With over two billion online shoppers and a 44% year-over-year increase of e-commerce sales in 2020, demand for accurate, timely, and detailed package data has never been higher. A shipper’s ability to predict demand, manage their product mix and inventory placement, and maintain healthy margins is advantageous in remaining competitive. But despite the abundance of data available across disparate systems, many shippers are wrestling with a key question: How can I best leverage the data available to achieve my two primary objectives – exceed customer expectations and reduce costs?

Problem: Shippers are limited by the timing, quality, and availability of carrier invoice data. Without thorough and real-time package and tracking information from a carrier, shippers are uninformed on their network, and consumers are left with little information about their highly anticipated order.

Imagine having information on exact SKU (stock keeping unit) numbers that are causing transit time delays, being able to pinpoint which size or type of box those SKUs were packaged in, and recording time stamps for each stage of the shipment process (packaging, pickup, shipping, delivery). Further, imagine having business intelligence capability to visualize and analyze this data to make real-time and informed decisions. Your ability to cross-reference and match carrier data with ERP (Enterprise Resource Planning), TMS (Transportation Management System), OMS (Order Management System), and WMS (Warehouse Management System) tracking data will enable a more robust data set, allowing further visualization and analysis into costs and inefficiencies within your network.

Benefit: Having access to robust and detailed package information can open a world of opportunities for your transportation network. Detailed tracking information can increase customer satisfaction and uncover opportunities to deliver packages more efficiently. In addition, a business intelligence tool allows you to synthesize the aforementioned data and use it most effectively.

Solutions: Here are three ways to enhance your carrier data and time in transit reporting capabilities.

Business Intelligence: Having a strong and robust business intelligence platform will allow shippers to regularly track critical time in transit metrics. Knowing these metrics will allow you to understand your typical customer’s experience. Additionally, such capability can allow you to understand customer experience by carrier, service level, location, etc. This type of granular view allows you as the shipper to easily monitor for blind spots or areas needing attention within your network.

Order Match: Order matching is a process where supplier data is collected upon manifest and is later cross referenced and matched with the invoiced data sent by the carriers. Examples of this data could include SKU information, box sizes, order number, order date, etc. This data is extremely useful in not only providing more details about each individual package, but also allows for a better estimation of costs that may be subsequently billed by the carrier (i.e. extra surcharges and fees).

Track and Trace: Track and trace solutions provide both customers and retailers real-time visibility to order and shipping status. With track and trace, shippers and consumers can see detailed information on the status of packages and have peace of mind in knowing when the package has made it to its final destination. Whether a package is stuck in the Suez Canal, or arrived 30 seconds ago on the wrong doorstep, it is important for consumers and suppliers to know the status of their packages.

Once you have some or all the solutions outlined above in play, it is essential to leverage the data in the most meaningful way possible. Here are some ideas to best understand your time in transit information.

Critical Metrics to Review

It is important to view time in transit metrics in multiple different ways. The way that a customer views time in transit will typically be different than how a carrier views time in transit. Additionally, slicing your data in multiple different ways can allow you to easily track customer experience based on what carrier and service are being utilized. The metrics outlined below are most critical when reporting on customer experience and can be easily modeled within most business intelligence platforms.

Time in Transit: These metrics should report time in transit based on a seven-day week. Therefore, if a package shipped on Thursday and arrived on a Monday, it would calculate as four days in transit. This view of your data is considered to be the true “customer experience” view of transit times as it most directly reflects what customers experience.

Business Days in Transit: These metrics use a five-day, M-F week or a six-day, M-S week to calculate days in transit. While this metric less accurately reflects what the customer experiences, it more accurately reflects how both shippers and carriers typically operate. Depending on the carrier or service level you are using, service may be offered on five, six, or even seven days per week. Customizing the business days in transit calculation based on these nuances will allow you to view time in transit through the same lens that a carrier would. Examples of this can be adding Saturday as a “Business Day” (Monday-Saturday), customization by carrier (Ex.: Carrier A uses five-day week; Carrier B uses six-day week), etc.

Additional metrics to track can include average transit days, average transit days over time, average transit days by zone, average transit days by service, percentage of shipments delivered in one day, two days, three days, etc. (by carrier, by service, or by zone), average transit days by zone and service combined (matrix), and average transit days by receiver state.

Advanced Time in Transit Metrics

While the metrics outlined above establish a critical reporting baseline, adapting your business intelligence tool to your specific business needs and internal KPIs can paint an even clearer picture of your operations. Here are a few examples of more advanced or “customized” time in transit metrics to model.

Customized breakouts specific to your business: You are not limited to breakouts by carrier/service/zone. Transit times can be calculated and visualized by your own internal categories. Examples include outbound locations, GL codes, fulfillment types, SKU numbers, order type, etc.

Score-carding against internal targets: If you have internal targets to achieve around time in transit, use your business intelligence capabilities to monitor the success rate of reaching these targets.

On-Time Performance: There is a critical distinction between “time in transit” and “on-time performance.” Time in transit represents the average number of days that packages take to arrive regardless of carrier, service, location, etc. On the other hand, on-time performance measures how well a carrier is meeting their delivery commitment times by service. This type of reporting typically considers “on-time” vs. “late” and is most often viewed as a percentage. This type of data can be more challenging to gain access to. However, if you do have access to such data, modeling overall on-time percentage by carrier, service, lane, receiver location, etc. can provide powerful insights.

The ability to manage detailed package information is becoming a necessity for shippers as the e-commerce market continues to boom. Success is where preparation meets opportunity. By taking steps to increase your data quality and subsequently having tools in place to effectively analyze that data, you are priming yourself for success. Data is just numbers; however, knowing how to effectively combine data sets and visualize critical analytics turns this data into information. With this information, opportunities are endless.

Anna Behrens is an Account Analyst with enVista Transportation Solutions. Anna has worked directly with clients to enable their success and achieve transportation goals through data analysis and solutions focused reporting. Quinn Nelson is Senior Account Analyst, enVista.

This article originally appeared in the July/August 2021 issue of PARCEL.