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Day: September 19, 2022

Understand Amazon’s Supply Chain

Posted on by Imarticus

Understand Amazon’s Supply Chain

If you are thinking of a supply chain management career, then you must learn how experts from different industries plan and execute their supply chains. One example of supply chain management that you can refer to is the American multinational e-commerce giant Amazon which has become a household name by winning the trust of millions of its customers.

Amazon made its debut in 1994 as a book-selling website. Today, it features one of the largest and most efficient supply chain models ever seen around the globe. How did Amazon achieve this? What are the fundamentals it focused on for the management of its supply chain? Let’s find out!

Before we get into the details of Amazon’s supply chain, an aspiring supply chain professional like you needs to get the conceptual basics correct so that you can understand these real-life examples well and in their entirety. 

For this, you may wish to enrol in our IIT Supply chain management certification course which equips you with the fundamental concepts of the field that are necessary for making data-driven strategic business decisions!

All About Amazon’s Supply Chain

Amazon’s Supply Chain refers to the entire process under which a product on offer by Amazon undergoes in its life journey. This is the process that starts right from the moment a product reaches Amazon’s warehouse, enters its inventory records, and goes on till the time it gets delivered to the customer.

To offer a robust platform to its suppliers, Amazon provides them with two types of fulfillment options – 

 

  • Fulfillment By Amazon (FBA) – Under this option, Amazon is responsible for storing, packaging, as well as shipping the product to the customer.
  • Fulfillment By Merchant (FBM) – Under this fulfillment option, the responsibility of storing the products in the warehouse, packing, and shipping them to the customers rests with the Merchant.

 

How Does Amazon Supply Chain Work?

Over the last 2 decades, Amazon has made several changes to the working of its supply chain, in a bid to crack an efficient system with which it can offer the fastest delivery options like same-delivery to its customers. 

As soon as Amazon receives an order, products get arranged into pallets either manually or with forklifts into the warehouses, where they get packed and screened for quality checks. After receiving a go-ahead from clearances, Amazon’s logistics team enters the supply chain and completes the delivery of the orders.

To achieve this efficiently, the e-commerce conglomerate puts its primary focus on two elements of the chain – Storage and Delivery. Let’s uncover how Amazon takes care of these two areas.

  • Storage 

Amazon has more than 20 Crore customers globally who have subscribed to its Prime membership, which means, it needs to deliver a huge number of its orders in just 1 day or a maximum time of 2 days.

To fulfill this promise of fast delivery, Amazon maintains a huge network of fulfillment centers for storing its products. Amazon currently has 185 fulfillment centers spread across different cities of the world with a carpet area of up to 10,00,000 square feet!

This huge storage capacity helps Amazon maintain a cutting edge over its competitors and maintain a smooth fulfillment of its orders. 

Another important aspect that Amazon has taken into consideration for its warehousing is its location. Most of its fulfillment centers are located near metropolitan cities from where it received huge and regular demand.

Moreover, to speed up warehouse operations, Amazon has deployed over 10 Lakh employees in its fulfillment centers globally. It also uses robots, drones, and other high-end technologies to run its warehouse ecosystem with greater efficiency. 

  • Delivery

The second most crucial segment of Amazon’s Supply Chain is its logistics and delivery teams. Amazon ships products mainly via two channels – road and air.  Amazon maintains a huge fleet of trucks, bikes, and vans for transporting products via roads. It has also partnered with various delivery services including FedEx and UPS.

Amazon also has a cargo airline of its own called Amazon Air which is used for transporting products. Amazon Air makes more than 160 flights every day with more than 85 planes, which include 11 Boeing Jets.

This is how Amazon can meet and many times exceed its customer’s expectations in terms of fulfillment and timely delivery of their orders!

Take Away

A Supply Chain Management System is an area that can either make or break a business. If you are interested in making an impact on a business by becoming a part of its supply chain decision-making, you must get a supply chain analytics certification today!

Want to know more about the supply chain management career options? Contact us through chat support or visit our nearest training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, 

Learning Hadoop Online – Your Guide To Building A Career In Big Data

Posted on by Imarticus

Learning Hadoop Online – Your Guide To Building A Career In Big Data

Big data is in demand because it has the potential to change the way we live and work. It can also help businesses solve problems faster, make better decisions, and deliver better customer experiences. 

At a projected CAGR of 13.4% over the forecast period, the global big data analytics market can increase from $271.83 billion in 2022 to $655.53 billion in 2029. With this growth also comes an increased need for professionals who understand how big data works and how organizations across all industries can use it.

What is big data?

Big data is a term used to describe the collection, storage, and analysis of large amounts of data. Data collection volume is increasing exponentially. The size of the data gets measured in terabytes. Big Data technologies have been around for years, but recently they have become viable tools for decision-making in business and government. 

Why is it in demand?

Hadoop and Bigdata are a buzzword. The term “big data” has been around for a while, but it’s only recently that businesses have used this phrase in their marketing materials and press releases. As more companies begin using big data analytics, it’s essential to understand what exactly you’re getting yourself into when you decide to go down this path of working with Hadoop—and what career opportunities exist there!

Hadoop allows users access to large datasets by processing them through various methods (e.g., MapReduce). There are many benefits associated with using Hadoop: they can get used across multiple industries, including finance, retailing, and healthcare; they allow organizations access to real-time insights into their customers’ buying habits; they provide improved operational efficiency through reduced costs on hardware infrastructure maintenance over time.

Role of the Hadoop developer.

A Hadoop developer is a person who has a strong knowledge of the Hadoop platform and can design and develop applications on it. They can be an analyst, software engineer, or data scientist.

The primary role of a Hadoop developer is to work with different tools and technologies to develop big data solutions using MapReduce functions, Pig scripts, and Hive queries. They also help manage storage requirements by using HDFS, which stores large amounts of data locally within clusters without downtime due to network issues.

Why do you need Hadoop online training and certification?

It is an open-source software framework for storing and processing big data. It has become a core technology for many companies, including Facebook, Yahoo, and Amazon. The tool has emerged as one of the most popular technologies in big data analytics over the years because it can efficiently process vast amounts of data. 

Discover a career in data analytics with Imarticus Learning

With this machine learning course, the student can learn data analytics and receive job offers. Learn machine learning using data science that produces vital business forecasts and insights by applying your new knowledge to practical use.

Course Benefits for Students:

  • The most widely used data science tools and methodologies, as well as data analytics and the fundamentals of machine learning, will be familiar to students.
  • To receive a data analytics certification course, students must complete 25 real-world projects and case studies directed by business partners. 
  • Using a data analytics tool to display data is one of the most in-demand skills in the market. Therefore, recent graduates and those starting their careers might want to think about enrolling.

 Contact us through the chat support system, or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

The Growth of Derivatives Markets in Emerging Economies

Posted on by Imarticus

The Growth of Derivatives Markets in Emerging Economies

Derivatives markets have been around since the beginning of time, becoming increasingly important as financial markets have evolved. However, derivatives are not just for large corporations; individuals can also use them to hedge against risk in their personal lives. 

This guide explores the evolution of derivatives markets in emerging economies, how that growth has affected financial systems there—and how this could change as more countries develop these markets!

Using derivatives in Emerging Markets.

Derivatives turnover has increased more quickly in emerging markets than in developed nations. Foreign exchange derivatives get the most frequently traded of all risk categories due to frequent currency turnover in emerging market currencies and an increasing share of cross-border transactions. 

The offshore trading of many emerging market currency derivatives has increased along with the global reach of emerging Asia’s financial centers. Trade, economic activity, and per capita income positively correlate with growth in derivatives turnover.

Derivatives are financial instruments generally based on other assets’ values. Derivatives can use for several purposes, including hedging risk or speculation. Most derivatives manage risk and reduce exposure to market movements by transferring it from one asset class to another.

The evolution of derivatives markets in emerging economies.

Derivatives markets are a significant part of financial systems in emerging economies, where the growth of derivatives gets driven by the need to hedge against risk. The evolution of derivatives markets in emerging economies can track back to the late 1980s when central banks began using them to manage domestic inflation and currency exchange rates. 

Impact of Derivatives Markets on Financial Systems.

The derivatives market has a positive impact on financial systems. Derivatives markets help in managing risk and make the financial system more efficient. They also provide liquidity to the market and help in price discovery.

The derivatives markets’ internationalization is driven by technological advances that have made it easier for investors to access information about global markets and regional integration through free trade agreements (FTAs). These two factors have resulted in a significant increase in cross-border trading activity over time.

Benefits of derivatives markets in EM

Derivatives markets are a great way to reduce risk and provide liquidity and transparency. This can help you manage interest rates, currency, commodity, and equity risks. It also reduces volatility in the market as well as helps manage credit/inflation risks.

The derivatives market has increased in emerging economies, with various benefits for investors. The benefits are not limited to the financial system or even the economy but extend into multiple sectors of society.

Learn investment banking courses with Imarticus Learning

With the help of the Certified Investment Banking Operations Professional program, students can begin a career in the financial markets. Students who complete this investment banking certification course will have the skills and knowledge necessary to succeed in banking, treasury, and clearing services at all stages of production. The investment banking course syllabus covers financing, strategic planning, financial management, and the valuation of investment prospects.

Course Benefits for Learners:

  • Students will gain knowledge of financial services, including managing complex securities and derivative products and their trade-life cycles. 
  • Students will get the assistance they need to start their careers in investment banking.
  • Students who complete the derivative markets training program and money market course will receive an industry-recognized certificate.

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

Understanding fintech in the cloud computing spectrum

Posted on by Imarticus

Fintech has been on the rise as a significant player in the financial sector in recent years. As we continue to see a shift towards digital solutions and away from traditional financial institutions, it’s essential to understand Fintech and how it operates within the larger landscape of cloud computing. 

In this blog, we will explore the world of Fintech and how it relates to cloud computing. We will look at the most popular fintech applications and see how they use the cloud to power their businesses. 

An overview of Fintech

 Fintech is a broad term that covers a wide range of financial technologies. These can include mobile payments and digital wallets to investment management and blockchain. If a financial service or product you can deliver digitally, it can be considered Fintech.

Fintech is growing for many reasons:

There is a growing demand for digital financial services. More and more people are using their smartphones and computers to manage their finances, and they are looking for convenient, user-friendly solutions.

Traditional financial institutions are starting to embrace Fintech. Banks and other financial companies invest in fintech startups, partner with fintech companies, and launch their fintech products.

The rise of mobile devices and digital platforms has made it easier for fintech companies to reach consumers and businesses. Fintech is also benefiting from the growth of cloud computing. Cloud-based solutions are often more scalable and flexible than on-premises software, a significant advantage for fintech companies that constantly innovate and expand their offerings.

How Is Fintech Related to Cloud Computing?

Fintech relies heavily on cloud computing. Most popular fintech applications would not be possible without the cloud. 

Here are a few examples:

  • Online banking: Customers can access their accounts from anywhere globally with an internet connection. It would not be possible without the cloud.
  • Trading platforms: Stock traders need to quickly access their portfolios and place trades. The cloud enables this by providing the necessary infrastructure.
  • Payment Processors: Fintech companies use the cloud to process payments.

The cloud provides the flexibility and scalability that fintech companies need to grow and scale quickly.

Discover Career in Fintech with Imarticus Learning

The Professional Fintech Certification is the first of its kind online FinTech course, providing extensive knowledge of all elements of FinTech and comprehensive, strategic, and hands-on training with several technologies, including API, Blockchain, Cloud Computing, AI, Machine Learning, IoT, and RPA.

Course Benefits for Learners:

  • Financial technology courses will educate students on applying machine learning algorithms and blockchain technology to their business needs, ensuring that they are well-equipped!
  • The future of Fintech is bright, and learners can stay up to date on the newest advances by attending networking events, job boards, or webinars.
  • With this fantastic new resource, students will have access to some helpful tools that can assist them in finding their dream job after graduation!

Machine Learning A-Z: Hands-on Python and R in Data science

Posted on by Imarticus

R and Python are both equally great programming languages. However, each has its own set of advantages that it offers to the user. For example, when we are talking about development processes or IT operations, Python is always a better option. But when it comes to statistical tasks or analytics, R can prove to be a much more suitable alternative.

This is because R has been created for Statisticians and for statistical projects. Skilled programmers can, however, employ any of the two languages to perform any task. For instance, Python can be used for using statistical techniques like Regression Analysis or Bayesian inference on datasets. Similarly, R can also be used for building new Data Science tools and data models. 

Python is quite easy to learn with simple syntax while R has various language boundaries. One can easily learn Python online with the help of a course. However, R is far superior in graphical and statistical procedures. R is more scientific in nature and will be much easier for statisticians or professionals who have worked with MATLAB, another scientific programming language.

Python is more focused on running algorithms and makes it easy to build programs in general. In Data Science, however, both are equally important and competent languages. R and Python can work with massive databases and are equally good at Machine Learning projects. 

R in Data Science

R is used in Data Science for graphical and statistical purposes. This language can help users create advanced visualizations and high-quality graphics as well as dynamic graphics. R is also great for data mining and statistical computing in general.

R has a set of functions that allows datasets to be loaded into the memory with program statements, but that is only required if you are going to be building R programs that will keep getting used for various functions. Otherwise, you can simply use the data import function in R Studio (IDE for R). R is able to work with data in two different formats in R studio, CSV and TXT. Once you select the dataset you wish to be working with, R will load the dataset and you can then work on it.

Python in Data Science

Python is a high-level programming level that does not require compilation before running code. This makes it very fast to use for all kinds of projects, including Data Science and Data Analytics. Python is extremely flexible and is a multi-paradigm language, thus, allowing Data Scientists to use different approaches and keep improvising the language with extensions and plugins.

Before loading a dataset in Python, you must first import pandas (library) and install them in Python. After that, a custom function must be created that loads the dataset for you. By creating a load_csv function, you can fundamentally provide an argument for your dataset’s file path.

Also, the readlines() are used in order to ensure that the program returns a list containing the lines inside the .csv dataset. You can also additionally present the data in a more visually appealing way by returning the dataset in a data frame format. This makes it easier to view the data as compared to the native list format or NumPy arrays.

Conclusion

Both the languages also are open-source and have huge communities behind them to keep providing new libraries. There are also a good number of updated tools and powerful IDEs (Integrated Development Environments) available for both R and Python.

If you wish to learn Python or R for Data Science, you should definitely opt for a solid Post Graduate Program in Data Science. You can also choose to take up a Machine Learning course with a placement or a Data Analytics course with placement as well.