d5.1_out

The PREMANUS project (285541) is co-funded by the European Union under the Information and Communication Technologies (ICT) theme of the 7th Framework Programme for R&D (FP7).

This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content.

Programme Factories of the Future PPP

Strategic Objective ICT-2011.7.3 Virtual Factories and Enterprises

Project Title Product Remanufacturing Service System

Acronym PREMANUS

Project # 285541

D5.1 - ALGORITHMS AND METHODOLOGIES FOR THE EOL PRODUCT RECOVERY

PROCESS

Work Package WP-5 Business Decision Support for PREMANUS

Lead Partner POLIMI

Contributing Partner(s) SAP, LU

Security Classification CO (Confidential)

Date 25-Mar-2013

Version 1.0

COPYRIGHT

© Copyright 2013 by <please add partners>

Legal Disclaimer

Project –No Date Classification

285541 25-Mar-13 CO

D5.1 – Algorithms and methodologies for the EoL product recovery process

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The information in this document is provided ‘as is’, and no guarantee or warranty is given that the information is fit for any particular purpose. The above referenced consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials subject to any liability which is mandatory due to applicable law. This document may not be copied, reproduced, or modified in whole or in part for any purpose without written permission from all of the Copyright owners. In addition to such written permission to copy, reproduce, or modify this document in whole or part, an acknowledgement of the authors of the document and all applicable portions of the copyright notice must be clearly referenced. The circulation of this document is restricted to the staff of the PREMANUS partner organisations and the European Commission. All information contained in this document is strictly confidential and may not be divulged to third parties without the express permission of all of the Copyright owners. All rights reserved. This document may change without notice.”


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Document history

Version Date Comments Author

0.1 21/12/12 Draft table of content Polimi

0.4 15/01/13 Edit table of content from LU LU

0.5 20/01/13 Final table of content Polimi

0.8 04/03/13 Draft Final for internal review Polimi

0.9 11/03/13 Final version after internal review Polimi

1.0 25/03/13 Final Version submitted Polimi The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no285541.


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Table of contents

1 EXECUTIVE SUMMARY ............................................................................................................................. 1 2 BDSS ROLE IN PREMANUS ....................................................................................................................... 2 3 STATE OF THE ART FOR ALGORITHMS AND METHODOLOGIES .............................................. 5

3.1 BRIEF LITERATURE REVIEW ........................................................................................................................ 5 3.2 ALGORITHM OBJECTIVES ............................................................................................................................ 7 3.3 EVALUATION ACTIVITIES IN REMANUFACTURING CONTEXT ....................................................................... 8

3.3.1 Approach in cost calculation .............................................................................................................. 8 3.4 EOL BOUNDARY CONDITIONS AND ALGORITHMS CONSTRAINS ................................................................... 9 3.5 END OF LIFE OPTIONS AND HIERARCHICAL DECISION MODEL .................................................................... 10 3.6 AN EXAMPLE OF HIERARCHICAL DECISION MODEL ................................................................................... 11

4 ALGORITHM FOR END-OF-LIFE PRODUCT STRATEGY DEFINITION ..................................... 18 4.1 STEPS OF DEFINITION PHASE ..................................................................................................................... 21 4.2 STEPS OF CALCULATION & TERMINATION PHASE ...................................................................................... 21

4.2.1 Algorithm for the maximum level of BoM ......................................................................................... 21 4.2.2 Algorithm for other levels ................................................................................................................. 22

5 ENVIRONMENTAL IMPACTS ASSESSMENT ..................................................................................... 24 5.1 INTRODUCTION TO LCA ........................................................................................................................... 24 5.3 MULTI-USE-PHASE ENVIRONMENTAL IMPACTS ......................................................................................... 28 5.4 AVAILABLE TOOLS & DATABASES ............................................................................................................ 30

6 IMPLEMENTATION OF ALGORITHMS INTO BDSS: ENVIRONMENTAL PERSPECTIVE ..... 31 6.1 ADAPTATION TO ALGORITHM TO USE CASES ............................................................................................. 31

7 ANNEX 1 – TOOLS AND DATABASES FOR IMPACT ASSESSMENT ............................................. 33 7.1 TOOLS ....................................................................................................................................................... 33 7.2 DATABASES .............................................................................................................................................. 38

8 BIBLIOGRAPHY ......................................................................................................................................... 40


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1 Executive Summary

The deliverable provides the foundations for BDSS development in the PREMANUS project, highlighting in particular the implications of eco-efficiency evaluations in remanufacturing processes and the implications of considering both the economic aspects of remanufacturing business in conjunction with environmental implications.

The following section present how it would be possible to integrate economic and environmental dimensions into BDSS and how different algorithms could be used. The main concepts enabling economic and environmental assessment are linked to:

• Activities carried out during remanufacturing processes at product or component level,

• Resources used to carried out activities,

• Relationship between specific activities at product/component level and different end-of-life alternatives, and

• External and product/component specific influencing factors.

All those elements are included into a hierarchical algorithm taking into account all EoL options for each item of the BoM, starting from the lower level, up to the product: decisions at lower levels influence alternatives in upper ones. Such aspects will also provide the foundations for the detailed and use-case specific activities of Task 5.2 and 5.3.

The eco-efficiency diagram is introduced, allowing the integration of economic and environmental dimensions into the decision making process. The deliverable details at the end the basic foundation for a streamlined environmental assessment of remanufacturing processes, providing a brief overview of the context, problems and complexities of LCA techniques and discussing so-called streamlined methodologies and multi-use-phase environmental impacts. An overview of available databases and tools for the purpose of impacts assessments is presented, building on existing work done under the Life Cycle Thinking coordinated by EU Joint Research Centre.


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2 BDSS role in PREMANUS

Business Decision Support System (BDSS) is one of the three pillars of PREMANUS (in conjunction with the Remanufacturing Information Services – RIS – and the Remanufacturing Service Gateway – RSG). This section will highlight the main functions and peculiarity of the PREMANUS BDSS; in particular why BDSS is crucial in remanufacturing businesses, how it works and the main users that will benefit from it. The role of environmental dimension in decisions will also be introduced in conjunction with a theoretical model for the eco-efficiency evaluator.

In D1.3 the high level scenario for PREMANUS usage has been defined and described, including the main requirements for user’s interactions and decisions to be supported.

In particular the key features of PREMANUS will enable (Figure 1):

• More reliable quotes for remanufacturing processes,

• Definition and verification of process KPIs,

• Data-supported decision on different alternatives and strategies at product/component level.

Sales managers but also production managers and shop-floor technicians will benefit from such features, at different stages of the entire remanufacturing business. Foundations of PREMANUS, enabling the above mentioned benefits, consist of:

• The ability of search and retrieval of relevant data for decisions from other IT System (e.g. MES, PLM, MRP…) or data entered by operators.

• Estimation and calculations of costs/time needed to carry out specific activities along the remanufacturing process,

• A proper reporting interface to allow different users to assess actual status of the product/component and to provide relevant information to support end-of-life decisions and define remanufacturing strategy.

Figure 1 - key features of PREMANUS

Decisions supported by BDSS combine the effects of costs, time, inventories and other relevant variables, including the environmental considerations related to the remanufacturing processes. Input and output of the BDSS could be clustered into 3 main groups as displayed in Table 1.


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Decisions Inputs Outputs

Cost Labour rates Workflow time Overhead Component costs Used resources Equipment used Cost virgin product/component

Mean process cost Range of process cost Estimate of remanufacturing cost Firm quote Cost breakdown Mean activity cost Range of activity cost Actual workflow cost

Time Activity time Work flow time Activity probability Delivery time Lead time

Mean process time Mean lead time Range of process time Range of lead time Mean activity time Range of activity time Time break down

Environment Resources used Energy used Bill of material Waste generated LCA inventories

Environmental impact of remanufacturing processes

Eco-Efficiency evaluations

Table 1 – Input and output of BDSS.

The two dimensions of cost and time are anyway closely interlinked as time needed for different activities in the process directly impact on resulting costs; for this reason cost will be considered as primary dimension together with the environmental one in the eco-efficiency evaluator of different EoL alternatives.

Remanufacturing process is a combination of multiple different activities carried out at product and component level.

The choice of activity enables different end-of-life strategies, thus having different implications on costs/time and also different environmental impacts.

The techniques enabling BDSS decisions will now be described in more detail.

The combination of economic and environmental dimension into an eco-efficiency evaluator allows the definition of different strategies for end-of-life of products/components. As presented in (Huisman, 2003) the eco-efficiency diagram allows the identification of four main areas (quadrants A–D, defined below). On the Y-axis of the diagram we can plot an economic indicator (in this case €) for the total costs occurring during the remanufacturing process. The X-axis represents the environmental indicator (LCA scores using any indicator like Eco-Indicator, Recipe, or any other available in standard inventories).

Positive values on the Y-axis represent economic revenues, while positive values on X-axis represent environmental gains. These areas represent “specific” bests from a purely economic or environmental perspective. Positive values on the Y-axis could represent local bests for companies in the remanufacturing business, while X-axis positive values could represent local best regulators and policy makers targets.

Applying eco-efficiency diagrams to end-of-life options and strategies in the electronics industry has been extensively used in the past decade in various research projects (Huisman, 2004) and in a recent study supporting the review of EU WEEE Directive (UNU, 2007).


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Figure 2 – Eco-Efficiency diagram.

Different end-of-life scenarios/options for the same product (or component), could end up in different areas of the diagram, depending on specific value of the input of the BDSS in different time-frames:

• Quadrant A: options/activities leading to profits but having negative environmental impacts. Usually these options should be prevented by local regulations.

• Quadrant B: options leading to profits with positive environmental impacts; these represent win-win situations and scenarios to be promoted.

• Quadrant C: options leading to losses and environmental burdens. Common sense should, generally speaking, avoid or prevent such alternatives.

• Quadrant D: options leading to losses despite having a positive environmental impact. Those cases are usually not directly pursued by companies (as leading to losses) but can be encouraged by policy makers and regulators, as leading to environmental benefits; this could be the case of mandatory recycling programs, where the financing of activities leading to environmental improvements or benefits is required trough Extended Producer Responsibility principles or other tools.


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3 State of the art for algorithms and methodologies

In this section, current attempts to develop models and algorithms for End of Life decision making will be addressed. Different approaches in the literature are briefly presented. Later in this section BDSS objectives are described. This will be followed by description of conditions which BDSS’s algorithm should deal with, whilst trying to recommend the best solution.

Later in the context of the End of Life domain a hierarchical decision model is described, based on which the algorithm is introduced. To do that, first, the available options in product recovery will be introduced. The different consequences of each option will be considered, namely the environmental impacts of each as well as the economic effects will be discussed.

Inderfurth, De Kok, & Flapper, (2001) explains that in many product recovery situations returned products can be reused in multiple ways; therefore reverse logistics will be required, and to do that, companies have to tackle the correspondent complexity. Huang & Su, (2013) state that: “In a closed-loop supply chain, product proliferation affects the reverse supply chain as well as the forward supply chain. Although increasing the number of product types can better satisfy diverse customer needs, complexity in the product recycling, remanufacturing, and resale processes may erode a firm’s overall profit”. Prahinski & Kocabasoglu, (2006) define the reverse supply chain as a series of activities required to retrieve a product from a customer in order to dispose of it or recover its remaining value.

In addition to product variability and the benefit incorporated in them, in some countries OEM1s are obligated to take products back at the end of their useful life. There are also some 3rd party companies active in collecting used products and making profit either by reselling them or through recycling. These firms select used products by comparing the revenue from recycle or resale of products’ components and the collection and reprocessing costs of the used products. (Pochampally, Nukala, & Gupta, 2009) find that making a decision on selecting the product and the appropriate choice on what to do with it after the End of Life stage is a complex decision which incorporates several drivers. The consequences of this decision are not limited to economic results but also environmental impacts need to be taken into account. The multi-object nature of this problem requires several information inputs to the decision making as well as boundary conditions, restrictions, preferences and priorities which turn the decision making procedure into a complex process. The aim of BDSS2 is to facilitate such processes which are a part of reverse supply chain processes. Huang & Su, (2013) mentions five key sequential steps in reverse supply chain processes: Product collection and acquisition, reverse logistics, inspection and disposition, remanufacturing or reconditioning.

3.1 Brief literature review

In the following section a brief state of the art on algorithms and methodologies aiming to assist decision makers for End of Life product recovery is presented, based on a review of different papers and research ((Gungor & Gupta, 1999; Huang & Su, 2013; Ilgin & Gupta, 2010; Pokharel & Mutha, 2009; “Reverse logistics network design: a state-of-the-art literature review,” 2009; Sbihi & Eglese, 2007; Subramoniam, Huisingh, & Chinnam, 2009; Williams, 2007).

Decision support algorithms are designed to support a decision making problem. This problem can be seen from different points of view. The goal of a decision is the main concern of the algorithm, and is one of these aspects. To decide for end of life recovery different objectives have been considered, for example some algorithms try to reduce costs of recovery processes such as disassembly activities

1 Original Equipment Manufacturer 2 Business Decision Support System


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(González & Adenso-Díaz *, 2005), some others to maximize economic profits of such processes so they try to maximise resulting revenue minus occurred costs. (H. B. Lee, Cho, & Hong, 2010); as another example some other algorithms target efficiency goals such as minimising lead time (Xanthopoulos & Iakovou, 2009).

In addition to the economic concerns, decision supporting algorithms may also consider environmental consequences of the decision and try to reduce the impacts of the decision on environment. For example, trying to reduce the amount of produced waste, reducing the emissions as well as resource consumption are some of the frequent objectives which have been considered by different researchers and experts so far (Amaya, Zwolinski, & Brissaud, 2010; Huang & Su, 2013; Ilgin & Gupta, 2010; H. B. Lee et al., 2010; Ma, Jun, & Kim, 2011; Seitz, 2007; Thinking & Assessment, 2011)

Finding out the best option for end of life is an important problem for OEMs as well as 3rd party companies. In order to develop a decision model, it is necessary to consider several factors. Different variables, boundary conditions as well as multiple objectives while added to the uncertainty and lack of proper information about the product and processes, make the task of development of this model complex. Traditionally, a considerable amount of previous attempts to develop a model and algorithm for EoL3 decision making and even in a broader sense, in deciding on closed or reverse supply chain, have been concentrated on economic sides of the phenomena, trying to minimize costs of product recovery or reverse logistics or trying to maximize the benefits. (Huang & Su, 2013; Krumwiede & Sheu, 2002; Prahinski & Kocabasoglu, 2006) Explicitly to aid decision making on product selection and on appropriate EoL options, mathematical models have been used by researchers to develop different algorithms for EoL option selection.(Ilgin & Gupta, 2010) H. R. Krikke, Van Harten, & Schuur, (1998) has used a dynamic programming approach to determine product recovery and disposal strategy for one product type. Their model tries to maximize the net profit while considering relevant technical, ecological and commercial feasibilities. Later in their following task, they have applied their model to real life cases: copier and a monitors recycling. (H.R Krikke, Van Harten, & Schuur, 1999; H.R. Krikke, van Harten, & Schuur, 1999) This attempt have been later further developed to address multiple disassembly processes and partial disassembly.(Teunter, 2006). (Das & Yedlarajiah, 2002) have proposed a mixed integer programing method which has been used to determine the optimal part disposal strategy based on the maximization of the net profit. A piecewise linear concave program to select between five disposal options (refurbish, resell, reuse, recycle, landfill) has also been used to maximize the overall return for disassembly products. (Jorjani, Leu, & Scott, 2004). The impact of reducing disassembly time and cost on the optimal EoL strategy has been also assessed. (Willems, Dewulf, & Duflou, 2006) Linear programming method has also been utilized to evaluate three options of repair, repackage or scrap.(Tan & Kumar, 2008) Multi Criteria Decision Making (MCDM) methodologies are used in some researches. A multi-objective method has been introduced in which economic and environmental impacts have been combined using a weight method. (S. G. Lee, Lye, & Khoo, 2001). (Hula, Jalali, Hamza, Skerlos, & Saitou, 2003) used a Genetic Algorithm (GA) method to trade-offs between economic and environmental impacts of different options of EoL. There are several other researches that have used multi-criteria decision methods and applied different approaches to resolve their issues regarding EoL decision making. Approaches such as ELECTRE III MCDM methodology to rank different EoL options (Bufardi, Gheorghe, Kiritsis, & Xirouchakis, 2004) which later were more developed to a methodology which considers complete ranking of EoL options under uncertainty environment.


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(Chan, 2008), also an evolutionary algorithm to maximize the recovery value considering multi-objective aims (Jun, Cusin, Kiritsis, & Xirouchakis, 2007). (Fernandez, Puente, Garcia, & Gomez, 2008) have also developed a fuzzy approach to evaluate five recovery options and one disposal option by considering four criteria: product value, recovery value, useful life and level of sophistication.as another approaches, (Wadhwa, Madaan, & Chan, 2009) propose a methodology to consider the knowledge of experts, the “Multi-criteria Matrix” by (Iakovou et al., 2009) and GP methodology proposed to address the optimal design of the recovery processes of the end-of-life (Xanthopoulos & Iakovou (2009).

The allocation of different quantities to different options for EoL decisions has been also investigated by integrating the uncertainty models into the algorithm. (Inderfurth et al., 2001) Zuidwijk & Krikke, (2008) have introduced a set of strategies for end of life based on available information of the returned product and by considering different levels of disassembly Bill of Materials in waste electrical and electronic equipment (WEEE) case. One of the pitfalls of these models is that in most of the conducted researches, the network of suppliers and remanufacturers with different stakeholder has not been taken into account. Walther, Schmid, & Spengler, (2008) tried to address this issue and provide a mechanism and structure for different role players in product recovery to be able to collaborate through negotiation, calling it as the recycling network. Dehghanian & Mansour, (2009) introduced a sustainable recovery network. The mechanism for optimizing this network in different stages is based on Genetic algorithms. They have considered both economical, environment factors as well as social impacts of the chosen option for end of life by using a LCA approach to assess different EoL options. (Dehghanian & Mansour, 2009)

An overview of different classifications of manufacturing decision support tools and their descriptions has been provided in deliver D1.1

3.2 Algorithm Objectives

H. B. Lee et al. (2010) have shown that while formulating the problem statement, how changing the objective statement affects the overall result and recommends considering maximization of the profit as the aim of algorithm. In particular they demonstrated how, in a simple disassembly for recovery scenario, the three potential strategies (or algorithm goals):

• Maximise (profits) – disassembly cost • Maximise (profits) – Minimize (disassembly costs), or • Maximize (profit – disassembly costs)

lead to different results. According to their model, the overall objective shall consider the difference between revenues and costs of an action to decide whether to carry it on or not.

With the aim of introducing not only a pure economic dimension in the BDSS, the maximisation of net margin, will be integrated with environmental considerations.

Deciding on the right strategy for product recovery 2 main pillars of sustainability will be than integrated:

• Economic consequences of the decision (within the scope of PREMANUS) • Environmental Impacts of the decision (within the scope of PREMANUS)

Therefore the developed algorithm for BDSS of PREMANUS will have as a goal a maximisation of economic and environmental benefits:

Objective: MAX {economic benefit + environmental benefits}


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3.3 Evaluation activities in remanufacturing context

From a business perspective interviews with remanufacturers, site visits and interactions with use case partners confirmed that the economic dimension of remanufacturing is essential to understand in detail, in order to make business decisions as well being able to provide customers a reliable quotation for the cost of a remanufactured product. However the cost to remanufacture each individual product will vary due to a variety of factors including the type of product, the condition of the returned product core and the cost of resources. In addition the level of uncertainty present within the remanufacturing system tends to be high, and it’s therefore advisable within a cost estimate to provide an interval estimate along with probability distributions, rather than relying upon a single point estimate which does not reflect the errors and uncertainty within the estimation (Angelis and Stamelos 2000).

Cost estimation is a well-defined research topic and is used within multiple disciplines where there is a great deal of uncertainty and different methods of cost estimation have been developed for these applications which can be broadly classed as intuitive, analogical, parametric or analytical (Niazi, Dai, Balabani & Seneviratne 2006, Ben-Arieh and Qian 2003).

Intuitive techniques, also referred to as expert judgement (Angelis and Stamelos 2000), are based on past experience of the estimator (Ben-Arieh and Qian 2003).

Analogical techniques use historical information of completed projects with known effort to estimate costs that are deemed similar to new projects (Anglelis and Stamelos, 2000).

Parametric techniques, also referred to as algorithmic (Angelis and Stamelos 2000), estimates costs based upon particular parameters of a project.

Analytical techniques allow evaluation of the cost of a product from a breakdown of the work required into elementary tasks, operations or activities with known (or easily calculated) cost (Ben-Arieh and Qian 2003).

3.3.1 Approach in cost calculation

Based on the requirements of remanufacturing a hybrid of analytical and analogical techniques can be used within the cost estimation of remanufacturing processes. Such an approach provides the main elements used in order to calculate main remanufacturing costs.

Figure 3 the four stage approach outlined within the cost estimation tool

• Analytical Model

The foundation of cost estimation tool is to use an analytical method by the summation of the cost of all the activities required by the remanufacturing process. From the business process model a good understanding of the activities conducted in remanufacturing are known. However each remanufacturing operation can require a unique effort due to variations in the product type, condition and customer requirements. This can therefore lead to variations within the total cost of each activity and the workflow path which occurs during remanufacture. In order to estimate these costs further, algorithms are used which utilise historical information to estimate specific costs and evaluate the level of uncertainty within the overall process.

Analy'cal model of remanufacturing

process

Deriva'on of ac'v'y cost using analogical approach

Sta's'cal analysis of cased based sample

to generate probability distribu'ons

Simula'on of analy'cal model using Monte Carlo

simula'on


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• Derivation of activity cost

Case based reasoning is a particular analogical technique which aims to match the current case to the most similar historical case. The historical cases are stored within database structures. The main challenge for analogical methods is in determining which previous project most closely matches the new case. Algorithms developed to tackle this problem include the nearest neighbour algorithm and inductive technique. Cased based reasoning elements include:

1. Case Representation 2. Indexing (manual or automated). This aims to reduce the computation time and speed

up searching. It isost likely to use a manual method which will allow the user to determine which attributes are most likely to affect the cost within a particular activity.

3. Storage. Relational database in this example 4. Retrieval (how to determine the most suitable comparative cases). Example

algorithms include nearest neighbour retrieval and inductive retrieval 5. Adaptation. If the case produced is not exact, how to adapt it.

• Statistical Analysis

A statistical analysis of the sample of ‘similar’ historical cases found using the analogical technique described above is conducted to provide a probability distribution for the cost model to be simulated. The choice of distribution type is a key factor within this stage to allow for the best representation of the data. For normal distribution mean and standard deviations are required to be calculated. Other distribution type may require more or less complex statistical analysis. This is currently an area of work under that requires further investigation by the researchers.

• Monte Carlo Simulation

Monte Carlo simulation will play two roles within the cost estimation tool:

1. To decide which activities will be conducted within the remanufacturing process when a choice exists. The probability of each activity will have been calculated from the data sources using the algorithms mentioned above.

2. To determine a cost for a particular activity based upon the probability distribution generated in the above algorithms.

After a cost has been simulated for each activity within the model, the values can be summed together to generate an overall cost for product remanufacture. By repeating this process multiple times a cumulative cost distribution can be generated which can represent the level of uncertainty within the estimation.

3.4 EoL boundary conditions and algorithms constrains

The algorithm should respect boundary conditions and existing restrictions, as well taking into account specific product/component conditions, as described in the following sections. Detailed evaluation of specific EoL constraints in the different Use Cases will be carried out in upcoming tasks 5.2 and 5.3. Boundary conditions of EoL management refer to:

• factors related to the specific product, and/or • factors related to operations at plant level.


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The decision depends on the status and condition of the specific product; for example if the received product, has been well maintained and is in a good condition, the manufacturer may decide to refurbish and resell it. Where the product is not in a good condition and has suffered serious wear, the manufacturer may decide to recycle it. Of course decision making is not simply based on these two factors and in practice several factors and conditions should be considered.

Boundary conditions are not limited to the product related items only. Elements to be considered, having a direct impact on remanufacturing business might include:

• actual plant capacity, including available tools/machines and resources, • operations carried out in different places with considerable handling/transportation distances, • warehouse related constraints and parameters, e.g. stock level, stock out and overstock costs, • prices of raw materials affects the decision on remanufacturing, recycling or landfill, • energy cost.

Another affecting factor in making decision of EoL is related to the waste and emissions produced by a decision. For example amount of waste water produced can be an important item. It has economic impacts as well as environmental impacts.

Production based on the demand or the prediction of demand is the major focusof current business models. Accordingly, integrating market information and status to the decision making process, can contribute to create value. Such results are not restricted to the economic results only. Satisfying an existing demand by recovering a used product, avoids total impacts related to production of a new product as well as disposing the used one to the environment and generating wastes.

3.5 End of life options and hierarchical decision model

A manufacturer has different options once he is in the position to decide on the end of life recovery for a product. Based on current conditions, for example the quality and wear of the product or the market demand for some of its components, the decision maker should decide between the available options he has. He may decide to reuse the product. In some cases this decision incorporates the need to repair the product. Another choice would be refurbishing while remanufacturing also would bring the product back to an acceptable level of quality to go back to the life and be employed again. In some cases the product is not in such a good condition that none of the above mentioned option is appropriate for it. In such cases, decision maker may decide to use some parts and components of the product or in a better way may decide on cannibalization. For elements which are suitable for reuse, a possible choice would be recycling the materials. Incineration and recapturing energy contents of the part is the next choice. The last option would be disposal, which in mostcases would be landfill. (Thierry, Salomon, Van Nunen, & Van Wassemhove, 1995)

EoL planning should be considered together with EoL options for subassemblies. (H. B. Lee et al., 2010) Options for EoL of a product were described in 3.3. These options can be applied to the product at different levels, i.e. for each level of BoM different choices exist on the menu. For each one of modules, components, subcomponents, parts and generally for any item of BoM, several choices exist. Economic and environmental impacts do not only depend on a single item in BoM of product but also on the links and connections between items. Usually a considerable amount of costs related to an item recovery is contributed to dismantling and disassembly activities to separate it from other items connected to it. So the final decision on a product EoL option requires consideration of existing possible options for items existing in its BoM. Also the connection between them shall be taken into account as sometimes it is more beneficial not to dismantle/disassemble some items and consider and process them as a unit entity. Of course the last point incorporates more information about the connection of items of BoM and correspondent costs and impacts in each option regarding such connections.


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As an example let’s assume that we have two possible actions, “remanufacturing” and “disposal”. And we have a product with 3 components to decide for. The decision on product P1, depends on the chosen options for its components. Clearly once we have made a decision on what we are going to do with C1, C2 and C3, we would be able to decide on what to do with P1. For instance, if we decide to dispose C3, we cannot reuse P1 anymore even with proper repair or remanufacturing but we have to replace C3 with a new item, therefore the decision on P1 depends on the decision on the bottom layers of the BoM. Sometimes the decision maker, instead of deciding to do a complete disassembly, may decide to dismantle C1 but keep and reuse C2 & C3 together to avoid costs or impacts related to connection between C2 and C3.

Considering above the mentioned items, the BDSS algorithm considers a bottom-up approach and for each level determine the best option for each item and then the best option based on an aggregation of the results will be recommended. BDSS algorithm, calculates impacts (economic & environmental) for each item of the BoM, and for each of possible paths calculates the aggregated economic and environmental consequence of that path in the tree.

3.6 An example of hierarchical decision model

So far we have briefly introduced different efforts reported in the literature regarding decision for end of life recovery. Objectives, Options, Constraints and boundary conditions have been indicated. In this section we present a simplified example, with fake data, to help the reader in comprehend the concepts and parameters of the algorithm. Product A consists of two components (C1 & C2) each one made of two raw materials. C1 and C2 required a disassembly activity to become separated from each other.

product A

C1

Fe Cu

C2

Fe Al

Figure 4 - sample product with 3 components


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Figure 5 – Example product A and hierarchical model.

Let’s imagine product A which has reached its end of life and a remanufacturing decision should be taken. The product consists of two components C1 and C2. Component C1 is consisted of 10kg of Iron and 2kg of Copper (total weight 12kg). C2 weight is 5kg and is made of 1kg of Iron and 4kg of Aluminium. A diagram showing the BoM of Product A is represented in Figure 6.

Figure 6 – Hierarchical Decision model.

Possible EoL options are displayed at product and component level:

• Product A has 4 options:

o Reuse

o Remanufacture (in case of refurbish, disassembly should be performed on A to access C1 & C2)

o Material Recovery4

o Disposal

• Components C1 & C2 have 3 options:

o Reuse

o Remanufacture

o Material recovery

• Part levels has only one option

o Material recovery (in this option, components C1 & C2 are disassembled to their parts. Each part is consisting of one type of raw material i.e. Fe, Cu or Al. Then raw materials will be recovered).

4 In case of material recovery different scenarios can be considered: product A can be sent for material recovery as a whole or can be disassembled to C1 and C2 level so that components can be sent for material recovery. Material recovery can be performed also at 3rd level of BoM (i.e. each component being disassembled to material level).

A

Reuse

Refurbish

C1 Reuse

Refurbish

C2 Reuse

Refurbish

Material recovery (MR1)

C1 Material Recovery (MR2_C1)

Fe (10 kg) Material Recovery (MR3_C1_Fe)

Cu (2 kg) Material Recovery (MR3_C1_Cu)

C2 Material Recovery (MR2_C2)

Fe (1 kg) Material Recovery (MR3_C2_Fe)

Al (4 kg) Material Recovery (MR3_C2_Al)

Disposal


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Assumptions are generally the input information to the algorithm. In the previous section, we have defined the structure and corresponding actions for each option for each entity in the BoM of product A. Now here, we describe our assumed values for prices resulting in income for the remanufacturer, costs and environmental impacts.

Revenues

One of the values and data categories considered as inputs to the algorithm is the price and income resulting by selection and execution of each option.

In this example we assume the prices displayed in Table 2.

Raw material Unit price Aluminum (Al) 4,00 €/kg Ferro (Fe) 0,40 €/kg Copper (Cu) 6,00 €/kg

Table 2 - assumptions for the example, price of raw materials

Considering different options for product A, the possible incoming revenue for each of them is reported in Table 3.

Table 3 – Assumptions for the example, revenue income at product level.

Refurbishing product A, requires disassembly of components C1 and C2. At component level, alternatives are displayed in Table 4.

Recovery option Comments Revenue

C1

Reuse Sell as is 1,50 € Refurbish Comprises refurbishing 3,50 € Material Recovery (MR2) Send for Recovery as is 2,00 €

C2

Reuse Sell as is 3,00 € Refurbish Comprises refurbishing 4,00 € Material Recovery (MR2) Send for Recovery as is 3,00 €

Table 4 - assumptions for the example, revenues coming from each of options of components C1 & C2

Costs and required activities for each option

In order to perform each of options some activities are required and shall be performed. These activities in this example would be:

• Disassembly of product A into components C1 and C2 • Refurbishing components C1 and C2 • Disassembly of component C1 into Fe and Cu parts • Disassembly of component C2 into Fe and Al parts

A summary of costs related to each of these activities is reported in

Recovery option for A Comments Revenue Refurbish Comprises disassembly and refurbishing of C1 and C2 10,00 € Reuse Sell as is 3,00 € Disposal Send to landfill -2,00 € Material Recovery (MR1) Send for Recovery as is 1,00 €


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Table 5.

Table 5 – Assumptions for the example, costs of activities

Environmental impacts of each option

The environmental impact for different procedures and options is reported in Table 6. It is necessary to consider that most of the options which try to reuse or recover the materials of the product, have negative or small impact on environment as they are avoiding impact of a production of a new product or component and hence the use of raw materials. In the Example, environmental impacts has been displayed using single score indicator (Eco-Indicator 99); values for different options are fake to demonstrate the eco-efficiency evaluator functioning.

BoM level Activity Environmental Impact (Pt)

Product A

Reuse_A -7,00 Refurbish_A -3,00 Disposal_A +5,00 Material Recovery at first level (MR1) -1,50

Component C1

Reuse_C1 -2,00 Refurbish_C1 -1,20 Material Recovery at first level (MR2_C1) -0,80

MR3_C1 Material Recovery C1_ Fe -0,20 Material Recovery C1_ Cu -0,90

Component C2

Reuse_C2 -3,00 Refurbish_C2 -1,80 Material Recovery at first level (MR2_C2) -0,90

MR3_C2 Material Recovery C2_ Fe -0,02 Material Recovery C2_ Al -0,65

Table 6 - Assumptions for the example, environmental impacts of different options

The objective in our example has two dimensions, maximizing profit and minimizing environmental impacts which are consequences of the chosen option. In our example there are multiple possible options which are a combination of different options of different entities in the BoM.

Here below we have listed possible options:

1. Reusing product A (Reuse_A)

Activity Cost Disassembly A to C1& C2 7,00 € Refurbish_C1 2,00 € Refurbish_C2 1,50 € Disassembly C1 to Fe & Cu 3,00 € Disassembly C2 to Fe & Al 10,00 €

Activity Cost Disassembly A to C1& C2 7,00 € Refurbish_C1 2,00 € Refurbish_C2 1,50 € Disassembly C1 to Fe & Cu 3,00 € Disassembly C2 to Fe & Al 10,00 €


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2. Refurbishing product A (Refurb_A) 3. Disposing product A (Dispose_A) 4. Material recovery for Product A (MR1) 5. Reusing both components C1 and C2 (Reuse_C1 & Reuse_C2) 6. Refurbishing both components C1 and C2 (Refurb_C1 & Refurb_C2) 7. Recovering materials of both components C1 and C2 (MR2) 8. Reusing C1 and refurbishing C2 (Reuse_C1 & Refurb_C2) 9. Refurbishing C1 and Reusing C2 (Refurb_C1 & Reuse_C2) 10. Reusing C1 and Material recovery for C2 (Reuse_C1 & MR2_C2) 11. Material recovery for C1 and Reusing C2 (MR2_C1 & Reuse_C2) 12. Refurbishing C1 and Material recovery for C2 (Refurb_C1 & MR2_C2) 13. Material recovery C1 and Refurbishing for C2 (MR2_C1 & Refurb_C2) 14. Material Recovery for both C1 and C2 at 3rd level (Fe, Cu & Al) (MR3)

There are other possible combinations but only the main ones have been displayed to present the use of Eco-Efficiency evaluator in the context of hierarchical decision.

For each of these possible options, corresponding revenue, cost and environmental impacts can be determined as a sum over the elements of activities required for each option (as shown in Table 7 below).

For example considering option 6 (Refurbishing both components C1 and C2):

• Revenues for Refurb_C1 and Refurb_C2 are derived from Table 4 (3,5 + 4 = 7,5 €). • Costs of disassembly of A to C1& C2 (7€) and of refurbishing C1 and C2 (2€ and 1,5€) are

given in Table 5. • Profits can be calculated as ‘Revenues – Costs’, giving 7.5 – (7.0 + 2.0 + 1.5) = -3€ • Environmental impact is derived from Table 6 (-1,2 + -1,8 = -3Pt).

options Revenues Costs Profit Env. impact

1- Reuse_A 3,00 € - 3,00 € -7,00 2- Refurb_A

10,00 € Disassembly Refurb_C1 Refurb_C1

-1,00 € -3,00 7,00 € 2,00 € 2,00 € 3- Dispose_A -2,00 € - -2,00 € +5,00 4- MR1 1,00 € - 1,00 € -1,50 5- Reuse_C1

& Reuse_C2 4,50 €

Disassembly Reuse_C1 Reuse_C2

-2,50 € -5,00 7,00 € - - 6- Refurb_C1

& Refurb_C2 7,50 €

Disassembly Refurb_C1 Refurb_C2

-3,00 € -3,00 7,00 € 2,00 € 1,50 € 7- MR2

7,00 € Disassembly MR2_C1 MR2_C2

- -1,70 7,00 € - - 8- Reuse_C1

& Refurb_C2

5,50 €

Disassembly Reuse_C1 Refurb_C2

-3,00 € -3,80 7,00 € - 1,50 € 9- Refurb_C1

& Reuse_C2

6,50 €

Disassembly Refurb_C1 Reuse_C2

-2,50 € -3,20 7,00 € 2,00 € -


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10- Reuse_C1 & MR2_C2

5,00 €

Disassembly Reuse_C1 MR2_C2

-2,00 € -2,90 7,00 € - - 11- MR2_C1

& Reuse_C2

4,50 €

Disassembly MR2_C1 Reuse_C2

-2,50 € -3,80 7,00 € - - 12- Refurb_C1

& MR2_C2

6,50 €

Disassembly Refurb_C1 MR2_C2

-2,50 € -2,10 7,00 € 2,00 € - 13- MR2_C1

& Refurb_C2

6,00 €

Disassembly MR2_C1 Refurb_C2

-2,50 € -2,60 7,00 € - 1,50 € 14- Disassembl

y and recycle materials

32,4€

Disassembly MR3_disassembly

12,40 € - 1,77

MR3 Fe

MR3 Cu

MR3 Fe

MR3 Al

7,00 € 1,5 € 1,5 € 5 € 5 €

Table 7 – Evaluation of different EoL alternatives.

In Figure 7 below different options are plotted into the eco-efficiency diagram; on Y-axis the economic dimension of the table is plotted. On X-axis the environmental dimension is plotted in reverse direction (negative values, indicating environmental benefits are on the right).

It should be noted how, for instance, given the actual data of the example, from a pure economic perspective the most convenient option is the “Disassembly & recycle material” option (#14), but from an environmental perspective the “reuse” (#1) is preferable.

Of course changing boundary conditions the ranking or positioning of alternatives vary on the diagram.


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Figure 7 – Eco-Efficency diagram for options of the example

As it can be seen in Figure 7, according to the preferences of the decision maker, importance of environmental impacts as well as economic benefits can be combined and which enables a proper decision to be made.

-‐4,0

-‐2,0

0,0

2,0

4,0

6,0

8,0

10,0

12,0

14,0

-‐8,0 -‐7,0 -‐6,0 -‐5,0 -‐4,0 -‐3,0 -‐2,0 -‐1,0 0,0 1,0 2,0 3,0 4,0 5,0 6,0

Rela%v

e Econ

omic Ben

efit

Rela%ve Environmental Impact

1-‐ Reuse_A 2-‐ Refurb_A 3-‐ Dispose_A

4-‐ MR1 5-‐ Reuse_C1 & Reuse_C2 6-‐ Refurb_C1 & Refurb_C2

7-‐ MR2 8-‐ Reuse_C1 & Refurb_C2 9-‐ Refurb_C1 & Reuse_C2

10-‐ Reuse_C1 & MR2_C2 11-‐ MR2_C1 & Reuse_C2 12-‐ Refurb_C1 & MR2_C2

13-‐ MR2_C1 & Refurb_C2 14-‐ Disassembly


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4 Algorithm for End-of-Life product strategy definition

This section describes main structure of algorithm to support decision at product or component level. BDSS algorithms should have the following characteristics:

• General i.e. applicable to different types of products and industrial sectors; • Multi objective i.e. considers jointly the economic and environmental dimension; • Multi-level supportive i.e. considers different levels of the BoM; • Interactive i.e. it should be able to interact with other systems, to use information coming

from the product. It shall allow the decision maker to contextualize the current status of the product when it arrives in the company and the current status of the company at that time.

This algorithm shall be able to analyse several dimensional factors. The main dimensions of interests in BDSS are the environmental and economic dimensions. Economically prices of the option, best and worth case costs, influencing factors and several other variables such as stock level can be important in decision making.

BDSS’s approach toward these two dimensions is by applying activity based costing (ABC) methodology for cost evaluation and streamlined LCA for environmental impact assessment.

The proposed algorithm is composed of four phases: Definition phase, initialization phase, calculation phase and termination phase. (Figure 8)

Figure 8 - Phases of BDSS algorithm

The definition phase is the phase in which information is gathered for the algorithm, i.e. in this phase BDSS interacts with other resources to get information about product, processes, influencing factors, results of inspections which may block some of the potential EoL alternatives, prices and other parameters related to the supply chain such as stock level for the product and its components. Environmental related information is also collected in this phase. This process is mainly carried out via interacting with other IT systems as well as the user.

Figure 9 represents steps of definition phase. Broadly speaking, in this phase two categories of information are gathered from the user or other information systems.

1) Product specific information, which mainly come from inspection reports, beginning of life and middle of life (usage phase) history as well as current information about demand and stock level.

2) Product general information, which mainly cover costs, prices, importance factors regarding stock-out and over-stock events, as well as activities carried out during the remanufacturing process for different alternatives existing within the plant.

In this phase according to the BoM of the product, for each component possible options will be addressed. Then for each option relevant activities occurring in the remanufacturing process to implement such an option will be introduced.

The next piece of information is to choose the relevant influencing factors for each of these activities. It should be determined whether a factor is blocking or not (f.i. the wear level of connection between components could be critical for a proper disassembly) and if so, then whether the activity is blocking the corresponding option or not (f.i. in case non-destructive disassembly due to the wear of connection between components is impossible, the remanufacturing of such a product/component is impossible).

Define Ini'al Phase Calcula'on Phase Termina'on Phase


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Besides determining if whether a factor is blocking, the range and importance weight for each influencing factor is inserted/updated to/ the system during the definition phase.

At the end of this phase, initial matrices of information will be updated according to system and product specifications and requirements. More details of figure 10 are given in section 4.1.

In the initiation phase the algorithm updates and generates matrices for each existing component of the BoM. Based on information provided in the definition phase, all elements which are necessary and required for the algorithm will have a value, which is either determined previously or at this stage is set to a neutral value like 0 or 1 depending on the operations using them.

The calculation phase determines, through iterations, the environmental value and economic value of each object of the BoM following a logic of backward iterations: it is strarting with the maximum level of the BoM (Bottom of BoM) and then it passes upward to the other levels until the first level (i.e. the product) is reached.

Generally this phase comprises three steps:

1) Calculations of economic value for each option which is the difference between the resulting revenue of an option an aggregation of different costs at different levels of the BoM and the required activities in each.

2) Calculation of the environmental value for each option, this is also an aggregation of what has been initialized for each item in BoM regarding the option of interests.

3) Determination of resolution method, i.e. how the algorithm is going to mix two dimensions of objectives i.e. economic and environmental impacts.

In the last phase which is the termination phase, results for different possible options along the BoM considering both economic and environmental impacts will be integrated and provided to the user based on the decided resolution method.


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Figure 9 - Definition phase of BDSS algorithm, in this phase predefined matrices will be updated according to the product state and other information which are available such as stock level, process, costs.

BoM

Product

Compo

nent

s

Yes/no

Component Option

Yes/no

Option

Activ

ity

Yes/no

Activity

Influ

encing

factor

Blocking?

Activity

Influ

encing

factor

Blocking?

Option

Activ

ity

DS flag ?

Option

Activ

ity

Cost min/MAX

Option

Activ

ity

Environmental Impact min/MAX Ac

tivity Range

Activity

Influ

encing

factor

Weight

Influ

encing

factor

Price

Component

Option

Margin

INV turnover

Class in the

matrix

Component

Level

Component

Stock level

% impact

Component

Over stock

% impact

Stock ou

t

MAX cost

Component

Over stock

MAX cost

Stoc

k ou

t

Definition phase

Product specific info. Matrix

Product general info. Matrix


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4.1 Steps of Definition phase

As described before in this phase the algorithm updates predefined matrices by interacting with the user and other information systems. In this step the BoM for the product is defined for the system.

Default options for each component in the BoM are being defined; for each EoL option the activities involved in the process need to be identified and selected. In this step the influencing factors in execution of each of the activities which were defined in previous step, are also defined. A literature review (Sundin, 2004) suggests the following items (ease of identification, ease of verification, ease of access, ease of handling, ease of separation, ease of securing, ease of stacking and wear resistance) as the influencing factors, but other elements can be also introduced by users of PREMANUS.

In this step, for each influencing factor of each activity, user can decide the importance of thiselement (values for High, Medium and Low). A weight is also assigned for each pair of (activity, influencing factor). This factor represents how much the influencing factor affects the move from the best case cost to a worst case cost. Here the aim is define how much the level of influencing factor affects the execution of the activity.

Blocking constraints need also to be defined at influencing factor and activity level: influencing factors that can block the activity are defined. If the activity is blocking for one option, a flag = “B” will be set. Clearly if any influencing factor is blocking this activity and its value is set to “blocking” then the algorithm will deselect the option.

In order to understand if disassembly is needed to have access to lower level of BoM, a flag “DS” is also defined. If an option has the flag= “DS” for an activity this means that it is necessary to drill down in the BoM.

Best case costs and the worst case costs are also defined, as well as revenues at product or component level. The same procedure will be carried-out for environmental impacts. i.e. steps which have been carried out so far, are repeated this time costs are replaced by environmental impacts. The required information regarding impacts of each option for each object is determined by performing a streamlined LCA.

4.2 Steps of calculation & termination phase

The algorithm has been divided into two groups of procedures: the first group is related to the maximum level of the BoM i.e. bottom of BoM, which is i=Imax; the second group is related to the upper levels of the BoM. In the Algorithm the calculation of Economic Value (Vomi) and Environmental Impacts (EIomi) for each level of BoM are carried out. The algorithm allows for calculation of such elements according to different methodologies and approaches, as will be done in task 5.2 and 5.3.

4.2.1 Algorithm for the maximum level of BoM

‘the algorithm starts with the bottom BoM level’ 1) i=Imax ‘increment of the object index’ 2) mi=m +1 Initialize the index of the option for the object mi’ 3) omi = 0 ‘Select the option of the object mi of the level i’ 4) omi= omi+1 ‘It is selected the activity of option omi for the object mi’ 5) ki=ki+1


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‘It is selected the influencing factor of the activity ki’ 6) fki=fki+1 ‘It is verified if the influencing factor is in a worst case and It is blocking for the activity ki’ 7) if vfki=0 and bfki=0 then ‘It is verified if the activity ki is blocking for the option omi’ 8) if FLAG= “B” then ‘the activity ki is blocking for the option mi and then the option is deselected’

I. deselect omi ‘iteration of the blocking verification’

II. go to the 4th step III. end if

‘increment of the influencing factor index’ 9) go to the 6th step until fki=fkmax ‘increment of the activity index’ 10) go to the 5th step until ki=Kmax ‘the activity ki is not blocking for the option mi and then It is calculated the economic value and the environmental value’ 11) Calculation of Vomi ;EIomi ‘the couple of Vomi and EIomi are added to the set of the solutions related to the object mi of the level I’ 12) Add 𝑆!",! = 𝑆!",! ∪ 𝑉!"#; 𝐸𝐼!"# ‘increment of the option index’ 13) Go to the 4th step until omi= Omaxmi ‘application of the resolution method to the set of solution couples of the object mi’ 14) Apply the resolution method to Smi,I à S*

mi,i ‘increment of the object index’ 15) Go to the 2nd step until mi = Mmaxi

4.2.2 Algorithm for other levels

‘decrement of the level index’ 16) i= i-1 ‘increment of the object index’ 17) mi = mi +1 ‘It is initialized the index of the option for the object mi’ 18) omi = 0 ‘It is selected the option of the object mi of the level i’ 19) omi= omi+1 ‘It is selected the activity of option omi for the object mi’ 20) ki=ki+1 ‘It is selected the influencing factor of the activity ki’ 21) fki=fki+1 ‘It is verified if the influencing factor is in a worst case and It is blocking for the activity ki’ 22) if vfki=0 and bfki=0 then

‘It is verified if the activity ki is blocking for the option omi’ I. if FLAG= “B” then

‘the activity ki is blocking for the option mi and then the option is deselected’ II. .deselect omi ‘iteration of the blocking verification’ III. go to the 4th step IV. end if


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‘increment of the influencing factor index’ 23) go to the 20th step until fki=fkmax ‘increment of the activity index’ 24) go to the 19th step until ki=Kmax the activity ki is not blocking for the option mi and for the level lower than the last level It is verified if the object has son/sons in the BoM’ 25) if Flag omi =”DS” then

It is calculated the economic value and the environmental value’ I. Calculation of Vomi ; EIomi

‘It is added to the economic value and environmental value of the object also the values of the son/sons, in the BoM, of the object analysed ’ II. Add 𝑉!"#; 𝐸𝐼!"# ∪ 𝑆!",!∗

!"# !"#$!" !"!#$%&'

‘the couple of Vomi and EIomi are added to the set of the solutions related to the object mi of the level i’ III. Add 𝑆!",! = 𝑆!",! ∪ 𝑉!"#; 𝐸𝐼!"#

‘if It is verified that the object has no son/sons in the BoM then It is calculated the economic value and the environmental value without the sons value’ 26) else Calculation of Vomi ;EIomi ‘the couple of Vomi and EIomi without the sons values is added to the set of the solutions related to the object mi of the level i’ 27) Add 𝑆!",! = 𝑆!",! ∪ 𝑉!"#; 𝐸𝐼!!" ‘increment of the option index’ 28) Go to the 18th step until omi= Omaxmi ‘application of the resolution method to the set of solution couples of the object mi’ 29) Apply the resolution method to Smi,I à S*

mi,i ‘increment of the object index’ 30) go to the 16th step until mi= Mmaxi ‘It is verified if It is the first level of the BoM and if It is true then the solution chosen by the resolution method in the 28° step is the optimal couple solution of the model’ 31) If i=1then 𝑆∗ = 𝑆∗ ∪ 𝑆!",!∗ ‘It is false the verification and It is decrement the level index’ 32) else Go to the 15th step until i=1 ‘TERMINATION PHASE OF THE MODEL’ 33) End


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5 Environmental Impacts assessment

This section highlights the main steps to assess environmental impacts of remanufacturing processes, providing a brief overview of the context, problems and complexities of LCA techniques and discussing so-called Streamlined methodologies and multi-use-phase environmental impacts. The effects of these techniques on BDSS will be considered. An overview of available databases, tools and services for the purpose of impacts assessments is presented, building on existing work done under the Life Cycle Thinking coordinated by Joint Research Centre of EU. These contents in addition to ELCD5 will be the main resources and references for PREMANUS BDSS model in assessing environmental impacts of EoL recovery. Those elements represent the foundation for assessment carried out in Task 5.2 and 5.3 of PREMANUS.

Traditional product development was trying to address costs, functionality and manufacturability of products. Nowadays sustainability is another dimension. Changes in products, legislations and other aspects, have increased the need to recover products at the end of their life. Product recovery has different motives and the decision maker is aiming to satisfy these motives. Seitz, (2007) summarizes motives for product recovery into 3 categories:

• ethical and moral responsibility, • legislations, • direct economic motive.

Products experience several life stages from cradle to the grave and potentially multiple use phases and recovery in between. To assess environmental impacts of a product, its information across all life stages shall be considered: if an assessment concentrates only on one stage of product life cycle for example its production stage or the use phase, the results may be misleading and while used, transfer the impact from one stage to another. Environmental implications of the whole supply-chain of products, goods and services, their use, and waste management, i.e. their entire life cycle from “cradle to grave” should be considered (ILCD, 2011).

5.1 Introduction to LCA

LCA is a scientific, structured and comprehensive method that is internationally standardised in ISO 14040 and 14044. Important characteristic of LCA is its holistic approach products/processes and their functions, considering upstream and downstream activities (“Introduction to LCA,” 2012). LCA strength is in avoiding the unwanted shifting of burdens, where reducing one kind of impact leads to an increase in another. It evaluates consumed resources, emissions and health impacts as well as environment impacts associated with a product.

LCA mainly helps to identify the best environmental options by quantifying environmental impacts, benefits and trade-offs. LCA has several types according to the life span which is covered during the assessment. In next section we briefly introduce each one and indicate the common term used for it.

There are several types of LCA in accordance to stages under assessment (Segers, 2011):

1. From cradle to gate (i.e. from mines to the gate at the warehouse)

2. From gate to gate (i.e. to calculate the eco-burden of a manufacturing facility)

3. From gate to grave (i.e. to calculate End of Life scenarios)

5 European Reference Life Cycle Database


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4. From cradle to grave (i.e. to calculate the total eco-burden of a product system from mine to end of life)

5. From cradle to cradle (closing the loop in the total product system)

Figure 10 – Different types of LCA

LCA is calculated by measuring and recording input from (resources such as energy, water, raw materials,…) and outputs to the environment (e.g. emissions such as CO2, Wastes, waste water,...).

In a Life Cycle Assessment, the emissions and resources consumed linked to a specific product are compiled and documented in a Life Cycle Inventory (LCI). An impact assessment is then performed, generally considering three areas of protection: human health, natural environment, and issues related to natural resource use. (ILCD, 2011). An LCA comprises five main phases: goal definition, scope definition, inventory analysis, impact assessment and interpretation. A reporting and review completes these 5 steps. (ILCD, 2011)

Figure 11 – Standard LCA phases.

Manufacturing Cradle Grave

1.Cradle to gate

3.Gate to grave

2.Gate to gate

4. Cradle to grave

5. Cradle to cradle (closing the loop)


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In goal definition, decision context, the intended applications and the intended audience of the LCA are determined. The need for comparative studies is also addressed. Also any other party which may be affected by the result of the study is distinguished at this stage.

The scope definition comprises clearly describing study related conditions and constraints i.e. the scope of the system under assessment (e.g. a specific brand), the functions of this system, the functional unit that is compared as the basis for a fair comparison, the life cycle stages to be assessed, the environmental impacts to be investigated, the Life Cycle Impact Assessment (LCIA) methods to be incorporated, the interpretation approaches to be used, the assumptions made about data and method issues, value choices, limitations, data quality requirements, type of critical review if any, and the type and format of the report required for the LCA. Clearly all of these items should be aligned with the study goal.

Data collection is addressed in The Life Cycle Inventory (LCI) analysis. This data is related to the resources used and emissions for the next process steps (e.g. manufacturing and packaging of a product), and the actual modelling of the life cycle of the analysed system. Background data is also included here. The first step of data validation will be carried out here.

In LCIA phase, LCI results are assigned to the selected impact categories. Categories such as climate change, acidification, human health, aquatic eco-toxicity, material resource depletion, land use, etc. are some examples. Then accordingly, potential environmental impacts in each category will be calculated.

During the interpretation phase, first significant issues are identified (e.g. the main processes and resources/emissions that quantitatively contribute most to the results): results of the LCI and LCIA phases are used. The interpretation comprises completeness, sensitivity and consistency analysis. Also the uncertainty and accuracy of the LCA outputs are checked. Then conclusion and recommendations are derived.

It is anyway difficult to perform a full LCA, i.e. to carry on an exact LCA covering all aspects of products for the whole life cycle in details. This is due to the fact that constructing a LCA needs a lot of information and is time consuming and this all are added to the uncertainty in information especially during the use phase (Arena, Azzone, & Conte, 2013; Hur, Lee, Ryu, & Kwon, 2005; Manmek, Kara, & Engineering, 2006). Nevertheless different attempts have been carried out to assess environmental impacts of products. Based on the ISO standards, LCA has matured over the past decade (Kumar, Azapagic, Schepelmann, & Ritthoff, 2010; Taisch, Cammarino, & Cassina, 2011). It has not yet reached mainstream use in industry, though carbon-footprint labelling schemes are becoming more established as an application based on life cycle information. The main reasons (Wolf et al., 2012) for this situation are:

• Reproducibility: results and recommendations which are being provided by current LCA, are dependent to practitioner’s interpretations in ISO standard framework.

• LCI data availability and quality: the quality and robustness of decision support is limited due to relatively low access to high quality and consistent data.

• Uncertainty of impact assessment methods and factors: There is yet no robust and fully practice-tested method.

• Quality assurance: selection of qualified and independent reviewers and practitioners is not straight forward and clear. Also guidelines on how the process of review should be conducted and the scope of study methods in order to have a widely accepted quality assurance for the life cycle data and assessment practices do not exist.

• Cost and complexity/lack of practicality: Reliable LCAs are often perceived to be too resource and time consuming, requiring sometimes dedicated experts.

Those aspects lead to two main streams of actions:


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• Standardization of data sources and methodologies (Handbook, Sala, & Pennington, 2012; Holsteijn et al., 2011; Hur et al., 2005; ILCD, 2011; Li, 2005; Wolf et al., 2012).

• Simplification by selecting most dominant factors in environmental impacts and accepting minimal errors and deviation (Arena et al., 2013; Garetti & Taisch, 2012; Hur et al., 2005; Manmek et al., 2006; Umeda et al., 2012).

5.2 Streamlined LCA

As explained in the previous section, conducting a full LCA, comprises some problems, LCA requires a reasonable amount of data which may not be available. Even though if such data is available it would be a time consuming and costly task to gather and analyse it. To avoid exhaustive data gathering and processing, some simplified methods have been introduced. Several attempts and different versions of simplified or streamlined LCA (SLCA) has been proposed so far (Arena et al., 2013; Eur 24708 en - 2010, 2010; Handbook & Assessment, 2010; Hochschorner & Finnveden, 2003; Hunt, Boguski, Weitz, & Sharma, 1998; Hur et al., 2005; Kumar et al., 2010; Li, 2005; Manmek et al., 2006; Segers, 2011; Weitz, Todd, Curran, & Malkin, 1996).

Broadly speaking, streamlining incorporates two approaches:

• the first involves modifying the methodology used to make it less burdensome to implement, • the second involves facilitating the process of performing an LCA, primarily by making data

more readily available to LCA practitioners.

Considering the group of approaches Weitz, Todd, Curran, & Malkin, (1996) classified different approaches to streamlining voiced by their respondents to be included in one of the following items:

• Narrowing the boundaries of the study, particularly during the inventory stage • Targeting the study on issues of greatest interest • Using more readily available data, including qualitative data.

According to Weitz, Todd, Curran, & Malkin, (1996):

• Academicians’ approach to streamlining is via: o Drawing boundaries at the firm door (i.e., gate to gate) and dealing with the upstream

and downstream impacts on a very qualitative basis, relating potential impacts to environmental issues.

o Scoping each product according to budget and areas which the companies can directly affect.

o Assessing the significant life cycle stages and impacts appropriate to the product or process

• Consultants: emphasis on eliminating indirect secondary and tertiary energy and materials. • Government Agencies: recommend to evaluate life cycle stages that are being affected while

moving from one option to another one. • Industries are interested in quantification, use of surrogate data as much as possible,

collecting all the life cycle inventory data first and then streamlining it in order to focus on the steps of life cycle that is possible to specify and control.

Generally in SLCA, reference drivers are selected. Usually the impacts of the units of study for such drivers or sub categories/groups of variables which affect these drivers, are available. An impact can be calculated by measuring the weight of the driver in the system under study and then looking up in the datasets to calculate the relative impact of the driver of interest accordingly.

Manmek et al., (2006) recommends five stages in the common life cycle: materials used, manufacturing processes, use phase and EoL options. Transportation can be involved across all


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stages. Within each stage the environmental impact can be evaluated with respect to both resource usage and emissions. A metric for each and the corresponding weight for that driver is being multiplied by the correspondent impact measure which is available in a predefined databases, obtained from averages of all available processes and items included in LCI.

5.3 Multi-use-phase environmental impacts

In order to understand multi-use-phase products we need to first consider the stages within a life cycle. It can be seen from Figure 12, a product during its life cycle passes different stages. And during each stage, has impacts on environment.

Figure 12 - life cycle of a new product adapted from (Amaya et al., 2010)

At different stages, different impacts exist. In 1st step, raw material extraction and preliminary processes, natural resources are used and clearly using natural resources imposes impacts on the environment by depletion of natural resources. The same also happens to the rest steps of 2, 3 & 4. Distribution and logistics costs are also problematic and may affect the environment by releasing emissions to the environment. Product use phase has also considerable effect and impacts on the environment. Energy consumption, waste, emission and pollutions produced by this product affects the environment. At the end of the product life, depending how the product is recovered, different impacts can be considered for the future.

The environmental interest of end of life recovery comes from lower energy and material compared to a production of a new product. but it is also necessary to assess the whole life cycle for the recovered product to verify if environmental impacts have increased by using remanufacturing (or processes under any other sort of EoL options). By choosing an option we avoid the production of a new product/component or intake of raw materials (in case of recycling or remanufacturing). In some cases, the net impact of recovery process may become negative. This is due to the fact that by replacing a new product/material with the recovered one will remove the impact of producing a new one.

Current LCA approaches and methodologies often cover linear LCA requirement, i.e. they cover single use phase life cycles and do not cover multiple use phase Life Cycle Assessment studies.

On the other hand, in the case of product remanufacturing products realize different number of usage phases (Figure 13). While performing lifecycle assessment, different parameters which can be considered in the lifecycle of the assessed product are (Amaya et al., 2010):

1. The “number of use phases” of the product. i.e. the percentage of the product which are going to experience the use case of interest.

2. The number of products not appropriate for remanufacturing, this is a complement to number 1, here we guess which percentage of the products are going to be recycled or landfilled.

3. The number of products/components recollected in the reverse logistic model. i.e. we need to estimate the percentage of the product which is retrieved according to the existing (or predicted future) reverse logistics structure.

1-‐Raw material

extrac'on & preliminary processes

2-‐Manufacturing & assembly of

the components

3-‐Components distribu'on

4-‐Product assembly

5-‐Product distribu'o

n

6-‐Product use

7-‐Reverse Logis'cs

8-‐End of Life


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4. The transportation distances. This item completes above variable and is related to the reverse logistics.

Figure 13 – Multiple use phases from Amaya et al., 2010.

Traditional LCA approaches with single-use-stage will be affected if the product is recovered. Product recovery depending on the selected option for EoL treatment, some of the impacts on environment will be avoided while some new ones will be introduced and imposed thereby. The following table represents some of these changes.

Option

Level of Disassembly

Effect on LCA Avoiding Imposing

Product L ife Cycle End of Use

End of l i fe

Raw material extraction & preliminary processes

Manufacturing & assembly of the components

Components distribution

Product assembly

Product distribution

Product use Re-use Recycle

Re-use Remanufacturing Recycle

Number of use phases

Remanufacturing

Product recovery

Transportation distance

%Net Remanufacturable


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Rep

air

Product level • A new product except replaced items

• Reverse Logistics • Repairing Process impacts • Replaced items waste • Differences in new use life between a repaired and a totally new one

Ref

urbi

shin

g

module level • A new product except replaced modules

• Reverse Logistics • Refurbishing Process impacts • Replaced modules (waste + resources) • Differences in new use life between a repaired and a totally new one

Rem

anuf

act

urin

g

Part level • New product/parts/components

• Reverse Logistics • Remanufacturing Process impacts • Replaced parts (waste + resources)

Can

niba

liza

tion

Selective retrieval of parts

• New parts/recycling for the rest

• Reverse Logistics • Cannibalization Process impacts • Non selected parts (recycle or disposal impacts)

Rec

yclin

g Material level • New materials

• Reverse Logistics • Recycling process impacts • Disposal of remaining materials

Table 8 – Effects of EoL options.

5.4 Available tools & databases

Many tools and databases are available to facilitate and/or execute LCA. European Union has developed an information Hub which includes life cycle thinking based data, tools and services. These information are cost free and independent and can be find at the address http://lca.jrc.ec.europa.eu/lcainfohub/toolList.vm

Two main resources which can be found in this information HUB are:

• The European ELCD database with the Commission's "European Reference Life Cycle Database" (ELCD) of Life Cycle Inventory (LCI) data sets, expectedto contribute data to the upcoming International ILCD Data Network (Wolf et al., 2012)

• LCA Resources Directory of Life Cycle Assessment studies (2012) and life cycle oriented services, tools and third party databases

Tables in Annex 1 summarize these resources directory of DBs and Tools.


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6 Implementation of algorithms into BDSS: environmental perspective

While assessing the environmental impacts of EoL recovery, it is necessary to consider the process and products which are being replaced by recovery, e.g. if a product is remanufactured the remanufacturing process will avoid production of a new product. This means that the total impacts related to the new product need to be deducted from the impacts which the remanufacturing process together the use phase of the renovated item impose upon the environment.

In the algorithm for the BDSS of PREMANUS, the differences between possible options are being taken into account for assessment. Detailed assessment on the basis of Use Case data will be performed in task 5.2. The assessment is going to be performed for two phases:

• In the first phase the environmental impact of remanufacturing processes (gate to gate LCA) as such will be considered; in this phase differences between processes and resources required to produce a complete remanufactured unit is assessed.

• In the second phase the impacts of avoided production and potential differences between new products/components and a remanufactured one will be evaluated.

6.1 Adaptation to algorithm to use cases

Streamlining the LCA according to BDSS specifications is performed from different points of view. Practicality, comprehensiveness, acceptance and conformity to scientific measures as well as usefulness and simplicity are different factors shall be taken into account. The table below shows how the Streamlining approaches defined in section 5.2 affect BDSS

Approach toward streamlining Implications for BDSS

Drawing boundaries at the firm door (i.e., gate to gate) and dealing with the upstream and downstream impacts on a qualitative basis, relating potential impacts to environmental issues.

The most important one is gate to gate during remanufacturing.

Secondly the new use phase would be important and performances and impacts intensity shall be compared to justify results of gate to gate LCA.

Scoping product and processes according to areas which the decision maker can directly affect.

Not asking for further evaluation study, or obligations to carry out LCA for new products. Some standard averages may be developed or retrieved from literature.

Assessing the significant life cycle stages and impacts appropriate to the product or process.

The significant life cycle stages are recovery stage the most and with lower importance the use phase after recovery).

Eliminating indirect secondary and tertiary energy and materials.

Just direct consumption of water, energy and main raw materials and main waste generated into processes is considered.

Evaluate life cycle stages that are being affected while evaluating different alternatives.

Mainly recovery (remanufacturing stage) is affected.

If the recovered product does not perform the same as a new one, the use phase will be also important.

In any case the recovered item (impacts and performances) shall be compared with the new one which is being replaced by recovery.


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Use of surrogate data as much as possible.

Available data sources (ELCD, JRC reference,…) will be used to quantify the impacts.

Focus on the steps of life cycle that is possible to specify and control.

Focus on possible options and their consequences.

Table 9 – Streamlined activities in Environmental Impact assessment for use cases.

The system under evaluation is the product which has reached its End of Life. Assessment is carried out on the product and its components. Here the comparison is made between different options for product recovery as shown in table 7. Clearly all options are compared with the base scenario which is disposal of available item and using a totally new one. Environmental impacts which are being investigated by BDSS are energy consumption, water consumption and waste generation. The life cycle stages which are covered are the recovery phase e.g. remanufacturing processes, recycling processes and the use cycle (reuse) phases. The method and data used are based on ILCD guidelines where possible.

Activities and resources needed for each option will be evaluated and gathered per each Use Case. Accordingly the generic environmental impact profile of each activity will be retrieved from available databases chosen from the tables in Annex 1.

Energy consumption, water consumption and waste and emission generation are considered for each activity. Then the impact for an option for the recovery phase is analysed considering these items.

For the use case, a profile for the product shall be provided which compares relative use phase performance (e.g. life duration) comparing to a new product as well as relative emission and energy consumption comparing to a new product. These items will be used to justify the results for comparison.

Activities for this phase are as follow:

• Determine product materials and evaluate them according to lookup tables (metagroups such as metals, plastic, …).

• Look up impact related to production of each, as well as disposal of each. And drive the correspondent impact value.

• Evaluate recovering (e.g. remanufacturing) impacts of processes correspondent with each option.

Such an approach is done through the bottom up approach in the BoM described in section 4 of the deliverable allowing the definition of Eco-Efficiency table at product and at component level.


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7 Annex 1 – Tools and databases for impact assessment

7.1 Tools

Tool + version N° Supplier Instruments Languages of Interface

AirConLCA Centre for Water and Waste Technology

English

AIST-LCA Ver.4 National Institute of Advanced Industrial Science and Technology (AIST)

Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Product stewardship, supply chain management

Japanese

BEES 3.0d National Institute of Standards and Technology (NIST)

Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Life cycle costing (LCC)

English

DPL 1.0 IVAM University of Amsterdam bv

Dutch

e!Sankey 1.0 ifu Hamburg GmbH Life cycle management (LCM), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

English

Eco-Bat 2.1 Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud

Life cycle impact assessment (LCIA), Design for environment (DfE, DfR)

French, Italian, English

Eco-Quantum IVAM University of Amsterdam bv

Dutch

ECODESIGN X-Pro v1.0

EcoMundo Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Legal Compliance checks

English

ecoinvent waste disposal inventory tools v1.0

Doka Life Cycle Assessments (Doka Okobilanzen)

Life cycle inventory (LCI) English

EcoScan 3.1 TNO Built Environment & Geosciences

Life cycle impact assessment (LCIA), Design for environment (DfE, DfR)

Spanish, German, Dutch, English

EIME V2.4 Bureau Veritas CODDE Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Design for environment (DfE, DfR)

English

EIME V3.0 Bureau Veritas CODDE Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Design for environment (DfE, DfR), Legal Complience checks

English

Environmental Impact Estimator V3.0.2

Athena Sustainable Materials Institute

Life cycle impact assessment (LCIA), Life cycle assessment (LCA), Design for environment (DfE, DfR)

English

EPD Tools Suit 2007 ITKE Enviornmental Technology Inc.

Life cycle inventory (LCI) Chinese

eVerdEE v.1.0 ENEA - Italian National Agency for New Technology, Energy and the Environment

Life cycle management (LCM), Life cycle assessment (LCA), Design for environment (DfE, DfR)

Spanish, Italian, German, English


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eVerdEE v.2.0 ENEA - Italian National Agency for New Technology, Energy and the Environment

Life cycle management (LCM), Life cycle assessment (LCA), Design for environment (DfE, DfR)

Italian, English

GaBi 4.2 PE International GmbH social LCA, Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Legal Complience checks, Product stewardship, supply chain management, Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

Japanese, Spanish, Portuguese, Danish, Thai, Chinese, German, English

GaBi DfX PE International GmbH social LCA, Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Legal Complience checks, Product stewardship, supply chain management, Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

Japanese, Spanish, Portuguese, Chinese, German, English

GaBi lite PE International GmbH Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Product stewardship, supply chain management

German, English

GEMIS version 4.4 Oeko-Institut (Institute for applied Ecology), Darmstadt Office

Spanish, Czech, German, English

Green-E, version 1.0 Quantis - Sustainability counts

social LCA, Life cycle management (LCM), Life cycle assessment (LCA), Design for environment (DfE, DfR), Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

English

JEMAI-LCA Pro ver.2 Japan Environmental Management Association for Industry (JEMAI)

Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA)

Japanese, English

KCL-ECO 4.0 Oy Keskuslaboratorio-Centrallaboratorium Ab, KCL

Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Product stewardship, supply chain management

English

LCA - Evaluator 2.0 GreenDeltaTC GmbH Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle assessment (LCA)

English

LEGEP 1.2 LEGEP Software GmbH social LCA, Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycleengineering (LCE), Life cycle assessment (LCA), Design for environment (DfE, DfR), Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

Italian, German

LTE OGIP; Version 5.0; Build-Number

t.h.e. Software GmbH Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA),

German


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2092; 2005/12/12 Design for environment (DfE, DfR), Life cycle costing (LCC)

Modular MSWI Model 1.0

GreenDeltaTC GmbH Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Life cycle costing (LCC)

English

Prototype Demolition Waste Decision Tool 1

IVAM University of Amsterdam bv

Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Design for environment (DfE, DfR), Product stewardship, supply chain management

Dutch

REGIS 2.3 sinum AG Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Legal Complience checks, Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

Japanese, Spanish, German, English

Sabento 1.1 ifu Hamburg GmbH Life cycle management (LCM), Life cycle inventory (LCI), Life cycleengineering (LCE), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Product stewardship, supply chain management, Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

German, English

SALCA-animal 1.0 Agroscope Reckenholz-Tänikon Research Station ART

Life cycle inventory (LCI) German

SALCA-biodiversity 061

Agroscope Reckenholz-Tänikon Research Station ART

Life cycle impact assessment (LCIA), Life cycle inventory (LCI)

German

SALCA-biodiversity 1.0



German

SALCA-crop 061 Agroscope Reckenholz-Tänikon Research Station ART


German

SALCA-crop 2.02 Agroscope Reckenholz-Tänikon Research Station ART


German

SALCA-erosion 061 Agroscope Reckenholz-Tänikon Research Station ART


SALCA-erosion 2.0 Agroscope Reckenholz-Tänikon Research Station ART


SALCA-farm 1.31 Agroscope Reckenholz-Tänikon Research Station ART


German

SALCA-farm 2.1 Agroscope Reckenholz-Tänikon Research Station ART


German

SALCA-heavy metals 061




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SALCA-heavy metals 1.0



SALCA-nitrate 061 Agroscope Reckenholz-Tänikon Research Station ART


SALCA-nitrate 4.0 Agroscope Reckenholz-Tänikon Research Station ART


SALCA-soil quality 061



German

SALCA-soil quality 1.1



German

SankeyEditor 3.0 STENUM GmbH

English SimaPro 7 PRé Consultants B.V. social LCA, Life cycle management (LCM), Life

cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Product stewardship, supply chain management, Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

Japanese, Spanish, Danish, Greek, French, Italian, German, Dutch, English

STAN 1.1.3 - Software for Substance Flow Analysis

Vienna University of Technology

Substance/material flow analysis (SFA/MFA) German, English

TEAM™ 4.5 Ecobilan - PricewaterhouseCoopers

Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Legal Complience checks, Product stewardship, supply chain management, Life cycle costing (LCC)

English

TEAM™ Web Simulator

Ecobilan - PricewaterhouseCoopers

Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Design for environment (DfE, DfR), Product stewardship, supply chain management, Life cycle costing (LCC)

TESPI ENEA - Italian National Agency for New Technology, Energy and the Environment

Design for environment (DfE, DfR) Italian, English

The Boustead Model 5.0.12

Boustead Consulting Limited


English

trainEE GreenDeltaTC GmbH Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Design for environment (DfE, DfR), Product stewardship, supply chain management, Life cycle costing (LCC)

English

Umberto 5.5 ifu Hamburg GmbH Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory

English


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(LCI), Life cycleengineering (LCE), Life cycle assessment (LCA), Substance/material flow analysis (SFA/MFA), Product stewardship, supply chain management, Life cycle sustainability assessment (LCS), Life cycle costing (LCC)

USES-LCA Radboud University Nijmegen

Life cycle impact assessment (LCIA) English

Verdee ENEA - Italian National Agency for New Technology, Energy and the Environment

Life cycle management (LCM), Design for environment (DfE, DfR)

Italian

WAMPS, betaversion IVL Swedish Environmental Research Institute Ltd

English

WISARD 4.0 Ecobilan - PricewaterhouseCoopers

Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Product stewardship, supply chain management, Life cycle costing (LCC)

French, English

WRATE UK Environment Agency Life cycle management (LCM), Life cycle impact assessment (LCIA), Life cycle inventory (LCI), Life cycle assessment (LCA), Life cycle sustainability assessment (LCS)

English


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7.2 Databases

Database + version Supplier Database Languages

CPM LCA Database Center for Environmental Assessment of Product and Material Systems - CPM

English

DEAM™ Ecobilan - PricewaterhouseCoopers English

DEAM™ Impact Ecobilan - PricewaterhouseCoopers English

DIM 1.0 ENEA - Italian National Agency for New Technology, Energy and the Environment

Italian English

ECODESIGN X-Pro database V1.0

EcoMundo English

ecoinvent Data v1.3 ecoinvent Centre Japanese English

EIME V8.0 Bureau Veritas CODDE Spanish French English

EIME V9.0 Bureau Veritas CODDE Spanish French English

erawsdf AQUA+TECH Specialities Aymara esu-services database v1 ESU-services Ltd. German English

Eurofer data sets EUROFER English

GaBi databases 2006 PE International GmbH Japanese German English

GEMIS 4.4 Oeko-Institut (Institute for applied Ecology), Darmstadt Office

Spanish Czech German English

IO-database for Denmark 1999 2.-0 LCA consultants English IVAM LCA Data 4.04 IVAM University of Amsterdam bv English

KCL EcoData Oy Keskuslaboratorio-Centrallaboratorium Ab, KCL English

LC Data Forschungszentrum Karlsruhe German English

LCA Database for the Forest Wood Sector

Bundesforschungsanstalt für Forst- und Holzwirtschaft (BFH)

LCA_sostenipra_v.1.0 Universitat Autònoma de Barcelona (UAB) Spanish Catalan English

MFA_sostenipra_v.1.0 Universitat Autònoma de Barcelona (UAB) Spanish Catalan English

Option data pack National Institute of Advanced Industrial Science and Technology (AIST)

Japanese

PlasticsEurope Eco-profiles PlasticsEurope English

ProBas Umweltbundesamt German

Sabento library 1.1 ifu Hamburg GmbH German English

SALCA 061 Agroscope Reckenholz-Tänikon Research Station ART German English

SALCA 071 Agroscope Reckenholz-Tänikon Research Station ART German English

SimaPro database PRé Consultants B.V. English

sirAdos 1.2. LEGEP Software GmbH German

The Boustead Model 5.0.12 Boustead Consulting Limited English


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Database + version Supplier Database Languages

Umberto library 5.5 ifu Hamburg GmbH German English

US Life Cycle Inventory Database Athena Sustainable Materials Institute English

Waste Technologies Data Centre UK Environment Agency English


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8 Bibliography

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Arena, M., Azzone, G., & Conte, A. (2013). A streamlined LCA framework to support early decision making in vehicle development. Journal of Cleaner Production, 41, 105–113. doi:10.1016/j.jclepro.2012.09.031

Bufardi, a., Gheorghe, R., Kiritsis, D., & Xirouchakis, P. (2004). Multicriteria decision-aid approach for product end-of-life alternative selection. International Journal of Production Research, 42(16), 3139–3157. doi:10.1080/00207540410001699192

Chan, J. W. K. (2008). Product end-of-life options selection: grey relational analysis approach. International Journal of Production Research, 46(11), 2889–2912. doi:10.1080/00207540601043124

Das, S., & Yedlarajiah, D. (2002). An integer programming model for prescribing material recovery strategies. Conference Record 2002 IEEE International Symposium on Electronics and the Environment (Cat. No.02CH37273), 118–122. doi:10.1109/ISEE.2002.1003251

Dehghanian, F., & Mansour, S. (2009). Designing sustainable recovery network of end-of-life products using genetic algorithm. Resources, Conservation and Recycling, 53(10), 559–570. doi:10.1016/j.resconrec.2009.04.007

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Garetti, M., & Taisch, M. (2012). Sustainable manufacturing: trends and research challenges. Production Planning & Control, 23(2-3), 83–104. doi:10.1080/09537287.2011.591619

González, B., & Adenso-Díaz *, B. (2005). A bill of materials-based approach for end-of-life decision making in design for the environment. International Journal of Production Research, 43(10), 2071–2099. doi:10.1080/00207540412331333423

Gungor, A., & Gupta, S. M. (1999). Issues in environmentally conscious manufacturing and product recovery: a survey. Computers & Industrial Engineering, 36(4), 811–853. doi:10.1016/S0360-8352(99)00167-9

Handbook, I., & Assessment, L. C. (2010). ILCD Handbook: Review schemes for Life Cycle Assessment (LCA) First edition EUR 24710 EN - 2010. doi:10.2788/39791

Hochschorner, E., & Finnveden, G. (2003). Evaluation of Two Simplified Life Cycle Assessment Methods. International journal of lifecycle assessment, 8, 119–128. doi:10.1065/Ica2003.04.114


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