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Supply Chain Analytics: What It Is & Why it is Important?
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
Finding solutions to optimize your supply chain might be the difference between providing an amazing client experience and improving profits. As a result, efficient supply chain management that optimizes cost, accuracy, and speed should always be a focus. However, optimizing is not necessarily the most difficult aspect. Understanding which sections of the supply chain require improvement is frequently difficult. Fortunately, current supply chain technology is meant to assist online firms in optimizing their supply chain through increased visibility, including access to advanced data and analytics.
Management of the flow of products, services, and information from suppliers to consumers is included in supply chain management. It is a complicated procedure that involves meticulous preparation, organization, and execution. Businesses have increasingly resorted to data analytics in recent years to help them manage their supply chains more effectively.
Supply chains contain a wide range of activities, people, and organizations, resulting in a massive volume of data. This is where supply chain analytics come in—they can transform that massive quantity of data into easily digestible dashboards, reports, and visualizations that affect crucial decisions and lead to better results. In an increasingly competitive world, easy access to these statistics has become important.
Supply chain disruptions are expected to continue in 2023, whether they are caused by existing or future geopolitical conflicts, inflationary pressures and the recessionary environment, climate change weather events, or other concerns that have yet to materialize. They can all have an influence on product access and how they flow to their eventual destination, cause port holdups, restrict container and ocean freight availability, and cause price increases, among other issues.
What is Supply Chain Analytics?
A supply chain is a complicated system with competing goals of cost reduction and customer pleasure. Data is becoming more important in supply chain management. The movement of products and services from the point of origin to the point of consumption is the focus of supply chain management. The ongoing COVID-19 epidemic has highlighted holes in global supply networks, causing enterprises to alter their processes. One way to improve is through supply chain analytics.
The gathering of data and information that provides insights into logistics operations, from inventory management to fulfilling and shipping orders, is referred to as supply chain analytics.
With the continued expansion of e-commerce, there is more demand to build a lean supply chain while also satisfying customer expectations for speedy, low-cost shipping. By using technology, tools, and other resources to collect real-time data on everything from inventory flow to shipping performance, online retailers may make better judgments about how to manage their supply chain to save money, speed up processes, and enhance accuracy.
Although supply chain analytics has been around for over a century, the mathematical models, data infrastructure, and applications that support it have progressed dramatically. With better statistical approaches, predictive modeling, and machine learning, mathematical models have improved. Cloud infrastructure, complex event processing (CEP), and the internet of things have all altered data infrastructure. ERP, warehouse management, logistics, and corporate asset management applications have evolved to deliver information beyond conventional application silos.
A supply chain is like a domino effect: each step in the network impacts the one after it, and any faults at any point might influence the capacity to satisfy consumer expectations.
Each of the aforementioned pieces of software may have its reporting capabilities that give insight into that specific phase in the supply chain, such as expected lead times for suppliers, current safety stock levels at the warehouse, or orders completed per hour. However, supply chain analytics are most effective when all of these systems are linked together, often through an Enterprise Resource Planning (ERP) system. Through dashboards or reports, the ERP or a standalone application may then show and illustrate data from throughout your global supply chain.
What is the significance of supply chain analytics?
Supply chain analytics may assist a company in making smarter, faster, and more efficient decisions. Among the advantages are the following:
- Reduce expenses while increasing profits
Access complete data to provide continuous integrated planning and real-time visibility into diverse data that fosters operational efficiency and actionable insights.
- Improve your understanding of hazards
By recognizing patterns and trends throughout the supply chain, supply chain analytics may uncover known hazards and assist forecast future concerns.
- Improve planning precision
Supply chain analytics may help a company better estimate future demand by analyzing client data. It assists an organization in determining which items may be reduced as they become less lucrative or in determining what consumer wants will be following the original order.
To analyze enormous databases and derive insights from them, data analytics use statistical and computational methodologies. Data analytics in supply chains entails gathering and analyzing information on many parts of the supply chain, such as transportation costs, inventory levels, manufacturing schedules, and customer demand.
Data analytics may improve supply chains in a variety of ways, including:
- Forecasting demand
Businesses may estimate future demand and manage inventory levels based on prior sales data. This can assist decrease the chance of stockouts and overstocking, increasing customer happiness, and lowering costs.
- Inventory management
Businesses may use inventory data analytics to detect slow-moving or surplus inventory and take efforts to minimize it. This can save money on inventory storage and free up warehouse space.
- Transportation planning
Businesses can determine the most cost-effective means of transportation and routes. This has the potential to minimize transportation costs while also improving delivery times.
- Supplier performance evaluation
Businesses may monitor supplier performance and discover opportunities to decrease costs and increase collaboration by analyzing supplier data. This can help to strengthen connections with suppliers and lessen the risk of supply chain interruptions.
- Quality assurance
Businesses may use it to discover areas for improvement and take action to decrease flaws and waste. This can assist to increase product quality while also lowering expenses.
Supply chain analytics aids in:
- Facilitating supply chain event planning and scheduling by giving real-time inventory data.
- Predicting consumer demand, managing resources, and keeping current on market trends
- Improving the SCM process by allowing organizations to track manufacturing materials and reduce procurement costs.
- Increasing client happiness and improving logistics by giving real-time data.
What Types of Data Are Used in Supply Chain Analytics?
- Product Data
- Financial Data
- Financial Data
- Warehouse Data
- Inventory Data
- Logistics Data
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There are five forms of supply chain analytics
With real-time data and reliable information, the possibilities for analyzing supply chain performance and efficiency are limitless. To get you started, here are the five most popular forms of supply chain analytics in the e-commerce business.
All of these supply chain analytics give comprehensive insights that lead to enhanced decision-making.
1.Descriptive Analytics
Descriptive analytics are statistical measurements of previous occurrences or data. This sort of analytical measurement examines past data to uncover patterns, allowing you to see trends and calculate return on investment.
2.Predictive analytics
To forecast potential trends, predictive analytics employs historical data, statistical algorithms, and machine learning. Predictive analytics may assist firms in identifying possible changes or interruptions, allowing them to plan ahead of time and develop a more flexible supply chain.
3.Prescriptive Analytics
To aid decision-making, perspective analytics use enormous volumes of data processed by computer tools. You may utilize supply chain analytics to plan the best possible move for your company.
4.Cognitive analytics
Cognitive analytics employs machine learning and artificial intelligence to assist in the generation of human-like reasoning and conclusions from enormous volumes of data. This makes the process much more efficient, allowing firms to make faster decisions.
5.Diagnostic analytics
Diagnostic analytics is the practice of ignoring overall performance in order to identify why mistakes, errors, and delays occur. This sort of supply chain analysis assists online retailers in determining the root cause(s) of problems such as shipment delays, procurement interruptions, and restricted carrier capacity.
While each sort of supply chain analytics serves a distinct purpose, they all play an important part in boosting supply chain efficiency.
Features of Supply Chain Analytics
- Data Visualization
- Stream processing
- Social media integration
- Natural language processing
- Location intelligence
- Digital twin of the supply chain
- Graph databases
Important uses of Supply Chain Analytics
- Improving risk management - detecting known hazards and forecasting future risks based on supply chain patterns and trends.
- Increasing planning accuracy - by examining consumer data to find factors that influence demand.
- Improving order management - by aggregating data sources in order to monitor inventory levels, forecast demand, and discover fulfillment concerns
- Streamline Procurement - by organizing and analyzing departmental spending in order to better contract negotiations and uncover chances for discounts or other sources;
- Increasing working capital - through creating models for predicting the inventory levels needed to meet service goals while investing as little as possible in the capital.
In conclusion, Supply Chain Analytics aids businesses in automating planning, budgeting, forecasting, and analyzing procedures to increase efficiency and develop timely, dependable plans. Establishing a cross-functional analytics team that helps in optimizing the supply chain analytics allows skilled planning, delivery, and analysis processes. Setting out clear business goals which ensure flexibility and agility is also possible with the help of Supply Chain Analytics. Businesses use data analytics tactics to improve supply chain operations. However, the availability of several analytics tools and vast volumes of data makes extracting value and real-time insights from supply chain data difficult. As a result, data analytics specialists are in great demand.
