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Towards Regenerative Supply Networks: A design framework proposal
Accepted Manuscript Towards Regenerative Supply Networks: A design framework proposal Vitor de Souza, Jacqueline Bloemhof-Ruwaard, Milton Borsato PII:
S0959-6526(19)30577-3
DOI:
https://doi.org/10.1016/j.jclepro.2019.02.178
Reference:
JCLP 15908
To appear in:
Journal of Cleaner Production
Received Date: 1 March 2018 Revised Date:
19 October 2018
Accepted Date: 16 February 2019
Please cite this article as: de Souza V, Bloemhof-Ruwaard J, Borsato M, Towards Regenerative Supply Networks: A design framework proposal, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.02.178. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Towards Regenerative Supply Networks: a design framework proposal 1,*
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Vitor de Souza , Jacqueline Bloemhof-Ruwaard and Milton Borsato 1
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Graduate School of Mechanical Engineering and Materials (PPGEM), Federal University of Technology - Parana (UTFPR), Campus Curitiba, Brazil. 2 Operations Research and Logistics Group, Wageningen University and Research, The Netherlands. *Corresponding author: [email protected]
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ACCEPTED MANUSCRIPT Word count: 7774 words. Keywords: regenerative development, transdisciplinary research, systems approach, sustainable supply network design, resilience.
1.
INTRODUCTION
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Industrial activity have provided humanity with wealth levels like never seen before in history (Ellen MacArthur Foundation, 2015; Kersley and Stierli, 2015), but not without severe consequences to the environment and society. Together, industrial processes and fossil fuel account for 65% of the Global Greenhouse Gas Emissions (IPCC, 2014). Downey and Willigen (2005) stated that mental health of neighbours surrounding industries was negatively impacted, while Turner (2014) reinforced the predictions of a societal collapse warned in Meadows et al. (1972). Sustainable Supply Network Design (SSND) is a research stream that has been transforming with the evolution of sustainability approaches. Eco,1 efficiency* (Verfaillie and Bidwell, 2000), is among the first approaches used to improve supply chain sustainability. As a business-oriented approach (Dyllick and Hockerts, 2002; Young and Tilley, 2006), it was focused on decreasing environmental impact, i.e., even with companies adopting such strategy, environmental problems continued to worsen (Hauschild, 2015; Turner, 2014). From another perspective, discipline-based solutions imply on unintended side effects, because sustainability represents a complex challenge that can hardly be tackled by a single discipline (Mauser et al., 2013; Sahamie et al., 2013). More systemic approaches arose evolving from inter-, multi-disciplinary towards transdisciplinary research, avoiding two problems observed in traditional, reductionist research (Ackoff, 1999): a) Taking separate parts and improving them separately will not result in the improvement of the whole; b) Problems are not disciplinary in nature: “effective research is transdisciplinary”. Transdisciplinary Research (TR) is a way forward to address the problem of sustainability, reaching “the common good” (Bergendahl et al., 2018; Brandt et 1
The concepts with an asterisk are described in the glossary of terms in Appendix I.
ACCEPTED MANUSCRIPT al., 2013; Sahamie et al., 2013), which can be interpreted in different ways (Hadorn et al., 2008). It should be applied when (i) there is not enough reliable knowledge about the problem, (ii) there is dispute over which practices must be transformed and (iii) solutions proposed shall have a profound impact in the whole society (Hadorn et al., 2006).
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In line with these theories, the eco-effectiveness* approach (Braungart et al., 2007; Carrillo-Hermosilla et al., 2010) focus on doing the right things for the environment, i.e., increasing environmental benefit, or regenerating ecosystems. Concepts like biomimetics* and the upcycle* (McDonough and Braungart, 2013) – “waste equals food” -, Industrial Symbiosis* (Chertow, 2000; Lombardi and Laybourn, 2012), Circular Economy (Ellen MacArthur Foundation, 2015) and Biobased Economy* (Lopes, 2015) – derived from this approach. In the field of SSND, Gruner and Power (2017) established theory grounding for the integration of resilient supply chains (SCs) with the environment. Banasik et al. (2016) applied optimization techniques to a closed-loop supply chain* of mushrooms production that reuses waste. Bergendahl et al. (2018) approached the food-water-energy nexus* with TR to enhance SC sustainability, investigating the impact on its relationships. Fahimnia and Jabbarzadeh (2016) also used mathematical modelling to integrate sustainability and resilience in SSND, investigating the trade-offs in performance. However, a SN design framework based in biocentrism*, regeneration, TR and optimization cannot be found in the literature. This paper aims to fill this gap by proposing a definition and a framework for the Regenerative Supply Network Design (RSND) process, grounded in Regenerative Development, Transdisciplinarity, Systems Thinking and Social and Design Sciences. The RSND framework is approached as an artefact* and developed using the Design Science Research Methodology. Through the RSND framework, a supply network can be designed focusing on environmental regeneration while economic feasibility is assured through network optimization. The design framework consists of six steps, where (i) the network surroundings are depicted as a Socio-Ecological System (SES), and a regenerative purpose is defined. With an eco-effective approach and based in Circular Economy, (ii) inputs and outputs are redesigned. (iii) The supply network is conceptualized as a Socio-Technical System (STS): its interactions
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with the surroundings are mapped and resilience is addressed, enhancing its regenerative effects through withstanding perturbations. In step (iv), the performance of the supply network is optimized and in (v), the most suitable network configuration is chosen. Finally, in step (vi), Implementation, the network becomes operational, fulfilling its function and regenerating the environment.
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The remainder of this article is organized as follows: Section 2 presents a literature review; in Section 3, methodological procedures are described. Section 4 includes the RSND definition and the framework. In Sections 5 and 6, discussions and conclusions are provided. 2.
LITERATURE REVIEW
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In this section, the three pillars of this research are presented, and Figure 1 illustrates the links between these pillars. First, Transdisciplinary Research, systems approach and the Socio-Technical and Socio-Ecological Systems views are introduced. The concept of Regenerative Development is disclosed, followed by a brief description of Circular Economy. From Social Sciences, Sustainable Supply Chain Design is reviewed, followed by the more systemic approach of Supply Networks and Resilience. Finally, Design Science Research, the third pillar, is the research methodology used, described in Section 0.
Figure 1 - Research Foundations.
2.1. Transdisciplinary Research
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Transdisciplinarity is defined in Bergendahl et al. (2018) as “the incorporation of a broad set of scientific and policy disciplines, including industries and actors, for addressing broad and complex problems, e.g. sustainability.” Transdisciplinary Research (TR) allows sustainability to becoming a concrete realization, rather then just a far objective (Brandt et al., 2013). TR is recognized as appropriate to:
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a) “grasp the complexity of problems; * b) take into account the diversity of life-world and scientific perceptions of problems; c) link abstract and case-specific knowledge, and; d) develop knowledge and practices that promote what is perceived to be the common good.” (Pohl and Hirsch Hadorn, 2007, p. 20).
Figure 2 – Disciplinary and Transdisciplinary Research compared. Source: adapted from Hadorn et al. (2006).
TR is regarded as a more integrative research approach to tackle the tremendous challenges ahead of a sustainable future (Mauser et al., 2013), that cannot be addressed by any discipline on its own (Brandt et al., 2013). Figure 2 summarizes the difference between disciplinary (a) and transdisciplinary research (b). In the left part, examples of scientific disciplines
ACCEPTED MANUSCRIPT are listed; at the centre, some of the current problem fields* faced by mankind; to the right, actors that deal with these problems in a daily basis.
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Disciplines are important in the search for solutions in any problem field; that is represented by the arrows in (a). However, arrows’ tips do not come close to the problem fields, due to the inherent impossibility of grasping a problem’s entire complexity (Hadorn et al., 2008, p. 34). Actors are not directly connected, although they have expectations about the research outcomes, especially when they provide these researches with funding and support.
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In (b), transdisciplinary research surpasses the boundaries of disciplines, merging knowledge bases in a less defined shape. Both scientific and practical knowledge can be applied towards a problem field – e.g., diseases –, what is represented by the dashed arrows from all the disciplines and actors in the list. Initially, any discipline or actor can be a source of knowledge for a certain problem field; the brackets represent their integration (Hadorn et al., 2008).
Figure 3 - Types of knowledge associated with TR. Source: Hadorn et al. (2008, p. 59).
Figure 3 represents the three types of knowledge produced with TR. Systems Knowledge is acquired when systems are investigated on their current function and behaviour – through theory-driven research; Target Knowledge, when the ideal future situation is proposed. Action (Transformation) Knowledge is achieved when the research focus is on the transformation of existing practices or the introduction of new ones within the technical, social and cultural dimensions (Hadorn et al., 2008). Target and Transformation knowledge are acquired with problem-driven research.
ACCEPTED MANUSCRIPT Transdisciplinary Research requires systems approach to be fully understood and inplemented (Jantsch, 1972), reviewed in the following subsection. 2.2. Systems Approach and the Socio-Technical and Socio-Ecological views
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Systems Approach is based in General Systems Theory, initially proposed in disciplines like biology (von Bertalanffy, 1950) and social sciences (Boulding, 1956). It was advocated as a structural framework where disciplines and information could be fitted within its structure, shaping a body of knowledge (Boulding, 1956).
Socio-Technical Systems were defined in Bostrom and Heinen (1977), and are structured in four interdependent, interacting elements. Structure and People (forming the Social System) and Technology and Tasks – that together form the Technical System. Figure 4 presents this structure, aimed at supporting designers to consider these aspects and dimensions during the design process. Two-way arrows symbolize interactions among elements.
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Ackoff (1994) stated that one should focus in the interactions among elements within a system, and among systems. There are two schools of thought to depict societal systems, both featuring complex, dynamic, multi-scale and adaptive* properties (Smith and Stirling, 2010): the Socio-Technical Systems (STS) and the Socio-Ecological Systems (SES).
Figure 4 - A depiction of a Socio-Technical System. Adapted from Bostrom and Heinen (1977).
The Socio-Ecological System view, is composed of a ‘bio-geo-physical’ unit and the actors and institutions related with it (Glaser et al., 2008). They are delimited by spatial boundaries – not too small that no detail is perceived neither too big that could hide its emergent* properties (Ostrom, 2009).
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Figure 5 presents the SES framework as a unit of analysis. It is composed of Resource Units (e.g. lobsters), Resource Systems (a lake), Users (fishermen) and Governance System (organizations and rules governing fishing) (Ostrom, 2009). Again, two-way arrows describe elements interacting, while the system interacts with other ecosystems, and social, economic and political settings.
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Figure 5 - The Socio-Ecological System Framework. Source: Ostrom (2009).
One of the primary concerns of the SES view is Resilience, defined as “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.” (Walker et al., 2004). The SES shall not be explored in such a way it cannot recover, with the risk of becoming damaged permanently. SES and STS can be understood as different perspectives of the same system. Smith and Stirling (2010) argues that the main differences between both views is that (i) SES considers technology as an exogenous factor, as it already entails enough complexity from ecological and social systems; (ii) SES view is place-bounded, while a STS can extend itself through more than one location (Smith and Stirling, 2010). Figure 6 represents the evolution of the integration of systems using the SES and STS views. In the top left, the SES is in a central perspective, merging with the STSs it nestles, and all systems that are resilient transform into a new, alternate state. In the bottom right, the STS is in a central perspective, and it interacts with multiple SESs – as is the case of a supply network -, and they merge into a transformed, more sustainable system.
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Figure 6 – integration between SES and STS. Source: adapted from Smith and Stirling (2010).
Such integration is supported by regenerative development, defined in this research as “the common good” - reviewed in the following subsection. 2.3. Regenerative Development The path towards environmental regeneration is defined by the transition from anthropocentrism – which is degenerating the environment -, to biocentrism, as represented in Figure 7 (Mang and Reed, 2012). As human consciousness gradually integrates with nature, it evolves from indiscriminately using resources to their efficient use and resource conservation. Anthropocentric efforts to reduce degeneration like eco-efficiency* - “doing things right” for the environment (Drucker, 1995, p. 33) are business-oriented (Young and Tilley, 2006). As such, they generate rebound-effects, where gains are likely to be lost by increased consumption – e.g., using more frequently an electrical car because it is electric (Bjørn and Hauschild, 2013). With a shift to biocentrism, nature affiliation (“biophilia”) begins, then evolves to mimic nature (biomimetics), restore nature, tend nature and finally, be nature – achieved through regenerative design and development, respectively (Mang and Reed, 2012).
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Figure 7 – The path towards Regenerative Development. Source: Mang and Reed (2012).
The regenerating stage is marked by nature-oriented approaches like ecoeffectiveness, i.e., to “do the right thing” for the environment, which can be defined by the degree of regeneration achieved with the desired. This relation is represented in Equation Error! Reference source not found.Error! Reference source not found., based on Enright (2012).
(1)
Different level of progress can be observed in each country or society. Developed countries are entering the regenerating phase (Bosman and Rotmans, 2016), while the less developed and underdeveloped countries are still striving to reduce degeneration. In both cases, economic development and environmental degradation are yet to be completely decoupled, due to e.g.
ACCEPTED MANUSCRIPT technological and/or economical restraints. Countermeasures like environmental impact minimization are still required until full integration between humans and nature is achieved. Based in eco-efficiency, ecoeffectiveness and regenerative development, the Circular Economy system is reviewed in the following section.
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2.4. Circular Economy
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Intensively explored by the scientific community (Su et al., 2013), the Circular Economy (CE) has its origins in Pearce and Turner (1990), featuring multidisciplinarity, eco-efficiency, cradle-to-cradle* design (Braungart, McDonough, & Bollinger, 2007) and industrial ecology (Peck, 1996). A recent definition was proposed by Geissdoerfer et al. (2017): “Circular Economy is a regenerative system in which resource input and waste, emission, and energy leakage are minimised by slowing, closing, and narrowing material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling.”
The degenerative open-loop, Linear Economy system of take, make and dispose must evolve to a closed-loop, regenerative, Circular Economy (Geissdoerfer et al., 2017). CE is based in the RESOLVE framework, which stands for REgenerate (shift to renewables, restore ecosystems), Share (assets, prolong lifecycle), Optimise (increase performance/efficiency), Loop (closed-loop), Virtualise (dematerialise) and Exchange (Ellen MacArthur Foundation, 2015). Figure 8 illustrates the three main principles of CE: Principle 1 - Enhance and preserve natural capital by controlling finite stocks and balancing renewable resource flows. Principle 2 - Optimise resource yields by circulating products, components and materials in the biological cycle (where organic elements circulate) and the technological products cycle. Principle 3 - Foster system effectiveness by revealing and designing out negative externalities*, regarded as “leakages” of the closed-loop system.
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Figure 8 – An outline of the Circular Economy. Source: Ellen MacArthur Foundation (2015).
The Circular Economy has been largely implemented through resarch on Sustainable Supply Chain Design, reviewed in the following topic. 2.5. Sustainable Supply Chain Design From the theory of Logistics Management, Bloemhof-Ruwaard (2015) defined a stepwise approach to develop zero-waste, zero-emissions supply chains without harming economic prosperity – represented in Figure 9. The approach is shaped around three phases: Assessment, where the supply chain’s sustainability level is determined; Evaluation, focused on benchmarking the desired level to be achieved, and Improving, where the current supply chain is redesigned, moving it from the current to the desired state, balancing environmental, social and economic performances.
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Figure 9 - The Sustainable Logistics Management Approach. Source: BloemhofRuwaard (2015).
Sustainable principles have been incorporated in the Supply Chain Design process (Eskandarpour et al., 2015). Table 1 describes the evolution of the sustainable supply chain design field (Kruczek et al., 2012). The adoption of recycling processes that reintroduce waste flows back into the production chain originated the Green Supply Chains, improving ecological efficiency and reducing pollution – a “single-objective”. Their performance was measured with CO2 emissions, or eco-indicator*.
With the introduction of eco-efficiency approach, the focus shifted towards improving the environmental performance of the chain while keeping profit levels maximized (“multi-objective”). This supply chain also generates less waste, “doing things right” for the environment through recycling processes under the linear design paradigm, cradle-to-grave. The latest, more evolved Supply Chain is based in the eco-effectiveness approach: closed loops are an inherent feature of the chain, which purpose is related with product lifecycle and cradle-to cradle design – doing the right things. Waste management is performed for upcycling, where a small amount of energy is required to reintroduce the waste in the production chain. Table 1 - Evolution of Sustainable Supply Chains. Adapted from Kruczek et al. (2012). Green/ Sustainable
Eco-efficient
Eco-effective
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Concepts General Purpose
Focus
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Design approach
Supply Chain Type
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Waste Management KPI (Key Process Indicator)
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Eco-efficient
Eco-effective
Eco-efficiency
Eco-effectiveness
Improving Ecological and economic efficiency zero waste emission, zero resource use and zero toxicity Doing things right
Improving product in life cycle from cradle to cradle (C2C) Quality in life cycle
Cradle to grave design
Cradle to Cradle design
Open and closed loop supply chain Recycling
Open and closed loop supply chain
Only closed loop supply chain
Recycling
Upcycling
CO2, ecoindicator, etc.
Integrated economic and environmental indicators
Indicators in life cycle from cradle to cradle (C2C)
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Main Idea
Green/ Sustainable Environmental Protection Improving Ecological Efficiency Pollution prevention Sustainable development Eco-design
Doing the right things
A summary of the findings from recent literature can be found in Appendix II, where the main concepts used in each research and a brief description of the main findings are described. The adoption of a systemic view over the Supply Chain evolved into the Supply Network, accounting also for its resilience. This subject is reviewed in the following subsection. 2.6. Supply Networks and Resilience The increasing complexity of Supply Chains demands a more appropriate approach than linear chains: the complex networks approach (Christopher and Peck, 2004). An organization should manage both the active and the inactive members of the system, characterizing the Supply Network (SN), defined in Braziotis et al. (2013): “[. . .] a set of active members within an organisation’s supply chains, as well as inactive members to which an organisation relates, that can be called upon to actively contribute to a supply chain if a need arises.”
Figure 10 illustrates the difference between active members – nodes connected with solid lines -, and inactive members - the nodes connected with
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dashed lines. The supply chain formed by the active members is highlighted in the central square, with the outer circle encompassing the supply network.
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Figure 10 - the Supply Chain and the Supply Network. Source: Braziotis et al. (2013).
Supply networks can be framed as Socio-Technical Systems (Behdani, 2013), i.e., as “complex physical-technical systems and a network of interdependent actors” (De Bruijn and Herder, 2009). A supply network is composed of, e.g., facilities, reprocessing companies, transporters, which are nested sociotechnical subsystems, interrelated in social networks. Overall, its behaviour is an outcome of the interactions within the networks and the interactions and interdependencies among systems, which influences, among other characteristics, their adaptiveness – the ability to change behaviour (Behdani, 2013, p. 89-93), i.e., to handle disruptions. Christopher and Peck (2004) grouped disruptions in three types: internal to the firm (related production processes and control), external to the firm but within the network (caused by variation in demand and/or supply) and external to the network (from environmental causes). They defined the resilient supply network from four general principles: (i) resilience should be designed in; (ii) corporations involved in the network must collaborate, (iii) a network must be agile and (iv) a risk management culture should be fostered. In the business and management context, Fiksel (2003) contributed with the fundamental concepts for the design of resilient supply chains. Pettit et al. (2013) mapped the vulnerability (e.g. resource limits, external pressures) and capability factors (e.g. efficiency, adaptability) influencing enterprise’s resilience.
ACCEPTED MANUSCRIPT In the following section, the last pillar of this research, the Design Science Research methodology is presented. 3.
DESIGN SCIENCE RESEARCH METHODOLOGY
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Design Science Research Methodology (DSRM) gives support to prescriptive research – where artefacts* are proposed to solve scientific problems (Van Aken, 2004). Figure 11 shows the iterative procedure to develop the RSND framework. The input is problem-centred: current supply chains produce unintended negative effects, as they were designed from an anthropocentric view, using disciplinary approaches.
Figure 11 - Research Methodology. Source: adapted from Dekkers (2017); Peffers et al. (2007).
A cause for the problem is identified: the lack of a biocentric, transdisciplinary approach. The supply chain design process is focused on fulfilling a primary function deployed from stakeholders’ needs, with activities and processes defined according to these requirements. The need for TR is evaluated answering three questions pointed in Hadorn et al. (2008, p. 34): •
•
Knowledge about a societally relevant problem field is uncertain: There are only a few studies aimed specifically at understanding how interactions occurring within the supply networks, or between the supply networks and the surroundings, influence environmental degeneration; Concrete nature of problems is disputed: There are a considerable number of solutions that improves the supply chain environmental performance: e.g., (i) improving eco-efficiency through different optimization techniques, (ii) proposing a variety of environmental indicators, weighting techniques and modelling paradigms and (iii), defining resilience with a multitude of dimensions, quantifying it
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•
through different analytical models, e.g., maximum entropy principle. Still, environmental degradation is still increasing, which suggests that the concrete nature of the problem requires a different design approach; There is a great deal at stake for those concerned by problems: Actors involved within the network are impacted, the surroundings ecosystems interacting with it, neighbouring companies and neighbourhoods, in different levels of scale.
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The objective for a solution to this problem requires that, first, the characteristics of the Regenerative Supply Network are identified. Second, the SN design process must begin from a biocentric perspective, which requires a formal definition for the Regenerative Supply Network Design process. Different concepts related with the artefact design process are assessed and merged accordingly. The Regenerative Supply Network is outlined in Table 2, based in Kruczek et al. (2012). Transdisciplinarity principles of purpose and “the common good” drive the beginning of the design process. From the biocentric perspective, the common good is environmental regeneration, the purpose of the regenerative SN design process. Network resilience is deployed to make the SN cope with disturbances and endure, inducing environmental benefits in the long term. The main idea is that the SN contributes with the reversion of environmental degradation identified in the surroundings where it operates. The SN is focused in “doing the right things right”, i.e., combining eco-effectiveness and eco-efficiency approaches, maximizing benefits and minimizing eventual environmental impacts. Eco-effective design approaches are biomimetics, upcycle design, and RESOLVE from Circular Economy. The supply network type is based in closed-loop networks formed through collaborative relationships and CE, regenerative by definition. The waste management of the Regenerative SN is performed following the 6R hierarchy, ranked by priority: reduce, reuse, recycle, recover, redesign and remanufacture. Finally, Key Process Indicator (KPIs) should be selected according to the regeneration type, system inputs and outputs and interactions between the SN and the surroundings. The KPIs should reflect the gains achieved in the sustainability dimensions, indicating positive results.
ACCEPTED MANUSCRIPT Table 2 – Regenerative Supply Network Outline.
Concepts
General Purpose Main Idea
Supply Chain Type
Closed-loop Networks; Circular Economy
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Design approach
Restore/Regenerate the environment Identify environmental degradation and help revert it Doing the right things right Biomimetics / Upcycle / RESOLVE
Focus
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Regenerative Transdisciplinarity; Network Resilience
Waste Management
6R
KPI (Key Process Indicator)
Context-related – Stressing benefits achieved
Comments A more effective way of addressing problems – resilience allows the SN to cope with disruptions* and endure the “common good” proposed in this research Understanding the SESs’ regeneration needs that match the SN function Using eco-effective approch to define flows, and eco-efficiency to optimize it Eco-effective flows can be designed through these design techniques Supply Networks instead of supply chains; CE is regenerative by definition Priorities for end-of-life destination: reduce, reuse, recycle, recover, redesign, remanufacture. Selected Indicator(s) must communicate the gains in sustainability dimensions
The following activity was to define a stepwise sequence for the design process, based in the diagram in Figure 9. The design process should start with the activity of defining which regenerative purpose the SN will contribute. Such definition impacts the SN’s input and output flows, demanding their redesign – what could be accomplished through one of the design approaches listed in Table 2. From the flows redesign, a new SN must be conceptualized, understanding its interactions with the surroundings and ensuring resilience features. From the perspective of an SES, an STS interacting with it is influencing e.g. its ecological configuration (crops land use) and its users (transportation vehicles * emissions), while the SES provides resources and ecosystem services – e.g. water supply. The conceptual system can be converted in a mathematical problem, that can be optimized in terms of economic, environmental and social performance. The optimization model can output multiple network configurations, each one with a different performance balance among the three dimensions. One network configuration may be selected for implementation. While the network
ACCEPTED MANUSCRIPT is operational, opportunities to expand the regeneration activities may arrive, which requires updating the system’s concept, and therefore a repetition of the design process cycle from that activity onwards.
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The design procedure is iterative, i.e., it can be repeated until coherence is achieved among the problem description, the objectives defined for the problem (e.g. should more disciplines be included?) and the designed artefact – does it effectively implement the solution objectives? When these questions have been properly answered, the artefact is ready. RESULTS
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The Regenerative Supply Network Design definition follows, merging the concepts of supply chain design (Melnyk et al., 2014), Sustainable Supply Network Design (Bals and Tate, 2018), regenerative design (Mang and Reed, 2012), supply networks (Braziotis et al., 2013), systems approach (Ackoff, 1994) and resilience (Chopra and Khanna, 2014): “from a regenerative purpose, identifying the strategic outcomes for the supply network and developing, implementing, and managing the resources, processes, interactions and collaborative relationships (along the network and with the socio-ecological wholes) in the long term, ensuring optimal functionality, environmental, social and economic feasibility, while adapting to system disruptions.”
The regenerative purpose is a starting point for the definition, stressing the shift for biocentrism. Systems thinking is addressed with interactions, networks and socio-ecological wholes. Collaboration is featured as means to establish value-oriented relationships (Seuring, 2013). Optimal functionality calls for operational efficiency improvement, while sustainability is defined by the longterm view and the environmental, social and economic dimensions. Last, resilience is represented by adaptability - to cope with disturbances while retaining its function. The RSND framework prescribes six steps, according to Figure 12: SES description and purpose identification, Redesign Outputs, SN Conceptualization, Optimize Performance, Choose Network Configuration and Implement Network. The design process is recursive; one could come back to any previous stage, if required. In stages one to three, indicators are chosen according to the purpose defined, the inputs and outputs types and SN
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characteristics. They are used during stage four – network performance optimization.
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Figure 12 - The Regenerative Supply Network Design Framework.
In the first step, a purpose related with environmental regeneration is identified; the process begins from a biocentric perspective. The surroundings are understood through the SES view. Environmental pressures, ecological configuration (e.g. how is the biodiversity of the SES?), resource units (renewable resources available?), ecosystem services* (water supply), wants and needs of its users and local legislation and policies are assessed, searching for opportunities in which the network can contribute. Other guidelines can be consulted during this analysis - e.g. the Sustainable Development Goals (see Folke et al., 2016), Greenhouse gases emissions, ocean contamination -, until a regenerative purpose that matches the network function is found. The SN will engage on reverting the environmental damages linked with the purpose defined. In Step 2, SN inputs and outputs are redefined using the eco-effective approach, through biomimicry, upcycle, Industrial Symbiosis*, or the RESOLVE. Product outputs can be transformed in service outputs, in a product-service system* (PSS) configuration. In the case of e.g. ocean plastic
ACCEPTED MANUSCRIPT contamination, replacing minerals by plastic collected as the raw material used for housing construction could be a redesign strategy adopted.
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Inputs and outputs redesign also implies on redefining the processing technology required within the value chain. Selection of manufacturing processes is performed, again with the eco-effective approach, seeking for the most environmental-friendly processes available, and indicators are selected to monitor their sustainable performance. Such transformation will require investment, which can be optimized in Step 4.
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In Step 3, the supply network system is approached as a Socio-Technical System, as depicted in Figure 13. In the inner dashed rectangle to the left, the Social System is composed of the Network Structure – i.e., the configuration of active and inactive members and flows directions among these members* -, and the People, representing the stakeholders involved in the network – e.g. suppliers, customers, transporters.
Figure 13 – Interactions model among the regenerative SN and surrounding SESs. Source: based in Behdani (2013); Bostrom and Heinen (1977); Oosthuizen and Pretorius (2016); Smith and Stirling (2010).
In the dashed rectangle to the right is the Technical System, composed of the Physical System – i.e., goods being transported, hardware used for production processes, software (or soft systems) used for management and control, and
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facilities like plants, warehouses. Function is performed by the supply network through realization of tasks. Arrows linking elements again symbolizes interactions among them; e.g., a certain function will imply in tasks that influences the type of companies involved in the network, which in turn impacts the network configuration and the hardware used in the production process.
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Figure 13Error! Reference source not found. also illustrates the interaction between the supply network and the multiple, surroundings SESs. In this case, the SN absorbs damage performed to SES #3, which ecosystem resilience is compromised, while affecting it through, e.g., transportation emissions. The SN is also nestled within SES #2, performing tasks that regenerate that system. The SN also impacts SES #1, which preserves its resilience. Such impacts should be minimized, and the SES #1 resilience should be monitored.
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Network resilience principles are used in this step to define requirements, e.g., will there be redundant suppliers and/or customers, multiple inputs and outputs, how much reserve capacity the production processes must feature. The output of Step 3 is a conceptual model, in the form of e.g. a flow diagram, which will be transformed in an optimization model. In Step 4, a mathematical model is developed based in the conceptual model, framed as an optimization problem to be solved – e.g. location-allocation or vehicle routing problem. System features are translated into sets, parameters, variables and equations. Equations can be defined from eco-efficiency, ecoeffectiveness and resilience: e.g., an objective function can minimize environmental impact, a constraint can define a percentage of waste used as an input, or reserve capacity value can be established. The problem is solved through a computer optimization model using Operations Research techniques. Network configurations are prescribed, featuring optimal economic, social, and environmental performances, measured with indicators previously defined. The network configurations define which suppliers are actively engaged, which are inactive, and material flows exchanged between companies. Step 5, Choose Configuration, is about the decision-making process for a network configuration. Economic, social, environmental performance and resilience are considered. Insights on the most suitable configuration are
ACCEPTED MANUSCRIPT drawn by stakeholders. Multi-criteria Decision Analysis* (MCDA) techniques like weighting are used, defining priorities among the indicators, and one configuration is chosen for implementation. Any network configuration that does not fulfil the SN purpose should be excluded from the decision process.
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Last step, Implementation, is where the selected configuration of the SN is put in place and initiates its operation. Many changes on the design definitions occur during this stage, which normally alters the configuration proposed (Slack et al., 2007, p. 290). One must guarantee that the implementation step does not alters the proposed design, keeping track of its features and monitoring performance. During its operation, organizations within the network must keep track of opportunities to improve regeneration. DISCUSSION
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In this section, the implications of the findings of this research are discussed. It is assumed that these implications hold if SNs are (re)designed according to this framework and implemented accordingly. This paper explores the socio-ecological intergradation defined by Gruner and Power (2017), proposing a design definition where the supply network is approached as a Socio-Technical System, the common good, considered as environmental regeneration and the starting point for the design process. The RSND framework adds on the scientific knowledge aimed at supporting practitioners (Van Aken, 2004): through its adoption, companies engage in reverting degradation and restoring ecosystems, realising the transformation from the business-oriented, anthropocentric approach towards a systemoriented, biocentric approach in the path towards regenerative development. The communication of positives instead of negatives – “the glass is half full” – increases motivation along the stakeholders involved (McDonough and Braungart, 2013, p. 214). Defining context-based indicators to measure the performance enhances such communication: if the purpose is linked with e.g. Greenhouse Gases (GHG) emissions, using net GHG (avoided minus emitted GHG by the system) gives a perspective of the gains achieved with the network configuration proposed. Using the framework as guidance to the design process does not completely mitigate the unintended effects. However, if the purpose is biocentric and regenerative, these unintended effects are probably beneficial – “celebrate your emissions” (McDonough and Braungart, 2013, p. 217). For example, if
ACCEPTED MANUSCRIPT landfills are recovered and transformed into recreational parks with the purposed of capturing GHG, the quality of life of the neighbours is also improved (Simis et al., 2016), as well as the quality of underground water (Danthurebandara et al., 2015), which in turn restores ecosystem’s resilience.
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Framing a SN around the three sustainable dimensions shift the primary focus on economic performance, meaning that profit will probably be compromised in favour of environmental and social performance. This can be regarded as a strong evidence of the shift from anthropocentrism to biocentrism, where the meaning of value is transformed. The role of optimization is fundamental to achieve network configurations that deliver a sound, sustainable performance, handling trade-offs between the three sustainable dimensions (Seuring, 2013).
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The framework enable desirable and conflicting design features: there is evidence from Fahimnia and Jabbarzadeh (2016) that environmental and social performance are not harmed by improving resilience. De Souza et al. (Unpublished) argue that resilience may be harmed if optimization is focused on environmental impacts minimization. Eco-efficiency is regarded as in opposition with eco-effectiveness: managing and balancing the trade-offs among these conflicting features is key to achieve high levels of performance in the SN. Mapping interactions between the SN (framed as a STS) with its surroundings - framed as SESs -, allow for a more integrative design, as the problem and its solutions can be understood and evaluated in multiple levels, in a similar proposition as in Joore and Brezet (2015). The synthetic approach allows for an improved harmonious fitting of the SN within the environments it is nestled. A regenerative purpose means that the system feeds itself from the SES and returning something back to it in retribution, similar to commensalism, realising the integration proposed in Smith and Stirling (2010). 6.
CONCLUSIONS
This research proposes a regenerative supply network design process based in three pillars: Regenerative Development, Transdisciplinarity and Systems Approach, and Design and Social Sciences. A definition for RSND is proposed, and a design framework is approached as an artefact and developed using DSR methodology. The framework consists of six steps, guiding the design process of supply networks that fulfil its function and engage on environmental regeneration.
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The supply network is approached as a Socio-Technical System that interacts with Socio-Ecological Systems, depicted from the perspective of environmental degradation and regeneration. Eco-effectiveness and ecoefficiency approaches are harmonized, and Resilience is deployed during the network conceptualization. After the SN is modelled as an optimization problem and solved, a network configuration is selected using MCDA and implemented. During its operation, opportunities to enhance regeneration may appear and should be evaluated, which could lead the design cycle back to a system conceptualization phase.
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The contributions of this paper can be framed within TR knowledge types. Understanding the current situation and sustainable performance of an operational SNs and the resilience state and of its surrounding environment contributes for the Systems Knowledge. The definition of a regeneration purpose for the SNs is a contribution in Target Knowledge. The target situation is based in the common good, where the SN plays a role in environmental regeneration and ecosystem’s resilience improvement, merging with the SESs it interacts with. The Transition Knowledge can be addressed through understanding the interacting SESs dynamic behaviour and addressing it during the design process, enhancing system’s adaptability. The research implications are limited as the DSR methodology was only partially followed. Demonstration and Evaluation activities have been overlooked, as the aim of this research was solely to propose a design framework. Future research could be aimed at performing these activities, improving the framework level of detail, exploring it through real case studies to understand other implications from its adoption for SN design, while monitoring the ecosystems that they intend to regenerate and if any unintended effects generated are positive. ACKNOWLEDGEMENTS This research was supported by the Coordination of Superior Level Staff Improvement – CAPES (grant number 88881.134782/2016-01) and by the Operations Research and Logistics research group from Wageningen University and Research. We also would like to acknowledge the Federal University of Technology – Campus Cornélio Procópio. REFERENCES Ackoff, R., 1999. A Lifetime of Systems Thinking. Syst. Thinker 10, 1–4.
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ACCEPTED MANUSCRIPT HIGHLIGHTS
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A definition for the Regenerative Supply Network Design is proposed; A six-step framework is proposed to design Regenerative Supply Networks. Environmental Regeneration is achievable through Transdisciplinary Research; Socio-Technical and -Ecological System views can be merged via Biocentric Design.
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S0959-6526(19)30577-3
DOI:
https://doi.org/10.1016/j.jclepro.2019.02.178
Reference:
JCLP 15908
To appear in:
Journal of Cleaner Production
Received Date: 1 March 2018 Revised Date:
19 October 2018
Accepted Date: 16 February 2019
Please cite this article as: de Souza V, Bloemhof-Ruwaard J, Borsato M, Towards Regenerative Supply Networks: A design framework proposal, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.02.178. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Towards Regenerative Supply Networks: a design framework proposal 1,*
2
Vitor de Souza , Jacqueline Bloemhof-Ruwaard and Milton Borsato 1
1
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Graduate School of Mechanical Engineering and Materials (PPGEM), Federal University of Technology - Parana (UTFPR), Campus Curitiba, Brazil. 2 Operations Research and Logistics Group, Wageningen University and Research, The Netherlands. *Corresponding author: [email protected]
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ACCEPTED MANUSCRIPT Word count: 7774 words. Keywords: regenerative development, transdisciplinary research, systems approach, sustainable supply network design, resilience.
1.
INTRODUCTION
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Industrial activity have provided humanity with wealth levels like never seen before in history (Ellen MacArthur Foundation, 2015; Kersley and Stierli, 2015), but not without severe consequences to the environment and society. Together, industrial processes and fossil fuel account for 65% of the Global Greenhouse Gas Emissions (IPCC, 2014). Downey and Willigen (2005) stated that mental health of neighbours surrounding industries was negatively impacted, while Turner (2014) reinforced the predictions of a societal collapse warned in Meadows et al. (1972). Sustainable Supply Network Design (SSND) is a research stream that has been transforming with the evolution of sustainability approaches. Eco,1 efficiency* (Verfaillie and Bidwell, 2000), is among the first approaches used to improve supply chain sustainability. As a business-oriented approach (Dyllick and Hockerts, 2002; Young and Tilley, 2006), it was focused on decreasing environmental impact, i.e., even with companies adopting such strategy, environmental problems continued to worsen (Hauschild, 2015; Turner, 2014). From another perspective, discipline-based solutions imply on unintended side effects, because sustainability represents a complex challenge that can hardly be tackled by a single discipline (Mauser et al., 2013; Sahamie et al., 2013). More systemic approaches arose evolving from inter-, multi-disciplinary towards transdisciplinary research, avoiding two problems observed in traditional, reductionist research (Ackoff, 1999): a) Taking separate parts and improving them separately will not result in the improvement of the whole; b) Problems are not disciplinary in nature: “effective research is transdisciplinary”. Transdisciplinary Research (TR) is a way forward to address the problem of sustainability, reaching “the common good” (Bergendahl et al., 2018; Brandt et 1
The concepts with an asterisk are described in the glossary of terms in Appendix I.
ACCEPTED MANUSCRIPT al., 2013; Sahamie et al., 2013), which can be interpreted in different ways (Hadorn et al., 2008). It should be applied when (i) there is not enough reliable knowledge about the problem, (ii) there is dispute over which practices must be transformed and (iii) solutions proposed shall have a profound impact in the whole society (Hadorn et al., 2006).
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In line with these theories, the eco-effectiveness* approach (Braungart et al., 2007; Carrillo-Hermosilla et al., 2010) focus on doing the right things for the environment, i.e., increasing environmental benefit, or regenerating ecosystems. Concepts like biomimetics* and the upcycle* (McDonough and Braungart, 2013) – “waste equals food” -, Industrial Symbiosis* (Chertow, 2000; Lombardi and Laybourn, 2012), Circular Economy (Ellen MacArthur Foundation, 2015) and Biobased Economy* (Lopes, 2015) – derived from this approach. In the field of SSND, Gruner and Power (2017) established theory grounding for the integration of resilient supply chains (SCs) with the environment. Banasik et al. (2016) applied optimization techniques to a closed-loop supply chain* of mushrooms production that reuses waste. Bergendahl et al. (2018) approached the food-water-energy nexus* with TR to enhance SC sustainability, investigating the impact on its relationships. Fahimnia and Jabbarzadeh (2016) also used mathematical modelling to integrate sustainability and resilience in SSND, investigating the trade-offs in performance. However, a SN design framework based in biocentrism*, regeneration, TR and optimization cannot be found in the literature. This paper aims to fill this gap by proposing a definition and a framework for the Regenerative Supply Network Design (RSND) process, grounded in Regenerative Development, Transdisciplinarity, Systems Thinking and Social and Design Sciences. The RSND framework is approached as an artefact* and developed using the Design Science Research Methodology. Through the RSND framework, a supply network can be designed focusing on environmental regeneration while economic feasibility is assured through network optimization. The design framework consists of six steps, where (i) the network surroundings are depicted as a Socio-Ecological System (SES), and a regenerative purpose is defined. With an eco-effective approach and based in Circular Economy, (ii) inputs and outputs are redesigned. (iii) The supply network is conceptualized as a Socio-Technical System (STS): its interactions
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with the surroundings are mapped and resilience is addressed, enhancing its regenerative effects through withstanding perturbations. In step (iv), the performance of the supply network is optimized and in (v), the most suitable network configuration is chosen. Finally, in step (vi), Implementation, the network becomes operational, fulfilling its function and regenerating the environment.
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The remainder of this article is organized as follows: Section 2 presents a literature review; in Section 3, methodological procedures are described. Section 4 includes the RSND definition and the framework. In Sections 5 and 6, discussions and conclusions are provided. 2.
LITERATURE REVIEW
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In this section, the three pillars of this research are presented, and Figure 1 illustrates the links between these pillars. First, Transdisciplinary Research, systems approach and the Socio-Technical and Socio-Ecological Systems views are introduced. The concept of Regenerative Development is disclosed, followed by a brief description of Circular Economy. From Social Sciences, Sustainable Supply Chain Design is reviewed, followed by the more systemic approach of Supply Networks and Resilience. Finally, Design Science Research, the third pillar, is the research methodology used, described in Section 0.
Figure 1 - Research Foundations.
2.1. Transdisciplinary Research
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Transdisciplinarity is defined in Bergendahl et al. (2018) as “the incorporation of a broad set of scientific and policy disciplines, including industries and actors, for addressing broad and complex problems, e.g. sustainability.” Transdisciplinary Research (TR) allows sustainability to becoming a concrete realization, rather then just a far objective (Brandt et al., 2013). TR is recognized as appropriate to:
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a) “grasp the complexity of problems; * b) take into account the diversity of life-world and scientific perceptions of problems; c) link abstract and case-specific knowledge, and; d) develop knowledge and practices that promote what is perceived to be the common good.” (Pohl and Hirsch Hadorn, 2007, p. 20).
Figure 2 – Disciplinary and Transdisciplinary Research compared. Source: adapted from Hadorn et al. (2006).
TR is regarded as a more integrative research approach to tackle the tremendous challenges ahead of a sustainable future (Mauser et al., 2013), that cannot be addressed by any discipline on its own (Brandt et al., 2013). Figure 2 summarizes the difference between disciplinary (a) and transdisciplinary research (b). In the left part, examples of scientific disciplines
ACCEPTED MANUSCRIPT are listed; at the centre, some of the current problem fields* faced by mankind; to the right, actors that deal with these problems in a daily basis.
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Disciplines are important in the search for solutions in any problem field; that is represented by the arrows in (a). However, arrows’ tips do not come close to the problem fields, due to the inherent impossibility of grasping a problem’s entire complexity (Hadorn et al., 2008, p. 34). Actors are not directly connected, although they have expectations about the research outcomes, especially when they provide these researches with funding and support.
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In (b), transdisciplinary research surpasses the boundaries of disciplines, merging knowledge bases in a less defined shape. Both scientific and practical knowledge can be applied towards a problem field – e.g., diseases –, what is represented by the dashed arrows from all the disciplines and actors in the list. Initially, any discipline or actor can be a source of knowledge for a certain problem field; the brackets represent their integration (Hadorn et al., 2008).
Figure 3 - Types of knowledge associated with TR. Source: Hadorn et al. (2008, p. 59).
Figure 3 represents the three types of knowledge produced with TR. Systems Knowledge is acquired when systems are investigated on their current function and behaviour – through theory-driven research; Target Knowledge, when the ideal future situation is proposed. Action (Transformation) Knowledge is achieved when the research focus is on the transformation of existing practices or the introduction of new ones within the technical, social and cultural dimensions (Hadorn et al., 2008). Target and Transformation knowledge are acquired with problem-driven research.
ACCEPTED MANUSCRIPT Transdisciplinary Research requires systems approach to be fully understood and inplemented (Jantsch, 1972), reviewed in the following subsection. 2.2. Systems Approach and the Socio-Technical and Socio-Ecological views
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Systems Approach is based in General Systems Theory, initially proposed in disciplines like biology (von Bertalanffy, 1950) and social sciences (Boulding, 1956). It was advocated as a structural framework where disciplines and information could be fitted within its structure, shaping a body of knowledge (Boulding, 1956).
Socio-Technical Systems were defined in Bostrom and Heinen (1977), and are structured in four interdependent, interacting elements. Structure and People (forming the Social System) and Technology and Tasks – that together form the Technical System. Figure 4 presents this structure, aimed at supporting designers to consider these aspects and dimensions during the design process. Two-way arrows symbolize interactions among elements.
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Ackoff (1994) stated that one should focus in the interactions among elements within a system, and among systems. There are two schools of thought to depict societal systems, both featuring complex, dynamic, multi-scale and adaptive* properties (Smith and Stirling, 2010): the Socio-Technical Systems (STS) and the Socio-Ecological Systems (SES).
Figure 4 - A depiction of a Socio-Technical System. Adapted from Bostrom and Heinen (1977).
The Socio-Ecological System view, is composed of a ‘bio-geo-physical’ unit and the actors and institutions related with it (Glaser et al., 2008). They are delimited by spatial boundaries – not too small that no detail is perceived neither too big that could hide its emergent* properties (Ostrom, 2009).
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Figure 5 presents the SES framework as a unit of analysis. It is composed of Resource Units (e.g. lobsters), Resource Systems (a lake), Users (fishermen) and Governance System (organizations and rules governing fishing) (Ostrom, 2009). Again, two-way arrows describe elements interacting, while the system interacts with other ecosystems, and social, economic and political settings.
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Figure 5 - The Socio-Ecological System Framework. Source: Ostrom (2009).
One of the primary concerns of the SES view is Resilience, defined as “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.” (Walker et al., 2004). The SES shall not be explored in such a way it cannot recover, with the risk of becoming damaged permanently. SES and STS can be understood as different perspectives of the same system. Smith and Stirling (2010) argues that the main differences between both views is that (i) SES considers technology as an exogenous factor, as it already entails enough complexity from ecological and social systems; (ii) SES view is place-bounded, while a STS can extend itself through more than one location (Smith and Stirling, 2010). Figure 6 represents the evolution of the integration of systems using the SES and STS views. In the top left, the SES is in a central perspective, merging with the STSs it nestles, and all systems that are resilient transform into a new, alternate state. In the bottom right, the STS is in a central perspective, and it interacts with multiple SESs – as is the case of a supply network -, and they merge into a transformed, more sustainable system.
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Figure 6 – integration between SES and STS. Source: adapted from Smith and Stirling (2010).
Such integration is supported by regenerative development, defined in this research as “the common good” - reviewed in the following subsection. 2.3. Regenerative Development The path towards environmental regeneration is defined by the transition from anthropocentrism – which is degenerating the environment -, to biocentrism, as represented in Figure 7 (Mang and Reed, 2012). As human consciousness gradually integrates with nature, it evolves from indiscriminately using resources to their efficient use and resource conservation. Anthropocentric efforts to reduce degeneration like eco-efficiency* - “doing things right” for the environment (Drucker, 1995, p. 33) are business-oriented (Young and Tilley, 2006). As such, they generate rebound-effects, where gains are likely to be lost by increased consumption – e.g., using more frequently an electrical car because it is electric (Bjørn and Hauschild, 2013). With a shift to biocentrism, nature affiliation (“biophilia”) begins, then evolves to mimic nature (biomimetics), restore nature, tend nature and finally, be nature – achieved through regenerative design and development, respectively (Mang and Reed, 2012).
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Figure 7 – The path towards Regenerative Development. Source: Mang and Reed (2012).
The regenerating stage is marked by nature-oriented approaches like ecoeffectiveness, i.e., to “do the right thing” for the environment, which can be defined by the degree of regeneration achieved with the desired. This relation is represented in Equation Error! Reference source not found.Error! Reference source not found., based on Enright (2012).
(1)
Different level of progress can be observed in each country or society. Developed countries are entering the regenerating phase (Bosman and Rotmans, 2016), while the less developed and underdeveloped countries are still striving to reduce degeneration. In both cases, economic development and environmental degradation are yet to be completely decoupled, due to e.g.
ACCEPTED MANUSCRIPT technological and/or economical restraints. Countermeasures like environmental impact minimization are still required until full integration between humans and nature is achieved. Based in eco-efficiency, ecoeffectiveness and regenerative development, the Circular Economy system is reviewed in the following section.
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2.4. Circular Economy
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Intensively explored by the scientific community (Su et al., 2013), the Circular Economy (CE) has its origins in Pearce and Turner (1990), featuring multidisciplinarity, eco-efficiency, cradle-to-cradle* design (Braungart, McDonough, & Bollinger, 2007) and industrial ecology (Peck, 1996). A recent definition was proposed by Geissdoerfer et al. (2017): “Circular Economy is a regenerative system in which resource input and waste, emission, and energy leakage are minimised by slowing, closing, and narrowing material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling.”
The degenerative open-loop, Linear Economy system of take, make and dispose must evolve to a closed-loop, regenerative, Circular Economy (Geissdoerfer et al., 2017). CE is based in the RESOLVE framework, which stands for REgenerate (shift to renewables, restore ecosystems), Share (assets, prolong lifecycle), Optimise (increase performance/efficiency), Loop (closed-loop), Virtualise (dematerialise) and Exchange (Ellen MacArthur Foundation, 2015). Figure 8 illustrates the three main principles of CE: Principle 1 - Enhance and preserve natural capital by controlling finite stocks and balancing renewable resource flows. Principle 2 - Optimise resource yields by circulating products, components and materials in the biological cycle (where organic elements circulate) and the technological products cycle. Principle 3 - Foster system effectiveness by revealing and designing out negative externalities*, regarded as “leakages” of the closed-loop system.
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Figure 8 – An outline of the Circular Economy. Source: Ellen MacArthur Foundation (2015).
The Circular Economy has been largely implemented through resarch on Sustainable Supply Chain Design, reviewed in the following topic. 2.5. Sustainable Supply Chain Design From the theory of Logistics Management, Bloemhof-Ruwaard (2015) defined a stepwise approach to develop zero-waste, zero-emissions supply chains without harming economic prosperity – represented in Figure 9. The approach is shaped around three phases: Assessment, where the supply chain’s sustainability level is determined; Evaluation, focused on benchmarking the desired level to be achieved, and Improving, where the current supply chain is redesigned, moving it from the current to the desired state, balancing environmental, social and economic performances.
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Figure 9 - The Sustainable Logistics Management Approach. Source: BloemhofRuwaard (2015).
Sustainable principles have been incorporated in the Supply Chain Design process (Eskandarpour et al., 2015). Table 1 describes the evolution of the sustainable supply chain design field (Kruczek et al., 2012). The adoption of recycling processes that reintroduce waste flows back into the production chain originated the Green Supply Chains, improving ecological efficiency and reducing pollution – a “single-objective”. Their performance was measured with CO2 emissions, or eco-indicator*.
With the introduction of eco-efficiency approach, the focus shifted towards improving the environmental performance of the chain while keeping profit levels maximized (“multi-objective”). This supply chain also generates less waste, “doing things right” for the environment through recycling processes under the linear design paradigm, cradle-to-grave. The latest, more evolved Supply Chain is based in the eco-effectiveness approach: closed loops are an inherent feature of the chain, which purpose is related with product lifecycle and cradle-to cradle design – doing the right things. Waste management is performed for upcycling, where a small amount of energy is required to reintroduce the waste in the production chain. Table 1 - Evolution of Sustainable Supply Chains. Adapted from Kruczek et al. (2012). Green/ Sustainable
Eco-efficient
Eco-effective
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Concepts General Purpose
Focus
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Design approach
Supply Chain Type
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Waste Management KPI (Key Process Indicator)
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Eco-efficient
Eco-effective
Eco-efficiency
Eco-effectiveness
Improving Ecological and economic efficiency zero waste emission, zero resource use and zero toxicity Doing things right
Improving product in life cycle from cradle to cradle (C2C) Quality in life cycle
Cradle to grave design
Cradle to Cradle design
Open and closed loop supply chain Recycling
Open and closed loop supply chain
Only closed loop supply chain
Recycling
Upcycling
CO2, ecoindicator, etc.
Integrated economic and environmental indicators
Indicators in life cycle from cradle to cradle (C2C)
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Main Idea
Green/ Sustainable Environmental Protection Improving Ecological Efficiency Pollution prevention Sustainable development Eco-design
Doing the right things
A summary of the findings from recent literature can be found in Appendix II, where the main concepts used in each research and a brief description of the main findings are described. The adoption of a systemic view over the Supply Chain evolved into the Supply Network, accounting also for its resilience. This subject is reviewed in the following subsection. 2.6. Supply Networks and Resilience The increasing complexity of Supply Chains demands a more appropriate approach than linear chains: the complex networks approach (Christopher and Peck, 2004). An organization should manage both the active and the inactive members of the system, characterizing the Supply Network (SN), defined in Braziotis et al. (2013): “[. . .] a set of active members within an organisation’s supply chains, as well as inactive members to which an organisation relates, that can be called upon to actively contribute to a supply chain if a need arises.”
Figure 10 illustrates the difference between active members – nodes connected with solid lines -, and inactive members - the nodes connected with
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dashed lines. The supply chain formed by the active members is highlighted in the central square, with the outer circle encompassing the supply network.
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Figure 10 - the Supply Chain and the Supply Network. Source: Braziotis et al. (2013).
Supply networks can be framed as Socio-Technical Systems (Behdani, 2013), i.e., as “complex physical-technical systems and a network of interdependent actors” (De Bruijn and Herder, 2009). A supply network is composed of, e.g., facilities, reprocessing companies, transporters, which are nested sociotechnical subsystems, interrelated in social networks. Overall, its behaviour is an outcome of the interactions within the networks and the interactions and interdependencies among systems, which influences, among other characteristics, their adaptiveness – the ability to change behaviour (Behdani, 2013, p. 89-93), i.e., to handle disruptions. Christopher and Peck (2004) grouped disruptions in three types: internal to the firm (related production processes and control), external to the firm but within the network (caused by variation in demand and/or supply) and external to the network (from environmental causes). They defined the resilient supply network from four general principles: (i) resilience should be designed in; (ii) corporations involved in the network must collaborate, (iii) a network must be agile and (iv) a risk management culture should be fostered. In the business and management context, Fiksel (2003) contributed with the fundamental concepts for the design of resilient supply chains. Pettit et al. (2013) mapped the vulnerability (e.g. resource limits, external pressures) and capability factors (e.g. efficiency, adaptability) influencing enterprise’s resilience.
ACCEPTED MANUSCRIPT In the following section, the last pillar of this research, the Design Science Research methodology is presented. 3.
DESIGN SCIENCE RESEARCH METHODOLOGY
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Design Science Research Methodology (DSRM) gives support to prescriptive research – where artefacts* are proposed to solve scientific problems (Van Aken, 2004). Figure 11 shows the iterative procedure to develop the RSND framework. The input is problem-centred: current supply chains produce unintended negative effects, as they were designed from an anthropocentric view, using disciplinary approaches.
Figure 11 - Research Methodology. Source: adapted from Dekkers (2017); Peffers et al. (2007).
A cause for the problem is identified: the lack of a biocentric, transdisciplinary approach. The supply chain design process is focused on fulfilling a primary function deployed from stakeholders’ needs, with activities and processes defined according to these requirements. The need for TR is evaluated answering three questions pointed in Hadorn et al. (2008, p. 34): •
•
Knowledge about a societally relevant problem field is uncertain: There are only a few studies aimed specifically at understanding how interactions occurring within the supply networks, or between the supply networks and the surroundings, influence environmental degeneration; Concrete nature of problems is disputed: There are a considerable number of solutions that improves the supply chain environmental performance: e.g., (i) improving eco-efficiency through different optimization techniques, (ii) proposing a variety of environmental indicators, weighting techniques and modelling paradigms and (iii), defining resilience with a multitude of dimensions, quantifying it
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•
through different analytical models, e.g., maximum entropy principle. Still, environmental degradation is still increasing, which suggests that the concrete nature of the problem requires a different design approach; There is a great deal at stake for those concerned by problems: Actors involved within the network are impacted, the surroundings ecosystems interacting with it, neighbouring companies and neighbourhoods, in different levels of scale.
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The objective for a solution to this problem requires that, first, the characteristics of the Regenerative Supply Network are identified. Second, the SN design process must begin from a biocentric perspective, which requires a formal definition for the Regenerative Supply Network Design process. Different concepts related with the artefact design process are assessed and merged accordingly. The Regenerative Supply Network is outlined in Table 2, based in Kruczek et al. (2012). Transdisciplinarity principles of purpose and “the common good” drive the beginning of the design process. From the biocentric perspective, the common good is environmental regeneration, the purpose of the regenerative SN design process. Network resilience is deployed to make the SN cope with disturbances and endure, inducing environmental benefits in the long term. The main idea is that the SN contributes with the reversion of environmental degradation identified in the surroundings where it operates. The SN is focused in “doing the right things right”, i.e., combining eco-effectiveness and eco-efficiency approaches, maximizing benefits and minimizing eventual environmental impacts. Eco-effective design approaches are biomimetics, upcycle design, and RESOLVE from Circular Economy. The supply network type is based in closed-loop networks formed through collaborative relationships and CE, regenerative by definition. The waste management of the Regenerative SN is performed following the 6R hierarchy, ranked by priority: reduce, reuse, recycle, recover, redesign and remanufacture. Finally, Key Process Indicator (KPIs) should be selected according to the regeneration type, system inputs and outputs and interactions between the SN and the surroundings. The KPIs should reflect the gains achieved in the sustainability dimensions, indicating positive results.
ACCEPTED MANUSCRIPT Table 2 – Regenerative Supply Network Outline.
Concepts
General Purpose Main Idea
Supply Chain Type
Closed-loop Networks; Circular Economy
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Design approach
Restore/Regenerate the environment Identify environmental degradation and help revert it Doing the right things right Biomimetics / Upcycle / RESOLVE
Focus
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Regenerative Transdisciplinarity; Network Resilience
Waste Management
6R
KPI (Key Process Indicator)
Context-related – Stressing benefits achieved
Comments A more effective way of addressing problems – resilience allows the SN to cope with disruptions* and endure the “common good” proposed in this research Understanding the SESs’ regeneration needs that match the SN function Using eco-effective approch to define flows, and eco-efficiency to optimize it Eco-effective flows can be designed through these design techniques Supply Networks instead of supply chains; CE is regenerative by definition Priorities for end-of-life destination: reduce, reuse, recycle, recover, redesign, remanufacture. Selected Indicator(s) must communicate the gains in sustainability dimensions
The following activity was to define a stepwise sequence for the design process, based in the diagram in Figure 9. The design process should start with the activity of defining which regenerative purpose the SN will contribute. Such definition impacts the SN’s input and output flows, demanding their redesign – what could be accomplished through one of the design approaches listed in Table 2. From the flows redesign, a new SN must be conceptualized, understanding its interactions with the surroundings and ensuring resilience features. From the perspective of an SES, an STS interacting with it is influencing e.g. its ecological configuration (crops land use) and its users (transportation vehicles * emissions), while the SES provides resources and ecosystem services – e.g. water supply. The conceptual system can be converted in a mathematical problem, that can be optimized in terms of economic, environmental and social performance. The optimization model can output multiple network configurations, each one with a different performance balance among the three dimensions. One network configuration may be selected for implementation. While the network
ACCEPTED MANUSCRIPT is operational, opportunities to expand the regeneration activities may arrive, which requires updating the system’s concept, and therefore a repetition of the design process cycle from that activity onwards.
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The design procedure is iterative, i.e., it can be repeated until coherence is achieved among the problem description, the objectives defined for the problem (e.g. should more disciplines be included?) and the designed artefact – does it effectively implement the solution objectives? When these questions have been properly answered, the artefact is ready. RESULTS
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The Regenerative Supply Network Design definition follows, merging the concepts of supply chain design (Melnyk et al., 2014), Sustainable Supply Network Design (Bals and Tate, 2018), regenerative design (Mang and Reed, 2012), supply networks (Braziotis et al., 2013), systems approach (Ackoff, 1994) and resilience (Chopra and Khanna, 2014): “from a regenerative purpose, identifying the strategic outcomes for the supply network and developing, implementing, and managing the resources, processes, interactions and collaborative relationships (along the network and with the socio-ecological wholes) in the long term, ensuring optimal functionality, environmental, social and economic feasibility, while adapting to system disruptions.”
The regenerative purpose is a starting point for the definition, stressing the shift for biocentrism. Systems thinking is addressed with interactions, networks and socio-ecological wholes. Collaboration is featured as means to establish value-oriented relationships (Seuring, 2013). Optimal functionality calls for operational efficiency improvement, while sustainability is defined by the longterm view and the environmental, social and economic dimensions. Last, resilience is represented by adaptability - to cope with disturbances while retaining its function. The RSND framework prescribes six steps, according to Figure 12: SES description and purpose identification, Redesign Outputs, SN Conceptualization, Optimize Performance, Choose Network Configuration and Implement Network. The design process is recursive; one could come back to any previous stage, if required. In stages one to three, indicators are chosen according to the purpose defined, the inputs and outputs types and SN
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characteristics. They are used during stage four – network performance optimization.
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Figure 12 - The Regenerative Supply Network Design Framework.
In the first step, a purpose related with environmental regeneration is identified; the process begins from a biocentric perspective. The surroundings are understood through the SES view. Environmental pressures, ecological configuration (e.g. how is the biodiversity of the SES?), resource units (renewable resources available?), ecosystem services* (water supply), wants and needs of its users and local legislation and policies are assessed, searching for opportunities in which the network can contribute. Other guidelines can be consulted during this analysis - e.g. the Sustainable Development Goals (see Folke et al., 2016), Greenhouse gases emissions, ocean contamination -, until a regenerative purpose that matches the network function is found. The SN will engage on reverting the environmental damages linked with the purpose defined. In Step 2, SN inputs and outputs are redefined using the eco-effective approach, through biomimicry, upcycle, Industrial Symbiosis*, or the RESOLVE. Product outputs can be transformed in service outputs, in a product-service system* (PSS) configuration. In the case of e.g. ocean plastic
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Inputs and outputs redesign also implies on redefining the processing technology required within the value chain. Selection of manufacturing processes is performed, again with the eco-effective approach, seeking for the most environmental-friendly processes available, and indicators are selected to monitor their sustainable performance. Such transformation will require investment, which can be optimized in Step 4.
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In Step 3, the supply network system is approached as a Socio-Technical System, as depicted in Figure 13. In the inner dashed rectangle to the left, the Social System is composed of the Network Structure – i.e., the configuration of active and inactive members and flows directions among these members* -, and the People, representing the stakeholders involved in the network – e.g. suppliers, customers, transporters.
Figure 13 – Interactions model among the regenerative SN and surrounding SESs. Source: based in Behdani (2013); Bostrom and Heinen (1977); Oosthuizen and Pretorius (2016); Smith and Stirling (2010).
In the dashed rectangle to the right is the Technical System, composed of the Physical System – i.e., goods being transported, hardware used for production processes, software (or soft systems) used for management and control, and
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facilities like plants, warehouses. Function is performed by the supply network through realization of tasks. Arrows linking elements again symbolizes interactions among them; e.g., a certain function will imply in tasks that influences the type of companies involved in the network, which in turn impacts the network configuration and the hardware used in the production process.
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Figure 13Error! Reference source not found. also illustrates the interaction between the supply network and the multiple, surroundings SESs. In this case, the SN absorbs damage performed to SES #3, which ecosystem resilience is compromised, while affecting it through, e.g., transportation emissions. The SN is also nestled within SES #2, performing tasks that regenerate that system. The SN also impacts SES #1, which preserves its resilience. Such impacts should be minimized, and the SES #1 resilience should be monitored.
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Network resilience principles are used in this step to define requirements, e.g., will there be redundant suppliers and/or customers, multiple inputs and outputs, how much reserve capacity the production processes must feature. The output of Step 3 is a conceptual model, in the form of e.g. a flow diagram, which will be transformed in an optimization model. In Step 4, a mathematical model is developed based in the conceptual model, framed as an optimization problem to be solved – e.g. location-allocation or vehicle routing problem. System features are translated into sets, parameters, variables and equations. Equations can be defined from eco-efficiency, ecoeffectiveness and resilience: e.g., an objective function can minimize environmental impact, a constraint can define a percentage of waste used as an input, or reserve capacity value can be established. The problem is solved through a computer optimization model using Operations Research techniques. Network configurations are prescribed, featuring optimal economic, social, and environmental performances, measured with indicators previously defined. The network configurations define which suppliers are actively engaged, which are inactive, and material flows exchanged between companies. Step 5, Choose Configuration, is about the decision-making process for a network configuration. Economic, social, environmental performance and resilience are considered. Insights on the most suitable configuration are
ACCEPTED MANUSCRIPT drawn by stakeholders. Multi-criteria Decision Analysis* (MCDA) techniques like weighting are used, defining priorities among the indicators, and one configuration is chosen for implementation. Any network configuration that does not fulfil the SN purpose should be excluded from the decision process.
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Last step, Implementation, is where the selected configuration of the SN is put in place and initiates its operation. Many changes on the design definitions occur during this stage, which normally alters the configuration proposed (Slack et al., 2007, p. 290). One must guarantee that the implementation step does not alters the proposed design, keeping track of its features and monitoring performance. During its operation, organizations within the network must keep track of opportunities to improve regeneration. DISCUSSION
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In this section, the implications of the findings of this research are discussed. It is assumed that these implications hold if SNs are (re)designed according to this framework and implemented accordingly. This paper explores the socio-ecological intergradation defined by Gruner and Power (2017), proposing a design definition where the supply network is approached as a Socio-Technical System, the common good, considered as environmental regeneration and the starting point for the design process. The RSND framework adds on the scientific knowledge aimed at supporting practitioners (Van Aken, 2004): through its adoption, companies engage in reverting degradation and restoring ecosystems, realising the transformation from the business-oriented, anthropocentric approach towards a systemoriented, biocentric approach in the path towards regenerative development. The communication of positives instead of negatives – “the glass is half full” – increases motivation along the stakeholders involved (McDonough and Braungart, 2013, p. 214). Defining context-based indicators to measure the performance enhances such communication: if the purpose is linked with e.g. Greenhouse Gases (GHG) emissions, using net GHG (avoided minus emitted GHG by the system) gives a perspective of the gains achieved with the network configuration proposed. Using the framework as guidance to the design process does not completely mitigate the unintended effects. However, if the purpose is biocentric and regenerative, these unintended effects are probably beneficial – “celebrate your emissions” (McDonough and Braungart, 2013, p. 217). For example, if
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Framing a SN around the three sustainable dimensions shift the primary focus on economic performance, meaning that profit will probably be compromised in favour of environmental and social performance. This can be regarded as a strong evidence of the shift from anthropocentrism to biocentrism, where the meaning of value is transformed. The role of optimization is fundamental to achieve network configurations that deliver a sound, sustainable performance, handling trade-offs between the three sustainable dimensions (Seuring, 2013).
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The framework enable desirable and conflicting design features: there is evidence from Fahimnia and Jabbarzadeh (2016) that environmental and social performance are not harmed by improving resilience. De Souza et al. (Unpublished) argue that resilience may be harmed if optimization is focused on environmental impacts minimization. Eco-efficiency is regarded as in opposition with eco-effectiveness: managing and balancing the trade-offs among these conflicting features is key to achieve high levels of performance in the SN. Mapping interactions between the SN (framed as a STS) with its surroundings - framed as SESs -, allow for a more integrative design, as the problem and its solutions can be understood and evaluated in multiple levels, in a similar proposition as in Joore and Brezet (2015). The synthetic approach allows for an improved harmonious fitting of the SN within the environments it is nestled. A regenerative purpose means that the system feeds itself from the SES and returning something back to it in retribution, similar to commensalism, realising the integration proposed in Smith and Stirling (2010). 6.
CONCLUSIONS
This research proposes a regenerative supply network design process based in three pillars: Regenerative Development, Transdisciplinarity and Systems Approach, and Design and Social Sciences. A definition for RSND is proposed, and a design framework is approached as an artefact and developed using DSR methodology. The framework consists of six steps, guiding the design process of supply networks that fulfil its function and engage on environmental regeneration.
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The supply network is approached as a Socio-Technical System that interacts with Socio-Ecological Systems, depicted from the perspective of environmental degradation and regeneration. Eco-effectiveness and ecoefficiency approaches are harmonized, and Resilience is deployed during the network conceptualization. After the SN is modelled as an optimization problem and solved, a network configuration is selected using MCDA and implemented. During its operation, opportunities to enhance regeneration may appear and should be evaluated, which could lead the design cycle back to a system conceptualization phase.
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The contributions of this paper can be framed within TR knowledge types. Understanding the current situation and sustainable performance of an operational SNs and the resilience state and of its surrounding environment contributes for the Systems Knowledge. The definition of a regeneration purpose for the SNs is a contribution in Target Knowledge. The target situation is based in the common good, where the SN plays a role in environmental regeneration and ecosystem’s resilience improvement, merging with the SESs it interacts with. The Transition Knowledge can be addressed through understanding the interacting SESs dynamic behaviour and addressing it during the design process, enhancing system’s adaptability. The research implications are limited as the DSR methodology was only partially followed. Demonstration and Evaluation activities have been overlooked, as the aim of this research was solely to propose a design framework. Future research could be aimed at performing these activities, improving the framework level of detail, exploring it through real case studies to understand other implications from its adoption for SN design, while monitoring the ecosystems that they intend to regenerate and if any unintended effects generated are positive. ACKNOWLEDGEMENTS This research was supported by the Coordination of Superior Level Staff Improvement – CAPES (grant number 88881.134782/2016-01) and by the Operations Research and Logistics research group from Wageningen University and Research. We also would like to acknowledge the Federal University of Technology – Campus Cornélio Procópio. REFERENCES Ackoff, R., 1999. A Lifetime of Systems Thinking. Syst. Thinker 10, 1–4.
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A definition for the Regenerative Supply Network Design is proposed; A six-step framework is proposed to design Regenerative Supply Networks. Environmental Regeneration is achievable through Transdisciplinary Research; Socio-Technical and -Ecological System views can be merged via Biocentric Design.
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