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Category: Management

Logistic Regression vs. Linear Regression: Which is Better for Machine Learning?

Posted on by Imarticus Learning

Logistic regression and linear regression are two integral algorithms of machine learning. These two are supervised learning methods that are used for making forecasts and predictions. 

Although both regression techniques are used for the purpose of machine learning, they still have a lot of differences in the way they are used. A good data analytics course with placement can help one understand the distinctions in a simple way. When talking about logistic regression vs linear regression, we need to understand that logistic regression is mainly used to solve classification problems, whereas linear regression is used to solve regression problems.

Read on to learn more about logistic regression vs linear regression – the regression techniques of machine learning.

Logistic Regression

Logistic regression is one of the most used machine learning algorithms. It is a supervised technique that is generally used for classification problems but can also be used for regression problems. It uses simple independent variables to make predictions on categorical and dependent ones.

Logistic regression works well with Python programming, which requires minimal coding and does the job of solving classification problems. the output of this technique ranges between 0 to 1. It is based on the institution of maximum likelihood estimate, which means observing data should be the most obvious.

Linear Regression

Linear regression is an old and simple form of a machine learning algorithm. It is a supervised technique that is specifically used for solving regression problems. This technique uses independent variables to make predictions on the continuous dependent variables. It is also compatible with Python programming, much like its counterpart. 

As the name suggests, a linear regression graph is always in the form of a straight line, and the main objective is to find the best-fit line for accurately denoting the variables. If only one independent variable is used, then it is called a simple linear regression, and if more than one independent variable is employed, then it is known as multiple linear regression.

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Logistic Regression vs Linear Regression – The Differences

Both regression techniques are widely used for machine learning, yet each one addresses particular problems. However, both can be used for data visualisation, and there are certain tasks that each performs separately. The key differences in logistic regression vs linear regression can be explained as follows:

Type of variable and output

Logistic regression is predominantly used to specifically predict and deal with the categorically dependent variables. A particular set of independent factors is associated with this regression technique. This technique is mostly based on probabilities.

On the other hand, linear regression uses only continuous dependent variables based on a particular set of independent variables. These independent variables take numeric values and reference from staying in any category or group.

Degree of complexity

Logistic regression has a more complex structure of equations, that makes it challenging to interpret and understand this model. 

Whereas linear regression is a simple model and involves a comparatively simple structure of equations. It is easily understandable and interpreted. 

Application and scope

Logistic regression is mainly applied to solving classification problems. It can, however, solve some regression problems as well. It is slightly wider in scope.

On the other hand, linear regression is only applied to solving regression problems. It cannot solve classification problems. Hence, it is comparatively narrow in scope.

Type of graphical representation

In logistic regression, the graph forms an S-shaped curve. Thus, the type of graph is S-shaped. It can be easily used for classifying samples.

In contrast, the graph of linear regression is a straight-line curve. It focuses on finding the proper fit line for precisely predicting the output. 

Method used

Logistic regression uses the maximum likelihood estimation method. In this method, the coefficients of the regression method are chosen to maximise the probability of the variables until the iteration processes are complete.

On the contrary, linear regression uses the ordinary least square estimation method. In this method, the coefficients of the recreation method are chosen to lower the sum of the squared variables to find the best-fit line.

Mathematical equation

The mathematical equation used in logistic regression is:

y(x) = e(a0 + a1x1 + a2x2 + … + aixi) / (1 + e(a0 + a1x1 + a2x2 + … + aixi))

On the other hand, the mathematical equation used in linear regression is:

y = a0 + a1x1 + a2x2 + … + aixi

Where, 

y stands for the response variable

xi stands for the predictor variable

ai stands for the average effect on y as xi increases by one (when all other predictors are fixed)

Type of output

Logistic regression must always have a categorical or numeric output that ranges between 0 to 1 or is present in the form of a clear yes or no answer, and so on.

On the other hand, the output of linear regression must always be in the form of a continuous value. For instance price, weight, age, and so on.

Correlation between independent variables

In the case of logistic regression, there exists no correlation between the independent variables.

On the contrary, in the case of linear regression, there may or may not exist a correlation between the independent variables. 

The Better Approach to Machine Learning

This is another debatable topic for many specialists. When it comes to supervised Machine Learning then linear regression is considered to be the best-suited approach. Linear regression is best suited for predicting continuous outcomes. 

For instance, linear regression can be used for predicting the weight of an individual if his height is known. 

The equation appears as follows:

Weight = 70 + 2 x (60) = 190 lbs

Where height= 60 inches

Here, the effect of one independent variable on the outcome is analysed. 

However, when unsupervised or semi-supervised Machine Learning is considered then logistic regression is the most suitable approach. It works best with assumptive data and refrains from using a rigid approach. Logistic regression is best suited for producing a discrete value.

For instance, logistic regression can be used for predicting whether a student will be able to crack a competitive examination or not. Whether a political candidate will win the election in an area or not. This approach presents binary outcomes where it has only two straightforward alternatives.

Conclusion

Regression is an essential Machine Learning tool that makes predictions and creates relationships among variables. Machine learning experts are in demand in every organisation nowadays. If you are looking to gain professional knowledge about regression approaches and learn what is logistic regression vs linear regression then a data analytics course with placement can help you to get a good piece of work. Sign up for the Postgraduate Program In Data Science And Analytics by Imarticus and kickstart your career in data science.

Linear Regression: Definition, Types, Examples

Posted on by Imarticus Learning

Linear regression has been used extensively in multiple industries for prediction purposes. It is an analytical method used in data science to study data and predict the value of an unknown dependent variable based on the known dependent variable.

This article aims to cover the definition of linear regression and its types with examples for better understanding.

What is linear regression?
Linear regression is a statistical method used to visualise the linear relationship between two variables. It quantifies the correlation between one or more independent variables and the dependent variable.

The two types of variables are:

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Independent variable: It is the variable whose value is known and is used to determine the value of the dependent variable.

Dependent variable: This is the variable whose value is being determined, which changes with the independent variable.

The value of ‘A’, the dependent variable, can be determined through ‘B’, the independent variable.
The graphical representation of linear regression is a straight-line graph, assuming a linear relationship between the variables. The method is highly accurate and reduces the discrepancies between the predicted and the actual outputs.

What are the uses of linear regression?

Linear regression has been used in scientific and academic research, behavioural science, and business. Some of the uses of linear regression are:

To predict future values like the estimate of revenue value upon changes in investment. To envision future trends in both science and commerce.

This can also be used to understand the strength of the relationship between the independent or predictor variable(s) and the dependent variable.

What are the types of linear regression?

There are two types of linear regression – simple and multiple.

Simple linear regression
Simple linear regression is used when there is only one independent variable at hand which is used to determine the dependent variable.

The equation for simple linear regression involves four coefficients:

A = 𝛽0 + 𝛽1B + ε

In the given equation, A is the regression coefficient or dependent variable, 𝛽o is the intercept, 𝛽1 is the slope, B is the independent variable which assumably is affecting A, and the epsilon (ε) is the adjustment of A or the error value in our estimate of A.

Simple linear regression makes four preconceptions:
The data has been collected using scientific methods without any bias and does not have any hidden relation.
The error size (value of ellipses) does not have any significant change across the independent variable values.
The data is symmetrically distributed and maintains a level of normality.

The relationship between the independent and dependent variables is always linear. It can never be a curve or hyperbola.

Simple linear regression is used in determining the value of a dependent variable that is directly influenced by the independent variable. Simple examples of this would be determining the marks a student may score depending on the hours of study, or the amount of increase in antibodies against the number of viral cells.

Multiple linear regression

Multiple linear regression quantifies the relationship between two or more independent variables and the dependent variable. The independent variable may be consequent values or grouped values.
The equation for multiple linear regression involves the coefficients:

A = 𝛽0 + 𝛽1B1 + 𝛽2B2 + …. + 𝛽nBn + ε

Therefore, the coefficients change in numbers. As seen in simple linear regression in the above equation:
A is the regression coefficient

𝛽o is the intercept
𝛽1B1 is the first independent coefficient, the slope (𝛽1) of the first independent or predictor value (B1).
… signifies the number of coefficient values, the slope of the subsequent independent variables present between the second and last variable value.

𝛽nBn is the last independent coefficient
ε is the error value.

The above-given equation visualises the linear relationship between the dependent variable (A) and all the predictor variables.

Examples of linear regression

Linear regression is used in multiple scenarios –
Linear regression can also be used to predict the sales of a product based on the advertisement done through different media channels. This will employ the multiple linear regression model.

In medicine, simple linear regression may be used to determine the blood sugar level of a patient upon administration of a particular dosage of insulin. This can be used similarly to determine the relationship between different drugs and the patient’s vitals.

Conclusion
Data science has become an indispensable part of multiple industries, and linear regression, an important aspect of data science, has too. Therefore, knowledge in data science is a high-demand skill, and the best way to head-start your career is to earn a degree in data science certification. Learning linear regression makes it easy to calculate predictive values and estimate the condition of commercial or scientific variables.

Imarticus Learning offers a Postgraduate Analytics degree, a data science course with job interview guarantee. The PG Data Science and Analytics course is a job-interview programme that is trusted by industry leaders for the resources and quality of training provided. In this futuristic programme, students learn data visualisation with Python, Tableau and Power BI. Thus, the programme also helps learners in adapting to regular upskilling requirements.

The importance of linear regression in machine learning

Posted on by Imarticus Learning

Are you interested in knowing linear regression and how it is used in machine learning? We got you.

Machine learning is a field of study that uses statistical and computational techniques. This enables machines to learn from data and improve their performance over time. Linear regression is a fundamental machine learning technique. It plays a critical role in predicting outcomes from data.

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If you want to become a data analyst and learn data analysis and machine learning, then machine learning certification courses like Postgraduate Program in Data Science and Analytics  are among the best options. In this article, we will discuss the importance of linear regression in machine learning. You can also find one of the best machine learning certification courses to improve your understanding.

Introduction to linear regression

Linear regression is a statistical technique. it is used to establish a relationship between two continuous variables. It is a popular method for predicting outcomes based on input data. The basic idea of linear regression is to find a line that best fits the data points. The line is defined by an equation of the form y = mx + c. Here y is the dependent variable, x is the independent variable, m is the slope of the line and c is the y-intercept.

Linear regression is widely used in various fields. This includes finance, economics, physics and social sciences. In machine learning, linear regression is used to predict the output variable based on one or more input variables.

Types of linear regression

Linear regressions are usually differentiated into two types: simple and multiple linear regressions.

Simple linear regression involves only one independent variable, while multiple linear regression involves two or more independent variables.

Simple linear regression is used to establish a relationship between two variables. Here one variable is dependent and the other is independent. For example, predicting the price of a house based on its size is an example of simple linear regression.

Multiple linear regression is used to establish a relationship between the dependent variable and multiple independent variables. For example, predicting the sales of a company based on multiple variables such as advertising budget, price and location is an example of multiple linear regression.

Importance of linear regression in machine learning

Linear regression is one of the most important techniques in machine learning. It is widely used in various applications such as:

Predictive modelling

Linear regression is used to predict the output variable based on input variables. For example, predicting the price of a house based on its size, location and other factors is a common application of linear regression.

Data analysis

Linear regression is used to analyse the relationship between two or more variables. It helps in identifying the significant variables that affect the outcome variable.

Feature selection

Linear regression is used to identify the most important features in a dataset. It helps in selecting the relevant features that are important for prediction.

Model evaluation

Linear regression is used to evaluate the performance of a model. It helps in determining the accuracy of the model and identifying areas for improvement.

Individuals who want to become data analysts should complete machine learning certification courses like Postgraduate Program in Data Science and Analytics to learn data analysis and machine learning concepts.

Importance of linear regression in machine learning

Postgraduate program in data analytics and machine learning

The Postgraduate Program in Data Science and Analytics is a 6-month job-oriented curriculum course for working professionals and young data analysts. Students who complete this course create analytical models that enhance company results. They can also comprehend how data science is used in practical settings.

With the course, you will gain helpful information about the uses of analytics and data science in actual organisations. You will also get equipped to work as a data science specialist in the developing industry. Also, it provides you with the opportunity to work with leading firms and learn about the most recent developments in the analytics field. Following are the various benefits of completing the Postgraduate Program in Data Science and Analytics:

The Postgraduate Program in Data Science and Analytics focuses on practical knowledge and a job-specific curriculum.

It offers guaranteed job opportunities where students have a chance to get placed in companies like Infosys, Goldman Sachs, Deloitte, KPMG, IBM and more.

Students who complete the program are provided with a KPMG certificate.

To sum up

Linear regression is a fundamental machine learning technique that plays a critical role in predicting outcomes from data. It is widely used in various applications such as predictive modelling, data analysis, feature selection and model evaluation. Understanding the basics of linear regression is essential for anyone working in the field of machine learning.

Those interested in becoming data analysts should enrol in our Postgraduate Program in Data Science and Analytics. For any queries, feel free to contact us.

How to Become a Supply Chain Analyst

Posted on by Imarticus Learning

A supply chain analyst develops an efficient way to make a robust supply chain strategy with the help of data analytics. Also, he/she interprets and leverages data to enhance performance resulting in overall improvement. 

As daunting as the career might sound, becoming a supply chain analyst is relatively easy. With the correct supply chain analytics certification, one can bag job opportunities as an analyst in leading companies. However, prospective candidates must know and understand the skills required to become a supply chain analyst. 

In this article, let’s learn about the job roles of a supply chain analyst and the steps to become one. 

The job role of a supply chain analyst

Besides being a professional who ensures the company’s supplies meet the customer’s demand, a supply chain analyst plans production, manages inventory management, sources raw material, plans demand, designs supply chain network and plans distribution. The other duties include:

  • Collection and analysis: A supply chain analyst collects and analyses different supply chain data by implementing accurate supply chain methods.
  • Management and coordination: Supply chain analysts have to manage and coordinate vivid supply chain operations with the staff involved in the process.
  • Quality assurance and inventory analysis: A supply chain analyst oversees quality assurance and inventory analysis. By doing inventory analysis, they design robust reports to help the enterprise in prospects.
  • Supply chain hazard identification and analysis: A supply chain has many hazards. A supply chain analyst helps to identify issues and develops strategies to resolve them quickly and efficiently.
  • Training and conflict resolution: A supply chain analyst must possess leadership skills to train his/her staff and resolve any conflict arising amongst team members.
  • Business administration and effective communication: A supply chain analyst is responsible for business administration and must have communication skills to deal with the clientele and their team.
  • Business expansion and strategic initiatives: A supply chain analyst takes up strategic initiatives to expand the business horizon. They adopt many ways to make the business approachable, thus incurring more profit.
  • Collaboration and evaluation: A supply chain analyst has to collaborate with people from different departments – sales and operations, design, marketing, etc. An analyst must use customer trends and previous activities to understand product demand effectively. They even analyse and evaluate routes and collaborate with transport companies to deliver products on time and in order. 

Skills required to become a supply chain analyst 

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The job of a supply chain analyst not only entails research and analysis but also has traces of leadership and effective communication. 

Technical skills 

A supply chain analyst should be well-versed in data analysis, mathematics, SQL, Excel, Hadoop and statistics. They analyse large and multi-dimensional data sets by incorporating various mathematical equations. These calculations also play a crucial role in the cost and savings of raw materials. 

Interpersonal skills 

Supply chain analysts have to work with various stakeholders. Hence, this requires top-notch interpersonal skills. It helps build effective relationships amongst stakeholders and teams and share ideas in a hassle-free way.

Organisational and project management skills 

Strong organisational skills would help a supply chain analyst to achieve long-term goals in a short time. Possessing the above-mentioned skills would not only help them to polish their leadership skills but also help them manage workload efficiently and pace up different projects. 

How to become a supply chain analyst: Eligibility 

A supply chain analyst needs to have specific prerequisite skills and qualifications. A bachelor’s degree in logistics or supply chain management is required to be eligible for the job. Other eligibility criteria are:

  • Candidates must have internship experiences from companies with a working supply chain for at least two years. They should have an understanding of inventory management.
  • An ideal candidate should have impeccable analytical skills and in-depth knowledge of logical reasoning and mathematics. They should also possess adequate knowledge of data collection and analysis.
  • A candidate must have sound theoretical and practical knowledge of data analysis and inventory management tools. It would help them develop useful insights from the data and act accordingly. 

Conclusion 

A supply chain analyst acts as a strategist in an organisation. This makes them an important part of an organisation where they are responsible for logistics, marketing, product management, quality assurance and other important sectors. They also provide insights into newer technologies and these tools further make the business efficient. 

If you are looking for a good supply chain management course, then check out Imarticus Learning’s Executive Certificate Programme in Global Chief Supply Chain and Operations Officer offered in partnership with IIM Raipur. This IIM Raipur supply chain management course will embark you on a journey to a new era of supply chain analytics. This 150-hour interactive session is taught by the skilled IIM Raipur faculty. 

Importance of Data-Driven Decisions in Supply Chain Management

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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. 

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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?

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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

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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?

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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.

Best Supply Chain Practices to Adopt in 2023

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Supply Chain Management (SCM) is the centralised management of the flow of goods and services from the manufacturer to the customer. Sales and operation planning is an integral part of SCM which ensures the successful functioning of a supply chain.

The supply chain sector continues to shift and evolve with emerging trends. Set your 2023 plan now and implement the best supply chain practices to keep your edge over others. This article will discuss some of the best supply chain practices for 2023. 

What is Supply Chain Management?

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Supply chain management includes all processes needed to transform raw materials into finished products. It simplifies a company’s supply chain operations to maximise customer satisfaction and have a competitive edge in the market.

Several problems plague today’s supply chains. This is why a data-driven supply chain is crucial for easily monitoring the flow of services, information and products. It offers a better understanding of the entire process from start to finish. 

Importance of Supply Chain Management

A robust SCM is essential for businesses to succeed and enhance customer satisfaction. Some major importance of supply chain management is as follows:

  • Lower Costs

The goal of Supply Chain Managers is frequently to cut expenses at every stage of the supply chain. 

Supply Chain Managers can cut costs through enhanced manufacturing procedures, supplier relationships and inventory management. 

  • Enhanced Client Services

Your customer service may be directly enhanced with efficient supply chain management. This is because SCM procedures guarantee that the correct number of goods is delivered on schedule while meeting quality standards. 

Sound connectivity between distributors and suppliers minimises delays and increases client satisfaction. 

  • Agility 

Supply chain management is crucial for businesses to stay flexible and be prepared for any unforeseen problems or unpredictability that may arise. 

Businesses can identify problems and find solutions rapidly by streamlining supply chain procedures and improving visibility. 

Top Supply Chain Management Practices of 2023 

Listed below are some supply chain practices that businesses must adopt in 2023. 

  • Utilising a More Flexible Business Model

A successful supply chain includes a lot of flexibility. A company can react more effectively to shifts in demand, natural catastrophes, political unrest and other unforeseen situations by having a flexible business model. 

Regular review intervals will help you stay adaptable as circumstances change; monthly touchpoints will keep the plan in line with current effects.

Create an easily comprehensible supply chain plan. Include benchmarks, objectives, presumptions, relationships and circumstances.

  • Tackling Supply Chain Threats

Supply chain risk management has become critical as companies have encountered more complex and unpredictable operating environments in recent years. 

Risks to the supply chain can come in many forms, such as natural disasters that obstruct the movement of goods or cyberattacks that may compromise private data. 

Strategic planning, threat assessment tools and a comprehensive company continuity plan work best for managing these risks.

Businesses can successfully handle any obstacle and maintain high levels of efficiency and effectiveness by anticipating potential threats and creating mitigation strategies. 

  • Introduction of Automation Methods

A vital component of a supply chain is automation. It can help you cut costs, enhance customer satisfaction and reduce environmental effects.

Supply chains are constantly changing as a result of new processes and technology. 

Implementing new automation techniques will keep you ahead of the curve as the world becomes more digitally connected. 

  • Putting the Consumer’s Interests First

In addition to maximising operations, supply chain management also focuses on providing top-notch customer support. 

Businesses must continuously seek new strategies to enhance the customer experience. 

This can be achieved by emphasising customer centricity, which refers to companies partnering closely with their clients to comprehend their needs and provide the most pertinent, customised services.

  • Work with Third-party Partners

By applying the skills or resources of another company when required, businesses can increase their flexibility through outsourcing or partnerships with other businesses. 

It allows businesses to concentrate on their core competencies while enlisting outside assistance as and when necessary.

For instance, outsourcing shipping services or working with local vendors when your supply requirements change due to unforeseen weather patterns like rain or snow (or droughts) can impact your services in more ways than one.

Your supply chain requires third-party partners to function correctly. However, a thorough knowledge of the partner’s compliance history and supply network is essential for the timely supply of goods and services. In addition, a contract should cover the following points:

  • Standards for the security of sales and inventory records
  • Terms of payment and procedures for resolving disputes
  • Requirements for product quality, including recalls

Conclusion 

Several factors contribute to the successful running of a supply chain. A supply chain requires proper sales and operation planning, procurement and distribution of raw materials and delivering quality finished products. 

A supply chain analytics course is ideal for anyone looking forward to making a career in supply chain management. 

Check out Imarticus’s Professional Supply Chain Management course, offered in partnership with IIT Roorkee, to get a head start on your career. This IIT supply chain management course, designed by industry experts and taught by notable IIT faculty, is perfect for those seeking careers as Supply Planners, Demand Planners, and Supply and Operations Planners. 

Partnerships and Collaboration in Supply Chains

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Partnerships and teamwork are essential for success in supply chain sourcing. Collaboration is the practice of working together to accomplish a shared aim, while a partnership is a long-term agreement between two or more people to achieve mutual objectives. Supply chains tend to be more effective, save money, and perform better when businesses partner and collaborate together. 

In order to remain competitive and fulfil the needs of customers, firms must engage in partnerships and collaborations as the global market expands and diversifies. Read on to know more about the benefits and types of partnerships and collaborations in the domain of supply chain management.   

Types of partnerships and collaboration in supply chains

Strategic Alliances

Strategic alliances in the supply chain may be created to accomplish mutual objectives like cutting costs, expanding market share, or enhancing product quality. Putting such a strategic partnership between a supplier and a retailer, for instance, may help both businesses innovate and enhance their supply chains. 

A consumer goods producer may partner with a logistics company to speed up and lessen the price of their items’ delivery to retailers and consumers alike. 

Joint Ventures

Joint ventures occur when two or more businesses agree to pool their assets, personnel, and capital in order to take on a larger enterprise. Joint ventures may be created between different parties in a supply chain in order to pool resources and expertise in order to boost operational efficiency and save costs.

For example, two different but complementary manufacturers team together to create a new product line by pooling their resources and knowledge.

Collaborative Planning, Forecasting, and Replenishment (CPFR)

Collaborating planning and forecasting (CPFR) is a method used in supply chain management that facilitates communication and cooperation between several businesses. It’s the practice of business partners exchanging information to coordinate logistics, manufacturing schedules, and stock levels.

A retailer and its suppliers, for example, can collaborate on a CPFR initiative to improve the accuracy of demand forecasting and reduce the risk of stockouts or overstocking.

Vendor-Managed Inventory (VMI)

With vendor-managed inventory (VMI), the supplier is in charge of the retailer’s stock levels. It is the supplier’s duty to keep tabs on the customer’s stock and restock it as required under VMI arrangements. By decreasing the need to stock products in inventory and increasing product availability, this method may boost supply chain efficiency.

A raw materials provider might be responsible for maintaining the client’s supply by tracking consumption rates and reordering supplies automatically in response to low levels.

Co-Managed Inventory (CMI)

Inventory management in a CMI supply chain is shared by the supplier and the customer. In CMI relationships, suppliers and customers work together to control stock, exchange relevant information, and improve the supply chain’s efficiency. This method has the potential to increase supply chain efficiency and cut costs for both parties.

A CMI partnership may be formed between a manufacturer and its component suppliers in order to manage raw material and component inventory levels, coordinate production schedules and enhance delivery times while decreasing inventory carrying costs.

Benefits of partnerships and collaboration in supply chains

Better communication and exchange of information

One of the benefits of building partnerships and cooperating is improved communication and sharing of information across the stakeholders in the supply chain. Coordinating efforts and exchanging important details like product details, shipping times, and stock levels are all facilitated by clear lines of communication.

Better decision-making and process streamlining may increase customer satisfaction when everyone in the supply chain has accurate information.

Increased efficiency and productivity

Collaboration and partnership have the potential to boost supply chain efficiency and output. When people from diverse backgrounds collaborate, they are better able to see inefficiencies and provide solutions that will make the process go more smoothly and efficiently.

 As people work together to create new goods and services, creativity often follows. The supply chain may be made more effective and productive if stakeholders work together to share their resources.

Cost savings

Partnerships and collaboration may lead to cost reductions for all supply chain participants. By collaborating, parties may negotiate better prices, combine resources, and divide expenses. Cooperation may also result in the more effective use of resources, leading to a reduction in waste and eventually cost savings.

Reduced risk

Sharing resources and working together may lessen the potential for harm throughout the supply chain. Potential threats, such as interruptions in the supply chain or problems with quality, may be identified and reduced by cooperative efforts.

Supply chains can be more robust and reliable when companies work together to develop effective risk management techniques. It is imperative to improve supply chain performance, customer happiness, and financial outcomes by decreasing risk.

Conclusion

By collaborating and partnering, organisations may also achieve mutual objectives such as increasing market share, and upgrading product quality. As the worldwide economy develops and diversifies, organisations must definitely participate in partnerships and collaborations to stay competitive and meet the requirements of their consumers.

Ultimately, successful partnerships and collaborations need stakeholders in the supply chain to develop strong connections and trust. Businesses may develop a more resilient and trustworthy supply chain that is better positioned to fulfil the needs of a continuously shifting market via continuing cooperation, communication, and collaboration.

Looking to take your supply chain management career to the next level? Imarticus has teamed up with IIM Raipur to offer an Executive Certificate Programme for Global Chief Supply Chain and Operations Officer. Focusing on state-of-the-art tactics and methods, this programme will prepare you to take the helm in the supply chain management sector.

With the IIM Raipur supply chain management course, you’ll gain the knowledge and skills you need to excel in today’s rapidly evolving business environment. During the supply chain analytics certification course, seasoned instructors will walk you through the newest innovations and best practices in supply chain management while also giving you extensive practice with analytics.

Don’t miss out on this incredible opportunity to take your supply chain management career to the next level. Enrol now in our Executive Certificate Programme for Global Chief Supply Chain and Operations Officer, and become a leader in your field.

Risk Management and Mitigation Strategies in Supply Chains

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Diversification: By sourcing from various vendors, regions, or modes of transportation, the supply chain can be made more resilient, and the effects of disruptions can be lessened.

Inventory Management: Risks associated with supply and demand can be reduced by maintaining proper inventory levels and optimising inventories across the supply chain.

Communication and Collaboration: Developing effective risk management techniques can assist companies in detecting potential risks and collaborating with suppliers, customers, and other stakeholders.

Technology and Data Analytics: Using data analytics and technology can enable proactive risk management and mitigation by allowing real-time monitoring and management of supply chain risks.

Contingency Planning: Creating contingency plans that specify what must be done during an interruption can lessen its effects and guarantee business continuity.

Building Resilient Supply Chains through Supplier Collaboration

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Building robust and effective supply chains requires cooperation with suppliers. By collaborating closely with suppliers, supply chain managers may identify possible hazards and create efficient risk management plans.

The following are some suggestions for working with suppliers:

Supplier Relationship Management: Building strong connections with suppliers founded on mutual respect, openness, and benefit can promote coopeSupply chains are a critical component of corporate operations for businesses to successfully source, produce, and deliver goods and services to clients. Supply networks are subject to several dangers that can impair reputation, disrupt business operations, and adversely affect profitability.

Supply chain managers must create solid supplier relationships, construct rigorous risk management plans, and use data analytics to make wise decisions to reduce these risks.

Acquiring the knowledge and skills to manage supply chain risks effectively can be achieved through a supply chain analytics certification course that covers key concepts such as supply chain sourcing, risk management, and data analytics.

Types and sources of risks in supply chains

Risks in the supply chain might originate from different places and impact different stages. Typical supply chain risks include the following:

  • Production, inventory control, and other internal processes and activities are the source of operational hazards.
  • Economic ambiguity gives rise to financial risks such as interest rate changes, payment defaults, and currency volatility.
  • Demand risks result from shifts in consumer demand for products or services, which can cause inventory to be either over- or understocked.
  • Natural disasters like earthquakes, floods, and hurricanes pose risks because they can impair supply chain operations.
  • Political and economic concerns, including trade restrictions, taxes, and sanctions, lead to geopolitical hazards.
  • Cyber dangers include data breaches, virus attacks, and phishing scams that cause cybersecurity concerns.

Developing a Risk Management Plan: Assessment and Prioritisation

It is critical to analyse and prioritise supply chain risks based on their likelihood and potential impact after recognising the different types and sources of risk.

Supply chain managers can concentrate on the most critical issues and create efficient risk management methods using prioritisation.

Assessment

The assessment process includes obtaining data on the recognised risks and assessing how they might affect the supply chain. This can be done using risk assessment methods such as risk matrices, scenario analysis, and probability analysis.

Prioritisation

Ranking the identified risks according to their likelihood and potential effects on the supply chain is known as prioritisation. It aids in developing efficient risk management and mitigation methods and helps supply chain managers concentrate on the most critical threats.

Supply Chain Managers can use various methods for prioritisation, including:

  • Risk scoring: The possibility and potential impact of each risk are given numerical scores according to this procedure, and the chances are then prioritised according to these numbers.
  • Risk mapping: Supply chain managers can identify and prioritise risks using this method, which graphically depicts supply chain risks on a map depending on their location and possible impact.
  • Cost-benefit analysis: This approach involves ranking each risk according to its potential impact on profitability while weighing the costs and benefits of risk mitigation.
  • Expert judgment: With this approach, subject matter experts are consulted to prioritise risks based on their expertise and experience.

Managing Supply Chain Risks: Effective Strategies and Recommended Practices

Developing and practising best practices and strategies to reduce the recognised and prioritised key risks is crucial.

Following are some typical tactics and recommended procedures for reducing supply chain risks:

ration and create more durable supply chains.

Supplier Risk Assessment: To detect potential risks and create efficient risk management methods, supplier risks can be evaluated based on elements including financial stability, quality control, and geography.

Supplier Performance Monitoring: Supply chain efficiency can be increased by keeping track of supplier performance based on key performance indicators (KPIs) such as delivery times, quality, and responsiveness.

Supplier Capacity Building: Supply chain resilience can be increased by enhancing supplier capacity through training, knowledge transfer, and other forms of assistance.

Supplier Collaboration: Building trust and enhancing supply chain resilience can be achieved by working with suppliers on risk management and mitigation plans, contingency planning, and other supply chain activities.

Monitoring and Evaluating Supply Chain Risks: Continuous Improvement and Adaptation

To ensure that supply chain policies and processes continue to be effective, it is crucial to continuously monitor and evaluate the risks that may arise in the supply chain.

Here are some recommendations for assessing and managing supply chain risks:

Develop a risk management plan: A thorough risk management strategy requires identifying, evaluating, and mitigating supply chain hazards. Supply Chain Managers should describe the methods and steps for assessing and monitoring risks.

Establish key performance indicators (KPIs): Monitoring key performance indicators (KPIs) frequently makes it easier to evaluate the success of risk management plans and spot potential areas for development.

Regular risk assessments: Frequent risk assessments help detect new risks and assess how well current risk management techniques work.

Leverage technology and data analytics: Supply chain managers may respond swiftly to disturbances and make wise decisions by monitoring and assessing supply chain risks in real-time.

Foster collaboration and communication: Partnering with suppliers, clients, and other stakeholders can support proactive risk identification and mitigation.

Continuously improve and adapt: To prevent new threats from arising, it is essential to review and enhance risk management procedures. Supply chain risks always change.

Conclusion

Effective risk management is crucial for supply chain professionals to navigate the complex and uncertain global supply chain landscape. Regular assessments, prioritisation, and mitigation strategies are necessary to minimise disruptions and ensure continuity.

Imarticus Learning’s IIM Raipur Supply Chain Management course offers a comprehensive certification that covers critical concepts such as supply chain sourcing, risk management, and data analytics.

Imarticus Learning’s Supply Chain Analytics Certification course is a valuable investment for professionals who want to stay competitive and add value to their organisations. Visit Imarticus Learning to learn more.

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