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Category: Analytics

Supply Chain Resilience: Strategies for Mitigating Disruptions

Posted on by Imarticus Learning

Supply chains have long remained the lifeblood of companies operating worldwide, and have exponentially grown in complexity over the past years. Also, with the increasing dependence on supply chains as the primary means of production, it has become all the more relevant for companies across the world to reassess the potential disruptions to their individual supply chains that can risk business operations. 

In this regard, Supply Chain Analysts are responsible for exploring possible risks, financial implications and other causative factors behind supply chain disruptions, and ideating ways to improve supply chain resilience against shocks and exposure to risks.

Ways to Mitigate Supply Chain Disruptions

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Supply chain resilience has emerged as a viable solution to mitigate disruptions in the supply chain, and eliminate the company’s exposure to risks. Supply chain management courses frequently highlight the following ways of mitigating shocks and disruptions as viable ways of establishing supply chain resilience.

Multisourcing 

Companies that tend to rely on supply chains originating from singular sources are often at a higher risk of supply chain disruptions. This is because, other than the lack of flexibility, companies can incur unavoidable losses in case of any inconvenience on the part of the supplier.

So, in order to avoid disruptions that could result in unwanted expenses, multisourcing is an effective solution to keep the supply chain uninterrupted. Not only do multiple suppliers offer additional security in times of need, but can result in greater flexibility and diversity. Having multiple suppliers over sole suppliers can also benefit companies operating from several locations.

Inventory and capacity buffers

Inventory and capacity buffers are some of the most prominent precautionary measures for any operating company, and have been recognised as one of the most effective means of establishing Supply Chain Resilience. At a glance, inventory and capacity buffers might appear expensive, but is completely worth their value provided their effectiveness as surge capacity expansions.

Inventory and capacity buffers are also beneficial as a cache for underutilized means of production and excess safety stock requirements, and allow companies to strategically utilise them in times of surge needs, such as product launches or new operating sites. 

Diversification of manufacturing networks

Diversifying manufacturing networks is an excellent way of ensuring Supply Chain Resilience and avoiding disruptions, as it essentially allows companies to widen their accessibilities, while ensuring seamless operation. 

Other than being beneficial to supply flexibility, diversification in manufacturing networks allows companies to improve their image and profitability, stay ahead of competitors, and operate profitably in times of economic crisis. Diversification of the manufacturing networks also means lowered order liability and lead times. Diversifying manufacturing networks has been on the rise in recent years due to its effectiveness in mitigating supply chain disruptions, and the cost of maintaining multiple suppliers has been recognised as the cost of doing business by major companies across the world.

Nearshoring

Geographic dependence on manufacturing networks is a major causative factor contributing to supply chain disruptions. Ranging from differences in timezones and natural disasters to political and strategic unrest, geographic dependence can often cause hindrances to the supply chain, and can also end in additional expenditures and liabilities. 

So, most companies around the globe prefer nearshoring, or sourcing their materials locally or regionally. Nearshoring can help companies overcome their geographic dependence, which benefits companies by improving supply chain resilience, and can simultaneously help shorten the cycle time of production. While nearshoring can be expensive in certain instances such as complications in the ecosystem or involvement of local factors, it offers companies more flexibility and security over inventory, and can significantly reduce supply chain disruptions. 

Ecosystem partnerships

Ecological crises and the need for environmental sustainability have made it crucial for companies to look into means of production that can be carried out effectively without any damage to nature and its values. So, companies have considered new ventures that promote environmental security without compromising their operation. In this regard, adopting ecosystem partnerships have emerged as a promising solution that allows corporations to uphold their environmental integrity, while making sure the supply chain is uninterrupted. 

Adopting eco-friendly measures for inventory acquisition, such as liaisons with global 3PLs and contract manufacturers can help companies achieve their desired goals, and mitigate supply chain disruptions without compromising environmental stability. Collaborating with external service providers also helps in boosting the resilience of future ventures. 

Product, platform and plant harmonisation

Streamlining is an effective way of ensuring supply chain resilience. In this regard, companies are often encouraged to standardise the components of the means of production, including the platform and plant that allows companies to seamlessly move products across networks and efficiently process supply chains. In addition to minimising the risks of supply chain disruptions, harmonising products, platforms and plants is also reflected in end-to-end transparency, improved opportunities and sourcing policies between manufacturing networks, and supply chain resilience.

Conclusion

With supply chains serving as one of the most crucial aspects of business operations in present times, it is vital for companies worldwide to adopt measures that contribute toward strengthening supply chain resilience, while simultaneously optimising production capacities for companies. Improved supply chain management by mitigating disruptions has allowed companies to avoid business risks, respond quickly to potential disruptions and secure assets, as well as improve end-to-end transparency.

Sign up for the Advanced Certification Program from Imarticus to unlock your potential in Supply Chain Management. You might also want to look into other supply chain management courses from Imarticus Learning and begin your journey as a Supply Chain Analyst.

Highest Paying Supply Chain Jobs in 2023

Posted on by Imarticus Learning

The need for an efficient and productive supply chain grows as businesses expand and globalise. Supply chain professionals play an essential role in streamlining operations, lowering costs, and increasing overall profitability for businesses. The job market for supply chain experts has seen extraordinary development and profitable opportunities as the complexity and demand for supply chain management have increased.

By leveraging the right supply chain analytics course, you can become a part of this high-paying domain. However, what exactly are the supply chain jobs that are paying well?

We have curated a list of the highest paying supply chain jobs in 2023 that aspiring supply chain professionals can explore and aim for with the right skills and courses!

Logistics Manager

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Logistics managers are in charge of organising the movement and storage of items in order to maintain a seamless supply chain flow. They manage transportation, warehousing, and distribution systems in order to maximise efficiency while lowering costs. Success in this profession requires knowledge of logistics software, problem-solving abilities, and the capacity to handle complicated processes. 

Average Annual Salary: INR 6,25,137

Supply Chain Analytics Manager

In supply chain management, data-driven decision-making is becoming increasingly important. Managers of supply chain analytics are in charge of gathering, analysing, and interpreting supply chain data in order to discover areas for improvement and cost-cutting potential. They collaborate closely with data scientists and supply chain teams to implement data-driven plans. 

Candidates must be competent in data analysis and applied analytics tools in order thoroughly understand supply chain procedures and flourish in this post. 

Average Annual Salary: INR 9,43,036

Global Commodity Manager

The position of a Global Commodity Manager is a highly important and well-compensated role in the business world. Global Commodity Managers are responsible for overseeing the sourcing, procurement, and supply chain management of critical commodities used in the company’s products or services. They are entrusted with developing strategies to optimise costs, manage risks, and ensure a stable supply of essential materials from various global suppliers.

 Average Annual Salary: INR 21,00,000

Strategic Chief Operating Officer

In today’s organisations, the function of a Strategic Chief Operating Officer (COO) is becoming increasingly important. Unlike traditional COOs primarily concerned with day-to-day operations, a Strategic COO adopts a more proactive and visionary approach, integrating operational strategies with the overarching business objectives established by the CEO and the board. This executive is responsible for establishing the company’s strategic direction, ensuring effective execution, and fostering long-term success.

Average Annual Salary: INR 30,21,577

Procurement Director

Procurement directors oversee the purchasing process, manage vendor relationships, and negotiate contracts to ensure that the organisation receives the best rates and terms possible. They seek to reduce supply chain risks, ensure timely deliveries, and keep costs low. This position requires a good history in procurement, negotiation abilities, and strategic thinking. 

Average Annual Salary: INR 44,38,980

Supply Chain Director

The role of a Supply Chain Director is of paramount importance as they are responsible for overseeing the entire supply chain process, ensuring seamless coordination between various departments, optimising inventory levels, and enhancing operational efficiency. Their strategic decision-making directly impacts cost reduction, timely delivery, and overall profitability. 

Due to the critical nature of their responsibilities and their substantial impact on a company’s success, Supply Chain Directors are highly sought-after professionals, commanding higher salaries and compensation packages.

Average Annual Salary: INR 54,00,000

Conclusion

The supply chain business is quickly evolving, providing diversified and well-paying employment opportunities for individuals from various disciplines. Supply chain professionals are in high demand as firms prioritise efficiency, sustainability, and cost-effectiveness.

Perceiving the growing significance of supply chain jobs, courses like Strategic Chief Operating Officer course can add great value to your resume.

One such course seeking interest from supply chain aspirants is Executive Certificate Programme for Strategic Chief Operating Officers offered by Imarticus in collaboration with IIM Raipur. With this IIM operating officer course, candidates are bound to experience a skyrocketing career in supply chain management!

Navigating Different Types of Supply Chain

Posted on by Imarticus Learning

The world of business is as intricate and diverse as the human civilisations that built it. In this vast expanse, one of the most crucial segments that dictate the rhythm of markets is the supply chain. 

This article takes you on a journey through the labyrinth of different types of supply chains, focusing on the integral element of procurement management.

Understanding Supply Chain Management: A Broad Overview

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Supply chain management (SCM) is an extensive field that intertwines numerous business functions. These functions encompass everything from product development to information systems, all operating in an orchestrated symphony to deliver value to customers. 

Navigating through these complex dynamics requires an in-depth understanding of the different types of supply chains, each with its unique set of challenges and opportunities.

1. The Push-Based Supply Chain

The push-based supply chain, sometimes referred to as the forecast-driven model, leans heavily on projected market needs to shape its production and distribution strategies. Even though this approach has held sway for a good while, it often faces critique for its rigidity and inability to promptly adjust to real-time shifts in market demand.

2. The Pull-Based Supply Chain

The pull-based supply chain is demand-driven, operating in response to actual customer orders rather than forecasts. This lean model minimises stockpiling and promotes efficiency, but it also requires a robust infrastructure to respond quickly to fluctuating demands.

3. The Hybrid Supply Chain

In this type of supply chain, the principles of both the push and pull models are utilised, creating a balanced approach. This strategy enables businesses to maintain a level of forecast-driven operations while remaining responsive to real-time market demands.

The Significance of Procurement Management in Supply Chains

An often underrated but pivotal component of SCM is procurement management. This involves strategising, sourcing, negotiating, and purchasing the goods and services required for a company’s operations. The effectiveness of procurement management can dramatically impact an organisation’s efficiency, costs, and, ultimately, its bottom line.

How Procurement Management Optimizes Supply Chains

Procurement management plays a crucial role in optimising supply chains by:

  • Minimising Costs: Effective procurement management allows businesses to negotiate better prices, terms, and delivery schedules, thus reducing operational costs.
  • Enhancing Quality: By choosing reliable suppliers and setting high-quality standards, procurement management ensures the quality of the final products.
  • Managing Risks: Procurement mitigates the risks of supply chain disruptions through supplier diversity and contingency planning.

The Role of Training in Procurement Management

A strong foundation in procurement management can be a game-changer. As such, professionals interested in this field often turn to dedicated procurement management courses to upskill and enhance their understanding of the dynamics at play.

An example of such a course is the Professional Certification in Supply Chain Management and Analytics by IIT Roorkee by Imarticus. This online supply chain management course offers a blend of theoretical knowledge and practical skills, providing a holistic understanding of the field.

Digital Transformation in Procurement and Supply Chain Management

In a world where technology never ceases to rewrite the rules, the realms of supply chains and procurement management aren’t bystanders. Adopting digital tools in these spheres is far from a simple upgrade; it represents a seismic shift in the way businesses conduct their operations.

A New Era Dawns with Technology

The wave of digital transformation sweeps across procurement and supply chain management, opening up unprecedented possibilities. Here are a few ways in which this techno-evolution is reinventing the landscape:

  • Automation: Routine tasks such as order processing, invoice matching, and tracking can be automated, freeing up time for procurement professionals to focus on strategic activities.
  • Real-Time Visibility: Digital platforms provide transparency across the entire supply chain, from sourcing to delivery. This visibility allows for better decision-making, forecasting, and risk management.
  • Supplier Relationship Management (SRM): Digital SRM systems help manage interactions with suppliers, facilitating communication, performance evaluation, and contract management.
  • Data Analytics: Big data and analytics enable more accurate forecasting and decision-making based on insights derived from data. This leads to improved efficiency and risk mitigation.

Preparing for the Digital Future

Adapting to this digital shift necessitates upskilling and continuous learning. Training programs like the online supply chain management course mentioned above provide a platform for professionals to acquaint themselves with the latest digital tools and methodologies.

By embracing digital transformation, businesses can streamline their procurement and supply chain processes, enabling cost savings, increased efficiency, and a competitive edge in the market.

Conclusion: The Road Ahead

As the world of commerce continually changes and broadens, reaching across borders like never before, the role of supply chain management—along with its vital subset, procurement management—grows increasingly significant. With appropriate education and training, professionals can confidently sail these intricate waters, making thoughtful, strategic choices that fuel their organisation’s journey to success.

Why Machine Learning is the Key to Unlocking Insights from Big Data?

Posted on by Imarticus Learning

Big data is becoming increasingly important in today’s business landscape. But what is big data, and why is machine learning the key to unlocking insights?

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In today’s digital world, we generate massive amounts of data every day.

From our social media interactions to online transactions, the data keeps growing.

However, the real challenge lies in understanding this data. Uncovering valuable insights from data can help us make better decisions.

This is where learning machine learning becomes essential. It is a key tool for extracting meaningful insights from large data sets.

What Is Machine Learning?

Machine learning is a part of artificial intelligence that focuses on developing algorithms and models.

It enables computers to learn from data. Instead of following strict rules, machine learning algorithms learn from patterns and trends in the data. Thus allowing them to make predictions, identify patterns, and reveal insights.

Importance Of Machine Learning In Unlocking Big Data Insights

Learning machine learning offers several advantages when it comes to analyzing large data sets. Traditional methods of data analysis struggle to handle big data due to its enormous volume,.

Humans would find it challenging to manually go through such vast amounts of data to extract insights. Machine learning algorithms, on the other hand, excel at processing and analyzing extensive data sets. It helps in revealing valuable insights that might otherwise remain hidden.

One of the main strengths of learning machine learning is its ability to identify complex patterns within the data.

These patterns can be challenging for human analysts to detect, especially when dealing with multi-dimensional data. Machine learning algorithms can identify and analyze intricate connections between variables.

Thus enabling organizations to uncover valuable insights and make data-driven decisions.

Furthermore, learning machine learning algorithms can continuously learn and adapt to new data. It improves their performance over time. This iterative learning process allows models to become more accurate and reliable. It helps, making them ideal for handling dynamic and evolving data sets. By learning from past experiences, machine learning models can make predictions and recommendations based on historical data. It helps businesses in anticipating future trends and making proactive decisions.

Imarticus Learning’s Postgraduate Program in Data Science and Analytics

Imarticus Learning recognizes the growing importance of machine learning in the field of data science and analytics.

The Postgraduate Program in Data Science and Analytics is a 6-month online course. This course offers numerous benefits to its participants:

Live Learning Modules

The program provides live learning modules. It allows students to interact with instructors. This interactive approach enhances the learning process and encourages active participation.

Dedicated Career Services

Imarticus Learning offers dedicated career services to support students in their job search. They provide guidance and assistance to help students find suitable employment opportunities in this industry.

Access to KPMG Organized Hackathons

Students enrolled in the data analytics course gain access to hackathons organized by KPMG. These hackathons provide an opportunity to apply machine learning techniques in a competitive setting.

Real-World Projects for Practical Knowledge

The program incorporates various real-world projects. It allows students to apply machine learning concepts to solve practical problems. This hands-on experience bridges the gap between theoretical knowledge and its practical application. It helps prepare students for real-world challenges.

Job-specific Curriculum

The program is designed to provide students with the specific skills required to excel in the field of data science. By focusing on the practical aspects of the industry, the curriculum ensures that students are well-prepared for the job.

Job Interview Guarantee

Imarticus Learning provides a job interview guarantee to eligible candidates upon successful completion. This assurance gives students confidence in their career prospects. It serves as an incentive to excel in their studies.

Alumni Success

Imarticus Learning’s alumni are currently employed by various esteemed companies. Some top companies are Accenture, KPMG, Infosys, Capgemini, IBM, Cognizant, Wipro, Standard Chartered, and Deloitte. The program’s strong industry connections provide graduates with excellent networking opportunities. This increases their chances of securing rewarding positions in top organizations.

Final Thoughts

Machine learning plays a vital role in extracting valuable insights from large data sets. Its ability to analyze large amounts of data, and identify complex patterns, makes it important in today’s data-driven world.

Imarticus Learning’s Postgraduate Program in Data Science and Analytics offers a comprehensive curriculum that covers all aspects of data science and analytics.

Join Imarticus Learning’s Postgraduate Program in Data Science and Analytics. Start your journey towards a rewarding career.

Real-World Applications: How a Data Science Certificate Program Prepares You for the Industry?

Posted on by Imarticus Learning

Data science has emerged as a crucial field in today’s digital age. With the increasing reliance on data-driven decision-making, businesses across various industries constantly need skilled professionals who can harness the power of data to gain valuable insights. 

Data science is an interdisciplinary academic discipline that draws information and insights from noisy, structured, and unstructured data using statistics, scientific computing, and scientific techniques, procedures, algorithms, and systems.

This blog post will explore how a Data Science Certification Program readies you for the field. Whether you are a seasoned professional looking to upskill or a recent graduate considering a career in data science, this post will shed light on the real-world applications and benefits of a data science certificate program.

How a Data Science Certificate Program Enhances Employability in the Industry?

In the field of data science, there is a growing need for qualified personnel. Employing data scientists who can assist them in making sense of the enormous volumes of data they get is a top priority for businesses. But how can you distinguish yourself from the competition when many seek these desirable positions?

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Earning a certificate in data science is one option. You can acquire the abilities and information required for success in data science through a certificate program. You’ll gain knowledge of data collection, cleaning, and analysis techniques, as well as practical programming knowledge in Python and R.

The following are some extra advantages of completing a data science certificate program:

  • Gained expertise: A certificate program will provide you with a thorough introduction to data science, including subjects like statistics, machine learning, andthis post will shed light on the real-world applications and benefits of a data science certificate program. data visualization. Additionally, you’ll get practical experience working with actual projects and data sets.
  • Increased employability: Having a certificate in data science will help you stand out as a candidate for positions in this expanding industry. Employers are seeking employees with particular data science knowledge and abilities more frequently.
  • Professional development: Maintaining current with the most recent data science trends is possible with a certificate program. Along with learning about cutting-edge tools and methods, networking with other data scientists will be a chance for you.
  • Personal satisfaction: Learning about data science may be enlightening and satisfying academically. You’ll get a greater comprehension of how the world functions and be able to apply your abilities to change it for the better.

What are the Real-World Applications of a Data Science Certification?

  • Understanding the Fundamentals of Data Science

Before delving into the practical applications of data science, it is essential to grasp the fundamental concepts and techniques. A reputable data science certificate program provides a comprehensive understanding of statistics, programming languages such as Python and R, data manipulation, and data visualization. 

  • Exploring Predictive Analytics

One of the key applications of data science is predictive analytics. Data scientists can make informed predictions and forecasts by leveraging historical data and statistical modeling techniques. A data science certificate program teaches you to apply predictive analytics in real-world scenarios. From predicting customer behavior and market trends to forecasting sales and optimizing supply chain management, making accurate predictions empowers businesses to make data-driven decisions that drive growth and success.

  • Unveiling the Power of Machine Learning

Machine learning is revolutionizing industries across the board. From healthcare and finance to marketing and e-commerce, machine learning algorithms are employed to automate processes, identify patterns, and extract valuable insights from vast data. A data science certificate program equips you with the necessary skills and knowledge to develop and implement machine learning models. This empowers you to create intelligent systems that can automate tasks, improve efficiency, and enhance decision-making processes.

  • Harnessing the Potential of Natural Language Processing

Natural language processing (NLP) has become a vital data science component as technology advances. NLP enables computers to understand and interpret human language, enabling applications like sentiment analysis, chatbots, and language translation. A data science certificate program familiarizes you with the principles of NLP and provides hands-on experience in applying NLP techniques to solve real-world problems. By mastering NLP, you can contribute to developing innovative solutions that enhance communication and user experience.

  • Navigating Big Data Challenges

The explosion of data in recent years has presented both opportunities and challenges. Organizations are grappling with efficiently storing, processing, and analyzing massive datasets. A data science certificate program equips you with the tools and techniques to handle big data effectively. From distributed computing frameworks like Apache, Hadoop, and Spark to data querying languages like SQL and NoSQL, you acquire the skills to tackle big data challenges and extract valuable insights from complex datasets.

The Final Words

A data science certification program offers many real-world applications and prepares you for a successful career in the industry. By understanding the fundamentals of data science, exploring predictive analytics, harnessing the power of machine learning, leveraging natural language processing, and navigating significant data challenges, you become equipped with the skills and knowledge needed to excel in the data-driven world. 

The Certificate Program in Data Science and Machine Learning from Imarticus Learning is here to help you start your journey into the world of data science and machine learning. This thorough curriculum, created with iHUB DivyaSampark @IIT Roorkee, presents the core ideas and subtleties of data science and machine learning, giving you the skills to use these ideas in practical settings. 

In this 5-month curriculum, carefully crafted by renowned IIT faculty members, you will delve into the worlds of data mining and machine learning technologies using Python and explore how to use data-driven insights to create major organizational success.

 So, take the first step towards a rewarding career in data science by enrolling in a reputable data science certificate program today!

Components of Supply Chain Management

Posted on by Imarticus Learning

The term ‘supply chain management’ first came into the picture in 1982, when Keith Oliver coined it. However, supply chain management has existed in practice long before that. The only way to successfully sell large quantities involves efficient supply chain management. 

With modern technology, AI, and various software tools, business owners find managing their supply chains increasingly simpler. Nevertheless, one must be aware of the different components of supply chain management in order to simplify and streamline their supply chain operations.

Let us dive into the world of supply chain and dissect each component of the supply chain to understand its functioning. 

Understanding Supply Chain Management: Definition and Objectives

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Essentially, supply chain management refers to the handling of all the processes involved in transforming raw materials into finished products and, subsequently, selling them. Supply chain management impacts other facets of businesses, such as financial management, operating costs, customer satisfaction, and more. 

The key objectives of supply chain management are: 

  • Improving efficiency
  • Optimising logistics
  • Improving customer satisfaction
  • Cutting down unnecessary costs
  • Improving distribution
  • Co-ordinating the overall business

Six Components of Supply Chain Management

While there are several different models of supply chain management, the most common one is the Supply Chain Operations Reference or SCOR model. This model has six components that are as follows:

  • Planning: The first and most crucial component in supply chain management is planning. One of the first things an entrepreneur would need to plan is how to make their product, domestically or internationally. Furthermore, this is also the time to decide whether you want to make each part of your product yourself or whether it would be more sensible to purchase some components from a third party. 

Additionally, planning will also require you to decide how you will produce and store your products. This means you should have a clear layout of whether you want to make and store your products beforehand or make them as you get orders. 

  • Sourcing: Once you are done planning, you will be moving on to the sourcing phase of your supply chain management. This is where business owners should reach out to potential vendors, verify the quality of raw materials, get the best-quoted price possible, and subsequently buy them. It is also essential to ensure all your raw materials are delivered on time. 

In addition, remember to assess and vet your suppliers continually for optimal supply chain management operations. 

  • Location: Location plays a very critical role in supply chain management. For example, if you own a beverage company, your manufacturing plant should not be established in a location with water scarcity. Therefore, set up your manufacturing unit based on the needs and requirements of your raw materials and finished products. 
  • Making: Once the previous three steps are fulfilled correctly, you will move on to making your goods. This stage involves assembling, testing, and packing of finished products. You can incorporate customer feedback at this stage to ensure your products are up to the mark. 
  • Delivering: Now, we enter into the logistics part of supply chain management. Apart from ensuring your goods reach your customers, this phase also involves processing orders, warehousing, and inventory management. A major part of the success of a company depends on this stage, and hence, you should put special efforts into ensuring a smooth delivery process. 

Some of the best delivery practices include automated inventory management, large warehousing networks, delivery fulfilment using air, sea, and road, and so on. 

  • Returns: While returns may seem to have a negative impact on your business, in reality, how you handle your returns and improve your products based on that will attract more buyers to your company. Therefore, make sure to create a simple and easy returns process that does not feel like a hassle to your buyers. 

Furthermore, you should have efficient reverse logistics in place to ensure your goods are picked up on time and returned to your warehouse for further inspection or processing. Other elements of the returns process are:

  • Identifying the condition of the returned product
  • Authorising the return
  • Scheduling a replacement
  • Processing refunds

Importance of Understanding Supply Chain Management for Growth

Needless to say, supply chain management is one of the most important aspects of businesses that help entrepreneurs to scale up while saving costs and offering high-standard services and products. Even though supply chain management might sound simple, it has a ton of intricacies, all of which need to be taken care of in order to get the best results. 

In this regard, we recommend enrolling in the IIT Supply Chain Management Course, taught by esteemed faculty members at IIT Roorkee and offered by Imarticus. 

This online course covers all the basics in addition to the components of supply chain management. Furthermore, you will also be able to learn coding languages and software tools like Python, Excel, and Tora. At the end of the course, you will get a certification from IIT Roorkee, which will further boost your portfolio and help you better manage your own business supply chain operations or establish yourself as an expert in this field. 

The Impact of Data Science on Various Industries

Posted on by Imarticus Learning

Data Science and Industrial Integration

Data science studies the extraction and derivation of meaningful insights from data. The approach to gathering the insight is multidisciplinary.

It takes practices and principles from a variety of fields. Some of these fields are computer engineering, statistics, artificial intelligence, and mathematics.

The applications of data science are unlimited as it answers the four important questions of problem-solving. These are what is the situation, why is it, what is the future course, and what would the result yield.

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This basic information gathering and problem-solving make the adoption of data science in different industries feasible.

Data science finds its application in industries such as retail, medicine, banking, finance, and telecommunications among others. Read ahead if you are planning to make a career in data science and are curious about the impact on various industries.

Impact of Data Science on various industries

A career in data science requires professionals to make use of raw data for the identification of patterns. These patterns are then used to derive actionable insights from the data.

With data science, it is possible to predict future outcomes with a higher level of accuracy. Here is how various industries are benefiting from the applications of data science.

1. Healthcare

Applications of data science in the healthcare industry include the use of data in taking important decisions and drawing conclusions. These include the implementation of medical knowledge gathered in the diagnosis of diseases.

Data science is also used to improve the safety and quality of healthcare by helping design prevention plans. It has also helped chart important health parameters such as sugar level, stress level, brain activity, etc using trackers. This can help deliver personalised care and precise prescriptions.  A deep learning technique is used in reading imaging data. This is also to reduce diagnostic failures.

2. Retail

In the retail industry applications of data science have led to the implementation of features such as recommendation engines. The impact helps companies design products according to the needs of the consumer. Market-based analysis allows the company to determine the likes, dislikes, and price points of consumers. This allows companies to target the right audience more efficiently.

The science has also helped the industry to analyze customer sentiments more accurately. Social media and other points of contact between consumers and companies help companies collect data. This is further used to enhance the feedback mechanism. The feedback is then used to design better products. Firms using data science can optimise prices efficiently. All these lead to an increase in sales and revenue flow in the industry.

3. Banking, Finance Services, and Insurance Industry (BFSI)

BFSI has benefitted largely from the application of data science in the industry. The industry has been able to minimize its losses with the help of fraud detection using science. It has impacted more efficient management of customer data, risk modelling, and customer support.

Data scientists can generate customer life value prediction that is used to recommend products and services to consumers as per their needs. BFSI companies are also able to minimize the impact of any risk to their businesses with the help of predictive and real-time analysis. These techniques have led firms to increase sales and operational gains.

4. Telecommunication Industry

If you are looking for a career in data science in the telecommunication industry you will be able to impact efficient data transmission and visualisation needs. Data science is applied for increasing fraud detection by firms. Using science, consumers benefit from increased network security.

The benefits for the company include price optimisation, real-time analysis, and prevention of customer churn. Companies can also benefit from features such as location-based promotion, predictive analysis, and targeted marketing.

5. The course you need for a career in data science

The example of the impact of data science on four industries is a mirror of the significance of data science in today’s world. If you are looking for a career in data science Imarticus Learning’s Postgraduate Program In Data Science And Analytics is the one.

The course is designed for early career professionals. This six-month program is designed based on a job-specific curriculum. The learning is accentuated by live learning modules, real-world projects, and KPMG hackathons. The certification is designed to launch your career in data science in an industry of your choice.

Summing it up

Applications of data science are wide. There are several industries apart from healthcare, retail, BFSI, and telecom that are using data science. Data science can help companies grow and the skills of a data scientist are always in demand. You can benefit from a quick start in your career by being a part of Learning’s Postgraduate Program In Data Science And Analytics.

Imaritcus Learning offers you benefits such as Job-assurance, specialized training, and placement drive. It has helped more than 56000 individuals attain placements. Be sure to visit us for other similar courses.

Why Learning Python Is Essential For Data Science

Posted on by Imarticus Learning

Python is a versatile programming language. When you learn Python, the programming language makes you competent for several job roles.

It finds applications in Data Science, Web Development, Analytics, Automation, Scripting, and Game Development.

Why is it so widely used?

Let’s explore the reasons to learn Python in the following sections.

What are the technical requirements of the Data Science industry?

Data Science and Analytics industry is based on mathematics and statistics. As a Data Scientist, technically, you will need some assistance to complete your job. These are listed below.

  • Fundamentals: You must know and learn Python programming language. Additionally, SQL is essential for understanding querying and database management.
  • Mathematics and Statistics: Creating reliable models is the main responsibility of a Data Scientist. This can only be done with sound knowledge of probability, regression analysis, hypothesis testing, and linear algebra.
  • Data Analysis and Manipulation: A Data Scientist performs data manipulation and analysis regularly. The programming language used for this job must help with feature engineering and data cleaning.
  • Big Data Technologies: Along with the fundamentals, you must learn frameworks. Apache Hadoop, Dask, or Apache Spark frameworks efficiently analyze and process massive databases.
  • Machine Learning: A Data Scientist must know machine learning algorithms. They help in performing regression, clustering, and classification.

Why should Data Scientists learn Python over other languages?

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When you learn Python for Data Science and Analytics, each lesson will speak of the language’s relevance in the field.

Listed below are the reasons that make Python important for Data Scientists.

If you’re wondering the reason for choosing Python over other languages, keep reading.

Simplicity: When compared with C, C++, or C#, Python is easy to learn. Data Scientists can learn the language easily and start coding in no time. The syntax of Python is simple and almost in the English language.

This enables you to focus on problem-solving instead of bothering yourself with the format of a new language. It does not just help you with the process, but it also enables teamwork. This means you can collaborate with others to complete a project on time.

Library: Python is popular for its vast library. When you talk about Data Science, the language offers one of the largest collections of frameworks and libraries. These are specific to Data Science and Analytics. For example, NumPy, Matplotlib, and Pandas are important libraries.

Support for Artificial Intelligence (AI): Tensorflow is one of the most useful libraries in Python for AI. The language also offers other frameworks and libraries. You can use these for building and training complex models. With Python, you can also integrate machine learning pipelines directly into the production systems.

Integration: In the previous section, you must’ve learned the importance of Big Data Technologies, SQL, and Visualization Tools. Python is compatible with SQL, Hadoop, and Tableau to enhance your capabilities as a Data Scientist. You can also integrate your Python code with other programming languages.

These reasons compel professionals to learn Python for Data Science and Analytics. However, you must note that programs like R, MATLAB, and Julia also help in Data Science. But, the simplicity of Python and its vast library has made it the top choice of professionals.

Join a job-assured Postgraduate Data Science & Analytics Course

As Python is lightweight, with extensive libraries, and customer support, the demand for this program is not getting down anytime sooner. When you become a data science expert, you must have a clear understanding of the fundamentals. Strengthen your concepts of mathematics and statistics and learn machine learning.

A Postgraduate Data Science and Analytics Course will direct your career in the right direction. Imarticus Learning presents a job-centric curriculum to make the most of your investment. You will also get assistance for participating in Hackathons that add value to your resume. Get ahead of your competitors by starting your journey today!

Artificial Intelligence (AI) in Demand Forecasting

Posted on by Imarticus Learning

Utilising Artificial Intelligence and machine learning helps us make better probability analyses and thus helps businesses to accurately foretell how much the goods might cost in the sales and operations planning accordingly by studying the seasonal fluctuations. 

We get to see the increasing use of AI in demand forecasting. This helps significantly reduce the percentage of errors that they commit in the networks of the supply chain and makes the entire process much more efficient in saving time. 

What do we understand by Demand Forecasting?

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Demand forecasting refers to the assessment of the various processes of a company that helps forecast the value of multiple products in the future supply chain. Doing this entire process manually is a pretty hectic job. This process is made more efficient by utilising Artificial Intelligence.

Demand forecasting helps in optimising the inventory of a business by forecasting the supply demand of the products in the future. This process includes analysing all the records of sales data.

What are the characteristics of Demand Forecasting?

There are several benefits of Demand Forecasting. These are listed as follows:

  • Demand Forecasting significantly cuts down the percentage of risk by making economic financial decisions. This in turn helps in managing cash flow, resource allocation, profit margins and many more. Overall it helps in preparing the budget of the company
  • Demand Forecasting helps in forecasting the demands of the customer and helps in aligning the production rate according to the demand rate and hence avoiding overflooding of inventory. 
  • Demand Forecasting manages the dynamic pricing of the products based on the consumers’ demand. It carefully studies the market and reads for potential opportunities which can help companies proliferate by incorporating the right marketing tactics. 

What are the various types of Demand Forecasting techniques?

The various types of Demand Forecasting techniques are as follows:

  • Macro-level forecasting: This only considers the broader external factors affecting the business and helps keep aware of the opportunities centring the expansion of the portfolio and other things.
  • Micro-level forecasting: This type of forecasting generally focuses on a specific industry or business
  • Short-term forecasting: This involves forecasting that involves a time span of almost 12 months. 
  • Long-term forecasting: This type of forecasting involves a time span of more than a year. Doing so, it analyses the seasonal demands by studying the annual patterns and the potential of production and expansion covering a long period. This helps in strategising long-term business plans.

What are the various advantages of using AI-based Demand Forecasting?

The various advantages of AI-based Demand Forecasting are as follows:

  • AI-driven forecasting is time efficient: There is a big time constraint while working on demand forecasting manually. AI-driven forecasting on the other hand is quite fast. Saving time for critical analysis, the primary data are efficiently arranged and analysed in no time. 
  • Accuracy at forecasting: As it has been mentioned earlier, making informed decisions is greatly aided by the accurate forecasting driven by AI. What gives AI an even better edge over manual forecasting is that the information is continually updated which makes the information that it provides so accurate. The rise in accuracy in forecasting is found to be directly proportional to the rise in sales.  
  • Coherence between external and internal data: The AI-generated data follows a close look out for seasonal variations and fluctuations in the external data. It specifically studies the demand patterns and then accordingly aligns the sales forecasting. Manually maintaining coherence between this external and internal data would have been quite difficult and prone to errors.  
  • Battling rapid fluctuations: In the case of events which is often commonly called the Black Swan events, the sales are heavily hampered. To cope with such a downfall of sales due to failure of predictions and immediate follow-up with good planning, AI models provide a solution by giving prompt analysis with appropriate planning that follows the analysis. This helps tide over such trying times. 
  • Early predictions: One of the biggest benefits of using AI-generated forecasting over manual forecasting is that AI can foretell fluctuations beforehand and makes room for appropriate adjustments and arrangements by laying out the future steps to be taken for the supply chain design.

Conclusion

In this article, we got to learn about demand forecasting and how the integration of AI helped make the entire process more efficient. Secondly, we got to know that AI-driven forecasting is much more accurate than manual forecasting. Moreover, AI ensures sound coherence between the external and internal data and keeps a continuous check on the rise and fall of demand rate in the market controlling tightly the flow of the sales. 

Check out Professional Certification in Supply Chain Management & Analytics provided by Imarticus if you want to give a boost to your career in this field. This supply chain analytics course is provided in collaboration with IIT Roorkee. One needs to have basic knowledge of programming and operations management to be eligible to enrol in this course.

Why Power BI is Essential for Data Scientists and Analytics Professionals?

Posted on by Imarticus Learning

In this present data-driven world, the field of data science has gained immense popularity.

Data scientists & analytics professionals are in high demand as organizations seek valuable insights from vast amounts of data.

Professionals in these fields require powerful tools to analyze and visualize data effectively. One such essential tool is Power BI.

The Growing Demand for Data Scientists and Analytics Professionals

The demand for skilled Power BI for data scientists and analytics professionals is rapidly increasing as businesses rely on data to drive decision-making processes. These professionals play a crucial role in collecting, analyzing & interpreting data to uncover patterns, trends, and insights that can drive business growth. 

By 2026, the US Bureau of Labor Statistics predicts a substantial increase of 27.9 percent in employment opportunities demanding Data Science skills.

With the exponential growth of data, organizations are looking for efficient ways to manage and make sense of the information.

What is Power BI?

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Power BI, developed by Microsoft, is a powerful business analytics tool.

It enables users to connect to various data sources, transform raw data into interactive visualizations, and share insights across the organization.

Power BI offers a user-friendly interface and a range of features that empower data scientists and analytics professionals to explore data, discover hidden patterns, and communicate insights effectively.

Benefits of Power BI for Data Scientists

Power BI provides several benefits that make it essential for data scientists:

  • Data Integration: Power BI integrates with diverse data sources, including databases, cloud services, and online platforms. This enables data scientists to access and analyze data from multiple sources within a single environment.
  • Interactive Visualisations: Power BI offers rich visualization options, allowing data scientists to create interactive dashboards, reports, and charts. These visualizations help present complex data in a visually appealing and easily understandable manner.
  • Advanced Analytics: Power BI incorporates advanced analytics capabilities, such as statistical modeling, forecasting, and clustering. Data scientists can leverage these features to gain deeper insights into data and make accurate predictions.

What role does Power BI play in data analysis for professionals in the analytics field?

For professionals aspiring to build a career in data science, having expertise in Power BI provides a significant advantage.

Power BI skills are in high demand across industries. Employers value professionals who effectively utilize Power Business intelligence to analyze and visualize data, making them valuable assets in data-driven organizations.

Power BI Features for Analytics Professionals

Power BI offers a range of features specifically designed for analytics professionals:

  • Data Modelling: Power BI allows analytics professionals to create robust data models by defining relationships between data tables. This helps organize data and enables efficient analysis.
  • DAX (Data Analysis Expressions): DAX is a formula language used in Power BI to create custom calculations and aggregations. Analytics professionals can leverage DAX to perform complex calculations and create custom measures for their analysis.
  • Data Security: Power BI provides robust security measures to protect sensitive data. Analytics professionals can define role-based access controls and implement data-level security to ensure data confidentiality.

How Power BI Simplifies Data Analysis

Power BI simplifies the data analysis process for data scientists and analytics professionals in several ways:

  •      Drag-and-Drop Interface: Power BI’s intuitive drag-and-drop interface makes it easy to import and manipulate data without writing complex code.
  •      Automated Refresh: Power BI can automatically refresh data from various sources, ensuring the analysis is always current.
  •      Natural Language Queries: Power BI enables users to ask questions in natural language to generate visualizations and insights. This feature allows data scientists and analytics professionals to explore data interactively and get instant answers to their queries.

Integrating Power BI with Other Tools and Platforms

Power BI seamlessly integrates with other tools and platforms, enhancing its capabilities for data scientists and analytics professionals. It can connect with tools like Excel, Azure Machine Learning, and SQL Server Analysis Services, enabling users to leverage existing workflows and combine data from multiple sources for comprehensive analysis.

Power BI vs. Other Data Visualisation Tools

While several data visualization tools are available, Power BI stands out due to its unique features and capabilities. 

When compared to other tools, Power BI offers the following:

  • Ease of Use: Power BI’s intuitive interface and drag-and-drop functionality make it accessible to users with varying technical expertise.
  • Scalability: Power BI can handle large datasets & can scale to meet the needs of enterprise-level data analysis.
  • Integration with Microsoft Ecosystem: As a Microsoft product, Power BI seamlessly integrates with other Microsoft tools and services, creating a unified ecosystem for data analysis and reporting.

The Future of Power BI in Data Science and Analytics

As the demand for data-driven decision-making continues to grow, Power BI is poised to play a crucial role in the future of data science and analytics. Microsoft continues to invest in Power BI, introducing new features & enhancements to meet the evolving needs of data professionals. With its ease of use, powerful capabilities, and integration with the Microsoft ecosystem, Power BI is expected to remain an essential tool for data scientists and analytics professionals.

Ending Note

Power BI certification and training programs are vital in enhancing the skills & knowledge of data scientists and analytics professionals. These programs validate an individual’s proficiency in Power BI and provide valuable credentials for career advancement. 

One renowned institution that offers comprehensive training in data analytics, including Power BI, is Imarticus Learning. Their Data Analytics course with placement covers a wide range of topics, providing participants with a solid foundation in data science and analytics. 

Enrolling in the Data Analytics course in india with placement by Imarticus Learning opens doors to a successful career in data analytics. Graduates and professionals looking to transition into the field can benefit from the extensive curriculum covering essential topics such as data analytics, business intelligence, and data visualization. The course equips participants with the necessary skills to thrive in the dynamic field of data science and analytics.

The Data Analytics course focuses on Power BI and comprehensively explains various analytical tools, techniques, and methodologies. By acquiring a diverse skill set, participants can excel in different areas of data analytics and make valuable contributions to organizations across industries.

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