Choosing the Greenest Compliant Box for Your Cold Chain

by Stefan Braun ,Managing Director SmartCAE

The pursuit of a compliant, cost-effective, and more environmentally friendly pharmaceutical cold chain has led various stakeholders to address these issues in various ways., We tackle this problem with a single tool using Digital Twins, which considers each contributing factor and then singles out the best solution. In the step- by-step process the optimal packaging is identified, transportation routes are optimised, and the total costs and the carbon footprint are quantified. This process leads the way to a greener, compliant supply chain.

CARBON FOOTPRINT IN PHARMACEUTICAL COLD CHAIN

The pharmaceutical industry releases approximately 52 million tonnes of CO2 into the atmosphere each year, contributing to roughly 2% of global annual emissions. We should, and can, reduce these enormous emissions by making our shipping practices and processes greener. The key question is: “To what extent can we decrease these emissions without disrupting the transportation and delivery of drugs?”

Our path towards a greener compliant supply chain (Figure-1) is:

  • Don’t underperform and don’t overengineer:
    • Use the right box for your requirements
    • Use the right lane & service for requirements
    • Match box and lane service
  • Quantify and reduce total cost and carbon footprint
Figure-1 Towards a greener compliant supply chain

CASE STUDY

To illustrate our approach, let’s apply it on an example. To work with a realistic lane, we had executed a test shipment, on which we installed an ELPRO realtime data logger to map out the individual waypoints of the lane. This test shipment gave us the information about the type of transport, stopover locations, and timing. This is our scenario:

  • Product: 2 to 8°C pharmaceutical goods.
  • Shipment Lane: from Singapore to Buchs, Switzerland, as recorded early 2024 with ELPRO realtime logger, illustrated in Figure-2
  • Shipper (container) options:
    • EPS all season, PU all season, PU summer, VIP all season and VIP winter;
    • all the shippers carry identical payload for comparison and are preconditioned identically.
Figure-2 Shipping Lane

OVERVIEW OF THE BOX PERFORMANCE

To assess how well each shipper option is suited to maintain the payload temperature requirement on the lane, we employ our Digital Cold Chain approach, and build a digital twin for each shipper. In our digital approach we can then go back in time, and send each shipper along the lane, on each day, for the last 5 years, using historical temperature data on the lane. The result is summarised in Figure-3 for each shipper and each month: a green circle indicates no temperature excursions, a red circle indicates potential hot excursions, a blue circle, potential cold excursions.

The results of the lane risk analysis on five different shippers are provided in Figure-3.

  • EPS all-season: only applicable in November to February;
  • PU all-season: applicable in all months except July;
  • PU summer: not applicable
  • VIP in all-season & winter configuration: applicable in all months.
Figure-3 Lane risk summary by month for five shipper options

From the results, we can now combine viable compliant shipper options that minimise the excursion risk:

a. 1 box: Use VIP shipper for all months;
b. 2 boxes: VIP during July, and PU all-season during other months;
c. 3 boxes: VIP during July, EPS all-season during November to February, and PU all-season during other months.

TOTAL COST OF OWNERSHIP ANALYSIS

The next question to address is, which solution is the most cost-effective, with the minimum Total Cost of Ownership. The factors contributing to TCO are illustrated in Figure-4. Cost of Packaging and Cost of Shipment are known in advance, whereas Cost of Compliance and Cost of Product Loss are only available when the rate of excursions and product losses are quantified. This is a significant advantage of the digital approach, as it allows to quantify the rates based on historical data, prior to any costly live shipment.

Figure-4 Factors constituting the Total Cost of Ownership in Temperature Controlled Logistics.

Assuming a cost of 6500€ per excursion for the CAPA analysis, and 10’000€ per product loss, based on product value, we determine the TCO for each shipper, as illustrated in Figure-5. In this case, the most cost- effective solution is the combination of the two shippers EPS all-season and PU all-season. From a cost perspective, it is in this case the optimum solution, to accept the relatively low failure risk for excursions in the month of July (1.3% for the month of July, corresponding to 0.11% over the year)). There are marginal savings of about 6660€ compared to using only the PU all-season shipper year round, with the same risk. So if the costs for maintaining two shipper options exceeds 6660€ per year, the optimum solution from a cost perspective would be to use only the PU all- season. The VIP shippers offer premium protection during all months, but the extra costs exceed the other solution by about 150’000€ per year.

Figure-5 Total Cost of Ownership of the five shipper options and the best combination

In Figure-6 we provide a month-by-month cost breakdown of the best combination. Excursions occur solely in July, resulting in associated costs for excursions and product loss.

Figure-6 Monthly breakdown of Total Cost of Ownership for the best shipper combination

CARBON FOOTPRINT CALCULATION

Having determined the most cost-effective solution, we next assess the carbon footprint of the different options. The following factors are considered to calculate the carbon footprint:

  • transport weight,
  • transport mode,
  • shipper CO2e based on full lifecycle analysis (mainly production and decommissioning),
  • mode-specific transport distance,
  • average CO2e factor per ton-km by transport mode.
  • the CO2e from cooling at warehouse or storage calculated by country-specific electricity emission.

The shipping lane, shown in Figure-2, comprises two long-haul and two short- haul flight segments. These contribute to a relatively high carbon footprint for each shipper option. The CO2 equivalent emissions are negligible for the truck segment Basel airport to Buchs. The detailed result of the CO2e for all shippers are provided in Figure-7.

Figure-7 Segment-wise CO2 split-up for shipping lane and the five shipper options

In Figure-8, we illustrate the total CO2 footprint for all shipper options, integrating overall contributions. The highest CO2 footprint is observed for the VIP all-season shipper, at 470,000 kg CO2e, with VIP winter and PU summer at a similar level. The two options EPS all-season and PU all-season feature significantly lower CO2e, at around 320,000 kg.

Figure-8 Total Carbon footprint comparison for shipping lane

The carbon footprint of the best combination, determined in the TCO analysis, is about 32% lower than compared to using a VIP shipper, see Figure-9.

This reduction corresponds to the equivalent of 6800 trees saved, see Figure-10.

Figure-9 Comparison of carbon footprint of best combination to using only a VIP shipper
Figure-10 Tree equivalent for the CO2e of best combination vs. VIP shipper

SUMMARY

A digital approach allows us to quantify and compare the Total Cost of Ownership and the Carbon Footprint of various shipping options in the cold chain. In our example we found that:

  • Using a combination of EPS and PU shippers for different months reduces the Total Cost of Ownership by about 145’000€ per year, compared to a VIP solution.
  • The carbon footprint is reduced by about 150,000 kg CO2e when using a combination of EPS and PU shippers, compared to a VIP solution.

About the Author

Stefan Braun
Managing Director
SmartCAE

Stefan Braun is the founder and managing director of SmartCAE and started the Digital Cold Chain 10 years ago. He is an engineer by education.

Read other articles from LogiSYM May 2024: