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Category: Analytics

What is supply chain analytics and what does it do

Posted on by Imarticus Learning

Supply chain analytics is an integral part of the logistics operations that organisations use for extracting value and gaining information from the huge datasets relevant to goods procurement, processing and distribution. As an essential element of SCM, it involves using analytics software to enhance forecasting, operational efficiency and responsiveness to customer demands. For instance, to use point-of-sale terminal data in a demand signal repository, predictive analytics is used. Businesses use it to predict consumer demand to cut down on costs and deliver products faster.

Supply chain analytics holistically requires compiling all information relevant to its use case, from procuring raw materials to production, distribution and post-delivery services. All integrations between supply chain execution and management platforms fall under a company’s supply chain umbrella. This type of integration aims to provide complete supply chain visibility (viewing all data on the movement of goods in SCM). 

Supply chain analytics: How it works

Supply chain analytics helps in compiling data related to the supply chain. It spans multiple applications, third-party sources, infrastructure and upcoming technologies. Implementing technologies like IoT helps enhance the decision-making process for strategic, tactical and operational efficiency in SCM. 

Supply chain analytics is used for synchronising planning and execution in the supply chain by improving visibility in real time. These processes impact the customers and the overall profit of the company. Increased visibility enhances flexibility in the network by helping evaluate tradeoffs between customer service and costs.

Data scientists are primarily involved in the operations of supply chain analytics because they understand the data-driven analytical aspects of the business. They may involve factors related to:-

  • Cash flow
  • Inventory
  • Waste  
  • Service levels

Supply chain analytics certification

A successful supply chain management career will require you to look for correlations amongst multiple data elements for developing predictive models to optimise the supply chain output. You will need to test out a number of variations to achieve the ultimate robust business model.

Supply chain analytics features

A supply chain analytics software has to include the following features to achieve operation efficiency:-

  • Data visualisation: The ability to assess data from every angle to enhance understanding and achieve insight.
  • Social media integration: Utilising sentiment data derived from social feeds for enhancing demand planning.
  • Stream processing: Acquiring insight across multiple data streams generated by IoT, weather reports, applications and third-party data.
  • Location intelligence: Using locational data for comprehending and optimising distribution.
  • Natural language processing: Deriving and organising obscure data found in news sources, documents and data feeds.
  • The digital twin of the supply chain: Compiling data and organising it into a systematic model of the supply chain shared across various kinds of users to enhance prescriptive and predictive analytics.
  • Graph databases: Restructuring information into relevant elements, making it easier to locate connections, pinpoint patterns and enhance product, facility and supplier traceability.

Supply chain analytics types

The main supply chain analytics types are based on the four capabilities of analytics in Gartner’s model. They are described below:-

  • Descriptive: This form of supply chain analytics uses reports and dashboards to interpret daily happenings. It involves using a number of statistical methods to search, summarise and structure any data relevant to supply chain operations.
  • Diagnostic: This is primarily used to figure out why something occurred or why something is not working as it should. 
  • Predictive: This form of supply chain analytics uses current data to help foresee anything likely to happen in the future. 
  • Prescriptive: This form of supply chain analytics helps automate or prescribe the most ideal course of action with the help of optimisation logic or embedded decision logic. It helps enhance the decision-making process regarding product launches, building infrastructure (factory or warehouse) or the best shipping strategy for every location.

Uses of supply chain analytics 

Companies depend on supply chain analytics to help match supply with demand by developing plans aligning corporate strategy with everyday operations. Down below, we have elucidated the number of uses relevant to supply chain analytics:-

  • Avoid risks: It is used in risk management by pinpointing known risks and forecasting future risks using patterns and trends derived from data relevant to the supply chain.
  • Enhance order management: It is used for optimising the order management process by compiling all data sources for assessing inventory levels, predicting demand and identifying fulfilment issues.
  • Optimise procurement: It is used for streamlining procurement by analysing and organising net expenditure across multiple departments to enhance contract negotiations and pinpoint discount opportunities or alternative sources.
  • Enhance working capital: It is used for optimising the working capital by enhancing models to determine inventory levels required for ensuring service goals with minimal investment of capital.

Conclusion

The supply chain is directly impactful to businesses as it is entirely customer-centric and directly involves delivering products to consumers. Therefore, companies greatly depend on the efficiency of supply chain analytics because it helps protect the reputation of businesses and sustainability. Therefore, a supply chain management career is a highly lucrative one since all companies demand an expert in this field. You can start by applying for courses with certifications for supply chain professionals to kickstart your journey in SCM. The IIT Roorkee supply chain management course offered by Imarticus is a world-class course to begin with. 

Advantages of data visualization with tableau certification

Posted on by Imarticus Learning

There are several prospects for Tableau professionals. It was formerly a data visualization suite, but you can see the data management add-on in its latest version. As a result, the need for Tableau experts is increasing rapidly. If you are still unconvinced about the benefits, it can offer to your career; this is the post for you.

What is Tableau?

Tableau is the business intelligence industry’s fastest-growing data visualization platform. It assists you in converting textual and numerical data into visual dashboards that allow people to see and comprehend their data. Tableau’s dynamic, simple, quick, and user-friendly design has made it highly popular. The beautiful thing about Tableau is that it does not require any technical or programming knowledge to operate. 

The Advantages of Completing Tableau Course

If coding isn’t your thing, but you want to work in an exciting field, being a Tableau specialist is an excellent choice. Furthermore, even if you have no technical capabilities, you may become a qualified professional. You have to choose the tableau training certification and start creating the groundwork for your profession. So, here are some considerations for deciding whether or not to study Tableau.

Tableau coursestableau courses provide several sorts of visualization possibilities. This improves the overall user experience. Tableau is simpler to learn than Python.

Those with no coding skills or understanding may also rapidly learn Tableau. Users may construct an extraordinarily interactive visual representation using Tableau’s drag and drop features.

Tableau can easily manage millions of rows of data. You may use significant volumes of data to generate various sorts of visualizations without interfering with the functionality of the dashboards. 

Additionally, Tableau has a ‘live’ option for connecting to other data sources such as SQL. Tableau Dashboard provides an excellent reporting tool to tailor the dashboard to devices such as smartphones or laptops. 

Future Roles in Tableau Professionals

Analytics knowledge and Tableau proficiency may propel you to the top of your field. We live in a competitive technological world driven by innovation and transition. Big Data, Artificial Intelligence, Machine Learning, Deep Learning, Cloud Computing, and the Internet of Things are just a few of the technologies permeating practically every sector to grow even more in the future. When seen through an analytics platform like Tableau, these developments may give rise to many jobs.

The need for Tableau professionals is expanding all the time. Thus more people are deciding to add this ability to their toolbox. The most effective method to accomplish this is to get Tableau training and certification from a reputable and competent training source.

Learn tableau course with Imarticus Learning

This tableau course will teach students to apply data science in the real world and build complicated models that yield critical business insights and projections.

Course Benefits for Learners:

  • Students should be familiar with data analytics, machine learning principles, and the most popular data science tools and methodologies.
  • To achieve a tableau certification, students must complete 25 real-world projects and case studies led by corporate partners.
  • Data visualization using tableau software is one of the most in-demand skills in today’s industry. Therefore fresh graduates and early-career professionals should consider enrolling.

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

Supply chain drivers can either make or break the forecasts – How?

Posted on by Imarticus Learning

In supply chain management, forecasting is crucial to planning and managing the flow of goods and materials. By understanding future demand, supply chain managers can make better decisions about inventory levels, production schedules, and transportation needs. Forecasting can help managers anticipate and mitigate disruptions to the supply chain.

How do supply chain drivers impact forecasts?

Supply chain drivers are the key factors that influence demand and supply in the supply chain. These drivers play a crucial role in shaping the overall performance of the supply chain and, as a result, have a significant impact on forecasts.

  1. Consumer Demand: Consumer demand is a significant driver of supply chain forecasts. It determines the number of products and services that must be produced and delivered to customers. Changes in consumer demand can significantly impact forecasts, mainly if they are unexpected or rapid.

  2. Lead Time: Lead time, or the time it takes to receive goods from suppliers, can significantly impact forecasts. Long lead times can result in longer wait times for customers and reduced inventory levels, while shorter lead times can improve inventory management and customer satisfaction.

  3. Capacity Utilization: Capacity utilization refers to the extent to which a company’s resources are used to produce goods or services. Changes in capacity utilization can impact forecasts, as they affect the speed and efficiency of production processes and, therefore, the ability to meet customer demand.

  4. Pricing and Costs: Pricing and costs are key drivers of supply chain forecasts. Changes in the cost of raw materials, labor, and transportation can impact the cost of goods and, therefore, the pricing of products and services.

  5. Government Regulations: Government regulations can have a significant impact on forecasts. Changes in laws can affect the cost of goods, the availability of raw materials, and the ability to transport goods to customers, all of which can impact demand and supply.

  6. Market and Economic Trends: Market and economic trends can also impact forecasts. Economic recessions, for example, can lead to reduced consumer spending and lower demand for goods and services.

Supply chain drivers significantly impact forecasts and are critical to understanding and predicting the performance of the supply chain. Companies must monitor and respond to these drivers & maintain accurate and reliable forecasts and ensure the success of their supply chain operations.

Supply chain drivers that have a positive impact on forecasts

Some Supply Chain drivers positively impact forecasts, making them accurate & reliable.

  1. Stable Consumer Demand: Stable consumer demand is a key driver of accurate forecasts. When demand is stable, companies can better predict the number of products and services that will be needed, allowing them to plan production, procurement, and distribution activities more effectively.

  2. Efficient Capacity Utilization: Efficient capacity utilization, or the extent to which a company’s resources are being used to produce goods or services, can positively impact forecasts. When capacity utilization is efficient, companies can better meet customer demand and maintain inventory levels, reducing the risk of overproduction or stockouts.

  3. Stable Pricing and Costs: Stable pricing and costs are also key drivers of accurate forecasts. When prices and costs are stable, companies can better predict their margins and profits, allowing them to make informed decisions about production, procurement, and distribution activities.

  4. Predictable Government Regulations: Predictable government regulations can positively impact forecasts. When regulations are stable and predictable, companies can better plan their operations and activities, reducing the risk of unexpected disruptions or costs.

  5. Favorable Market and Economic Trends: Favorable market and economic trends can also positively impact forecasts. For example, a strong economy can lead to increased consumer spending and higher demand for goods and services, allowing companies to make more accurate demand forecasts.

Learn and Grow with Imarticus Learning:

For those searching for a leadership program for supply chain professionals, enrolling with Chief Supply Chain and Operations Officers online training from Imarticus Learning can be an excellent idea to advance your career in the Supply Chain and Operations industry. The program at IIM Raipur is tailored to senior supply chain professionals, offering a comprehensive set of skills, including strategic, operational, personal, and technological, to help you become a successful SCM manager. Students connect with other industry professionals throughout the course and embark on a unique learning journey.

Course USPs:

  • Equip individuals with the necessary skills & knowledge for the SCM industry
  • Knowledge and understanding from innovative leaders
  • Chance to network with former students from IIM Raipur
  • Be a part of the Alumni community of professionals at IIM Raipur

What are the advantages of pursuing data science and machine learning certification course

Posted on by Imarticus Learning

Machine learning (ML) and data science have distinct functions and goals. Machine learning is a set of methodologies that enables computer technology to learn from data, whereas data science is a strategy to extract insights from data.

However, there is a similarity between data science and machine learning. Both the fields employ arithmetic, statistics, and algorithms to derive value from data. If you want to pursue a career in data science or machine learning, then a certificate program in data science and machine learning will help you get a headstart in your career. 

What is data science?

Data science deals with data to gain essential business insights. It is a multidisciplinary way of analysing vast volumes of data. The demand for data scientists has increased in today’s market.

What is machine learning? 

best data science and machine learning course

Software programmes can predict outcomes more correctly with machine learning, a subset of artificial intelligence, without needing explicit instructions. ML algorithms come up with new output values through the previous data. Machine learning is vital because it helps create new things and provides businesses with a picture of trends in consumer behaviour. For many companies, machine learning has emerged as a necessary vital differentiation. An IIT Roorkee machine learning certification course will be perfect for you if you want to pursue a career in the field.

Role of machine learning in data science  

Machine learning  is implemented in data science  in the following steps: 

Step 1: Data collection

Data collection is the first step in machine learning. Notably, machine learning assists in gathering and analysing any kind of data (structured, unstructured or semi-structured) from any database throughout systems following the business challenge. 

Step 2: Data preparation and cleansing 

Data preparation uses various ML techniques for assessing the data and making features according to the business requirements. When defined correctly, ML systems comprehend the elements and connections between them.

Data in the real world is dirty and polluted with inconsistencies, partial information, noise,  and missing values. Thus, you will have to clean the data after data preparation is finished.

Machine learning allows us to quickly and automatically identify missing data, do data imputation, encode category columns, and eliminate outliers, duplicate rows, and null values.

Step 3: Model training 

The machine learning method’s selection and the training data’s calibre are essential components of model construction. The requirements of the end user are used to select ML algorithms.

You should also consider the model method’s complexity, performance, interpretability, computer resource requirements, and speed if you want your models to be more accurate.

After selecting the best machine-learning technique, the training data set is divided into two halves for training and testing. This is done to determine the bias and variance of the ML model.

The model training procedure will produce a functional model that can be further evaluated, validated, and deployed.

Step 4: Model prediction 

Understanding prediction mistakes is essential when discussing model prediction (bias and variance).

Having a solid understanding of these issues would make it simpler to construct accurate models and prevent the flaws of overfitting and underfitting the model.

To further reduce prediction errors, a good data science project should find the right balance between bias and variance.

Advantages of pursuing a data science and machine learning certificate course

The best way to learn about the two is through a certified course. Here we have listed some of the benefits of a data science and ML certification course:

  • You can use your technical expertise and knowledge of ML and data science principles like probability, deep learning, mathematical modelling, statistical analysis, and data optimisation to impact your work.
  • You get familiar with the ideas behind supervised and unsupervised learning models that can forecast various types of behaviour.
  • The most prestigious businesses in virtually every industry across international marketplaces have a high demand for certified data science and machine learning training specialists.
  • Obtaining a professional data science and machine learning certification is one of the best methods to stand out. It will demonstrate to employers that you possess the skills and knowledge required to succeed in your position.
  • Data science and machine learning are both new and constantly growing in demand. A certified course in these areas will put you a step ahead in your career.

Conclusion 

A certified course is the best way to learn if you want to know more about machine learning and data science. The Imarticus IIT Roorkee Data Science and Machine Learning course spans 5 months and offers live training by IIT faculty and industry experts. In addition, one can avail personalised mentorship for building start-ups and funding. 

Importance of blockchain in supply chain management

Posted on by Imarticus Learning

Supply chain management (SCM) is mainly involved in moving raw materials and work-in-progress inventory from the point of origin to the point of manufacture and finally to the end customer while keeping the expenses at the lowest possible cost. This entire process is dedicated to making a loyal consumer base and enhancing brand loyalty which finally adds to the company’s growth. This whole process has to follow a strict schedule to deliver the product to customers as per their demands.

best supply chain management and analytics courses

On the other hand, blockchain is a digital bookkeeping mode or a ledger that deals with cryptocurrency and can be distributed. Cryptocurrency can be tracked and can also be used in smart contracts, payment and exchange of agreements. Every blockchain transaction is recorded into blocks that are a part of the blockchain. It is slowly being integrated into supply chain management in recent times. To become a supply chain analyst, knowledge about blockchain is becoming a mandatory requirement as well. Let us delve deeper into how blockchain works in supply chain management. 

Blockchain in supply chain management

With nearly every aspect of our day-to-day lives digitalised, the supply chain world is also steadily moving in this direction. This is where blockchain in supply chain management comes in handy. From order tracking to secure transactions, blockchain is paving its way into the core of SCM, especially in industries like retail and consumer goods that are experimenting with its use case. As a permanently extant and safe digital ledger, it helps reduce fraudulence, errors, and even tracking fees.

Applications of blockchain in supply chain

With blockchain gaining significant popularity, robust corporations and startups are experimenting with the variegated uses of technology and blockchain innovations to carry out multiple needs. 

Tracing food/meat

Blockchain has been widely used in the food industry, and one striking example is Walmart. The company has started using blockchain for product tracking to the end users. Walmart tracks products like pork meat sourced from China and uses blockchain to keep a record of every piece related to the placement of the order to the consumer. This tracking function also helps investigators to locate and remove unreliable sources and blacklisted factories that provide meat carrying food-borne diseases.

Payment processes for automotive suppliers

Blockchain can help in approaching businesses with a global supply chain easily. One great example is Tomcar, which is an Australian vehicle manufacturer. It pays its suppliers with Bitcoin. 

Solar-powered microgrids

The solar industry uses smart contracts to redistribute excess power. Blockchain has a  Transactive Grid application that helps keep track of and redistribute energy. They use a platform called Ethereum to run this program to help with smart contracts and automate the sale of green energy by cutting down on cost and pollution.

Healthcare industry

A patient’s medical records can now be accessed from anywhere in the world with the help of blockchain. The healthcare industry of many countries is now providing key access such as patient fingerprints via which their entire medical history can be viewed. This can alert a paramedic to help them identify any problem and prescribe the necessary medication.

Importance of blockchain in supply chain

Integrating blockchain in SCM has its benefits, from saving money and time to preventing potential risks of cyberattacks and hacking. Businesses are steadily implementing blockchain in their logistics operations to reap the following advantages:

  • To help trace the material supply chain with ease by meeting corporate standards
  • To improve compliance and visibility when it comes to outsourced contract manufacturing
  • To cut down on losses from grey (counterfeit) market trading
  • To reduce administrative costs and additional paperwork
  • To enhance corporate fame by providing transparency of raw materials used in products
  • To reduce any risk of supply chain malpractice 
  • To engage stakeholders
  • To enhance trust and credibility of shared data

Legal considerations in blockchain use

Every organisation should consider legal issues when using blockchain in SCM and business logistics. We have listed a few that businesses need to follow:

  • Blockchain-based agreements
  • Enforcing awards against foreign entities
  • Privacy protection laws
  • Disputing resolution mechanisms
  • Transporting regulatory prerequisites
  • Risks of cybersecurity 

Disadvantages of using blockchain

Blockchain might seem to answer all your problems relevant to supply chain management. However, you should keep the following downsides in mind if you are considering implementing it in your business operations:

  • Lack of standards
  • Budget
  • Regulatory issues
  • Lack of industry support
  • Not seeing value
  • Lack of awareness

Conclusion

The supply chain industry faces multiple challenges daily when it comes to cost, quality, and speed. These parameters are slowly and steadily being achieved with the help of blockchain in SCM. It is still under experimentation and almost deemed a niche concept. The job market is seeing a demand for expert professionals in this field. To become a supply chain analyst, you need to have specific certifications for supply chain professionals. This can be acquired by taking up a reliable supply chain management course with analytics offered by Imarticus.

Machine Learning and Artificial Learning – Explain its interrelation

Posted on by Imarticus Learning

The computer industry has presented us with boons to advance our future in unimaginable ways with machine learning (ML), artificial intelligence (AI) and data science. These three components are dominating the industry and have been making lives easier since their inception. 

These things might be separate branches, but they are interrelated with one another. If you are taking up an AI certification separately, then you must also understand machine learning and data science as a whole. Due to their interrelation, job opportunities in these fields are trending, making them the industry’s most demanding professions. 

In this article, we will discuss the interrelation between AI and ML, their scopes, applications and benefits in the current world. 

What are artificial intelligence and machine learning? 

According to Statista, the forecast for the global growth of AI in the software market from 2019-2025 would be approximately 54%. The forecast size would reach more than US $22.6 billion. AI and ML reduce human labour and make it possible to gain accurate results in no time. 

But the question arises, what are AI and ML? Well, let’s find out. 

Artificial Intelligence (AI) 

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Artificial intelligence is the field of computer science that mimics human intelligence to perform various tasks like speech and image recognition, decision-making, and calculations. It is built using techniques and algorithms of natural language processing or computer vision. AI intends to be intelligent enough to replace human activity in the future. 

Machine Learning (ML) 

Machine learning is a subfield under AI. It is used to make future predictions over a particular problem based on pre-loaded data. One of the finest examples of the usage of ML is to decipher whether an email is spam or not. The email provider helps you detect spam by using various algorithms like regression and classification. ML also has a subfield called deep learning inspired by the working procedure of the human brain. 

Interrelation between AI and ML 

AI and ML fall under the same domain. Machine learning is a subfield of artificial intelligence. Even though they are related inherently, they are not synonymous. Let’s read about their connection. 

Focus on developing new programs

AI and ML have similar outcomes – managing intelligent software and handling more complex tasks. Both AI and ML are incorporated into companies to take over human tasks and finish them quickly. The procedure might differ between the two, but the objective is the same. 

AI intends to mimic human activity, whereas ML uses historical data to automate the results. 

Machine learning as an extension of artificial intelligence 

Machine learning can be used in AI applications to improve upon them. Ml is capable of answering on its own without the need for additional programming. 

Reliance on data 

Both ML and AI rely on data for gaining results. It is often overlooked, even though they play a crucial role in computer programming. The predictions are made via ML, acting as a middleman between artificial intelligence and data science. AI extracts and understands the data, and ML, on the other hand, helps in concluding and proceeding with the data. 

A fine example of the collaborative work between AI and ML is self-driving vehicles. Sensors collect the images, which are then processed through ML, helping to make quick, concise decisions. However, gathering accurate data is the key here. 

Benefits, scopes and applications of artificial intelligence and machine learning

McKinsey and Company have predicted that AI and ML will develop an additional economic output of around $1.3 trillion in 2030. Servion Global Solutions have also specified that by 2025, 95% of customer interactions would be taken over by AI. 

Here we have listed the different benefits, scopes and applications of AI and ML. 

Benefits 

One of AI and ML’s primal benefits is increasing the organisation’s performance. Since data is an extremely valuable component of an organisation, utilising that data is the key to the organisation’s success. For example, since manual data preparation takes a considerable amount of time, automating the procedure would be easier and allows the data to be streamlined. This, in turn, would lead to a seamless business performance without the wastage of any resources. 

Besides, AI and ML also help transform the customer experience by personalising the customer experience. Due to this, the rate of customer retention is more than the traditional selling experience. 

Scope 

ML and AI possess tons of scope both in the present and future. 

  • Automation: AI aims to automate any repetitive high-volume task by setting up a reliable system saving loads of time. ML uses historical data to formulate future forecasts.
  • Producing intelligent products: AI and ML can turn conventional products into intelligent commodities. It works much better when they are paired with conversational platforms and bots.
  • A progressive learning method: AI and ML algorithms can train machines to perform any desired functionality. These algorithms also work as predictors and classifiers.
  • Data Analysis: Machines learn data from analysing and identifying accurate data sets. This becomes very important in any organisation. Furthermore, the use of neural networking helps immensely in training these machines. 

Applications 

Machine learning and artificial intelligence are applied in day-to-day tasks. A few examples are listed below:

  • Chatbots 
  • Image recognition systems 
  • Curating smart content 
  • Voice assistants 
  • Autonomous vehicles 
  • Traffic prediction 
  • Product recommendations 
  • Email spam and malware filtering 
  • Online fraud detection

Conclusion 

The evolving aspect of AI and ML make them hot topics with innumerable future job prospects. If you wish to switch your career for the better, then you can check out Imarticus Learning’s artificial intelligence and machine learning certification course by IIT Guwahati, presented in collaboration with IIT Guwahati. Taught by top IIT faculty, this IIT artificial intelligence course would give you good job opportunities. 

What is the purpose of a leadership program for supply chain professionals

Posted on by Imarticus Learning

A leadership program for supply chain professionals has been designed to provide opportunities to supply chain professionals to help them perform better and assume leadership positions for manufacturing and logistics businesses. It impacts a supply chain executive or manager positively and equips him/her with technical, functional and leadership skills to handle a variety of roles within the two industries mentioned above. The program’s purpose is to accelerate talent early on to help the business and provide career growth to the professional.

Purpose and significance of online leadership program for supply chain professionals

A leadership program will help you to develop the skills mentioned above in the following domains of the supply chain industry:  

  • Manufacturing
  • Purchasing
  • Warehousing
  • Inventory management
  • Demand planning
  • Transportation
  • Customer service

Supply chain management consists of complex organisational operations and it affects the full spectrum of business and helps create and develop industry-based products and services. When the supply chain professionals are trained effectively in leadership, then the organisations benefit in areas like sales, production, distribution and marketing.

It helps you, as a professional, to gain an outlook on the industry from the perspective of senior personnel in management or as an owner of a company. During the program, you will understand how to optimise operations and how to take decisions to improve the business. After the leadership training is concluded, the operations become quicker and the chances of committing errors are reduced.

A leadership program for supply chain professionals also trains you in the following: 

  • Building an effective business development plan
  • Strengthening the core competencies of the organisation

Every business is experiencing the boon of globalisation, resulting in the rise of MNCs. This is why supply chain logistics management has become excessively complicated. Since the business is spread across different economies, there are different markets, labour laws, tax systems and import/export regulations. Therefore, managing a business’s supply chain network in an MNC is all about handling multiple departments, partnering organisations and personnel, and departments.

On the other hand, in smaller companies and startups, supply chain managerial leaders in a company can handle all the logistics operations by themselves or with a comparatively small team. However, leadership skills are still required to manage it efficiently.

Additionally, managing relationships is one of the core responsibilities of supply chain managerial leaders who need to execute operations successfully with the help of liaising. Supply chain leaders help in integrating management and logistics. They constantly analyse market trends and use the required information and all the sources of big data. Then they deliver the decisions after the perusal of this data. This helps the organisation to scale up and grow.

These are some core reasons why business leadership skills are critical for candidates enrolled in the best supply chain management courses.    

Imarticus Learning offers the best online program for supply chain management leadership

Imarticus Learning offers a professional certification in supply chain management and analytics. This professional certification program provides the best supply chain management and analytics program, which helps a candidate develop top-notch leadership skills. The course has been designed jointly by CEC and IIT Roorkee. The faculty comprises industry leaders who provide students with a cutting-edge academic experience.

The analytics in the supply chain enables supply chain managers to make data-driven decisions at every level of the chain. And, professionals who have such insights are in high demand.

Moreover, this certification prepares you for several job roles in the industry. Some of these roles are supply planner, supply and operations planner, demand planner and data scientist.

Some other exemplary features of the Imarticus Learning Supply Chain course

The chief supply chain and operations officers online training by Imarticus Learning has some good features. They are as mentioned below:

  • Learn job-relevant skills

You will get prepared for a highly successful career in supply chain and operation by getting to know modern-day skills, tools and training in real-world projects, which will give you relevant experience on the way the industry functions.

  • Get the valuable IIT tag

You will receive the IIT tag for your supply chain management (SCM) course. Your analytical skills will be top-notch because you are trained by the best faculty and have certification endorsed by the top-ranked institute, IIT Roorkee. You also get to learn from the IIT faculty through live online sessions and discussions.

  • Receive mentoring from industry experts

The best thing about this course is that you get 1:1 mentoring sessions from industry proponents in supply chain management.

Parting thoughts

We have enlisted here all the benefits of chief supply chain and operations officers online training. If you want to know more about the course, then please feel free to contact Imarticus Learning.

The Ultimate Cheat Sheet On Data Analysis With Tableau

Posted on by Imarticus Learning

Tableau is one of the best data visualization tools out there. It’s flexible and easy to use, and it comes with a ton of built-in functions and features that make it very powerful.

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Tableau is a popular data visualization tool that can be used to analyze and present data. It is used by people from all walks of life, from social media managers to data scientists, to create visually appealing and informative reports. It allows you to easily create dashboards for visual analysis, which you can use to share your findings with others in your organization. 

Data visualization with Tableau has three main components:

Data Connections

These allow you to connect Tableau to your data sources. Tableau supports connecting to the most popular databases and cloud storage providers like Google Sheets, Amazon Redshift, Azure SQL Database, etc.

Visualizations 

This is where you create charts, graphs, and other visualizations based on your data connections. You can also create calculated fields that give you additional flexibility when creating visualizations. For example, if you want a bar chart but don’t have enough data points to create multiple bars (for example), creating a calculated field will let you do just that!

Dashboards

Dashboards are collections of visualizations that provide insights into key performance indicators (KPI). They can also include filters that limit what data is displayed.

Types of charts

Line Chart

A line chart is a great way of tracking trends over time. A line chart will be your best option if you have data that changes slowly over time (like sales numbers). Line charts can also be used to compare two sets of data over time. The lines themselves typically connect points that fall on the same date, making it easy to see changes over time.

Bar Chart

Bar charts are similar to line charts, but they show more than one variable at once—for example, showing sales by brand and product category. Bar charts are also good for comparing multiple sets of data at once. Suppose you have a list of items in different categories. In that case, using a bar chart will let you compare them side-by-side on one visual element instead of having separate visuals for each category or variable.

Scatter Chart

A scatter chart is a great option for comparing two different variables. It’s also a good choice when you have a large number of data points or when the data is fairly sparse, and you want to see clusters or trends in the data.  

A scatter plot has two axes: one for the x-coordinate and the other for the y-coordinate. The x-axis represents time or some other variable, while the y-axis represents other variables like revenue or profit. The values on both axes are usually represented by numbers or words as opposed to lines or shapes (such as bars). 

Scatter plots are useful because they allow you to see whether there is any correlation at all between your variables. They also allow you to see if there’s any pattern in how these variables change over time (this will be more apparent when multiple points are plotted on your scatter plot).

Combined Axis Chart

A combined axis chart is a useful tool for comparing two variables at once. It allows you to look at the values of both the variables on one graph with different scales for each axis. You can also use this type of chart to compare multiple sets of data (such as regions). 

A combined axis scatter chart combines both axes into one axis, allowing you to visualize three variables at once. It’s useful when you want to compare multiple variables over time or across different categories—for example, comparing sales by the department overtime or comparing sales by category in different markets.

Tree Chart

A tree chart is a good choice when you want to show hierarchical data, such as a company’s organizational structure or product line structure. Tree charts are also useful if your data set has multiple levels within each category, such as the different levels in an organization’s hierarchy or the different tiers of products produced by an organization.

Stacked Bar Chart 

Stacked bar charts are ideal for displaying how two factors interact with one another—for example, how much more money someone makes if they have a college degree compared with those who don’t have one. Stacked bar charts can also be used to show percentages or proportions over time. 

Imarticus learning understands the need of the hour and has curated a tableau certification course that covers all the industry-standard methodologies. Get in touch with us through our chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Gurgaon and Delhi for tableau courses and tableau certification related queries.

Career Upskilling: How to learn data mining with python

Posted on by Imarticus Learning

With the introduction of an ever-growing need for businesses to gain intelligence about their competitors and customers, the field of data mining has been the next big thing. Data mining has proved itself a valuable tool that can get used in almost every industry. Data mining is fast becoming a buzzword in many industries, from real estate and marketing to finance. This post will show you how to teach yourself the basics of Python data mining.

Introduction to Data Mining with Python

Data mining is discovering patterns and knowledge from large amounts of data. It involves using statistics, machine learning, and artificial intelligence techniques to extract insights and make predictions from data.

Python is a popular programming language for data mining because it has many frameworks and libraries that make it easy to perform complex data analysis tasks. To start data mining in Python, you must have a basic understanding of programming concepts and some experience with the Python language. 

Prerequisites for Learning Data Mining with Python

To learn data mining with Python, you should have a basic knowledge of the following concepts:

  • Programming: Data mining requires a good understanding of programming concepts such as variables, data types, loops, and functions. Python is relatively easy to learn for beginners, but some experience with programming is necessary.
  • Statistics: Data mining involves analyzing large amounts of data, so a basic understanding of statistics is essential. You should know the mean, median, standard deviation, probability, and hypothesis testing.
  • Linear Algebra: Some data mining techniques, such as principal component analysis, require a basic understanding of linear algebra.
  • Machine Learning: Data mining is a branch of machine learning, so it’s helpful to have a good understanding of machine learning concepts such as supervised and unsupervised learning, decision trees, and neural networks.
  • Python: Familiarity with the Python programming language is necessary as all the libraries used in data mining are in Python.

How to Get Started Learning Data Mining with Python?

To get started learning data mining with Python, you can follow these steps:

  • Learn the basics of Python programming: If you are new to Python, start by learning the language basics. You can find many resources online, such as tutorials and documentation, to help you get started.
  • Review the basics of statistics and linear algebra: You’ll need a solid understanding of statistics and linear algebra to succeed in this career. You can find many online resources, such as tutorials, videos, and books, to help refresh your knowledge.
  • Learn about machine learning: Familiarize yourself with machine learning concepts and algorithms since data mining is a branch of machine learning.
  • Learn about the Python data mining libraries: Learn about the most popular Python libraries for data mining, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and Keras.
  • Get a dataset to work with: Find a dataset you can work with and start applying the concepts and techniques you’ve learned to analyze it.
  • Start with basic data mining techniques: Begin with simple data mining techniques such as data cleaning, feature extraction, and basic modeling.
  • Progress to more advanced techniques: As you gain more experience, you can move on to more advanced data mining techniques such as deep learning and ensemble methods.
  • Practice and Experiment: Try out different algorithms and techniques with different datasets to better understand.
  • Keep learning and stay up-to-date: The field of data mining is constantly evolving, so it’s crucial to stay up-to-date with new techniques, algorithms, and tools.

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Importance of sales and operations planning for businesses

Posted on by Imarticus Learning

Sales and operations planning (S&OP) is a critical process for businesses of all sizes and industries. It involves aligning sales and production activities to ensure that the organization can meet customer demand while maintaining operational efficiency. S&OP helps businesses achieve their goals and provides a framework for making informed decisions and addressing challenges as they arise. 

In this blog, we will delve into the importance of S&OP for businesses, discussing its benefits and the key components that make it effective. Whether you are a small startup or a multinational corporation, understanding the role of S&OP can help you optimize your operations and drive success.

What is Sales and Operations Planning?

Sales and Operations Planning (S&OP) is the glue that holds a business together, seamlessly linking its sales, marketing, and operational efforts to drive success. It aligns an organization’s sales and production goals with its operational capabilities, ensuring customer demand is met while maximizing efficiency and profitability. 

Supply Chain analytics course

Think of S&OP as a high-level strategic roadmap that helps businesses make informed decisions, adjust to changes, and overcome challenges. With S&OP, companies can take control of their operations, align their teams, and drive growth in a coordinated and effective manner. Whether in manufacturing, retail, or any other industry, S&OP is an essential tool for success. Embrace this powerful process and watch your organization soar! 

Benefits of Sales and Operation Planning in Supply Chain

Sales and Operations Planning (S&OP) offers numerous benefits to businesses of all sizes and industries. 

Some of the most significant advantages include the following:

  • Improved alignment: S&OP helps bring all departments of an organization together, ensuring that everyone is working towards the same goals and using the same information.
  • Better decision-making: S&OP provides a comprehensive view of the business, allowing decision-makers to make informed choices based on accurate data.
  • Increased efficiency: By aligning production with sales demand, S&OP helps businesses avoid overproduction or stockouts, improving operational efficiency.
  • Better forecasting: S&OP helps businesses create more accurate demand forecasts, reducing the risk of stock shortages or excess inventory.
  • Enhanced profitability: S&OP can lead to significant cost savings and increased profits by optimizing operations, reducing waste, and avoiding stockouts.
  • Improved customer satisfaction: S&OP helps businesses build a positive reputation and retain customers by ensuring that products are available when customers want them.

S&OP provides a strong set of advantages that, taken together, can aid businesses in achieving their objectives and fostering success. Adopt S&OP and enjoy the benefits immediately if you want to advance your company.

Common Challenges in Implementing Sales and Operations Planning

Implementing Sales and Operations Planning (S&OP) is a complex process that requires planning and execution. 

Some common challenges in implementing S&OP include:

  • Resistance to change: Getting buy-in from all departments and team members can be challenging, especially if they resist change.
  • Lack of data quality: S&OP relies on accurate and up-to-date data, so ensuring the quality of this data can be a challenge.
  • Inadequate technology: S&OP requires a robust and flexible technology platform, so upgrading or implementing new systems can take time and effort.
  • Difficulty in aligning teams: S&OP requires close collaboration between sales, marketing, and operations, so aligning these teams can be challenging.
  • Difficulty in forecasting: Accurately forecasting demand can be challenging, especially if there is limited historical data or significant market volatility.
  • Resistance to cross-functional collaboration: S&OP requires cross-functional collaboration, so getting different departments to work together can take time and effort.

Despite these challenges, implementing S&OP can lead to significant benefits, including improved alignment, better decision-making, and increased efficiency. It is crucial to involve all stakeholders, create a clear plan, and choose the right technology and resources to overcome these challenges. With the right approach, businesses can successfully implement S&OP and achieve their goals.

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Professionals looking to maximize their expertise in Supply Chain And Logistics Management should consider certification to equip leaders with the necessary tools to drive tactical, operational, and strategic decisions. 

 Course Benefits For Learners:

  • By fusing a traditional supply chain management course with analytics, you can get ready to help develop more innovative and tactical global supply chains.
  • With the help of IIT’s supply chain program, you can advance your career and become a supply chain analyst. Utilize this chance to start your career as a data scientist!
  • Students who study Python develop a data-centric mindset, which will improve their decision-making in the supply chain.