One of the most common key performance indicators (KPIs) for transportation departments was to measure on-time performance when visibility solutions first became available. A carrier scorecard was being reviewed for every quarter. The ocean and air carriers were being measured based on their on-time performance. The rail carriers would report and measure theirs based on schedule departures while motor carriers tracked their own by service types.
It was very rare for companies to look at the performance of multi-mode or multi-leg shipments in the past. It was even more uncommon to follow the best practice KPIs that are now available today; those that extend across financial, physical and regulatory aspects in the supply chain.
There is a lot more data available to the shippers today, thanks to the immediate evolution of real-time tracking and GPS datasets, the Internet of things and the technology that enabled global trading partners via mobile solutions. But there are still companies that narrowly define their carrier performance.
If the on-time performance of a carrier is identified to be at 99.5 percent, a lot will consider this to be the best-in-class. Yet what If the data you used to figure this out only covered 60% of the carrier’s shipments? And what if the carrier has not worked effectively with your downstream trading partners to make inland movements and customs clearance more efficient? How timely do the motor and parcel carriers report their deliveries? There are so many gaps in data that leave these questions unanswered without complete information.
To evaluate opportunities to have a better supply chain performance, the best KPIs today now focus on metrics across functions and trading partners instead of narrowly focusing on the performance of isolated operations in a carrier.
An international shipment’s average cycle time is 21 days, with more or less six days variability. This is in contrast with the domestic shipments where the average cycle time is four days with one day of variability. Since international shipments require the involvement of more trading partners compared to domestic shipments, it is easy to recognize the increased cause for variability.
To be able to obtain the appropriate conclusions from big data, the information from all parties needs to be timely, complete and accurate. They should also synchronize the heterogeneous forms of data that pertain to locations and time zones. At a more sophisticated level, this data must also be able to record the end-to-end supply chain starting with the sourcing and ending with the revenue recognition for the delivery of finished goods. To evaluate the KPIs of big data, an evaluation criterion needs to be made and a data quality management program for the dataset present.
There are many opportunities to leverage big data to extend and improve carrier performance. The best-in-practice KPIs go past trading partners and business processes. By being able to analyze the KPIs of the end-to-end supply chain and the contributing factors to variability, companies will be able to achieve a more predictable, cost-efficient and agile supply chain.