Utilization of Big Data in Logistics - Data Analyst

Utilization of Big Data in Logistics

There is no doubt that information has become an important element in the strategy of competing. Currently, many companies are trying to obtain and use data for sharp and accurate business and operational decision making. Some important information such as sales volume prediction, customer preferences, and optimization of operating capacity, has become an important need for managers in managing a business in today’s era of competition. Data has become an important strength and resource for companies in managing their business successfully..

Instal Data Analysts App

The thing is, the data today is so plentiful and complex. Managers need to sort and process data into relevant and accurate information for business decision making. Especially in the information age like today, with the growth and internet access that extends to various communication media (Internet of Things–IoT) and everything using the internet (Internet of Everythings), data can be obtained easily, instantly, and in large quantities.

In recent years in the digital age, data availability has doubled on average each year. In 2010 the data volume was 2,000 exabytes, while in 2015 it had become 10,000 exabytes. It is predicted that the volume of data in 2020 will not be less than 40, 000 exabytes. In addition to the large volume of data, the characteristics of the data are now substantially different from the characteristics of the data in previous years.

First, data is currently increasingly flowing in large quantities from various equipment connected to the internet, such as smartphones, RFID, webcams, and network sensors. This equipment generates data flow data continuously without human intervention.

Second, today’s data is very varied and unstructured, ranging from images, voices, blog entries, discussion forums, and e-commerce catalogs. All this data is in large volumes, fast, and varied (3Vs: volume, velocity, and variety) which in its development becomes characteristic of Big Data. Currently the company manages and analyzes Big. Data to increase the value of information in business decision making.

Big Data Value Dimensions

When companies manage and use Big Data as a strategy in competing, the first question asked is what kind of Big Data can drive increased organizational value? In terms of the value of Big Data information, there are three dimensions of Big Data utilization in business organizations: operational efficiency, customer experience, and new business models.

The use of Big Data for operational efficiency is obtained through:

  • Increased level of transparency.
  • Optimization of resource consumption.
  • Quality and performance improvements.

Companies can exploit data to improve the quality of the interaction process with customers in using the company’s products and services, so that they can:

  1. Increase customer loyalty and retention.
  2. Accurately present in segmentation mapping and customer targeting.
  3. Optimizing customer interaction with the service.

The third dimension of the value of Big Data is that companies can develop new business models through data capitalization to increase revenue streams of current products and create new revenue streams from new product development.

Benefits of Big Data in Logistics

Currently, companies are learning to turn data at scale into information as a competitive advantage. Precision in market forecasting, radical customization of services, and management of entirely new business models demonstrate the exploitation of previously untapped data.

Apart from being a best practice today, Big Data will also have the potential to become a disruptive trend in the logistics industry.

In the logistics industry, Big Data analysis can provide a competitive advantage because five different characteristics can be effectively applied in the logistics industry.

1. Operational optimization

Optimization of operating processes such as delivery times, resource utilization, and geographic coverage is an inherent challenge in logistics. Large-scale logistics operations require data to efficiently manage logistics operations.

2. Delivery of tangible goods

Delivery of tangible goods requires direct customer interaction at the time of pick-up and delivery. On a global scale, millions of points of interaction with customers every day can create opportunities for market intelligence, product feedback or even demographics. Big Data provides a versatile means of analytics to generate valuable insights into consumer sentiment and product quality.

3. Sync with business customers

Modern logistics solutions integrate into the production and distribution of processes in various industries. Strict integrase levels with customer operations allow logistics service providers to feel the heartbeat of an individual business, vertical market, or region. The application of this broad knowledge analytic methodology reveals supply chain risks and provides resilience to disruptions.

4. Information network

Transportation and delivery networks are very important data sources. In addition to using data to optimize the network itself, it can provide valuable insights into the flow of global goods flows. The power and diversity of Big Data analysis moves the level of observation to a microeconomic point of view.

5. Global coverage, local presence

Local presence and decentralized operations are a must for logistics services. Fleets of vehicles move across the country to automatically collect local information along transport routes. The processing of Big Data streams coming from large delivery fleets creates a display of valuable information for demographics, environmental statistics, and traffic.

Last Mile Optimization

The term “Last mile” originated in telecommunications terminology and describes the last segment in the communication network that actually reaches the customer. In the logistics sector, “Last mile” is a depiction of the activity of the final part of the supply chain, where the goods are handed over to the recipient.

Constraints in achieving high operational efficiency in the distribution network occur on the “Last mile” in the supply chain. Therefore, the optimization of Last mile is carried out by using big data to reduce the cost of the product.

There are two basic approaches to making data analysis to improve the efficiency of “Last mile”. The first approach, a large flow of information is processed to further maximize the performance of conventional delivery fleets. This is mainly achieved through the optimization of delivery routes.

The second is an approach that makes more use of data processing to control an entirely new Last mile model. With this approach, each vehicle receives a continuous update of information from the order of delivery that takes into account geographical factors, environmental factors, and the status of the recipient.

Leave a Comment