Within the supply chain, big data lies at the center of every process, asset movement and decision made. Big data is often thought about in four parts, otherwise known as the 4 V’s: volume, variety, veracity and velocity. As we covered in our last blog, when your organization successfully addresses the challenges of working with data in the supply chain, you will be able to unlock its full potential.
In the world of logistics service providers (LSPs), data is being created every minute—from an order first being placed, an unexpected delay, or a customer asking a question about the location of their shipment. For a logistics manager, understanding and utilizing big data can either be the path to their organization’s success or stand in the way of it.
How Data Addresses the Concerns of a Logistics Manager
Logistics managers are tasked with heading overall operations within their supply chain, ensuring that all partners and processes are working optimally. To do this effectively, they need insights into their operations and timely responses to arising issues and market trends. In this week’s Guide to a Data-Driven Supply Chain, learn how the 4 V’s of data can empower your logistics organization.
A. High Volumes of Data
The first V of data is volume, the amount of data that your organization has access to or possesses. In order to arrive at insights through technologies like artificial intelligence and machine learning, you need high volumes of data. Essentially, the more data you have, the more meaningful your insights will be.
Within a logistics organization, data-driven decisions are key, as data can help you to make knowledgeable decisions and implement a cost-effective supply chain. One example in which data can be strategically utilized is in the process for selecting carriers. Having key data points like service levels by lane, tender acceptance rates, accessorial fees and other freight costs will help your organization choose the best priced and most time efficient carrier for your shipment.
B. Variety of Data
Another V of data is variety, or different types of datasets. In troubleshooting when issues arise or markets change during shipments, a wide variety of data will empower logistics managers to best respond. For instance, during holiday seasons, data from previous years surrounding number of orders placed, busiest order dates, most popular items and more can inform logistics managers for how to best prepare for the influx. This information will empower logistics manager to adjust and scale operations accordingly for the holiday seasons, allowing them to have consistency in service and continue to provide value for their customers.
C. Veracity for Your Data
The next V stands for veracity of data, or the trustworthiness of the data you gather. If the data that your supply chain organization gathers is untrustworthy, then you will be unable to relay accurate information to your customers. As competition greatly increases in the world of logistics, providing high levels of customer service is all the more important. A key component of this for a logistics manager is the ability to relay information regarding accurate real-time tracking of shipments to customers. With reliable data at hand, logistics managers will be able to confidently notify customers of expected delivery dates and unexpected delays.
D. Data Velocity
The last V of data is velocity, which refers to the speed with which your data is processed. Similar, to the topic of latency in data that we discussed last week, data is rendered useless if not received in a timely manner. In order to meet your customer’s needs for real-time data for shipping and delivery, the data needs to be quickly processed. Additionally, when logistics managers receive data in a timely manner, they can better prepare when unexpected delays arise. One example, a weather alert for a moving storm can help you anticipate potential delays in delivery to certain locations, initiate earlier communication with customers and prepare potential disaster plans.
Follow along on our series about a data-driven supply chain by checking out our past blog on data challenges and reading our next on unexpected sources of data.
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