Measuring on-time performance was once the most common key performance indicator (KPI) for transportation departments when visibility solutions first became available. Each quarter, a carrier score card was reviewed. Ocean and air carriers were measured by on-time performance. Rail carriers reported and measured theirs as scheduled departures. And motor carriers were measured by their service types. Very rarely were companies looking at the performance of multi-mode or multi-leg shipments. Even more uncommon were the best practice KPIs that are available today; those which extend across the physical, financial, and regulatory aspects of the supply chain.
Today, even more data is available to shippers, enabled by the rapid evolution of real-time tracking and GPS datasets, the IOT (internet of things), and the technology enablement of global trading partners through mobile solutions. Yet many companies still narrowly define carrier performance. If a carrier’s on-time performance is 99.5 percent, many would consider that carrier to be best-in-class. But what if you only have data that covers 70 percent of the carrier’s shipments? And what if that carrier has not been able to effectively work with your downstream trading partners to make customs clearance and inland movements or final mile arrangements more efficient? How timely do your parcel and motor carriers report deliveries? How does that impact your omni-channel customer satisfaction scores and revenue recognition? Do you know if your carriers are over-charging you based on your contractual rates? Or the opposite, do you know how you are performing against your MQC’s (minimum quantity commitment) with your ocean carriers? Are you optimizing your allocated free time or are you paying more detention and demurrage costs than planned? Does your dataset give you enough insight to create these KPI’s? Or are you struggling to link big data together? There are so many blackholes of data that leave these and other questions unanswered without better information.
When evaluating opportunities for supply chain performance, best practice KPI’s now focus on metrics across functions and trading partners rather than narrowly focusing on the performance of the isolated operations of a particular carrier.
The average cycle time of an international shipment is 21 days with 6 days of variability. This contrasts with domestic shipments where the average cycle time is 4 days with 1 day of variability. With international shipments requiring the involvement of many more trading partners than domestic shipments, it is easy to recognize the increased cause for variability. More than just carrier performance, our customers are measuring the performance of the end-to-end supply chain. The dataset they are evaluating spans the business processes across the many legs (and modes) of an international shipment, the multiple parties, as well the compliance, financial, and logistics functions. Best-in-class KPI examples include evaluating the landed cost at a line item level by tradelane, evaluating the performance of a transloader by how accurately their line item data reflects the good being received at the free trade zone, or how timely the data is being provided for document creation and customs filings.
In order to derive the appropriate conclusions from big data, the information from all parties must be accurate, timely, and complete. It must also synchronize the heterogeneous forms of data pertaining to locations and time zones. At a more complex level, the data must also be able to record the end-to-end supply chain beginning with sourcing and ending with the revenue recognition of the finished goods delivery. In order to evaluate KPIs of big data, there needs to be an evaluation criteria and data quality management program for the dataset. Best-in-class practices now include stating and evaluating data quality expectations in carrier and 3rd party contracts.
The opportunities to leverage big data extend far beyond improving carrier performance. Best-in-practice KPIs traverse trading partners and business processes. Through analyzing the KPIs of the end-to end supply chain and the contributing factors of variability, companies can achieve a cost-efficient, more predictable, agile, and compliant supply chain.
Stephanie Miles leads Amber Road’s global support team for the company’s global trade management solutions as well as the on-demand professional services team. Prior to joining Amber Road, Stephanie ran the supply chain visibility company, BridgePoint, for 7 years as a first tier subsidiary of CSX. While at BridgePoint, she held the positions of Senior Vice President and General Manager, and also served as a Board Member. Stephanie entered the supply chain management industry in 1992, where she held numerous positions including product and project management, and Manager of Government Programs. She holds a degree in mathematics from Pennsylvania State University. She is a member of the National Defense Transportation Association and the Council of Supply Chain Management Professionals.
Stephanie, very insightful article on leveraging big data! Big data is very useful for supply chain management. Many uses of big data have a measurable positive impact on outcomes and productivity. Areas such as record linkage, graph analytics, deep learning, and machine learning have demonstrated being critical to help in the applications you mention as well as to fight crime, reduce fraud, waste and abuse in the tax and healthcare systems, combat identity theft and fraud, and many other aspects that help society as a whole. It is worth mentioning the HPCC Systems open source offering which provides a single platform that is easy to install, manage and code. The built-in analytics libraries for Machine Learning make it easy for users to analyze Big Data. The free online introductory courses allow for students, academia and other developers to quickly get started. For more info visit: hpccsystems.com