DHL Solutions & Innovations Temperature monitoring of non Temperature monitoring of non- actively cooled pharmaceutical actively cooled pharmaceutical transportation transportation A Amir Mousavi i M i September 2010, Bonn
Agenda Introduction Scope Methodology Results Summary Summary Forecast 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 2
Status temperature controlled shipments in Germany Market situation Increasing temperature controlled shipment capacity I i t t t ll d hi t it Increasing volume of temperature sensitive goods e.g. life sciences and pharmaceutical products, chemicals and food e g e sc e ces a d p a aceut ca p oducts, c e ca s a d ood Cargo load Swap bodies ntity > 10 t Quan Temperature ranges 5 – 10 t 2 – 5 t frozen : < - 20 °C < 2 t chilled: + 2 °C to + 8 °C ambient: +15 °C to + 25 °C Year 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
Status temperature controlled shipments DHL DHL cold chain services Temperature controlled transportations 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 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
Temperature monitoring approaches Observed monitoring approaches For frozen and chilled products temperature monitoring usually takes place on 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 level b d t i l l 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
Agenda Introduction Scope Methodology Results Summary Summary Forecast 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 6
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 g q y 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
DHL SMART SENSOR TEMPERATURE DHL SST in a glance Sensor Sensor Reading device Reading device Web portal Web portal Wireless Wireless Data logging Data read-out Data analysis …is an end-to-end monitoring solution developed by DHL Target Market …offers transportation & temperature monitoring from one single source Life Sciences …uses future-proof technologies Food and other perishables …is delivered ready-to-go Chemicals … no change to your existing IT-system necessary …enables automated data distribution enables automated data distribution External Partners …provides 24/7 data availability , worldwide … shelf life algorithms implemented 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 8
Agenda Introduction Scope Methodology Results Summary Summary Forecast 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 9
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 Conflict of objectives Minimal numbers Highest quality of of sensors temperature data Reason of conflict of objectives: Lack of information 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 10
Methodology Approach Solving the lack of information via interpolation of temperature data S l i th l k f i f ti i i t l ti f t t d t Reducing the cost through replacing hardware sensors by software sensors Method Adaption of the geo-statistical Kriging method Pro: P Statistical interpolation method Usage of variography to optimize the results 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 Complexity in calculations 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 11
Methodology Experimental a nd theoretical variograms*: Experimental variogram: Experimental variogram: - from measured data - Scatter plot p Theoretical variogram: - Exponential variogram function Exponential variogram function - Method of smallest squares * The variogram is a location-independent method which indicates the mean statistical spread g p p 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
Methodology Gathering of temperature allocation 63 sensors for temperature data harvesting 63 sensors for temperature data harvesting in a 3x3x7 matrix alignment 24 hours measuring duration for each test g 14 minutes measuring intervals DHL Swap body SmartSensor Z 7300 mm X 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 13
Agenda Introduction Scope Methodology Results Summary Summary Forecast 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 14
Results of temperature gathering 1/2 Mean temperature in the swap body Measurement of 09.09. Measurement of 22.09. C ure in ° C emperatu Mean te time 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 15
Results of temperature gathering 2/2 Mean temperature of swap body outer walls 09.09. – 10.09. C ure in ° C emperatu Mean te ti time left right roof floor door rear 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 16
Relation between no. of sensors and mean interpolation lapse Optimization of measuring network according to max error- and Kriging-Variance ° C Error in ° no of sensors no of sensors max. error mean error max. error mean error (Error) (Error) (variance) (variance) 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 17
Verification Interpolation: Measurement of 22.09.2010 with 14 sensors Interpolation error Interpolation error ° C time time mean error max error defined mean error 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 18
Agenda Introduction Scope Methodology Results Summary Summary Forecast 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 19
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 allocation from evening time h t t ll ti f i ti 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 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
Agenda Introduction Scope Methodology Results Summary Summary Forecast 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page 21
Forecast Methodology enhancements based on results Further analysis of the Kriging- method y g g Analysis of different load situations in swap bodies Technological enhancements Analysis of further condition parameter e.g. humidity and shock More detailed and longer observation of monitoring M d t il d d l b ti f it 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
Thank you for your attention! Thank you for your attention! Time for questions Time for questions 4th International Workshop - Cold Chain Management | Bonn | September 2010 DHL | Page
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