Big Data, this term is the new obsession of organizations across the globe. The term refers to sets of data that are so huge or complex that traditional software tools are inadequate to capture, curate, manage, and process them. Big Data Analytics is an analytical technique that enables businesses to predict the likelihood of an event and take timely business decisions.

The logistics industry is complex with critical sub-areas such as transportation, inventory, warehousing, material handling, packaging and security that need to come together for actionable insight. The huge scale of operations makes it increasingly difficult for the Logistics Service Providers to gain visibility across the supply chain and ensure efficient customer service. Following is the analysis of how Big Data is changing the logistics industry globally and in India.

Increased Efficiency: 
Powerful data-processing and analysis capabilities increase the efficiency of distribution, logistics, and production networks. Big Data Analytics enables companies to share data with partners across the supply chain that results in the development of new services, improved demand forecasting accuracy and discovery of new demand patterns. For instance, gaining information from unstructured customer data can generate useful insights on product placement, pricing strategies, optimization strategies, layout optimization, operational risk management, and improved product/service delivery.

Real-Time Analytics: With Big Data Analytics, the Logistic and Supply Chain companies have an end to end visibility for acting rapidly on prospective loss of revenues and profits that could occur at various points in the chain. It increases asset uptime and expands throughput, enables preventive maintenance of assets and resource optimization along with conducting near real-time supply planning using dynamic data feeds from production sensors and the Internet of Things. This clear visibility ensures better operational planning resulting in enhanced service and increased efficiency for their customers.

Data-Driven: Logistic industry rely on enormous data to make key decisions. Business Data Analytics provides granular insight. Big Data Analytics highlights the importance of data in terms of its volume (referring to the amount of data), velocity (referring to frequency or speed by which data is generated and delivered), veracity (referring to data quality) and value (referring to the benefits from the analysis and use of big data). Predictive analysis helps to hire and retain talent, forecast staffing needs and improve employee satisfaction. As there seems to be exponential growth in the Logistic sector verticals, organizations are abandoning legacy systems and embracing analytics for better transparency.

Network Optimization Model: With the presence of more variables and more scenarios than ever before, organizations can integrate their analyses with many other interconnected business systems. Analytics on warehouse layout, product inventory and demand can help optimize operations within the warehouse also enabling alerts on depleted inventory or potential roadblocks. As the number of warehouses gets smaller, the remaining warehouses grow bigger and more efficient. By pooling customer demand across a smaller network of bigger warehouses, the company can decrease the variability of demand and can, therefore, hold lower levels of inventory.

Big Data Analytics helps companies solve much more intricate optimization problems than in the past. Usage of big data and advanced analytics to simplify distribution networks also helps logistic companies accrue savings that range from 10 to 20 percent of freight and warehousing costs, in addition to large savings in inventories. This technology has also shortened order-to-delivery cycle times and improved cost to serve.




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