Business

How AI Consultancy Works in Manufacturing

Manufacturing is a very broad term when you look at how the industry is defined; “any industry that makes products from raw materials by the use of manual labour or machinery and that is usually carried out systematically with a division of labour”. Nonetheless, this is a space that benefits from automation arguably more so than any other industry. That’s another subject to get into but essentially, time, money, and efficiency is where the appeal to automation stems from.

A company on its own can’t just look for the metaphorical “band-aid” to put over its current processes and magically scale performance. For this reason, they outsource to consultants; a team that can see not just the current shortcomings but ones the company can expect down the line. 

What we as vendors know about manufacturing companies in North America in 2023, is that procurement and sourcing, as well as supply chain disruptions, are among the critical challenges faced. Artificial intelligence is a great resource to overcome these challenges, but the specifics that an AI system needs to address in the context of manufacturing are intricate and multifaceted.

Solving Problems Across Industry Models

As we’ve already covered, the industry is broad and every business will have different outcomes they’re looking for. With that said, here are some practical application examples that can be leveraged across multiple business models:

AI-Powered Procurement Platforms: By analyzing information from supplier databases, market trends, historical data, and other relevant data sources, AI procurement platforms can match specific procurement requirements with supplier capabilities and qualifications. Resources for this include Globality, Zycus, and JAGGAER.

Spend Analysis: AI can analyze spending patterns and identify opportunities to save money by scrutinizing large volumes of procurement data. It also offers insights into spending behaviour, supplier performance, and contract compliance. Some platforms for this could be SpendHQ or SAP Ariba

Supplier Risk Management: These AI tools can assess supplier risk by analyzing various data sources, which might include financial data, news articles, and social media. They’ll help identify potential risks and enable proactive risk mitigation strategies. Platforms for this might include Resilinc and Zycus iContract.

Contract Management: Platforms like Icertis or Evisort can automate contract management processes by extracting key information from contracts, identifying risks and opportunities, and ensuring compliance with contractual terms and conditions.

Demand Forecasting: This is simply for optimizing inventory levels, reducing stockouts, and enhancing the efficiency of your supply chain overall. For this, you’ll want to look at BlueYonder.

Where AI Consulting Comes In

Consulting at a base level is about creativity, bringing an outsider perspective into your organization with expertise in problem-solving to create solutions that will optimize your business long term, great. With AI in the picture, this process looks the same, but even more promising - specifically when it comes to data management. Whether you’re a small-scale manufacturer or a large industrial player, data is at the heart of your operations and it can be extremely tedious and time-consuming. 

Most companies with in-house development teams may either be trying to navigate multiple priorities or simply might not have the expertise to construct a system that can manage data using machine learning and AI. For a company this is a problem, for a consultancy this is a massive opportunity. When you say the words “trouble managing data”, a consultancy correlates wasted time and resources, which would push them to create a roadmap looking something like this: 

Know the Plan: The consultants start by understanding what needs to be done. They figure out what problems and good things we want to achieve with your information.

Make a Smart Plan: Next, they think about how to do it in a smart way that matches your needs. They decide what kinds of information we need and how to keep it safe.

Choose the Right Rules: They make rules like a game. They say where the information comes from, what it looks like, and how to take care of it. This helps everyone use the information the right way.

Think about AI's Job: They talk about how AI can help. They make sure the AI does what you want and is safe.

Stay Safe: They check to make sure the AI is working well and doesn't do anything bad. They also make rules to stop the AI from doing bad things.

Keep Everything Nice: They make sure the information stays good and not messy. They also make sure it's always correct and up-to-date.

Make It Even Better: They look at how things are going and think about how to make them even better. They use the plan to keep improving how you take care of your information.

The System

At the core of AI's prowess lies its ability to learn. Think of it as a virtual apprentice that gets better with experience. Initially, AI algorithms are fed historical data - production records, machine performance, and even external factors like market trends. Through sophisticated techniques like machine learning, AI identifies hidden relationships between variables and develops predictive models.

An ERP system infused with AI is the ultimate vision for manufacturing companies. For anyone who’s never imagined that, it’d look something like this: As raw materials flow into the factory, embedded sensors send real-time data to the AI-powered ERP system. The AI, like an orchestra conductor, processes this influx of information. It then predicts maintenance requirements for the machines before they break down, optimizes production schedules based on market demand, and even fine-tunes the supply chain to minimize delays.

Too good to be true? We think not. 

The Takeaway

AI consultancy isn’t so much about thinking up and designing systems as it is about giving companies the best shot at success for the least amount of risk. It’s not an easy process, but that’s what experts are for. We’d love to make your problems our solutions, so be sure to bring us all of them. 

Written By Ben Brown

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

 
 

Comparing Kotlin Vs. Java for Android Apps

Kotlin has come out on top against Java when it comes to favourable options for Android development and is now Google's preferred language for new mobile applications. Kotlin is a statically-typed language; it’s interoperable with Java and examines variable types during compilation, which prevents runtime errors and boosts execution speed. Usually with dynamically-typed languages like Python and JavaScript, types are determined at runtime, which enables faster coding but can lead to verbosity.

While initially designed for Java Virtual Machine (JVM), Kotlin also compiles JavaScript and native binaries using Kotlin Native. This enables cross-platform execution on various systems and produces bytecode, JavaScript, or native binaries as needed.

The main point to be aware of is that developers need to understand the difference between Java and Kotlin as languages especially when transitioning between the two. For that reason, we’ll be looking at the strengths and weaknesses of each, and the different use cases.

Similarities Between Kotlin and Java

Java and Kotlin share similarities in a few ways that are important to know as an Android developer. The first point is that they share the same platform (Java Virtual Machine) which allows them to communicate easily; you can call code from either end. 

Also, Java is applicable in just about any domain which includes web front-end tasks, enterprise solutions, server-side applications, databases, embedded systems, and mobile apps. When it comes to Kotlin, the versatility extends to Android platforms, the JVM, JavaScript environment, and Kotlin/Native framework. Kotlin is also very effective for server-side, web, and desktop development. 

With all this considered, we can’t ignore that Java has a lot more mileage than Kotlin with its introduction to the landscape coming 20 years before Kotlin 1.0. However, in that window, Kotlin has become the preferred choice over Java for Android development. Yet, nobody is saying to replace one with the other.

Comparing the Key Strengths of Each

Java:

When it comes to Java, the community is obviously much bigger which helps with support and resources but also means the developer pool is much greater. Additionally, its speed for both development and compilation is often regarded as better than Kotlin. 

Kotlin:

Kotlin is well known for not needing as many lines of code as Java but finding a solution to a task is still often quicker with Java because of its range of coding processes. However, with Kotlin comes modern programming paradigms and a mixed-language database.

A more general overview of Java and Kotlin’s strengths and weaknesses looks like this: 

Java strengths:

  • Easier to learn

  • Complex programming language

  • More established with a larger developer community

  • More support from the Android community

Java weaknesses:

  • Complex programming language

  • More verbose

  • More formalities and rules

Kotlin strengths:

  • Clearer

  • More flexible and widely applicable

  • More expressive

  • Less code

  • Mixed-language database

  • Faster than Java overall

  • Offers modern programming paradigms

Kotlin weaknesses:

  • Younger than Java with a smaller developer community

  • Initial code readability can be difficult

  • May cause problems with Android API design

  • Test-driven development requires writing more code and carries a higher risk of programming errors and bugs

Use Case Comparison

To give you some insight as to how different these two are, here’s a quick side-by-side of custom logging utility:

Java:

LoggerUtils.logMessage("Hello, this is a log message.");

Kotlin:

"logMessage"("Hello, this is a log message.")

When it comes to an overview of use cases for each, we can uncover distinct strengths tailored to various scenarios:

Java Use Case:

Enterprise-Level Apps: Java's ecosystem and well-established libraries make it great for developing large-scale enterprise apps. Its compatibility with legacy systems is also an upper hand for complex business solutions.

Kotlin Use Case:

User-Friendly UI: Kotlin's syntax and expressive nature help create clean user interfaces. With that said, its intuitive code can lead to faster UI development and better user experiences overall.

Java Use Case:

Cross-Platform Compatibility: Java's compatibility across platforms allows developers to write code that can be reused for Android as well as other environments, which makes it optimal for projects with diverse platform requirements.

Kotlin Use Case:

Modern Features and Concurrency: Kotlin's support for modern programming paradigms, along with built-in support for coroutines, simplifies the management of concurrent operations, which is good for apps that require multitasking.

Java Use Case:

Data-Intensive Apps: Java's extensive libraries and performance optimizations are great for data-intensive applications, such as those involving complex calculations or heavy data processing.

Kotlin Use Case:

Prototyping: Kotlin's syntax allows developers to quickly prototype and iterate on app features. This is helpful in fast-paced development cycles and when testing new ideas.

Java Use Case:

Legacy Code Integration: For projects that involve integrating with existing Java codebases, Java is the seamless choice due to its native interoperability with the Android platform.

Kotlin Use Case:

Null Safety: Kotlin's null safety features reduce the likelihood of null pointer exceptions, which is good for apps where data integrity and app stability are priorities.

Development Expertise

Java's syntax, while verbose, stands as one of the most popular object-oriented programming languages. Practically every developer possesses a certain degree of familiarity with Java, which makes it a fallback option for Android app ideas. 

Kotlin, with its succinct and less verbose code structure, offers an easier learning curve for seamless development. Yet, despite this user-friendly aspect, Kotlin's compiler can yield complex errors. For instance, smart casts allowing raw types, while not identified as errors during compilation, can lead to runtime crashes. Finding proficient developers capable of efficiently debugging and crafting error-free programs can be challenging, especially under tight timelines.

In cases where your development team lacks end-to-end Kotlin expertise, Java will be the more suitable choice.

Project Scope

Securing developers capable of readily delivering solutions for your Kotlin application can be a costly and challenging pursuit. While Kotlin's support community is expanding, it’s still not on par with Java's vast reach. Consequently, Kotlin's full-stack development journey may not be as smooth as desired for intricate, multifaceted projects.

Java's maintenance and development cycles tend to be longer due to its familiarity, which entails parsing through more code. For instance, Java's checked exceptions mandate explicit handling or re-throwing, sometimes leading to time-consuming empty catch blocks that necessitate debugging.

However, these considerations don't negate the strengths of either language. For smaller projects with limited scalability requirements, Kotlin shines. Even for larger projects, a substantial budget can render Kotlin viable with a promising return on investment Netflix stands as a prime example. On the other hand, Java is just simply reliable for big undertakings, assuming that a proportional budget is allocated for project support.

The Takeaway

We’ll likely see many more programming language showdowns, as the choice between languages continues to be influenced by factors like project size, team expertise, and evolving development trends. When a company begins looking for support on projects like app dev, make sure that the team you’re working with understands the scope of your project and has the expertise that’ll guide you to success. 

Written By Ben Brown

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

 
 

8 Trends in .NET Development in 2023

There’s so much opportunity for .NET developers in 2023 when it comes to growth and innovation. With that said, as user demands grow, so too does the pressure to enhance .NET applications. Of course, we’re seeing Microsoft consistently innovate the framework to empower .NET developers. However, gaps in updates can be big setbacks for companies and stakeholders, leading to a chain of functional setbacks and unmet expectations. To avoid this, grasping the latest .NET trends is crucial. 

Commonly, the gaps disrupt 5 key areas; performance, compatibility, documentation, security, and third-party dependencies. These missing pieces can expose security vulnerabilities in applications. Given the prevalence of cyber attacks, this is typically enough for companies to reevaluate their applications. 

How Relevant is .NET in 2023?

The Stack Overflow Developer Survey from last year unveiled that ASP.NET Core & ASP.NET are the favoured web frameworks. 

With that said, here’s a look at some of the hottest trends in .NET development right now that are shaping the way developers enhance applications to mitigate these risks:

1) ML.NET

Machine learning has taken center stage, and Microsoft's ML.NET framework is at the forefront of this transformation. By integrating machine learning in the .NET ecosystem, developers can infuse their applications with AI-driven insights and access pre-built models that span across applications, everything from natural language processing to image recognition.

Additionally, ML.NET is open-source, which means the framework is continuously evolving and benefitting from the collective expertise of the development community. The last thing that should be noted is just how user-friendly this platform is for developers of all skill levels. That aspect comes from its graphical user interface (GUI) called Model Builder, which is a tool that integrates into Visual Studio. The GUI allows developers to build, train, and ship custom machine learning models in their .NET applications.

2) Cloud-native Development

Leveraging cloud computing in app development is an approach born out of the reliance, we could even say “attractiveness”, of cloud computing environments. Creating and managing applications in this environment is a developer's paradise. It’s scalable, it’s flexible, and it provides resources on demand.

Cloud-native development breaks applications down into microservices that scale on their own which in turn makes the application more adaptable and responsive to user demand. 

3) .NET MAUI for Native Apps

Creating native applications for different platforms is very intense as every developer knows. .NET MAUI lets you create native mobile and desktop apps with C# and XAML. It’s beating out Xamarin. Forms and is meant to build apps that run on Android, iOS, macOS, and Windows from a single shared codebase.

MAUI uses .NET 6 as a foundation, offering a consistent runtime and framework for crafting applications from a range of platforms. This integration utilizes contemporary capabilities like hot reload, which enables real-time code adjustments without necessitating a complete app rebuild. MAUI lets developers create apps on either PCs or Macs and upon compilation, then turns them into native app packages. 

For instance, MAUI constructs Android apps using C#, which is subsequently converted into an intermediate language (IL), then just-in-time (JIT) compiled into a native assembly upon app launch.

4) ASP.NET Core

Originally introduced in 2016, ASP.NET Core is a far more advanced version of .NET where developers can build websites and web applications using HTML, CSS, and JavaScript. It stands as an open-source framework tailored for crafting cloud-based applications. This framework is a reimagined edition of ASP.NET, and it features architectural enhancements to build a modular structure.

Unlike ASP.NET, the Core version has cross-platform adaptability and the development process is a lot more streamlined. Its framework integrates MVC and Web API, alongside the inclusion of Razor Pages for simplified creation of page-centric applications. Additionally, it offers a lightweight and efficient HTTP request pipeline and is compatible with hosting on Windows, macOS, and Linux.

5) Blazor

Microsoft created Blazor as a means to build web apps using .NET and C#. The idea is that developers can write client-side web UI experiences using HTML, CSS, and C# instead of relying heavily on JavaScript.

Blazor presents two methods of hosting: Blazor WebAssembly and Blazor Server.

Blazor WebAssembly: In this approach, the entire application operates within the user's browser. The application is fetched as a WebAssembly binary and executed within the confines of the browser. This facilitates a dynamic user interface, akin to conventional single-page applications built using JavaScript.

Blazor Server: In this alternative, the application's visual elements are generated on the server side and then conveyed to the client through a live connection. User interactions are managed by the client-side code, which then relays them back to the server for processing. This model does streamline the client-side experience which makes it ideal for scenarios demanding real-time updates or when client-side resources are more limited.

6) Xamarin for Mobile Development

Xamarin is a cross-platform mobile development framework that developers can use to build native iOS, Windows, and Android apps using C#. It uses a single codebase for building apps across multiple platforms, which can save time and resources. This trend is you’ll see continue to grow as more businesses look to build mobile apps for multiple platforms.

7) Movement Towards Open-Source

What you’ll find with most tools nowadays is that they’re open-source models, which are primarily driven by Microsoft's efforts and the desire for flexibility and community-driven improvement. Also, many businesses are using open-source software internally and contributing to open-source projects which has been a big driver in the adoption of open-source in .NET development. 

8) .NET in IoT

Given the proliferation of IoT networks for security and automation in various enterprises, the demand for compatible applications has surged. In response, .NET enables the construction of applications specifically for IoT devices and sensors. It also provides a specialized framework, known as the Nano framework, created for crafting applications for embedded devices.

The libraries .NET offers are good for interactions between applications and a range of IoT input devices, sensors, System-On-Chip components, RFID elements, GPIO, I2C, and port interfaces.

Moving Forward as a .NET Developer

C# and .NET programmers are highly sought after by both startups and major tech corporations. For large-scale applications, these technologies are very commonly used which makes being proficient in them a superpower for developers in 2023.

Written By Ben Brown

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.