There is a lot of uproar going on about the influential technologies that are dominating the business market for almost every sector. Big data, artificial intelligence, and machine learning are being discovered and analyzed and with passing time people are coming with new applications for a better world and an improved future. While there are some who are still not aware of the potential and capabilities of Machine learning and the value it can add to their business. Machine learning draws insights from raw data and provides solutions to complex business problems based on analysis. Data processing in AI and ml algorithms help computers find hidden insights without any programming or human assistance. It is a technology of the future that ensures heightened efficiency, enhanced customer service relationship, and boosting sales for any business. With big companies like Amazon, Google, and Microsoft bringing in Cloud machine learning platforms, the credibility of artificial intelligence and machine learning just springs up.
There are now several applications of the insights that machine learning draws from raw data. These are used to solve complex business problems, increase efficiency, improve customer relationships, and hike up sales.
NATURAL LANGUAGE PROCESSING
The tech industry just like any other industry faces several challenges from time to time and recently one of them has been the capability for machines to analyze, manipulate, and understand human language. And NLP, a field of machine learning, helps with just that. It gives computers the ability to understand human language by building systems that are able to make sense of text so that they could translate, recommend text, and check grammar. However, certainly there are NLP equipped tools that are used to derive insights that can be used to automate tasks. Algorithms are used to build intelligent systems like voice activated personal assistants - siri, google assistant, alexa, and AI-powered chatbots that are capable of solving issues by translating the language when and where required for machines and humans.
Now when we come to the mechanism on how this works, we will first have to understand the two techniques that go into helping machines understand text. Syntactic analysis that assesses text using basic grammar rules to understand the structuring of sentences. Semantic analysis emphasises on understanding the meaning of the text, first the meaning of individual words and then the meaning when those words are combined.
Business enterprises are getting better at understanding online conversations and how their customers perceive them and their products. They are also able to automate tedious tasks and make them more efficient. Language translation, sentiment analysis, text extraction, topic classification, and chatbots are some of the important applications of NLP known in the technical world.
SUPPLY CHAIN & LOGISTICS MANAGEMENT
Experts predict that 95% of supply chain planning vendors will rely on supervised and unsupervised machine learning for their solutions by 2020. (Source: Fronetics)
We are all aware of the wonderful capability of machine learning to allow systems to learn and improve from experience without any human assistance. Adding to that, is the progress in speed, resilience, accuracy, and efficiency. It is the way that machine learning algorithms are built and this how they work. Having said that, the supply chain market has its own challenges like lack of transparency, uncertainty, and instability in processes. Especially during the pandemic, transportation hassles, remote working, and shortage in products due to increased demand are some of the other supply chain vulnerabilities that have arised. Machine learning and Artificial intelligence has bought out ways to make a shift from traditional and linear approaches to a smart and networked approach that is more connected, scalable, and customizable.
Machine learning in the supply chain is helping distributors, suppliers, retailers by adding value to the supply chain management operations. The machine learning models provide the businesses with predictive analysis by identifying hidden patterns in the development sets in ML applications to detect issues in advance and control any damage that could have been caused. This gives the business the flexibility to respond to emerging problems on time. One of the challenges being the manual quality inspections during transit which was slow and lacked precision. Machine learning opened a range of possibilities for automated quality inspections in the supply chain. Image recognition ability helps reduce the chance of delivering substandard goods to your customers and improves the overall store operation. Then, there was also the problem of visibility and tracking that is considered the crux on which a supply chain business extensively relies on. With the help of machine learning and deep learning analytics, businesses were able to improve visibility in the processes and hence provide the customers with a faster delivery and an enhanced experience.
You may also read: How DL is Revolutionizing Healthcare
MANUFACTURING
The manufacturing industry is one such vertical that has been using AI and ML technology to its full potential. And, Smart factory or Industrial 4.0 speaks plenty about the former statement I just made. From the times when it was all traditional, and the operations were carried out in a linear fashion to now when the processes are networked and completely flexible. We can say that times have indeed changed and businesses have become intelligent. Smart factory has introduced the manufacturing industry to a system that’s transparent, connected, agile, proactive, and optimized. This solution not only is cost saving but the more optimized quality processes allows to produce better quality products with fewer faults keeping in mind the workers safety and environment sustainability. Integration of machine learning throughout the manufacturing process and predictive algorithms in machine maintenance makes the absolute manufacturing process seamless and smart.
You may also read: Integration of Artificial Intelligence (AI) in Mobile Apps
According to a Global Market Insights report, global machine learning in manufacturing is going to skyrocket from $1 billion in 2018 to $16 billion by 2025. (Source: Deepsense)
When talking about the future, there is a great razzmatazz about the robotics-powered manufacturing that is expected to make the industries self-reliant and far dependent on human resources. This machine learning technique will provide a great potential for reinforcement learning through robotics. It is said that machines will be able to adapt to the changes happening in their environment and still be able to shape new strategies to achieve objectives that were otherwise impossible to accomplish with definitive programmed solutions.
CUSTOMER-CENTRIC MARKETING
There are large amounts of data that one can receive around their existing and potential customers. The real challenge lies in extracting valuable insights from the data that could be used for data-driven customer segmentation, customer experience optimization, and digital marketing optimization. With Big data, machine learning has become immensely valuable for retailers and businesses. The Machine learning recommendation system that recommends things to the user is based on several factors and the recommendations are by and large the products that they are interested in and are likely to purchase. However, the benefits of the machine learning system reached both the users and the service providers. Then, using reinforcement learning, neural networks, and machine learning, a dynamic pricing system has been another defining use case that has been used by retailers to increase e-commerce sales and profits. With over thousands of products to reprice regularly, the dynamic pricing system has come to be a powerful means that is giving businesses a competitive edge in the market. Depending upon a whole lot of factors like customer behavior, demand fluctuations, market trends, purchasing power, and others, the pricing targets different groups of buyers. Other than that, the data collected is analyzed to calculate the revenue-optimal price. It saves a lot of time and allows that time to be invested in some task that demands human assistance.
“The global machine learning market is expected to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The main driving factors for the market are proliferation in data generation and technological advancement”. (Source: Marketsandmarkets) That being said, it’s growth here shows the incredible work that is being done in this sphere of technology. Likewise, Evon Technologies is one such AI development company that has been doing its part to contribute. It has a team of skilled and proficient programmers that provide AI and ML services to other companies to help find their edge in the market. To know more about us, get in touch with us or email at This email address is being protected from spambots. You need JavaScript enabled to view it.
