E-commerce is driving the demand for last-mile delivery, causing parcel shipping costs to increase at twice the rate of inflation. This effect is magnified as the expectation for low or free shipping and expanded delivery services is further established by Amazon. Since this trend isn’t likely to change anytime soon, shippers who can contain the cost of shipping by maximizing efficiency and optimization throughout their shipping network will be on firm ground heading into the future.
Analytics: An enterprise approach
With parcel shipping in the eye of the e-commerce last mile storm, it is more important than ever to use advanced analytics to monitor delivery performance and look for cost savings opportunities. Data-driven analytics enables you to make cost-effective transportation decisions.
However for most shippers, parcel spend is poorly understood because shipping data is often locked up in point solution silos, such as carrier-provided systems and websites, in a variety of different formats. Moreover, some costs, such as office and inbound shipping expenses, aren’t tracked at all, and the reporting that does exist is sketchy at best.
Despite all the attention given to the last mile, e-commerce logistics involves the entire organization. This means that an enterprise approach is necessary to continuously fine-tune all processes and strip away wasteful costs and behaviors — allowing you to maximize both profits and customer retention.
Enterprise Parcel TMS: Pulling it all together
Reducing parcel shipping costs starts with effective planning. The first step is to collect all the data required to support optimal decision-making within the shipping function. A big part of this is getting all the related information into a single data repository. Enterprise parcel TMS platforms provide this capability by capturing data at all points of shipping, including fulfillment centers, drop ship suppliers, stores, offices, and returns.
For more about how a parcel TMS can help you pull all the pieces together and put in place enterprise shipping controls from ordering through returns, see our blog post “Improve Parcel Shipping through Better Planning and Execution”.
The Data Warehouse: The treasury of your enterprise
All of this data has to be kept somewhere, and just like SKUs, it’s stored in a warehouse. William H. Immon, known as the “Father of Data Warehousing,” described a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data that supports management’s decision-making process. In non-techy terms, a data warehouse is an electronic storage area filled with a variety of information gathered over time that is organized by subject and isn’t lost when the computer is turned off. This information can then be used in an infinite number of ways to aid in making decisions that will benefit the shipping operation and overall business.
Companies used to be limited in how much data they could store by the physical size of their on-premise computing equipment. But no longer. Thanks to DaaS (Data as a Service) cloud-based options like Amazon Web Services and Microsoft’s Azure, even the sky is no longer the limit for data storage and processing. It’s easy to see the potential for finely detailed and highly accurate data analysis when the system can draw on such a vast amount of specific, instantaneously searchable information.
Another advantage of cloud-based data warehouses is that they’re seemingly limitless and scalable — they’ll grow as your data volumes grow — at low cost. Some, like Azure, only charge for the time you actually spend accessing your data, so you’re not paying for the time your information is sitting idle. And when you do need to tap that information, they deliver lightning-fast responses.
Analytics: A three-pronged approach to optimization
As a company now with several billion bits of data sitting around in your warehouse, how are you going to use them to gain actionable insight into your shipping processes? That’s where analytics comes in. There are three main types of analytics — descriptive, predictive, and prescriptive — each of which gives you different information that can support and guide your decision making. The latter two are the most useful, but it’s important to understand how each can be used to improve the efficiency and profitability of your parcel shipping.
Descriptive analytics – What has happened
As the name implies, descriptive analytics gives you a description of what’s already happened. It will show you the history of your past deliveries and how your resources were used to fulfill those deliveries. Descriptive analytics can provide dashboards with key performance indicators and insights into cost by carrier, cost per pound, and on-time delivery performance. It will uncover unnecessary spend such as Air that could have been downgraded to Ground, address corrections, and DIM weight fees — enabling you to find opportunities to implement cost-saving measures. By importing carrier invoice data, descriptive analytics can compare expected vs. actual costs, and uncover cost recovery opportunities such as rating efforts, guaranteed delivery refunds, and lost or damaged shipments.
Predictive analytics – What will happen
This is the method that allows you to predict the future — or at least an estimate of the future grounded in hard data. Using predictive analytics, you can forecast what efficiencies might be possible from certain changes in your shipping behavior. For these predictions, key inputs and historical data are required, such as package types, weights, origin/destination pairs, lead times, service levels, as well as educated guesses to fill in gaps in the data. By anticipating future volume with certain probability, you can see the likely demand for particular products, the need for sorting and distribution centers in particular locations, and what resources will be necessary to optimize last-mile delivery to certain areas.
Prescriptive analytics – What can happen
Prescriptive analytics is a way to automatically generate an action that will lead toward an optimal result. With prescriptive analytics you can prescribe what actions to take to attain certain desired outcomes, such as how to change your shipping behavior to achieve savings of X amount while still making that last touchpoint with the customer an excellent experience. For example, if a carrier’s delivery performance is actually better in a particular zone than the published delivery times indicate, you could set the TMS to automatically downgrade the service level and achieve the desired delivery for the same or lesser price — thus achieving the same level of customer service at lower cost.
And prescriptive analytics can be applied to the whole organization: purchasing, order entry, shopping carts, fulfillment, customer service, human resources, and finance. When the results of a prescription are loaded into an enterprise-level management system, then routings, employee shifts, contracts, etc. can be re-optimized in real time or close to it. That means major savings in time, increased business agility, and a significant, positive impact on profits.
Machine Learning: Getting smarter and solving problems
Taking analytics one step further, machine learning uses massive computing power to recognize patterns in data that humans could never see, and with each new piece of data, the machine gets smarter and more accurate. All of which happens in real time.
The power of machine learning comes from leveraging data from both internal and external sources, such as GPS systems, weather reports, and real-time traffic patterns. And in a kind of virtuous cycle, more stored data => more created data => more accurate and useful analytics to drive more cost-effective transportation decisions.
For parcel shippers, machine learning can aid you in making faster and better decisions to optimize such processes as carrier selection, rating, routing, and quality control. What’s more, by being able to gather and analyze millions of pieces of information at lightning speed, machine learning can point out a problem you didn’t know existed, and then help you solve it.
Parcel TMS Data Analytics: The philosopher’s stone
In a type of modern-day alchemy, a parcel TMS — your philosopher’s stone — enables you to transmute the base metal of your data into the gold of increased profits. And it allows you to do it in a way that doesn’t compromise the customer experience, especially in that last stage of getting the parcel to the door. So start gathering data today that you can leverage into cost savings tomorrow.