March 31, 2023 - Finance, Tech & Analytics Career Resources

Importance of Data-Driven Decisions in Supply Chain Management

Posted on March 31, 2023April 8, 2024 by Imarticus Learning

Data-driven decision-making has become increasingly important in Supply Chain Sourcing and Management. Organisations require experts in supply chain analytics to collect, analyse and interpret data, thus optimising supply chain operations and achieving business objectives.

Pursuing a supply chain analytics certification course can provide individuals with the necessary skills and knowledge to make data-driven decisions and excel in supply chain management.

This article will explore the importance of data-driven decision-making in supply chain management and how it can benefit organisations.

What Is Data-Driven Decision-Making?

Data-driven decision-making involves using data to inform and validate a course of action before committing to it. It can take various forms in business, such as collecting survey responses or launching products in test markets.

Integrating data into the decision-making process will depend on business goals and the types and quality of data available.

While data collection and analysis have always been significant for large corporations and organisations, modern technology allows businesses of all sizes to collect, analyse, and interpret data into actionable insights.

Here are some examples:

Collecting survey responses to identify products, services, and features that customers desire.
Conducting user testing to observe customer behaviour and identify potential issues before an entire release.
Launching new products or services in a test market to gauge performance and understand customer needs.
Analysing shifts in demographic data to determine business opportunities or threats.

Benefits of Data-Driven Decision-Making

Data-driven decision-making offers several benefits that can improve business performance and competitiveness.

Some of the critical benefits of data-driven decision-making are:

Objective decision-making: Data-driven decision-making removes bias and subjectivity, ensuring fact-based decisions rather than opinions or assumptions.

Better accuracy: By leveraging data and analytics, organisations can make more accurate decisions aligned with their business goals and objectives.

Improved efficiency: Data-driven decision-making enables organisations to make faster and more informed decisions, reducing the time and resources required for decision-making.

Risk mitigation: Data-driven decision-making enables organisations to identify and mitigate risks by analysing data and identifying patterns and trends.

Cost reduction: Data-driven decision-making can reduce costs by identifying inefficiencies and optimising processes.

Improved customer experience: By analysing customer data, organisations can gain insights into customer behaviour and preferences, improving customer experience and loyalty.

Competitive advantage: Data-driven decision-making can give organisations a competitive advantage by enabling them to make more educated and effective decisions than their competitors.

Applications of Data-Driven Decision-Making in Supply Chain Management

Data-driven decision-making has numerous applications in supply chain management, including:

Demand forecasting: Supply chain managers can effectively estimate product demand using data-driven decision-making by analysing historical data and market trends, which enables them to optimise inventory and production planning.

Inventory optimisation: Supply chain managers can optimise inventory levels and save costs associated with excess or stock-out inventory by analysing data on inventory levels, lead times, and demand patterns.

Supplier selection and management: Supply chain managers may make informed judgements about which suppliers to engage with and how to manage those relationships by analysing supplier data on quality, cost, delivery time, and reliability.

Logistics optimisation: Supply chain managers may optimise logistics and cut down on transportation expenses by analysing data on lead times, delivery performance, and transportation costs.

Quality management: Supply chain managers can leverage data on product defects and customer complaints to make informed decisions about quality management and identify areas for improvement in the supply chain.

Risk management: Supply chain managers can make data-driven decisions regarding risk management and build strategies to mitigate risks by analysing data on supply chain interruptions and vulnerabilities.

Sustainability: Supply chain managers can establish methods to lessen their environmental effects and make data-driven decisions regarding sustainability by analysing data on waste, energy use, and carbon emissions.

Challenges in Implementing Data-Driven Decision-Making in Supply Chain Management

While data-driven decision-making offers many benefits in supply chain managementhttps://imarticus.org/blog/benefits-of-data-driven-decisions-in-supply-chain-management/, there are also several challenges that organisations may face when implementing it.

Some of the key challenges are:

Data quality: Data accuracy, completeness, and consistency can impact the effectiveness of data-driven decision-making. Ensuring data quality requires proper data management processes and tools.

Data integration: In supply chain management, data comes from various sources, such as suppliers, logistics providers, and internal systems. Integrating and analysing this data can be challenging, as it may come in different formats and structures.

Data analysis: Analysing large amounts of data can be time-consuming and complex, requiring specialised skills and tools. Ensuring data is appropriately analysed and interpreted is critical to making informed decisions.

Change management: Implementing data-driven decision-making requires organisational culture, processes, and technology changes. Resistance to change can be a significant challenge that businesses must address.

Cost: Implementing data-driven decision-making requires investment in technology, tools, and resources. The cost of implementing and maintaining these systems can be a challenge for some organisations.

Data privacy and security: As data-driven decision-making involves collecting and analysing large amounts of data, ensuring the confidentiality and security of that data is critical. Organisations must ensure that data is protected adequately from breaches and misuse.

Human error: Human error can impact the accuracy and reliability of data-driven decision-making. Proper training and processes can minimise the risk of errors.

Best Practices for Effective Data-Driven Decision-Making in Supply Chain Management

To ensure effective data-driven decision-making in supply chain management, organisations should follow these best practices:

Define clear objectives: Define the business objectives and key performance indicators (KPIs) you want to achieve using data-driven decision-making.
Identify relevant data sources: Identify the data sources and types needed to achieve the objectives.
Ensure data quality: Ensure the data collected is accurate, consistent, and complete. Implementing data quality controls and monitoring makes it achievable.
Analyse data: Analyse the data to identify trends, patterns, and insights that can help inform decision-making. Use advanced analytics and visualisation tools to present the data meaningfully.
Integrate data: Integrate the data from various sources and systems to gain a holistic view of the supply chain. It will help identify dependencies and potential bottlenecks.
Establish data governance: Establish practices to ensure the security, privacy, and compliance of the data collected and analysed.
Involve stakeholders: Involve stakeholders from different areas of the supply chain in the decision-making process. It can help ensure that business decisions align with the business objectives.
Continuously monitor and improve: Monitor the data and KPIs to identify areas for improvement and refine the decision-making process.

Conclusion

Data-driven decision-making is crucial in complex and global supply chains. Effective implementation is necessary to overcome data quality, integration, and governance challenges.

Many options are available for those interested in pursuing a career in supply chain management. Imarticus Learning’s IIM Raipur Supply Chain Management course is one such option.

Additionally, the Supply Chain Analytics Certification course offers various certifications. These courses help professionals gain the skills and knowledge to excel in supply chain sourcing and management.

Visit Imarticus Learning for more information.

What are Deep Neural Networks and How are They Different From Artificial Neural Networks?

Posted on March 31, 2023 by Imarticus Learning

Neural networks have become a more widely used data science and machine learning technique for various applications. Neural networks have used artificial neural networks (ANNs) for many years.

But, as technology has developed, Deep Neural Networks (DNNs) have become a more advanced variety of neural networks.

Machine learning requires a solid grasp of the distinctions between ANNs and DNNs, and working with them successfully necessitates a solid grasp of programming languages like Python.

By learning machine learning with Python, you can gain the skills to work with ANNs and DNNs to build more powerful machine-learning models for real-world applications.

This blog will explore the differences between ANNs and DNNs, their structures, types, advantages, and applications.

Artificial Neural Networks

What is ANN?

ANNs comprise layers of interconnected nodes, or neurons, that process input data, output results, and receive input data. With an ANN, each neuron is linked to several other neurons, creating a network of nodes that can handle sophisticated data processing.

The single-layer perceptron is the most fundamental type of ANN; it comprises a single layer of neurons that analyse input data and provide a binary output.

Types of ANN

There are several types of ANNs, including:

Radial Basis Function Networks

ANNs with the aim of approximating functions are known as radial basis function networks (RBFNs). RBFNs use radial basis functions to convert the input data into a higher-dimensional space divided into classes or categories.

Self-Organizing Maps

Self-Organizing Maps (SOMs) are artificial neural networks (ANNs) made to figure out how to organise high-dimensional input, like pictures or sounds.

SOMs map the input data into a low-dimensional grid using a competitive learning process to visualise and analyse the data.

Feedforward Neural Networks

The simplest type of ANN is a feed-forward neural network, which has a single layer of input neurons, one or more hidden layers of processing neurons, and a layer of neurons for output.

Without any feedback connections between the layers, data travels from the input layer via the concealed layers to the output layer.

Advantages of DNN

Better Accuracy: In tasks requiring higher accuracy, like speech recognition, image identification, and natural language processing, DNNs can outperform traditional ANNs.

Reduced Feature Engineering: DNNs can automatically learn features from raw data since they require less feature engineering than classic ANNs, which results in more accurate and efficient models.

Improved Training: DNNs are more resilient and better equipped to manage data variances since they can be trained using massive volumes of data.

Applications of ANN

ANNs have numerous practical applications, including:

Image recognition
Natural language processing
Speech recognition
Robotics
Financial forecasting

Python packages like TensorFlow and Keras can be used to implement ANNs. These libraries have built-in tools for visualising ANN structures and functions for developing and training ANNs.

In machine learning methods, particularly ANNs, Python objects and lists can store and modify data.

Deep Neural Networks

DNNs comprise several layers of neurons, each of which processes incoming data before transmitting the results to the layer above it. Depending on the particular requirements of a task, a DNN’s structure can be modified to include different numbers of layers and neurons in each layer.

Types of DNN

There are several types of DNNs, including:

Long Short-Term Memory Networks

RNNs with Long Short-Term Memory (LSTM) is made to retain information for extended periods. To better handle sequential data, LSTMs include a gating mechanism that allows them to recall or forget data from earlier inputs selectively.

Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a specialisation of Deep Neural Networks (DNNs) that handle multi-dimensional input such as photographs.

CNNs use convolutional layers to separate features from the input data, which they subsequently utilise to find patterns and objects in the data.

Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a subset of DNNs that handle time series data and plain language. RNNs utilise feedback connections among the network’s neurons, enabling them to recall prior inputs and draw on that knowledge when processing new inputs.

Advantages of DNN

Above other machine learning methods, Deep Neural Networks (DNNs) provide many advantages, including:

Improved accuracy: DNNs are more accurate than conventional machine learning models, especially when handling complex data.

Better scalability: DNNs are appropriate for big data applications since they can handle massive datasets with millions of samples.

Flexibility: DNNs are adaptable to many data formats and can be used to solve various issues, including natural language processing and picture recognition.

Applications of DNN

DNNs have numerous practical applications, including:

Image and video recognition
Natural language processing
Speech recognition
Autonomous vehicles
Healthcare
Finance

DNNs are very effective at tasks that call for high levels of accuracy and a lot of data. They are now an essential artificial intelligence component and have many possible applications in several fields.

Differences Between ANN and DNN

Machine learning algorithms like Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are made to recognise patterns in data and learn from them. Yet there are several significant distinctions between ANNs and DNNs.

Number of Layers

The number of network layers distinguishes ANNs from DNNs, which is one of their primary differences. Whereas DNNs can have dozens or even hundreds of layers, ANNs typically only have one or two layers of neurons.

It enables DNNs to analyse more intricate patterns and analyse complex data.

Complexity

DNNs have more layers and a more extensive architecture than ANNs, making them more complex overall. DNNs can evaluate more complex data and find subtler patterns thanks to this complexity.

Training Data Requirements

Due to their greater complexity and number of parameters, DNNs often need more training data than ANNs. DNNs might take longer and use more resources to train than ANNs do.

Computational Power

DNNs need more processing power than ANNs because of their bigger size and more intricate architecture. Particularly for large datasets, it may make it more difficult and expensive to train DNNs.

Performance

On complex tasks requiring the recognition of complicated patterns in data, DNNs typically perform better than ANNs. Yet, ANNs can still be helpful for more focused tasks that call for recognising simpler patterns.

Conclusion

The field of Artificial Neural Networks (ANNs) is rapidly expanding. It is being utilised in various industries and fields, making it an exciting area to explore in data science and machine learning.

If you are interested in pursuing a career in this field. In that case, having a solid foundation in Python programming, particularly in dealing with Python Objects and List, and a thorough understanding of machine learning with Python is essential.

Imarticus Learning’s Certificate Program in Data Science and Machine Learning course offers a comprehensive curriculum that covers all aspects of data science and machine learning, including Python programming, machine learning algorithms, data visualisation, and more.

Take the first step towards a fulfilling career in the exciting field of data science by visiting Imarticus Learning and checking out our IIT data science course today.

Waste Reduction With Lean Supply Chain Management

Posted on March 31, 2023April 8, 2024 by Imarticus Learning

A company’s capacity to run effectively and efficiently while cutting waste is crucial to its success. Waste is a problem that is particularly prevalent in the supply chain management industry.

By implementing lean supply chain management practices, companies can reduce costs, boost customer happiness, and improve operational performance.

Those pursuing a supply chain management career, such as a Supply and Operations Planner, will find their knowledge of waste reduction principles critical to their success.

The importance of waste reduction in supply chain management, lean supply chain management principles, the advantages of waste reduction, and best practices for applying it will all be covered in this article.

What is Lean Supply Chain Management?

Eliminating waste and maximising efficiency in the supply chain are vital components of the lean supply chain management philosophy. Finding and removing waste and inefficiencies in every part of the supply chain entails a continuous improvement process.

By reducing waste and maximising value, lean supply chain management seeks to build a lean and flexible supply network that can swiftly respond to consumer demands.

It is based on the ideas of lean manufacturing, which Toyota pioneered. Achieving a shared waste reduction and continual improvement objective entails integrating all supply chain stakeholders.

Creating a customer-centric supply chain that is responsive, effective, and sustainable is the ultimate goal of lean supply chain management.

Why Waste Reduction Matters in Supply Chain Management

Waste reduction is crucial to supply chain management since it affects the system’s effectiveness and efficiency. There are many different types of waste, which can increase expenses, lower product quality, and lengthen lead times.

Reducing waste can help businesses manage risks and supply chain interruptions while enhancing operational performance, customer satisfaction, and sustainability.

By putting waste reduction first, businesses streamline their operations, boost productivity, and lessen their environmental impact, promoting customer loyalty, increasing brand perception, and improving stakeholder relations.

Critical Principles of Waste Reduction in Lean Supply Chain Management

Lean supply chain management’s guiding principles for reducing waste are based on those of lean manufacturing and include the following:

Value – Eliminate the tasks that do not create value for the consumer and concentrate on those that do.

Flow – Ensure that products, information, and materials move through the supply chain promptly and efficiently.

Continuous improvement – Continuously identify and eliminate waste and inefficiencies by making ongoing progress.

Standardisation – To reduce variability and boost efficiency, standardise processes and procedures.

Visual management – Use visual tools to identify waste, perform monitoring, and share information throughout the supply chain.

By concentrating on these concepts, businesses may develop a culture of waste reduction and ongoing development, leading to a lean and effective supply chain that is responsive to consumer demand, minimises waste, and maximises value.

Benefits of Waste Reduction in Supply Chain Management

There are many benefits of waste reduction in supply chain management, including:

Improved operational performance – Companies may streamline their operations and boost efficiency by decreasing waste, leading to shorter lead times, higher-quality products, and enhanced performance.

Increased customer satisfaction – By decreasing waste and enhancing operational performance, firms may produce products and services that meet or exceed consumer expectations, resulting in better customer satisfaction and loyalty.

Decreased costs – Waste reduction enables businesses to cut back on wasteful spending, boost profitability, and create a supply chain that is more cost-efficient.

Improved sustainability – By minimising waste production and resource consumption, businesses may create a more ecologically friendly and sustainable supply chain.

Enhanced risk management – Companies can better manage risks and disruptions and lessen the effect of unplanned events on their operations by developing a more effective and agile supply chain.

Improved stakeholder relations – Companies can boost their brand, strengthen stakeholder relations, and draw in customers that respect environmentally friendly company practices by supporting waste reduction and sustainability.

Competitive advantage – Businesses may attract clients who respect environmentally friendly business practices by promoting trash reduction and sustainability.

Enhanced supply chain visibility – Companies can boost visibility into their supply chain and find inefficiencies and chances for improvement by employing standard processes and visual management tools.

Best Practices for Implementing Lean Supply Chain Management to Reduce Waste

Here are some best practices for implementing lean supply chain management to reduce waste:

Identify and prioritise waste – The first step in minimising waste is to recognise where it occurs in your supply chain. Perform a waste analysis to determine waste sources and prioritise which areas to address.
Standardise processes – Developing standardised procedures aids in minimising waste and reducing unpredictability. Implement regular work practises and visual management tools to make operations more dependable and manageable.
Foster a culture of waste reduction – Engage staff members at all organisational levels to advance a culture of waste reduction and continual improvement. Motivate workers to recognise and report waste and reward those helping reduce waste.
Optimise inventory – In the supply chain, excess inventory is a substantial source of waste. Use just-in-time inventory management to reduce waste, increase cash flow, and lower inventory levels.
Streamline transportation – Waste might also happen during transportation. Optimise transportation modes and routes to minimise waste and lessen environmental impact.
Improve supplier relationships – Establish waste reduction and ongoing improvement partnerships by working closely with suppliers. Work with suppliers to streamline procedures, shorten lead times, and cut waste.
Embrace technology – Use technology to track and analyse data, automate procedures, and find areas that need work. Use supply chain management software to improve operations and visibility.
Continuously monitor and improve – Continuous improvement is a fundamental lean supply chain management principle. To reduce waste and boost efficiency, constantly assess supply chain performance, pinpoint problem areas, and make necessary adjustments.

Conclusion

Lean supply chain management relies heavily on waste reduction, which boosts operational efficiency, increases sustainability, and lowers risk. Companies may boost productivity, foster teamwork, and gain a competitive edge by applying best practices for waste reduction in the supply chain.

To gain a deeper understanding of supply chain management and learn how to reduce waste effectively and optimise supply chain performance, consider pursuing a professional certification in supply chain management.

Imarticus Learning’s Professional Certification in Supply Chain Management & Analytics course is designed for individuals seeking to enhance their skills and knowledge in supply chain management.

Graduates can pursue various roles, including Supply and Operations Planner, and gain a competitive edge in the job market while contributing to waste reduction efforts. Enrol now at Imarticus Learning to take the first step towards a successful career in supply chain management.

10 Things to Look for in a Fintech Course

Posted on March 31, 2023April 2, 2024 by Imarticus Learning

Fintech works at the intersection of banking and technology. It uses technology to enhance and streamline financial services and procedures, such as banking, payments, investing, and insurance. Fintech refers to a wide variety of technologies and applications, such as mobile banking apps, online financing platforms, digital wallets, robo-advisors and blockchain-based solutions. Fintech is changing the financial industry by making it more approachable, efficient, and customer-centric.

Fintech training can help you become more fiscally savvy and make better financial decisions. Taking a fintech course is like investing in a high-growth stock – it’s a smart move that can pay off big in a rapidly advancing global digital economy.

Why a fintech certification course is important for jobs in India?

Here are the reasons why a certificate in fintech is valuable in the Indian market:

With innumerable innovations and technological headways in finance, professionals with a certificate have a first-hand understanding of their work and its implications on the industry and its services.
Fintech courses in India help professionals understand technological advancements and their impact on the industry, which can give them an edge over other candidates during job interviews.
Fintech courses can aid students in securing their prospects.

Fintech is a lucrative sector that attracts many applicants competing for jobs, intensifying competition.

10 factors to consider while choosing a fintech course

The fintech industry’s rapid growth has increased demand for qualified personnel. As a result, several educational institutions are now offering fintech courses. They ensure you receive the best training possible. If you’re considering enrolling in a fintech certification course, then there are a few things you should look out for.

Curriculum: The curriculum is the first consideration when selecting a fintech course. Any pertinent fintech subjects, such as blockchain, cryptocurrencies, and digital payments, should be covered in the curriculum. Also, it must be current and pertinent to ongoing market trends.

Faculty: The course’s faculty should also be taken into account. The teachers should be seasoned experts with job experience in the fintech sector who are well-equipped to teach the material. To guarantee a well-rounded education, look for classes taught by a combination of academic experts with business professionals.

Industry collaborations: Because the fintech sector is developing quickly, selecting a course that offers collaborations with top fintech firms is critical. You can develop a network of connections in the business and obtain practical experience through these collaborations.

Flexibility: Courses in fintech should be adaptable enough to fit your schedule and learning preferences. Search for programmes that allow for online or hybrid learning, as well as part- or full-time enrollment.

Accreditation: It is crucial to ensure that the course you select satisfies the requirements established by regulatory and industry organisations. Search for programmes that have earned accreditation from respected bodies like AACSB or EQUIS.

Practical knowledge: The fintech sector highly values practical knowledge; thus, the school you pick should offer practical application of theoretical knowledge in simulated real-world scenarios. Look for programmes that provide case studies, internships, and other hands-on learning opportunities.

Career assistance: To shift into the fintech business, search for schools that offer career support. Assistance with preparing a résumé and cover letter, networking opportunities, and job placement services are some things to look out for.

Improve your skills: The fintech sector is quickly developing; therefore, keeping up with innovations and technology is essential. Look for programmes that host seminars, conferences and other industry-related events offering certifications.

Develop your network: In every industry, networking is essential, and the fintech sector is no exception. Top fintech institutes have well-placed alumni that can add to your network. In addition, to expand your network and get insightful information, join industry events, sign up for clubs and associations, and connect with industry leaders on LinkedIn.

Understand the fintech industry: Finally, understanding the fintech business and its numerous segments is vital before pursuing a career. Make sure your chosen course covers fintech firms and the most recent trends, breakthroughs, and technology in the field.

Your success in this ever-changing field will depend on the fintech course you choose. You can ensure that you receive the greatest education possible and are adequately equipped to pursue a career in fintech.

Conclusion

The SP Jain School of Global Management and Imarticus Learning have partnered to offer a financial technology course. It gives students a unique and valuable learning experience by fusing the latest industry knowledge and methods with a rigorous academic curriculum. The programme covers many topics, like digital banking, blockchain technology, digital payments, and alternative financing. Additionally, you will receive the remarkable SP Jain fintech certification.

Role of Investment Banking in Sustainable Finance

Posted on March 31, 2023May 29, 2025 by Imarticus Learning

Investment banking is a rewarding way in which corporations and individuals can raise funds. Investment banking helps to bridge the gap between the parties who are seeking funds and investors who are in search of profitable avenues where they can invest their money. Not only this, but investment bankers also advise you on the most profitable investment tools that will reap you the best benefits.

However, investment decisions these days are made on more than just the basis of which avenue will provide the most profitable income. Investment decisions are made based on ESG (Environmental, Social, and Governance) conditions which help to determine how a company will utilise the opportunities and manage the risk that arises on these three fronts.

In this article, we will understand the role of money market funds in supporting sustainability and how an investment banking course can help an individual gain a boost in his career as an investment banker. Read on to find out.

Environmental, Social, and Governance – The 3 Facets of Sustainable Finance

Before we delve deeper, we need to understand what sustainable finance is. Sustainable finance is the process of deciding on financial investments while considering ESG factors. This helps ensure that investment decisions do not negatively impact the environment or society and maintain good governance in the long run.

Sustainable finance maintains some regulations to make investment decisions based on ESG (Environmental, Social, and Governance) considerations. During ESG investing, investors stress the fact that the companies that they invest in are socially responsible, do not pose any threat to the environment, and are led by managers who are accountable. The three factors are explained below:

Environment

Businesses should be aware that the practices they perform should not pose any threat to the environment. Such issues may include problems like improper waste disposal, pollution, energy use, climate change, improper treatment of wildlife, degradation of natural resources, and so on. ESG considerations help to evaluate the risks that a company’s practices might cause to the environment and suggest how the risk can be mitigated.

Social

The way a company treats its employees speaks volumes about its culture. Investors also look forward to socially responsible investing, which means investing in companies that fight against stigmas like discrimination based on caste, sex, and colour, and promote ethics like inclusion, diversity, and social justice.

Governance

Good governance is another facet of ESG. Investors seek to invest in companies that have fair practices like transparency in accounting methods, accountability to shareholders, and integrity.

Therefore, from investing in projects that put renewable energy to use or curbing the use of plastic to combatting discrimination and promoting inclusion simply by having more female employees on board, companies can embrace ESG practices effectively!

How does Investment Banking Contribute to Sustainable Finance?

There are countries whose domestic resources are not enough to meet their development goals. Through investment banking, investors can chip into their growth while also earning a certain amount as a return which will be passed on to the clients. However, as has been mentioned above, investments should be decided in a socially responsible manner.

A number of ESG assets, such as green bonds, social bonds, sustainability bonds, etc., are gaining prominence. Green bonds are instruments made available to corporations that they can use to fund projects like carbon-neutral projects or projects related to water and sanitation. These projects will have a positive impact on the environment as well as society, thereby adhering to ESG norms.

Advantages of ESG Investing

Money markets are embracing ESG factors to ensure the sustainability of investments. By now, we already know that ESG investing is good both for your pocket as well as for the planet.

Some of the primary benefits of ESG investing are listed below:

ESG investing helps to reduce the risk for the investors: ESG helps investors in effective risk management. Investors who invest in ESG assets usually refrain from investing in irresponsible companies with questionable practices. If a company has poor practices, it is evident that the price of the stocks can go down considerably when they are ready to be sold.

By avoiding such companies, investors can avert the risk of losing money and earn better returns by making the right investment choices. Businesses that are committed to ESG practices adhere to government regulations. Therefore, they have great reputations and are less likely to follow dubious practices.

Cost-effective for the companies: Companies that adhere to sustainable and socially responsible practices are less likely to make exorbitant expenses like huge advertisement campaigns or investing in luxury items. For instance, companies can invest in renewable energy, which can reduce the consumption of energy and lower operating cost.
Diverse portfolio: When investing in ESG, investors collaborate with companies that work in different environmental and social sectors. This results in the portfolio diversification of the investors.

ESG investments help to instil a sense of satisfaction and purpose among a company’s employees and help to boost overall productivity. Therefore, ESG investing creates a win-win situation for both the investors as well as the companies!

Conclusion

Are you willing to pursue a career as an investment banker? Having a certification can help you validate your skills in the given field. If you are willing to establish a career as an investment banker, an investment banking course from Imarticus might be of immense help to you.

This certification course, offered by Imarticus in partnership with Academy-Euronext Group, will prepare you for the job market by teaching you the relevant skills. The course in investment banking will begin with teaching the basics of financial markets and move forward to components like trade life cycle, risk management, etc.

Alongside the theoretical knowledge that is made available through live interactive modules, learners also get to implement their learnings through real-world case studies and projects. This 150-hour-long investment banking course from Imarticus will equip you with the right set of skills that will open up a plethora of new career opportunities for you.

Enrol now!

How to run an awesome in-person onboarding session for your new hires

Posted on March 31, 2023April 2, 2024 by Imarticus Learning

Estimates from a survey conducted by BambooHR say that around 33.87% of new hires quit their jobs within the first month of hiring. The numbers go up to about 68.06% after the first three months. Why are they leaving their job too soon? What is going wrong? Have you cared to ask if your organisation has an onboarding process?

corporate training

Yes, although often overlooked, a good onboarding program is crucial to an organisation’s success. The right employee onboarding process touches almost every aspect of the business. Several surveys conducted from across the world conclude that onboarding programs have great perks. For one, let’s say, it helps you work with an employee who is committed and engaged with your company’s values.

On the other hand, a poor onboarding process often leads to disengagement in the workplace. Do not trust us? Here are some statistics to prove our point.

A few onboarding statistics

Effective onboarding provides new hires with the information they need to do their job. It will introduce them to the company culture and help in building trust. When employees and organisations are well-adapted to each other’s ways, the probability of quitting jobs is less. That is why companies with effective onboarding programs retain 50% more new hires than their competitors.
The right onboarding process will prepare your employees for their specific roles. When employees are not juggling between multiple roles, they can focus entirely on the work that they were hired for. Thus, organisations with standardised onboarding programs have 50% greater productivity.
Turnovers, as we know, are expensive. The total cost involved in replacing an employee can vary from 100–300% of the individual’s salary.

Despite having great benefits, 1 out of 5 businesses does not have an onboarding program. Also, just having an onboarding session isn’t enough. Following the right new employee onboarding process will ensure retention. So, here are a few best practices that can turn your new hires into productive and lasting employees.

5 onboarding best practices

1. Reach out to them before they begin

Giving your new hires the silent treatment even before they start working isn’t a sign of a great start. Maybe your HR can send an email briefing them about the first day. Or their manager can get in touch with them for a small introduction. Whatever your method is, be sure to reach out to them before they start the job.

2. Let the first day be memorable

Trust us, first impressions have a huge impact. So, make sure they start with a positive experience. The following are a few suggestions you can do for your new employees on their first day:

Prepare their desks with everything they need including forms, ID badges, passwords etc.
Be ready with welcome gifts and messages from office mates.
Schedule a lunch

3. Prepare a structured schedule

There are two advantages to having a structured schedule.

They will know what exactly to do after completion of a particular task.
This gesture from your side will help in building trust.

4. Let them be familiar with the whole company

It takes a whole company to nurture its new hires. Make sure that your new employees get some one-on-one time with the already existing employees of every department. This will help them identify their role in contributing to the company’s success. It also fosters collaboration.

5. Communicate the Culture

More often than not, employees quit their jobs because their values do not match with company culture. Make it a point to communicate the company culture early on. Giving an honest review of the culture helps them decide whether to stay or not.

Since the chances of people staying at a job for a long time are very less now, an effective employee onboarding process is vital to ensure business success. A diligent onboarding process will help in preparing them for the job and build trust. Employees can leave for several reasons. But, many potential issues can be resolved by having a proper onboarding program. If you are interested in acquiring industry-specific skills and improving your productivity, Imarticus Learning has several courses to offer.