 2019 European Union regulatory requirement, the Strong Customer Authentication (SCA), should enable making online transactions even safer. SCA compliance entails possession (e.g. a hardware token issued to the client specifically), inherence (a physical attribute of the client, like biometric identification), and knowledge (something only the client knows, e.g. a PIN). The drive toward achieving maximum security of digital banking will continue, as fraudulent actions adopt the newest solutions as well – protection needs to be at least one step ahead of wrongdoers.

Payments

The financial sector comes up with newer and newer ways of improving and simplifying payment methods. The last 50 years have seen a journey from checks, through bank transfers to contactless payments, using smartphones, and other wearable devices, to the recently emerging payment chips to be implanted for the ultimate convenience of not carrying around any financial resources. Microchips, roughly the size of a grain of rice, are to be connected with a credit card and recommended to be placed into users’ hands for an undoubtedly hard-to-lose payment option.

The impact of digital transformation in financial services

The digitalization of the financial sector took off during the pandemic and there are no signs for tech adoption to stop. Other industries aren’t slowing down and banking needs to implement technologies to achieve its objectives. Examples of digital banking innovations mentioned in this article give a glimpse into the future of modern financial services – there’s still more to come in the years ahead.

Digital innovation in banking and financial services was never solely about creating responsive websites for brick and mortar branches. It’s a comprehensive approach to technology, paying enough attention to modern infrastructure, creating compelling products and services, and ensuring the right customer experience. All of which is possible by employing the right tools and approaches.

After all, even though there’s not that much of that clinking, clanking, clunking sound anymore, money still makes the world go ‘round. And state-of-the-art digital solutions are here to make the flow safe and efficient.

Curious about what custom software solutions can do for your enterprise? See our case studies to read about the success stories of our customers. Enjoyed the read and looking for more? Book your one-hour free consultation to talk about your idea and find out which digital transformation services will be the most beneficial.

Tech & Biz – NeuroSYS: AI & Custom Software Development

What is user research? Overview of types and methods

In this article

What is user research?

What is key in user research?

Questions to ask yourself

Types of user research

What?

How?

When?

Five user research methods

Competitor analysis

Target group observation

Focus groups

User interviews

Surveys

User research examples

Research objectives

Test types

Areas verified

In-depth user research means combining methods

How to invest in tech startups?

How to tell if your tech startup of choice is legit?

The good, the bad, and the wisely advertised

How to recognize the real unicorn? Or: Which startup is best to invest in?

Not all that glitters is gold

Time to say: Check!

The things to verify:

Feasibility of the idea

A working code

Potential for delivering the promised results

In case you’d wonder, why should you choose us?

The takeaway

Scrum vs Kanban, what’s the difference? Which one to choose?

In this article

What is Agile?

Four Agile values

Waterfall approach

Scrum methodology

Scrum roles

Ceremonies and events

Kanban methodology

Roles and tasks in Kanban

Kanban board

WIP limits

What is the difference between Scrum and Kanban?

Is Kanban better than Scrum?

Scrum vs Kanban wrap-up

Digital transformation in the automotive industry

Is the future already here?

How has technology changed cars?

Autonomous vehicles

Self-parking systems

Biometry

Digital twins

Generative Adversarial Networks (GAN)

Quality assurance

Shared mobility

Knowledge retention & management

Will the top technology trends in the automotive industry grow?

Pain points in the industry for technology to solve:

Artificial intelligence does the trick in digital transformation

Why is AI in digital transformation important?

Examples of artificial intelligence solutions in digital transformation

Computer vision

Natural language processing

Data science

Predictive modelling

Sound recognition

How to improve process effectiveness with digital transformation

Digital transformation and how should it be done

How to decide whether an industrial or business process needs improvement?

Analysis

Audits

Key Performance Indicators (KPI)

Benchmarks

Which processes can be suspected to lack effectiveness in the first place?

How does digital transformation improve process efficiency?

How does it look from a digital service provider perspective?

The first pancake is always spoiled