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96 results for “supply chain”
Short Food Supply Chains Business and Marketing Models Categorisation
<p>The present dataset contains information about the categorisation of Business Models for Short Food Supply Chains, in the context of the <a href="http://agrobridges.eu">agroBRIDGES project</a> (Horizon 2020, GA No. 101000788). Regional information about existing and new business models for Short Food Supply Chains were collected from 12 regions and countries of focus for the project (Beacon Regions) through desk research, interviews and co-creation workshops.</p> <p>The datasets collected information provided by Beacon Region Leaders in the co-creation workshops about models applied and newly or potentially developed ones and available in the literature, to categorise them into stylized models with specific and distinguishable features and attributes, enabling a systematic approach in the development of the sustainability assessment framework. An initial ranking of local business models was provided at a regional / country level. </p> <p>Through analysis of the aggregate ranking results of business models at local level, a final list of business models for SFSCs was derived. Five different Business Model types have been addressed and developed ( i. Community Supported Agriculture - CSA, ii. Face to face trade, iii. Online Food Trade, iv. Local Food Trade, and v. Improved Logistics). The final categorization list provided valuable insights in order to represent all regional contexts and eventually offer value-added information about the main factors for developing a successful business model that can enhance market success in the long term; as well as to strengthen farmer´s strategy that eventually enhances their position within the whole value chain and improve their customer experience.</p> <p>The Value Proposition Canvas and Business Model Canvas was defined for each of the most common SFSC represented in each of the targeted regions.</p> <p><br> The dataset contains:<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation_2021.12.01_v1 [zip file]: </strong>The initial list of business models collected from the co-creation workshops<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation-Tool_2021.12.01_v1 [.xlsx file]: </strong>Internal Assessment tool for Business models regional data collection: A data collection tool was built in-house and shared to all Beacon Region Leaders in order to enable them to fill it regarding their assumptions and conclusions after the co-creation participants discussed and presented their insights from regional activities.<br> • <strong>agroBRIDGES_SFSCs-BModels-CategorisationResults_2021.12.01_v1 [.xlsx file]:</strong> SFSCs BM type results in each of the regions.<br> • <strong>agroBRIDGES_BusinessModels_Canvas_2021.12.01_v1 [zip file]: </strong>The Business Model and Value Proposition Canvas of the 5 BM categories</p>
Supply Chains of 36 Corporations (CERTH DB)
<p>These are supply chains extracted from web sites and pdf files of 36 big corporations resulting in 25,938 relationship metrics answers.</p>
SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
<p><strong>Abstract:</strong> Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.</p>
Data to explore circular manureshed management in beef supply chains of the United States and western Canada
Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.
Data and R Code from "A novel approach to sustainability assessment of food supply chains using networks of ecosystem services"
<p>Data and R code from this paper applying network analysis (iGraph) to two case studies pre and post agroecological transitions in Central America and Tanzania, Africa from the IPES-Food report. Further descriptions of this data and code can be found within the extended manuscript. R Code relies on the data from the scenarios (e.g., Nodes and Relations CSVs) and creates the output network metrics (e.g., Node Metric CSVs). </p>
Land, carbon and biodiversity data for supply chain impact calculations
<p>Monitoring, halting and reversing land conversion is fundamental to meeting international biodiversity and climate targets, and agriculture is the major driver of land conversion. We present an open access set of global data for calculating land use change impacts of agricultural supply chains. These data, originally prepared for the LandGriffon service, include indicators of deforestation, conversion of natural ecosystems, greenhouse gas emissions, and loss of intact or high integrity ecosystems following international standards and guidelines for reporting and target setting in the agriculture, forestry, and land use sector. In order to assign impacts to agricultural production, we prepare data using a spatial adaptation of the statistical Land Use Change (sLUC) accounting approach distributing impact to human activities across the local area using a 50km radius. The results are high resolution global maps of impact per hectare of land occupation. These can then be combined with land footprint data, cropland extent, or productivity maps to calculate land use change related impacts for specific crop volumes sourced from specific regions. Carbon and deforestation results are validated against FAO statistics at the national level.</p>
TAMPAR: Visual Tampering Detection for Parcels Logistics in Postal Supply Chains
<p>TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our paper and for visual samples our <a href="https://a-nau.github.io/tampar/">project page</a>. Features are: </p><ul><li>>900 annotated real-world images with >2,700 visible parcel side surfaces</li><li>6 different tampering types</li><li>6 different distortion strengths</li></ul><p>Relevant computer vision tasks:</p><ul><li>bounding box detection</li><li>classification</li><li>instance segmentation</li><li>keypoint estimation</li><li>tampering detection and classification</li></ul><p>If you use this resource for scientific research, please consider citing our WACV 2024 <a href="https://arxiv.org/abs/2311.03124">paper</a> <i>"TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains".</i></p>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
Data of paper "Global supply chains amplify economic costs of future extreme heat risk"
<p>This is the database of articles "Global supply chains amplify economic costs of future extreme heat risk". The database contains the number of deaths caused by future heat waves in regions around the world under different SSP scenarios (e.g. SSP119, SSP245, SSP585), as well as global health losses, labor losses, and indirect losses as a percentage of regional or sectoral value added under different SSP scenarios. The regions of the database are aggregated using the GTAP 141 aggregating schema.</p>
Simulation of the efficiency of a reversed supply chain of wood biomass using different types of transport units (NCN) DEC-2020/39/I/HS4/03533
<p>Data describing simulations related to the standardisation of loading units for the transport of wood biomass. The effectiveness of assumptions relating to the use of different types of packaging were verified from the perspective of the number of vehicles required and their emissions. The relationship between the size and specification of the wood biomass load and the packaging used was indicated. The study was funded by National Science Centre in Poland under agreement National Center of Science (NCN) through grant DEC-2020/39/I/HS4/0353</p>
Investigating the correlation of control parameters in reversed wood biomass supply chains (NCN) DEC-2020/39/I/HS4/03533
<p>Results of calculations of mutual correlations between parameters of reversed wood biomass supply chains, referring to the statistical yearbooks of the State Forests (in Poland). Identification of predictions of increasing values of individual parameters of reversed wood biomass supply chains.<br>The study was financed by National Science Centre in Poland under agreement DEC-2020/39/I/HS4/03533</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Good Practices and Success Cases for Short Food Supply Chains connecting consumers and producers
<p>The agroBRIDGES Good Practices (GPs) is a compilation of 51 Good Practices prepared by TEAGASC in collaboration with other agroBRIDGES project partners, where the Good Practices, success cases and opportunities related to Short Food Supply Chains (SFSCs) across Europe are highlighted. The Good Practices are identified and collated from SFSCs in Denmark, France, Greece, Italy, Latvia, Lithuania, the Netherlands, Poland, Turkey, Finland, Ireland, Spain and other international representations across Europe.</p> <p>The dataset contains a spreadsheet containing the 51 Good Practices collected. The included cases are presented in alphabetical order and numbered from 1 -51:</p> <p>1. "Bios Coop" Social Consumer Cooperative </p> <p>2. AGFORISE (AGroFOod clusters platform with common long-term Research and Innovation) </p> <p>3. Agriloops </p> <p>4. Airfield Estate </p> <p>5. Benedicto Turgus (Benedict’s Market) </p> <p>6. Bienvenue à la ferme </p> <p>7. Bioalverde </p> <p>8. Burgerboerderijen </p> <p>9. CASETTA ROSSA Solidarity Purchase Group / Gruppo di Acquisto Solidale (GAS) Casetta Rossa </p> <p>10. Castlegregory Market </p> <p>11. Cloughjordan Community Farm </p> <p>12. Conservas artesanales contigo </p> <p>13. Digital Agriculture Market </p> <p>14. Drumanilra </p> <p>15. Evidence of Public Procurement of local food in the EU </p> <p>16. Fru Møllers Mølleri </p> <p>17. Gastroteca </p> <p>18. Kindergarten "Duzgynelis" </p> <p>19. Københavns Fødevarefællesskab (KBHFF)/Copenhagen Food Co-operative (CPH) </p> <p>20. La Colmena que dice sí </p> <p>21. La OSA Coop </p> <p>22. La OSA Coop </p> <p>23. La Ruche qui dit Oui/Boeren en Buren/L'Alveare Che Dice SÍ </p> <p>24. Local food program </p> <p>25. M**bun </p> <p>26. Manna Organic Store </p> <p>27. Más Que Lechugas </p> <p>28. Mobilus Turgelis </p> <p>29. Naturecode </p> <p>30. Né d'une seule ferme </p> <p>31. Podkarpacki E-bazarek </p> <p>32. Podkarpackie Smaki (Subcarpathian Flavors Cluster) </p> <p>33. Pora Na Pola </p> <p>34. American Farm School of Thessaloniki </p> <p>35. Public Food Services </p> <p>36. QualityLowInputFood (QLIF) </p> <p>37. Rechtstreek </p> <p>38. REKO food collective </p> <p>39. School milk and fruit </p> <p>40. Self picking berries </p> <p>41. Streeckgenoten</p> <p>42. SVAIGI - online shop </p> <p>43. Telaraki.com </p> <p>44. The Agricultural Marketing Platform </p> <p>45. The Cold Chain of Raw Milk </p> <p>46. The Eco-Cabinet (Økoskabet) </p> <p>47. The Little Cheese Shop </p> <p>48. Tiešā pirkšana - Direct shopping group </p> <p>49. TomaTomate </p> <p>50. Traditional United Europe Food (TRUEFOOD) </p> <p>51. Via Amerina and Forre Bio-District</p>
Regional assessment of the current status of Short Food Supply Chains
<p>The assessment of the current status of the Short Food Supply Chains (SFSCs) in each of the 12 Beacon Regions (focal regions) of the agroBRIDGES project is one of the different key aspects that jointly conform to the overall project concept.</p> <p>It is important to study the existing knowledge and SFSCs in each region, to identify and later stimulate the wide adoption of sustainable SFSC-based business models already existing successfully in one or some of the regions. The traditional/current business models, that will be spotted here, like the new SFSCs business and marketing models to be developed later, be categorised, and analysed from an economic (with special attention on producer’s income), environmental and social point of view.</p> <p>The 12 Beacon Regions portrayed in this report are listed in the table below:</p> <table> <thead> <tr> <th> <p><strong>Country</strong></p> </th> <th> <p><strong>Region</strong></p> </th> <th> <p><strong>Beacon Region Leader </strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>Greece</p> </td> <td> <p>Central Macedonia</p> </td> <td> <p>Q-PLAN</p> </td> </tr> <tr> <td> <p>Finland</p> </td> <td> <p>All</p> </td> <td> <p>VTT</p> </td> </tr> <tr> <td> <p>Netherlands</p> </td> <td> <p>All</p> </td> <td> <p>WU</p> </td> </tr> <tr> <td> <p>Italy</p> </td> <td> <p>Lazio and Piemonte</p> </td> <td> <p>CREA</p> </td> </tr> <tr> <td> <p>Ireland</p> </td> <td> <p>All</p> </td> <td> <p>MTU</p> </td> </tr> <tr> <td> <p>Denmark</p> </td> <td> <p>All</p> </td> <td> <p>FBCD</p> </td> </tr> <tr> <td> <p>Spain</p> </td> <td> <p>Andalucia</p> </td> <td> <p>CTA</p> </td> </tr> <tr> <td> <p>Poland</p> </td> <td> <p>All</p> </td> <td> <p>UNMF</p> </td> </tr> <tr> <td> <p>Latvia</p> </td> <td> <p>All</p> </td> <td> <p>UNMF</p> </td> </tr> <tr> <td> <p>Lithuania</p> </td> <td> <p>All</p> </td> <td> <p>UNMF</p> </td> </tr> <tr> <td> <p>France</p> </td> <td> <p>Bretagne, Pays de la Loire, Auvergne – Rhône Alpes</p> </td> <td> <p>VGP</p> </td> </tr> <tr> <td> <p>Turkey</p> </td> <td> <p>All</p> </td> <td> <p>SUF</p> </td> </tr> </tbody> </table> <p>All Beacon Regions have contributed data and insights about their specific region collected through desk research. They have focused on 2 appointed sectors that is of importance for the region. The focal sectors have been settled in the application for this project. The knowledge generated in the desk research is qualified through interviews with members of the regional Multi Actor Platform (MAP). Some interviews are summarized in the document, while others are integrated into the description.</p> <p>For each Beacon Region the current status of SFSCs is described in these chapters:</p> <ul> <li>Overall status</li> <li>Main regional drivers</li> <li>Environmental aspects</li> <li>Economic aspects</li> <li>Social aspects</li> </ul> <p>Along this information you will find a description of 5-10 important business or marketing models for each region together with statistics on market sectors and size. The description of the models is based on Business Model Canvas.</p> <p>The dataset contains the 12 regional reports, outlining the core topics of the regional assessment described above. In some regional reports, the notes from interviews with local actors are available and provided in the respective Annex. </p>
Supply chain sustainability standards in temperate farming commodities
<p>Could companies contribute to farmland biodiversity in temperate landscapes? Most of the business efforts to enhance sustainability in agricultural supply chains have been focused on tropics. Important temperate crops like cereals and oilseeds are left largely unchecked. This results in a missed opportunity to reverse biodiversity decline in about 47% of the world's cropland. But implementation of voluntary sustainability standards to restore biodiversity in temperate and Mediterranean biomes would require doing things differently than in tropical commodities. Temperate agriculture differs both in its operations (lack of divergence between staples and cash crops, crop rotation) and landscape context (lack of agricultural expansion frontiers, complex pressures on biodiversity). Consequently, agricultural certification and similar schemes need to be linked primarily to a unit of space rather than a specific commodity. However, necessary tools such as advanced criteria, metrics and data infrastructure are lacking in temperate agriculture, and their development depends on input from conservation scientists and practitioners.</p> <p>Supporting information for<em> Supply chain sustainability standards in temperate farming commodities </em>consists of:</p> <ol> <li>Dataset for Figure 1;</li> <li>Additional information on methodology (VSSs uptake in retail industry) and sources (LEAF Marque case study, references for evidence of impact of feasible objectives for VSSs in conventional temperate agriculture).</li> </ol> <p> </p>
Supply Chain Shocks due to extreme weather events
<p>Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period</p>
Data, materials, methods and codes for publication Reis et al., "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.
<p>Explanation of the data and code used for the article: "Understanding the stickiness of commodity supply chains is key to improving their sustainability", 2020, One Earth.</p> <p> The file: "StickinessAnalysisCompleteGeneric_CleanUpdate18-5-2020.R" is the R code/ script containing all the data preparation and the stickiness analysis.</p> <p>The file: "BRAZIL_SOY_V2.3_WITH_DOMESTIC_TRADERS.csv" contains the raw data of Brazil's soy exports and domestic consumption from trase.earth. This dataset can also be obtained from trase.earth in the latest version.</p> <p>The file: "NodesCiS.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesCiR.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesCiSMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesCiRMunCountry.csv" is a table with the Ci (stickiness on linkages) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p> <p>The file: "NodesWPiS.csv" is a table with the WPi (stickiness on flows) measured for the types of supply chain relationships: A. logistics hubs supplying traders, and D. traders supplying countries. They are put together in the same table because they are all "sending" relationships.</p> <p>The file: "NodesWPiR.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: C. traders sourcing from logistics hubs, and E. countries sourcing from traders. They are put together in the same table because they are all "receiving" relationships.</p> <p>The file "NodesWPiSMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: B. logistics hubs supplying countries (directly not passing through traders). This is separate in another table because it is a direct sending relationship from LHs to countries.</p> <p>The file "NodesWPiRMunCountry.csv" is a table with the Ci (stickiness on flows) measured for the types of supply chain relationships: F. countries sourcing from logistics hubs (directly not passing through traders). This is separate in another table because it is a direct receiving relationship from LHs to countries.</p>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
data set of scopus about technology and halal meat supply chain publications
<p>This is the dataset of papers related to the topics of technology and halal meat supply chain collected from Scopus. The duration of year was 2008-2022</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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