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Category: Analytics

Sales and Operations Planning in Supply Chains Management

Posted on by Imarticus

Sales and Operations Planning (S&OP) is a vital process in Supply Chain Management that aligns the organisation’s strategies and operations to achieve its business objectives. 

It is a continuous process that enables a company to balance its demand and supply, optimise inventory levels, and increase customer satisfaction.

S&OP is a collaborative effort that involves different departments, including Sales, Operations, Finance, and Supply Chain. 

Its primary objective is to establish a balance between the company’s demand and supply by developing a forecast of demand and supply, which then leads to an integrated plan.

What is Sales and Operations Planning (S&OP)?

supply chain management courses

Sales and Operations Planning (S&OP) is a process in Supply Chain Management that involves cross-functional collaboration to develop an integrated plan that aligns the organisation’s strategies and operations to meet its business objectives. 

The process involves a continuous review of the demand and supply of goods and services to ensure that they are balanced.

The Sales & Operation Planning process typically involves representatives from different departments, including Sales, Operations, Finance, and Supply Chain. 

The objective is to align the demand forecast with the supply plan to ensure that the company can meet its customer’s requirements while optimising its inventory levels and maximising its efficiency.

The Process of Sales and Operations Planning

The sales and operations planning process generally involves the following steps:

  • Demand Planning: This step involves creating a forecast of the expected demand for the company’s products or services. The forecast is based on historical sales data, market trends, and customer feedback.
  • Supply Planning: This step involves determining the resources required to meet the expected demand. This includes creating a production plan, scheduling resources, and determining inventory levels.
  • Pre-S&OP Review: This step involves reviewing the demand and supply plans to ensure that they are aligned and identifying any gaps or issues that need to be addressed.
  • S&OP Review: This step involves a formal review of the demand and supply plans by senior management to ensure that they are aligned with the company’s strategic objectives and to make any necessary adjustments.
  • Executive S&OP Review: This step involves a review of the S&OP plan by the executive team to ensure that it is aligned with the company’s overall strategy and to make any final adjustments.

Benefits of Sales and Operation Planning

The Sales department plays a crucial role in the sales and operations planning process by providing insights into market trends, customer behaviour, and demand patterns. This information is then used to fine-tune the demand forecast, ensuring that it aligns with the current market conditions.

The Finance department is also involved in the process, providing insights into the financial implications of the S&OP plan.

The sales and operations planning process helps companies to achieve several benefits, including:

  1. Improved customer satisfaction: By aligning demand and supply, companies can ensure that they meet their customer’s requirements, resulting in improved customer satisfaction.
  2. Reduced inventory levels: The S&OP process helps to optimise inventory levels, reducing the amount of working capital required to maintain the inventory.
  3. Increased efficiency: By aligning their operations with their strategies, companies can improve their efficiency, reducing lead times, and increasing productivity.
  4. Improved financial performance: By optimising inventory levels, reducing lead times, and increasing efficiency, companies can improve their financial performance.

The challenges of sales and operations planning

Sales and Operations Planning (S&OP) is a crucial process in Supply Chain Management, but it can also be challenging. Some of the challenges that companies face in implementing an effective S&OP process include:

  • Data Availability and Accuracy: One of the most significant challenges of S&OP is having access to accurate and timely data. Companies may struggle to gather data from different departments or systems, leading to inaccurate forecasts and plans.
  • Cross-Functional Collaboration: S&OP requires cross-functional collaboration and communication. However, different departments may have competing priorities or perspectives, making it challenging to reach a consensus and create an integrated plan.
  • Resistance to Change: Implementing an S&OP process may require significant changes to the organisation’s structure, processes, and culture. Resistance to change can slow down the implementation process and hinder its effectiveness.
  • Lack of Executive Support: Without strong executive support, it can be challenging to prioritise and implement the necessary changes to establish an effective S&OP process.
  • Uncertainty and Volatility: The external environment can be unpredictable, with changes in demand patterns, supply disruptions, and economic fluctuations. These uncertainties can impact the S&OP process and require companies to continuously monitor and adjust their plans.
  • Lack of Technology: S&OP requires advanced technology and tools to support data integration, analysis, and collaboration. Without these tools, companies may struggle to implement an effective S&OP process.

What’s next for sales and operations planning?

With the advent of new technologies, changing customer needs, and increasing market volatility, sales and operations planning is evolving to meet the changing demands of the business environment. Here are some of the trends and developments that are shaping the future of S&OP:

  • Digital Transformation: The adoption of digital technologies, such as Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics, is transforming the way companies approach S&OP. These technologies can help companies improve their forecasting accuracy, optimise their inventory levels, and enhance their decision-making capabilities.
  • Demand-Driven Planning: Demand-driven planning is a customer-centric approach to S&OP that focuses on meeting customer demand while minimising inventory levels and lead times. This approach involves using real-time data and advanced analytics to forecast customer demand and adjust production and inventory levels accordingly.
  • Sustainability and Resilience: With growing concerns about climate change and supply chain disruptions, companies are incorporating sustainability and resilience into their S&OP processes. This involves developing plans that take into account environmental, social, and governance (ESG) factors and building more resilient supply chains that can withstand shocks and disruptions.

Conclusion

The future of sales and operations planning is characterised by digital transformation, integrated business planning, collaborative planning, demand-driven planning, and sustainability and resilience. Companies must address the challenges they face by investing in technology, encouraging cross-functional collaboration, and ensuring strong executive support to establish an effective S&OP process. 

Companies that embrace these trends and developments will be better equipped to meet the challenges of the future and achieve their business objectives.

If you are interested in pursuing a career in sales and operations planning, consider enrolling in Imarticus Learning’s supply chain management certification program offered by IIT. To learn more about Imarticus Learning’s supply chain management certification course, visit their website.

Demand Forecast and Planning

Posted on by Imarticus

Demand forecasting and planning refer to estimating future demand for a certain product or service and developing a strategy to meet that demand. This requires studying previous sales numbers, market patterns, and other variables to forecast future demand accurately. 

The fundamental goal of demand forecasting and planning is to guarantee that the right items are available in the right quantities and at the right time to fulfil the demands of consumers. Demand planning is a step in the sales and operations planning (S&OP) process that uses statistical sales forecasts and the experience of other areas to estimate future demand.

Demand forecasting and planning have become essential elements of supply chain management in today’s fast-paced corporate atmosphere. Planning and forecasting demand with accuracy may assist organisations in maximising inventory levels, cutting expenses, and enhancing customer happiness. 

How to Do Demand Forecasting and Planning?

Demand forecasting and planning can be a complex process, but it can be simplified by following these steps:

Collect Data: The initial stage in demand forecasting and planning is gathering data on previous sales, market trends, and other pertinent elements that might influence demand.

Analyse Data: After gathering the data, the subsequent step is to analyse it to uncover patterns and trends. This study can assist you in identifying demand-influencing elements like seasonality, promotions, and economic situations.

Develop a Forecast: Based on the data analysis, you can anticipate future demand. This projection should be as precise as feasible, considering factors that influence the market.

Plan Inventory: After creating a prediction, the following step is to plan inventory levels to satisfy that demand. This process includes choosing how much inventory to purchase, when to order it, and how to maintain inventory levels to minimise stockouts and overstocking.

Types of Demand Forecasting Techniques

There are several types of demand forecasting techniques:

  • Survey methods, which involve collecting data through customer surveys
  • Statistical methods, which use historical data to identify trends and patterns
  • Trend projection, which consists in extrapolating past trends into the future
  • Market research, which uses customer data to identify demand patterns
  • Sales force composite, which involves aggregating sales forecasts from individual salespeople
  • Delphi method, which consists in collecting and aggregating expert opinions

Each technique has strengths and weaknesses, and the appropriate strategy will depend on the specific forecasting problem.

Factors Affecting Demand Forecasting

Here are some of the most important factors that influence demand forecasting:

Type of goods: The type of goods being sold can have a significant impact on demand forecasting. It can be goods that are already established in the market, or it can be new goods that are yet to be introduced.

Economic conditions: Economic conditions such as GDP, unemployment rate, inflation rate, and consumer confidence are key factors that affect the level of demand for products or services.

Product cost: The price of goods is also a factor affecting forecasting. Even a subtle change in pricing can have a significant impact on demand forecasting.

Competition: The number of competitive products also influences product demand. Additionally, new products can also shift the market dynamics. Demand forecasting becomes challenging and complex in this situation.

Other factors affecting demand forecasting include market fluctuations, cyclical economic trends, and seasonal highs and lows in demand.

Best Practices in Demand Forecasting and Planning

To ensure that your demand forecasting and planning process is effective, here are some best practices to follow:

Use Multiple Data Sources: To develop an accurate forecast, it is essential to use multiple data sources, including historical sales data, market trends, and customer feedback.

Collaborate with Stakeholders: Demand forecasting and planning should be a collaborative process that involves stakeholders from across the organisation, including sales, marketing, and operations.

Use Technology: There are many tools available that can help simplify the demand forecasting and planning process, such as demand planning software and inventory management systems.

Monitor Performance: Once you have implemented your demand forecasting and planning process, it is essential to monitor performance regularly to identify areas for improvement and make adjustments as needed.

Benefits of Effective Demand Forecasting and Planning

Demand forecasting and planning provide several benefits to businesses. These are:

  • Optimising inventory by predicting future sales, which allows demand managers to make informed business decisions about inventory planning, warehousing needs, running flash sales, and meeting customer expectations.
  • Reducing risks and making profitable financial decisions that increase profit margins and opportunities for expansion while also making the allocation of resources, inventory accounting, staffing, cash flow, and overall spending more efficient.
  • Providing valuable information regarding prospects in current and other markets, allowing managers to make informed pricing, business growth plans, and market potential decisions.
  • Maximising revenue and reducing costs by helping businesses strike the right balance between sufficient inventory levels and customer demand.
  • Creating accurate budgeting by estimating demand, including sales timing, allows firms to be more precise.
  • Reserving cash or negotiating bridge loans or credit terms in advance by forecasting demand, which helps businesses avoid an inability to supply customers.
  • Helping businesses better plan production, financing, labour, and marketing by determining the timing of sales. 

Future Outlook on Demand Forecasting

Accurate demand forecasting is critical for organisations to satisfy consumer demand, optimise inventory levels, and maximise profitability. There are several approaches and tools available for demand forecasting, including statistical models, machine learning algorithms, and market research. While predicting overall market demand might be tough, it can give useful insights into future market circumstances and help organisations make better strategic decisions.

supply chain management courses

IIT supply chain management course on demand forecasting and planning covers the components like, including averages, linear trend, seasonality, and cyclical, irregular, and random factors. Getting certified in such a course will help one build a career in supply chain management.

Conclusion

Imarticus Learning offers a Professional Certification in Supply Chain Management and Analytics in collaboration with the Department of Management Studies (DoMS) and the E-learning Centre at IIT Roorkee. The programme is designed to provide candidates with cutting-edge experience to enter the operations and supply-chain industry. 

Supply chain performance, drivers, metrics, demand forecasting, inventory management, logistics, and supply chain analytics are all covered in the curriculum. Supply chain analytics allows managers to make data-driven choices at the strategic, operational, and tactical levels.

The certified IIT supply chain management course prepares candidates for high-demand job roles such as demand planner, data scientist, and supply sales and operations planning. The programme delivers live online training sessions and discussions with some of the best faculties in India and leaders from the industry. 

What are Artificial Neural Networks?

Posted on by Imarticus

This is the age of supercomputers, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning where computing power has gone much beyond our imagination. With such scientific and technological advancements, it has become possible to process huge volumes of data within a fraction of a second for getting valuable insights. 

become a Data Analyst

A common term, which is extensively used when we talk about AI or ML is Artificial Neural Network (ANN). Artificial Neural Network is a model which imitates the way in which various nerve cells function in the human brain. There are many aspects and interesting layers in this network. Read on to learn more about Artificial Neural Networks, how they work, their advantages and other related things. 

What Are Artificial Neural Networks?

Artificial Neural Networks refer to a subfield of Artificial Intelligence, modelled after the human brain. These are algorithms which are based on brain functions for forecasting issues and modelling complicated patterns. The Deep Learning method emanates from the concept of biological neural networks. 

The main aim of developing Artificial Neural Networks was attempting to replicate the functioning of the human brain. If you study closely, you will find that the functioning of ANN is similar to that of biological neural networks. ANN algorithms work on structured and numeric data. 

The learning algorithms that ANNs use can make adjustments independently. Precisely, they can learn as they receive any kind of new input. This is the reason they are highly effective for non-linear statistical data modelling. 

The Architecture of Artificial Neural Networks

To understand Artificial Neural Networks, you must have knowledge about the architecture of ANNs.

Layers in the Artificial Neural Network

There are three or more layers in an Artificial Neural Network, which are interconnected to one another, namely the input layer, hidden layer and output layer. Because of multiple layers, ANNs are often called Multi-layer Perceptron (MLP).

The first layer has input neurons. These neurons send data to the other deeper layers in the network. After the data is processed through these layers, the final output data is sent to the last output layer. 

In the ANN, all inner layers are hidden. These layers are made of units which adaptively alter the data received from one layer to another through a long series of transformations. The hidden layer is also referred to as the ‘distillation’ layer as the most relevant and useful pattern is extracted from the inputs and sent for further analysis to the next level. Redundant information gets discarded in the process. 

Each layer in the ANN functions as both an input and output layer for understanding complex subjects well. Collectively, all the inner layers are called neural layers. 

Using backpropagation

Using backpropagation is an essential part of Artificial Neural Networks. Backpropagation is a process in which the ANN adjusts the output results by considering the errors in the account. 

During the supervised training phase, every time output is labelled as an error, it is sent backwards through the process of backpropagation. Every single weight is updated to understand how it contributed to the error. 

The error recalibrates the weight of the ANNs unit connections for understanding the difference between the actual outcome and the desired outcome. The ANN will gradually learn to minimise the chances of unwanted results and reduce the number of errors as well. 

Practical Applications of Artificial Neural Networks

Artificial Neural Networks have unique properties and therefore they are used in various applications. Some of the practical applications of Artificial Neural Networks are as follows:

  • Forecasting

Artificial Neural Networks help in making forecasts which impact business decisions greatly. Regular business decisions include capacity utilisation, the financial allocation between goods, sales, etc. 

Apart from these ANNs also help in making forecasts on the stock market, monetary and economic policy, finance and other subjects. Forecasting is quite a complex thing as there are many underlying variables concerned, some of which are known and some are unknown. 

There are things that could be improved in traditional forecasting models. On the other hand, ANNs can extract previously known correlations and unknown characteristics, thus providing accurate forecasts with minimal errors. 

  • Image Processing and Character Recognition

Artificial Neural Networks have a significant role in character and picture recognition. This is because these networks can take in innumerable inputs, process them and derive complicated and hidden, non-linear correlations. 

For instance, handwriting recognition is an integral part of character recognition and is used extensively in various applications like fraud detection and national-level security assessments. 

Apart from character recognition, image recognition is a fast-evolving discipline. Image recognition is now used in various applications right from cancer detection in medical science to facial identification in social media, from defence purposes to satellite image processing, agricultural purposes and so on. 

Deep Learning includes deep neural networks, which have opened up transformative and new advances in natural language processing, speech recognition and computer vision. One of the best examples in this regard is self-driving vehicles. 

Advantages of Artificial Neural Networks

Some of the most prominent advantages of Artificial Neural Networks are as follows:

  • Data storage on the entire network

Data used in traditional programming is not stored on a database; but rather on the whole network. When a few pieces of data disappear in one place, it does not prevent the network from functioning. 

  • Parallel processing capability 

Artificial Neural Networks have the capability of performing more than one task simultaneously. This is possible because ANNs have a numerical value. 

  • ANNs can function with incomplete knowledge 

Post-ANN training, the output will be produced from the given information even with inadequate data. The loss of performance depends upon the significance of missing information. 

Summing it up

Artificial Neural Networks have myriad applications and are being extensively used in natural language processing, spam email detection, predictive analysis in business intelligence, chatbots and so on. 

A course in Data Science and Analytics helps you know more about Artificial Neural Networks. Imarticus Learning offers a Postgraduate Program in Data Science and Analytics for building a career in analytics and data science. The programme offers guaranteed job assurance. The 6 Months programme has almost 300+ learning hours with 25+ projects and 10+ tools. Give your tech career the needed boost with this course. 

Tips on how to choose the right BBA program?

Posted on by Imarticus

Know about BBA

The Bachelor in Business Administration or BBA is a 3-year undergraduate program for students who have their minds set on a management career just after completing school. The BBA program revolves around the administration of business and business operations management. Students learn about entrepreneurship and management roles in the program.

The best part of BBA is that it offers the course to students from every stream. Students of Humanities, Commerce, and Science can apply for any BBA program.

bba in business analytics

BBA promises you a great future in Management. Many universities across India provides BBA program in various modes. However, before jumping to a long-term program, you must consider the impact a BBA program will have on your job opportunities.

A good BBA program comes with great exposure to the industry before you complete your course. Selecting the right college is a tedious task just because of the number of options available offering near-similar benefits. Here are few

Tips on how to choose the right BBA program?

1. Make a list

Make a list of what your desired universities have to offer. It could include specializations, fees, mode of learning, placement, faculty, and more. It will help you to narrow down to BBA programs that are best for you. Lists are critical since they help you to make informed decisions. They also save a lot of time and can be useful in cases where there are plentiful options.

2. Research

It is important to research before you jump into a BBA program. Research can help you find loopholes and eliminate the programs that are not helpful for you. It also helps to set goals and cut the additional costs you might incur without proper research.

3. Find pros and cons

Check out the pros and cons of a particular BBA program so that you can clear idea of options.

What do you need to consider in a BBA program?

You have to follow a few parameters and look for certain specific things while choosing the best BBA program for you.

1. Offered Specialization

There are multiple specializations offered in the BBA program. This includes:

Business administration

International Business

Computer application

Marketing

Finance

Sports management

The specializations will depend on what you are good at and have an interest in. You can also research what curriculums cater to what you want to study and are beneficial in the future.

2. Faculty

Faculty is one of the biggest USPs of any BBA program and you must ensure that you are benefitted from it. Check the number of faculties depending on whether they are full-time or part-time. Faculties that have industry experience are a plus sign you should look out for. It helps the learners to engage with the industry even before they start their internships.

3. Curriculum

A highly text-based curriculum might not be the best choice when it comes to choosing a BBA program. Management courses are rarely about taking notes. It must include case studies, real-world projects, skill development sessions, opportunities for internships, and more.

4. Placement opportunities

Placement is the single most important criterion that should be included in your research list. Make sure that you check out the information on the partners and collaborations of the university beforehand. partnerships with companies ensure maximum placement. You might also want to check out the median salary, the number of people placed, offered positions, etc for the same.

Why choose Imarticus Learning?

Imarticus Learning brings BBA in business analytics in collaboration with Geeta University.

The curriculum curated by Imarticus Learning in association with Geeta University has been deemed as new-age and industry aligned by industry leaders. It includes topics like:

Fundamental of business analytics

Fundamentals of business intelligence

Design thinking

Critical thinking and decision making

Data visualisation

Statistical analysis using SAS &R

Marketing analysis

Financial analytics

Supply chain analytics

You will also be familiarised with tools such as Microsoft Excel, SQL, Power BI, SAS, R and more. You will also get to work on capstone projects that enable the students to link their projects to issues of community and utilise their learning experience. You will also get the skill enhancement certifications and benefits of a digitally-enabled campus.

Along with this, the BBA program offers summer internships in top companies. After the completion of the program, you will also get career support that includes placement. Career support also includes mock interview sessions, resume-building classes, and more.

Imarticus Learning has worked to get more than 56000 students placed and enable 45,000+ career transitions. That is not all. To provide the best assistance in the competitive job market for our learners, we have collaborated with more than 500 industry leaders as our partners for maximum placement opportunities.

Our alumni work at:

Deloitte

Genpact

Accenture

Infosys

KPMG

IBM

Capgemini

Goldman Sachs

Cognizant

Standard Chartered

Wipro

Moody’s analytics

Check out more about the BBA course here.

Interactive Dashboards with Microsoft Power BI

Posted on by Imarticus

Are you struggling to generate meaningful insights from the wide datasets of your enterprise for creating a business dashboard? Without the right insights, your decision-making will become vulnerable. 

Implementing Microsoft Power BI is the best solution to this issue. The drag-and-drop options, simple user interface and not requiring coding make Power BI a favourite tool for building business dashboards and data visualisation. 

Building an excellent dashboard with Power BI

Big data is a massive asset, which businesses leverage for transforming business operations, enhancing decision making and increasing the overall productivity of the organisation. Power BI helps in analysing data effectively along with providing interactive insights for creating visually immersive visualisations and interactive dashboards. 

Building a dashboard with Power BI has become a favourite for businesses. In a Power BI dashboard, you can assemble all crucial data elements on a single page. The dashboard acts as a gateway to the underlying reports and datasets so that you can reach the reports from the dashboards whenever needed. 

By using Power BI dashboards, you can visualise your data and share insights across the organisation. You have the benefit of embedding the dashboard into your app or website. 

How to create dashboards in Power BI?

A powerful visualisation tool, Microsoft Power BI transforms raw data into actionable insights. Create custom dashboards for displaying crucial metrics and giving complete visibility to your business for informed decision-making. 

Let us look at the various steps to follow for creating dashboards in Power BI:

  • Data Import 

The first step towards creating dashboards in Power BI is data import. You have to move data to Power BI first. 

  • Click on the ‘Get Data’ button at the left corner bottom of the screen.
  • Choose the source from where you want to import data – OneDrive, Excel, Azure SQL database or SharePoint. 
  • After importing the data, Power BI generates a blank canvas with a menu where you can select visuals which represent the metrics best on the dashboard.

  • Selecting the visuals

You must choose proper visuals for the reports and the data so that the important pointers are highlighted. Here are some common Power BI visualisations you can choose from:

  • Bar charts – These are ideal for showcasing the comparison between various data categories. They are great for analysing monthly expenses and sales product performance. 
  • Scatter plots – These represent a relationship between two attributes. 
  • Pie charts – Great for representing customer demographics, pie charts represent the composition of a whole in terms of percentage. 
  • Stacked charts – With these charts, you can present multiple data types within a single bar. They are useful for highlighting monthly budget breakdowns. 
  • Line graphs – These are useful for tracking changes over time. They are mainly used for plotting annual revenue. 

  • Attaching visuals to the dashboard

While customising the Power BI dashboard, you have to pin the most valid and suitable visual or chart. 

Click on ‘Add Title’ > ‘Create a Visual’ > Pin Icon to attach the icon to the custom dashboard. With the drag-and-drop feature, you can arrange the tiles as you feel. 

  • Dashboard themes

There are options for changing the dashboard themes in Power BI so that your dashboard has a new look. With every Power BI subscription, you have some inbuilt basic themes. You also have the option of downloading custom themes from the Microsoft Themes Library. With themes, you can work on the organisation’s branding. Themes also help in differentiating the dashboards on various parameters. 

  • Targeting the audience

With Microsoft Power BI, you can create effective dashboards for specific users. You can target your audience specifically for better results. For instance, if you are creating an accounting dashboard, you can highlight things like product performance, monthly expenses, revenue generation and other things. To capture the attention of your audience, Power BI lets you expand the charts and drill into the reports. Refrain from cluttering your dashboard with unnecessary information and data. 

  • Sharing the dashboard

The best thing about Microsoft Power BI is that you can share the dashboard with colleagues, peers and major decision-making professionals in the organisation. You can edit the imported databases by adding or modifying content in the datasets. The visuals and charts in the Power BI update automatically with the edits. As a result, users can see the updated data, which helps in boosting the transparency and productivity of the organisation. 

Items that are customisable in the Power BI dashboards

The drag-and-drop functionality of Power BI simplifies the presentation and data extraction process in interactive data visualisations. Here are certain items which are customisable in the Power BI dashboards:

  • Security filters

You have the option of setting up access filters for ensuring that viewers only see information which is relevant to them. You can overcome the risk of unauthorised access. 

  • Machine Learning

Power BI users can make Machine Learning (ML) models, incorporate Azure Machine Learning, and access image recognition and text analytics. 

  • Real-time data

With Power BI, you can update dashboards in real-time. This implies that data is streamed continuously letting viewers solve issues and determine opportunities on the go. Live data can go from business apps, social media or any other source. Some of the data is time-sensitive as well. 

  • Cortana Integration

Cortana, the digital assistant, can be integrated into Power BI. with this integration, you can put questions in your natural language for accessing any kind of information. This is a very useful feature for users who access Power BI through their mobile devices. 

  • Publication and distribution

Power BI dashboards enable direct uploading of visualisations and reports instead of uploading large files to the shared drive or sending them via email. As soon as the underlying dataset is updated with fresh data, the dashboard data is refreshed every time. 

Summing it up

Data has a superpower today. With Microsoft Power BI dashboards, you can make your data understandable to users. You can use data from various sources and develop visually immersive and impressive insights for informed decision-making in businesses. Add more value to your business with Microsoft Power BI dashboards. 

If you want to unleash the power of Power BI and understand data better, taking up a course in the subject will be of great help. Many universities and institutes offer a Data Science course with placement opportunities to interested candidates. 

Imarticus Learning offers a Post Graduate Programme in Data Science and Analytics. Build your career in Data Science and Analytics with guaranteed job assurance with this course. The course covers Python programming, logistic regression, data visualisation and related topics. The course includes a live learning module, real-world projects, a job-specific curriculum, dedicated career services and many more features. 

Apply now for the course!

Supply Chain Analytics and Data-Driven Supply Chain Management

Posted on by Imarticus

Data-driven supply chain management and supply chain analytics are two related concepts used to optimise and boost the effectiveness of supply chain operations. Data analytics and sophisticated analytical methods are used in supply chain analytics to examine vast amounts of data pertaining to supply chain operations. On the other hand, data-driven supply chain management uses analytics and data to facilitate tactical and supply chain management strategies. 

Supply chain analytics and data-driven supply chain management work together to empower organisations to use data and analytics to obtain important insights into their supply chain operations, discover areas for improvement, and make data-driven decisions. All in all, both are involved in enhancing the efficiency of their supply chains by utilising the power of data.

What is Supply Chain Analytics and Why is it Important?

The application of data analytics and advanced analytical tools to analyse and optimise supply chain operations is referred to as supply chain analytics. It entails gathering, analysing, and interpreting data pertaining to different parts of the supply chain. The main goal of supply chain analytics is to obtain insights, discover patterns and trends, and make data-driven supply chain management strategies to improve overall performance in the field. 

It is significant because it gives firms expanded visibility, better decision-making capabilities, higher operational efficiency, proactive risk management, better customer service, and a competitive edge in the highly complex and ever-evolving sector of supply chain management.

What is Data-Driven Supply Chain Management? 

The process of leveraging data and advanced analytics to make educated decisions and optimise supply chain operations is referred to as data-driven supply chain management. It entails gathering, analysing, and using data from numerous sources across the supply chain in order to obtain insights, detect patterns, and drive data-driven activities for enhanced performance, efficiency, and effectiveness.

The main constituents of data-driven supply chain management include:-

  • Data collection
  • Data integration
  • Data analysis
  • Data-driven decision-making 

Machine learning, predictive analytics, and prescriptive analytics are common approaches used to analyse massive and complex data sets, find patterns, trends, and abnormalities, and provide suggestions or improve choices.

Importance of Data Analytics in Supply Chain Management

Data analytics is critical in contemporary supply chain management, providing considerable advantages in terms of efficiency, cost savings, and strategic decision-making. These are some of the most essential reasons why data analytics is vital in supply chain management:-

  • Improved Visibility: Data analytics enables real-time visibility into many parts of the supply chain, such as inventory levels, transit routes, and demand trends. This enables supply chain executives to monitor operations, resolve issues or disruptions, and quickly respond to changing events, resulting in increased overall visibility over the supply chain.
  • Better Forecasting: Data analytics helps supply chain managers to develop accurate demand projections by analysing historical data, consumer preferences, market trends, and other pertinent aspects that result in better demand planning and customer service levels.
  • Improved Operational Efficiency: Data analytics aids in the identification of inefficiencies and opportunities for improvement in supply chain processes that leads to greater operational effectiveness and reduced expenses.
  • Streamlined Inventory Management: Data analytics allows supply chain managers to improve inventory levels by examining data on demand patterns, lead times, and other factors that contribute to the enhancement of working capital management and lower inventory expenses.
  • Optimised Supplier Management: Data analytics aids in the evaluation and selection of suppliers based on performance data and also in the identification of possible supply chain hazards. This aids in the overall maintenance of a stable and efficient supply base.
  • Development of better supply chain management strategies: Data analytics delivers insights and knowledge to supply chain managers, allowing them to make more informed and strategic decisions and data-driven choices for the supply chain’s long-term performance.

How is Supply Chain Analytics Related to Data-Driven Supply Chain Management?

Data-driven supply chain management is a key constituent of supply chain analytics. It refers to the use of analytics techniques and tools for data analysis in order to acquire insights into supply chain operations, performance, and effectiveness. One of the important components of data-driven supply chain management is supply chain analytics, which comprises the gathering, integration, analysis, and interpretation of data to facilitate decision-making and improve supply chain operations.

Supply chain analytics Data-driven supply chain management
Supply chain analytics involves the evaluation of data from multiple sources, supply chain analytics use data-driven techniques such as descriptive, predictive, and prescriptive analytics. In contrast, data-driven supply chain management involves the application of data and analytics to streamline decision-making and optimise supply chain operations holistically in the supply chain.
It relies on insights garnered from historical sales data, transportation data, production data, inventory data, and other data points.  It relies on data-driven aspects like data collection, data governance, data integration, and decision-making based on the insights extracted from data. 
These approaches aid in the identification of trends, connections, and irregularities in data, as well as the generation of insights that may be utilised for decision-making and process improvement. Data-driven supply chain management employs supply chain analytics as a tool to garner data-driven insights and facilitate decision-making. It also includes the integration of larger strategies and techniques of leveraging data to optimise supply chains.

Conclusion

In the dynamic and complex field of supply chain management, supply chain analytics, and data-driven supply chain management enable organisations to use data and analytics to make informed decisions, streamline operations, lower costs, enhance customer satisfaction, and gain a competitive edge. Hence, there is a growing need for experts in supply chain analytics. Firms are increasingly understanding the value of data-driven decision-making in supply chain management. 

Companies in a variety of industries are looking for supply chain analytics experts to help streamline their supply chains and enhance performance. To get started in this career field, you can take up an Operations and Supply Chain Management course offered by Imarticus Learning. 

Difference between Object-oriented and functional programming

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Programming paradigms are different approaches that are taken to provide solutions for a variety of tasks. It consists of various rules, strategies and principles that a software developer can utilise to develop new software or enhance the efficacy of an existing one. Out of the various programming paradigms out there, Object-oriented programming and functional programming are two very important but different approaches to programming. 

All the programming languages follow at least a particular programming paradigm or are a composition of a few of them. It is upon the needs of the software and the discretion of the developer which programming paradigm is to be used. 

Read on to know about object-oriented programming and functional programming along with their differences.

What is Object-oriented Programming?

Object-oriented programming is a type of programming paradigm that works on the basis of classes and objects. Developers, in object-oriented programming, write codes that are then classified into various classes and as various objects. Developers majorly use OOP for data frame manipulation as it is well suited for the task.

Classes are blueprints or a particular set of instructions used to build a data structure for each of the objects in a specific manner. Objects are categorised based on the variables that they contain and function in the way variables behave. 

Difference between object based and object oriented programming discussion

Object-oriented programming is concerned with determining the operation and performance of the variables. It can be said that classes serve as templates to build objects and the objects are instances of those particular classes. Additionally, objects also contain codes in the form of procedures and data in the form of fields. It is also known as attributes and methods.

What is Functional Programming?

Functional programming is a programming paradigm that is declarative in nature. Developers use functional programming to write codes only in pure functions. Codes in pure functions imply that functional programming does not alter old variables into new characters but only generates new variables. 

Functional programming is great for data visualisation but cannot conveniently be used for data frame manipulation. It can be said that pure functions cannot be influenced by external factors and the result is only based on the input parameters. Additionally, functional programming helps in avoiding mutable and shared data.  Continue reading “Difference between Object-oriented and functional programming”

Which are the best data analytics courses that offer placements?

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Data analytics is a critical aspect of the modern business landscape. The ability to process, analyze and extract meaningful insights from large amounts of data is essential to drive business growth and profitability. 

Data analysts should not be confused with data scientists. Although data scientists and analysts both use data, the things they do with it vary. Business executives are assisted in decision-making by data analysts who use data to answer predetermined questions.

According to the US Bureau of Labor Statistics, analysts will have 23% more job opportunities between 2021 and 2031, a rate much higher than the country’s anticipated average employment growth of 5%.

become a Data Analyst

As a result, the demand for skilled data analytics professionals has grown in recent years. If you want to build your data analytics career, selecting the top data analytics courses with job placement is essential. 

What is Data Analytics and Its Importance?

Data analytics works to examine raw data to extract valuable insights. It involves using computational and statistical techniques to identify data patterns and trends. Data analytics is critical to businesses to make informed decisions and optimize processes.

  • Data analytics aids in the discovery of trends, correlations, and patterns that may point to the fundamental causes of your performance.
  • Data analytics is more than simple math and graphs. It involves comprehending your target market, foreseeing their wants, and designing your goods and services to satisfy those demands.
  • Data analytics is more crucial than ever in the data-driven world of today. Businesses may improve their bottom line and gain a competitive edge by using data analytics by making data-driven choices.
  • Businesses may streamline processes, save expenses, and boost revenue by adopting data analytics. They can spot new chances for development and innovation.

The Growing Demand for Data Analytics Professionals

Data analytics courses with job interview guarantees are in greater demand each year than there are qualified candidates to occupy those positions.

According to estimates, there are currently 97,000 open positions in data analytics in India alone per year. The need for data analytics has resulted in a significant 45% growth in jobs in the Indian industry. 

Due to the steep learning curve and high skill set requirements in the area, there is a growing gap between the need and supply of these individuals. The education and training required for these jobs have begun to receive more attention and momentum in India.

How to choose the right data analytics course for job opportunities?

If you’re looking for data analytics and data visualization courses, there are some essential things to consider before deciding. 

Here are some essential factors that you should keep in mind:

  • Check if the institution is accredited and has a good reputation. This ensures that the course meets the required standards and is recognized by employers. 
  • Look at the curriculum and course content. Ensure the course covers fundamental statistical analysis, visualization, and data mining. 
  • Consider the quality of the faculty and teaching staff of the quantitative analysis courses. Look for experienced teachers with the necessary qualifications and experience in data analytics. 
  • Look for courses that have connections and partnerships with the industry. This will provide you with real-world experience and networking opportunities.

Considering these factors, you can find a data analytics course that’s right for you and can help you achieve your career goals.

Launch Your Data Science Career with Imarticus Learning – Become an Expert in Analytics Today!

Are you aiming to become a pro in analytics and data science? Look no further than the comprehensive course from Imarticus Learning! 

With this curriculum, which guarantees job interview opportunities, you’ll learn how to create analytical models to improve corporate results. This curriculum is ideal for anybody wishing to launch their career in data science, whether you’re a seasoned professional or just getting started.

As you progress through this program, you will get practical expertise with Python, SQL, data analytics, Power BI, and Tableau while learning about the real-world applications of data science and analytics. With these abilities, you’ll be well-equipped to work in the developing field of data science and analytics.

Also, this curriculum includes a job interview guarantee that allows you to get hired by the 500 top-tier partner companies actively seeking machine learning and artificial intelligence specialists. 

Conclusion

The expansion of data analytics and data science occupations is driven by the increasing digitization of Indian businesses and the expanding footprints of global firms. The demand for experts who can gather, analyze, and visualize enormous volumes of data is always increasing rapidly.

Therefore, data analytics is a critical aspect of the modern business landscape, and the demand for skilled professionals will only increase in the coming years. The correct data analytics course is essential to getting the necessary training and skills to succeed. 

Join the Imarticus Learning course today for this fantastic chance to launch your data science career!

Does Learning Data Visualisation With Tableau Shape Your Career as a Data Scientist?

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Does Learning Data Visualisation With Tableau Shape Your Career as a Data Scientist?

In today’s world, data analysis professions are highly in-demand. Every organisation now requires data scientists. There are all sorts of complex data which need to be tackled by efficient professionals. Data scientists use tools such as Excel, Tableau, SAS, BigML, Apache Spark, Google Charts, etc. These tools help make the data more understandable and usable, making the information valuable. Massive amounts of information can become chaotic very quickly, which is why these tools help in organising them. 

Data scientists use data visualisation with these tools to efficiently tackle vast amounts of information. A data scientist career requires them to know how to use essential data tools, which adds more value to their existing skills. There are ample sources of data science courses for online mode of studies, such as the IIT Roorkee data science and machine learning course, which helps acquire data science and machine learning skills.

Advantages of Using Tableau in Data Visualisation

It Creates a Neat Visual Representation

One of the most vital reasons why data scientists choose to use the tableau tool is because it categorises all the data comprehensively. Since the quantity of information is huge, an organised data dashboard helps tackle the data efficiently. Tableau also helps in calculating complex problems and data blending. It makes the data look manageable instead of working on it in a pile of chaotic spreadsheets.

It Helps in Data Designing

 

Tableau includes many helpful features. One of its most liked features by data scientists is the ‘drag-and-drop’ feature. Data scientists can add or remove any information using this feature while designing the data’s outlook. There are no limitations to the changing data designing format in this tool. It provides the data scientist complete freedom and creativity. On the other hand, if the created data design has certain professional flaws, then the tool helps and guides the data scientist to make those changes. You can be certain that the outcome will be of good quality nonetheless.

Easy to Learn

Unlike other tools, Tableau is very simple to learn and implement and It does not come with any complex instructions. People who don’t have much knowledge of coding and other computer skills can also learn to use Tableau. 

It Accommodates a Large Amount of Data

It is hard to expect a data tool to offer features of well-structured designs and good storage to carry out other data management functions. Tableau provides them with both. Millions of rows and columns of data can be operated in Tableau without impacting the designs on the dashboard. It also offers another unique feature which is called ‘live’. This feature allows data to be shared and managed using different sources simultaneously.

It Uses Scripting Languages

As mentioned earlier, Tableau can use other sources to manage data. It also helps it to use the function of the merged tool. Data scientists can use Python’s scripting language in Tableau. In a data scientist career, they are taught about various programming languages. As the use of a scripting language helps solve complex mathematical problems, it adds more value to the existing features of Tableau. It improves the overall performance of this tool and makes sure that there is no data overload at any point. 

It is Supported by Mobile Phones

One of the most convenient features of using Tableau in data visualisation is that it can be accessed through mobile phones. This feature adds another layer of promise to its problem-solving, as any data can be changed at any time by the data scientist using their phones. Tableau has created certain features only for mobile phones so that there is no compromise in the tool’s performance. Mobile phones cannot handle all functions of a desktop device or a laptop. Therefore, this feature makes it very accessible and efficient.

Security and Updates

Tableau provides end-to-end encryption to its users. Data scientists can be assured that their changes will stay safe and secure and the servers will run smoothly. Each data is tackled single-handedly, ensuring it will be kept safe. Another essential feature of Tableau is its ‘auto-update’ and ‘auto-save’ feature. Human beings are prone to make mistakes and they can erase a bulk of data by mistake or forget to save important data, which can cause a huge problem. To prevent careless errors that can disrupt the smooth functioning of data management, Tableau auto-saves the changes made to keep it safe and secure. Its auto-update feature allows it to make overall modifications that help with the performance of the data management. 

Conclusion

It can be safely said that using Tableau allows data scientists to manage data visualisations efficiently. This tool can enhance the skills of the data scientist, which in turn improves their career prospects in data science. Students should prepare to take data science courses such as the IIT Roorkee data science online course, which helps in up-skilling and opens up more job opportunities for data scientists.

Top Data Mining Algorithms Data Scientists Must Know in 2023

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Data mining is an essential part of data analytics and one of the primary areas in data science. Various data analytic methods are employed to spot repetitions and glean information from large data sets. Businesses can identify future patterns and make more informed decisions using data mining techniques and tools.

Data Science Course

As a data scientist, it is necessary to learn data mining in detail to comprehend unstructured raw data. This blog will discuss the top five data mining algorithms data scientists must know in 2023. 

What is data mining? 

Businesses use data mining to transform unstructured data into helpful information. Businesses can discover more about their customers to create better advertising techniques, boost sales, and cut costs by using software to search for patterns in large batches of data. 

Effective data gathering, warehousing, and computer processing are prerequisites for data mining.

  • Businesses can use data mining for various purposes, such as determining what products or services their consumers want.
  • Based on the information customers provide or request, data mining programmes analyse patterns and relationships in data.
  • Apart from spotting repetitive patterns, data mining is also used to observe anomalies to detect fraud and scams in the financial sector. SaaS companies use data mining to filter out fake accounts from their database.

Why is data mining important?

Successful analytics efforts in organisations depend on data mining. The data it produces can be used in real-time analytics applications that look at live streaming data, business intelligence (BI) and advanced analytics applications that analyse historical data.

Planning effective business strategies and managing operations are just a few ways data mining can help. In addition to manufacturing, supply chain management, finance, and human resources, this also involves front-office activities like marketing, advertising, sales, and customer support. 

Multiple other crucial business use cases, such as fraud identification, risk management, and cybersecurity planning, are supported by data mining. Management, scientific and mathematical research, and sports are other fields that use data mining extensively.

Top data mining algorithms that data scientists must know

Below are some of the best data mining algorithms important in 2023. 

C4.5 Algorithm

Developed by Ross Quinlan, C4.5 produces a decision tree-based classifier from previously classified data. A classifier is a tool for data mining that uses previously classified data to identify the class of incoming new data.

There will be a unique collection of attributes for each data point. The decision tree in C4.5 categorises new data based on the responses to questions on attribute values.

Since the training dataset is labelled using lasses, C4.5 is a monitored learning method. Compared to other data mining algorithms, C4.5 is fast and well-liked because decision trees are always easy to understand and analyse.

Apriori Algorithm

One form of data mining technique is to use association rules to find correlations between variables in a database. The Apriori algorithm learns association rules and is then used on a database with many transactions. 

The Apriori algorithm is categorised as an unsupervised learning method because it can find intriguing patterns and reciprocal connections. Although the method is very effective, it uses a lot of memory, takes up a lot of disk space, and is time-consuming.

K-means Algorithm

K-means, one of the most popular clustering algorithms, operates by forming k groups from a collection of objects depending upon their degree of similarity. Although group members won’t necessarily be alike, they will be more comparable than non-members. 

Standard variations state that it is a non-monitored learning algorithm because k-means understands the cluster without outside input.

Expectation-Maximisation Algorithm

Like the k-means algorithm for information discovery, Expectation-Maximisation (EM) is employed as a clustering algorithm. The EM algorithm iterates to increase the likelihood of seeing recorded data. 

Then it uses unobserved variables to determine the parameters of the statistical model, producing some observed data. The Expectation-Maximisation (EM) algorithm, another example of unsupervised learning, uses unlabelled class knowledge.

kNN Algorithm

A classification method that uses lazy learning is kNN, which only saves the training data during the training procedure. Lazy learners start categorising when new, anonymous data are presented as input.

On the other hand, C4.5, SVN, and Adaboost are fast learners and start developing a categorisation model during training. kNN is regarded as an algorithm for supervised learning because it is given a labelled training dataset.

Conclusion 

Learning about the various data science algorithms is essential for a data scientist. Check out Imarticus’ Certified Data Science and Machine Learning course to learn more about data mining. 

This IIT data science course has been created with iHub Divya Sampark to help you learn data science from scratch. In this course, esteemed IIT faculty members teach machine learning with Python and ways to use data-driven insights in a business setting.