Customers have traditionally focused on the 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.
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.
“Meaningful data can only be achieved by the 3PL through examination of data quality or with data governance efforts”
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.