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Category: Analytics

Collaborating in Supply Chain Design: What are the Factors Involved?

Posted on by Imarticus Learning

Step into the world of supply chain design! It’s intricate. It’s dynamic. It’s powered by collaboration and integration. These are key factors. They shape types of supply chains. Are you curious? Do you want to understand why? Let’s embark on this journey. Let’s unlock the secrets of supply chain design.

Collaboration: A Driving Force in Supply Chain Design

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Collaboration is a force. It fuels the supply chain. It unites stakeholders. It connects suppliers, manufacturers, and customers. Collaboration enhances efficiency. It improves performance. It ensures smooth operations. The result? A more effective supply chain.

But how does it work? It’s about sharing. It’s about communication. It’s about common goals. When stakeholders work together, they can overcome hurdles. They can solve problems. They can meet customer needs better.

This is the power of collaboration. Collaboration fosters trust. It builds strong relationships. It enables the sharing of resources, knowledge, and ideas. This creates opportunities for innovation. It drives continuous improvement. A collaborative supply chain is a resilient supply chain. It can weather challenges. It can adapt to change.

Integration: Linking the Pieces of the Puzzle

Integration is crucial. It’s another key factor in supply chain design. It links the parts. It connects processes. It unites systems. With integration, data flows smoothly. Information is accessible. Decision-making improves.

Consider the logistics process. It involves many steps. There’s procurement. There’s warehousing. There’s transportation. The integration connects these steps. It streamlines operations. It reduces errors. It increases efficiency. Integration means leveraging technology. It means using tools like ERP ( Enterprise resource planning ) systems, IoT (The Internet of Things) devices, and cloud computing. These tools enable real-time data sharing. They improve visibility. They help to monitor and manage the supply chain. Integration fosters agility. It empowers businesses to respond to changes quickly. It ensures a competitive edge in the market.

Types of Supply Chain: Choosing the Right Design

There are many types of supply chains. Each has its benefits. Each has its challenges. The right choice depends on many factors. It depends on the business. It depends on the industry. It depends on the market.

A few examples can be – 

There’s the lean supply chain. It focuses on efficiency. It aims to reduce waste.

Then there’s the agile supply chain. It values flexibility. It responds quickly to market changes.

Understanding these types is crucial. It’s a key step in successful supply chain design.

Factors of Supply Chain: Influencing Design and Performance

Many factors influence supply chain design. They shape its performance. They include demand patterns. They include product characteristics. They include business strategy. Understanding these factors is important. It helps to tailor the supply chain. It helps to meet specific business needs.

Demand patterns can be stable or volatile. They can be predictable or uncertain. Product characteristics can vary. There are perishable goods. There are durable goods. There are high-value items. Business strategies can focus on cost leadership. They can focus on differentiation. They can focus on niche markets. Each factor plays a role. Each factor influences supply chain design. By considering these factors, businesses can create a supply chain aligned with their goals. This alignment ensures success. It ensures a competitive advantage.

The key factors that influence supply chain management include:

Demand Patterns: The fluctuations in consumer demand greatly affect supply chain operations. Stable demand allows for more accurate forecasting and efficient planning, while unpredictable demand can pose challenges.

Product Lifecycle: The stage of the product in its lifecycle (introduction, growth, maturity, or decline) can impact supply chain strategies.

Product Characteristics: The nature of the product, such as its perishability, fragility, weight, or value, will influence logistics and transportation decisions.

Supplier Reliability: The dependability of suppliers plays a key role in maintaining steady product availability and managing production timelines.

Technology: The use of technology, like AI, IoT, and blockchain, can significantly enhance supply chain operations, including inventory management, demand forecasting, and logistics.

Regulations and Compliance: International trade laws, environmental regulations, and safety standards can affect various aspects of the supply chain, from sourcing to delivery.

Economic Conditions: Global and local economic trends can impact supply and demand, pricing, and overall supply chain strategy.

Geographical Scope: The physical distance between suppliers, manufacturers, and customers can impact transportation costs and timelines.

Business Strategy: The overall business strategy (cost leadership or differentiation) will influence supply chain decisions.

Risk Management: The ability to identify and mitigate potential disruptions in the supply chain is a crucial factor in maintaining operational efficiency.

Balancing the Supply Chain Design Act

Supply chain design is a balancing act. It needs collaboration. It needs integration. It needs the right type of supply chain. It needs an understanding of key factors. Together, these elements form a powerful supply chain. They enhance performance. They drive business success.

Ready to master the art of supply chain design? Take the first step with the IIT Guwahati Advanced Certification in Digital Supply Chain Management. This course will equip you with the knowledge and skills to design effective supply chains. Visit Imarticus today. Your journey to becoming a supply chain expert starts here!

Ensemble Methods: Combining Multiple Models for Improved Performance

Posted on by Imarticus Learning

Machine Learning models developed for more accurate prediction are trained with a variety of different methods. Some of these essential methods are the ensemble methods that can assist in gaining a more accurate result.

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In brief, ensemble methods combine the predictions of several methods to form a more accurate result. And anyone who is seeking a career in data analytics should care about it more as it will direct them toward creating models that are more precise. 

What Are Ensemble Methods?

Ensemble methods simply combine several individually trained models through machine learning and statistical techniques with the objective of giving out the most precise result possible. Thus, not only the final result is accurate but also improves the robustness of predictions. 

By applying this method, one can even reduce the risk of overfitting while increasing the stability of predictions. All of this falls into place by aggregating the output of multiple results. Thus, solving the most complicated machine learning problems, like regression and classification, in no time. 

In particular fields like finance, healthcare, and autonomous systems where accuracy and reliability are important, the application of ensemble methods can do wonders.

Benefits of Ensemble Methods

  • Compared to the other individual models, ensemble methods have increased predictive accuracy.
  • Given its precision, the result of ensemble methods is less prone to any errors.
  •  It also helps in overcoming the limitations of individual models by combining the strengths of multiple models to achieve better results.
  • The ensemble methods perfectly manage both linear and non-linear types of data in the datasheet.
  • Bias/Variance can be reduced when using the ensemble method to produce results.  
  • Both the process and the end result after the ensemble of models are less noisy and more stable in nature.
  • Given the use of ensemble methods, it can be applied to various machine learning tasks, such as classification, anomaly detection, and regression.

Ensemble Method Groups

Ensemble learning methods are mostly categorised into two groups;

Sequential Ensemble Methods

As the name implies, in this ensemble method, the base learners are dependent on the results obtained by previous base learners. Although, every subsequent base model corrects the results of its predecessor by fixing the errors in it. Thus, the end result leads to a more improved performance. 

Parallel Ensemble Methods

Contrary to the above one, there is no dependency on base learners in this method. Here, the results of all the models, executed parallelly are combined at the end to make an accurate prediction. 

There are two Parallel Ensemble Methods with different approaches to their base learner;

  • Homogeneous- A single machine learning algorithm is used
  • Heterogeneous- Multiple machine learning algorithms are used.

Types of Ensemble Methods in Machine Learning

In order to have a robust and reliable predictor, ensemble methods have a few advanced techniques to carry out the process. To learn about the process in depth, one can opt for a machine learning certification as well. 

Here are the three types of ensemble methods that are put to use:

  • Boosting

It is a sequential ensemble learning technique carried out on the most difficult-to-predict examples. In boosting method, models are iteratively trained so at the end, several weak base learners can also build a powerful ensemble. Here, the final prediction is based on a weighted average of the models. This method is used to decrease bias errors and also can avoid overfitting of data with parameter tuning.

Some boosting algorithms are AdaBoost, XGBoost, and LightGBM.

  • Bagging

Unlike boosting, in the bagging method, multiple models are trained on a randomly generated sample of the original datasheet. It then combines the predictions from all to aggregate them through averaging or voting. Bagging or Bootstrap Aggregation is a parallel ensemble learning technique to reduce the variance in the final prediction.

A few examples of it would be Random Forest and Bagged Decision Trees. 

  • Stacking

This method is also known as a stacked generalisation, referring to the ensemble technique that works by combining multiple machine learning algorithms through meta-learning. Here, the base models are trained on the entire datasheet. But the meta-models or level 1 models are trained on the predictions of base-level models. It helps to reduce bias or variance in base models.

Some libraries for Stacking are StackingClassifier and StackingRegressor.

  • Voting

This ensemble learning method creates multiple models of different types, which go through some simple statistics like calculation mean or median to combine the prediction. This result will then serve as additional input for training to make the final prediction. Similar to other ensemble methods, it is also implemented through Python programming and with the help of tools like Power BI, which makes the process of implementing the models much easier.

Conclusion

A single algorithm might disappoint one by its inaccurate prediction for a given data set. But if we build and combine multiple models, the chance of boosting the accuracy in overall performance increases. This is where ensemble methods are put into use to carry out precise results.  

As we understood from the above information, ensemble methods combine several predictions to churn out the most accurate and robust prediction. However, it is often not preferred in some industries where interpretability is more important. But that being said, no one can deny the effectiveness of these methods. Further, their benefits, if appropriately applied, are tremendous. 

Thus, to learn these ensemble methods, one must skill up in Python programming and using power BI. And all of these can be easily covered in a machine learning certification. 

For those who are looking to develop their skills and move ahead in their career in data analytics, Imarticus Learning offers the Postgraduate Programme in Data Science and Analytics. Here, you will get the expertise in working with the necessary tools with complete knowledge of the subject. 

Visit Imarticus Learning to learn more about data science and machine learning.

Unearthing the Data World: Top Resources to Learn Data Mining for Beginners

Posted on by Imarticus Learning

Imagine you’re an explorer, setting sail on the vast sea of data. Your compass? Data mining skills. Your treasure? Valuable insights are hidden within the data. Learning data mining isn’t just about acquiring a new skill, it’s like embarking on an exciting adventure. You’ll uncover hidden patterns and unravel the secrets buried deep within data.

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The first step in this journey is understanding what data mining is. Essentially, data mining is the process of extracting valuable information from large volumes of data. It’s like panning for gold in a river of data. It’s about finding those precious nuggets of information that can help businesses make informed decisions. But, don’t worry. You won’t be left stranded on this adventure alone. There are plenty of resources to help you learn data mining. Let’s explore some of them.

What Is Data Mining?

Data mining, as the name suggests, is the process of ‘mining’ insights from large amounts of data. From predicting customer buying behaviours to detecting fraud, data mining helps businesses make informed decisions and strategies.

Books to Kickstart Your Data Mining Journey

Many beginners start their journey to learn data mining with books. They offer in-depth knowledge, real-life examples, and they cover a wide range of topics. Here are a few that could be helpful:

“Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten and Eibe Frank

“Data Mining: Concepts and Techniques” by Jiawei Han and Micheline Kamber

“Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management” by Gordon S. Linoff and Michael J. A. Berry

Online Courses and Tutorials

In today’s digital world, online courses are a fantastic resource. They provide you with the flexibility to learn at your own pace and often include practical projects for hands-on experience.

Websites like Coursera and edX offer a multitude of courses, both free and paid, to help you learn data mining. YouTube also houses a plethora of tutorials from channels like ‘DataCamp’, ‘Sentdex’, and ‘Siraj Raval’.

Software Tools

To become proficient in data mining, you’ll need to familiarise yourself with data mining software tools. These tools, such as RapidMiner, WEKA, and Orange, are designed to aid in the extraction of data and allow you to apply various data mining techniques.

Communities and Forums

Joining data mining communities and forums is another great way to learn and stay updated. Websites like Kaggle and GitHub have active communities. Here, people share datasets, ask questions, and discuss data mining techniques.

Podcasts and Webinars

In the digital age, podcasts and webinars have emerged as popular means of learning. They provide bite-sized information that you can consume on the go. Podcasts like “Data Skeptic”, “DataFramed”, and “Linear Digressions” regularly discuss data mining topics and feature industry experts. Webinars, on the other hand, provide a more interactive learning experience with real-time Q&A sessions.

Academic and Professional Journals

If you’re interested in the theoretical and advanced aspects of data mining, consider reading academic and professional journals. Journals like “The Data Mining Journal” and “The Journal of Big Data” publish high-quality, peer-reviewed articles that discuss the latest advancements, techniques, and case studies in data mining.

Online Coding Platforms

Getting your hands dirty with coding is an integral part of learning data mining. Online coding platforms such as Codecademy, LeetCode, and HackerRank offer practice problems and projects related to data mining. These platforms help you apply theoretical knowledge and improve your coding skills.

Blogs and Articles

There are numerous blogs and articles available on the internet that provide a wealth of information on data mining. Blogs by data science and tech companies, like ‘Towards Data Science’, ‘KDNuggets’, and ‘Analytics Vidhya’, regularly publish articles on data mining techniques, applications, and industry trends.

Free Public Datasets for Practice

Practicing with real-world datasets is an excellent way to learn data mining. Websites like Kaggle, UCI Machine Learning Repository, and Google’s Dataset Search provide free public datasets that you can use to apply data mining techniques and build projects.

Exploring Different Data Mining Techniques

Finally, as you dive deeper into data mining, you’ll encounter various techniques used in the field. Some common ones include association, clustering, classification, prediction, and sequential patterns. Each technique has its own set of rules and methodologies. For example, the association is used to find relationships between items in a large dataset.

Similarly, clustering involves grouping related data points. By learning these techniques, you’ll have a wider range of tools to solve complex data-related problems.

Remember, learning data mining is a journey. It will require time, practice, and a lot of learning. But, the rewards you’ll reap in terms of knowledge and career opportunities make it all worthwhile. So, take the first step today and embark on your path to becoming a data mining expert!

Taking the First Step in Your Data Mining Journey

Starting to learn data mining might seem daunting at first. But with the right resources and determination, you’ll soon be uncovering valuable insights from datasets. Remember, the journey of a thousand miles begins with a single step.

Whether you’re taking your first step or looking to further your data mining skills, the Certificate Program in Data Science and Machine Learning by Imarticus Learning in collaboration with Divyasampark could be an excellent option for you. With comprehensive coverage of data mining and other related fields, you’ll learn from industry experts and gain hands-on experience. So why wait? Start your data mining journey today! Check out the program here.

Lean Manufacturing: Principles and Implementation

Posted on by Imarticus Learning

Lean Manufacturing aims to eliminate wastage and improves the efficiency of production processes across businesses. Focused on customer values and continuous improvement in this ever-changing world, Lean Manufacturing has proven to be an effective strategy for organisations to improve productivity, reduce costs and provide higher-quality products and services, thereby maintaining stable profits.

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By the end of this blog, you will have a complete understanding of Lean Manufacturing as a form of supply chain design and its potential to transform operations across industries. 

Principles of Lean Manufacturing

Some of the major  principles of this supply chain strategy are:

  1. Identifying customer activities: Understanding customer needs and separating value-adding activities from non-value-adding activities. 
  2. Value Stream Mapping: Visualise the production process, identify bottlenecks, and analyse value stream and cycle times. 
  3. Create Flow: Streamline processes, eliminate disruptions, and enable just-in-time production for continuous product flow. 
  4. Create a Pull: Respond to customer demands by implementing a pull-based manufacturing system that reduces inventory and waste. 
  5. Pursue perfection: Continuously improve processes, eliminate waste, involve employees in solving problems, and set high-quality standards.

Implementation of Lean Manufacturing 

Specific ways to implement this process are as follows: 

Management and management responsibility

  1. Top-level support and participation in Lean Manufacturing initiative management. 
  2. Set clear goals and objectives according to Lean principles.  
  3. Provide resources and training to support implementation. 

Value stream analysis and mapping 

  1. Identify value-added and non-value-added activities in the production process.  
  2. Create a current state value stream map to visualise the flow and identify waste. 
  3. Create a future state value stream map to define the desired lean process.

Continuous development initiatives: 

  1. Involve employees in problem-solving and process improvement. 
  2. Promote a culture of continuous learning and adaptation.

Standard operation and visual control

  1. Document and standardised work processes to ensure consistency.  
  2. Use optical inspection techniques to monitor and control production processes. 

Total employee participation

  1. Enable employees to provide ideas and suggestions for improvement.  
  2. Provide training and skill development to improve employee skills.
  3. Foster a culture of teamwork, collaboration, and continuous improvement.  

Benefits of Lean Manufacturing 

Implementation of Lean Manufacturing brings many benefits to organisations such as efficiency, cost reduction, improved quality, and increased customer satisfaction, as well as employee empowerment and competitive advantage in the market.

  1. Improved efficiency: Streamlined processes and optimised use of resources improve operational efficiency. 
  2. Cost reduction: Eliminating waste reduces costs associated with excess inventory, overproduction, and unnecessary operations.
  3.  Better quality: Focus on defect elimination and continuous improvement to improve the quality of products and services. 
  4. Greater flexibility and responsiveness: Lean practices allow organisations to respond to rapidly changing customer demands and market conditions. 
  5. Employee Engagement and Empowerment: Lean Manufacturing promotes a culture of employee participation that increases satisfaction and motivation. 
  6. Customer satisfaction: Lean practices bring added value to customers through efficient processes, shorter delivery times, and better quality.
  7.  Competitive advantage: Lean organisations gain a competitive advantage by delivering products faster, at lower costs, and with higher quality. 
  8. Sustainable practices: Lean Manufacturing promotes waste reduction, resource optimization, and environmental sustainability.

Challenges and Solutions in Lean Manufacturing Implementation

Certain problems that are faced during implementation and their solutions are as follows:

Resistance to change

  1. Overcome employee resistance through effective communication and training.
  2. Proactively address fears and concerns and highlight the benefits of Lean Manufacturing.  
  3. Celebrate success and recognise the contributions of employees to motivate and engage them. 

Lack of a culture of continuous improvement

  1. Create a supportive environment for experimentation and learning.
  2. Encourage feedback and create open communication channels.  
  3. Recognize and reward innovative and improvement efforts to promote continuous improvement.

Scale lean manufacturing across the organisation

  1. Start with small controlled pilot projects to demonstrate the benefits of Lean.  
  2. Gradually expand Lean practices to other departments and units. 
  3. Provide the necessary training and support for wider implementation.

Case studies of successful implementation of Lean Manufacturing 

Some of the examples of successful implementation of the lean manufacturing process are as follows:

Toyota: 

Toyota is a well-known example of a successful implementation of Lean Manufacturing. Toyota Production System with its introduction, Toyota revolutionised the automotive industry.  They focused on waste reduction, continuous improvement and employee empowerment.  Toyota’s Lean Manufacturing practice has led to significant improvements in efficiency, quality, and customer satisfaction.

General Electric (GE): 

GE has implemented Lean Manufacturing principles in its various business units.  They adopted Lean Six Sigma methods to improve work efficiency and reduce waste. GE achieved significant savings, shorter lead times, and better product quality by implementing Lean.

Wiremold: 

Wiremold, which produces electrical and telecommunications systems, has successfully implemented Lean Manufacturing. They applied Lean principles to their production processes, focusing on waste reduction and continuous improvement.  As a result, Wiremold has significantly reduced lead times, improved productivity, and improved customer satisfaction.

Nike: 

Nike, the world’s leading manufacturer of athletic shoes and apparel, has adopted Lean Manufacturing practices to improve efficiency.  They used Lean principles to optimise their supply chain, reduce inventory and improve responsiveness.  Nike’s Lean initiatives result in shorter lead times, faster product delivery, and better customer service.

Herman Miller: 

Furniture manufacturer Herman Miller adopted Lean Manufacturing to streamline manufacturing processes.  They implemented Lean technologies such as mobile manufacturing and pull manufacturing systems. As a result of Lean implementation, Herman Miller increased productivity, reduced lead time, and improved product quality.
These case studies highlight the successful implementation of Lean Manufacturing in various industries. They show how organisations have achieved significant gains in efficiency, cost savings, quality, and customer satisfaction using Lean principles and methods.

Conclusion

In summary, Lean Manufacturing is a transformational approach that optimises processes and creates value for customers. It improves efficiency and quality and lowers costs thereby providing a competitive advantage. Adopting Lean practices promotes continuous improvement and empowers employees for sustainable success in today’s business environment.

The Executive Certificate Program for Global Supply Chain and Operations Officer is a specialised program that focuses on strategic supply chain strategy formulation, operational optimization, and global business. Upon graduating from this operations and supply chain officer certificate course, participants will be equipped with the tools and strategies to achieve operational excellence and lead successful supply chains and teams around the world.

Integrating S&OP in Business Planning

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Effective planning and coordination across all parts of a company’s operations are critical in today’s continuously changing business world. Sales and operations planning, otherwise known as S&OP, has evolved as an effective structure for streamlining sales projections, production plans, and inventory management. Organisations may streamline processes, increase customer satisfaction, and generate long-term growth by incorporating S&OP into overall business strategy. 

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This article delves into the main concepts and benefits of incorporating S&OP into corporate planning and practical tips for building an efficient S&OP process.

What is S&OP?

Sales and operations planning (S&OP) is an integrated business management approach that enables firms to achieve focus, alignment, and synchronisation across all functions. It is a strategic planning process aligning demand, supply, and financial planning to support executive decision-making and approve material and financial plans that are viable and financially rewarding.

The primary purpose of S&OP is to balance customer demand and the organisation’s ability to supply that demand effectively. S&OP entails the integration of numerous data sources, such as customer relationship management (CRM), engineering, independent systems, and external databases. This integration improves the supply chain’s overall health and gives enterprises a competitive advantage. 

Benefits of integrating S&OP in business

Integrating sales and operations planning in a corporation can result in many significant advantages. Here are some of the benefits: 

  • Enhanced decision-making: Integrating S&OP delivers a complete and robust business image, allowing executives to make better-informed decisions. S&OP supports improved strategic decision-making that analyses the full impact on the business by synchronising demand, supply, and financial planning.
  • Improved alignment and coordination: S&OP ensures alignment and coordination among various business operations, encouraging improved coordination and collaboration. It integrates departments like sales, operations, finance, and supply chain, allowing them to work towards common goals and objectives. This collaboration breaks down barriers and improves cross-functional communication.
  • Optimal resource allocation: S&OP assists in optimising resource allocation by matching supply and demand. It allows companies to adapt production and inventory levels to customer demand, minimising overstocking or stockouts. Companies can reduce expenses, improve operational efficiency, and increase customer satisfaction by aligning resource allocation with expected sales.
  • Improved customer service: By integrating S&OP, firms can improve their customer service levels. Organisations can ensure timely delivery and satisfy customer expectations by aligning production plans and inventory management with customer demand. As a result, client satisfaction, loyalty, and retention improve.
  • Improved financial performance: By using S&OP, firms can improve their financial performance. Organisations can maximise revenue generation, reduce costs, and increase profitability by aligning sales projections, production plans, and financial targets.

Key steps to integrate S&OP in business planning

The key steps involved in incorporating sales and operations planning into broader business planning are:

  • Establishing a cross-functional S&OP team

Involving representatives from sales, operations, finance, and other departments in the S&OP process ensures diverse perspectives and expertise are considered, leading to more informed decision-making.

  • Defining clear objectives and performance metrics

Setting clear goals and metrics is crucial for a successful S&OP process. Measuring performance with metrics like sales revenue, customer service, inventory turnover, and forecast accuracy allows for continuous improvement and accountability.

  • Developing an integrated demand and supply planning process

Aligning sales, production, and inventory is crucial for optimising resources and customer service. Organisations can avoid stockouts, minimise bottlenecks, and enhance customer satisfaction by synchronising sales projections with production capacities and optimising inventory levels. Collaboration between sales and operations teams leads to improved efficiency and cost savings.

  • Enhancing data visibility and analytics

Advanced analytics and technology solutions enable firms to access and evaluate massive amounts of data in real time, facilitating scenario analysis and boosting decision-making. It provides greater insight into market trends, customer behaviour, and operational performance. Forecasting algorithms, predictive modelling, and data visualisation are examples of advanced analytics techniques that can help spot patterns, estimate demand more precisely, and simulate multiple scenarios to evaluate their impact on business outcomes.

  • Implementing a regular S&OP review cycle

Conducting regular S&OP reviews is crucial for evaluating performance, detecting gaps, and implementing required changes to the business plan. These reviews enable the S&OP team to learn valuable lessons, adjust forecasts based on market fluctuations, resolve bottlenecks or inefficiencies, and align plans with strategic objectives. Moreover, they provide an opportunity to communicate outcomes to stakeholders and ensure lasting commitment.

Conclusion

Organisations can improve operational efficiency, strategic alignment, and, ultimately, long-term success by adopting effective S&OP practices. Sales and operational planning assist in harmonising conflicting goals between different departments. A holistic S&OP process can unify the whole supply chain, from supplier to customer. Enrol in a supply chain course to upskill and learn more about S&OP.

The Professional Certification in Supply Chain Management and Analytics offered by Imarticus and IIT Roorkee is the perfect course for candidates wishing to succeed in the supply chain industry. Visit the website for more details.

Big Data Analytics in Supply Chain Optimisation

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Only 14 per cent of the supply chain management executives had realised the importance of big data in 2014. By 2022, the percentage had increased to 50 per cent and has been increasing ever since, thus proving the change that big data analytics has brought to the scene of supply chain management.

From closely monitoring quality in real-time to predicting and preventing risks, massive data sets have become the new solution to optimising supply chains.

Transformative Impact of Big Data on Supply Chain Management

For optimisation of production planning

While it can be quite daunting a task to create the most realistic production plan and schedule, Data Analytics can help tackle this challenge. Also known as logistics management, it helps clear up the scheduling constraints that manufacturing units often face due to a multiplicity of schedules. With the help of data analytics, the manufacturing facility can compare the prepared schedule and the real-time output to ensure that the plan most accurately represents the actual quantity that can or will be produced. 

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Using integrated data sourced from the supply chain, companies can perform both catalogue management and planning for restocking, as well as monitor delivery situations. Since the time lapse between the scheduled delivery and actual delivery, both early and delayed can be expensive, data signals can be used to track the delivery speed and location of goods and identify the best routes for delivery, staffing and so on. 

Analysis of consumer behaviour 

Behaviour analysis of customers has formed a significant part of data analytics due to the volatile market conditions and the imminent possibility of a recession. By using data analytics to understand customers’ preferences, organisations can adjust their schedules and production stocks. By predicting the demands, it is easier to predict the requirements of the customers and provide them with a unique experience.

Behavioural analytics can be applied to internal stakeholders and vendors as well. The executive officers who work in the domain of supply chain management can assess the behaviour and requirements of every participant in the business, and offer support while mending gaps wherever required. 

Maintenance of machines

Maintenance of big machines has been much easier with the aid of data analytics. Supply chain enterprises often have to encounter the risk of sudden equipment breakdown, malfunctioning due to rust, and other unexpected obstacles. The maintenance department can benefit from combining large data systems with the Internet of Things (IoT) to transmit alerts for any kind of irregularity detected in the equipment. For instance, they can use detectors to monitor production, find any abnormality, and inform the controller about the need for routine maintenance when the time comes. 

The detectors are installed to mirror the operations of the machine and the data retrieved in real-time can help predict machine failure, and thus augment the efficacy of the maintenance department. In the long run, this is a highly cost-saving move for it minimises expenditure on repairs and prevents unscheduled downtimes.

Management of supplier relationship

IndusSupplier Relationship Management can be greatly improved with the aid of Data Analytics. If the company can efficiently collect supplier data and analyse it with data analytic tools, then it can proactively monitor supplier behaviour and minimise obstacles.

For instance, decisions made on purchase orders are hugely influenced by supplier lead time, especially concerning timing and sizing. Considering that there can be fluctuations in supplier lead times, professionals can use big data analytics to accurately forecast lead times and avoid considerable variations. Qualitative data in the form of assessments and audits can be used by the companies to keep track of the suppliers’ activities for future purposes as well as to choose the right kind of suppliers. In case the primary supply chain is disrupted or violated, then the companies can easily pick an alternative reliable supplier for delivery and avoid losses.

Product design and quality control through predictive maintenance

Industries such as food processing, agriculture and chemicals often need to be constantly supervised and controlled, especially in specific elements. For instance, temperature control is a significant factor in ensuring the quality of the product, as even the slightest fluctuation in temperature can render the end product completely unusable.

One use of big data analytics is in cold chain monitoring technology, where data logging facilitates logistics for temperature-sensitive products. Furthermore, managers can control the heating and cooling equipment during packaging, transit, and delivery in real-time as needed.

 In supply chain management, product designing is also another important factor, and the designers can use data about the changing preferences of consumers to incorporate changes into product structure.

The Future of Predictive Big Data Analytics in Supply Chain Operations 

According to a survey by Gartner, among the supply chain leaders, 76 per cent have reported that they are increasingly facing supply chain obstacles. Hence, businesses are seeking out more applications of data analytics in predicting and preventing disruptions rather than remedying them. It is assumed that cross-functionality, or collating multiple supply chains to get a singular perspective and thus make quick strategic decisions in a fluctuating market, will be the biggest contribution of big data analytics in supply chain optimisation. Moreover, predictive data analytics will always be an integral part of the three main vital steps in supply chains: procuring goods, tracking inventories, and logistics management. 

Apart from the logistical strategies, big data analytics can also be impactful in endorsing sustainable practices in supply chain management. ESG (Environmental, Social, and Governance) issues, such as the eco-friendliness of the products, and exploitative labour practices can be addressed by the companies who retrieve data from their supply chain networks.

Conclusion

Managing supply chains in a digital mode has become increasingly in demand due to the cost-effectiveness and the greater amount of information to be retrieved. Aspirants seeking to make a career in this lucrative profession can hence train themselves in a Supply Chain Management Certification Course. 

One such programme is the Digital Supply Chain Management with E&ICT, offered by Imarticus Learning in partnership with IIT Guwahati. The classes will be held live virtually and will run for 6 months. The curriculum is focused specifically on industry orientation and students learn to use technology to get real-life experience in supply chain management. After completing this Supply Chain Management Certification Course, students are guaranteed to get placed in companies such as Nestle, Amazon, Microsoft or Paytm.

Supplier Performance Analytics and Inventory Optimization With Safety Stock Analysis

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Supplier performance analytics is a game-changing tool for optimising supply chain design. It provides valuable insights into supplier performance, enabling informed decision-making. This data-driven approach enhances collaboration, fosters innovation, and improves efficiency. 

Combining supplier performance analytics with safety stock enables companies to identify cost savings opportunities. This holistic perspective drives transformative change, streamlines processes, and maintains a competitive edge in today’s fast-paced market.

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If you’re considering upskilling yourself with a supply chain analytics course, then keep reading and embark on a learning journey that will enrich your understanding and expertise in the realm of supplier performance analytics before you make a sound choice for your career. 

Why is supply performance analytics important?

Efficient sales and operation planning and optimising supplier performance is a critical yet complex endeavour. It goes beyond merely focusing on price, as suppliers adhering to agreed-upon pricing might still fall short in terms of service quality or provide substandard goods. Achieving your savings targets requires a holistic approach.

Supplier performance management grants you comprehensive visibility into the risks associated with a supplier, empowering you to implement measures that mitigate or eliminate those risks within your supply chain design.

For companies aiming to maximise profits, timely delivery, price reductions, and service quality from suppliers are paramount. The effective management of supplier performance directly impacts the overall quality of the entire supply chain. 

Establishing an efficient mechanism to enhance supplier performance becomes essential, enabling accelerated improvement and ensuring the delivery of high-quality services and products. By prioritising supplier performance, you set the stage for a successful supply chain ecosystem.

Different types of supplier performance analytics 

Here are the four primary types of supplier performance analytics:

Operational Analytics: This type hones in on the operational data within a company’s supply chain. It delves into critical aspects like supplier performance, on-time delivery metrics, and quality measures. Operational analytics provide insights into the efficiency and effectiveness of suppliers’ operational processes.

Financial Analytics: This type concentrates on the financial data associated with a company’s suppliers. It delves into key financial factors such as invoices, payment history, and credit risk assessment. By analysing financial analytics, businesses can gain a deeper understanding of the financial stability and reliability of their suppliers.

Contract Analytics: This type revolves around analysing the contractual data related to a company’s suppliers. It focuses on crucial elements like pricing structures, terms, and conditions outlined in supplier contracts. Contract analytics enables businesses to assess adherence to contractual obligations, identify potential risks, and optimise supplier relationships.

Social Media Analytics: This type zeroes in on the social media data connected to a company’s suppliers. It entails monitoring and analysing online reviews, ratings, and feedback provided by customers or stakeholders about suppliers. Social media analytics offers valuable insights into a supplier’s reputation, customer satisfaction levels, and overall brand perception.

How to implement supplier performance analytics?

Implementing supplier performance analytics successfully requires careful consideration of key factors. Here’s a guide to ensure a smooth implementation:

Acquire accurate supplier data: Gather precise and reliable data on your suppliers from various sources such as financial reports, customer surveys, and supplier performance evaluations. This comprehensive data will serve as the foundation for your analysis.

Identify relevant KPIs: Determine the key performance indicators (KPIs) that align with your organisation’s goals and will effectively evaluate supplier performance. Consider metrics like on-time delivery, product/service quality, and total cost of ownership. Tailor the selection of KPIs to your specific needs.

Track and analyse KPIs: Regularly monitor and analyse the identified KPIs to gain insights into supplier performance. Track trends, identify areas for improvement, and spot any anomalies or patterns. This ongoing analysis will enable proactive decision-making.

Develop a scorecard system: Establish a scorecard system to track and evaluate supplier performance against the selected KPIs. The scorecard serves as a quantitative tool to assess suppliers, aiding in decision-making for future partnerships. It provides a standardised framework for supplier evaluation.

Maintain open communication: Foster open and transparent communication channels with your suppliers. Provide both positive feedback for commendable performance and constructive criticism when necessary. Collaboration and effective communication contribute to continuous improvement within your supplier base.

Why is safety stock important?

Safety stock is an essential component of inventory management that involves holding extra inventory beyond normal demand. Its importance can be summarised in the following points:

It accounts for fluctuations in customer demand, minimising the risk of stockouts during unexpected spikes in demand or supply disruptions.

It compensates for uncertainties in supplier lead times, guarding against delays in receiving materials or finished goods.

Safety stock provides a cushion during supply chain disruptions, such as natural disasters or labour strikes, ensuring business continuity.

It reduces the risk of stockouts caused by variations in order cycle time, enhancing service levels and customer satisfaction.

Safety stock minimises the likelihood of backorders, ensuring product availability and customer loyalty.

It accommodates seasonal demand fluctuations, allowing businesses to meet increased customer requirements during peak periods.

Safety stock acts as a buffer for uncertain demand forecasting, providing a safety net against demand forecast errors.

Overall, safety stock plays a crucial role in mitigating supply chain risks, maintaining customer satisfaction, and ensuring smooth operations.

When do you not need safety stock?

There are valid reasons why having safety stock may not always be the optimal choice for your business. Instead of applying a blanket rule to every product in your inventory, it’s important to strategically evaluate its necessity. Here are some considerations for not having safety stock:

Firstly, investing a significant amount of money in inventory ties up your cash until those products are sold. If a substantial portion of your capital is locked in safety stock, it may limit your ability to address unforeseen expenses or capitalise on business expansion opportunities.

Secondly, managing retail inventory is both time-consuming and costly. The more inventory you hold, the higher the expenses associated with holding costs, such as storage units, warehouse space, and labour.

In cases where products consistently sell at a predictable rate, safety stock may not be essential. Instead, focus on investing in additional units of items that experience occasional unpredictable surges in demand.

Lastly, if your suppliers are reliable and consistently deliver products as agreed upon, you may not require safety stock. Furthermore, having multiple suppliers for the same product provides a contingency plan, reducing the need for excess inventory.

Upon carefully assessing these factors, you can make informed decisions about when and where to allocate resources for safety stock, optimising your sales and operation planning and maximising your business’s overall efficiency.

Conclusion 

Supplier performance analytics is an indispensable asset for any business striving to thrive in the ever-evolving business landscape. It empowers organisations to adapt, optimise, and seize new opportunities, ultimately leading to sustainable growth and a competitive edge in the market. 

If you have an interest in expanding your knowledge of supply performance analytics, consider enrolling in the supply chain analytics course by Imarticus- the Professional Certification in Supply Chain Management and Analytics, in collaboration with IIT Roorkee.

Feature Engineering: Transforming Data for Machine Learning

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Raw input data are generally available in tabular formats, where rows highlight observations or instances and columns show attributes or features. Feature engineering is a tactical process which is used to transform raw data into valuable features that can be utilised for creating accurate predictive machine learning models. This uses Python programming and Power BI as key visualisation tools. 

Business Analyst

Feature engineering helps to prepare models with reasonable prediction even when a few missing raw data are missing. This is possible when the work is done using the most relevant features that eliminate undesirable or non-influential ones.  

The Process of Feature Engineering

Feature engineering in machine learning broadly consists of four processes. They are as follows:

Feature creation

Feature creation is a process that uses the human brain’s creativity and is performed by addition, deletion or rationalisation of existing data variables. This activity is done by professionals who have chosen a career in data analytics. 

Transformation

The process of adjusting the selected variable so that it may contribute effectively towards the accuracy and performance of the predictive model is known as transformation. The process ensures that all the features follow the same scale. It also helps to make the model flexible to accept a variety of data inputs.

Feature extraction 

Feature extraction is an automated method of generating new meaningful variables out of the raw data provided. This makes the predictive model more reliable and accurate by reducing the input data volume. The process involves text analytics, cluster analysis, edge detection algorithms, and principal components analysis.

Feature selection

Feature selection is the process of selecting the most useful variables out of many for incorporating them into the predictive model. Irrelevant or noisy data are left out since they are useless to the model and negatively affect the model when infused into the system.

Tools of Feature Engineering

Many feature engineering tools help make good predictive models. A few of them are described below:

FeatureTools

FeatureTools helps to perform auto-feature engineering. It is particularly good at converting meaningful raw data to useful features in machine learning.  

AutoFeat 

Linear predictive models with automated feature engineering and selection process is a key strength area of the AutoFeat tool. AutoFeat helps us to select the unit of useful variables.

TsFresh 

TsFresh is an open-source Python package tool that helps to correlate and automatically calculates a large number of time series data. It helps to extract details such as peak, average value, time reversal symmetry statistics etc. Knowing Python programming is of immense importance in today’s world.    

OneBM 

This tool works on the raw data, irrespective of whether they are relational or non-relational to the predictive model. It can generate both simple and complicated features.

ExploreKit

It is a structured framework to produce automated features. It can combine multiple data and may unearth common useful features thereby eliminating duplication. This makes the predictive model compact and error-free. 

Feature Engineering Techniques in Machine Learning

Some of the regular feature engineering techniques used in preparing data for machine learning models are as follows:

Imputation 

The most common problem is missing data, which arises out of the following typical cases of human errors, data flow interruptions, privacy issues etc. Numerical and categorical imputations are applied in these cases.

Handling outliers 

This is a process of suitably dealing with specific data which is exceptional in terms of value and category. When several outliers are very few, the process of removal is applied. However, if the number of outliers is quite a few, then removal will cause us to lose enormous data and hence be avoidable. In these cases, the process of replacing values, capping or discretisation is applied.

Log transform 

Logarithms are used to convert data of a skewed distribution into that of a normal distribution. This process is also used to handle confusing data. The efficiency of this tool may be best expressed visually with Power BI.    

Scaling 

It is the process of bringing all data under a common scale by scaling up or down, as required. The purpose is to make the features similar in terms of their range. The two standard procedures adapted here are normalisation and standardisation.

Binning

Excessive and irrelevant data and unwarranted numbers of parameters deter the performance of models. Binning is the process of segmenting several data and features and eliminating unwanted ones from the system.

Feature split 

This is a process of segregating features into two or more parts to closely monitor the same with the help of the data available. This characteristic produces meaningful features with better algorithms and is better numerically representative.

One hot coding 

It is a commonly used technique in machine learning. It is used to convert categorical data in a specific form which can be easily interpreted by machine learning algorithms and can be used in creating successful predictive models. 

Benefits of Feature Engineering in Machine Learning Models

Using feature engineering in machine learning applications has some notable advantages, which are as follows:

Flexibility 

Better features impart better model flexibility. Even if a wrong model is chosen by mistake, the flexibility of features will generate good predictions.

Simplicity 

Flexible featured models are simple and quick to operate.

Better Results 

With the same available data, the selection of better features gives way to better results in predictive models. 

Conclusion

A career in data analytics is a booming option for modern youth. A data science course with placement assistance makes this opportunity lucrative. Having a machine learning certification is very necessary for a prospective candidate. Several reputed institutes in India offer machine learning certification courses.

The Postgraduate Program in Data Science and Analytics at Imarticus will give the prospective candidate a perfect start to their career. This is a data science course with placement and the duration of the program is 6 months. The classes are held on weekdays where the mode of teaching is both online as well as classroom training. 

Visit the official website of Imarticus Learning for more course-related details.

The Impact of Technology on Management Practices

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For a business, the most important thing is to achieve success. And in order to accomplish that, they must follow a set of management practices. This not only aligns the tasks of the organisation but also oversees all the aspects of the business to form strategic decisions. Further, these decisions help in driving success to the organisation. 

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There are several management practices, like establishing quality control and providing adequate training to employees. These practices have a huge role to play in getting the company’s employees to achieve their target with the given resources. But as we know, with time, technologies have advanced greatly. And like any other realm of human existence, it has highly impacted management practices. 

Here, we will discuss different management practices and the role of technology in impacting them. To understand concepts like these in-depth, one can also go for a BBA in business analytics or an online BBA course.

Different Business Management Practices

The management of an organization is responsible for the well-being of its employees, as well as its stakeholders. Thus, business management practices are of immense importance. Below are some useful management practices that should be applied in every organization.

Communication

It’s essential for every organization to have a clear chain of communication with its employees. This practice helps the company communicate its goals and expectations to the staff and make them understand their roles that fit in the business strategy. 

Assessing Operations of Business

Improving a business and meeting success isn’t an overnight thing. It needs a constant effort from everyone associated with it. Thus, close attention should be paid to every detail of the business. And to do this, an assessment of business operations with the right tools and practices on a regular basis is needed. 

Strategic Planning

Far-sightedness is crucial when one aims for a successful business. That being said, strategic planning is one of the most important practices of an organisation aiming to yield some long-term benefits. Here, quantitative data can be used for making informed decisions. According to the collected data, training can also be reshaped for better results in the future. 

Engagement of Employees

The management practices also include the job of engaging the employees to solve problems, take initiative, and come up with innovative ideas. All these can be done only if the employees and passionate about their job which will happen once they’re aligned with the company’s vision. Also, studies have found that companies with high employee engagement and more profitable. 

Open Management Style

Developing a culture where your employees feel free to come up to you with their ideas and queries gives a boost to the overall functioning of the organisation. Open management style also projects the management as a helping hand for the employees rather than an enforcer. Thus, it empowers the employees and makes them feel an important part of the organisation. 

Impact of Technology on Management Practices

Technology can easily create a huge impact for betterment in any space if used judiciously. Thus, there are plenty of enhancements in management practices with the use of the latest technologies. Some of the crucial impacts are listed below. 

Enhances Decision Making 

One of the most important roles of a manager is to come up with effective plans and decisions. To survive in this competitive environment, one must have the latest technologies with them to collect accurate data at an improved speed. This will then faster the decision-making process with more efficiency. Technology also helps in accessing these data to draw a precise conclusion that will assist the business in being on track to meet its goals. 

Employee Collaboration

With time and the advancement in technology, our lifestyle has completely changed. One major impact is also seen in the way people communicate or collaborate with each other. Especially in the professional setup, collaboration in former conference rooms has changed to video conferencing, allowing them to communicate and share their ideas in the blink of an from where ever they are. To enhance the quality of work along with building a culture in the organisation, some real-time collaboration tools like cloud-based file sharing are essential. 

Efficiency in Operational Tasks

There are many tasks in a workplace that needs to be done on a daily basis or are repetitive in some sense. This needs to be done manually and ends up taking a lot of time for the employees. But with the right technology, one can transform this old setup, saving the time of employees from doing time-consuming and repetitive tasks. Further, they can engage themselves elsewhere without sticking to inefficient manual tasks. 

Management of Business Information

Along with building a business comes the responsibility of managing its confidential information. Thus, it becomes the utmost priority of the management to have a structured and efficient way of managing those documents and contents. And to help the management in doing so comes cutting-edge technology at the rescue. 

Digital Transformation

Be it any kind of business, one needs to keep up with technological advancements and grow alongside social trends to attain success. This means that businesses must transform themselves digitally to survive in the competitive surrounding. 

To do this, there must be a good digital strategy that can be planned only after identifying the gaps present in the organisation. After finding it out, the business should look for suitable technologies and out the transformation one step at a time.

Conclusion

Managerial practices are important for the smooth processing of all the workings of an organisation. It keeps a regular check on various activities of different departments and thus, plans out strategies for their best. To further enhance this process, new-age technologies can be a great boon. Be it security, or communication, there are hi-tech innovations to assist in every possible domain of an organization. 

Thus, it is advisable for any fresher or an existing professional to upgrade themselves with a recently crafted online BBA course that will educate them about the application of the latest technologies in management practices.  

For in-depth knowledge on this, one can enrol for BBA in Business Analytics that empower future business leaders. Here, you will get hands-on experience with new-age tools that will enhance your management skills even more. 

Visit Imarticus Learning to know more about business analytics.

Benefits of Batch Tracking and Periodic Inventory

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Businesses are built on two crucial factors, product quality and customer service. 

Batch tracking is an efficient approach to inventory management which plays a crucial role as it impacts on both product quality and customer service. 

global chief supply chain and operations officers course

In addition, periodic inventory management, through keeping track of an inventory in periodic segments, helps to maintain the balance between supply and demand patterns to maximize profit.

While batch tracking enables a business to recognize the source of a problem (quality issues) in the larger supply chain, periodic inventory management reduces the chances of such problems as they help to tailor the goods to meet the customer’s demand.

Methods of Batch Tracking

The three most effective methods of batch tracking in the supply chain are:

Push Strategy

It is a method in which goods are “pushed” down, that is, it essentially tracks the downflow of the product from the initial manufacturing process to the reception by the customers. A company must accurately predict product demand in order to successfully use the push strategy. It is important to know how much of a product is required and when, throughout the year. Businesses can gain valuable insights into how inventory has been used over time and how much inventory they may need to order over the course of a year by using inventory management software.

For instance, a home appliances business might stock hundreds of air-conditioners and coolers in the spring and summer but only a few in the winter. These shops decide how many grills to purchase based on sales records of appliances previously sold. For companies that can precisely predict their customers’ needs, the push technique becomes the most favorable and efficient of all.

Pull Strategy

A pull strategy is an inventory management method in which a store, warehouse, or company “pulls out” goods only when ordered by a customer or employee. Items move upstream in the supply chain instead of moving downstream. This can take a lot of time. Pull techniques are used mainly for luxury or novelty items (products with unpredictable demand). 

For example, many wedding dresses for brides are “not taken off” until the purchaser submits their credit card. These women choose dresses from dress samples and websites, and the final dress is either made or sourced at the time of purchase. It can take months, but customers usually have no problem using it because it is an industry-standard. However, the pull strategy is not suitable for all businesses, especially when customers have immediate access to the same product from another supplier.

Just-in-Time Strategy

Just-in-time inventory strategies are similar to pull strategies. That is, companies order inventory “just in time” to meet customer orders and business needs. Additionally, to successfully implement and accomplish a just-in-time inventory management strategy, one requires reliable suppliers, vendors and third-party logistics partners. Without these, businesses cannot meet customer’s demands.

 Just-in-time warehouse management helps companies reduce inventory costs, reduce inventory, and improve cash flow. On the other hand, businesses that rely on JIT inventory cannot always keep up with demand and may have to overpay for products just to get them to their customers faster.

Methods of Periodic Inventory

The three most effective and commonly used methods of Periodic Inventory can be categorized as FIFO, LIFO, and FEFO. These strategies can be tailored to suit the requirements of the business, as well as, their customers, by adjusting to a favorable interval of inventory management which can be accomplished by determining the approaches mentioned below.

FIFO

FIFO stands for First In, First Out. This strategy of management is mostly useful if the business deals with fast-moving and/or products for consumption, as it will ensure the oldest inventory is sold first.

LIFO

LIFO stands for Last In, First Out. This strategy is not the most commonly used. However, businesses involved with products that do not have a natural date of expiration are often found implementing this strategy. 

FEFO

Lastly, FEFO stands for First Expiring, First Out. This strategy is used in businesses that deal with products with clear expiration dates, as products with expiration dates closest to the date of purchase are sold, used, or otherwise disposed of first, regardless of when they were manufactured or purchased. This strategy is often preferred by businesses in the food industry (perishable products) over FIFO because it focuses on the date of expiration over the date of manufacturing.

Advantages of Batch Tracking

The main advantage of batch tracking is the full traceability it enables. There are many benefits for companies when they monitoring the shelf lives and quality of their products from raw materials to point of sale. Such as,

Safety and Quality control

Batch tracking is essential to inventory monitoring to ensure quality control and safety, as it allows anyone to quickly and efficiently identify all items in the relevant batch when a problem is identified.

Expiration-date Tracker

Expiry date data is used for supply sequencing strategies and marketing and promotions. With batch tracking, one can easily assign expiration dates to entire batches instead of individual items and track inventory that is nearing expiration, so they can initiate promotions, for example,  to increase sales.

Automated Sequencing

Batch tracking also enables businesses to integrate automated sequencing strategies for their inventories like FIFO or FEFO. This practice results in the extraction of the optimum value of inventory by minimizing potential waste.

Recall Process Ability

No one wants a product removed from the market, but removing it before it causes major problems is critical to long-term customer satisfaction. When a recall becomes necessary, lot tracking software allows companies to more quickly send appropriate notifications to their supply chains and affected customers.

Better Product Quality

Knowing what materials are used in a quality batch of products allows you to continue ordering from the best suppliers and avoid those who do not provide the right materials. Overall, this results in better product quality, increased customer satisfaction, and increased sales.

Hassle-free Supply Chain 

Just like the ability to identify good batches that lead to better product quality, so can your supply chain. Batch tracking helps business owners identify the best and most cost-effective vendors and close more deals.

Financial Benefits

Batch tracking supports more informed decision-making and saves costs. Simply knowing the best time to sell a batch of your product based on the expiration date can prevent your inventory from aging past the sell-by date. Knowing when a product needs to be recalled also helps avoid replacement shipping costs and potential legal fees.

Improved Accounting Competency

Processes automated with the help of batch tracking technology can decrease accounting errors and data misinterpretation. Improved visibility makes it easier to monitor the location of all items in batches to see if they are still in stock, in transit, or already sold.

Benefits of Periodic Inventory

The practice of Periodic Inventory management provides several benefits which eventually enable the system to track both purchases and sales over a set period. By utilizing a periodic inventory system you can determine how much money was spent on what, and how many items were sold. Periodic inventory, therefore, not only improves the general quality of the inventory stocks, but also has economic benefits. Some of the benefits of this practice are,

Simplicity

A periodic inventory system primarily simplifies the process of inventory management and documentation. It reduces manual efforts by doing less math, using less paper, and saving time which can be used for other management tasks required.

Economic Efficiency

One only needs to count their inventory at regular intervals. Thus, they might want to buy some barcode stickers and scanners, but these are a cheaper alternative than running sophisticated computer systems that monitor your inventories continuously (or in real-time).

Accuracy

As the main objective is to count the cost of goods sold and the closing inventory, one needs to be patient until the physical check is finished. These physical checks, although time-consuming, provide accurate and in-depth knowledge of the inventory stocks which cannot be achieved by automated systems.

Conclusion

Batch tracking and practices of periodic inventory management, are thus one of the most effective management practices that provide benefits to most businesses through increased visibility and exactness in inventory management. By extension, as a result of these benefits, both indirectly help businesses in maintaining and/or improving their customers’ satisfaction, as well as in raising their product quality. 

To enhance skills in inventory management in a supply chain and other related areas, professionals can enroll in supply chain management courses such as the Supply Chain And Operations Officer course or the IIM Raipur Supply Chain Management program.

For senior executives looking to develop their skills further, Imarticus Learning offers the IIM Raipur Executive Certificate Programme For Global Chief Supply Chain And Operations Officers, providing them with the tools to excel in their roles and drive success in their organizations.

Visit Imarticus Learning to learn more about Inventory management in a supply chain.