Big Data Analytics and Its Impact on Logistics

Big Data Analytics and Its Impact on Logistics
Aug 16,2016          Logistics Solution          Be the first person to comment

Use of various internet-based systems have led to an exponential growth of data across all sectors, and Big Data is turning out to be a useful. Gartner IT Glossary defines Big Data as high-velocity, high-variety and high-volume information assets that involves innovative and cost-effective methods of processing information for better decision making, process automation and enhanced insight.

Businesses now require better data management and analytics to adapt the changing scenario. Big Data Analytics has revolutionized the way organizations perform their business. Industries that have already implemented big data technology has witnessed dramatic change in terms of business growth and cost reduction. It has now become increasingly important for the logistics companies to invest in Big data and experience a rapid growth.

Logistics and Big Data Analytics

Logistics and Big data should ideally be a perfect combination. If forecasts are to be believed then logistics companies or service providers will expect a consistent growth. Data sets gathered from different sources should be converted to meaningful information to aid decision making. Service providers gather information like origin, destination, size, weight and the current location of the shipment. But, they lack the process to generate meaningful insights from the data.

Big Data Analytics provide insights about buying patterns of customers, market trends, and maintenance cycles. It highlights the methods to lower cost and facilitates a targeted decision making process. In logistics and supply chain management, big data helps in improving competitiveness, efficiency and productivity.

Each company has hordes of data stored, but these need to be refined and transformed to improve business value. Logistics service providers and researchers have recognized Big Data Analytics as the game changer. From unstructured data, decision makers can generate insights about pricing strategies, product placement, optimization strategy, improved service delivery, layout optimization and risk management.

Value addition with Big Data in Logistics

For logistics service providers and companies, capitalizing information is an important strategy to implement effective data-driven business model. While including Big data in Logistics planning process, the decision makers and entrepreneurs might face questions about its viability. Questions might surface as what it will yield or how will it affect the overall efficiency and productivity! So, it is essential for data scientists to understand the impact or the value that Big Data Analytics will yield. Three areas where big data add value are:

  • Improves operational efficiency by optimum resource utilization, increasing transparency, process performance and quality.
  • Enhance customer experience so that there is a rise in customer retention and loyalty. Insights help in segmenting the customer groups and start communication with them.
  • Implementation of effective business models to earn better revenue.

To get competitive advantage, it is important to exploit the untapped data and contribute to operational efficiency.

Big Data Applications in Logistics

Data is generated every day. Logistics collect data from different sources- customer feedback, review and blogs on social platforms, sensors used to get real-time insight, internal processes and mobility solutions. It is difficult for conventional systems to process it and generate meaningful insight. So, they are now shifting to a more sophisticated process.

Already few logistics companies have integrated Big data to the existing system to monetize data. In the logistics sector, few big data based activity are

  • Predicting the volume of shipment on days, week or month is a chief concern for logistics companies as budget and resource allocation depends on it. Analytics help the service providers to predict peak shipment volume and plan accordingly for better productivity.
  • Routing paths, facilities, transparency and cost are important factors integral to logistics companies. Data scientists or experts study the data sets and provide insights on the route and facilities that are cost-effective, reliable and will yield a profit. These insights make it easy for the decision makers to choose the warehouse services, transporters and facilities that are cost-effective, viable for growth and formulate an effective business plan.
  • Predicting the exact delivery time of shipment is an uphill task for logistics companies. Different variables like traffic status, number of consignments, vehicle health and driver efficiency determine the time of delivery. Data analysis provides accurate information about the status of consignment and relevant information. This information helps in providing better customer experience.

To drive productivity across the different segments of the logistics and supply chain it is important to invest on Big Data in Logistics.

Factors determining the success of Big Data in Logistics

Logistics providers can leverage the power of big data to improve their performance. But there are few things that entrepreneurs need to emphasize on

  • To implement Big Data in Logistics successfully, it is important for the IT experts to stay abreast with the objective and benefits of the process and risks involved in it. A mutual understanding between the management/ decision makers and IT team about the process and challenges is important for determining success.
  • The success of big data depends on three factors- locating data that is already present, ambiguity in data mapping and governance. So, it is important to maintain transparency about information, have structured data and upkeep the data quality. Make sure that the data is cleansed in order to remove duplicate, obsolete and incomplete data.
  • Often to gather customer insight, personal data also get exposed, so at the onset consider setting regulations about privacy issues and data protection.
  • Big data comprises of techniques that convert data into valuable information, so it is important to have a team proficient in data manipulation and analysis techniques. IT departments need to assure that the technology used for the process conforms to the particular use cases.

In the years to come, data will take the driving seat in the logistics arena. It is important to tap on the potential of Big Data in Logistics to improve the productivity, and implement practical business plans.