logisticstechoutlook

"Big Data" in the 3PL World

By Joe Johnson, Vice President of Information Technology, Wagner Logistics

Joe Johnson, Vice President of Information Technology, Wagner Logistics

Customers have traditionally focused on visibility of their inventory and data when outsourcing their logistics needs to 3PL providers. Historically, metrics such as inventory accuracy, product handled per hour, and inventory turns were among the standard information requirements. With the increased demand around “Big Data,” customers are looking to their 3PL providers for complex and innovative metrics and are expecting more value-added services in the scope of data reporting and dashboards. This requires 3PL’s to take a deeper look at their existing toolsets, infrastructure, and processes.

"Meaningful data can only be achieved by the 3PL through examination of data quality or with data governance efforts"

To be competitive in the market today, 3PL providers must invest in technology that will allow them to mine the data they receive and process through their systems so that they can provide value-added services to their customers. Investment in tools for EDI (electronic data interchange), ETL (extract, transform, and load), data warehouses, and BI (business intelligence) is critical to providing these complex metrics and services. In the space of EDI, 3PL’s need the flexibility of a tool that can receive and send data in a variety of different formats. Many customers are not interested in sending data in legacy EDI formats and now have systems that transmit data with web services or API (application programming interface) calls. Advancement of BI tools, increased functionality like ETL and data modeling allows more flexibility with reporting and metrics than in the past.

Customers are looking for more predictive analytics, forecasting, and cost avoidance capabilities. Some examples of this are the ability for the 3PL to let the customer know days, and even weeks, in advance when inventory will be running low based on past shipping and ordering patterns. Other areas of new interest are predicting future sales forecasts and patterns based on past orders which consider seasonality that might exist in their business model. In addition, data analytics that aid in the reduction of chargebacks and detention fees are a great resource for customers. It is critical for 3PL providers to realize that a substantial portion of “Big Data” needed by their customers to perform these functions is within their own systems. For 3PL’s this requires a review of all their systems to ensure that data has common keys or attributes that allow this information to be mined in a meaningful manner. This may occur not just within the WMS (warehouse management system) or TMS (transportation management system), but also may include information from systems such as appointment scheduling, invoicing, pricing, and time reporting.

From these systems, a wealth of information can be garnered about past performance and how it might impact future growth or behaviors, including opportunities for continual improvement efforts. Real world applications for this data already exist in the 3PL world now. With the data connectivity, there are opportunities for 3PL providers to help customers determine the best locations for future warehouses based on delivery times, along with transportation and labor rates. Other areas of application include determining short-term logistical needs based on natural disaster and emergency relief areas. These data modeling scenarios require a large amount of data with an array of information from transportation rates to hourly labor rates. However, this will only be successful if the data is meaningful.

Meaningful data can only be achieved by the 3PL through examination of data quality or with data governance efforts already in place and enforced. For example, incorrect or partially filled out address information on orders may lead to incorrect mappings of orders on a heat map, which in turn could skew projecting future sales or growth for that area. Data governance should account for uniqueness, consistency, accuracy, timeliness, and completeness of that data to ensure that all aspects are addressed when considering data quality. In addition, significant effort must be made when modeling the data from various systems to ensure the integrity of the data or referential integrity is in place.

Increased opportunities exist today for 3PL providers to mine the “Big Data” they are receiving from their customers. The days of providing basic metrics on performance are gone. 3PL’s must have the right tools and processes in place, along with a data governance strategy in order to utilize this data effectively. They must look beyond the WMS and TMS systems to evaluate and connect systems that are likely cloud-based or on-premise. Customers are beginning to expect this “Big Data” mining and reporting from their providers, not as added value, but as a standard requirement. Increasing focus on efficiencies and predictability allows for data analytics to become the feature within the 3PL space of the future. Now is the time for 3PL providers to ensure they are harnessing all the data available at their fingertips so that they can provide exceptional service to their customers.