Last-Mile Lessons For Depot-To-Site Delivery
By Eshaan Jain, Senior Consultant, Mphasis
Last-mile delivery accounts for 41% of total logistics supply chain cost, according to Capgemini Research Institute, more than manufacturing, warehousing, or long-haul transport combined.1. Inefficient last-mile execution can cut retailer profitability by as much as 26%.1 E-commerce companies treat the last mile as its own discipline, with its own metrics, its own systems, and its own dedicated engineering attention, precisely because it is where the most expensive failures happen closest to the customer.
Clinical trial supply chains have a last mile too: the leg from depot to site. It gets a fraction of the design attention the manufacturing and central depot network receives, even though it is where temperature excursions, missed windows, and reconciliation failures actually surface. About 70% of trial participants live more than 2 hours from their study center, and roughly 6% of direct-to-patient shipments fail due to participant unavailability or temperature issues.2 That failure rate compounds every time a protocol adds a site or a decentralized arm.
I spent years on last-mile logistics at Amazon, inside a supply chain contract portfolio worth more than $40 billion a year. The problem that caused the most damage was rarely the delivery itself. It was the gap between what the shipment tracking system said and what the inventory system said at the exact moment a package changed hands.
The Reconciliation Gap, In E-Commerce And In Trials
A carrier's tracking system marks a package "delivered" the moment a driver scans it at the door. The warehouse inventory system, on a different update cycle, might not decrement that unit for hours. In between, both systems are technically correct and mutually contradictory: one says the package is gone, the other says it is still in stock. At enterprise scale, that gap window, even measured in hours, created real problems: double-counted inventory, phantom stockouts, and customer service calls about packages the system insisted had not shipped.
The same gap exists between a depot's shipment record and a site's drug accountability log. The depot's IRT marks a kit as shipped the moment it leaves the facility. The site logs it as received only after someone on-site manually enters it, sometimes a day or more later. For a biologic requiring 2 to 8 degrees Celsius storage, or an ultra-cold product requiring minus 70 to minus 80 degrees Celsius, that gap window is exactly when a temperature excursion is most likely to go unnoticed, because neither system is actively watching the shipment during the handoff.2
The fix closed the gap between the two existing systems rather than replacing either one: pairing the carrier's scan event with an automatic decrement in the inventory system at the same timestamp, instead of on two separate update schedules. The two systems still did their original jobs. They just stopped disagreeing about what had happened and when.
Why The Depot-To-Site Leg Is Harder Than A Retail Delivery
A retail last-mile failure means a late package. A depot-to-site failure can mean a dosing visit gets rescheduled, a patient misses a treatment window, or a batch has to be quarantined pending an investigation into a storage excursion nobody can fully reconstruct because the two systems recorded different timelines for the same shipment.
A retailer can also absorb a bad last-mile week by issuing a refund and sending an apology email. A clinical trial cannot refund a missed dosing window. A rescheduled visit for a treatment with a narrow administration window can mean the patient is no longer eligible to continue in the study, turning a logistics delay into data loss that the biostatistics team has to account for months later.
The math gets worse at scale. A trial with sites spread across a country, or across several, multiplies the number of depot-to-site legs the same way a retailer's last mile multiplies with every new delivery zone. Unlike a retailer, a clinical supply team cannot solve this by adding same-day delivery options, because the constraint is the patient's ability to reach the site at all. That is exactly why 70% of participants living more than 2 hours away is the number that should set the design requirements, not the courier's standard service level.
3 Ways To Close The Gap
Three things separate a depot-to-site leg that reconciles cleanly from one that does not:
- Automatic reconciliation between systems, not manual entry: If the depot's IRT and the site's inventory log both automatically feed a shared record, timestamped at each handoff, the gap window shrinks to the slowest system's update cycle rather than the slowest staff member's data entry cycle.
- Exception-based alerts on the handoff itself, not just on the final delivery: A courier's temperature logger should trigger an alert the moment a reading crosses a threshold, rather than waiting for someone at the site to notice it during intake, by which point the exposure window has already closed.
- Courier SLAs built around the site's actual constraints, not the courier's default service tier: A 4-hour excursion tolerance negotiated because it was the courier's standard offering, rather than because it matches what the product and the protocol can actually tolerate, is a number chosen for the wrong reason.
None of these three require replacing your depot, your courier, or your IRT. They require deciding that the depot-to-site leg deserves the same design attention as the manufacturing and central depot network, rather than being treated as a solved problem simply because the courier has an SLA on file.
A Reconciliation Problem Or A Logistics Problem?
Pull the timestamp gap between your depot's shipment record and your site's receipt record for your last 10 deliveries. If that gap regularly runs longer than your product's temperature excursion tolerance, you have a reconciliation problem hiding behind what looks like a logistics problem, and it will not show up until an excursion happens during that exact window, on a shipment you were already tracking in two systems that never agreed with each other.
References:
- Capgemini Research Institute, cited in Capgemini's analysis "Navigating the complex web of last-mile deliveries," on last-mile cost share of total logistics supply chain cost and profitability impact: www.capgemini.com/insights/expert-perspectives/navigating-the-complex-web-of-last-mile-deliveries
- Sachit Verma, "Solving Pharmacy And Clinical Supply Challenges In Decentralized Trials," Clinical Supply Leader, on participant distance from study centers, direct-to-patient shipment failure rates, and cold chain temperature ranges: www.clinicalsupplyleader.com/doc/solving-pharmacy-and-clinical-supply-challenges-in-decentralized-trials-0001
About The Author:
Eshaan Jain is a senior consultant at Mphasis and serves as the lead product owner for Salesforce/Vlocity CPQ and CLM at T-Mobile, engaged through Mphasis’s consulting services. He previously worked as a senior technical program manager at Amazon on last-mile logistics across a $40 billion annual supply chain contract portfolio. He is an IEEE senior member and holds professional membership with Forbes Tech Council, ACM, IEEE, Isaca, and AAAI.