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Category: Management

Network Optimisation for Efficient Distribution

Posted on by Imarticus Learning

An efficient distribution network is the heart of a successful supply chain. In today’s world, companies that are operating on e-commerce platforms are gaining a competitive edge with better logistics and distribution networks. When we speak about distribution network optimisation, we mean the availability of enough distribution centres at locations in proximity to delivery points and adequate numbers of vehicles to transport the materials there. 

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Network optimisation is also required for the time taken in loading and unloading materials and during planning the inventory for each distribution centre. All these may be achieved by strong sales and operation planning.   

Factors Affecting Distribution Network

Some of the factors which affect the distribution network are as follows – 

Customer analysis 

The demand volume history and financial credibility of the buyer need to be researched. Simultaneously the payment cycle or policy of the customer also needs to be assessed. 

Analysis of suppliers 

Purchase order history and logistic details of suppliers are to be monitored. Besides this, the commitment to quality and timeliness of supply needs to be maintained. 

Inventory assessment

The inventory assessment for each distribution point should be prepared. Inventory management should be considered keeping in mind the sudden upsurge in demand.

Financials

Cash flow, capital investment, availability of working capital, legal obligations etc. are to be monitored. At the end of the day, the objective of any organisation is to make a profit out of a business. 

Trade zone analysis

Preparing a detailed report on geography with a proposal for a new distribution centre is essential. It needs to be checked whether the benefits of a free trade zone are available or not. Marketing potential also needs to be reviewed.

Sales and Operation Planning

Before setting up an efficient distribution network, any manufacturer should have a master plan for its sales and operation. The planning should be aligned with the demand and supply of its products and its financial planning.

Strategic decisions of the sales team include elements like whether the demand for a product in a specific geography should be more than it was in the last year or whether this demand should shift to a more promising geography. These strategic sales decisions influence tactical operational decisions like whether to increase or reduce the production capacity and manpower. Other long-term vendor management policies are also determined accordingly.

All these planning parameters are to be backed up by strong financial planning or budget. There are several challenges to a proper error-free sales and operation planning process. They are as follows –

Zeroing down on accurate information regarding supply and demand at a given point in time is a rigorous task.

In cases where demand shifts significantly from its previous earlier values, sales and operation plans need to be amended with the approval of top management.

Making a presentation to the top management incorporating all real and assumed parameters for the purpose of decision-making is challenging. Assimilating a database from multiple systems for making visible interactive reports is a complex activity.

Planning for new products as a result of demand shift or merger and acquisition of companies leads to newer hurdles for the team.

The latest technology trends have been incorporated into the sales and operation planning exercise. The usage of Enterprise Resource Planning (ERP) software and Supply Chain Management (SCM) software have become quite common nowadays. Besides the above-mentioned, Artificial Intelligence (AI) and the Internet of Things (IoT) have been introduced. 

Supply Chain Design

A real ground working model that elaborates on the structures or distribution outlets of the supply chain and the available logistics network for calculating the time and cost to deliver goods to the market is loosely understood as supply chain design. The model points out the errors committed in the system during the planning stage and flags potential risks involved in the process under different given conditions.

It aims towards reducing inventory, working capital and logistics costs and, in turn, increases operational efficiency, transparency and cost savings. The design model also aims to match supply to its demand under uncertain business scenarios by leveraging on its efficient inventory management skills.

It deals with strategic parameters like the number of distribution centres, location and size of the centres and deals with all global and domestic sourcing strategies. The design model is fully equipped to respond to all possible “what if” scenarios. It also has the flexibility to adjust to any shift in strategic decisions due to changes in the supply-demand curve.

Tips for Maintaining an Optimised Distribution Network

Optimising the distribution network is an ardent activity. Dynamic business conditions and varying parameters make the task difficult. However, a few tips may be followed to optimise the distribution network. They are as follows – 

Early engagement of top management of business owners and other stakeholders is a must-do-thing. A high-level meeting regarding network optimisation at the beginning clarifies many issues, which otherwise could have posed a serious threat due to wrong assumptions.

The meeting should be done in the presence of cross-functional leaders from both sides so that several overlapping functions get clarity right at the very beginning. All the parameters affecting network distribution should be chalked out and debated in a thread-bare manner.

The usage of commercially available modelling tools is better to tackle complex problems than homegrown spreadsheets. The model should be flexible to record all relevant parameters and able to show visually a tangible solution.

Inventory distribution should be the top planning criterion.  

A study or research on network distribution typically takes three to six months’ time. Organisations must allow this time for a stress-free operational experience in future. 

Conclusion

The supply chain management system has come a long way. A supply chain analytics course equips a prospective candidate with all the lessons to be learned. The candidates may find lucrative placement offers in the port and logistics companies besides other opportunities.     

The Professional Certification in Supply Chain Management and Analytics by Imarticus will enable the prospective candidate to boost their career towards a bright future. With the help of this supply chain analytics course, the candidates learn job-relevant skills from experienced IIT faculty. 

Visit the official website of Imarticus for more course-related details. 

Image Recognition and Computer Vision: Extracting Information from Images

Posted on by Imarticus Learning

Image recognition is a result of the incredible fusion between artificial intelligence and computer vision that has led to the emergence of this technology. Image recognition software or applications take the help of camera technology and various AI models.

Image recognition technologies can distinguish and identify objects, people, texts and so on by extracting information from the images it captures. The emergence of this technology has revolutionised the industrial platform, be it pharma companies or retail shops and made brilliant opportunities for building a career in data analytics.

How does computer vision help in image recognition?

The role of computer vision is very pivotal in image recognition. It incorporates various processes like Optical Character Recognition (OCR) with the help of which it extracts textual data from images it captures. Two technologies are employed in this case: convolutional neural networks (CNN) and deep learning which generally use Python programming.

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With the aid of machine learning, computers can distinguish between images without the necessity of detailed programming. This is done by breaking down images into minute pixels and then making predictions based on that. By undergoing repetitive iterations, the predictions are made more accurate which is almost parallel to human perception.

Using CNN for image recognition

The Convolutional Neural Network inculcates a strong algorithm which it uses for image processing. It uses three different layers for analysing the images namely, Convolutional Layer, Pooling Layer and Fully-Connected Layer. 

In the Convolutional Layer, a small portion of input neurons is connected to hidden neurons. The dimensionality of the feature map is reduced by the Pooling Layer which follows the Convolutional Layer. In the end, the Fully-Connected Layer assesses the input data from the previous two layers and helps make assessments through predictions based on memory.

How does image recognition work?

There are a series of steps that are followed to convert images into textual data. These steps include:

  • Acquisition of image: this is the first step where the image is retrieved from an external source
  • Enhancement of image: this step involves changing the picture quality for better assessment
  • Restoration of image: this utilises certain mathematical tools for improving the quality of the image
  • Multiresolution processing: here the image is divided into smaller wavelets for the compression of data
  • Morphology-based processing and segmentation: analysis of images is done based on their shapes and then subdivided into smaller individual components
  • Description and representation: each component is analysed and quantitative information is derived

At the end of these steps, image recognition is made possible where the objects are tagged with a label centred on their characteristic features.

This entire process includes pre-determined signal processing methods which are employed to derive the information from the captured images. These methods include object visualisation, recognition, pattern measurement and so on.

Challenges faced in image recognition

The evolution in image recognition has brought with it several technological advancements. However, the advancements are augmented with various challenges and limitations which need to be overcome. The challenges are as follows:

  • Model generalisation improvement: the challenge here is to ensure that the system can run well in real-world scenarios that can differ from training and test sets. One finds varying distributions in real-world scenarios like different viewing angles, size of the objects and camera features.
  • Failure to read small and huge data sets: here the challenge is to enable the system to learn new data by introducing it to small and limited datasets in the beginning and utilising deep learning and machine learning to learn new information and ultimately recognise new objects. Similarly, another challenge here is that the current models lack the efficiency to read huge datasets to perform critical tasks.
  • Limitations to cognitive understanding: the challenge here is the inefficiency to go beyond just object recognition and achieving a cognitive understanding of objects to interpret inter-relationships between objects like humans to humans, humans to cars and so on.
  • Limitations to automate engineering of networks: the challenge here is that, instead of focusing on some specific features, the efforts are now to build novel network architectures. However, this is quite a difficult task involving myriads of parameters and choices.

Applications of image recognition

Some of the major arenas where image recognition is used are as follows:

  • Face Recognition: This is used in surveillance and security works. 
  • Remote Sensing: various sensors are used to extract information about a distant object. This is used in ships, aircraft and satellites to name a few.
  • Medical sectors: image recognition is being used in image diagnosis of a disease in medical sectors. It is also used in augmenting Computational Tomography (CT) scans and Magnetic Resonance Imaging (MRI).
  • Processing of Video: it is used to process visual data in television sets and other visual electronic systems.

Conclusion

A massive revolution in the industrial sector has been brought about by the advancements in technologies supporting image recognition and computer vision. Utilising deep learning and machine learning integrated neural connecting systems have been developed which aim at getting much better in the coming days. 

However, it has yet to overcome a number of challenges to attain its maximum potential. To gain expertise in such technological backgrounds you can check out Postgraduate Program In Data Science And Analytics provided by Imarticus. This 6-month long program will help data science aspirants with a better chance of securing a career in data analytics with a machine learning certification. 

The Role of Emotional Intelligence in Leadership

Posted on by Imarticus Learning

Leadership requires lots of admirable qualities, one of which is possessing the right amount of emotional intelligence. Emotional intelligence is known as the ability to perceive one’s own or others’ emotions. It means comprehending what the other person is feeling through their sentiments and their signals. 

Emotional intelligence is the crux of becoming a successful leader. Even though one’s technical skill might guarantee them the job interviews, to become efficient in management in future, emotional intelligence plays a critical role. 

In this article, let’s learn in detail about the significance of emotional intelligence as a leader in the workplace and how you can also be a part of an IIM Leadership Programme. 

What is Emotional Intelligence? 

Emotional intelligence or EQ was first coined in 1990 by Professor John D Mayer and Peter Salovey. It is often thought of as a ‘glue’ that can help bind relationships in the long run. They have defined it as an ability to manage and control one’s emotions. This, in turn, would foster long-term relationships not only in personal life but also in one’s professional life. 

What is Emotional Intelligence in Leadership? 

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Every effective leader in a workplace is an embodiment of being the most emotionally mature individual ever. It is an essential skill that helps to solve and recognise the problems of one’s team members. 

Emotional intelligence enables the individual to understand and manage one’s emotions and put themselves in the other one’s perspective. It is also an effective parameter that checks the leadership effectiveness of an individual. Sometimes IQ, technical skills and communication skills fall short in a corporate sector if the candidate lacks emotional intelligence. 

Thus, the use of emotional intelligence has now been popularised. Many leaders enforce emotional intelligence in their leadership style to incite innovation, and job satisfaction and create a positive work environment. 

Components of Emotional Intelligence in Leadership 

Various components of emotional intelligence should be reflected in leadership roles. Let’s find them out. 

Self-awareness 

This is the ability to understand the individual’s strengths as well as their weaknesses. It also makes the leader control their emotions to understand complex emotional turmoil easily affecting the team members. This in turn makes the leader sufficiently self-aware to guide and control their team. 

Moreover, self-awareness also has tons of admirable qualities. They are: 

  • Helps the leader to make a profitable and sound decision to bring prosperity to the company. 
  • To instigate personal growth among the team members for smooth functioning. 
  • Taking 360-degree feedback which in turn is the best way to estimate the self-awareness quotient of the leader. 

Self-management 

This is also considered to be a crucial component when it comes to emotional intelligence as a leadership quality. Even in challenging business situations, this component comes in handy to test one’s leadership skills. It is an admirable quality that keeps emotions in check even during difficult situations. There are also other upsides of self-management in the workplace. They are: 

  • It helps in maintaining a positive outlook in the leaders even during adversities. 
  • It equips the leaders with various ways to tackle different situations. 
  • It also helps the leader to know how to react during a tough predicament by emphasising calmness and mental peace. 

Social awareness 

Since a business organisation does not operate in solitude, social awareness is an essential factor for emotional intelligence. This would help the leaders to be well aware of their work and business environment. There are several upsides to social awareness. They are: 

  • It helps to formulate business strategies and implement potent plans to adapt their employees to heterogeneous market conditions.
  • Social awareness is not only limited to its external factors: it also enhances the leader’s capability to understand and manage people’s emotions in the workplace.
  • Leaders who excel in social empathy help them to understand the perspective and emotions of the people which promotes effective communication and collaboration. 

Relationship management 

This component comes in handy when it is time to resolve conflicts in the workplace. This is a vital component for influencing and coaching the team members at the workplace. This is also an admirable ability to keep conflicts and miscommunications at bay in an organisation. 

Relationship management also talks about increasing job satisfaction and promoting growth. It also helps to maintain peace between all parties within the organisation. 

Emotional Intelligence: Scope in Leadership 

Even though several leaders prolifer in technical and communication skills, they often overlook emotional intelligence. Emotional intelligence, on the other hand, becomes an inevitable part of a managerial skill set. Any leader who might lack emotional intelligence will not survive within the tides of a complex business environment. The scope of emotional intelligence in leadership are: 

  • Positive work culture: Emotional intelligence amongst the leaders produces a positive work culture which increases efficiency and productivity vicariously. 
  • Instigates growth: Emotional intelligence promotes growth, creativity and innovation in an organisation and their stakeholders. 
  • Team motivation: Emotional intelligence helps in motivating team members and also encourages them to put their best foot forward. 
  • Correct decisions: Emotional intelligence is an important component which helps in making correct decisions even during difficult times. 
  • Strong bond: Emotional intelligence promotes a strong bond between leaders and their teams. 

Conclusion n

Be it communication skills, technical skills or anything else, nothing surpasses emotioal intelligence. Since it is the most important part of leadership, it is now being incorporated into many management courses. 

If you are interested in learning about leadership and its essential to become a potential global leader, check out Imarticus Learning’s Global Senior Leadership Programme with IIM Lucknow. This IIM course for working professionals is specifically designed to transition one’s career to a high-potential business leader. It makes it easier for your journey to become a CMO, CFO or COO. 

To know more, check out their website right away! 

A Guide to Getting Datasets for Machine Learning in Python

Posted on by Imarticus Learning

Welcome to the world of Machine Learning! 

Data gathering can be challenging when creating your first machine-learning project, especially for beginners. Finding datasets for machine learning is essential, but it may also be one of the most difficult parts of the process. Your ML model’s dataset serves as its building block, and you cannot train your model to provide reliable predictions without it.

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But don’t worry; this blog will demonstrate locating and getting the appropriate datasets for your Python ML project. You’ll discover where to hunt for datasets and how to obtain them using Python, whether you’re a professional or a student. 

Before diving into how to get datasets for machine learning in Python, let’s first understand what is machine learning.

What is Machine Learning?

Machine learning is basically a field of computer science and artificial intelligence that involves developing algorithms and statistical models. In other words, it’s a way for computers to automatically improve their performance at a specific task by learning from experience rather than being explicitly programmed.

If we talk about types, there are numerous machine learning types, such as supervised learning, unsupervised learning, and reinforcement learning, each with its own set of algorithms and techniques. In general, machine learning involves three main steps: preparing the data, training the model, and using the model to make predictions or decisions.

Furthermore, Machine learning has numerous applications, from image recognition and natural language processing to self-driving cars and personalized recommendations. It’s a rapidly growing field, with new techniques and models being developed all the time, and it’s expected to play an increasingly important role in many industries in the years to come.

Why is Python Used for Machine Learning?

Python has become a favored linguistic medium for machine learning due to its ease of use, versatility, and an extensive assortment of libraries and utilities. Python was the third most in-demand language among recruiters in 2022, according to Statista.

Some of the key essentials why Python is used for machine learning are:

Easy to learn: Python has a simple and intuitive syntax that makes it very easy to learn and use, even for those without a background in programming.

Rich library ecosystem: Python has a vast collection of open-source libraries that support various machine learning tasks, such as data preprocessing, feature selection, model building, and evaluation.

Strong community support: Python has a large and active community of developers who directly contribute to developing machine learning libraries and tools, making it easier for users to find resources and get help with their projects.

Versatile: Python is a universal language for various tasks beyond machine learning, such as web development, data analysis, and scientific computing.

Scalability: Python has robust support for distributed computing, making it possible to scale up machine learning applications to handle large datasets and complex models.

How to Find Datasets for Machine Learning in Python?

Choosing the right dataset is crucial for the success of your machine learning project. Here are some ideal factors to consider when choosing Python Machine Learning Dataset Libraries:

  • Size: The size of the dataset must be large enough to be representative of the problem you are trying to solve. However, it should also be manageable and not too large that it becomes difficult to work with.
  • Quality: The quality of the dataset is also essential. Ensure that the dataset is accurate, reliable, and free from errors or biases.
  • Relevance: Choose a dataset that is relevant to your problem statement. The dataset should contain useful features for solving the problem you are trying to address.
  • Data Type: Consider the data type you are working with, whether numerical, categorical, or text. Choose a dataset that matches the data type of your problem.

How to Preprocess Datasets?

Preprocessing datasets is an essential step in machine learning that involves cleaning and transforming raw data into a correct format for machine learning algorithms. Here are some common preprocessing techniques:

  • Data Cleaning

 Data cleaning involves removing or correcting errors and inconsistencies in the dataset. This step is crucial in ensuring that the dataset is accurate and reliable.

  • Data Transformation

Data transformation simply involves converting the data into a format that machine learning algorithms can quickly analyze. Common techniques include normalization and standardization.

  • Feature Engineering

Feature engineering involves selecting and creating relevant features for the problem statement. This step can improve the model’s accuracy and reduce the data required to train it.

Ending Note

Obtaining high-quality datasets is essential to any successful machine-learning project. With the tools and resources available in Python, it’s easier than ever to collect and preprocess data for use in machine learning models.

Imarticus Learning Certificate Program in Data Science and Machine Learning is a great place to start for those who want to learn more about data science and machine learning. This curriculum, developed with iHUB DivyaSampark @IIT Roorkee, gives students a solid foundation in data science and machine learning ideas and the practical skills they need to put these concepts into practice and apply them to real-world issues.

With the right training and resources, you can become a skilled machine learning practitioner and make a real impact in data science.

Why a Data Analytics Course is the Next Best Thing

Posted on by Imarticus Learning

With the internet ushering us into an age of information, accessing data has never been easier. Data is widely prevalent in every industry and all stages of human society. From governments to MNCs, businesses of all sizes depend on data for their continued growth and existence. This is precisely why a career in data analytics right now is highly rewarding, with innumerable growth opportunities.

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Data analytics is a fast-expanding field that involves studying big data sets to gain information so as to make educated choices. Opting for a data analytics course can be the best course of action for those who wish to develop the relevant skills and knowledge required to become a professional data analyst.

Read on to learn how pursuing a data analytics course in 2023 will benefit you in the near future.

How data analytics can help professionals

Data analytics is a dynamic and demanding field requiring unique analytical skills and resourcefulness. As a data analyst, you will collaborate with various teams to provide insights on improving their processes. 

To become a Data Analyst, you must have certain technical abilities, such as statistics and R or Python programming. Even those who do not wish to become full-fledged Data Scientists can benefit from learning data analytics. 

Being data-literate assists one in finding answers hidden in vast datasets that can address a range of issues. Hence, enrolling in a data analytics course can be an ideal method to gain the requisite knowledge and expertise for a successful career in data analytics or to improve one’s data literacy.

Reasons to study data analytics 

Training in data analytics can help candidates land various job roles across industries, for instance, Data Scientist, Data Engineer, Data Architect, or data analytics professional. The growing scope of the sector, along with its other advantages, make it a lucrative career option for students as well as working professionals looking for a career switch.

Below is a list of reasons you should choose a data analytics course.

  • Fast-track your career 

Data analytics training can help you fast-track your career and apply for top-paying jobs. Studies reveal working professionals who completed a data analytics course experienced a considerable salary hike.

  • High demand 

The main driving force behind the increasing popularity of data analytics courses is a high demand for skilled data analysts across various industries, including finance, healthcare, and technology. 

One study found that data analysis skills are so in demand that even non-technical managers can expect a significant raise in their salary by learning these skills. LinkedIn states it is among the top skills employers seek in the current job market.

  • Flexible online learning options 

Many certification and certificate courses are available online, allowing flexible learning options that fit your schedule. For example, Google’s Data Analytics Professional Certificate is a flexible online data analytics course.

  • Specialisation options 

Depending on your interests and career goals, you can specialise in different areas of data analytics, such as data visualisation, machine learning, or predictive analytics. This allows you to adjust your learning to your career objectives and enhance your expertise in a specific area. Some popular domains of data analytics include Marketing Analyst, Financial Analyst, Sales Analyst and Operations Analyst.

  • Hands-on experience

Many data analytics courses go beyond theory-based learning to offer hands-on experience with real-world datasets, allowing you to hone your practical skills and apply your learning in a professional setting.

  • Improve decision-making

By gathering new insights from data, data analytics skills can assist organisations in making better decisions pertaining to their daily operations and future too. You can acquire skills that can be employed in a range of jobs and sectors by taking a data analytics course. 

  • High earning potential

Data analytics specialists have incredible income potential, with an average annual salary of INR 903,864 in India. Earning potential is predicted to rise as the need for experienced workers in this industry grows.

  • Broad working spectrum

A career in data analytics offers prospective candidates the freedom to choose from a wide set of industries according to their personal preferences. You can land jobs in marketing, business intelligence, finance, sales, data assurance, data quality, etc., departments of an organisation. Professionals with a data analytics course can expand within the same organisation by switching job roles.

Conclusion

Do you wish to bolster your technical skills, enhance your decision-making abilities, and be a part of an exciting, fast-growing sector with enormous potential? Then a data analytics course is the next best thing for your career. Help organisations turn data into valuable insights and have a meaningful impact on the world with the necessary skills and knowledge.

Enrol in Imarticus’s Postgraduate Programme In Data Science And Analytics to avail yourself of exciting career opportunities. This 6-month long course is taught in hybrid mode through online and classroom learning. With a job-centric curriculum teaching practical applications of SQL, Power BI, data analytics, Python, tableau and much more, candidates will land assured jobs with top-tier companies. 

For more details, visit their website now!

An Introduction to Marketing Management

Posted on by Imarticus Learning

Marketing management involves making potential consumers aware of a company’s products and services. It involves formulating plans and creating a company’s brand name and identity. Developing and implementing a company’s business plan depends heavily on marketing management.

The concept of marketing management is all about planning, creating and implementing various marketing strategies to achieve greater organisational objectives. This manual briefly explains marketing management so that one can get an idea of the concept, its importance, functions and so on. 

What is marketing management?

Marketing management is a concept that refers to all the analysis tools, strategies and business plans used to promote a brand or the name of the business and the products or services it provides. It helps track a business’s growth and find opportunities to capture the market. It aims to create a huge customer base by coordinating advertisement campaigns on various marketing platforms.

Marketing management also entails looking at the results of previous marketing campaigns and consumer outreach initiatives to improve a brand’s overall marketing strategy. It aims to streamline the business process and form better relationships with consumers. It works two ways: increasing the business’s productivity and satisfying customers.

Objectives of marketing management

The main objectives of marketing management are to build a large customer base and maximise profits. However, there are other objectives as well that are enumerated as follows:

  • The prime goal of marketing is to attract more buyers to invest in the products or avail of the company’s services.
  • Marketing keeps in mind the taste and demands of the customers as they launch products according to consumer preferences.
  • Increasing the company’s profitability to ensure the long-term survival of the company by the marketing team.
  • Marketing also aims to enhance the standard of living of the target audience. It is done by conducting thorough market research and implementing new techniques.
  • Managing the marketing mix is another key objective of marketing management. It is the 4 P’s ━ product, price, promotion and place.

Features of marketing management

Understanding the features of marketing management is important to understand its functionality. The following are the features of marketing management:

  • It is a managerial process that includes planning, organising, directing decision-making, controlling and forecasting.
  • All marketing activities are consumer-centric, as the brand provides what the consumers want. 
  • It involves performing research analysis to understand the needs and demands of customers. 
  • Marketing considers the planning and development of the brand’s products and services. 
  • It includes the promotional and communication activities the brand performs to reach out to new and potential customers.
  • Marketing also includes management and controlling activities to streamline the process.

Functions of marketing management

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The main functionality of marketing management is to integrate sellers and buyers. It organises and integrates all the elements of marketing to effectively operate the system and build a consumer pool. The other functions of marketing management are:

  • Assessing market opportunities: It helps to determine and assess the marketing opportunities for a business’s growth. The marketing world is dynamic; hence marketing teams must develop targeted programmes in sync with current demand.
  • Planning and organising market activities: Planning is a part of the managerial process that enhances marketing strategies. It formulates policies about price, product, distribution channels etc.
  • Regulating and motivating employees: Companies must have appropriate motivation policies in place. Modern ways of motivational strategies are implemented to improve employee efficiency, which in turn attracts more customers.
  • Evaluation and control of marketing initiatives: Evaluating and keeping track of marketing efforts is very important when running a business venture. It helps marketing professionals to ascertain which strategies are working and which need to be changed.

Steps of marketing management

The marketing process involves the following steps:

  • Setting marketing goals: The most important step is understanding what one wants from a marketing campaign. It helps the team to understand and find proper market opportunities.
  • Analysing marketing opportunities: Evaluating the boons and banes of the various marketing areas and the marketing campaigns based on their strengths and weaknesses.
  • Choosing the target audience: After scanning through market opportunities, the marketer selects the target audience best suited for its product and services.
  • Designing marketing campaigns: Businesses must prepare well-defined marketing strategies per the target audience’s preferences. It comprises basic marketing decisions like marketing mix, product pricing, product distribution etc.
  • Planning marketing programmes: The company needs proper marketing programmes to achieve organisational objectives effectively. These programmes include important decision-making related to numerous marketing parameters that will convert potential customers into actual buyers.
  • Organising, implementing and controlling the marketing initiative: Planning is abstract, but the actual test is when the plan is executed. This concluding step in the marketing process applies corrective measures in case of any diversion.

Conclusion

Marketing is an ever-growing field that is witnessing constant change. With businesses vying for online presence, marketing has gained paramount importance. Building a career in marketing now would be the best decision.

To have a career in marketing management, sign up for the IIM Lucknow Global Senior Leadership Programme by Imarticus. This programme will help you grasp all the leadership areas, including financial accounting, analytics, operations, etc. This one-stop course will help you master marketing skills and establish yourself in a highly competitive world.

Ultimate Machine Learning Guide for 2023

Posted on by Imarticus Learning

As we approach 2023, machine learning (ML), a subset of artificial intelligence, has shown to be a crucial skill in the digital commerce industry. With the increased demand for intelligent systems and automation, businesses are increasingly resorting to ML to stay ahead of the competition. 

The future of machine learning is promising, with a projected CAGR of 38.8%, reaching $209.91 billion by 2029. The tech industry is enhancing productivity, decision-making, product and service innovation, and customer journey by deploying machine learning-based solutions. 

This blog will look at machine learning principles, cover the latest trends and breakthroughs, and present tools to assist you in tackling the area in 2023.

What is machine learning?

Machine learning (ML) is a subfield of artificial intelligence that leverages artificial neural networks and focuses on creating computer systems that may improve their performance via experience and data analysis. 

Simply put, machine learning is the process of developing models or systems that can learn from data without being explicitly programmed for specific tasks. Instead, these algorithms are intended to recognise patterns, form predictions, or perform actions depending on the data they are subjected to.

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Machine learning algorithms can be classified into various types, including supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning – Models learn from labelled data in supervised learning when the desired output or target is provided with the input data. 

Unsupervised Learning – It is the discovery of patterns or structures in unlabeled data in the absence of explicit target labels.

Reinforcement Learning – Reinforcement learning teaches agents how to perform in a given environment to achieve maximum rewards or outcomes.

Key concepts and techniques of machine learning

  1. Deep Learning: A subclass of machine learning, deep learning involves artificial neural networks inspired by the human brain. It possesses distinguishing features, including its capacity to learn hierarchical representations from unstructured input and its applications in image identification, natural language processing (NLP), and other areas. 
  2. Data Preparation and Feature Engineering: Data preparation and feature engineering are essential steps in the machine learning workflow to improve the performance and effectiveness of machine learning models. 

Data preparation involves cleaning, transforming, and organising the data, while feature engineering involves creating new input variables from existing raw data. Transformations, interaction terms, and domain-specific knowledge can be used to generate new features. 

Model Training and Evaluation: Model training and assessment are critical processes in the machine learning workflow, encompassing the process of training and evaluating a machine learning model on a dataset. 

Model training is the process of teaching a machine learning model to produce correct predictions by learning from the available dataset. The training set trains the model by feeding it input data and known outputs, adjusting its parameters through optimisation algorithms.

Advanced topics and applications

Some of the major applications that are shaping the future of machine learning are -: 

  • Explainable AI: Explainability is becoming increasingly important as machine learning systems become more complex. Explainable AI is the development of AI models and systems that can provide understandable explanations for their outputs and decision-making processes. Various methods for evaluating and explaining machine learning models help improve transparency, trust, and moral concerns. 
  • Reinforcement Learning: Various domains have benefited from successful applications of reinforcement learning, including robotics, games, recommendation systems, and autonomous vehicles. Agents in these applications are trained using RL algorithms like Q-learning and policy gradients, which enable them to learn optimal strategies or policies through trial and error. 
  • Generative Models: Generative models are machine learning models that learn the underlying probability distribution of data and generate similar samples. Generative models have various applications, including image generation, text synthesis, and data augmentation. Examples of generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models.

Trends and forecast for 2023

Edge Computing: Edge computing in machine learning is the practice of performing computation and data processing at the network’s edge, closer to the source of data generation or the end user. It reduces latency and network bandwidth requirements, enhances privacy and data security, and enables offline or intermittent connectivity scenarios. Edge computing in ML has proved useful in healthcare monitoring systems, autonomous vehicles, industrial IoT, and video surveillance.

Federated Learning: This ML approach trains models across various decentralised devices while maintaining data privacy and security.

Responsible AI: It is a methodology for designing, evaluating, and implementing AI systems in a safe, trustworthy, and ethical manner. It emphasises the importance of possible effects and outcomes of AI systems at every stage, including their creation, implementation, and utilisation.

Conclusion

Machine learning will affect a variety of businesses in 2023 as well as in future years. You can harness the great potential of machine learning in this fast-expanding world by grasping the underlying ideas, keeping yourself up-to-date on the newest developments, and honing your abilities through practical applications and learning materials. 

If you are still wondering what is machine learning, enrol in Imarticus’s Certificate Programme in Data Science and Machine Learning to learn the core concepts of the field and start on an exciting career path.

Top Supply Chain Analytics Courses To Look Out For in 2023

Posted on by Imarticus Learning

In today’s dynamic business world, supply chain analytics plays a vital role in assisting organisations in making informed decisions, optimising their supply networks, and gaining the upper hand over their competitors. 

Whether you are a supply chain professional looking to advance your career or an aspiring analyst looking for an introduction into supply chain analytics, enrolling in a top-tier supply chain analytics course can give you the knowledge and expertise you need to succeed. 

This blog will look at some of the best supply chain analytics courses to watch out for in 2023.

Introduction into supply chain analytics

Supply chain analytics is critical in enhancing supply chain management, operations, and efficiency by employing data analytics methodology and tools. It entails analysing massive amounts of data modern supply chains produce to obtain insights, identify trends, and address shortcomings. The introduction of computer-based technologies, such as artificial intelligence (AI) and machine learning (ML), has dramatically improved supply chain analytics in recent years.

Best supply chain analytics courses

Top Supply Chain Analytics Courses

Pursuing a supply chain analytics course can help expand your expertise and contribute to better decision-making, and drive efficiency in supply chain operations. Here are some of the top courses you can consider:

  • Supply Chain Analytics: Rutgers the State University of New Jersey

Offered by Rutgers University, this comprehensive course focuses on supply chain analytics and equips learners with the necessary skills to generate actionable insights for effective decision-making. It covers data-driven decision-making, demand forecasting, inventory management, and optimisation techniques. Through hands-on exercises and real-world case studies, students gain practical experience in applying analytics to supply chain scenarios.

Duration: 5 months approximately

Fees: N/A

  • RA: Data Science and Supply Chain Analytics (A-Z with R) by Udemy

This course on Udemy provides a comprehensive overview of data science and supply chain analytics using the R programming language. With a focus on practical applications, this course empowers learners to analyse supply chain data and extract valuable insights. Students will learn to leverage data science techniques for supply chain optimisation, inventory management, forecasting, and revenue management.

  • Professional Certification in Supply Chain Management & Analytics by Imarticus Learning

Designed in collaboration with IIT Roorkee, the course covers supply chain performance, drivers and metrics, designing the supply chain or distribution network, planning and coordinating demand and supply, sales and operations planning, managing uncertainties in a supply chain, determining the optimal level of product availability and more. The programme offers project-based learning focused on helping candidates tackle real-world scenarios using cutting-edge tools and technology

  • Executive Programme in Supply Chain Management and Analytics at IIT Delhi

This online programme is specifically designed for working professionals aspiring for a role in logistics and supply chain management. The key learning aspects of this course include an in-depth knowledge of supply chain analytics, multi-criteria decision-making techniques like AHP, TOPSIS, DEA, regression models, etc. This case-based training programme helps participants understand and solve real-world problems. Post course completion, candidates get the prestigious IIT Delhi certificate.

  • Supply Chain Analytics at MIT Cambridge

This online course is part of MIT’s MicroMasters programme with edX. Designed by eminent MIT faculty, the course exposes students to industry-relevant, real-life case studies training them to handle various analytical tools and techniques to manage supply chains better. The programme commences with an introduction to probability and moves on to regression analysis and statistics.

  • Demand and Supply Analytics at Columbia University, New York

The course provided by edX in collaboration with Columbia University is ideal for students looking for a career switch or professionals looking to bolster their knowledge in demand and supply analytics. The programme focuses on basic analytical methods, inventory management, applying analytical methods in real-life supply chain problems, and stochastic inventory management. It also helps students develop predictive abilities to identify and mitigate issues before they can hinder operations.

Conclusion

To excel in the field of supply chain analytics, professionals need to acquire the necessary skills to work effectively with data. Supply chain analytics has become increasingly vital in today’s business landscape, and keeping up with the latest techniques and tools is essential for professionals in this field. Whether new to the field or looking to upskill, enrolling in a supply chain analytics course offers valuable knowledge, practical experience, and industry-relevant insights. 

Imarticus Learning’s Professional Certification in Supply Chain Management and Analytics, designed by IIT Roorkee, can land high-demand job roles like Demand Planner, Supply and Operations Planner and Supply Planner. By enrolling in this supply chain analytics course, you can expand your expertise, contribute to better decision-making, and drive efficiency in supply chain operations.

A Guide to Basic Python Programming

Posted on by Imarticus Learning

To learn Python means to practice Python. Python is a popular high-level, interpreted, interactive, and object-oriented programming language invented by Guido van Rossum in 1991. Its popularity rests on its simplicity, readability, and ease of use, making it a popular option for novices and professionals. 

best big data analytics course

It may be processed in a procedural, object-oriented, or functional style and operates on an interpreter system. It features a clean and focused syntax centred on readability and a vast and active community offering assistance, tools, and libraries to help developers solve issues and build new things.

Getting started with Python

Here are some key points to consider:

Choose an Integrated Development Environment (IDE) or code editor to develop and execute Python code. Some prominent choices are:

  • PyCharm 
  • Visual Studio Code 
  • Sublime Text 
  • Atom 
  • IDLE

Install Python on your PC. You may grab the latest version of Python from the official website: https://www.python.org/downloads/

Learn the basic principles of Python programming language, including:

  • Variables and data types
  • Operators
  • Control structures (if/else statements, loops)
  • Functions
  • Modules and packages

Practice developing Python code by tackling exercises and challenges. Some resources for Python exercises are: https://pynative.com/python-exercises-with-solutions/

https://www.w3resource.com/python-exercises/python-basic-exercises.php

Join online groups and forums to network with other Python developers and seek support when required. Some popular communities are:

  • Reddit’s r/learnpython
  • Python Discord server
  • Stack Overflow

Read Python documentation and tutorials to learn more about the language and its capabilities. Some resources for Python tutorials are: 

https://docs.python.org/3/tutorial/index.html

https://realpython.com/ 

Data types in Python

Python includes various built-in data types used to represent different sorts of data. Here are the most popular data types in Python

Numeric types: int, complex, float 

String type: str

Sequence types: list, range, tuple

Set types: frozenset, set

Mapping type: dict

Boolean type: bool

None type: None

Binary types: memoryview, bytes, bytearray

Here are a few examples in detail:

Integers: Whole numbers, such as 1, 2, 3, and so on.

Floating-point numbers: They are decimals, such as 7.21, 5.168, etc.

Tuples: Tuples are ordered, immutable collections of elements, such as (1, 2, 3), (“apple”, “banana”, “cherry”), and others.

Strings: They are sequences of characters, such as “hello”, “world”, etc.

Dictionaries: Dictionaries are unordered collections of key-value pairs, such as {“name”: “John”, “age”: 30}, {“fruit”: “apple”, “colour”: “red”}, etc.

Lists: They are ordered collections of elements, such as [“apple”, “banana”, “cherry”], and others.

Here’s an example of how to create variables of different data types in Python:

a = 42

b = 3.14

c = 2 + 3j

str_var = “Hello, world!”

lst_var = [1, 2, 3]

tpl_var = (4, 5, 6)

rng_var = range(10)

dct_var = {“name”: “Alice”, “age”: 30}

st_var = {1, 2, 3}

fst_var = frozenset({4, 5, 6})

bool_var = True

bts_var = b”hello”

ba_var = bytearray(bts_var)

mv_var = memoryview(ba_var)

none_var = None

Functions in Python

Blocks of code called functions may be reused to carry out different tasks. Using the def keyword in Python, you may create your functions. Here is an example of a function that computes a number’s factorial:

def factorial(t):

    if t == 0:

        return 1

    else:

        return t * factorial(t – 1)

The following is how your Python code should call this function:

def factorial(n):

result = 1

for i in range(1, n + 1):

result *= i

return result

num = 5

print(factorial(num)) # Output: 120

Control structures in Python

Python has several control structures that let you manage the application’s flow. These consist of try-except blocks, for loops, while loops, and if-else expressions. An overview of various control structures is provided below:

If-else clauses: If-else clauses let you run several chunks of code in response to a condition. For instance:

if y > 0:

print(“y is positive”)

else:

print(“y is non-positive”)

For loops: For loops allow you to iterate over a sequence of elements. For example:

for k in range(20):

    print(k)

While loops: While loops let you continually run a section of code as long as a condition is met. For instance:

x = 0

while x < 10:

print(x)

x += 1

Try-except blocks: Try-except blocks allow you to manage potential exceptions in your code. For instance:

try:

    num = int(input(“Enter a number: “))

    result = 1 / num

except ValueError:

    print(“Invalid input”)

except for ZeroDivisionError:

    print(“Cannot divide by zero”)

else:

    print(“Result:”, result)

This guide covers Python fundamentals such as data types, control structures, functions, and modules. The best way to learn Python is to start with the basics and then slowly master it by practising coding or taking part in a workshop. 

Conclusion 

Imarticus Learning offers a Postgraduate Programme in Data Science and Analytics (PGA). This programme uses data science and creates analytical models that enhance corporate performance. Machine learning and Python programming are among the basic and advanced data science and analytics techniques covered in the curriculum. The course is one of the best to learn Python for beginners. It offers certification programmes and job assurance.

An Introduction to Warehouse and Order Management

Posted on by Imarticus Learning

Warehouse and order management are critical components of supply chain operations, ensuring efficient goods flow and storage. Warehousing simply means working on where and how to keep goods before they are delivered directly to customers or shops or before being displayed. 

Order management, on the other hand, deals with the entire process starting from order placing to order delivery.

Employing the best warehouse and order management practices is essential for businesses, regardless of their size, to optimise space use, minimise time lost in finding items, maintain optimal product condition and avoid delivery delays. 

This article will answer what is warehouse order management and discuss in detail its importance, fundamental principles, and best practices.

Introduction to warehouse operations and management 

best advanced certification program in digital supply chain management course

Warehouse management entails effectively controlling and optimising numerous activities within a warehouse facility. Its primary goal is to guarantee that products move smoothly from receiving and storing to collecting, packing, and shipping. Efficient warehouse management reduces inventory holding costs, optimises space usage, and improves processes pertaining to the completion of orders.

Key functions of warehouse management

The primary tasks involved in warehouse management include : 

Receiving: Incoming items are inspected and recorded, quantities and quality are verified, and inventory records are updated. Accurate and timely receiving improves inventory accuracy and allows for efficient planning of subsequent warehouse operations.

Inventory and Storage Management: Warehouse managers must strategically plan and store products to maximise space utilisation and enable effective order pickup. Inventory management systems are used to keep track of stock levels, monitor replenishment requirements, and avoid stockouts or overstock situations.

Order Picking: The process of removing products from storage locations to complete customer orders is known as order picking. It entails using effective picking methods such as batch picking, zone picking, or wave picking to boost production and reduce trip time inside the warehouse.

 Packing and Shipping: Orders must be carefully packed after they are picked to ensure that the goods are not compromised during shipment. Warehouse management involves packaging, labelling, and working with shipping carriers to ensure on-time and precise order delivery.

An overview of order management

Order management deals with the entire process of receiving, processing, and filling client orders. Coordination among other divisions, such as sales, inventory, and logistics, is required to maintain smooth order flow and customer satisfaction.

Key components of order management

Order Processing: Order processing includes processes such as order entry, validation, and verification, as well as customer information, product availability, and pricing. Efficient order processing ensures accuracy, minimises errors, and allows for timely order fulfilment.

 Order Fulfilment: Order fulfilment involves collaborating on the collection, packing, and shipping processes within the warehouse. The aim is to provide clients with accurate items, on schedule and in perfect condition.

 Order Tracking and Customer Communication: Providing customers with real-time order tracking information is an integral part of order management. Effective communication about order status, shipment tracking information, and anticipated delays increase customer satisfaction while minimising queries.

Returns and Exchange Management: Order management includes handling of product returns, exchanges, and refunds. Streamlining the refund and exchange process and managing customer expectations increase consumer loyalty while reducing operational disruptions.

Best practices in warehouse and order management

Implementing the best warehouse and order management practices ensures the overall improvement of operations, safety, productivity, space utilisation and inventory control. Given below is a list of the best practices that can help businesses manage the entire process efficiently.

  • Warehouse Management Systems (WMS) Implementation: WMS software automates and optimises warehouse operations, promoting inventory accuracy, order fulfilment efficiency, and overall productivity. 
  • Using Technology: Employing technology like barcode scanning, RFID (Radio Frequency Identification), and automation systems improves inventory visibility, minimises errors, and accelerates order processing. 
  • Continuous Process Improvement: Reviewing warehouse and order management procedures regularly, finding interruptions, and adopting process changes can result in increased operational efficiency and customer satisfaction. 
  • Collaboration and Integration: Effective warehouse and order management requires seamless coordination and information exchange among multiple parties involved, including suppliers, manufacturers, and transportation providers.

Conclusion

Warehouse and order management are essential elements of supply chain operations, ensuring that commodities are delivered from suppliers to clients promptly and efficiently. Businesses may optimise their operations, improve customer experience and satisfaction, and gain a competitive advantage in the market by knowing the fundamental concepts and best practices in the warehouse and order management.

Imarticus Learning and E&ICT and IIT Guwahati offer advanced certification in Digital Supply Chain Management. The course specifically trains SCM professionals in using technology in logistics, procurement, inventory and vendor management. The new-age digital programme has an industry-focused curriculum and offers the unique opportunity to learn from real-life case studies.   

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