The world of logistics is all-encompassing and ever-expanding. Those in the logistics industry often offer a bevy of other services that cater to their customers’ needs. Any given company in the logistics space may provide a veritable grab bag of services, including mail, print, auxiliary, facility management, and more. Handling many diverse operations is a daunting task for any business to take on, much less in a time-sensitive and cost-driven environment.

Logistics technology is often regarded as cutting edge and forward-thinking. An oft-mentioned aspect of this ever-burgeoning field is the competitive nature of the technology itself. Organizations and companies are continually adopting new technology to gain a competitive edge. Software and hardware alike are furiously taken on by professional teams, often without a clear understanding of how best to use those tools. Yet, typically, the most straightforward tools, when used effectively, produce the largest impact.

Take something as simple as database management. Database management is often the pillar of typical business operations, as regardless of the size of a business, there are always mountains of client data to track. But what if you pair a database management tool with a complex yet forward-thinking technology? What if there was a tool that could discern specific client information without needing an exact query match? Fortunately, that tool is here: the concept of fuzzy logic allows for imprecise searches.

What is Fuzzy Logic?

Fuzzy logic is a mathematical tool that allows for an expansive or a grey area to exist between any group of given properties. This logic is something that human beings can do naturally as we process the complex world around us. However, computers, which exist in a binary logical setting, are designed to see things as true or false, black or white, one thing or another. With fuzzy logic, computers learn to see degrees of truth.

How Does Fuzzy Logic Work?

As they were originally built, computers were programmed to see information through a binary lens. With fuzzy logic, the goal is to create desirable results by teaching computers how to discern commonality without precisely matching up words.

For example, a mailroom worker who sees the address “651 S 8th St.” can think of its various spelling iterations and match it to the database with the same address listed as “651 South Eighth Street.” Ordinarily, this would be an incredibly complex task for a computer. However, with fuzzy logic, an in-building logistics platform can match up search queries and database entries to recognize these human inconsistencies and common variations. This is helpful when applied to search algorithms, allowing for non-exact matches to pull up correct results.

Why is Fuzzy Matching Important?

Human beings are not precise in the way that computers have to be. We use partial search terms, don’t always spell things correctly, and rarely ever include context. Fuzzy matching helps computers think like us by translating our flawed search terms into the database’s actual entries.

Fuzzy matching can be particularly important when it comes to UPS or FedEx API integrations. Software that integrates information from separate platforms and databases significantly cuts down on labor time because staff can access data from a single system. Fuzzy matching provides a way for those databases to communicate with one another, allowing each database to see a different set of conventions, use alternate spelling, and identify abbreviations as they share information. Ultimately, if you want to cut down on time spent searching for items and increase your team’s efficiency, a fuzzy matching integration provides a way to achieve this goal.

Are you interested in learning more about fuzzy matching integrations? Let us know in the comments or email us at [email protected].