Geofencing Data in Logistics: Importance of Standardization

Since the introduction of geofencing to the logistics and transportation industries, companies have improved their geo-tracking, data accuracy, alert systems, routes, delivery times, and much more. Even though geofencing has been an exceptional technological push forward, the lack of standardization concerning transportation makes it difficult for logistics professionals and carriers to collaborate, share data, and get everyone on the same page.

This guide explores geofencing standardization and its role in logistics and freight. You’ll learn why data standardization matters, data standards within the transportation industry, problems with geofencing data standardization, and the costs of not implementing geofencing data standards in your own operations.

Why Data Standardization Matters in Logistics and Freight

Nearly every industry has unique standards, codes, rules, and regulations that have been tested and iterated upon, often for many years. Since geofencing in the transportation and logistics industry is relatively new, there has yet to be much time for standardization.

Without standardization, the landscape becomes like the wild west, with anyone choosing the data formats, layouts, or procedures they wish to use and follow at a whim.

Unfortunately, this often results in incompatibility and the inability of carriers, shippers, and other important stakeholders to work with each other without excess complexity. When data formats, procedures, and other elements are standardized, everyone has clear expectations on accessing, implementing, and utilizing the data in question. This type of self-sufficient organization yields improved productivity and better business results in the long term.

Why Data Standards Matter

LinkedIn’s CEO, Jeff Weiner, once said, “Data really powers everything that we do.” This especially holds true in the logistics and freight space, where precise, accurate data is increasingly available on demand. When we have access to vast amounts of data that allows us to make near real-time business decisions and adjustments on the spot, as well as monitor long-term trends, there must be a standardized way of storing this data, retrieving it, and analyzing it over time.

Standards not only allow access to valuable data on demand, but also provide a valuable platform for developing flexible storage formats that let multiple users take advantage of different technologies and participate in the data-sharing “ecosystem.”

With this type of access and sharing, users can effortlessly transmit data amongst themselves without conversions, manual translations, or other tedious transformations that often take significant time and money to complete. Data standards establish common rules that govern data transfers within a specific industry.

Data Standards in Logistics & Freight

Within the logistics and freight industries, data standards help workers understand and retrieve important details and communicate that information with other carriers, shippers, employees, and anyone who requires visibility to the information.

Here are a few real-world examples of data standards in this industry:

• GS1 Standards – GS1 is a global non-profit organization that provides standards for product identification and communication, traceability, and data synchronization. GS1 standards are used across the supply chain, from product design to product delivery.
• UN/EDIFACT Standards – EDIFACT (Electronic Data Interchange for Administration, Commerce and Transport) is an international standard for electronic data exchange in the supply chain. EDIFACT standards are used for exchanging business documents such as orders, invoices, and purchase orders.
• Open Transport Initiative (OTI) – OTI is a set of standards for data exchange in the transport and logistics industry. The standards provide a common data format and communication protocol for exchanging transport-related information.
• International Air Transport Association (IATA) Standards – IATA standards provide a set of standards for sharing information between airlines, airports, and other organizations involved in air transport. The standards include a common language for exchanging flight information, passenger data, cargo data, and additional related information.

These standards and others play an integral role in aligning stakeholders’  operations, communications, data storage, formatting, and other internal procedures. This unified approach helps achieve high-integrity geofencing that enables organizations to implement accurate positional tracking, real-time alerts, shipping notifications, and other features that improve their daily efficiency. Well-defined data standards also help organizations train their employees in geofencing, which decreases the risk of human error and alleviates safety concerns.

To keep everything running as smoothly as possible, you need to evaluate your current data standards and be prepared to develop them further.

Using Standardized APIs to Ingest Geofencing Data

Application programming interfaces (APIs) are some of the most critical innovations of the cloud computing era, especially as they relate to data accessibility and standardization. APIs also come with their own important standards in both computing language and structure, and how different APIs can benefit from each other if, and only if, they are compatible due to the use of data standards across platforms.

Using different geofencing data providers and “mixing and matching” datasets can quickly create a mess of your most crucial storage databases if each new dataset has a different API format, with different data formatting flowing through it. Suppose one data provider uses an  API structure and data format that isn’t compatible with what other industries already use for their geofencing data. In that case, it becomes almost impossible to collaborate and share data and ensure that you are working off a data standard. This creates additional work for end users who need data to build products and solve real problems in the supply chain. For example, if a track and trace platform has integrated geofences, but those geofences are only available to the same users on that platform due to a unique format (think of Apple and their annoying use of unique chargers for their devices when everyone else in the world uses USB-C), then that forces other carriers or shippers in that ecosystem to create or access their own geofences and the standard now has fallen apart.

Kestrel Insights has developed APIs with standardized formats that allow geospatial data to be ingested in a common, reliable storage format for ease of use and accessibility.

The Problems With Geofencing Data Standardization in Logistics

While geofencing is an excellent freight and logistics technology, there are several common problems with the industry’s current state of geofencing data standardization. We will discuss circular geofences and their negative impact on having a data standard and look at how manually drawn geofences can be susceptible to human error and lead to the creation of thousands of data silos.

Circular Geofences and Their Impact on Standards

Circular geofences are inherently accurate since no location is a perfect circle and can vary widely in size across different platforms. This becomes especially problematic in dense areas where geofence boundaries require high levels of precision and is a major problem for both intra-company and inter-company communication.

For intra-company communication, let’s take an example of The Home Depot Distribution Center located at 2320 Beckleymeade Ave, in Dallas, Texas. Looking at a map of the area, we see a Love’s Travel stop within ½ a mile from this distribution center. ½ a mile is very much within the commonly used radii for a circular geofence, which typically ranges between 0.5 – 2 miles. These distances are used to ensure all areas of a given site are covered by the geofence, which can be a tall order given the simple geometric shape of a circle.

Teams are often required to set a standard radius across all their geofences, meaning that if a team has 100 geofences, they need to choose either 0.5 miles, 1 mile, or 2 miles to be used across all geofences since that’s how they are produced. Teams often choose large distances to ensure their geofences work for all of their locations.

Let’s step back to our Home Depot example: With that Love’s Travel Stop located 0.5 miles from the distribution center, teams who have previously installed 0.5-mile radius geofences were getting inaccurate detention reports that read 6 – 8 hours spent on site.

This occurred when a truck would arrive at the Love’s Travel Stop, fill up on fuel, reset its clock, and then go to its appointment. All of this was happening inside of the established geofence and therefore led to major breakdowns in intra-company communication due to reporting inaccuracies compared to what was happening from the driver’s perspective.

We can also use the Home Depot Distribution Center for inter-company communication as an excellent example. There are over 100 different carriers that engage with that center, dropping off and picking up loads continuously. It is a widespread practice for those carriers to work with Home Depot on detention reporting, delays, and other timestamp-based issues and opportunities.

What happens if all 100 carriers are using a different geofence? A situation like this creates very inefficient communications between stakeholders that require constant reporting and verification.

To solve this problem, Kestrel Insights has developed a geofencing data lake so that all stakeholders can operate from the same geofence for all locations.

With this geofencing data lake implementation, everyone works off the same data and speaks the same language. Not only that, but shippers can also access these geofences to obtain locations that their carrier partners are using and then utilize this information to provide updates and verification to the geofences Kestrel Insights has stored for them. This creates a single touch point for all location data users for every node of the supply chain.

In short, a geofencing data lake can improve the location accuracy of your data and drive higher-level key performance indicators (KPIs) and decision-making.

• Since there isn’t an established standard around geofences in the transportation and logistics space, companies traditionally generate geofences by manually inputting a radius to create a circular geofence or manually drawing a polygon and inputting it into their specific system of record.
• With circular geofences, there is no integrity relative to the actual boundaries of the location being “fenced-in,” leading to insufficient data and poor decision-making in the long run.

Manually Drawing Geofences and Data Silos

Polygon geofences provide greater accuracy than circular geofences, but manually drawing these areas opens the door to human error and excessive manual labor. There is no master quality assurance (QA) or quality control (QC) process to verify geofenced targets and ensure data quality and integrity. Furthermore, manually drawn polygon geofences have no scale or network effect, making it difficult to grow and expand your operations.

• Manually drawing geofences requires trained personnel and employees to be available and make changes on-demand; hiring additional teams for the sole purpose of drawing geofences can become expensive fast.
• To solve these problems, Kestrel Insights has developed precise polygon geofences that can be drawn automatically at scale, removing the need for hiring a team dedicated to geofences and allowing your organization to draw accurate polygon boundaries for your areas of operation.

The Costs of No Geofencing Data Standards

With everything we just looked at together, it’s clear that without geofencing data standards, the costs and losses among logistics, shipping, transportation, and freight partners can be high and result in the following:

• Poor decision-making
• Insufficient data quality
• Increased human capital spending
• Losses related to positioning and security
• No standards to audit

Firms can spend tens of millions of dollars annually creating and using poor-quality geofence data. Fortunately, there is a better way by utilizing Kestrel Insights automated polygon geofencing solutions.

Standardizing Your Geofencing Data With Kestrel Insights

Kestrel Insights has developed precise and automated geofencing solutions that are robust and can significantly amplify your bottom line. When you’re ready to begin standardizing your geofencing data and eliminating all of the capital losses that come with the lack of standardization, reach out to our team. We’ll be happy to show you what geofencing standardization can do for your business.