Retail and e-commerce brands are flush with logistics data, but so much information can be overwhelming to analyze, let alone used to optimize areas of a business. From transactional and inventory data, to data collected from Warehouse Management Systems (WMS), Order Management Systems (OMS), and additional sources like IoT devices, it’s hard to know which data is the most valuable. However, having a wide aperture for data collection is invaluable, as it enables brands to see what’s working well and identify problems to make quick and data-informed business decisions. 

Whether handling data collection in-house or relying on a third-party logistics (3PL) partner to provide it and give recommendations, data-backed insights are key to running an efficient logistics operation.  

Here are four tips for turning individual logistics data points into meaningful insights. 

Determine the “North Star” Measurements. 

With so much data available to brands, some leaders mistakenly believe they need to review every type of data that’s collected. But when it comes to knowing what logistics data to prioritize, it often comes down to a few key areas: 

• Order Fill Rate — is everything getting out the door that should be?
• Units per Hour — how fast can the team pick, pack, and ship those orders?
• Mispick Rate — how many shipments are getting received by customers with the wrong product?
• On-Time Delivery Rate — are the delivery timeline promises for customers being met?
• Orders per Truck — are the network assets being used efficiently?
• Perfect Order Rate — how often is everything going right? 

These North Star metrics shine light on the health of logistics operations. If everything is running smoothly and these guiding metrics are within the right range, it’s a sign of being on track. However, if the data shows areas of concern, such as long delays to delivery or continuous mispicks, these metrics can help leaders address problem areas quickly and pinpoint how to take action and get back on track. 

Turn to trusted tools for visibility into key data. 

By diving into logistics data, companies can gain insights to efficiently identify and address rising issues. Fortunately, it doesn’t require groundbreaking tools to do so. Even the simplest tools, like a pivot table, can reveal meaningful insights — but there’s a caveat. Whether working with Excel, business intelligence tools or enterprise-grade reporting software, it’s important to differentiate between what data should be looked at daily, weekly, and monthly.  

On-time delivery rate, for example, highlights an area with very real consequences for customers, and can require intervention after even one bad day. For brands managing this in-house, utilizing the tools in which a team is well-versed helps companies identify their problems quickly, and lead to better resolutions. Alternately, a tech-forward 3PL will offer reports of their own, often flagging down issues that arise, and saving brands the trouble of analyzing this information on their own.  

Understand the context around data before taking action. 

Warehouse and customer service teams have a plethora of granular order data at their fingertips to manage their work, day to day. On the other hand, logistics leaders and 3PL partners are required to look at data from a zoomed out, panoramic vantage point to identify positive and negative logistics trends over time. While it can be incredibly valuable for teams to spend time auditing raw data to gain an understanding of the business, context here is critical.  

Let’s go back to the on-time rate. Say the data shows a truck broke down in a brand’s busiest region one week. This one-off occurrence could be isolated. But, if a brand has consistent on-time rate issues and weekly truck breakdowns for months on end, that points to larger vehicle maintenance and asset management issues that need to be addressed. The key here is context — that’s what differentiates an incident from a pattern. By looking at data sets over different periods of time, brands can identify trends and act quickly to solve them without getting distracted by outliers.  

Make analyzing logistics data a high priority. 

Data-driven decision making is impossible without consistent tracking and purposeful analysis. Once a brand identifies their North Star logistics metrics, leaders need to prioritize setting time aside to analyze this information. Reviewing data and reports shouldn’t be a background goal. Establishing a cadence, and deliberately setting aside time to review key metrics daily, weekly, and monthly gives brands visibility to make more informed decisions, streamline operations and protect the customer experience.  

The ability for logistics companies to make better decisions comes down to one thing: data. Collecting, analyzing, and interpreting data is the best way to gain actionable insights and improve logistics processes. Whether managing this in-house or relying on a tech-forward 3PL, these best practices will turn logistics data into meaningful insights. 

Mark Ang is a co-founder of GoBolt.