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.