Incorporating Big Data Analytics in Supply Chain

With the vast amount of data available today, it has become difficult for companies to choose the right data for analysis. Different industries and functions are facing the issue and same is the problem with supply chain. In solving this problem, companies are taking help of big data analytics. New research reveals the strategies they are adopting to help them harness the power of big data.

Technology helps supply chain organizations to gather information from an expanding variety of sources. The main purpose is to analyze the available data and find the scope of innovation and improvement. However, few companies still struggle to ensure the quality of data and how to analyze it effectively.

To solve the problem, a large number of C-level executives are incorporating big data analytics into their supply chain practices. A leading global analytics solution provider said that almost every industry is looking for new ways to integrate big data and advanced analytics into their business processes to help streamline operations. Experts believe that big data can play an important role in improving productivity and efficiency in supply chain practice and can provide an edge over the competitors. Apart from improving efficiency and productivity, it can also help companies to identify new business opportunities.

Here are the ways big data analytics can be incorporated into the supply chain system:

Supply Chain Traceability

One of the major challenges is to find the appropriate pinpoint where the products are placed in the supply chain. Advances in digital technologies along with barcode scanners have enabled supply chain managers to precisely track where in the supply chain their products are located. Sensors along which internet of things can enable end-to-end traceability which helps the companies quickly identify the instances of any issues.

Proper Procurement

After finding the appropriate pinpoint where the product has to be delivered, next step is to find the appropriate volume and quantity to be supplied. Data generated by procurement department are immense and processing such data with machine learning and optimization algorithms can help uncover patterns and associations amongst the datasets to make both strategic and tactical procurement decisions. Also, mining historical data can unveil future trends and help identify risks including supplier risk, pricing risk, geographical risk etc. Moreover, predictive analytics helps companies to be prepared for the future scenario.

Optimized Logistics Network

Now the next step is to find the optimized travel routes owing to the advent of IoT and sensors technology. Vehicles communicate with each other by telematics to coordinate on best routes and alert on the less desirable routes. The vast amount of data can be used to improve driving performance, on-time delivery, and minimize risk areas.

Improved Warehousing Efficiency

Big data helps supply chain managers to maintain an optimum level of inventory so that they don't face stockout situation without overstocking the inventory. Big data is not limited to only stock optimization, but it can also suggest where to strategically place warehouses, what the optimum size should be and how to arrange the stock to facilitate easy check-ins and dispatch.

Conclusion

Use of big data analytics in the supply chain has helped a lot of companies in improving efficiency and productivity. It helps in making the whole step of logistics easy and effective. With artificial intelligence, and IoT companies can further improve their work efficiency and productivity.