Big data analytics is a much-hyped topic in the world today. Almost every industry is looking for ways to incorporate big data and advanced analytics to help improve their business operations. The supply chain and logistics industry is no exception to this trend. The massive amount of data generated by a company’s supply chain and logistics function can be fruitfully used to generate meaningful insights and give strategic direction to the company. A majority of C-level executives have already begun looking for ways to incorporate big data analytics into their supply chain systems. Big data can significantly improve productivity and efficiency in supply chains and provide an edge over competitors. Additionally, big data can also help companies identify new business opportunities. So, how and where can big data be used in the supply chain and logistics function?
Speak with our analytics experts today to know more about applications of big data in supply chain and logistics network, warehousing efficiency, procurement efficiency, and supply chain traceability.
Supply Chain Traceability
One of the most critical success factors for a supply chain is the ability to pinpoint where the products are in the supply chain. Advances in digital technologies along with barcode scanners and RFID devices have enabled supply chain managers to precisely track wherein the supply chain their products are located. Additionally, traceability is also a sensitive matter in the food industry as factors such as disease outbreaks, chemicals used, and processing systems should be attributed to responsible parties. To track down where exactly the fault has occurred in the food supply chain, traceability is a must to know where the items originated and what processing took place at each level. This way, food and beverage sector companies can not only avoid hefty fines but also ensure customer satisfaction by providing safer and healthier food. Such sensors technologies, along with IoT and big data can enable end-to-end traceability, which helps the company quickly identify the instances of food contamination.
The volume and quantity of data generated by the procurement department are immense. 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. Spend data, contract data, and supplier-related data can provide detailed information on the supplier and help identify the ideal vendor. Additionally, mining historical data can unveil future trends and also help identify risks including supplier risk, pricing risk, compliance risk, geographical risk, and disaster risks. Predictive analytics helps the procurement teams to be prepared for a future scenario.
Optimized Logistics Network
Big data can help the logistics industry players to optimize travel routes due to the advent of IoT and sensors technology. Today, it is possible for vehicles to communicate with each other using vehicle telematics to coordinate on best routes and alert on less desirable routes. The vast amount of data regarding routes preferences, type and size of the vehicle, traffic densities, and weather conditions can provide clues to improve driving performance, determine the exact delivery time, and identify & minimize risk areas.
Improved Warehousing Efficiency
A warehouse is a piece of real estate where companies store their valuable commodities. And with all such real estate, the problem is it costs too much money, not only the rental or lease costs, but also facility management costs. Big data can help supply chain managers to maintain an optimum level of inventory just to the point that they face fewer stockout situations without overstocking the inventory. The power of big data is not only limited to 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.