The Standardization of Data for Geofencing in Logistics & Supply Chain

The increasing globalization of supply chains and logistics has made it abundantly clear that data is at the heart of successful operations. A lack of data standardization in many vital facets of the supply chain impedes efficiency, communication, and accuracy, and the standard or lack of standard in geofencing technology is no exception.

What exactly does standardization accomplish, and why does it matter? These are essential questions so your business can move forward purposefully and get the best value from your data.

Why Is Data Standardization Important in Logistics & Supply Chain?

Supply chain and logistics are constantly dancing between multiple parties across hundreds of tasks that range from procurement and scheduling to detention reporting and payments. Data Standardization allows businesses to streamline operations worldwide, communicate and collaborate more effectively with multiple parties, and increase efficiency. Geofencing is a much-needed technology that supports these tasks.

To establish data standardization, companies first need to centralize the access to available quality data via a data lake or through APIs. These requirements are necessary to bring value to any market where a data standard is needed and valuable.

Data Quality

Today’s business processes are incredibly reliant on accurate data to function properly. Unfortunately, experts estimate that organizations lose an average of $12.9 million annually to poor data quality. The issue of data quality is a growing problem for several reasons:

A single business may use multiple systems to capture or interpret the same data point, with no consistency in managing that data.
Manual data entry is susceptible to human error.
Often, data rules are set once and never updated.
Trusted data sources may have inaccuracies.

Standardizing data is necessary to address each of these problems and, in turn, improve business performance.

The Role of Data Lakes for Data Accessibility & Quality

A data lake allows storing and processing of large amounts of data, including operational databases, in a central location. It also enables you to crawl, catalog, and index the data to fully understand what the lake contains. In addition, you can centralize and standardize data by accessing a data lake via an API, which makes it possible to use a consistently updated data repository for reporting and analytics.

You may also bring your API data into a data lake first. In this case, you can treat the data lake like a relational database and use it to access and analyze current and historical data.

Data Standardization in Geofencing

There is no existing system of standardization for geofences in transportation and logistics. This has damaging consequences, as problems like fence breaks and bad data can lead to poor decision-making and financial ramifications.

Kestrel Insights can provide raw data via API or bulk download to a data lake that you can then use to pinpoint accurate location data with automated geofencing. Other geofencing data lakes cannot offer standardization across the industry because they are in silos and only accessible to certain users or paid members. However, the data from Kestrel Insights is available to anyone for use across any and all platforms that leverage geofences.

Use Cases of Applied Geofencing Data in Logistics Scenarios

Modern supply chains rely on access to real-time data for geofencing services and applications. Therefore, if data and geofencing logistics results are accurate, they should offer:

On-demand access to real-time data for trucks, containers, or shipments as a whole.
Digital records and data analytics focused on industry trends and consumer needs.
Real-time visibility via precise perimeter pings and settings for in-transit inventory shipments. This enables users to understand freight transportation milestones, such as departure from a supplier warehouse to arrival at a fulfillment center, so operational teams and end consumers can accurately predict the arrival time.
Ownership of fleet management data instead of relying on a third-party software application. This makes it possible for users to avoid downstream issues, including data latency, added costs, cumbersome data access, and a lack of new third-party application features.
Reliable tracking and proactive monitoring of exceptions through customized settings and alerts. These notify fleet managers within their system of record or logistics tech platform of exception events, such as a vehicle crossing a perimeter or deviating from a planned route, which could indicate a wrong delivery or vehicle theft.
Increased value from raw data allows users to extract value, including subsequent analysis, prescriptions, and predictions, to optimize asset utilization, plan routes, and forecast demand.
Delivery flexibility allows for better activity planning, reduced wait times for carriers, and anticipating demand for unloading facilities.
Improved operational efficiency and accuracy via automated yard checks and timesheet verification.

These benefits can reframe logistics and supply chain operations by creating a less labor-intensive system with greater accuracy.

Standardizing Data for Geofencing with Kestrel Insights

Polygon geofencing is an excellent way to increase efficiency and customer satisfaction, but only if the right information is available. With Kestrel Insights, your business can improve its geofencing technology with standardized data. Contact the team to learn how using high-quality data for geofencing can enhance your decision-making and save time and money.