A system dynamics approach to product design and business model strategies for the circular economy

Journal of Cleaner Production 241 (2019) 118327

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A system dynamics approach to product design and business model strategies for the circular economy Maria A. Franco Department of Engineering and Information Technology, Bern University of Applied Sciences, Quellgasse 21, CH-2501, Biel, Switzerland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 8 June 2018 Received in revised form 3 September 2019 Accepted 6 September 2019 Available online 6 September 2019

By means of a system dynamics computer simulation model, this paper analyzes the systemic effects of combining multiple product design and business model strategies for slowing and closing resource loops in a circular economy. In the model, green (i.e., recycled) and brown (i.e., traditional linear) products, as well as products under a PSS model, flow in a closed-loop supply chain. The model structure covers issues related to product design, product use and replacement, second-hand use, product discard, and finally, collection and processing for product recycling. Results from this research provide insights into the relationship between design considerations at the beginning of a product's life and their implications for the product's take-back stage when a new manufacturing cycle begins. The main contributions of this paper are the considerations of time delays and reinforcing feedback loops in the design of an effective closed-loop supply chain. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Prof. Jiri Jaromir Kleme
s Keywords: Circular economy Circular product design Circular business models System dynamics Simulation

1. Introduction It is nowadays widely acknowledged that for the circular economy (CE) to advance at the firm level, corporate strategies should focus on both product design (PD) and business model (BM) innovation (Bocken et al., 2014; den Hollander et al., 2017; Lieder and Rashid, 2016; Rashid et al., 2013; Urbinati et al., 2017). Implementing both types of strategies in conjunction is expected to allow stakeholders across the value chain to reap all the economic and environmental outcomes that the CE has promised to deliver. While eco-design encompasses the principles, strategies, and methods used to design products with environmental considerations in mind (e.g., design for longevity, design for maintenance, and design rez-Belis, 2012), sustainable business for recycling) (Bovea and Pe models, such as leasing or sharing, concentrate on replacing capital ownership with the use of services (Boons and Lüdeke-Freund, 2013). Despite the apparent agreement on the need for firms to incorporate circular design and business model principles into their strategies, quantitative studies aimed at testing the wider effects (i.e., benefits, trade-offs, and impacts) of isolated and/or hybrid strategies for circularity are still not available for academics,

E-mail addresses: [email protected], maria.francomosquera@bfh. ch. https://doi.org/10.1016/j.jclepro.2019.118327 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

policymakers, and market players alike. In fact, most of the research related to product design and business model innovation for the purpose of the CE is, so far, mainly qualitative and often supported by case studies only (Bocken et al., 2016; Manninen et al., 2018). This is not a surprise given that qualitative methodologies are well suited for studying novel phenomena such as the CE, where the knowledge of relevant actors, strategic processes, and new forms of network organizing is still limited (van Dijk et al., 2014). The research question that this study proposes to investigate is: What interesting dynamics can be observed when testing isolated and combined applications of various product design and business model strategies for slowing down and closing resource loops in a CE? To conceptualize and test various combinations of product design and business model strategies, a system dynamics computer simulation model is developed. Simulation models mirror the operation of real world processes, systems, or events (Law and Kelton, 2000) and are especially useful when the problem under study is longitudinal, and nonlinear, or when empirical data are difficult to obtain (Davis et al., 2007). Rather than looking at the specific financial, environmental, and social value that is created through product design and business models for circularity, this paper looks at the broader picture to understand the general dynamics present in a closed-loop value chain where a hypothetical manufacturing firm commercializes a generic product.

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List of abbreviations BM CBM CE DfD DI EOL GIF IE M&P MCI PSS RI SD T&E

Business model Circular business model Circular Economy Design for disassembly Disassembly index End-of-life Green image factor Industrial ecology Mature and pervasive Material compatibility index Product service systems Recyclability index System dynamics Trendy and emotional

The model's feasibility is tested by using data derived from the literature and by conducting structure and behavioral tests, as well as by testing model assumptions for selected product categories. The main purpose of this article is to provide a holistic and reliable decision-support tool for industrial stakeholders at the intersection of product design (and consequently of post-use) and business model strategies in a CE. All the possible configurations associated with different strategy bundles bring manufacturing companies to an uncertain and even risky position because their effects are unknown. This work is therefore aimed at promoting the systematic exploration and quantification of different product design, business models, and post-use strategies, within the context of the manufacturing industry. This paper is structured as follows: Section 2 provides a brief review of the circular economy, circular product design and business models for circularity. Sections 3 and 4 introduce the system dynamics methodology, describe the steps in the model-building process, and, most importantly, review in detail the body of literature used to conceptualize the model structure. Section 5 presents the analysis of the simulation results, while Section 6 discusses the conclusions of this study, its practical and theoretical implications, and finally its main limitations and future research avenues. 2. Literature review 2.1. The circular economy (CE) The CE is perceived as representing the business operationalization of the much-discussed and sometimes blurry concept of sustainable development, because it offers firms the opportunity to achieve product differentiation, competitive advantage, and ultimately growth (Ellen MacArthur Foundation, 2013; Kirchherr et al., 2017; Murray et al., 2017; Saidani et al., 2017). As industries and economies face the devastating effects of pollution, global warming, and resource scarcity, legislation in favor of the CE has gained increased popularity across the globe. In 1996, Germany integrated a semi-circular directive called the “Closed Substance Cycle and Waste Management Act” into its national laws, an action that was later followed by Japan's 2002 “Basic Law for Promoting the Creation of a Recycling Oriented Society” and China's 2009 “Circular Economy Promotion Law” (Geissdoerfer et al., 2017; Korhonen et al., 2018; Lieder and Rashid, 2016). Although different definitions have been proposed for the CE (Ghisellini et al., 2016; Lieder and Rashid, 2016; Su et al., 2013), the description used in this paper is based on the concept of material

flows used in industrial ecology (IE) (Stahel, 2010). According to the IE tradition, the basic idea in a CE is that the economic and environmental utility and value of materials in products are preserved at their highest level for as long as possible, either by lengthening products' lifetimes or by looping products’ components back into the system to be reused and/or recycled (den Hollander et al., 2017; Saavedra et al., 2018). In other words, in a closed-loop or circular production and consumption system, waste becomes input for new processes, thus reducing the need for virgin raw materials in new production cycles. In contrast to an open-loop model where products become waste at end-of-life, the CE provides a framework where resources are kept in loops in order to create more systemic value and for longer periods (McDonough and Braungart, 2002; Su et al., 2013). For circular products to mimic a closed-loop system, material flows need to remain accounted for, before, during, and after the products' end of life (den Hollander et al., 2017). It is within this context that certain guidelines on product design and business models must be considered by product manufacturers in the development of truly circular industrial systems. While design practices focus on maintaining materials at their highest value at any point in the products’ lifecycles, business models are mainly concerned with the way products are commercialized and “consumed.” A summary of the relationship between these two concepts and the CE is presented next. 2.2. Eco- and circular product design The idea of eco-design is not new and has been present since the 1970s (De los Rios and Charnley, 2016; Lieder and Rashid, 2016). Design strategies addressing ecological challenges typically fall under the umbrella of various concepts, including product stewardship (Hart, 1995), green design (Fullerton and Wu, 1998), design for the environment (DfE) (Chen, 2001), sustainability-driven product design (Byggeth et al., 2007; McLennan, 2004), design for sustainability (DfS) (Arnette et al., 2014), ecological product design (EPD) (Hartmann and Germain, 2015), eco-design (Deutz et al., 2013), cradle-to-cradle design (Braungart et al., 2007), and regenerative design (Lyle, 1996). Most of these concepts emphasize the need to promote resource efficiency, the restriction of hazardous or non-renewable materials in manufacturing, efficiency during the product's use phase, and component reuse or recycling at the end of the product's lifetime (Calcott and Walls, 2005; Dyllick and Rost, 2017; Snir, 2001; Wong et al., 2012). In general, the bundle of product materials and production technologies chosen during the design stage of a product will determine the pollutants and wastes it will release during its lifetime, the energy it will consume, and the ease with which its components will be reutilized in subsequent uses and manufacturing cycles (Rashid et al., 2013; Tsoulfas and Pappis, 2006). Circular design rests on the notion that products must be conceived with multiple life cycles in mind, where material quality is to be maintained and waste is to be avoided. That is, a product is considered circular when it is engineered to be remain in a state closer to the original product (i.e., product integrity), ideally “eliminating environmental costs when performing interventions to preserve or restore the product's added economic value” (den Hollander et al., 2017; Lieder and Rashid, 2016). Previous publications in connection with circular product design have been mostly conceptual in nature, covering topics such as product design frameworks for the CE (Bocken et al., 2016), the difference between circular and eco-design (den Hollander et al., 2017), product life extension through design (Bakker et al., 2014), the new set of skills and capabilities required by circular designers (De los Rios and Charnley, 2016), product design and consumer behavior in a CE

M.A. Franco / Journal of Cleaner Production 241 (2019) 118327

(Poppelaars et al., 2018), tools for the design of circular products (Sinclair et al., 2018; Wastling et al., 2018), and the challenges circular product manufacturers face during the design stage (Franco, 2017). Later sections will provide a more detailed account of which circular design strategies were included and how they were quantified in the simulation model featured in this article. 2.3. Circular business models (CBMs) Transitioning to a CE will require not only changes to the way products are designed, but also radical changes to how they are commercialized and consumed. Circular business models lie at the core of the CE (Lewandowski, 2016) and constitute a prerequisite for CE diffusion, because they “enable economically viable ways to continually reuse products and materials” (Bocken et al., 2016). Furthermore, CBM innovation allows firms to go beyond prevalent sustainable business model configurations that focus only on efficiency, productivity, and greening the supply chain (Bakker et al., 2014; Geissdoerfer et al., 2018). Due to the early character of the CE and the many interpretations that are currently associated with it, a consolidation and understanding of the concept of circular business models is just emerging (Lewandowski, 2016; Nubholz, 2017; Pieroni et al., 2019). All in all, CBM is the label used to describe business models in a CE that are aimed at achieving resource efficiency by incorporating strategies that slow and close resource loops (Bocken et al., 2016; Geissdoerfer et al., 2018; Nubholz, 2017). Because the CE demands the lifetime maximization of products and materials, CBMs must be oriented towards designing systems around products that can generate revenue by creating, delivering, capturing, and maintaining value over time (Bakker et al., 2014; Boons and LüdekeFreund, 2013; Osterwalder et al., 2005). Extant publications in relation to CBM have conceptually discussed topics such as the barriers and enablers to circular business model implementation (Linder and Williander, 2017; Rizos et al., 2016; Vermunt et al., 2019), the role of CMBs in sustainability and resource efficiency (Hofmann, 2019; Manninen et al., 2018; Whalen, 2019) and classification frameworks for CBMs (Bocken et al., 2014; Rosa et al., 2019; Urbinati et al., 2017). As pointed out before, although various typologies for classifying CBMs have been proposed in the literature (see van Renswoude et al. (2015); Moreno et al. (2016); Bakker et al. (2014)), most authors agree that the value proposition of a firm operating in a CE should entail schemes to slow and close resource loops (Bocken et al., 2016; Nubholz, 2017). On one hand, BMs to slow loops encourage long product life and reuse and are classified according to three main models: (i) access and performance (e.g., product-service system (PSS) models including sharing and leasing), (ii) extending product value (e.g., exploitation of the product's residual value through reuse, refurbishing, and remanufacturing), and (iii) classic long life (linked to design for long life and design for maintenance). On the other hand, BM for closing loops involve capturing value from “waste” or “by-products” through schemes such as industrial symbiosis. The PSS model (coupled with repair and maintenance services) as well as the reuse model will be the two main CBMs conceptualized in this article. Section 4.1.2 elaborates more on each of these strategies and on how they were incorporated to the model structure.

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consumption systems in a circular economy. System dynamics is a computer simulation methodology that has its roots in the theory of nonlinear dynamics and feedback control developed in mathematics, physics, and engineering. It is aimed at enhancing the understanding of complex feedback systems while simultaneously supporting the policy formulation process for decision makers (Sterman, 2000). SD asserts that the dynamic complexity in a system is derived from the presence of various factors, namely: (i) accumulation (i.e., the accumulation and depletion of resources), (ii) non-linearities (i.e., an effect is rarely proportioned to its cause), (iii) feedback structures (i.e., both balancing and reinforcing), and (iv) time delays (i.e., adding a time delay to a negative feedback loop creates oscillations, thus reducing a decision maker's ability to control for confounding variables and design-appropriate policies). A system dynamics model consists of an interlocking set of differential and algebraic equations developed from a broad spectrum of relevant data and depicted by means of a simulation software (Homer and Hirsch, 2006). SD makes use of stocks, flows, and parameters to model the complex behaviors of different systems. Stocks or “state variables” represent the accumulation of the inflows minus the outflows and are expressed in whole units (e.g., widgets, units, people). Changes in stocks can only result from “flows” or “rates” (i.e., inflows and outflows), which are always expressed as a function of a unit of time (e.g., widgets/year, units/ month, people/day). Hence, stock levels increase when the inflows exceed the outflows, fall when outflows exceed inflows, and remain in equilibrium when inflows equal outflows. Overall, the stocks will integrate the difference between the inflows and the outflows, while the flows will be functions of the stock and other parameters (Sterman, 2000). Parameters are numerical values that remain constant throughout the simulation and that affect other variables and flows in the model. A systemic approach to the CE has been proposed by various authors, including Ghisellini et al. (2016), who conceptualize the CE as “systemic” by highlighting the need to cause simultaneous changes at a macro (i.e., city, province, region, nation), meso (i.e., eco industrial parks), and micro (i.e., single firms) systems-level. Furthermore, it is widely acknowledged that the circular economy has the ambition of “optimizing systems rather than components” (Ellen MacArthur Foundation, 2013) and as such, requires a tool that is capable of quantitatively modeling the diverse number of elements, links, and non-linearities that circular value chains exhibit (Velte and Steinhilper, 2016). Given its suitability for the analysis of complex industrial and environmental systems, system dynamics has already been widely used in applications related to the CE, including sustainable development (Meadows et al., 1972), closed-loop supply chains (Georgiadis and Besiou, 2008; Georgiadis et al., 2006; Golroudbary € ter, 2003; Vlachos et al., and Zahraee, 2015; Spengler and Schro 2007), recycling and remanufacturing (Wang et al., 2014), sus€uscher, 2016), producttainable business models (Abdelkafi and Ta service systems (Lee et al., 2012), and the CE itself (Asif et al., 2016). As a methodology, SD is not free of criticism though. The utility of SD is often questioned because of the models’ complexity and the great amount of quality data they demand. In despite of this, their power to provide useful insights and policy recommendations, even when confronted with limited data, is undisputable. The subsections below describe in greater detail the model formulation process.

3. Methodology 3.2. Model building stages 3.1. System dynamics (SD) This work draws on the system dynamics methodology to capture the complex and systemic nature exhibited by production and

Modeling is an iterative process of scope selection, hypothesis generation, causal diagramming, quantification, reliability testing, and scenario analysis (Homer and Hirsch, 2006; Sterman, 2000).

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The following sections describe these steps and how they were adapted for this research.

3.2.1. 1st stage: Problem identification The first stage in the modeling process, problem identification and definition, is concerned with building a model for the purpose of understanding or analyzing a specific problem rather than modeling a whole system (Sterman, 2000). The initial characterization of the problem in this paper is given by the research question introduced in Section 1 and the model assumptions outlined in Table 1, which analyzes the dynamic implications stemming from different combinations of circular product design and business model strategies.

3.2.2. 2nd and 3rd stages: System conceptualization and model formulation The second modeling step is concerned with the system conceptualization or the development of what is called a “dynamic hypothesis.” The dynamic hypothesis tries to explain the researched problem's dynamics, and it is embedded in a model's underlying feedback and stock-and-flow structure. The formal modeling process started by building and testing small model structures and then building progressively toward complex ones until the model was developed completely (Martinez-Moyano and Richardson, 2013). To formulate and test the model, I used the proprietary system dynamics modeling software Dynaplan® Smia, developed by Dynaplan AG. Smia allows modelers to make use of advanced modeling functions along with easy-to-build model interfaces and professional output graphs. For the different iterations of the model structure, information for the relevant variables was collected by reviewing prominent literature on the topics of sustainable and circular product design and business models, as well as on closed-loop or circular supply chains. The use of qualitative data from published literature is, in fact, one of the most popular techniques for building SD models (Luna-Reyes and Andersen, 2003). The coding software ATLAS. ti was used to systematically record, review and code (by means of open coding) the different concepts that emerged throughout the literature review. Codes were then grouped according to their place in the circular value chain and the most prominent ones were used to conceptualize the different model subsectors. The literature review by means of concept coding not only facilitated the identification of key themes and concepts (i.e., stocks, flows, and parameters), but also of the relationships among them (see Fig. 1). From the beginning of the modeling process, dimensional or unit consistency for all model equations was also ensured (Martinez-Moyano and Richardson, 2013). Fig. 2 combines causalloop and stock-and-flow diagrams to present the structure of the simulation model.

Fig. 1. Overview of the model formulation stage. Adapted from Asif et al. (2016).

4. Description of the conceptual model The following subsections describe in the detail the theoretical foundations used for the development of the model structure (see Table 2).

4.1. Circular product design To model product design, I adhered to the taxonomy proposed by Bocken et al. (2016), who suggest categorizing product design for a CE according to strategies for slowing loops (S) and strategies for closing loops (C). While the first group includes designing for longevity and designing for ease of maintenance and repair, the second group incorporates designing for disassembly and reassembly as well as designing for a technological cycle (i.e., recycling) (see Table 3).

Table 1 Overview of model assumptions.
The time horizon of the simulation is 40 years in order to witness the delayed and indirect effects of potential policies (2017e2057).
The model is built from the perspective of a single manufacturing firm that sells generic product units.
The product sales rate determines the number of products that are purchased and are in-use, regardless of the firm's manufacturing capacity. This means that the SD model structure does not account for flows of product ordering or work-in-progress (i.e., raw material conversion to the final product).
The model does not account for any dynamics related to product storage and transportation.
Although certain products exhibit a “baseline” demand in society (i.e., household electronics, bicycles, etc.), the model assumes all product types must be adopted first by curious consumers by means of the Bass model.
Customer demand for brown and green product purchases, as well as for customers opting for PSS, is exogenous and deterministic.
Return and reusability rates are also exogenous.
The initial number of adopters or customers is 1000 people, with each person buying 1 product unit.

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Fig. 2. Overview of the SD model structure.

Table 2 Design and business model strategies included in the SD model. For slowing resource flows (S)

Design strategies

1. 2. 1. 2. 1. 2.

For closing resource flows (C) Business model strategies

For slowing resource flows (S)

Design for long-life Design for ease of maintenance and repair Design for dis- and reassembly Design for a technological cycle/recycling Access and performance (PSS and maintenance) Extending product value (Reuse)

Table 3 Product design strategies for slowing (S) and closing loops (C) and their corresponding relevant model variables. Design strategy

Quantitative operationalization in the SD model

Source

Relevant model variables

1. Design for long-life (S)

Shape of the distribution using the Bass model (short- and long-life products)

Georgiadis et al. (2006) Sterman (2000)

2. Design for ease of maintenance and repair (S)

A higher breakdown rate diverts man-hours to solve unplanned repair tasks first

Sterman (2000)


Adoption fraction
Contact rate
Product lifetime
Residence time
Fraction of products-in-use being replaced
Fraction of discarded units being replaced
Breakdown rate
Disassembly index
Man-hours available for repair tasks
Man-hours available for scheduled maintenance 1: easiest to disassemble 4: most difficult to disassemble 1: parts are made only of one material 4: parts and modules are composed of fully incompatible materials

3. Design for dis- and reassembly (C) Disassembly Index (DI) ranging from 1 to 4 4. Design for the technological cycle Material Compatibility Index (MCI) ranging from 1 to 4 (C)

4.1.1. Design strategies for slowing loops 4.1.1.1. Design for long-life and the demand distribution. Design for durability, longevity, or long life is concerned with developing

de Aguiar et al. (2017) de Aguiar et al. (2017)

durable products that do not to break down easily (Bocken et al., 2016). Central to the idea of product longevity is the well-known four-stage process of a product's life cycle, consisting of an

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introduction, growth, maturity, and decline stage. Similar to the SD formulation proposed by Georgiadis et al. (2006), the concept of short and long product lifecycles is used to emulate products designed for short and long lifetimes, respectively. While products purposely built for obsolescence (i.e., short lifecycle) display a narrow-spread distribution with a high peak, products designed for longer lifetimes display a wider-spread distribution with a lower peak (Briano et al., 2010; Georgiadis et al., 2006). In the model, lifetime distributions determine the product demand distribution, and consequently the shape of the new product adoption flow. To mirror the distributions for short and long product lifecycles, the model uses the Bass diffusion model in which word of mouth and advertising move customers from a stock of potential adopters to a stock of adopters. The Bass model, however, is often described as a first-purchase model, since it does not capture situations where the product is consumed and discarded, a situation that would naturally lead to repeat purchases (Sterman, 2000). As a result, first-time and repeat purchases are modeled separately. The purchase rate then consists of the sum of two flows: the repeat purchase flow and the new product adoption flow. The model formulation for product replacement by means of repeat purchases is described next. 4.1.1.1.1. Product replacement rate. The product replacement rate will depend on the product's lifetime and on the residence time. A product's lifetime, or physical time, is the average time during which a product is operational before it experiences a functional failure that is beyond recovery (den Hollander et al., 2017). On the other hand, the residence time, or use time, refers to the amount of time the product is kept by the user, before it becomes unwanted and is discarded or replaced (Murakami et al., 2010). Past research suggests product replacement can be determined by: (i) mechanical, physical, or performance obsolescence (i.e., end of the product's lifetime), (ii) fashion or technological motives that induce the consumer towards the replacement of well-functioning products (i.e., products have become outmoded) (Bakker et al., 2014; Guiltinan, 2009; van Nes and Cramer, 2005), or (iii) a combination of both wear-and-tear and new desires (van Nes and Cramer, 2005). The model assumes that more than half of the short life cycle products which are in use and are still in good condition are replaced due to fashion trends or impulse purchases. These products stop being used or are discarded, not because they have broken down or are worn out, but because customers perceive them as being outdated. In contrast, I assume that long life cycle products are mostly discarded, and replaced, because they have broken down (Cox et al., 2013). In contrast to short life cycle products, the fraction of buyers who replace long-life products prematurely because of fads is relatively low. Note also that some products may be discarded after a certain period of use, even though they are still fully functional or in good working order. This group of products is represented in the model by the outflow “discard rate of functional products.” 4.1.1.2. Design for ease of maintenance and repair. Design for ease of maintenance and repair is the second major strategy for slowing resource loops (S). Accordingly, the model displays structures for both reactive (repair) and scheduled (proactive) maintenance. A static fraction determines the proportion of product units that move from being available to being in the stock of unplanned or reactive maintenance. When the ratio of “damaged units to total units” (i.e., the breakdown rate) surpasses a threshold, priority is given to resolve reactive maintenance tasks. That is, the total number of man-hours available, given the number of available mechanics, is split between scheduled and reactive maintenance jobs, with the latter receiving priority depending on the breakdown

rate. After repair, units become available again and the stock of units under reactive maintenance is reduced. The remaining number of man-hours, discounting those being used for repair, are assigned to scheduled maintenance tasks. Finally, it is also assumed that the disassembly index (explained below) affects the number of manhours required by planned and reactive maintenance jobs. Thus, when products are difficult to disassemble, both reactive and scheduled maintenance tasks take longer. 4.1.2. Design strategies for closing loops 4.1.2.1. Design for disassembly (DfD) and the disassembly index (DI). Design for disassembly is a key element for transitioning to the CE (Andrews, 2015). A product designed to be easily disassembled brings about various benefits throughout the product's lifecycle, including efficiencies in manufacturing and assembly, maintenance or servicing, and recovery at end-of-life both through remanufacturing and recycling (Gu and Sosale, 1999; Vanegas et al., 2017). Although many analytical models have been developed that evaluate the degree of a product's disassembly capacity (see, for instance, Desai and Mital (2003)), the model uses the disassembly measures proposed by de Aguiar et al. (2017). The authors compile published research to estimate 4 disassembly indexes that range from 1 to 4, where 1 is an ideal state (i.e., easiest to disassemble), and 4, an undesired state (i.e., almost impossible to disassemble). The disassembly index score influences three variables in the model, namely: (i) the recycling delay, (ii) the fraction of recyclable materials in products, and (iii) the man-hours required for scheduled and reactive maintenance tasks. First, the less a product has been designed for disassembly, the more the time a recycling party will need to sort, clean, and process its parts. I assume that when the disassembly index equals 1, the recycling time can be cut by a maximum of 50% (e.g., see Vanegas et al. (2017), where disassembly time is cut by approximately 40% when DfD guidelines are introduced in the design stage). The 50% ceiling assumes that the recycling delay is not only affected by disassembly, but also by other activities. The disassembly index also affects the fraction of recyclable materials in a product. Hence, when a product has not been designed for disassembly, component parts cannot be efficiently separated and reused, thus reducing the product's fraction of recyclable materials. The same is assumed with the number of man-hours assigned to scheduled and reactive maintenance tasks. When the disassembly index equals 1 (i.e., the most desirable state), the number of man-hours allotted for unplanned and scheduled maintenance will remain unchanged, and when it equals 4 (i.e., undesired state), the time for both types of maintenance tasks will double. 4.1.2.2. Design for the technological cycle and the recyclability index (RI). A product designed for the technological cycle is a product whose technical component parts (e.g., metals, and plastic) can be recycled into high-quality raw materials to be looped back into the industrial system. The degree of material recycling is primarily affected by the number of materials used in a product and their mutual compatibility. Overall, the fewer the materials in a product, the easier the recycling, since less separation work is involved (Franco, 2017; van Schaik and Reuter, 2007). This principle has proved to be true in products ranging from textiles to common household electronics. Although various possible levels of detail can be defined when conceptualizing the degree of material recycling (see, for example, van Schaik and Reuter (2010)), the model uses the Material Compatibility Index (MCI) suggested by de Aguiar et al. (2017). The authors propose a MCI that ranges from 1 to 4, where 1 refers to a

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situation in which parts are made only of one material, and 4 refers to a situation in which parts and modules are composed of fully incompatible materials. To operationalize the MCI in the model, it is assumed that when the MCI equals 1, 100% of the product's materials can be recycled. Similarly, at an MCI value of 2, 70% of the materials are recyclable, at a value of 3, 30%, and at a value of 4, only 5% of the materials are recyclable. The MCI or recyclability index partially affects a product's net recyclability fraction and determines the number of units flowing from the “collected for recycling” stock to the “recycled product inventory” stock. 4.1.2.3. Closing the technological cycle through recycling. At end-oflifetime (EOL), only a fraction of all sold products will be collected for post-processing, with the remaining fraction flowing through the “uncontrollable disposal rate,” whereby products end up in a landfill or being incinerated. Upon collection, I assume that some of the materials in products will be suitable for recycling according to the recyclability index described in the previous section, and that the remaining non-recyclable materials will constitute postconsumer waste. The net fraction of recyclable materials in a product is then defined as the percentage of recyclable materials (defined by both the disassembly and the recyclability indexes) multiplied by a measure, ranging from 0 to 1, that reflects the efficiency of the recycling process. Finally, the flow of recycled products is regulated by a recycling delay that mirrors the time that processing parties take to sort, clean, disassemble, and process products’ component parts (see Table 3). Table 4 displays some of the variables that were used to test extreme case scenarios. Note that scenarios 1.1 and 1.2 show the parameter values of key model variables for short-lifetime products, while scenarios 2.1 and 2.2. Show the parameter values for long-lifetime products. Results from this exercise are presented in Section 5. 4.2. Circular business models 4.2.1. Product service systems (PSS) The concept of PSS has now been discussed for more than a decade in connection with its role in sustainability (Annarelli et al., 2016; Beuren et al., 2013; Reim et al., 2015), and more recently in relation to its contribution to the CE (Lewandowski, 2016; Lieder and Rashid, 2016; Linder and Williander, 2017; Tukker, 2015). The PSS model shifts the focus from selling products (complemented by services), to selling services that substitute products, thereby contributing to dematerialization and offering the opportunity to decouple economic gain from material consumption. An example

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of a PSS is Rolls Royce “Power by the Hour” scheme, which allows airlines to purchase the use of aircraft engines instead of having to buy them (Baines et al., 2007; Ellen MacArthur Foundation, 2016). Although not evident in the model structure, it can be assumed that the manufacturing firm, the PSS provider, and the reverse logistics party are all different stakeholders interacting in the same simulated industrial ecosystem. Research literature on PSS has investigated some of the factors that contribute to the low levels of PSS adoption among customers, including: the low level of PSS maturity and lack of enthusiasm, PSS acquisition costs and risks (Gesing et al., 2014), hygiene and safety concerns (Catulli, 2012; Catulli and Reed, 2017), the physical aspects of ownerless consumption (Manzini et al., 2001; Mont, 2002), and the insecurity of consumers with regard to what they are getting (Rexfelt and Hiort af Orn€ as, 2009). Instead of accounting for each adoption factor discretely, the model sets an exogenous fraction of customers who opt for services instead of acquiring the physical product. The remaining fraction (1-PSS fraction) is then split between customers who buy green (recycled) products and those who buy brown (conventional) products. Note that throughout this paper, green products will refer to sustainable goods that contain a fraction of recycled materials, whereas brown products will refer to conventional products that have been manufactured for a linear economy. In fields such as marketing and energy management, these labels have been extensively used to distinguish between products that are clean, benign or resourceful (i.e., “green”) and polluting or toxic (“brown”) (Champagne and Matharu, 2016; Garrett-Peltier, 2017; Yenipazarli and Vakharia, 2015). 4.2.1.1. Product lifetime and discards under a PSS model. The model assumes a product under the PSS model can reach end-of-life either because the product's lifetime has ended or because excessive use has shortened it. The first scenario is straightforward and accounts for the product's lifetime to determine the flow of product discards. The second scenario accounts for the intensity of use, which is linked to the decisions taken by the final consumer while using the product (Ellen MacArthur Foundation, 2015; Iraldo et al., 2017). Previous research has highlighted the link between user behavior and product durability by suggesting that improper use and overuse lead to shorter product lifetimes (Bobba et al., 2016). The intensity of use is expressed through a measure of “functional units,” which reflects the extent to which a product is used to its full capacity (e.g., a kilometer for a car or a wash cycle for a washing machine). I have included this measure, because, according to past research, the level of a product's intensity of use depends on whether the product is purchased or used as a service. Hence,

Table 4 Parameter values for selected extreme-case scenarios. Key model variable

Potential adopters Adoption fraction Advertising effectiveness Contact rate Initial sales per adopter Perceived functional risk Average residence time Fraction of products in-use being replaced Average product lifespan Fraction of discarded units being replaced Fraction of products collected for recycling Disassembly index Recyclability index

Short-lifetime products

Long-lifetime products

Scenario 1.1

Scenario 1.2

Scenario 2.1

Scenario 2.2

1000 people 0.05 0.01/yr 100/yr 1 unit/people 0.5 2 yrs 0.7 3 yrs 0.1 0 4 4

1000 people 0.05 0.01/yr 100/yr 1 unit/people 0.5 2 yrs 0.7 3 yrs 0.1 0.5 1 1

1000 people 0.02 0.01/yr 60/yr 1 unit/people 0.5 5 yrs 0.1 6 yrs 0.7 0 4 4

1000 people 0.02 0.01/yr 60/yr 1 unit/people 0.5 5 yrs 0.1 6 yrs 0.7 0.5 1 1

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customers under an ownership scenario are more careful with the products, thus leading to less wear and tear. In contrast, when customers utilize but do not own the asset, overuse often occurs (Velamuri et al., 2013). Product use is thus modeled as the number of functional units accumulated during the product's use phase, divided by the average number of functional units which the product is assumed to have according to industry standards. The outflow of products at EOL will, again, be the maximum between the flow of products that have been disposed of because their lifetimes have ended (as dictated by design choices), and the flow of disposal as dictated by product overuse (as reflected by the functional unit ratio).

when functional risk reaches its highest value (1), only true green consumers will buy the recycled product. Prior research has estimated this last measure to be approximately 10% (Rex and Baumann, 2007). In addition to the functional risk, the perceived green image factor (GIF) also affects the fraction of customers opting for green products. The GIF is a delayed or smoothed variable modeled as the ratio of the “recycling rate” to the “product collection rate for recycling.” This ratio reflects the delayed market awareness that a producer cares about remanufacturing or recycling (Vlachos et al., 2007).

4.2.1.2. Product lifetime and scheduled maintenance. In a PSS model, service and maintenance tasks are taken over by the manufacturer or the retailer in order to lengthen the asset's lifetime (Bocken et al., 2016). Hence, the model assumes that, besides design characteristics and patterns of use, the lifetime of a product is also affected by its level of scheduled or preventive maintenance (e.g., substitution of components and cleaning of the product parts) (Bobba et al., 2016). The effect of preventive maintenance is incorporated in the model as the delayed ratio of the number of product units under preventive maintenance divided by the total number of units under both preventive and reactive maintenance. When this ratio equals or is close to 1, a product's average lifetime remains the same. However, when the ratio starts falling below 1, because little or no preventive maintenance has been performed, so does the value for product lifetime. Finally, it is also assumed that regardless of the level of scheduled maintenance, product lifetime can never fall below 50%.

One important question related to this modeling exercise is whether simulation results are different when the model is populated with parameter values corresponding to a specific product group (e.g., household appliances, clothing, cars, etc.) rather than a “generic product.” To answer this question, I searched the literature for key parameter values, and in the absence of suitable information, assigned certain variables values based on common sense. In Tables 5 and I have used the label “T&E” (i.e., trendy and emotional) to denote products which are fashion-affected (i.e., models are more important than functionality), and “M&P” (i.e., mature and pervasive) to refer to mature products, with established design cycles, that are not affected by fashion fads. While the latter coincides with the description of products designed for a long-life, the former matches the definition of short-lived products. Results from this exercise are discussed in Section 5.2.

4.2.2. Product reuse Product reuse is the second CBM portrayed in the model. It is incorporated as a ratio that diverts a portion of the product intended to be discarded to a stock of products that are intended for second-hand use. This decision rule delays the number of products flowing to recycling or the landfill. I have included a structure for reuse in the model because it is widely acknowledged that many products are discarded even though they are still in perfect working order (Bayus, 1991) and because looping products through reuse is a major CE strategy.

5.1. Extreme-case scenarios

4.3. Other relevant model structures 4.3.1. Functional risk and the green image factor The fraction of customers opting for green products versus the fraction of customers opting for brown products is determined by the product's degree of functional risk and the green image factor. Functional risk refers to the perceived risk that customers attach to a product, which can be psychological, financial, performancerelated, physical, and social. A repaired tire, for instance, may be perceived as embodying a high functional risk, in that it poses a high risk of accidents, while clothing produced with recycled fibers is perceived as presenting a low functional risk. Hamzaoui-Essoussi and Linton (2010) found that perceived functional risk appears to have a statistically significant impact on consumer purchase decisions, so that when functional risk in recycled products is perceived as high, customers’ willingness to purchase (WTP) is low, and vice versa. The measure of functional risk is determined by a lookup function of the form Y ¼ f(X) ranging from 0 (low) to 1 (high), such that at zero functional risk, an estimated maximum of 80% of customers will choose recycled products (this is because even at zero perceived risk there will still be some customers who will prefer to buy new products) (Michaud and Llerena, 2011). On the other hand,

4.4. Assessment of selected product groups

5. Results and discussion

Results from the extreme-case scenario test (Table 4) demonstrate how product longevity affects disposal and replacement rates in such a way that products designed for a short-lifespan exhibit higher and faster disposal and replacement rates than long-lasting products (Cooper, 2005; Franklin-Johnson et al., 2016). Additionally, these results show the extent to which different lifecycle lengths and residence times partially determine the shape and number of discarded products as well as the number of products intended to be remanufactured or recycled. The following sections present the results of selected model scenarios. 5.2. Short-life products with a poor disassembly index A disassembly index (DI) of 4 in scenario B means that a product's component parts are extremely difficult to separate, thus complicating service, remanufacturing, and recycling tasks for the relevant parties. In scenario B, not only are there fewer products flowing into recycling (i.e., recycling rate), but there is also evidence of higher amounts of recycling waste as a result of the poor disassembly performance of the products. Additionally, because a DI of 4 translates into a smaller fraction of recyclable materials contained in products, a much smaller amount of the “products collected for recycling” becomes converted into the “recycled products inventory.” In contrast, top scores for disassembly and recyclability in scenario A ensure that the collected products flow smoothly into the stock of “recycled products inventory.” Although the level of green product demand is the same in both scenarios, the “recycled products inventory” stock in scenario A grows significantly more than it does scenario B. This is because there are many more units that are in a suitable condition to be recycled in scenario A, and thus the inventory accumulates more and does not deplete quickly. Overall, in the case of short-life products, a combination of

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Table 5 Decision rules and parameter values for selected product groups. Product types

Household appliances (e.g., refrigerators, washing machines)

Industrial products (e.g., forklift trucks, soil compactors, excavators)

Cars

IT personal products (e.g., Clothing small IT accessories, mobile phones)

Furniture (e.g., pushchairs)

Lifecycle as dictated by current design Product type Suitable for remanufacturing (RM) or recycling (RC) Suitable for PSS Average residence time in years Median lifespan in years Willingness to purchase (WTP) recycled/ refurbished products as determined by functional (safety) or hygiene risk

Long [a] M&P RM

Long [b] M&P RM

Long [c] M&P RM

Short [a] T&E [f] RM/RC

Short [d] T&E [d] RC

Short [e] T&E [g] RM/RC

Yes 9 [author] 11 [k] Low (high functional risk) [l]

Yes [b][h] 5 [j] 10 [j] High (high functional risk but familiarity) [l]

Yes 8 [author] 10 [c] High (low functional risk) [m]

No 1.5 [j] 4 [f][k] Low (high functional risk) [m]

No [i] 0.5 [author] 2 [f] Low (hygiene and safety risks) [n]

No [g] 2 [author] 5 [e] High (functional risk) [o]

Sources: [a] Huisman et al. (2012), [b] Sundin et al. (2009), [c] Kagawa et al. (2006), [d] Birtwistle and Moore (2007), [e] Costa et al. (2015), [f] Cox et al. (2013), [g] Besch (2005), [h] Dongmin et al. (2012), [i] Armstrong et al. (2015), [j] Georgiadis et al. (2006), [k] Wang et al. (2013) found in Bakker et al. (2014), [l] Hamzaoui-Essoussi and Linton (2014), [m] Hamzaoui-Essoussi and Linton (2010), [n] Catulli (2012), [o] Catulli and Reed (2017).

conscious design for recycling and disassembly produces a higher amount of recycled product units at an earlier time. It is important to highlight that the conclusions outlined in this section do not significantly change when the scenario that is opposite to scenario B is tested (i.e., short-life products designed with a good disassembly index and with a poor recyclability index).

scores for disassembly and recyclability, a poor DI in scenario B reduces and delays the number of product units flowing into the recycled products inventory. Similar to the test in the previous section, there is no significant difference between long-life products that are designed with a poor disassembly index and with a good recyclability index and the scenario where the opposite holds (see Fig. 5).

5.3. Long-life products with a poor disassembly index Compared to short-life products, the stock of “products collected for recycling” for long-life products in scenario B (Fig. 4) exhibits a more constant and steady shape than scenario B in Fig. 3. The number of products waiting to be recycled is both lower and more constant in scenario B of this test, not only because products are designed for a longer life, but also because replacement purchases caused by technical failure occur primarily at the products’ end-of-life (see second hump in the “products collected for recycling” stock). Also, compared to scenario A, where products flow directly to the recycled products inventory because of excellent

5.4. Short-life products with average indexes for disassembly and recyclability In this test, I compare two scenarios with different disassembly and recyclability scores under the assumption that neither is optimal. Although for both scenarios the product collection rate is the same, scenario A accumulates fewer units in the “recycled products inventory” stock when compared to scenario B (see Fig. 5). In contrast, an average index of 2 for both disassembly and recyclability indexes in scenario B allows for a greater amount of product materials to be recovered and therefore, enables more

Fig. 3. Short-life products with an optimal and a poor disassembly index.

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Fig. 4. Long-life products with an optimal and a poor disassembly index.

Fig. 5. Short-life products with average indexes for disassembly and recyclability.

products to be recycled and pile up in the recycled products inventory (the recycled product inventory stock grows considerably because demand cannot keep up with the raising inflow of products). This test showed that better results are achieved, in terms of the recycled product inventory, when indexes for both disassembly and recyclability are only “good enough.” Model runs involving the same comparison for long-life products yielded the similar results (see Fig. 6). Finally, in both scenarios almost the same amount of waste (resulting from non-recyclable materials) was generated.

5.5. Variation in the functional risk After a product is manufactured, it must flow within the consumer system during a certain amount of time before users dispose of it and recycling parties can recover and recycle it. Overall, tests involving changes in the degree of functional risk indicated that producers must carefully plan the alignment of: (i) production volumes with the desired inventories of recycled products, and (ii) the forecasted inventories of recycled products with the forecasted demand for such products.

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Fig. 6. Long-life products with average indexes for disassembly and recyclability.

In scenarios B and D (see Fig. 7), when the degree of functional risk is low, and consequently the demand for green products is higher than the demand for brown products, the scant number of units in the recycled product inventory stock at the beginning of the simulation generates low sales levels for both green and brown products (the sales rate for green products is set to be the minimum between the demand and supply rates). With low sales levels for both product categories, fewer than the desired number of product units are sold, used, collected, recycled, and subsequently stored in the inventory of recycled units. That is, an initially low inventory of recycled goods combined with high levels of green demand leads to a reinforcing feedback loop, whereby initial low inventories of green products cause sales of both green and brown goods to be low, in turn leading to low future levels of collected, recycled, and, again, stored recycled goods. In contrast, when the demand for brown products is higher than the demand for green products, as is the case in scenarios A and D (because of a high functional risk), the stocks for sold, disposed of, and recycled products successfully build up. Paradoxically in this case, the recycled products inventory can cope with the demand for green products. Furthermore, it is only at this point where a variation in the functional risk, like the one introduced in scenarios B and D, can generate the desired effects.

parties who rely on substantial volumes of product returns to sustain their continuity in a CE. 5.7. The PSS model versus the purchase-only model: a quick test To elucidate some of the differences between a PSS and a purchase-only model, two simple model structures were built. Results from this test showed that more units need to be purchased at time zero under the PSS model, than is necessary under the product purchase scenario, to satisfy the same level of hypothetical customer demand. This is so because under a PSS model each unit is kept by the user for a certain amount of time, and, only after this time has elapsed will the product unit be returned to the stock of “PSS units available.” Also, although the initial investment required by a PSS model is much higher for the sponsoring firm, at EOL the number of discarded units (regardless of whether they are landfilled, remanufactured or recycled) proved to be the same under both the purchase-only model and the PSS model. Finally, because the difference between these two models resides in how many units are in operation throughout the product's useful life (with their corresponding environmental impact), it can be concluded that PS is not a panacea unless product units under this model are also designed with circular design guidelines in mind and their use is regulated somehow (see Fig. 7).

5.6. Variation in the PSS fraction for short- and long-life products 5.8. Testing the model for different product types With the introduction of PSS demand, fewer products are purchased and go directly into use and disposal flows. That is, products which might have been used by only one customer can now be used by many. Results of this test show that, compared to short-life products, long-life products exhibit a lower stock of recycled products. Contrary to the rather logical belief that a combination of long-life design (i.e., the first of the two design strategies for slowing loops) and a PSS model (i.e., the most common dematerialization strategy) would be ideal in a CE, results show that for long-life products, product returns are fewer in quantity because they are distributed over a longer time period. These findings are relevant to manufacturers, as well as to collection and processing

Fig. 8 shows that the simulated behavior for the different model variables is strikingly similar among products in the long-life category (see red, blue and black curves), as well as those in the short-life category (see green, pink, and orange curves). This is so, because residence times and lifetimes for all items in each of the product groups coincide to a great extent. Products designed for a short-life are landfilled, collected, and recycled in bigger amounts, compared to the products in the long-life category. Also, given that all long-life products in Table 5 are suitable for PSS, a fixed number of these products are distributed among the purchases of brown and green products, as well as among products under a PSS model.

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Fig. 7. Average scores for disassembly and recyclability with variations in the functional risk for short- and long-life products.

This, in turn, influences the number of product units accumulated in the stock of “products collected for recycling,” which, as previously stated, is smaller for long-life than for short-life products. Last, the results from this test evidence that even though it is critical to look at different product groups separately to study their behavior in a CE over time, the decision to use a “generic product” for this simulation model is justified. 6. Conclusions Important conclusions from this exercise are plentiful, both theoretically and practically. First, comparisons between scenarios covering design for longevity (i.e., short and long life) showed that

short-lived products, compared to long-lived ones, generated a higher amount of disposed of and collected product units earlier in time, not only because their lifetimes were shorter, but also because their replacement times were faster. Here, the readiness of manufacturing and processing parties to deal with product returns, and the preparedness of customers in different markets to buy them, will have to be assessed. Also, tests measuring whether it is better to design a product that excels in one of the design frameworks (e.g., DI ¼ 1) and not in the other (e.g., RI ¼ 4), or to design a product with good-enough indexes for both disassembly and recyclability (e.g., DI ¼ 2 and RI ¼ 2), revealed the second scenario produced better outcomes since the inventory of recycled goods increased by a higher amount. Naturally, it did so earlier for the

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Fig. 8. Model output variables for different product groups.

short-lived products than for its long-lived counterparts. This conclusion speaks particularly to firms wanting to redesign their product offering according to CE principles or to start-ups wanting to achieve the best circular results from the outset. Tests examining variations in the functional risk also produced somewhat counterintuitive results, especially when considering the units that accumulate in the inventory of recycled goods. Hence, when a low value is given to the functional risk and therefore green products are preferred over brown products, one would expect sales and inventories of recycled green products to soar. Counterintuitively, CE implementation is sabotaged. One must remember that the simulation starts with a low inventory of recycled goods (at time 0 the first product purchases are being made and consequently, there is nothing yet to regenerate), and that the flow of green product sales is set as the minimum between green product supply and green product demand. Although the demand for regenerated products in this scenario is high (due to a low functional risk), only a few products can be offered for purchase and, consequently the number of product units to regenerate and sell back to the market is low. This situation can be seen as a reinforcing feedback loop of scarcity such that in the presence of an initially low production and sales level of green products, only a few units (designed and manufactured with sustainable guidelines in mind) can be

regenerated and resold, despite the high green product demand. In contrast, when the availability of green products is ensured beforehand (e.g., through government incentives, for instance), the existing high demand for green products can be satisfied, enough products can accumulate for reprocessing, and inventories of green products can pile up (see similar results in Franco (2017)). Model results also showed than when the PSS model is introduced to the simulation, there is a need for firms to align their future supply and demand recycling flows. This is because, due to longer and more intermittent use patterns, PSS units accumulating in the recycled products inventory are distributed over a longer period of time. Although it is believed that a combination of a PSS model and products designed for a long life is highly desirable in a CE, it is important that either recycling or remanufacturing firms consider the flow of products to be regenerated under a PSS model for their planning activities. Finally, this model building exercise evidenced the extent to which many contextual conditions beyond the manufacturers’ control (e.g., product purchase and replacement behaviors, product reuse and return rates, volume, timing, and quantity of returns, as well as unexpected or postponed product disposal) need to be satisfied for a circular model to properly function and be accurately assessed.

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6.1. Practical and theoretical implications Because there are no official indicators, methods, or tools available for supporting and measuring businesses’ transition from a linear to a circular industrial system (Ellen Mac Arthur Foundation, 2015), this system dynamics model might be particularly useful to manufacturers, service providers, and collection parties seeking to test or evaluate the performance of CE strategies and the viability of self- or third-party imposed collection targets. Each of these players could use the model to assess how different assumptions in different parts of the value chain affect CE implementation at the macro and micro level (Kalmykova et al., 2018). These findings also support policy making and academic research on EPR (extended producer responsibility), because to make EOL management more efficient, legislation and industry should take into consideration the delayed effects and complex dynamics of combined strategies for design, business models, and collection schemes. Future model versions could also help governmental and non-governmental institutions test the wider network economic effects of a broad range of CE interventions at the firm or micro level. For academics, the development of the proposed model fills the gap in the literature of a systemic, integrated, interdisciplinary, and visual tool to support the understanding and performance management of circular production systems, from product design and manufacturing up business models and product return and reprocessing. Finally, this modeling exercise reveals the paradox of the circular economy, which seems to require ever increasing volumes of production and consumption to make the closing of material loops economically profitable for recycling parties. In fact, in the presence of no link between end-of-life and primary production flows, a CE could encourage even more production, partially offsetting the resource efficiencies gained in the first place (especially when secondary goods are not equivalent replacements for primary goods). 6.2. Limitations and future research Populating model parameters with data derived from literary sources, rather than a real case study, is arguably the most salient limitation of this work. There are some reasons why data was not readily available or difficult to obtain (Ameli et al., 2019; Asif et al., 2016; Lieder et al., 2017). First, while some firms have focused either on circular design, business models, or on product take back and reprocessing schemes, hardly any firm has implemented all three sets of strategies simultaneously (see, for instance, the case studies presented by Franco (2017)). Second, long time delays in product use and returns have prevented those scant number of firms that have incorporated any sort of circular strategy to be endowed with detailed, reliable and longitudinal data on the quantity and quality of material flows in their circular value chains (Gupta et al., 2018; Kirchherr et al., 2018; Whalen et al., 2018). Because of all this, retrieving fragmented data from previous publications became imperative for this study. Furthermore, although an attempt to test the model structure for different product groups was made in Section 5.2, future research could assess the applicability and reliability of this generic tool for a real firm. By parameterizing the model to fit a specific industrial case, one could check whether the conclusions from this modeling exercise vary and whether recommendations for the future would be different. Additionally, the present SD model is not meant to be final and comprehensive. Instead, it must be developed further to grasp all the missing details that could be relevant to manufacturers, service providers, refurbishing, remanufacturing or recycling parties, and policy makers. Modeling varying product adoption mechanisms

and demand profiles for different business models, or environmental (e.g., CO2 production), organizational (e.g., marketing initiatives and policies), and economic variables (e.g., costs of recovered materials and investments for reverse logistics and recovery infrastructure) could, as examples, increase the utility of the model. One could also think about endogenizing the model structure for manufacturing so that, in some cases, primary production is decreased (with its associated costs) when secondary/recycled products are considered suitable replacements for primary/virgin goods. Finally, to ease the use of this tool for managers and other relevant parties, the development of a user interface is recommended. Acknowledgements I thank all the reviewers who, throughout the various rounds of revision, contributed to make this manuscript more accurate and insightful. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.118327. References €uscher, K., 2016. Business models for sustainability from a system Abdelkafi, N., Ta dynamics perspective. Organ. Environ. 29 (1), 74e96. Ameli, M., Mansour, S., Ahmadi-Javid, A., 2019. A simulation-optimization model for sustainable product design and efficient end-of-life management based on individual producer responsibility. Resour. Conserv. Recycl. 140, 246e258. Andrews, D., 2015. The circular economy, design thinking and education for sustainability. Local Econ. 30 (3), 305e315. Annarelli, A., Battistella, C., Nonino, F., 2016. Product service system: a conceptual framework from a systematic review. J. Clean. Prod. 139, 1011e1032. Armstrong, C.M., Niinim€ aki, K., Kujala, S., Karell, E., Lang, C., 2015. Sustainable product-service systems for clothing: exploring consumer perceptions of consumption alternatives in Finland. J. Clean. Prod. 97, 30e39. Arnette, A.N., Brewer, B.L., Choal, T., 2014. Design for sustainability (DFS): the intersection of supply chain and environment. J. Clean. Prod. 83, 374e390. Asif, F.M.A., Lieder, M., Rashid, A., 2016. Multi-method simulation based tool to evaluate economic and environmental performance of circular product systems. J. Clean. Prod. 139 (Suppl. C), 1261e1281. Baines, T., Lightfoot, H.W., Evans, S., Neely, A., Greenough, R., Peppard, J., Roy, R., Shehab, E., Braganza, A., Tiwari, A., Alcock, J.R., Angus, J.P., Bastl, M., Cousens, A., Irving, P., Johnson, M., Kingston, J., Lockett, H., Martinez, V., Michele, P., Tranfield, D., Walton, I.M., Wilson, H., 2007. State-of-the-art in product-service systems. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 221 (10), 1543e1552. Bakker, C.A., Wang, F., Huisman, J., den Hollander, M., 2014. Products that go round: exploring product life extension through design. J. Clean. Prod. 69, 10e16. Bayus, B.L., 1991. The consumer durable replacement buyer. J. Mark. 55 (1), 42e51. Besch, K., 2005. Product-service systems for office furniture: barriers and opportunities on the European market. J. Clean. Prod. 13 (10), 1083e1094. Beuren, F.H., Gomes Ferreira, M.G., Cauchick Miguel, P.A., 2013. Product-service systems: a literature review on integrated products and services. J. Clean. Prod. 47, 222e231. Birtwistle, G., Moore, C.M., 2007. Fashion clothing e where does it all end up? Int. J. Retail Distrib. Manag. 35 (3), 210e216. Bobba, S., Ardente, F., Mathieux, F., 2016. Environmental and economic assessment of durability of energy-using products: method and application to a case-study vacuum cleaner. J. Clean. Prod. 137 (Suppl. C), 762e776. Bocken, N., de Pauw, I., Bakker, C., van der Grinten, B., 2016. Product design and business model strategies for a circular economy. J. Ind. Prod. Eng. 33 (5), 308e320. Bocken, N., Short, S.W., Rana, P., Evans, S., 2014. A literature and practice review to develop sustainable business model archetypes. J. Clean. Prod. 65, 42e56. Boons, F., Lüdeke-Freund, F., 2013. Business models for sustainable innovation: state-of-the-art and steps towards a research agenda. J. Clean. Prod. 45, 9e19. rez-Belis, V., 2012. A taxonomy of ecodesign tools for integrating Bovea, M.D., Pe environmental requirements into the product design process. J. Clean. Prod. 20 (1), 61e71. Braungart, M., McDonough, W., Bollinger, A., 2007. Cradle-to-cradle design: creating healthy emissions e a strategy for eco-effective product and system design. J. Clean. Prod. 15 (13e14), 1337e1348. Briano, E., Caballini, C., Giribone, P., Revetria, R., 2010. Using system dynamics for short life cycle supply chains evaluation. In: Proceedings of the 2010 Winter Simulation Conference, pp. 1820e1832.

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