Post on 02-Jun-2015
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1DTI / Titolo principale della presentazione
Andrea Emilio Rizzoli
EcoLogTex: a software tool supporting the design of sustainable supply chains for textiles
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Motivation
High Complexity Product and Supply Chain
Raising standards based on LCARaising standards based on LCAEU Ecolabel for Textiles,EU Ecolabel for Textiles,
EU Product Footprint Category Rules (PFCR), EU Product Footprint Category Rules (PFCR), French Environmental Labeling Project “Grenelle”French Environmental Labeling Project “Grenelle”
Product Carbon Footprint (PCF)Product Carbon Footprint (PCF)ISO 14067 – Carbon Footprint of ProductsISO 14067 – Carbon Footprint of Products
Information Costumers and Organizations
Scientific fact based approach
TimeTraditional LCA tools are time consuming
CostsLCA study today needs an expert
Improvement continuously improve efficiency
• Improve quality and environmental and social responsibility;
• Provide consumers with accessible and transparent information
• Face future cost internalisation of their carbon footprint.
• Manage the increased complexity added by LCA
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EcoLogTex objectives
Ecologtex enables traceability of the value chain and assess all impacts of a products’ life cycle
Climate Change ResourcesHuman Health
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EcoLogTex products
Supply Chain Designer Cost TimeEnviron
ment
Benchmarker
Reporter
Evaluate supply chain and its resulting impact for a specific desired product during design phase - > design for environment
Continues improvement throughout fact based analyses and anonymous benchmarking-> e.g. LCA studies done by Cotton Inc.
U.S. Cotton’s Footprint Reduction 1987, 1997 and 2007
Inform fact based on environmental impacts of the production phase, the use phase and final disposal -> Product traceability and impact assessment
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The benchmarker
• Quick and easy data entry in a user-friendly online questionnaire
– specific user data is complemented with background data
– e.g. average energy consumption values for known processes
• Cradle-to-gate self assessment
– calculate and compare the upstream LCI
– anonymised comparison with competitors
• Contribution assessment
– which factors weight more
• Use and share real LCI data
– beneficial information enrichment cycle
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Some general facts on the cotton impact for textiles
• Cotton is the dominant textile material, accounting for approximately 43%, but also in terms of impact shows the highest share (EU27)
• Some key areas of eco-innovation identified to reduce the environmental impact include:
– Organic cotton, often at a very high % of content,
– Cotton that is produced using less pesticides (for example, BCI) and less irrigation water
IMPRO study : Impacts of textile consumption in the EU 27, midpoint and endpoint indicators, fibre production phase, broken down by fibre
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State of the Art of textile Life Cycle Assessments (LCA)
• A few textile LCA analyses e.g for jeans, t-shirts, .. exist but based on high amount of literature data
• Often cotton data referring to the LCA study done by Cotton Inc. like for example sustainability indexes or fiber categories
• Cotton impact can vary depending on the type of cultivation method and the region.
• Public, certified available Cotton LCI data as a base of an accurate LCA are rare
Carbon dioxide emissions Stockholm Environment Institute (2005)
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Data collectionMid point impacts according EU requirements*Page 109 ILCD Handbook:
Climate Change
Ozone Depletion
Human Toxicity
Respiratory inorganics
Ionizing radiation
Photochemical Ozone formation
Acidification (land and water)
Eutrophication (land and water)
Ecotoxicity
Land Use
Resources depletion (minerals, fossil and renewable energy
Online data questionnairefor first level high quality data
Product and Process data
Process Life Cycle Inventory
Product Life Cycle Inventory
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The information flow: from data to impacts
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The supply chain designer
• Traditional supply chain design problem
– decompose your chain as a sequence of activities/processes
• procure raw cotton
• transport to processing site
• weaving and dying
• transport to processing site
• shirt manufacturing and assembly (other processes converging)
• transport to distribution sites
• transport to marketing sites
• All of this can be modelled as a mathematical programming problem
– costs of processes
– time of processes
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Partial supply chain (till secondary production plants).
Transportation to hubs and warehouses is not depicted
Given a product with some selected characteristics: Multiple transportation modes can be consideredAlternative supply chains are available
The supply chain designer: the layout
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For each possible arc of the graph (transportation operation), we have 3 indicators:
1. economical cost2. environmental impact3. travel time
Each of these indicators can be uncertain. Examples: The travel time can be modeled as an interval (between 10 and 14 days)The travel time can be modeled as a probability distribution (normal distribution with mean 8 days and standard deviation 2.3 days)
The supply chain designer: indicators
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The target of the algorithm is to propose the user different alternative supply chains, optimized according to the different objective functions, taking uncertainty into account
•Supply chain A, has a total economical cost of [2500, 3200], an average impact of 35.3 (standard deviation 3.2) and a total production+travel time of [3.5, 4.2] months•Supply chain B, has a total economical cost of 4000, an average impact of 15.3 (standard deviation 3.2) and a total production+travel time of 5.3 months•Supply chain C, has an average economical cost of 8400 (standard deviation 223.4) , an impact of 18.4 and a total production+travel time of 2.2 months
The supply chain designer: the algorithm
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Conclusions
• EcoLogTex aims to deliver a support tool for the design of sustainable supply chains
• A lot of effort needs to go on data collection
• The EcoLogTex model assumes the collaboration of suppliers and their involvement in a “virtuous information enrichment circle”
• Opens the possibility of “ex-ante” impact assessment
• The use of recent OR techniques can effectively help decision makers in the efficient exploration of the space of possible solutions
• A first prototype is expected to be ready towards June 2013
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The project team
This research is supported by the Commission for Technology and Innovation of the Federal Department of Economic Affairs of the Swiss Confederation, project number 12400.1 PFES-ES.