Classically, attempts have been made to quantify lane risk by making test shipments, or running tests in a climate chamber, both of which are very costly and time-intensive processes. If the test shipment fails, you have to change the container and run another series of tests. If the test succeeds, the implications are of limited use: the result gives you a performance snapshot of only a single day out of many different possible transportation scenarios. You would have to run many tests on different days and seasons to get a viable lane-specific characterisation of the container's thermal performance. For another lane, you would have to do similar tests all over again, or resort to reference profiles. This is a very time and money-consuming process, which can still leave great and innumerable uncertainties.
SmartCAE's virtual cold chain is an effective tool for finding the best solution for a transportation lane through robust quantification of the lane risk. The company prepares virtual models of containers and exposes them in thermal simulation to air temperature data characteristic of the assessed transportation lanes. Since thermal simulation is very fast, it is easily possible to assess thousands of lane temperature scenarios, corresponding to years of test data, within less than a single day. The results can be used to compare the thermal performance of different containers on a lane. Alternatively, the results can be put into use to test the suitability of a particular container on different lanes. Being cheap and fast, this approach explores all parameters to minimise the lane risk, and also vary the container and its materials.
Let's find the best solution for shipping temperaturesensitive pharmaceutical products at 2-8°C along two transportation lanes: Dusseldorf via Atlanta to Chicago, and Chicago via Miami to Sao Paulo. We need to decide between three available container packouts: summer, winter and all-season. The question is, do we use a summer and a winter seasonal packout, or do we use a single all-season packout? If we use the seasonal packouts, at what time of the year do we need to switch between the two? The virtual models of the three packouts are shown in the image above.
When quantifying lane risk we also need the ambient thermal condition on the transportation lane. This can be obtained from historical temperature records of weather stations along the lane. In the image on the facing page, the ambient air temperature profiles along the two example lanes are shown. Each grey line represents a single day during the time between 2011-16, a total of about 2,000 individual temperature profiles per lane. During flight intervals, the ambient temperature is assumed to be controlled and set at 15°C.
As simulation is fast, it is no problem to run each temperature scenario of the selected time interval. The result compiles the thermal performance of each packout for each lane on every day during 2011-16.
For the lane ORD-MIA-GRU, best performance is obtained using only the single all-season packout for the entire year. For the lane DUS-ATL-ORD, a single packout is not sufficient. Deeper analytics show that the best performance is obtained by switching between summer packout May-September, and all-season packout October-April.
The analysis shown here has not been possible in the past with the sole implementation of classical approaches. To collect the substantial amount of data required to correctly perform this analysis, it would be necessary to combine data from about 12,000 individual climate chamber tests: boxes×lanes×profiles, thus 3×2×2,000 ~ 12,000.
As each test ultimately takes around two days, this would lead to a total required testing time of about 65 years. Naturally, this would prove to be an unfeasible task, even if multiple parallel chamber tests were carried out.