Contact us anytime to know more — Kuldeep K., Founder & CEO CISIN
Industry 4.0 is the abbreviation for the fourth industrial revolution. The Fourth Industrial Revolution, which transformed digital technologies manufacturing through big data, the Industrial Internet of Things, and AI/machine learning, introduced intelligent factories.
A smart factory solution is digitization's ultimate objective.
High degrees of sophisticated production automation are present in intelligent factories. As a result, industrial procedures can operate without any human involvement.
Industrial IoT technologies, which combine hardware (sensors and actuators) and software, make this automation possible (big data, machine-learning tools).
Software Development in Areas Where AI is a Factor
AI is essential in software design, code generation, and testing. Let's discuss each topic in more detail.
Gathering Requirements
In the SDLC, the requirement collection step is a conceptual one. Only minimal human involvement is needed. Various technologies and methodologies, like Google ML Kit and Infosys Nia, automate some tasks requiring little human involvement.
Before creating, this stage focuses on identifying vulnerabilities. A method for AI is natural language processing, a sort of AI that uses natural language processing to comprehend user needs and automatically produce high-level software models.
This approach has its limitations, including the difficulty of balancing existing systems. It is a popular area of study today.
Software Design
To plan and design projects, you need specialized knowledge and experience. Designers are often unable to decide on the right design for each stage.
Design changes such as forward investigation plans and retractions can force designers to modify dynamically until the client finds the right artificial intelligence solutions. The most efficient methods for designing projects can be automated by using AI tools. Designers may comprehend client demands and wishes and create the ideal project with the help of AIDA (Artificial Intelligence Design Assistant).
AIDA is a website-building tool that evaluates various software design configurations and displays the best bespoke design to satisfy clients' needs.
Automated Code Generation
Writing code for a significant project from a business insights idea might take a lot of time and effort. Experts have suggested a way to write code before you start development.
This addresses both time and money concerns. This method does not perform effectively when there is doubt, as there is in the target code's goal. It takes far longer to gather these details than to write new code.
A programming aid with AI intelligence will help to reduce this load.
Imagine that your project idea could be explained in natural language, and your system would interpret it and turn it into executable software.
Although it may sound like science fiction, software engineering artificial intelligence can change the story! Natural language processing and AI techniques will make it possible.
AI in Testing Services
The phase of software development known as software testing is crucial. It ensures that the product meets its quality standards.
Software testing can become costly and time-consuming if repeated every time the source code changes. AI in software testing is here to save the day.
Numerous AI tools are available for developing test cases and running a regression analysis. These AI tools automate testing and assist in ensuring that it is error-free.
These platforms employ machine learning and AI to automate testing. Appvance, Functionalize, and Testim.io are a few examples.
Control of Deployment
AI machine learning technologies have also improved the efficiency and effectiveness of deployment control activities, which is a benefit to the software deployment phase.
The software development paradigm includes the deployment phase, where developers often upgrade programs and apps to more recent versions.
Developers who fail to execute a process during software upgradation properly pose a risk. AI can help developers avoid such vulnerabilities and decrease the chance of deployment failures.
Artificial intelligence also allows for the analysis of deployment processes using machine learning algorithms.
Smart Factories Offer Many Benefits
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Agile Production Process
Thanks to intelligent factories, manufacturers can quickly adjust to shifting customer demands, financial constraints, and standards for product quality.
The integration between many systems (such as an IIoT solution, ERP, MES, and SCM) and robust data analytics capabilities make this viable.
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Increased Efficiency in Manufacturing Operations
This sensor network can gather information on equipment, the environment, and production. The cloud program immediately analyzes this data.
This allows manufacturers to adjust parameters quickly, such as the operating conditions of their equipment. Additional analysis of the sensor-generated data can be performed to find trends and enhance opportunities throughout the production process.
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Increased Reliability in Manufacturing Operations
The likelihood of human mistakes in manufacturing activities is decreased in smart factories by high levels of automation.
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Improved Product Quality
Smart factories employ AI technologies to regulate quality. Computer vision algorithms in cameras quickly spot flaws, and sophisticated analytics tools can pinpoint the issue.
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Increased Visibility to Shop Floor Operations
Through constant real-time data about production activities and the condition of industrial assets, the IIoT provides producers with better visibility into shop floor operations.
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Information Security
Data security can be ensured by access control, at-rest and transit encryption, access control, and AI-powered detection and prevention of unusual user activity in smart factory solutions.
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Predictive Maintenance
The IIoT enables data about numerous equipment factors that affect its performance and health to be sent in almost real-time to the cloud.
To help machine learning algorithms find unusual patterns, it is paired with metadata. It is possible to anticipate equipment failures and take appropriate measures.
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Increased Worker Safety
Robots can take over dangerous jobs from human workers.
Are you ready to start your journey toward manufacturing transformation?
The Intelligent Factory Uses Technologies
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Cloud Computing
Cloud platforms like AWS and Azure allow extensive data to be securely processed, stored, and analyzed.
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Radio Frequency ID (RFID)
RFID can be used to track industrial equipment, machinery, finished goods, and objects in smart factories.
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Big Data
This technology allows for the continuous collection, storage, and analysis of large quantities of production-related information.
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Machine Learning (ML) and Artificial Intelligence (AI)
AI and ML can automate the entire production process and monitor equipment. These technologies also enable advanced analytics insights, such as predictive maintenance and detection of quality improvement opportunities.
Smart manufacturing is the future for the entire industry. You will need a strategy for intelligent factories to get there without getting lost or going off track.
What is the strategy? Is there any one right way for every manufacturer to do things? Or do you have to tailor each step to your plant's needs? What is the best way to create a roadmap for the future?
Are you Really in Need of an Intelligent Factory Strategy?
There are times and places where it's appropriate not to be able to see the broader picture as you proceed. Improvisation, speculation, and guesswork can be like putting a bandage on a wound.
They fail to address the core issue. Additionally, you'll miss out on intelligent manufacturing's most significant advantages.
It is important not to rush in, as this could lead to over-investment and no ROI. Instead, go through each step of the implementation process slowly.
First, you need to determine what you are doing.
Smart factories are built on the same principles that guide traditional plants. However, they are empowered by data and artificial intelligence to optimize and empower them.
It takes time. It resembles piecing together a puzzle in terms of how it works. To realize the idea of the smart factory, you are assembling manufacturing technologies rather than using small bits.
Your industrial revolution has occurred.
Smart manufacturing is a similar term, but it's not synonymous. It refers to digitalized operations and the technologies that guide them.
Smart manufacturing refers to the approach, and an intelligent factory is a facility that uses this approach.
Your strategy is built on intelligent manufacturing. Smart manufacturing should assist with the early stages of implementation, such as:
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You are identifying and setting goals. To make your factory feel like your factory, spend some time brainstorming plans.
What is your vision for the future? What manufacturing problems, production inefficiencies, and other frustrations do you hope to overcome?
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Understanding your strengths and limitations: Digital transformation does not mean abandoning the progress you have made.
Instead, focus on your strengths and create your smart factory strategy around these. You should also identify your weaknesses to target them when choosing intelligent factory technology.
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Understanding what to expect from intelligent manufacturing: It is essential to be optimistic and realistic about the future of smart manufacturing.
This approach will help you to understand what is possible (such as optimized processes) and what is unlikely to occur (such as perfection).
After you have a firm grasp of the fundamentals, developing your intelligent manufacturing strategy is time. Three steps are required to accomplish this: technology, people, and evolution.
Read More: AI In Manufacturing – From Science Fiction To Reality: Robotics In The Smart Factory
Step 1: Learn About Intelligent Factory Technologies
You may think of digitized, advanced manufacturing as a factory that is "all tech, every time." But that is not the case.
This approach to intelligent manufacturing can lead to severe problems.
Technology is only necessary when it solves a problem. It is effective only when technology adoption results in weaker processes, greater efficiency, or better ROI.
This is not manufacturing wisely. It's only a wasteful procedure that masquerades as intelligent production.
Intelligent factory technology will yield better results if you view it as a solution to specific problems. These are some ways you can incorporate that into your smart technology-factory strategy.
Big Data is What you Should be Thinking About
You may quickly and swiftly collect data with the help of intelligent manufacturing technology. In other words, you'll have a lot of it.
Extensive data collection, or "big data," are helpful in this situation.
Manufacturing companies can achieve operational excellence by properly using data. If mishandled, data might quickly spiral out of control, take a long time to store, retrieve, and capture, and provide very few insights.
It is essential to be prepared for big data as soon as it appears. Instead of hoarding data like a dragon hoards silver, an intelligent factory strategy should be built on the effective asset, operational, and process analysis and use.
Two things are required to achieve this:
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It is essential to understand the nature, quantity, and accuracy of data that you will have access to. This will be far more than what you can capture using traditional manufacturing techniques.
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This data can be compiled from different sources within your factory to create a cohesive view of the truth.
Make the Most of Existing Technology
Modern technology is not necessarily necessary for an innovative factory solution. Making the most of current technology, such as mobile devices, could benefit you.
You can give your factory floor personnel the authority to collect data without interfering with their work or the manufacturing process. This allows you to take advantage of well-known technologies to get a deeper and more thorough understanding of what's happening in your plant.
Don't Get Complacent About Security
A factory may treat automation and artificial intelligence as regular tools very quickly. Data theft and unfettered access are two security issues to which innovative industrial technologies may be susceptible.
You must be able to defeat these online dangers. Therefore, plan and include security procedures and tools in your intelligent manufacturing strategy.
Step 2: Get your Employees Involved in the Intelligent Factory Vision
Any smart manufacturing must include technology. But it can't work by itself. Only the capability to execute the appropriate action can make the data from sensors and integrated systems valuable or of sufficient quality.
This could involve altering a procedure, bringing supplies, selecting which issue to prioritize, etc. Humans perform all these functions. Therefore smart manufacturing must enable rather than replace people.
You'll need a few key strategies in your intelligent factory strategy.
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The employees are the creative thinkers, problem solvers, and innovators in your factory. Every innovative factory solution must be built to offer workers genuine benefits, whether it comes in the form of readily available data, insightful analysis, or more efficient operations.
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Tools ought to be helpful. Employee comfort with the use of intelligent manufacturing technology is essential. Your systems and devices will be challenging to utilize.
This undermines the goal. Your intelligent factory strategy should be intuitive and user-friendly to meet the expectations of modern workers.
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Moving parts are abundant in your factory. Having people together is made more accessible by this. Ensuring that all those "components," which are people, function as a unit are even more crucial.
For instance, your intelligent factory strategy should facilitate good communication between the IT and Operations editorial team. This will allow Operations to communicate their needs to IT and IT to find the best tech manufacturing it solutions without creating new obstacles.
Step 3: Integrate Evolution into Your Smart Factory Strategy
The last and most crucial component of your intelligent manufacturing plan is evolution. You don't want to develop a roadmap that restricts your options or prevents innovation.
Remember that intelligent manufacturing needs to expand continuously. Not a goal but a journey.
These are some ways you can incorporate evolution into your smart factory strategy's DNA.
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Allow for creativity: Although automation solutions make it simpler to standardize production floor processes, you shouldn't fall into a rut.
It would help if you experimented with smart manufacturing with your team. This can help you gain a competitive edge.
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Take it from yourself: Your intelligent factory approach should be geared towards sharing information and creating new best practices.
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You can evolve organically: As your experience with intelligent factory technology improves, you will realize that evolution does not have to be a constant focus.
Instead, you should be able to use new insights to improve processes, pivot, and eliminate inefficiencies.
Artificial Intelligence in Software Development
The main focus of software development is product innovation. The software development life cycle (SDLC) is lengthy and complicated, especially for larger projects.
Companies are looking for reliable software that is innovatively developed. AI can be applied to create more consumer-focused products.
How can AI transform existing applications using AI tools into intelligent ones? This software is used to assist in the development of applications using AI tools, such as NLP or machine learning with its sub-technology Deep Learning.
Software Developers Use AI
Software developers included it in their engineering practices. They design and code software using AI technology daily, including bug identification, natural language processing, software testing, and software development.
It was converted into something different through AI-driven coding. We got the chance to develop such a project and chose to share our insights.
This essay will explain artificial intelligence, its advantages, and practical applications for everyday life. We'll talk about machine learning and the potential applications of software development powered by AI.
Read More: Top Mobile App Development Trends for 2022
What is Artificial Intelligence?
AI is the artificial intelligence of machines that simulate human intelligence. It involves learning, perceiving, solving problems, and even fixing problems.
Weak AI and strong AI are the two sorts.
The first AI, also known as a narrow AI, is a concentrated AI that can do a single task. An artificial general intelligence (or powerful AI) is a creature that is capable of understanding, picking up on, completing, or choosing to do any intellectual job that a human being is capable of.
Although weak AI exists daily, strong AI is hypothetical and has no concrete examples.
A Few Clarifications
What distinguishes machine learning, deep learning, and artificial intelligence? Because they are frequently used synonymously, these phrases might be challenging to understand.
Let's look at how deep learning, machine learning, artificial intelligence, and machine learning relate to one another to know how they differ.
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Artificial Intelligence: refers to any technique that allows machines to mimic human behavior. This includes problem-solving, thinking, learning, and thinking.
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Machine Learning: A subset of AI which can learn on its own. It becomes more proficient at performing specific tasks as it receives more data.
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Deep Learning: is an area of machine learning where artificial neural networks adapt and learn from large amounts of data.
Artificial Intelligence: What a Great Idea!
Risk and artificial intelligence are frequently linked in science fiction films. AI can run in specific industries for very long periods, just like software development.
It must offer some benefits based on this. What are these benefits? Let's have a closer look.
AI Algorithms apply to all industries, even development processes. AI works all day without interruptions. This means it can take over for us when we are busy with other things and will complete the task as usual.
It can also help with daily chores at home.
AI Technology facilitates strategic decision-making and improves recommendation systems. It can be used for data analysis and project planning.
AI allows you to perform specific tasks and complex processes quickly without spending much time. This reduces costs and improves efficiency.
AI Algorithms process data quicker and more accurately than human beings, which results in fewer errors and better data integrity.
It is extremely useful in quality assurance and makes software development much more accessible.
Our Experience With Artificial Intelligence (AI) in Software Engineering
As we havealready indicated, we also produced a few projects that included AI algorithms. Let's now talk about how AI technology has helped us create innovative digital products and solutions.
We ensure that the client knows the critical business needs during software development by conducting thorough research.
This enables us to modify the project to match the market's needs and minimize the revisions needed. We constantly search for novel solutions to make the project stand out. Utilizing AI-powered software or incorporating AI algorithms could be a solution.
The Software Development Process in the Case of Image Manipulation
But now, let's discuss earlier construction initiatives. The first experiment to show how to make slow-motion effects was this one.
The issue was that you couldn't slow down the action enough to make smooth transitions if there weren't enough frames per second. The answer? Synthetic intelligence. Algorithms for machine intelligence or machine learning. This deep neural network can produce extra frames through multi-frame interpolation.
It seems easy, right?
It was laborious. We did our homework, modified the implementation to suit our needs, and then trained an artificial network to complete the assignment.
In the end, all the effort was worthwhile. The transition is more seamless and natural thanks to deep learning than a conventional app effect. This is the benefit of using AI algorithms in the software project.
Real-Time Recommendations: The Case
In the second project, a dropshipping platform was used to lead customers through the website and present them with new possibilities based on their preferences.
We employed two algorithms for recommendation systems during the AI software development. Machine learning was used again to achieve the best results. We observed how users interact with products. We ranked products based on customer interactions.
We generated interaction matrices using consumers' choices, behavior, and interests. This allows us to track how users interact with the products.
The information was afterward utilized to educate the AI systems to look for related products in the store. Real manufacturing Users may choose products that meet their needs thanks to our ability.
The most popular items may also be sold via this technique because they were the easiest to click. These products received more interactions than other products, which led machine learning algorithms to rate them higher.
Like any algorithm in recommendation systems, more users and items translate into higher sales.
Conclusion
You must combine all three steps and create a solid, intelligent manufacturing strategy. To provide answers to these queries, visibility and the appropriate data are needed.
To help you achieve your goals for the intelligent factory, Cyber Infrastructure Inc. offers the basis by generating insights and making data available to you. Having the right platform is the first step to excellence in intelligent manufacturing.
