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Day: December 30, 2021

5 tips for supply chain management and analytics in the age of AI

Posted on by Imarticus Learning

Undergoing supply chain management training is a prominent goal of several in the management industry. To become a supply chain analyst, one must complete a certification course. There are various certifications for supply chain professionals, available online. While pursuing this career one must understand how the SCM works in this new age of AI. 

Nowadays, AI is an integral part of the competitive market. Businesses are constantly increasing their profit margin using AI. The supply chain market is volatile with the change in several factors and using AI businesses can keep up with the changes and make necessary changes in their system as needed. 

There are several ways in which AI helps in supply chain management (SCM). One of the most prominent methods it adopts is to analyze the available data, both internal and external. Here are some tips for supply chain management in this AI era.  

 

  • Plan for the IoT Data

 

The various data applications in the supply chain make up one-third of the total IoT data. So it needs proper planning to collect, integrate and utilize it. Since the volume of data is ever-increasing, it needs proper tools to manage it effectively and AI comes in as the best option. It can handle data collection of any volume and streamline it properly. 

While doing so, make sure to bring in variety with the data so that it can help with unprecedented methods and ways that detect any anomalies or disruptions in the SCM system. 

 

  • Make use of external data

 

In supply chain management, the volume of internal data itself can be vast. When using AI, one must also think outside the box and bring in outside data such as the local weather, customer reviews from external sources, vendor details, details about the competitor, etc to have a comprehensive database. 

 

  • Increase reactivity faster with AI

 

AI helps achieve a competitive edge in terms of responsiveness to any issues. It can detect problems and create alerts to take necessary preventive steps or find alternatives. 

 

  • Prioritize root cause analysis

 

AI is an effective tool in detecting issues and finding the root cause of the said problem. It can save time by early detection and gives an unbiased analysis of the root cause. 

 

  • Automation in the management system

 

AI can automate the various steps involved in SCM. It can automate administrative jobs, shipment updates, warehouse management, route planning, quality control, and shipping processes. The collective efforts can improve overall customer satisfaction or supplier selection. 

What do you need to study to become a supply chain analyst?

Supply Chain Management is a popular career option and many are eager to become supply chain analysts. But, what do you need to study to become a supply chain analyst? It requires you to get some kind of supply chain management training. 

Supply Chain Management Certification Course

Though a bachelor’s degree seems to be the basic qualification mark, having a master’s degree is an added advantage. To become an analyst one must take certification courses in the form of Professional Certification In Supply Chain Management & Analytics that provide expert guidance and job placement assistance. 

Conclusion

The popular AI-assisted processes in supply chain management are GPS tracking of the shipment for both the company and customer, regular weather updates to help the shipping industry plan their shipments, keeping inventory to help with warehouse management, etc. Depending on AI has helped businesses to reduce their cost, customize their products, and reach more customers with better customer satisfaction. 

The Fintech Bubble: Principles of investing in Fintech

Posted on by Imarticus Learning

Since its emergence, fintech has been one of the growing industries worldwide. People immediately preferred fintech services over financial services offered by traditional banks. Many fintech start-ups came in recent years and, some of them even became successful. The market cap of fintech is continuously increasing due to more and more customers preferring digital transactions.

Traditional banks are arranging fintech training courses for their employees to undergo digital transformation. If you are looking to invest in a fintech start-up or start your fintech firm, you should have a basic understanding of the fintech bubble. Read on to know about some principles for investing in the fintech industry.

Did you notice the fintech bubble exploded?

Gone are the days when only a handful of fintech companies were there in the market. At present, many fintech firms are competing with each other. In 2015, there were more than 350 fintech start-ups that caught headlines. However, the number of fintech start-ups decreased as the fintech bubble exploded.

Many fintech firms had already established themselves at the top and it got hard for newcomers. However, this does not mean that fintech training courses are of no use.

Even if the fintech bubble exploded, the global market cap of the fintech industry is continuously increasing. The predicted CAGR (Compound Annual Growth Rate) for the fintech industry is also high. The only thing that is challenging in the fintech industry is the increased competition. At present, you will have to compete with many fintech giants to build your market share.

The top fintech firms have already gained the trust of customers and, it is hard to displace their market. However, with the right business strategies and reliable services, you can still become a fintech giant even after starting late.

Principles for investing in the fintech industry

Some of the principles for those looking to invest in the fintech industry are as follows:

  • If you are buying shares of any fintech company, look for those that are continuously innovating. There is no compulsion that you should buy shares of a fintech giant. A fintech company that is constantly innovating itself is moving in the right direction.

  • If you are investing in a fintech start-up, look for the technology stack used by the fintech platform. Invest in a fintech platform that uses blockchain for making digital transactions secure and fast. A financial technology course can help you understand the technologies used for creating a fintech platform.

  • Invest in a fintech platform that offers many financial services to customers. Besides facilitating customers with digital transactions, a fintech platform can also provide P2P lending, gold/stock trading, and many other services. Third-party integrations also make a fintech platform more popular than others.

  • Due diligence is required before investing in the fintech industry. If you are starting your fintech firm, perform due diligence to know the right time to start. You should consider market disruptions, trends, and financial reports before investing in the fintech industry or starting your fintech firm. A financial technology course can help you understand the driving features of a fintech platform.

How to learn financial technology?

You should obtain a fintech certificate online to become an expert in financial technology. We at Imarticus Learning ensure industry-oriented FinTech courses that can help in knowing the industry practices. Besides investors or entrepreneurs, our fintech courses are beneficial for young enthusiasts looking to build their careers.

Our fintech courses will make you work on several hands-on projects and case studies. Job aspirants will also receive placement support to kickstart their careers in the fintech industry. Obtain your fintech certificate online with Imarticus right away!

Regression and classification metrics with python in AI/ML

Posted on by Imarticus Learning

Python is one of the most popular languages used in data science. It has a massive library that makes it easy for anyone to conduct machine learning and deep learning experiments. In this blog, we will be discussing regression and classification metrics with python Programming in AI/ML.  

We will show how to use some of these metrics to measure the performance of your models, which can help you make decisions about what algorithm or architecture might work best for your application or dataset!

What is a regression metric?

A regression metric measures how accurately a machine learning model predicts future values. To calculate a regression metric, you first need to collect predicted and actual values data. Then, you can use various measures to evaluate how well the model performs. 

How to use classification metrics with python Programming in AI/ML?

A classification metric or accuracy score measures how accurately a machine learning model predicts the correct class label for each data point in your training dataset. Once you have a classification metric, you can evaluate your machine learning model’s performance. 

You can use many different classification metrics to measure performance for a classifier machine learning model. Common ones include accuracy score, precision, recall, actual positive rate, and recall at different false-positive rates. You can also calculate the Matthews correlation coefficient (MCC) to measure how well your model performs.

Accuracy Score:

Accuracy score measures how often the predicted value equals the actual value. It’s also known as error rate, accuracy, or simply classification accuracy. You can calculate the accuracy score by dividing the total number of correct predictions from all predictions made.

Precision:

Precision is the number of correct predictions divided by the number of predictions made. 

Recall:

Recall, or valid positive rate is the number of correct predictions divided by the number of positives. You can calculate how well your model performs for different classes by plotting a ROC curve and calculating the AUC.

False Positive:

False-positive is also known as Type I Error or alpha error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class, but it belongs to another.

False Negative:

False-negative is also known as Type II Error or beta error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class but belongs to another, and the actual value isn’t present in training data. 

Matthews Correlation Coefficient (MCC):

The Matthews correlation coefficient measures how well your model predicts the labels of unseen instances from training data. 

Area Under Curve (AUC):

The AUC score measures how well your model predicts future values by plotting a ROC curve and calculating the area under it.

Discover AIML course with Imarticus Learning

This artificial intelligence course is by industry specialists to help students understand real-world applications from the ground up and construct strong models to deliver relevant business insights and forecasts. 

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies.
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners.
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

Contact us via the chat support system, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon.