“There is an immeasurable distance between late and too late.”
While driving to work the other day I listened to a discussion on the longest time listeners had waited for a parcel to arrive. One caller said they were still waiting for their purchase from almost a year ago, to which the response from the radio hosts was “. . . looks like you waiting for a Christmas present . . . from 2018!”.
Given the festive season is almost upon us it got me thinking of one misunderstanding of single supply chain performance measures used by many delivery organisations to show us (the customer) how reliable they are in delivering their goods within the guaranteed delivery times. One such performance measure is DIFOT, or Delivered In-Full, On-Time. DIFOT is a relative measure in percentage, of the number of orders successfully delivered “in-full” (where there is more than 1 item) and “on-time” (based on the agreed delivery date at the time of purchase).
DIFOT is simple to use and easy to understand. But what about our unfortunate caller whose order was not delivered in-full and on-time? How do we track the performance of “failed” deliveries; that is deliveries that were not delivered? Moreover, what if the only commercial consequence for the seller was DIFOT? Is there any incentive for them to complete the late order? While many of you may be thinking this is a negative opinion of many seller’s who want to ensure customer satisfaction, consider the following.
In 2017 Amazon shared some data on its Prime delivery services focused on two-day delivery with more than 5 billion items shipped worldwide with the two-day Prime delivery service. So even if DIFOT was 99.9999% only for the Prime two-day delivery service this means that 5,000 deliveries we not delivered in full, on-time. But what happens to these “failed deliveries”? How to we track, and potentially incentivise, the delivery of these late orders?
One option is to include another performance measure representing the seller’s performance in quickly delivering the “failed deliveries”. One such performance measure is Average Days Late which represents the average number of days for all failed deliveries after the agreed delivery date / time that customers are waiting for their late order.
Looking at the scenario below we can see that the majority of the deliveries were delivered in-full and on-time, and are captured by the first Key Performance Indicator (KPI). However, there was one delivery that was not. In this instance, the second KPI then addresses this failed delivery. So combining the 2 performance measures, DIFOT and Average Days Late, results in a more accurate and representative picture of delivery performance compared to just DIFOT.
The key takeaway here is that single performance measures typically do not accurately represent performance (see previous article, The Allure of a Single Measure). Therefore, in designing our Performance Management Framework (PMF) we should ensure measurement of not only success, but also failure. Since at the adage goes, “plan for failure, but hope for success”, although this festive season I hope all your wishes are delivered in-full and on-time!