DHL Solutions & Innovations. Amir Mousavi September 2010, Bonn

Transcription

1 DHL Solutions & Innovations Temperature monitoring of non- actively cooled pharmaceutical transportation A i M i Amir Mousavi September 2010, Bonn

2 Agenda Introduction Scope Methodology Results Summary Forecast 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 2

3 Status temperature controlled shipments in Germany Market situation Increasing temperature t controlled shipment capacity Increasing volume of temperature sensitive goods e.g. life sciences ces and pharmaceutical aceut ca products, chemicals ca and food Cargo load Swap bodies Temperature ranges frozen : < - 20 C chilled: + 2 C to + 8 C ambient: +15 C to + 25 C Quan ntity > 10 t 5 10 t 2 5 t < 2 t Year Status of temperature controlled transports in Germany Source: Federal Office for Motor Traffic (Kraftfahrt Bundesamt) 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 3

4 Status temperature controlled shipments DHL DHL cold chain services Temperature controlled transportations FTL / LTL services Partially temperature monitoring vehicles End-to-end temperature monitoring very difficult to realize 87 % are ambient shipments DHL SmartSensor temperature service focuses on end-to-end temperature monitoring intelligent temperature sensor RFID interface Temperature monitoring on all levels of transportation 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 4

5 Temperature monitoring approaches Observed monitoring approaches For frozen and chilled products temperature monitoring usually takes place on shipment level For ambient products temperature monitoring takes place on shipment, pallet, swap body or container levell Ambient transportations are mostly not shipped with temperature controlled vehicles In cases one sensor is linked to all shipments in a swap body Temperature data not evaluated on swap body level 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 5

6 Agenda Introduction Scope Methodology Results Summary Forecast 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 6

7 Scope Questions How many sensors are necessary to allocate the temperature in a swap body and conclude the whole environment temperature? Which relation is given between number of sensors and the quality of monitored temperature? Subject of analysis and Measuring device: DHL SmartSensor Temperature DIN EN 284:2006 Swap body 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 7

8 DHL SMART SENSOR TEMPERATURE DHL SST in a glance Sensor Reading device Web portal Wireless Wireless Data logging Data read-out Data analysis is an end-to-end monitoring solution developed by DHL offers transportation & temperature monitoring from one single source uses future-proof technologies is delivered ready-to-go no change to your existing IT-system necessary enables automated data distribution provides 24/7 data availability, worldwide shelf life algorithms implemented Target Market Life Sciences Food and other perishables Chemicals External Partners 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 8

9 Agenda Introduction Scope Methodology Results Summary Forecast 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 9

10 Methodology Challenges for temperature monitoring of ambient products transported in swap bodies high cost for temperature monitoring on shipment level high cost for data harvesting and management difficulty of placement of sensors Conflict of objectives Minimal numbers of sensors Highest quality of temperature data Reason of conflict of objectives: Lack of information 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 10

11 Methodology Approach Solving the lack of information via interpolation ti of temperature t data Reducing the cost through replacing hardware sensors by software sensors Method Adaption of the geo-statistical Kriging method Pro: Statistical interpolation method Usage of variography to optimize the results Able to solve 3D problems Best linear unbiased estimator (BLUE) Contra: Requests high quality of data Complexity in calculations 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 11

12 Methodology Experimental and theoretical variograms*: Experimental variogram: - from measured data - Scatter plot Theoretical variogram: - Exponential variogram function - Method of smallest squares * The variogram is a location-independent method which indicates the mean statistical spread of the differences between two random variables through the vector h and is the degree of the spatial relation between these two variables. 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 12

13 Methodology Gathering of temperature allocation 63 sensors for temperature data harvesting in a 3x3x7 matrix alignment 24 hours measuring duration for each test 14 minutes measuring intervals DHL SmartSensor Swap body Z 7300 mm X 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 13

14 Agenda Introduction Scope Methodology Results Summary Forecast 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 14

15 Results of temperature gathering 1/2 Mean temperature in the swap body Measurement of Measurement of C ure in C emperatu Mean te time 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 15

16 Results of temperature gathering 2/2 Mean temperature of swap body outer walls C ure in C emperatu Mean te time left right roof floor door rear 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 16

17 Relation between no. of sensors and mean interpolation lapse Optimization of measuring network according to max error- and Kriging-Variance C Error in max. error (Error) mean error (Error) no of sensors max. error (variance) mean error (variance) 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 17

18 Verification Interpolation: Measurement of with 14 sensors Interpolation error C time mean error max error defined mean error 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 18

19 Agenda Introduction Scope Methodology Results Summary Forecast 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 19

20 Summary Temperature situation in the swap body Temperature allocation at daytime not constant over daytime high temperature differences through the spatial dimensions very homogenous temperature t allocation from evening time Temperature differences for loaded swap body must be considered Temperature monitoring with lowest numbers of sensors Kriging-method to eliminate information lapse Optimization of measuring network according to the maximum interpolation error delivers good results Defined mean interpolation error is only excided very shortly Results under the given sensor tolerance of ± 0,5 C 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 20

21 Agenda Introduction Scope Methodology Results Summary Forecast 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 21

22 Forecast Methodology enhancements based on results Further analysis of the Kriging- g method Analysis of different load situations in swap bodies Technological enhancements Analysis of further condition parameter e.g. humidity and shock More detailed d and longer observation of monitoring i Risk of increasing technology costs Mathematical methods can be used to reduce technology costs 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page 22

23 Thank you for your attention! Time for questions 4th International Workshop - Cold Chain Management Bonn September 2010 DHL Page