Food Chain from farm to fork. Bjarne Kjær Ersbøll Professor, PhD

Transkript

1 Food Chain from farm to fork Bjarne Kjær Ersbøll Professor, PhD

2 Big Data Societal Partnership DABAI WP5 2 DTU Compute, Technical University of Denmark Vitenparken Ås

3 Big Data fra jord til bord 3 DTU Compute, Technical University of Denmark 3 Vitenparken Ås

4 Big Data value chain Sense Think Act Data Origins The Internet, sensors, machines, etc. Data Collection Web log, sensor data, images/audio, RFID and videos, etc. Data Storage Technologies supporting data storage Analytics: Predictive analytics, patterns in data, decision making Consumers: Business processes, humans, and applications 4 DTU Compute, Technical University of Denmark Vitenparken Ås

5 Stakeholders in FOOD value-chain Value chain Feed/plants Animals Processing Consumers Actors Feed producers Plant producers Equipm. producers Farmers Abbatoir Dairy Retail sector Export Big Data Data Eg feed quality Eg growth rate of animals Eg efficiency in slaughtering process Consumer patterns and food quality 5 DTU Compute, Technical University of Denmark Vitenparken Ås

6 How do we proceed? 4 steps to ensure utilisation of data between parties 1. Business development 2. Exchange of data 3. Cross-disciplinary Big Data tools 4. Data security and privacy 6 DTU Compute, Technical University of Denmark 6 Vitenparken Ås

7 Recommendation 1: Pick low-hanging fruit Eg. DLG/Svinevet/Vitfoss GAINMAX simple effective! 7 DTU Compute, Technical University of Denmark Vitenparken Ås

8 Recommendation 2: Create framework for closer interaction on sharing data 8 DTU Compute, Technical University of Denmark 8 Vitenparken Ås

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10 Information to be used to cut in three 10 DTU Compute, Technical University of Denmark Vitenparken Ås

11 CT as tissue assessor W total = V fat β fat + V meat β meat + V bone β bone Based on weight measurement High precision of weight reference Owen-Hjort-Mohn! 11 DTU Compute, Technical University of Denmark Vitenparken Ås

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13 Traceability lost when cutting in three and hanging on X-mas tree Loss of traceability of the individual piece Only able to trace batch Solution is to recognize individual pieces

14 Daisy feature descriptor + Dictionary training K-means clustering of training data in 200 dimensional space

15 Use image analysis to trace individual cuts? Experiment 40 back pieces Photographed on two consecutive days Bag of visual words Daisy features Frequency of occurrence of visual words In 8 regions Histogram comparison

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17 Monitoring Animal Wellbeing in Slaughterhouses With Danish Meat Research Institute Objective to monitor humane handling of livestock in slaughterhouses Recordings are available on livestock arriving at and being handled in the slaughterhouse The first step is to convert the image data to numerical data that we can use for classification of the behavior of the animals The next step is to monitor the behavior of the animals automatically using control charts Many challenges in the classification and also in building the control chart due to the special characteristics of the data Various potential application possibilities in crowd monitoring 17 DTU Compute, Technical University of Denmark Vitenparken Ås

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19 Some of the family members -9 Main previous strategic foundation for Danish Crown: ü Uniform raw material quality (especially pigs) ü Constant raw material supply (especially pigs) ü Constant and predictable global markets

20 A New World Order? CO-OP SUPPLY SLAUGHTER / DE-BONING / CO-PRODUCTS FOOD PROCESSING END USER Unpredictable volatility in raw material supply Reduction in animal supply Increased production costs in primary production Relatively low degree of utilisation of digitalisation between actors along the supply chain Increased diversity in market requirements Surplus meat at EU markets Export markets are being increasingly volatile Increased niche product volumes challenge full carcass utilisation Customers/governments put increasing demands on transparency/documentation/ sustainability

21 Eksempler på data(baser) i vores værdikæder LEVERANDØRER SLAGTNING / UDBENING FORÆDLING (SLUT)KUNDE/MARKED - GLR/CHR - Flyttedatabase Svin - Transportdatabase - VetStat - Leverandørregister - Kødkontrolregister - Zoonoseregister - SPF-databasen - Kvægdatabasen - Kødkontroldata - Carcass balance scans - Færdigvareanalyse - Processtyring/PAT - Intern logistik - Kunde/ordrestyring - Sporbarhed/batchID - Energi/vand/gas - Tidsstudie/procesflow - Råvareanalyse - Processtyring/PAT - Færdigvareanalyse - Kunde/ordrestyring - Sporbarhed/batchID - Energi/vand/gas - Intern logistik - Tidsstudie/procesflow - Markedsdata - Kundeindsigt - Metadata (marked) - E-commerce/PoS - Vejr/klima - Zoonoser - Pandemier Andet: - Foderdata (udnyttelse) - Ressourceudnyttelse - E-kontrol DANISH CROWN

22 Muligheder/udfordringer - værdiskabelse fra big data Øget databaseret dokumentation som grundlag for ressourceoptimering Bedre styring af differentierede kvalitetskoncepter Bedre forecasts/kravspecifikationer på råvarekvalitet/kvantitet Bedre indsigt i hastigere skiftende markedstendenser Øget loyalitet hos råvareleverandører/kunder Forberede os på skift på afsætningskanaler (e-commerce m.m.) Uensartede datakilder (pga. løbende udvidelser, M&As) Stor fragmentation af data (platforme, ejerskab, typer) Frygt for afsløringer /regulering ift. åbning af data Inerti i omstillingsevne (tung infrastruktur) Fokus på hele værdikæden er kompleks Har vi kompetencerne? Vi har ikke tid (til at blive disrupted?) DANISH CROWN