Big Data in Supply Chain Management: Impacts and Applications

Introduction

 

The evolution of information technology, increased customer expectations, economic globalization, and other modern competitive priorities have caused firms to adjust in today's competitive environment. 

 

As a result, the rivalry between businesses is replaced by competition between businesses and their supply chains. Supply chain experts are straining in today's competitive market to handle massive amounts of data to achieve an integrated, efficient, effective, and agile supply chain. 

 

As a result, the rapid expansion in volume and variety of data types throughout the supply chain has necessitated the development of systems that can intelligently and quickly evaluate enormous amounts of data.

 

To improve supply chain management, big data analytics is becoming increasingly important. It addresses several issues at the strategic, operational, and tactical levels. Big data is affecting every aspect of the supply chain. It includes anything from reducing the communication gap between manufacturers and suppliers to boosting delivery times. 

 

Decision-makers can use analytics reports to increase operational efficiency and monitor performance to boost productivity. To cut costs and improve service levels, supply chain analytics supplement data-driven decisions.

 

(Also Read: Use Cases of AI in Logistics)

 

What is big supply-chain analytics, and how does it work?

 

Big supply chain analytics makes better decisions for all activities in the supply chain by combining data and quantitative methodologies. It adds two new features in particular. 

 

For starters, it broadens the dataset for analysis beyond the usual internal data stored in ERP and SCM systems. 

 

Second, it uses advanced statistical methods to analyze both new and old data sources. This generates fresh insights that aid supply chain decision-making, ranging from improving front-line operations to strategic decisions like choosing the best supply chain operating models.

 

Impact of Big Data analysis on several stages of the supply chain:

 

Big Data analysis necessitates the use of a variety of tools, processing systems, and algorithms to extract information from large amounts of data.

 

When performing a coordinated supply chain analysis, the application's focus moves from simple automation to forward-thinking data integration and enhanced decision-making. This generates new insights that can be used to improve supply chain management, from front-line operations to strategic decisions.

 

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Supply chain analysis facilitates supplier network collaboration and integration by utilizing real-time data in a variety of organized and non-structured formats, as well as the power of the 3Vs (volume, speed, and variety). In a systemic sense, this goes from one extreme to the other, affecting each stage of the chain.

 

1. Big data for planning:

 

Demand forecasting can be improved with the use of integrated data from the entire supply chain and statistical models. This has an impact on inventory management and replenishment planning. Having the chance to organize them ahead of time, for example, ensures that there are no out-of-stock situations. 

 

An appropriate Big Data model study at this stage takes into account not just real-time and historical data, but also macroeconomic aspects, market trends, and even competition data.

 

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2. Big data for provisioning:

 

Managing purchase coordination is always a challenge, but there is a lot of room for improvement and cost savings. Using supply chain analysis to analyze supplier performance and compliance in real-time on-site on a quarterly or annual basis can assist us in seeing possibilities and intervening early on difficulties.

 

Monitoring and collecting data from suppliers can help us build a transparent connection that is free of hidden expenses, based on real data, and in real-time.

 

 

3. Big data for performance:

 

Big Data assists in reconfiguring the numerous flexible sections of the supply chain, optimizing available resources (space, tools, materials, human resources, and so on), and maximizing productivity throughout implementation.

 

The IoT sensors application in the manufacturing industry can give real-time machine status data. Not only can this be used to increase asset performance and production capacity, but it can also be used to run predictive scenarios to predict problems or scheduled maintenance.

 

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4. Big data for delivery:

 

Everything is focused on speed (getting the product out on time), precision (ensuring shipments arrive at the correct location), and efficiency (identifying the best route / combining deliveries) throughout the delivery stage. Real-time delivery data combined with external data such as traffic and weather trends can increase logistics management performance significantly.

 

 

5. Big data for inverse logistics:

 

Return efficiency is a critical component for businesses that want to stay profitable. Given the complexity of reverse logistics, the goal is to optimize restocking and transportation costs to return the goods to the store/warehouse, general shipping costs to deliver another product to the consumer, and decision costs on the evaluation of the returned product. 

 

By merging data from inventory and sales systems, as well as inbound and outgoing flows, analytics can assist reduce these expenses and give the information required for trouble-free returns.

 

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Big Data Benefits in Supply Chain Management

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Benefits of big data in supply chain management

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1. Improved Supply Chain Process Traceability:

 

Successful supply chain operations require product traceability. Using barcode scanners and connecting radio frequency identification devices to particular products, supply chain managers may readily trace a product. 

 

Businesses can use big data analytics tools to acquire accurate product information, allowing operators to keep on top of their distribution cycle. For example, it will be simple for F&B managers to predict when food will spoil.

 

Improved traceability allows commodities to be tracked from production to retail. Businesses can better collaborate with supply chains with improved traceability.

 

Improved traceability allows commodities to be tracked from production to retail. Businesses can better interact with supply chain stakeholders to streamline distribution with improved traceability.

 

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2. Consumer Behavior and Usage Patterns:

 

Leading telecom companies are investing heavily in big data analytics to better understand their consumers' usage patterns and habits. Businesses can use the data acquired from the analytics report to keep their subscribers and enhance income dramatically.

 

Jio, for example, uses big data analytics to forecast network growth and efficiently plan network expansions.

 

 

3. Streamlined E-commerce:

 

Big data analytics is used by online retailers such as Snapdeal and Flipkart to streamline their management procedures. 

 

Flipkart, for example, uses big data to ensure excellent supply chain management. Through innovative mobile technology, Flipkart enhances its algorithms to precisely estimate delivery dates, increase warehouse automation, and optimize routes.

 

( Must Read: Latest E-commerce Platforms )

 

 

4. Temperature control and product quality:

 

Many industries, including food, agriculture, pharmaceuticals, and chemical processing chains, require constant monitoring and control of certain supply chain constituents. Even a small temperature shift of a few degrees might have an impact on the product's quality - or even render it worthless.

 

Unfortunately, roughly 30% of temperature-controlled products are damaged or spoiled before they reach their destination due to a lack of technological support to maintain control.

 

(Must Read: Role of Big Data in the Healthcare Industry? )

 

In healthcare, temperature monitoring and logistical support are critical, especially for the distribution of the COVID-19 vaccination. Many vaccination vials can only be used for a certain time at a specific temperature. Much of this supply is regrettably wasted due to poor management.

 

The solution is cold chain monitoring technology, which uses data logging to support temperature-sensitive product logistics. During packaging, shipping, and delivery, managers can monitor temperature swings in real-time and modify cooling or heating systems as needed.

 

Big data platforms can also assist in the prevention of potential interruptions caused by fluctuating data, such as weather or traffic delays. This results in a comprehensive control system for successful supply management from beginning to end, eliminating waste and preventing product faults.

 

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5. Enhanced Inventory Management: 

 

Big box retailers and top online companies with large inventories face some obstacles. Operational managers can use big data analytics to acquire a minute-by-minute snapshot of operations and detect bottlenecks that slow down supply chain processes. 

 

Consumer trends also help businesses market their best-selling products and manage their inventories.

 

Amazon, for example, employs big data analytics to manage its inventories. To cut distribution expenses, it chooses warehouses based on the proximity of its providers and customers. Amazon distributes products based on customer preferences in a certain location using big data analytics.

 

( Must  Read: Applications of IoT In Warehouse Management )

 

 

6. Order Fulfillment and Real-time Tracking:

 

Both for business productivity and consumer pleasure, efficient order fulfillment, and traceability are critical. Amazon has revolutionized the shipping industry by providing extraordinarily fast delivery timeframes, as well as warnings for projected drop-off timings and minute-by-minute tracking.

 

Businesses across various industries may use big data to provide similar experiences for their customers and clients. By optimizing route deployment, delivery timetables, and item location, up-to-date shipment information can also assist in minimizing expenses with delivery fleet management.

 

( Suggested Blog: How does Amazon Use Warehouse Technologies? )

 

Every step of UPS's shipping process includes supply chain data analysis. As packages move through the supply chain, radars and sensors collect data. The delivery routes are then optimized by big data tools to ensure that packages reach on time. Overall, UPS has saved 1.6 million gallons of gasoline in its trucks each year, greatly lowering delivery costs.

 

 

7. Machine Maintenance:

 

Unexpected problems with machinery caused by breakdowns, poor maintenance, or aging equipment can cause major headaches for businesses. According to Industry Week, the manufacturing industry loses $50 billion a year due to unplanned downtime caused by machinery faults.

 

When big data systems and IoT devices are coupled, they can send out alerts for any abnormalities or irregularities in machinery. Sensors can be used to track production, predict problems, and alert you when routine maintenance is required to keep your machinery running smoothly.

 

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This helps to lower total expenses in two ways: first, it reduces repair costs and eliminates unscheduled downtimes. Predictive maintenance technology, on the other hand, aids in efficient manufacturing. Overall, according to a McKinsey study, big data and predictive maintenance technology may reduce machine downtime by 50% and even prolong machine life by 40%.

 

Conclusion

 

Integrating big data technology into each phase of the supply chain management process can yield huge benefits. Supply chain managers can now have the tools they need for strategic decision-making by combining strong data sets with predictive analytics and IoT.

Although investing in big data can be intimidating at first, the benefits greatly outweigh the costs for many businesses. Without a question, businesses will continue to invest in and rely on big data technologies in the foreseeable future.