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Category: Management

Statistics in Analytics: Essential Statistical Techniques

Posted on by Imarticus Learning

In today’s data-driven world, businesses are increasingly relying on analytics to gain insights and make informed decisions. One of the fundamental pillars of analytics is statistics, which involves using mathematical methods to collect, analyse, and interpret data. From predictive modelling to hypothesis testing, statistics play a crucial role in uncovering meaningful patterns and trends in data.

In addition to mathematical techniques, data visualisation is also a key component of statistical analysis, as it allows us to present complex data in a way that is easy to understand and visually appealing.

Let us explore essential statistical techniques that are commonly used in analytics and how they can enhance our understanding and interpretation of statistical results.

Essential Statistical Techniques Used in Analysis

There are a plethora of statistical techniques you can employ in order to get valuable insights from the data you possess. Down below are listed a few of them with real-world examples for each:

Descriptive Statistics

Descriptive statistics as defined by the word itself ‘describes’ the essential features that one can gather from a dataset. Let’s take a small example to understand it better.

Imagine a hypothetical scenario where a business is tasked with analysing the sales data for a product over the past year. The dataset they have at their disposal includes a wide range of variables such as the number of units sold each month, the average price per unit, and the total revenue generated.

To gain a more granular understanding of the data, the business could employ descriptive statistics techniques. This would allow them to summarise and describe key features of the dataset in a more intuitive manner.

The main techniques employed in descriptive statistics:

  • Mean
  • Median 
  • Mode
  • Variance
  • Standard Deviation

Inferential Statistics

Inferential statistical techniques are employed to ‘infer’ the differences amongst groups of data and then make an assumption about the vast population pertaining to the insights gained from the inference.

Let’s say that you own a healthcare company and want to determine whether a new medication is effective in reducing blood pressure. You conduct a randomised controlled trial where you randomly assign patients to receive either the new medication or a placebo. After the trial, you collect data on the blood pressure readings for both groups. 

To draw insightful inferences about the effectiveness of the new medication, you could leverage inferential statistics techniques to analyse the data. By calculating the difference in the mean blood pressure readings between the two groups, you can gain a deeper understanding of the impact of the medication on blood pressure levels using data visualisation.

Following the inference, you could employ a hypothesis test to determine if the difference in blood pressure readings between the two groups is statistically significant. This would allow you to draw strong conclusions about the effectiveness of the new medication in reducing blood pressure.

The main techniques employed in inferential statistics:

  • Hypothesis Testing
  • Confidence Intervals

Correlation Analysis

Correlation Analysis is a statistical technique used to determine whether or not there is a link between two variables/datasets and the strength of that relationship.

Let’s say a company wants to investigate the relationship between advertising spend and sales revenue. They have collected an extensive dataset that contains information on the amount of money spent on advertising and the corresponding sales revenue for each month over the past year.

To unravel the intricacies of the relationship between advertising spend and sales revenue, the company could use correlation analysis techniques. This would involve calculating the correlation coefficient, which is a numerical measure that reveals the strength and direction of the linear relationship between two variables.

In the case mentioned above, the variables would be advertising spend and sales revenue. The most widely used correlation coefficients are ‘Spearman’s Rank Correlation Coefficient’ and the ‘Pearson Product-Moment Coefficient’.

If the correlation coefficient is positively skewed, it would indicate a strong positive relationship between advertising spend and sales revenue, signifying that as advertising spending increases, sales revenue also tends to increase. Conversely, if the correlation coefficient is negatively skewed, it would indicate a negative relationship, implying that as advertising spending increases, sales revenue tends to decrease.

Regression Analysis

Picture a world where you’re looking to understand the relationship between two or more variables. In walks regression analysis, a statistical method that helps you do just that. This technique is heavily utilised in an array of fields, including economics, finance, marketing, and social sciences.

It aims to pinpoint a mathematical equation that can predict the value of one variable based on the values of other variables. The variable being predicted is known as the dependent variable, while the variables that are used to predict it are known as independent variables or predictors.

Let’s say a car manufacturer wants to predict the fuel efficiency of its vehicles based on various factors such as engine size, weight, and transmission type. To achieve this, they conduct a regression analysis to identify the most significant predictors of fuel efficiency.

The manufacturer compiles data on fuel efficiency, engine size, weight, and transmission type for each of their car models. They then utilise regression analysis to construct a mathematical equation that optimally foresees fuel efficiency grounded on these variables.

Upon scrutinising the data, the regression model reveals that engine size and weight wield significant influence on fuel efficiency, whereas transmission type has no substantial impact on fuel efficiency. The car manufacturer can exploit this knowledge to reconfigure its production methods and make adjustments to the design of their cars to fine-tune fuel efficiency.

The dependent variable in the scenario above is the fuel efficiency of the vehicles. This is the variable that the manufacturer is trying to predict based on the values of the independent variables.

The independent variables are the engine size, weight, and transmission type of the vehicles. These are the variables that are used to predict the fuel efficiency of cars.

The most widely used techniques in regression analysis are:

  • Linear Regression
  • Logistic Regression
    NumPy, a popular Python library, is used for regression because it provides a fast and efficient array operation, mathematical functions, linear algebra operations, and interoperability with other libraries. These features of NumPy make it an ideal tool for handling the computations involved in different types of machine learning including regression modelling.

Conclusion

As you can understand from the aforementioned, statistical techniques play a crucial role in insights for businesses to function optimally. Though we discussed a few of the plethora of techniques, it is important to remember that these techniques are used in most types of machine learning.

If you’re interested to know more about techniques such as cluster analysis, time series analysis, and many more, then you should check out the Postgraduate Programme in Data Science and Analytics offered by Imarticus Learning. With expert instructors, hands-on projects, and a industry-relevant curriculum, this programme can help you launch your career in the dynamic field of data science. Don’t wait, click now to learn more and enrol today!

What are the 4 types of machine learning with examples

Posted on by Imarticus Learning

Welcome to the world of machine learning! It’s no secret that machines are taking over more human life as technology develops. Machine learning has become the most crucial area in computer science with the development of artificial intelligence. Since it is engaging, several experts and computer enthusiasts are interested in this topic.

The practice of teaching machines to recognize patterns in data and take actions without explicit programming is known as machine learning. In other words, a computer system can use data to enhance performance on a particular job over time.

According to IDC, the market for AI software in India would increase at a CAGR of 18.1% from USD 2,767.5 million in 2020 to USD 6,358.8 million in 2025. 

Machine learning is therefore being embraced quickly due to its enormous potential to impact businesses all over India. With thousands of new opportunities being created daily, there is a tremendous demand for workers who can learn data science. We’ll discuss the four types of machine learning in this blog and give examples of each.

What is Machine Learning?

Artificial intelligence, known as “machine learning,” enables computer systems to learn from their past performance and advance. In other words, machine learning algorithms are trained to learn and develop independently instead of being programmed to do a certain task.

Types of Machine Learning

Supervised, unsupervised, semi-supervised, and reinforcement learning are the four primary categories of machine learning.

Let’s examine each category in more detail and give instances of their use.

  • Supervised learning

The most popular kind of machine learning is supervised learning. The method is trained on a labeled dataset in supervised learning. Each dataset’s data point includes a label indicating the desired result. The algorithm learns how to map inputs to outputs based on the labeled samples given during training. 

Supervised learning examples:

  • Image classification: The algorithm guesses the item in an image given only the picture. This is often applied in projects like medical image analysis, self-driving automobiles, and facial recognition.
  • Spam detection: It uses an algorithm to determine whether a given email is spam. Email filtering systems frequently employ something like this.
  • Predictive Maintainance: Using information about a machine, an algorithm may forecast when the machine is most likely to break down. In manufacturing and industrial applications, this is frequently utilized.

  • Unsupervised Learning

The method is taught on an unlabeled dataset in unsupervised learning. This indicates that the data lacks labels or categories. The algorithm learns to detect patterns or structures without understanding what the data represents.

Unsupervised learning examples include:

  • Clustering: When given data points, the algorithm clusters them according to similarity. Market segmentation, social network analysis, and picture segmentation frequently employ this.
  • Automatic detection: The method discovers the data points that are noticeably distinct from the rest of the data when given a batch of data points. This is frequently applied to medical diagnostics, network intrusion, and fraud detection.

  • Semi-Supervised Learning

The system is trained on a dataset that includes labeled and unlabeled data in semi-supervised learning. The algorithm learns to predict outcomes for the unlabeled cases using the labeled examples.

Semi-supervised learning examples include:

  • Language Translation: The system can translate new, unheard sentences given a small set of identified utterances. Applications for machine translation frequently use this.
  • Sentiment Analysis: The program can forecast the sentiment of new, upcoming reviews given a small number of tagged reviews. This is frequently used in consumer feedback analysis and social media monitoring.

  • Reinforcement Learning

A kind of machine learning called reinforcement learning teaches an algorithm to make decisions depending on input from its surroundings. The algorithm is taught to maximize a reward signal by choosing behaviors that produce the greatest reward.

Reinforcement Machine Learning examples: 

  • Video games: Real-time learning algorithms are widely used in game applications. It is utilized to perform at a superhuman level. The video games AlphaGO and AlphaGO Zero are examples of well-known RL algorithms.
  • Resource Management: To reduce average job slowness, the “Resource Management with Deep Reinforcement Learning” study demonstrated how to automatically utilize RL in computers to train and arrange resources to wait for various workloads.
  • Robotics: Several applications of RL are found in robotics. Robots are deployed in the industrial and manufacturing sectors, and reinforcement learning increases their power. 

Ending Note

Powerful technology like machine learning can completely change a variety of sectors. Understanding the many forms of machine learning is essential for companies and people wishing to use new technology. 

Each of the four categories of machine learning—supervised, unsupervised, semi-supervised, and reinforcement learning—has distinct properties and uses. Understanding the advantages and disadvantages of each form of machine learning will help you select the one that will work best for your needs and produce the best results. 

To develop the Certificate Program in Data Science and Machine Learning, Imarticus Learning collaborated with iHUB DivyaSampark @IIT Roorkee. Students interested in learning data science and machine learning course should start with this program.

The Challenges of Pursuing a Global Senior Leadership Programme

Posted on by Imarticus Learning

You could be experiencing excitement, anticipation, and even fear as you consider starting your journey. After all, this is not a choice that should be made hastily. A worldwide senior leadership program needs a major time and financial commitment, as well as a great deal of effort and attention.

But here’s the thing: the benefits are outstanding. You’ll not only develop your leadership abilities and increase your understanding of international business, but you will get the chance to network with people worldwide who share your interests. You may become a member of a community of passionate, ambitious people dedicated to changing the world by enrolling in a global senior leadership program.

So, congrats if you’re willing to commit and pursue a global senior leadership program! You are starting a journey that will push your boundaries, broaden your perspectives, and change you in ways you never imagined. As you face the difficulties of enrolling in a Global Senior Leadership program, we hope this blog will be a helpful resource and a source of inspiration. 

Let’s get started!

Why is a global senior leadership program important?

global senior management programme

People are becoming aware of the need for global leadership training. Organizational skills and leadership qualities are required in today’s dynamic, ever-changing business environment, which differs from the past. Senior staff members are now expected to make important decisions that support business strategy and culture. The performance of business executives is crucial.

How leaders negotiate and maneuver in the workplace has changed due to this change in the current corporate environment. The business landscape has altered due to various variables, including the rapidly expanding technical landscape, unusual corporate dynamics, and the ability to cross sociocultural divides and forge deep connections. 

What are the challenges of pursuing a global senior leadership program?

Overcoming the Challenges in Global Senior Leadership Programme

Challenge #1: Balancing work, study, and personal life

Finding the time and energy to manage your profession, studies, and personal life is one of the main obstacles to enrolling in a worldwide senior leadership program. You must balance several deadlines and obligations while still doing top-notch work in your profession and preserving your connections with family and friends.

How to deal with it: Prioritizing and planning are the keys to dealing with this difficulty. Create a realistic timetable that allows time for your work, study, and personal life, and try your best to keep to it. Learn to say no to requests and unwanted interruptions that might jeopardize your objectives. Delegate work that can be completed by others and seek assistance when necessary.

Challenge #2: Adapting to different cultures and perspectives

Another hurdle is adapting to other cultures and viewpoints while completing an international leadership development program. Interacting with people from other backgrounds and experiences who may hold different values, views, and perspectives than your own will be necessary. 

How to overcome it: Being courteous and open-minded is the secret to conquering this difficulty. Never criticize or evaluate someone based on preconceived notions or biases. Instead, try comprehending their perspective and what they are attempting to convey. To understand their culture and viewpoint more, be inquisitive and ask questions. Recognize and value the diversity and depth they bring, and use it to your advantage.

Challenge #3: Managing Stress and Uncertainty

Managing Stress and uncertainty is a third difficulty of pursuing a global senior leadership program. You will encounter novel, challenging circumstances that might test your knowledge and abilities. You’ll also have to deal with adjustments and disturbances that might interfere with your plans and objectives. At times, you can feel overburdened, impatient, or nervous.

How to deal with it: Resilience and adaptability are the keys to dealing with this difficulty. Don’t let anxiety or tension hold you hostage or prevent you from attempting new things. Instead, welcome them as chances to improve and learn. Use healthy coping techniques like breathing exercises, meditation, or talking to a trusted friend. Consult with your peers, mentors, or coaches for advice and encouragement. 

What are the latest global business trends in senior leadership training?

  • Use technology, such as online learning platforms, to offer training information.
  •  Focusing on high-potential talent development is another trend, particularly in the context of the hybrid work patterns that have evolved due to the COVID-19 epidemic. 
  • Additionally, there is a shift toward flatter, non-hierarchical organizational structures, project-based working, and a growing emphasis on creating agile leaders who can act quickly in times of change or crisis. 
  • Finally, arguments about justice and equity are at the center of many debates about the future of work, and they are rapidly emerging as defining challenges for organizations. 

The Final Words

It isn’t easy to pursue a worldwide senior leadership program. It calls for commitment, diligence, and readiness to stretch yourself. But the benefits are enormous. You’ll not only increase your network and acquire useful leadership skills, but you’ll also get the chance to benefit the world.

Remember that the difficulties you encounter along the path are simply chances for you to develop and learn. Accept them and continue moving forward. You can do everything you want if you have endurance and dedication. 

In collaboration with IIM Lucknow, Imarticus Learning provides a game-changing Global Senior Leadership Programme to advance the careers of ambitious corporate executives to the coveted C-Suite level. 

The curriculum meets the particular requirements of aspiring CFOs, CMOs, and CHROs by giving them the crucial abilities and information needed to steer their firms toward success. The program is the ideal chance for current CFOs, CMOs, and CHROs to refresh their skill sets and strengthen their leadership capacities to succeed in difficult circumstances in today’s fast-paced business environment. 

With the help of this curriculum, you may learn cutting-edge skills and achieve the self-assurance you need to lead your company to success. Join the Global Senior Leadership Programme to realize your full potential and advance your career.

Sales and Operations Planning in Supply Chains Management

Posted on by Imarticus Learning

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.

Valuation: Calculating the Value of a Business

Posted on by Imarticus Learning

When assessing a business’s value, cash flows and valuation techniques are important factors to consider.

Understanding a company’s financial health and market value is crucial in making informed decisions. But it’s not about the numbers – organisational behaviour management also plays a pivotal role in determining a business’s value.

senior leadership management program

A company’s internal culture, leadership, and employee behaviour can impact its performance.

In this article, we’ll learn about the relationship between cash flows, valuation, and how organisational behavior is essential for calculating the value of a business.

What Is a Business Valuation?

Business valuation also referred to as company valuation, involves the assessment of a business’s economic worth.

It includes a deep analysis of all aspects of the business, including its various departments or units, to determine its value.

A company valuation serves many purposes, such as establishing the fair value of a business for potential sale, determining ownership stakes among partners, assessing tax implications, and even in divorce proceedings.

Business owners often seek the expertise of professional business evaluators to get an unbiased and reliable estimate of their business’s value.

What Are the Advantages of Knowing Your Company’s Worth?

Knowing your company’s worth can offer several advantages, including

  • Accurate pricing: Understanding the true value of your business allows you to set a realistic and accurate asking price when selling, which can help attract serious buyers and increase the chances of a successful sale.
  • Negotiation power: Armed with the knowledge of your business’s value, you can negotiate from a position of strength during sale or acquisition negotiations, maximising your chances of getting a favourable deal.
  • Financial planning: Knowing the value of your business can assist with financial planning, including retirement planning, estate planning, and tax planning. It offers a clear understanding of the assets you own and their potential worth.

When Would a Business Valuation Be Needed?

Valuation is an important part of business management, involving the assessment of a company’s worth.

Many situations call for a business valuation, where understanding the link between cash flows, organisational behaviour management, and valuation techniques becomes critical.

Some of the situations where a valuation is necessary are:

Stakeholder changes: New shareholders, potential investors, or partners seeking a merger or acquisition will want to know the value of a business to make informed decisions.

Selling or merging a business: When looking to sell or merge a business, potential buyers or partners will need a thorough understanding of the business’s value to negotiate.

Pricing options for equity compensation: Young startups offering equity or stock options as part of compensation packages need a reliable business valuation to price these options.

Financing: Bankers, creditors, and potential investors will need to assess the intrinsic value of a business for loans, refinancing, or investment decisions.

Tax reporting: The government may need a business valuation for tax reporting purposes, such as changes in ownership, estate tax returns, or gifting a business.

Personal reasons: Business valuations are often necessary for divorce settlements or estate planning to divide or determine the value of marital assets acquired during the marriage.

Understanding the importance of valuation in these critical situations, and the impact of cash flows, organisational behaviour management, and valuation techniques, is crucial for making informed decisions in the world of business.

How To Determine a Business’s Value?

Calculating the value of a business involves using one or a combination of several valuation methods. These methods may include:

Examination of management and capital structure

Assessing the competency and effectiveness of the company’s management team and evaluating its capital structure, including debt and equity ratios, can provide insights into the business’s value.

Market value of assets

Evaluating the market value of the business’s assets, such as tangible assets like property, equipment, and inventory, as well as intangible assets like patents, trademarks, and customer relationships, can be a significant factor in determining its value.

Historical performance and predictions

Reviewing the company’s historical financial performance and forecasting future projections can help assess its growth potential and influence its value.

Industry trends and competition

Considering the current trends and competitive landscape of the business’s industry can provide insights into its market position, competitive advantage, and potential risks or opportunities that can impact its value.

Ownership/management strength

Evaluating the experience, skills, and track record of the business’s ownership and management team can be a critical factor in assessing the strength and potential of the company.

What Are the Various Methods of Business Valuation?

There are several different methods used for business valuation, including:

Market Capitalisation

The market capitalisation method is a straightforward approach to valuing a business. It involves multiplying the company’s share price by its outstanding shares.

Earnings Multiplier

It is a valuation method that adjusts the price-to-earnings (P/E) ratio for current interest rates to get a more accurate picture of a company’s value based on its profits as an indicator of financial success. 

It accounts for prevailing interest rates by adjusting future profits against cash flow that could be invested at the current interest rate over the same period of time.

Discounted Cash Flow (DCF) Method

The discounted cash flow (DCF) business valuation method is like the earnings multiplier. It is based on projecting future cash flows and adjusting them to determine the company’s current market value. 

But, unlike the earnings multiplier, the DCF method also considers inflation in its calculations to arrive at the present value.

Revenue/Earnings

The revenue business valuation approach involves evaluating a company’s stream of revenues over a specific period and applying a multiplier that varies depending on the industry or economic conditions. 

Similarly, the earnings multiplier method is used to make a more accurate prediction of a company’s future financial success. 

This method involves making adjustments to the price-to-earnings (P/E) ratio to account for prevailing interest rates.

Book Value

The book value is the value of a company’s shareholders’ equity, as shown on its balance sheet statement. It is calculated by subtracting the company’s total liabilities from its total assets.

Liquidation Value

The liquidation value is the net cash a business would receive if its assets were liquidated and all liabilities were paid off immediately. It estimates the smallest value a business could generate in a liquidation scenario.

Conclusion

Valuing a business requires careful consideration of cash flows and valuation and effective management of organisational behaviour. Leaders who have strong leadership skills and strategies can impact a company’s performance and valuation.

IIM Lucknow’s Global Senior Leadership Programme course provides comprehensive training in organisational excellence, team performance, and business growth to develop these skills further.

Learn more at Imarticus Learning.

Demand Forecast and Planning

Posted on by Imarticus Learning

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?

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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?

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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

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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

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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. 

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