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Category: Management

AI, Data Science, Machine Learning Terms You Need to Know in 2022!

Posted on by Imarticus Learning

In the present paradigm of technical knowledge, it is imperative to be aware of certain concepts to survive and thrive. Whether you are pursuing a career in artificial intelligence (AI), have a cursory interest in data analytics, or simply wish to broaden your horizons, here are some artificial intelligence, data science, and machine learning terms you need to know in 2021. Read on…

  1.     Natural language processing: 

Both humans and computational devices use their own modes of language to communicate and share ideas to the extent of imparting and debating on the information. The languages, however, are different in their basic forms and formats. Using natural language processing, or NLP, artificial intelligence can decipher many human languages to suit specific functions that may range from the academic study of linguistics to providing utility to hearing-impaired people.

  1.   Data warehouse: 

A data warehouse, as the name suggests, contains a large ensemble of data pertaining to businesses and learnings from past successes and failures to provide better services. One who is not entirely proficient in data architecture may yet take the advantage of data warehouses to gather business analytics courses and make far better decisions. This method allows one to find new ways to process old data and change future iterations of that data with his/her actions. 

Career In Data Analytics   Data journalism: 

This is a mode of journalism that is slowly gaining greater prominence and is proving its necessity in combating the ever-growing trend of fake news. In this form of news reporting, one focuses on proving his/her assertions through the collection and presentation of reliable data. This may be done through human and/or AI collection and calculations. Soon, we may be able to have a collated base of data obtained through AI learning. This will make it very hard for individuals and/or groups to spread misinformation.

  1.   Deep learning:

This uses artificial intelligence to construct structures that mimic the human neural network – starting from simple problems to finding layers of hidden information. Meanwhile, it makes errors and learns from them with the program often ending up with a different solution than what was expected by its programmers and set parameters. Using this process, we can identify and solve possibly any real-world problem. The degree of human supervision in this process can be ascertained at various levels of this process.

  1.   Cybersecurity: 

Both defenders and attackers of databases are getting smarter, escalating the never-ending battles between cybersecurity and hackers. Often, the strategies used by either group are similar to the point of being indistinguishable. Here, any large organization employs AI and/or deep learning to be one step ahead of the threats that plague them.

The above-mentioned terms are only the tip of the iceberg when it comes to talking about new technology-related topics. Hopefully, they have provided you with new avenues to look into as per your interests, or at least recapitulated some of the basic terminologies.

How to succeed in your supply chain management and analytics job search

Posted on by Imarticus Learning

Introduction

The field of supply chain management is exploding. Several new technologies are coming into the space, making it a very lucrative industry. From e-commerce companies to manufacturing companies, all of them use supply chain planning and management. If you want to make a successful career in supply chain management, you can take a supply chain management course with analytics and make yourself ready for the industry. 

There are hundreds of opportunities in the supply chain management area. You can either become a supply chain analyst or a manager in the area based on your experience. Let’s check out how you can make a career in supply chain management.

Making a Career in Supply Chain Management

If you are willing to succeed in supply chain management, you will have to brace yourself for an incredibly high-paced environment. You’ll have to figure out how to save money on inventory and shipping. However, you must prioritize customer service in everything you do. You’ll also have to deal with rising manufacturing costs and monitor the entire process. To summarise, to get the supply management career of your dreams, you’ll need a diverse set of talents.

Understand the career paths

The tasks of supply chain management are numerous. It all depends on the particular career route you choose. You must take some time and do your study to find a course that you can stick with. You can go for a supply chain analytics course training to make yourself aware of the job.

You can also work as a supply chain analyst, which entails tracking and analyzing a portion of the supply chain. You’ll also need to concentrate on process streamlining and ongoing improvement. Working as a buying manager, you’d be in charge of supplier interactions and contract negotiations. You could also work as a Demand Planner, Supply Planner, Transportation Analyst, or Inventory Specialist.

Highlight your skills on your resume

You’ll need to demonstrate softer qualities in addition to your great data analysis and financial prowess. Update your resume to reflect a commitment to effective communication. Include anything that has to do with relationship management and bargaining.

You should also demonstrate that you pay close attention to details. This is crucial because you’ll almost certainly have to undertake site audits. You can also compare and contrast various modes of transportation. To kickstart your supply chain management career, you may need to develop some of these talents. Look through internet job postings and recruitment websites to learn more about these changes.

Compensate for your lack of experience

It will be challenging to transition directly from a logistics course or a profession in a related field to supply chain management. Still, there are methods to compensate for the lack of experience. Internships are one way to get into a supply chain management field that requires prior experience, but they’re generally out of reach for experienced professionals wishing to change careers.

Training and certificate courses, which involve a minimal time commitment and provide a résumé, are an alternate kind of education offered.

Conclusion

We at Imarticus are known for our supply chain management courses. Several SCM courses will help you crack the industry code and then perform well in the industry. We also offer a Professional Certification in Supply Chain Management and Analytics by IIT Roorkee.

This course highlights everything that is there to know about supply chain management, and it also covers the application of analytics in the course. You can take up this course if you are interested in having a career in supply chain management. You will also get a certificate on the completion of the course, which you can showcase in front of the recruiters. It’s time to enroll in the course!

Overcoming privacy challenges in supply chain planning

Posted on by Imarticus Learning

Introduction

Supply Chain Planning is a crucial step in the supply chain management process. When you plan the several factors that can affect your supply chain process, you need to consider the privacy concerns that may arise out of it. Preserving any data involved in the supply chain planning process is crucial, and you should take all the necessary actions to ensure that privacy is maintained.

If you aspire to have a career in supply chain management, you can take up a supply chain analytics course to understand how privacy can be maintained in the entire process. 

A supply chain analytics certification will help you understand the privacy concerns but will also aid in understanding the entire supply chain division with both the advanced and the basic concepts. 

Ways in which privacy challenges can be tackled 

There are several ways to overcome the privacy concerns you might face while supply chain planning. Some of these ways are:

 

  • Go Digital

 

Switching from traditional processes and technology like fax, phone, and email is slow but necessary. Your organization establishes secure data transfer within your group and external trading partners, suppliers, and customers by upgrading to contemporary technologies.

Updating your software and procedures offers you access to data security methods such as:

  • Encryption
  • Tokenization
  • Monitors and alarms for file access
  • Preventing data loss.

You can train your people on how fraud can be prevented by using a digital system and how cybersecurity risks can be reduced if the company’s focus shifts to the digital growth and digitization of the entire supply chain process.

 

  • Encryption and Identification of Data

 

The National Institute of Standards and Technology (NIST) recommends businesses build defenses on the assumption that a breach would occur. As a result, you must protect all forms of data you store or communicate. You can utilize discovery tools to locate and classify files holding confidential information, financial data, or personally identifiable information. 

With this comprehensive view of all your data, you can implement current encryption standards to safeguard your most precious assets. As firms become increasingly reliant on online transactions, enhanced controls such as digital signatures, session breaks, and multi-factor authentication can help to strengthen supply chain security.

 

  • Risk Management through third-party

 

More and more organizations in the supply chain are collaborating to store, transport, and use data. This necessitates more comprehensive risk management, including end-to-end security.

Shared risk assessment among stakeholders is the foundation of effective third-party risk management. You’ll need to break down barriers between your technical and business teams, as well as enlist the help of vendors and partners. You can protect the supply chain’s most valuable assets by banding together. After that, you can determine the extent of any potential operational damage. If data is poorly managed, compliance issues, or any data breach, interference would be required immediately.

Conclusion

We at Imarticus aim to provide the best courses that help make all the students seasoned industry experts. You can enroll yourself in the Professional Certification in Supply Chain Management and Analytics by IIT Roorkee and Imarticus learning. This course in the SCM domain will make you ready for all the challenges that you will come across in the supply chain industry. 

Also, with a certification in supply chain management, you can get into high-paying jobs where you can prove to your recruiters that you are trained to work in the industry. Handling privacy issues is vital in the supply chain industry, and a course will help you understand and manage these issues. Enroll in this course today and reap all the benefits that the course has to offer.

Here’s how to develop a NLP model in Python

Posted on by Imarticus Learning

NLP or Natural Language Processing is one of the most focused upon learning models in modern times. This is especially due to how popular chatbots, sentiment analytics, virtual assistants, and translation tools have become. NLP empowers machines with the ability to process, understand and get meaning out of textual data, speech, or human language in general.

NLP allows other applications or programs to use human language. For example, the NLP model that powers Google understands what the user is searching for and fetches the results accordingly. Python online training can definitely help when one wishes to delve into NLP.

NLP models go much further than just finding the exact type of information and can also understand the context of the search or the reason and fetch similar or related results as well. NLP-powered machines can now identify the intent and sentiment behind the human language.

Developing Learning Models in Python

Python is a great language to use for NLP models as one can take the help of the NLTK package. The Natural Language Toolkit is an NLP package for Python. Additionally, you can also install the Matplotlib and NumPy libraries in order to create visualizations.

First, you need to have Python 3.5 or any of the later versions installed. After this, you must use pip install for installing packages such as NLTK, LXML, sklearn. If you decide to work with random data, you must first preprocess the data. You can use the NLTK library for text preprocessing and then carry on with analyzing the data. 

Here are the 4 steps involved in developing a learning model using Python:

  • Loading and data preprocessing
  • Model definition
  • Model training
  • Model evaluation

How to Develop an NLP Model using Python

Let us learn how to develop an NLP Model in Python by creating a model that understands the context of a web page. Once you have installed the NLTK library, you should run this code to install the NLTK packages:

import nltk

nltk.download()

After this, you will be asked to choose the packages you wish to install, since all of them are of very small size, you can install all of them.

Then, you must find a web page that you want to process. Let us take the example of this page on computers. Now, you must use the urllib module for requesting websites:

import urllib.request

response =  urllib.request.urlopen(‘https://computer.fandom.com/wiki/Main_Page)

html = response.read()

print(html)

Now, we can use the Beautiful Soup library for pulling the data out of the XML and HTML files. Also, this will help us clean the text of HTML tags.

Once, this is done, we can go ahead with converting the text into tokens using this:

tokens = [t for t in text.split()]

print(tokens)

Once the output is returned as tokens, we can use the FreqDist() function in the NLTK library for removing unnecessary words such as (for, the, at, a, and etc.) from our text and then plot a graph for the words that occur the most number of times. After this, the model identifies the most relevant words and then the context of the web page.

Conclusion

The auto-completion suggestions that we are given, the voice searches that our devices carry out for us are all possible with the advancements we have made in NLP. The PG in Data Analytics and Machine Learning offered by Imarticus is a great Data Analytics course with placement and can definitely help you delve deeper into concepts such as Deep Learning and ANN (Artificial Neural Network).

How Machine Learning is Reshaping Location-Based Services?

Posted on by Imarticus Learning

Today life is a lot different from what it used to be a decade ago. The use of smartphones and location-empowered services is commonplace today. Think about the driving maps, forecasts of local weather and how the products that flash on your screen are perhaps just what you were looking for.

Location-enabled GPS services, devices that use them and each time we interact and use them generates data that allows data analysts to learn about our user-preferences, opportunities for expansion of their products, competitor services and much more. And all this was made possible by intelligent use of AI and ML concepts.

Here are some scenarios where AI and ML are set to make our lives better through location-based services.

Smart real-time gaming options without geographical boundaries.
Automatic driver-less transport.
Use of futuristic smartphone-like cyborgs.
Executing perilous tasks like bomb-disposals, precision cutting, and welding, etc.
Thermostats and smart grids for energy distribution to mitigate damage to our environment.
Robots and elderly care improvements.
Healthcare and diagnosis of diseases like cancer, diabetes, and more.
Monitoring banking, credit card and financial frauds.
Personalized tools for the digital media experience.
Customized investment reports and advice.
Improved logistics and systems for distribution.
Smart homes.
Integration of face and voice integration, biometrics and security into smart apps.
So how can machine learning actually impact the geo-location empowered services?

Navigational ease:

Firstly, through navigation that is empowering, democratic, accurate and proactive. This does mean that those days of paper maps, searching for the nearest petrol station or location, being late at the office since the traffic pileups were huge and so many more small inconveniences will be a thing of the past. We will gracefully move to enhanced machine learning smartphones that use the past data and recognize patterns to inform us if the route we use to commute to office has traffic snarls and provide us with alternative routes, suggest the nearest restaurant at lunchtime, find our misplaced keys, help us locate old friends in the area etc all by using a voice command to the digital assistant like Alexa, Siri or Google.

Machine Learning can make planning your day, how and when to get to where you need to be, providing you driving and navigational routes and information, and pinging you on when to leave your location a breeze. No wonder then that most companies like Uber, Nokia, Tesla, Lyft and even smarter startups that are yet to shine are investing heavily on ML and its development for real-time, locational navigational aids, smart cars, driverless electric vehicles and more.

Better applications:

Secondly, our apps are set to get smarter by the moment. At the moment most smartphones including Google, Apple, Nokia among many others are functioning as assistants and have replaced those to-do lists and calendar keeping for chores that include shopping, grocery pickups, and such.

Greater use of smart recommendatory technology:

And thirdly, mobile apps set smartphones apart and the more intelligent apps the better the phone experience gets. The time is not far off when ML will be able to use your data to actually know your preferences and needs. Imagine your phone keeping very accurate track of your grocery lists, where you buy them, planning and scheduling your shopping trips, reminding you when your gas is low, providing you with the easiest time-saving route to commute to wherever you need to go and yes, keep dreaming and letting the manufacturer’s know your needs for the future apps. The smart apps of the future would use your voice commands to suggest hotels, holiday destinations, diners, and even help you in budgeting. That’s where the applications of the future are headed to.

In summation, ML has the potential to pair with location-using technologies to improve and get smarter by the day. The future appears to be one where this pairing will be gainfully used and pay huge dividends in making life more easily livable.

To do the best machine learning courses try Imarticus Learning. They have an excellent track record of being industrially relevant, have an assured placement program and use futuristic and modern practical learning enabled ways of teaching even complex subjects like AI, ML and many more. Go ahead and empower yourself with such a course if you believe in a bright locational enabled ML smart future.

The best research and investment tools for a machine learning course

Posted on by Imarticus Learning

As machine learning becomes more popular, many people look to get into the field. But what are the best research and investment tools for a machine learning course in 2022?

This post will discuss the critical tools you will need to succeed in a machine learning course. So, if you are pursuing a career in machine learning, make sure to read this blog post!

Why are research and investment tools necessary?

Research and investment tools are essential because they allow you to research and invest in new technologies. In a machine learning course, you will need to complete a lot of research to keep up with the developments in the field. Additionally, you will need to invest in new technologies to improve your skillset. Thus, research and investment tools are essential for any machine learning course.

What are some of the best research and investment tools?

Many different options are available for research and investment tools for machine learning. Each has its benefits and weaknesses, so choosing the right tool for your needs is crucial.

Here are some tools for machine learning course in 2022:

#01: Python

Python is one of the most popular programming languages for machine learning. It has a large community, and there are many open-source libraries available. Additionally, it is easy to learn, and you can use Python in your research projects because it is an interpreted language with dynamic typing and garbage collection.

#02: TensorFlow

TensorFlow is a popular open-source library for machine learning. Google developed it, allowing you to perform complex mathematical operations on data. TensorFlow is also widely used in the industry, so it is a great tool to learn if you want to pursue a machine learning career.

#03: Keras

Keras is an open-source neural network library written in Python. François Chollet developed it, and it allows you to design quickly and train deep learning models using a few lines of code.

#04: PyTorch

PyTorch is another popular machine learning framework based on Torch, an open-source machine learning library. PyTorch is for deep learning, and it allows you to develop and test your models quickly.

These are just a few research and investment tools available for machine learning courses in 2022. Make sure to explore all different options before choosing the right tool for your needs.

Discover Artificial Intelligence And Machine Learning Course with Imarticus Learning

This IIT AIML course gives students the skills they’ll need for positions in today’s digital workplace. This intensive Artificial Intelligence certification will prepare the student as a data scientist, analyst, or engineer-a professional who can use AI tools from machine learning through reinforcement algorithms and deep neural networks while developing their understanding of how these technologies work under different circumstances.

Course Benefits For Learners:

  • The Expert Mentorship program provides AIML expertise through practical experience for those who want to learn more about this exciting field of study, leading them to careers as artificial intelligence professionals or experts!

  • This course will help students gain access to attractive professional prospects in Artificial Intelligence and Machine Learning.

  • Academic professors will help students construct Data Science concepts, while industry specialists will teach students how to utilize Machine Learning, Deep Learning, and AI approaches in real-world applications.

How a machine learning course will transform your resume in 2022?

Posted on by Imarticus Learning

An artificial intelligence (AI) technology that trains computers to learn and better itself based on experience without being explicitly designed is termed Machine learning (ML). It is a set of computer programs trained to retrieve and use data. Machine learning enables computers to observe the data and provide a result without any human intervention or observation.

Machine Learning with Python

AI is the machine intelligence that leads to the practical solution to the problem, and machine learning takes AI technologies a step further by employing algorithms to examine data, learn, and make intelligent conclusions. 

For AIML, the program developers use the programming language python because it has many libraries and frameworks to make coding easy, and it also saves time.

Thus, machine learning is all about application, and if you know python, you can grasp machine learning fast. To implement anything, you should know how to code it.

Machine Learning Course

At Imarticus, we offer you an extensive program to become a data scientist, data analyst, machine learning engineer, or AI engineer, and, by becoming analytics, you can build machines and systems that will react as humans do.

In the Data analytics certification, we will teach the technique to create a machine learning model that will accurately work to give suitable and best outcomes. We will develop your analytical abilities to choose the correct algorithm as per the model compatibility and your requirement.

The first requirement of a machine learning model is data collection and its interpretation. Therefore, at Imarticus, we give you the knowledge of data manipulation, analysis, and visualization. 

As analytics, you learn to extract ideas from your team, choose proper tools, use a machine learning framework, and stay up to date with the latest development. 

The key responsibilities of analytics are:

  • Collect data, study, and then convert it into data science prototypes
  • Research for the appropriate machine learning tools and algorithm
  • Build a machine learning application that will meet the industry requirement
  • Choose the correct data and the visualization methods
  • Perform machine learning tests
  • Execute statistical analysis from the test results.
  • Set the model for accurate results

Machine Learning Resume

Your resume is your introduction and first impression for recruiters, but writing perfect codes and preparing a good model may not get you your dream job. You have to delve deeper.

Furthermore, if you want to survive in the job market, you should not only have the skills, but you should also know how to endorse these skills to your name. Furthermore, you should have an exceptional and organized resume. Hence, you must include the following points in your resume:

  • You are a certified machine learning engineer
  • Briefly mention your projects and your contribution
  • Describe your work experience in one-liner points
  • List down every information in reverse chronological format
  • Prepare a summary of your resume while highlighting your contributions

 Machine learning has a promising future, and these professionals are high in demand. At Imarticus, we know this so, the expert mentors will give you a practical understanding of AIML. They will help you to develop skills to unlock lucrative career opportunities. 

A Complete Guide On How To Approach A Machine Learning Problem For Beginners!

Posted on by Imarticus Learning

As beginners in machine learning, you will want to have questions answered to common problems. Questions like how to approach, how to start, which algorithm fits best, and so on.

Common problems in machine learning for beginners

Here, we will help you resolve those problems by answering common questions:

Where can you use machine learning?

You can use machine learning for problems when:

  • Automation is involved
  • Learning from data is needed
  • An estimated outcome is required
  • Need to understand pattern like user sentiments and developing recommendation systems
  • Object required to identify or detect an entity

How to solve machine learning problems?

Here are steps to solve problems in machine learning:

  • Read data from JSON and CSV
  • Identify dependent and independent variables
  • Find out if there are missing values in the data or if it is categorical
  • Apply pre-processing data methods if there are missing data to bring it in a go to go format
  • Split data in groups for testing and training for concerned purposes
  • Spilt data and fit into a suitable model and move on validating the model
  • Change parameters in the model if needed and keep up the testing
  • An optional step is to switch algorithms to get different answers to the same problem and weigh the accuracies for a better understanding – this explains the accuracy paradox
  • Visualize the results to understand where the data is headed and to explain better while representing it

What algorithm should you use?

You need to understand what labelling is to answer this. Labels are the values we need to make an estimate. This represents the Y variable, also known as the dependent variable.

Here is a small example to help you understand this:

if

dependent_variable_exists==True:

supervised learning()

else:

unsupervised learning()

Machine Learning CourseWhile you’re learning from a machine learning course, you will understand that your supervision and training refers to supervised learning. This means that the results need to be compared by a frame. The frame here is the dependent variable. However, there is no reference for frame under unsupervised learning, which is why the name.

It is time to figure out how algorithms are served. However, it is essential to note that this is a generalized approach. The situations can differ, and so will be the usage of algorithms:

  • Numeric data for linear regression
  • Logistic regression when the variable is binary
  • Multiple category classification through a linear discriminant approach
  • Decision Tree, Naive Bayes, KNN, and Ensembles for regression and classification

Machine Learning Course

As you grow in your machine learning career, you will learn how to take random XG boost, forest, Adaboost, among other algorithms for ensembles. You can try these for both regression and classification.

Ensembles, as the name goes, refer to a group of at least two classifiers or regressors. Moreover, it doesn’t matter if it is the same or if working towards the same goals.

Building visualizations

Here are some of the things to remember when visualizing reports:

  • You can show class clustering with a scatter plot
  • Avoid scatter plot if there are several data points
  • Class comparisons can be explained through histogram
  • Creating pie charts help comparative breakdown
  • Line charts can help analyze reports with frequent deviations like stocks

If a scatter plot has too many data points, it will look clumsy. It will not be a presentable representation to show stakeholders. In such cases, you should use scatter charts.

Final thoughts

These points will help a beginner in machine learning career to become more aware of how to solve problems. You now know the essential things to do and things to avoid to get accurate results.

Developing digital health care solutions with an artificial intelligence and machine learning course

Posted on by Imarticus Learning

In the current times, digitization is seen in every sector, and healthcare organizations are not far behind. Artificial intelligence with machine learning and algorithms is the newest aspect of the technological developments that can help to automate various processes.

If you are interested in implementing AI in healthcare, you can opt for Imarticus Learning’s artificial intelligence and machine learning course. The course includes relevant use of technology across industries, including healthcare. 

How to Implement Artificial Intelligence and Machine Learning in Healthcare? 

Artificial intelligence has various roles in the healthcare industry. If you choose to get an artificial intelligence certification, you will learn more about the following aspects. 

 

  • Prediction of Treatments

 

Artificial intelligence and machine learning can be implemented for the accurate analysis of patient information. AI solutions can analyse medical conditions and help doctors arrive at accurate treatment plans that will be beneficial to the patients. While reviewing all medical information is necessary for correct diagnosis, doing so manually increases workload and may even lead to errors. Artificial intelligence and machine learning can automate specific processes and ensure error-free treatment plans. 

 

  • Improvement of Workflow

 

From the IT infrastructure in healthcare organizations to diagnostic tasks, workflows can be automated and optimized. This will improve business processes and ensure better outcomes. All organizational tasks will be seamless and less time-consuming. 

 

  • Detection of Anomalies

 

Most healthcare organizations include digital databases and rely on workflow automation. While AI can assist in automation, it can also monitor the entire system. Failure of systems in any industry leads to loss, however, in the healthcare industry, anomalies can lead to loss of lives and not just revenue. Therefore, it is important to use artificial intelligence and machine learning tools to detect gaps within the system so that professionals can take better precautions. 

 

  • Introduction of Opportunities for Clinical Trials

 

While artificial intelligence solutions are capable of predicting treatment plans through a thorough analysis of symptoms, they can also assist in clinical trials. Artificial intelligence can be used to determine if certain patients are suitable candidates for trials. Such solutions can also help doctors predict patient responses to trials. AI and machine learning create space for safer clinical trials by ensuring that patients can withstand treatments. 

How Can Imarticus Learning’s Al ML Course Prepare You for a Career in Healthcare? 

If you wish to enter the healthcare sector and work in the digitization of healthcare solutions, then Imarticus Learning’s Certification in Artificial Intelligence & Machine Learning is a great option. Our course is in collaboration with E&ICT Academy and IIT Guwahati. So, you will have access to lectures and curricula designed by renowned academicians and industry professionals.

At Imarticus Learning, we ensure that the IIT AI ML course prepares students for a long and rewarding career in data science and machine learning engineering. You will be attending live sessions for eight hours every week and we encourage you to interact with all teachers and peers. Imarticus Learning creates opportunities for students to network and hones their soft skills while preparing for work in the industry.

To ensure hands-on experience, we offer twenty-five projects that are based on real business issues and more than one hundred assignments. 

The certificate course in artificial intelligence and machine learning at Imarticus Learning is ideal for students who have completed graduation in computer science, engineering, statistics, mathematics, science, or economics. If you have a minimum of 50%, you can enroll in our program and receive education and industry training from experts.

How blockchain is adding transparency and efficiency to supply chain management

Posted on by Imarticus Learning

Blockchain technology is quickly becoming one of the most important innovations in recent history. It has the power to transform every sector, from manufacturing to finance and more. Blockchain can help businesses streamline their supply chain management by providing transparency while reducing costs for all parties involved. 

This blog post will explore how blockchain benefits supply chain management and why it is essential for business owners to understand this new technology!

What is blockchain, and how does it work?

The blockchain is a technology that stores information in blocks, each block containing data of any size. Once data is stored, it can’t be changed or removed. It makes the blockchain incredibly secure and tamper-proof.

Blockchain is the world’s leading software platform for digital assets. Offering the largest production blockchain platform globally, we are using new technology to build a radically better financial system. Blockchain enables transformation across every business, government, and institution.

One of the key ways to use blockchain in supply chain management is to add transparency and efficiency. Blockchain can help businesses keep track of their inventory and get the best deals on supplies by creating a tamper-proof ledger of transactions. Additionally, blockchain can help companies save money and time by reducing the need for intermediaries.

What are some of the benefits of using blockchain in supply chain management?

The key benefits of using blockchain in supply chain management include increased transparency, reduced costs, and improved efficiency. By creating a shared ledger of tamper-proof and secure transactions, businesses can reduce the need for intermediaries and create a more efficient supply chain.

As it becomes easier for businesses to track the provenance of their products, consumers can also feel confident that they are buying ethically sourced goods. For example, if someone buys diamonds mined in South Africa on De Beers’ blockchain-based platform Tracr, they will be able to trace the provenance of those diamonds through that supply chain.

The ability to track goods from origin to end-user means a more transparent and therefore traceable product journey, enabling businesses to prove compliance with governmental regulations for food safety, quality assurance, or other aspects of their offerings. 

What challenges need to get addressed before you can widely adopt blockchain in supply chain management?

 One of the key challenges is that blockchain is still in its early stages, and many businesses are unsure how to implement it. Additionally, there are some concerns around security and privacy when sharing data on a blockchain network.

Another challenge facing blockchain in supply chain management is the lack of standardization. Because different businesses use different blockchains, it cannot be easy to transfer data between them. You could address it by developing a universal standard for blockchain technology.

Explore Supply chain Management Career with Imarticus Learning.

Supply chain management certification online is in partnership with DoMS and E-learning Centre, IIT Roorkee, and industry professionals to equip candidates interested in entering the operations and supply chain business with a cutting-edge experience.

Course Benefits For Learners

  • Supply chain management online course prepares students for jobs such as Demand Planner, Data Scientist, Supply Planner, and Supply and Operations Planner, which are in great demand.supply chain management courses

     

  • With a certification authorized by one of the top-ranked IITs, IIT Roorkee, students can impress employers and demonstrate their new-age SCM and Analytics abilities. 
  • Students Attend 1:1 mentorship sessions and get their questions addressed by Supply Chain Management industry leaders.
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