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Category: Analytics

Importance of Data-Driven Decisions in Supply Chain Management

Posted on 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. 

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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?

Posted on 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

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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.

Tableau for Advanced Data Analytics and Data Visualization: Examples

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Tableau is an extremely popular data visualization tool. This tool has been rapidly adopted by the Business Intelligence sector and is being used for a variety of applications. The main task of Tableau is to simplify raw data in a straightforward and comprehensive manner. 

Companies are adopting Tabaleu because it can create simple data that is cognisable for professionals at every level. It is also useful for non-technical users as it allows them to make their dashboards and worksheets. These dashboards can be customised at any time. 

Normally the Tableau tool does not require any complicated codes to operate. When this tool collaborates with data analytics, they transmute various text information into visual forms at a very high pace. This process of visual rendering is often termed data visualization with Tableau.  

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With the assistance of an excellent machine learning certification course, one can learn more about Tableau. It will also allow an individual to create a strong career in data science. 

Let’s drive in this article to learn about Tableau, data analytics, and data visualization with help of examples.

 What is Advanced Data Analytics?

Advanced data analytics is a group of advanced procedures that allows any venture to foresee future patterns and trends. This technique can easily predict future patterns and trends by deeply analysing the pieces of information and data of their potential customers. 

Various techniques like machine learning, data mining, visualization, and pattern matching all fall under the umbrella of advanced data analytics. It also uses various analysis methods to function properly. These analysis methods include cluster, semantic as well as sentiment analysis. 

Advanced data analytics provides small and big businesses with data insights and well-organised annual plans. It also makes better business decisions than human beings. Frauds are quite common in any venture therefore advanced data analytics is designed to reduce business risks as well. It also keeps a check on any future threads. 

Advanced Analytics Projects and Tableau 

Data Scientists are utilising Tableau’s tools for completing advanced analytics projects in a short period. Tableau’s predictive tool is used to complete these advanced projects. 

There are various ways by which Tableau is used for finishing off an advanced analytics project. These ways of using Tableau are mentioned below:

Predicting or forecasting is one of the essential capacities that Tableau possesses. This is because it was designed with several predictive technologies. Professionals use this particular technology to figure out inactive threats or variables. 

Besides, Tableau allows foreseeing a statistical graph by simply adding any data or trend line to it. It also allows one to select these predictions and drag them to a new graph with the help of a right click. 

  • Segmentation 

The full form of segmentation is drag-and-drop segmentation. With help of this tool, Tableau can easily boost the cohort analysis and intuitive flow. 

This tool will help to build a dashboard on any subject. It will contain all details regarding that subject. 

  • Intricate Calculation 

One can make wrong calculations and manipulate complicated data while working with Tableau. This is because it has a strong calculation programme that improves any wrong analysis. 

Level of Detail (LOD) Expressions and Table Calculations are two characteristics of Tableau that help to enhance any wrong analysis or calculation. With this technique, it is easier to calculate logical problems, arithmetic sums as well as specific operations. Hence, Tableau makes any advanced analytics project a lot easier. 

  • What-If Analysis 

Test scenes can be altered by simply linking Tableau’s front end with its strong input capacity. It also allows a user to alter any calculation pretty quickly. 

What-If Analysis also allows a user to change filters and select data from the dashboard. It also permits one to generate an interactive report. 

  • R Integration

R integration allows Tableau to access any function that is present in the R data. Tableau can also send data to R by linking to the Rserve process. It also allows changing of any model that is made in R with the assistance of Tableau. 

These are the procedures by which Tableau can help any venture to complete advanced analytics projects rapidly.

A real-life example of the usage of Tableau 

Walmart is one of the largest American retail ventures that use Tableau. They use it to collect various analytics from their customers. Many companies purchase Tableau to collect information like customer information, sources, implementation of the law, IT-related information, and more about industries. 

Many other companies have purchased Tableau for Analytics purposes. They are as follows:

  • Amazon is an American retail organization that gives employment to many across the globe. It is one of the most famous customers of Tableau. 
  • CVS Health Corporation is an American Healthcare company that also used Tableau. 
  • European companies are also the popular purchaser of Tableau. The English gas company BP has purchased Tableau for further enhancement in their business. 
  • Apple Ireland, an Irish manufacturing company has also purchased Tableau. 

All these companies earn billions and have given employment to many. To improve the infrastructure, these companies have bought Tableau. There are many other companies around the globe that have also purchased Tableau for running their venture smoothly. 

Conclusion 

Data Visualisation with Tableau is fairly a new concept. Professionals are still grasping it. Thus, before initiating a career in data science one must learn data mining, one of the main components of Tableau. 

Imarticus Learning has brought an IIT data science course for those who are willing to commence a career as a data science professional. This online course will cover every aspect of data visualisation with Tableau and will also incorporate vivid knowledge about Neural Networks. 

This online data science training course is a collaboration between Imarticus Learning and IIT. Therefore, the top-notch faculty of IIT will teach the learners with extreme dedication. To grab this opportunity, get yourself enrolled in this course without any further delay. 

Disaster Response and Recovery in Supply Chain Management

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In today’s world, supply chain disruptions caused by natural disasters, political instability, and pandemics are becoming more frequent, emphasising the need for a robust disaster response and recovery plan in supply chain management.

best supply chain management and analytics training

Professionals with a certification in supply chain management, mainly those trained in supply and operations planning, play a crucial role in ensuring that businesses are prepared for potential disruptions and can recover quickly. 

With their expertise, they can help organisations navigate challenges and minimise the impact of disasters on supply chain operations.

Importance of Disaster Preparedness in Supply Chain Management

Supply chain disruptions caused by natural disasters, geopolitical events, and other unexpected circumstances can significantly impact businesses, causing delays, shortages, and loss of revenue. 

Some of the importance of disaster preparedness in the supply chain is as follows:

Risk Mitigation

Disaster preparedness aims to reduce the risk of supply chain disruptions caused by natural disasters, accidents, or other unexpected events.

This includes identifying potential risks and developing strategies to mitigate them, such as:

  • Conducting risk assessments and mapping out potential supply chain disruptions
  • Identifying critical suppliers and developing backup plans for alternative sourcing
  • Creating redundant systems to ensure continuity of operations in case of a disruption

Cost Savings

Preparing for disasters can save supply chain managers high costs in the long run. This includes:

  • Avoiding stockouts and lost sales due to disruptions
  • Minimising costs associated with rush shipping or expediting production to catch up on lost time
  • Reducing the need for emergency response services or additional labour to handle disruptions.

Reputation Management

Supply chain disruptions can also damage a company’s reputation, particularly if it cannot meet customer demands during or after a disaster. By being prepared, companies can:

  • Maintain a positive image by ensuring business continuity and customer satisfaction
  • Demonstrate their commitment to sustainability and corporate social responsibility by minimising the environmental impact of disruptions.

Key Elements of an Effective Disaster Response Plan for Supply Chains

An effective disaster response plan is essential for minimising supply chain disruptions caused by natural disasters, accidents, or other unexpected events. 

Here are some key elements that businesses should include in a comprehensive disaster response plan for supply chains:

  • Risk Assessment: A risk assessment should identify potential risks and vulnerabilities in the supply chain network. This includes mapping out potential supply chain disruptions, identifying critical suppliers, and assessing the impact of disruptions on business operations. 
  • Business Continuity Plan: A business continuity plan should ensure critical business functions can continue during a supply chain disruption. It includes identifying alternative sourcing strategies, developing redundancy plans, and establishing communication protocols and emergency response procedures for all stakeholders in the supply chain. 
  • Technology: Technology can be critical in disaster response efforts, including real-time supply chain operations monitoring and data analytics to identify potential disruptions. 

By including these key elements in a comprehensive disaster response plan, supply chain managers can be well-prepared to navigate unexpected events and minimise the impact of disruptions on business operations.

Role of Technology in Supply Chain Disaster Recovery

Technology is critical in supply chain disaster efforts by enabling real-time monitoring, data analytics, and supplier communication. Here are some specific ways technology can support disaster recovery in the supply chain:

Real-time Monitoring: Technology enables real-time monitoring of supply chain operations, helping managers quickly identify disruptions such as weather conditions and traffic patterns. This allows for prompt action to minimise the impact on the supply chain.

Data Analytics: Data analytics can identify potential disruptions in the supply chain, allowing managers to adjust their operations proactively and avoid potential issues, such as changes in demand or disruptions in logistics.

Communication: Cloud-based supply chain management systems use technology to communicate with suppliers, track shipments, and manage inventory levels. Real-time visibility into operations allows managers to make informed decisions and respond promptly to potential disruptions.

Automation: Automation is a valuable tool for streamlining supply chain operations and minimising disruptions caused by human error. Businesses can achieve it through automated warehousing systems, autonomous vehicles, and other technologies that enhance speed and efficiency.

By leveraging technology in these ways, supply chain managers can improve their disaster recovery efforts and ensure that their operations are resilient to potential disruptions.

Best Practices for Managing Supply Chain Disruptions During a Disaster

Disasters such as natural calamities, cyber-attacks, and geopolitical issues can cause significant disruptions to global supply chain networks. 

Here are some best practices for managing supply chain disruptions during a disaster:

Collaboration

Collaboration is key to managing supply chain disruptions during a disaster. Supply chain managers should work closely with suppliers and customers to identify alternative sourcing strategies and develop contingency plans.

Redundancy

Redundancy is critical to ensuring business continuity during a disaster. Supply chain managers should identify critical suppliers and establish redundancy plans to ensure that essential products and services can continue to be delivered.

Flexibility

Flexibility is essential during a disaster, as disruptions occur quickly and unexpectedly. Supply chain managers should adjust their plans and operations to respond to the changing situation.

Risk Assessment

A risk assessment should identify potential risks and vulnerabilities in the supply chain network. It includes mapping out potential supply chain disruptions, identifying critical suppliers, and assessing the impact of disruptions on business operations.

Contingency Planning

Companies should develop a contingency plan to ensure critical business functions can continue during a supply chain disruption. 

Contingency planning includes identifying alternative sourcing strategies, developing redundancy plans, and establishing communication protocols and emergency response procedures for all stakeholders in the supply chain.

Testing and Training

Regular testing and training ensure that the disaster response plan is effective and that all stakeholders respond to potential disruptions. 

Testing and training include conducting tabletop exercises, reviewing emergency response procedures, and providing training to supply chain employees.

Case Studies: Lessons Learned from Supply Chain Disasters and Recovery Efforts

There have been several high-profile supply chain disasters over the years, which have provided valuable lessons on how to manage supply chain disruptions and recovery efforts. Here are a few examples: 

The 2011 Thailand floods disrupted the hard disk drive supply, causing a global market shortage. Companies that had diversified their suppliers were better able to weather the disruption.

Companies that relied on a single supplier scrambled for alternatives and paid a premium for scarce inventory. The floods highlighted the importance of supply chain risk management and the need for redundancy.

The 2011 earthquake and tsunami in Japan disrupted the supply of electronic components, semiconductors, and automotive parts. Companies with a detailed understanding of their supply chain networks were better able to respond to the disruption.

Many companies implemented contingency plans and alternative sourcing strategies to minimise the impact of the disruption. The disaster highlighted the importance of supply chain visibility and the need for agile response capabilities.

Conclusion

Effective disaster preparedness and management are crucial for maintaining a resilient and efficient supply chain. With the help of technology and automation, supply chain managers can better monitor and adjust their operations, minimise the impact of disruptions, and prepare for potential disasters. 

Data analytics can provide valuable insights for identifying weaknesses and improving processes.

If you want to advance your supply chain management and analytics career, consider enrolling in Imarticus Learning’s Professional Certification in Supply Chain Management & Analytics.

This program provides a comprehensive understanding of supply chain management concepts and tools, including the role of a Supply and Operations Planner. Visit Imarticus Learning for more information.

Benefits of Data-Driven Decisions in Supply Chain Management

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One of the major attributes driving business success is customer satisfaction. With proper data in hand, companies can work on their supply chain management strategy to cater to customer demands and combat delivery inefficiencies. Supply chain as well as procurement process management produces huge volumes of data. It is important for leaders to adopt the right approach to using valuable data for improved operations. 

Supply chain leaders like the Chief Supply Chain and Operations Officer uses Machine Learning and AI models for improving supply chain operations. Automating a data pipeline is also of great help. A data-driven supply chain provides higher agility and greater productivity, particularly when there are chances of disruptions. Professionals who have completed a supply chain management certification course deal with this subject better. 

Understanding data-driven supply chain management

Data-driven supply chain management includes data usage strategically for better prediction of inventory and production changes, which is closer to real-time. This directly plays a crucial role in quicker decision-making. 

When it comes to a data-driven approach to devising a supply chain management strategy, primarily there is the use of new data sources like AI or ML technologies for making predictions. Data-driven supply chains provide a complete and vivid picture of the entire supply chain performance. 

Benefits of data-driven decisions in supply chain management

As a Chief Supply Chain and Operations Officer, you cannot undermine the importance and benefits of data-driven decisions in supply chain management. Let us look at some of these benefits:

  • Actionable insights

With the use of data-driven decisions within the supply chain, businesses can gain insights into the demands of various products and materials. This helps in getting more accurate forecasts. With access to data in real-time, organisations are in a position to adjust to demand fluctuations or emerging trends. This opens up opportunities in generating more revenue. 

Businesses also get an insight into the logistical capacity requirements thus dealing with inventory undersupply or oversupply issues successfully. Data-driven decisions help in meeting a balance between demand and supply, leading to cost-savings and more importantly, enhanced customer experience. 

  • Improved accountability and end-to-end visibility

When businesses implement data-driven decisions in devising strategies for supply chain management, they have improved accountability of the whole work. Moreover, there is a detailed record of every step in the supply management chain offering end-to-end visibility. 

Transparency in various operations is a vital point of a sustainable supply chain. With real-time data-driven decisions, organisations have complete transparency, which proves to be useful for all stakeholders. Transparency is inclusive of the due diligence measures in supplier appointments, identifying procurement sources as part of procurement process management and the metrics used for calculating carbon emissions. 

  • Improved inventory and logistics management

Inventory and logistics management are integral parts of the supply chain management process. With data-driven decisions, vendors and suppliers have immediate updates if there are any kinds of order backlogs. Along with attending to the backlogs immediately, businesses can devise strategies to adjust the inventory management process.  

The logistics department also benefits from data-driven decisions as they receive real-time information regarding their consignments, different cargo batches and updated delivery status of each consignment. With enhanced operational management, you can track your goods conveniently. The final result is a happy and satisfied customer. 

  • Better planning capacity

Every business wants to expand and for that proper planning is a prerequisite. Data-driven decisions in supply chain management provide valuable insights into emerging trends and the demand for a product. This proves to be highly useful for future project planning and devising and implementing effective business strategies. 

For instance, in a manufacturing company, real-time data and visibility offer valuable insights into various things including manufacturing inefficiencies, production volumes, various challenges related to raw material sourcing, etc. When businesses get more accurate information from data, they can have better planning. With planning, a business gets a competitive edge over others. 

  • Cost-savings

Data-driven decisions help in saving good amounts of money in supply chain operations. This is done by making sure that the demands for raw materials and related products are fulfilled timely. This is important so that there is no delay in the production plants or in the assembly lines. Supply chain operations can be made cost-effective if the products move on the quickest routes in the shortest time span. 

With necessary insights and transparent visibility from data-driven decisions, supply chain executives are successful in addressing various challenges in the supply chain.  Some of these include boosting profitability, reducing wastage and increasing operational efficiencies. 

  • Enhanced customer experience

Whether it is procurement process management or supply chain management, the main aim of any business is to provide the best customer experience. A Chief Supply Chain and Operations Officer implements data-driven decisions for enhanced customer experience. The majority of customers have a positive and smooth experience, making them happy customers. 

With real-time data, officers and managers monitor and analyse supply chain operations closely. They can work on ways to increase accuracy and minimise waiting times for product deliveries to customers. As a result, customers receive their orders on or before time and in good condition. 

Conclusion

Businesses are realising the value and importance of data and data-driven decisions in supply chain management and procurement process management. Right from making improved data-driven decisions for a particular supply chain management strategy to enhancing end-to-end operations, businesses are leveraging data in the best possible manner. 

If you are interested in making a career in the supply chain industry as a Chief Supply Chain and Operations Officer, a supply chain management certification course will be of great help. You can take up an IIM supply chain management programme and become a leader in the supply chain and operations industry. 

Imarticus Learning in collaboration with IIM Raipur offers an Executive Certificate Programme for Global Chief Supply Chain and Operations Officer. This 10-month programme targets senior supply chain professionals and helps them in acquiring operational, technological, strategic and personal skills for carving a niche in the supply chain industry. The study programme has six modules, covering various critical features of supply chain management and operations. 

Why is Noise Removal Important for Datasets?

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Noisy data in datasets impact the prediction of meaningful information. Studies stand evidence that noise in datasets leads to poor prediction results and decreased classification accuracy. Noise impacts algorithms in missing out patterns in any dataset. To be precise, noisy data is equivalent to meaningless data. 

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When you learn data mining, you get to know about data cleaning. Removing noisy data is an integral part of data cleaning as noise hampers data analysis significantly. Improper data collection processes often lead to low-level data errors. Also, irrelevant or partially relevant data objects might hinder data analysis. For enhancing data analysis, all such sources are considered noise.  

In data science training, you will learn the skills of removing noise from datasets. One such method is data visualisation with tableau. Neural networks are also quite efficient in handling noisy data. 

Effective ways of managing and removing noisy data from datasets

You must have heard the term ‘data smoothing’. It implies managing and removing noise from datasets. Let us look at some effective ways of managing and removing noisy data from datasets:

  • Regression

There are innumerable instances where the dataset contains a huge volume of unnecessary data. Regression helps in handling such data and smoothens it to quite an extent. For the purpose of analysis, regression helps in deciding the suitable variable. There are two variables in regression, which are as follows:

  • Linear Regression 

Linear regression deals with finding the best line for fitting between two variables so that one is used for predicting the other. 

  • Multiple Linear Regression

There is the involvement of two or more variables in multiple linear regression. By using regression, you can easily find a mathematical equation for fitting into the data. This helps in smoothing out the noise successfully to quite an extent. 

  • Binning

When you learn data mining, you will surely learn about binning. It is one of the best and most effective ways of handling noisy data in datasets. In binning, you can sort the data. You can then partition this data into bins of equal frequency. You can replace the sorted noisy data with bin boundary, bin mean or bin median methods.

Let us look at the three popular methods of binning for smoothing data:

  • Bin median method for data smoothing

In this data smoothing method, the median value replaces the existing values that are taken in the bin. 

  • Bin mean method for data smoothing

The mean value of the values in the bin replaces the actual value in the bin in this data smoothing process. 

  • Bin boundary method data smoothing

In this data smoothing method, the maximum and minimum values in the bin values are then replaced by the boundary value that is closest.

  • Outlier Analysis

Outliers are detected by clustering. It is evident from the name that close or similar values are organised in clusters or in the same groups. The values which do not fit into the cluster or fall apart are considered outliers or noise. 

However, outliers provide important information and should not be neglected. They are extreme values which deviate from other data observations. They might be indicative of novelty, experimental errors or even measurement variability. 

To be precise, an outlier is considered an observation which diverges from a sample’s overall pattern. Outliers are of different kinds. Some of the most common kinds are as follows:

  • Point outliers

These are single data points, which rest away quite far from the rest of the distribution.  

  • Univariate outliers

These outliers are found when you look at value distributions in a single feature space. 

  • Multivariate outliers

These outliers are found in an n-dimensional space containing n-features. The human brain finds it very difficult to decipher the various distributions in n-dimensional spaces. To understand these outliers, we have to train a model to do the work for us. 

  • Collective outliers

Collective outliers might be subsets of various novelties in data. For instance, it can be a signal indicating the discovery of any new or unique phenomena. 

  • Contextual outliers 

Contextual outliers are strong noises in datasets. Examples to illustrate this include punctuation symbols in text analysis or background noise signals while handling speech recognition. 

  • Clustering 

Clustering is one of the most commonly used ways for noise removal from datasets. In data science training, you will learn how to find outliers and also the skills of grouping data effectively. This way of noise removal is mainly used in unsupervised learning. 

  • Using neural networks

Another effective way of removing noise from datasets is by using neural networks. A neural network is an integral part of Artificial Intelligence (AI) and a subset of Machine Learning, in which computers are taught to process data inspired by the human brain. It is a kind of Machine Learning process known as Deep Learning where interconnected nodes are used in a layered structure for analysing data. 

  • Data visualisation with tableau

Tableau is a data processing programme which creates dynamic charts and graphs for visualising data in a professional, clean and organised manner. While removing noise from datasets, this programme proves to be truly effective. Clear identification of data is possible with data visualisation with tableau. 

Conclusion

Almost all industries are implementing Artificial Intelligence (AI), Machine Learning (ML) and Data Science tools and techniques in their works. All these technologies work with huge volumes of data, using the most valuable ones for improved decision-making and forecasting trends. Noise removal techniques help in removing unimportant and useless data from datasets to make them more valuable. 

If you are looking to make a career in data science, you can enrol for an IIT data science course from IIT Roorkee. You can also go for a Machine Learning certification course in conjunction with a data science programme. 

Imarticus Learning is your one-stop destination when you are seeking a Certificate Programme in Data Science and Machine Learning. Created with iHub DivyaSampark@IIT Roorkee, this programme enables data-driven informed decision-making using various data science skills. With the 5-month course, learn the fundamentals of Machine Learning and data science along with data mining. Acclaimed IIT faculty members conduct the course. Upon completion of the programme, you can make a career as a Data Analyst, Business Analyst, Data Scientist, Data Analytics Consultant, etc. 

Enrol for the course right away!