1 Process Network Modularity, Commonality, and ...

Process Network Modularity, Commonality, and Greenhouse Gas Emissions

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Kevin J. Dooley Department of Supply Chain Management, WP Carey School of Business Arizona State University Tempe, AZ 85287-4706 Surya D. Pathak School of Business, University of Washington, Bothell Bothell, WA 98011 Thomas J. Kull Department of Supply Chain Management, WP Carey School of Business Arizona State University Tempe, AZ 85287-4706 Zhaohui Wu College of Business, Oregon State University Corvallis, OR 97331-2603 Jon Johnson Department of Management, Sam M. Walton College of Business University of Arkansas Fayetteville, Arkansas 72701 Elliot Rabinovich Department of Supply Chain Management, WP Carey School of Business Arizona State University Tempe, AZ 85287-4706

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Process Network Modularity, Commonality, and Greenhouse Gas Emissions

ABSTRACT A process network is a complex system of linked unit processes that constitute the life cycle of a product. In this paper, we consider how the structural and functional characteristics of a product’s process network impact the network’s collective greenhouse gas emissions. At a unit process level, greenhouse gas emissions are primarily related to process efficiency. We hypothesize that a process network’s greenhouse gas emissions will be less when the process network has a modular structure, and when its constituent unit processes are more functionally similar. A modular process network architecture promotes autonomous innovation, and improved knowledge management and problemsolving capabilities, leading to more efficient processes. Functional commonality in a process network enables economies of scale and knowledge spillover, also leading to process efficiencies and thus reduced greenhouse gas emissions. We test these two hypotheses using a sample of 4,189 process networks extracted from an environmental life cycle inventory database. Empirical results support our hypotheses, and we discuss the implications of our findings for product development and supply network design.

Keywords: process, network, modular, nearly-decomposable, commonality, environmental performance, sustainability, greenhouse gas, carbon footprint, life cycle

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1.0 INTRODUCTION The observed and predicted impacts of climate change have led to unprecedented attention to reducing greenhouse gas (GHG) emissions. In 2015, 155 countries agreed to GHG emissions targets as part of the Paris Climate Conference. Most participating countries have developed regulations and incentives to reduce GHG emissions, and move to renewable energy sources that are less carbon-intensive (Williams et al., 2012). Companies and industry sectors have in turn made GHG reduction commitments, renewable energy commitments, and invested in processes and systems that are less energy intensive or capture GHG emissions (Hoffman et al., 2014; Obama, 2017). Business organizations have concentrated on reducing GHG emissions within their own operations as part of their efforts to curb their direct operating costs (Corbett and Klassen, 2006; Russo, 1997). To reach global, national, and corporate targets, however, GHG emissions across the whole life cycle of a product or service need to be addressed (Dooley, 2014). According to CDP (2017), greenhouse gas emissions related to a company’s own operations account, on average, for less than 20 percent of a product or service’s greenhouse gas emissions; the majority of emissions occur in the company’s supply chain. Pressure from customers, competitors, regulators, and civil society around life cycle or supply chain GHG emissions is becoming common (Kleindorfer, Singhal, and Van Wassenhove, 2005; Meinrenken et al., 2014; Piore, 2012). Because of both supply and market reputation risks, manufacturers are held accountable to not only their own actions but also the actions and impacts of their upstream suppliers (Awaysheh and Klassen, 2010; Carter, 2005; Dooley and Johnson, 2015).

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Walmart’s recent commitment to remove one gigaton of GHG emissions collectively from their supply chain is an example of how companies are actively considering the risk and opportunity associated with GHG emissions in their supply chain (Business Wire, 2017). Reducing supply chain GHG emissions typically leads to cost reductions because of efficiency improvements, which aligns with a corporate strategy of managing costs. Additionally, it reduces the cost risks associated with any future carbon tax or energy price volatility. Likewise, the positive momentum of the product environmental footprinting efforts in Europe (Finkbeiner, 2014) suggests that life cycle accounting for GHG emissions will be increasingly used for purchasing decisions, despite the moderate success so far of product carbon footprinting efforts. There is a paucity of research that links characteristics of supply chains to GHG emissions. Our paper models the activities that constitute the product life cycle as a process network (Ruddell and Kumar, 2009). A process network is a directed network of unit processes that yields a final product, where each node represents a transformation process and connections represent physical flow of process inputs and outputs. There is little understanding of what drives the level of a network’s GHG emissions, other than having unit processes with low or high emissions. Process networks are complex in that they involve many parts acting in a nonsimple way (Simon, 1962), and are adaptive in that the organizations (i.e. agents) who design or operate unit processes within the process network make decisions over time that impact unit process performance (Pathak, et al., 2007). Most real-world process networks involve hundreds or thousands of units processes, thus the network is not under the control of any single organization, but rather its structure and function are emergent

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from the actions and interactions of many organizational decision makers. If process networks are similar to other complex systems, then it is possible that structural and functional characteristics of the network itself, beyond the node-level effects, may also have an impact on the network’s performance (Simon, 1962). Thus, our research question is: How do the structural and functional characteristics of a process network impact its collective GHG emissions? To answer this question, we consider theory and empirical studies related to the benefits of modularity and commonality in product, process, organization, and supply networks (Barabasi, 2007; Bellamy et al., 2014; Cheng, 2011; Danese, and Filippini, 2013; Fixson, 2007; Jacobs et al., 2007; Lau et al., 2010; Randall and Ulrich, 2001; Thatte, 2013; Ulrich, 1995; Worren et al., 2002). As a process’s GHG emissions stem mostly from the energy and fuel it uses in production, GHG emissions are reduced as processes become more productive and efficient. Our theory suggests that a modular process network architecture promotes more autonomous process innovation, and improved knowledge management and problem-solving capabilities, leading to more efficient processes. Additionally, functional commonality of processes in a process network enables economies of scale and knowledge sharing and spillover, which also leads to process efficiencies and thus reduced network greenhouse gas emissions. We test these two hypotheses using a sample of 4,189 process networks extracted from an environmental life cycle inventory database. We use recent graph theory methods to operationalize modularity (Leicht and Newman, 2008, Dugue and Perez, 2015, Fortunato and Hric 2016), and process commonality of using an entropy-based measure. We test the hypotheses using a hierarchal linear model involving the two main

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explanatory variables and appropriate firm level, industry level, and network level controls. Empirical results support our hypotheses, namely, that more modular process networks, and networks with more commonality amongst their constituent unit processes, have lower GHG emissions, ceteris paribus. Our study makes several unique contributions. First, the study addresses modularity and commonality across the entire life cycle, which contrasts with previous studies that only focus on the relationship between a manufacturer and their first-tier suppliers (e.g., Jacobs, Vickery, and Droge, 2007). Likewise, in contrast to studies of product architecture which focus only on components and materials present in final products (e.g., Danese and Filippini, 2013), our model and data are the only we know of that includes intermediary processes used at multiple tiers of the supply network. Second, most studies of modularity have used perceptual measures of modularity (e.g., Jacobs, Vickery, and Droge, 2007); in contrast, our study uses secondary data, enabling a more objective measure of modularity. Third, our study is the first in this area to use network community detection algorithms (Blondel et al., 2008; Fortunato and Hric 2016) to operationalize network modularity. Fourth, our study is the first to examine the linkage between modularity, commonality, and greenhouse gas emissions (or energy intensity). Finally, our study uses a sample roughly an order of magnitude larger than any previous study of modularity and commonality, encompassing the whole manufacturing sector. We first review the relevant literature, which provides some general insight to our research question. We present our theoretical proposition and argument, linking process network modularity, commonality, and greenhouse gas emissions. The paper then

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discusses the study’s methodology and empirical findings, and concludes with implications for theory and future research. 2.0 BACKGROUND AND LITERATURE REVIEW In this section, definitions are first provided for clarity in description. Second, systems theory and economic literature is reviewed to discuss how division of labor and specialization result in modularity within economic systems, and how this is both widely observed and empirically demonstrated to be beneficial. Third, literature on modularity and commonality within product, process, and supply networks is reviewed. Finally, gaps in the literature are summarized that yield the need for the current study. 2.1 Definitions The literature on modularity or commonality (or diversity) examines these issues at various scales – process, product, and supply network. In each case, the system can be represented as a collection of entities that are connected by their interactions, i.e. a network. In the context of network, modularity is defined as the degree to which the network can be decomposed into clusters, where there are relatively fewer interactions between entities (nodes) in different clusters than between entities in the same cluster (Newman, 2006). For example, in a modular product, the components of the product can be clustered into subsystems, where each subsystem is relatively autonomous from the others, and subsystems are connected by standard interfaces that facilitate information, energy, and material flow (Sosa, Eppinger, and Rowles, 2004). In a modular process, lower-level task operations can be clustered into subprocesses, to the same effect. In practice, a network that is computationally nearly-decomposable is considered modular, and we will use the term modularity when discussing theory and methods.

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Commonality is defined as the degree to which the entities (product components, processes, or suppliers) within a network are functionally similar (Fixson, 2007). In a process network, higher commonality would indicate that unit processes were functionally similar to one another. For example, unit processes that occur within industrial chemical production tend to have both chemical inputs and outputs, require heat, and often involved purifying and filtering processes. On the other hand, these chemical reaction processes share less in common with the unit processes involved in the upstream mining that provides their chemical inputs. 2.2 Division of labor and specialization From a general systems perspective, Simon’s architecture of complexity model (Simon, 1962) addresses why systems evolve towards division of labor and specialization, in turn leading to modularity within those systems. The model states that systems will naturally improve by evolving to more hierarchically-modular states over time. He proposed that as any living system evolves, parts of the system break down tasks into subtasks where possible, and parts differentiate themselves via the specialized tasks that they perform. When a system decomposes its requisite behaviors into specialized subtasks the system can perform more complex tasks, and it can optimize overall performance more easily, since nearly independent submodules can seek optimality (nearly) independent of one another. Thus, the more modular a system’s architecture is, the better it will perform because of efficiencies and robustness gained from task decomposition, specialization, and ease of optimization. Recent computational work (Clune, Mouret, and Lipson, 2013; Frenken and Mendritzki, 2012) confirms that

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living systems will tend to evolve towards modularity when the system’s fitness function includes the cost of connecting modules. From Adam Smith onwards, economists have advocated and demonstrated the positive impact of division of labor and specialization to economic growth (Romer, 1987). The general argument is that division of labor and specialization yield supply and demand side benefits, which promote productivity and economic growth. On the supply side, it leads to economies of scales (Becker and Murphy, 1992) and concentration of relevant knowledge (Rosen, 1983), leading to better productivity. On the demand side, specialization enables a greater variety of outputs to be created from the same inputs, enabling a better matching of market needs with product or service offerings, and thus opportunity for growth. Krugman’s model of specialization and international trade (1981) suggests that while division of labor and specialization may lead to low commonality in production activities between countries, it may lead to high commonality within countries, due to the knowledge accumulation benefits that occur within intra-industry differentiation. Chaney and Ossa (2013) extend Krugman’s model to explicitly consider the structure of supply chains and pose that it is growth in demand that leads to division of labor and specialization, which in turn leads to productivity improvements. Specialization may or may not lead to less commonality. Specialization is the process where entities within a system differentiate themselves from one another. If the differentiation between entities is small, then commonality is high; if differentiation is significant, then commonality is low. Thus, when division of labor and specialization occur in an economic system, the corresponding network becomes more modular, but the commonality amongst its entities may change in either direction.

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Division of labor and specialization is documented by many historians of premodern society, in the form of craft specialization (Brumfiel and Earle, 1987). More modern economic empirical studies validate the theoretical models discussed above. For example, Hummels, Ishii, and Yi (2001) found that vertical specialization, i.e. specialization within supply chains, had increased by 30 percent from 1970 to 1990 and it accounted for 30 percent increase in inter-country exports. In a study of the semiconductor industry, Kapoor (2013) documents the counter-vailing forces that led to a balance of integration and specialization within that industry sector. Specifically, certain firms played the role of systems integrators, exploiting their design and project management expertise, while allowing component suppliers to specialize in sophisticated product and process technologies. With risks being mitigated by the sector’s culture of knowledge sharing and collaboration, this specialization has led to supply and demand side benefits. 2.3 Modularity While studies have examined the demand side benefits of modularity and commonality, we will concentrate on the supply side benefits, as our question relates to energy efficiency of production processes. The supply side benefits of modularity stem from it facilitating autonomous decision making and thus innovation (Langlois and Robertson, 1992). To the extent that modules have fewer interactions with other modules, the team or organization responsible for design and operation of the module can pursue improvements and innovations with minimal coordination with other teams or organizations. If improvements and innovations require investment or involve intellectual

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property and modules are controlled by different organizations, minimizing dependencies between modules simplifies decision making around potentially strategic considerations. Modularity has been linked to the knowledge that drives product and process improvement and innovation. A study of patents by Yayavaram and Ahuja (2008) empirically demonstrated that the more modular a knowledge base is (i.e. it has knowledge elements that can be clustered into modules based on similarity of content), the more it is capable of adaptation, and the more useful the inventions derived from it are. Their theory is that inventive ideas come by balancing the breadth and depth of knowledge search, and that a nearly-decomposable knowledge base creates a balance between specialized and general knowledge, and the ability to leap from one knowledge domain to another via weak ties. Modularity has also been demonstrated as a benefit to problem solving. Autonomous decision making enables more rapid trial-and-error learning, leading to more learning cycles within a fixed period of time (Langlois and Robertson, 1992). Brusoni et al. (2007) develop a model to show that as a system is more modular, it effectively breaks down system-level problems into more easily solved sub-problems; isolated optimization may however lead to solution lock-in within modules. A study of vertical integration in the automotive industry suggests that firms will tend to make make-buy decisions based on minimizing complex design decision making between firms (Novak and Eppinger, 2001). Ethiraj and Levinthal (2004) demonstrate that systems tend to improve most effectively when the search process is appropriately tuned to the degree of modularity inherent in the system. Specifically, they found that over-modularizing led

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to greater learning detriments than under-modularizing the search within the solution space. A number of empirical studies confirm the benefits of modularity. A study of the automotive industry (Jacobs, Vickery, and Droge, 2007) found empirical evidence that modularity, as defined as the degree of independence between a buyer and their first-tier suppliers and measured by managerial perception, was positively related to perceptions of cost, quality, flexibility, and cycle time. A cross-sector study of U.S. manufacturing by Cheng (2011) defines modularity by an econometrics-based index combining contracting manufacturing, alliances, and alternative employment. The study finds a positive relationship between organizational modularity and economic performance (capacity utilization, ROA, ROI). Danese and Filippini (2013) use a survey of managerial perceptions and demonstrate a positive relationship between product modularity and new product development performance. Vickery, Koufteros, Droge, and Calantone (2016) use a survey of managerial perceptions and demonstrate a positive relationship between product and process modularity and new product development introduction. Fixson’s (2007) review of modularity literature also cites a number of studies that link modularity and product and process cost and flexibility. In the context of a process network, modularity implies that unit processes can be separated into clusters, where there are few interactions between unit processes in different clusters, but numerous interactions between unit processes within the same cluster. We expect, and will reason that, process network modularity also can confer the benefits of more effective learning and thus improved performance. 2.4 Commonality

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Commonality provides demand side benefits, as variations of components can be mixed and matched into many end variations in a modular system, but there are supply side benefits as well. Fixson’s extensive review of the literature (2007) indicates that commonality can improve productivity, cost, quality, and time, as well as inventory management. From a manufacturing perspective, commonality amongst unit processes enables supply management, process improvement, and maintenance to be shared, yielding scale economies. The knowledge management literature also suggests that commonality of product subcomponents or processes will create value by facilitating knowledge sharing and spillover (Dyer and Nobeoka, 2000). For example, a manufacturing organization may employ several different techniques to perform molding of different types of plastic, but new knowledge in plastic pellet composition, or molding technology in general, will likely benefit all of their plastic molding processes. Functional diversity, the opposite of commonality, induces coordination costs and thus loss of efficiency. Becker and Murphy (1992) discuss the balance between the value of diversity, in terms of enhanced productivity, versus the coordination costs of interactions between those diverse entities. An empirical study of manufacturing processes in U.S. factories demonstrated that factories that employed job or batch manufacturing processes, in order to deal with great demand diversity, had higher costs due to loss of efficiency (Safizadeh et al., 1996). 2.5 Summary In summary, there is pressure for economic systems to evolve to more modular architecture via division of labor and specialization. Relative to our research question, if a more modular network will be more productive, and greenhouse gas emissions are related

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to productivity, a linkage is suggested. The economics literature also suggests that while specialization is beneficial, some degree of commonality (i.e. small amounts of differentiation) is beneficial for scales of economy and knowledge management. The literature on modularity suggests that its supply side benefits accrue from autonomous decision making and innovation, more effective knowledge management, and better problem solving. The literature on commonality suggests that its supply side benefits are generated through economies of scale and knowledge sharing and spillover. Empirical studies mostly confirm this reasoning. 3.0 THEORY DEVELOPMENT 3.1 Systems overview and terminology Manufacturing a product typically involves hundreds or even thousands of discrete processes, encompassing activities from raw material extraction to final product assembly and distribution, i.e. cradle to grave. The smallest collection of activities that commonly occur together, and for which physical inputs and outputs have been measured, is called a unit process (Weidema, et al., 2013). These processes are linked together by economic and physical transactions that give rise to a process network (Skipper et al., 2008). A process network can be modeled as a directed graph that yields a product in its ego node. For a given product, each discrete process in its life cycle is represented as a node in its process network, while the sequential dependencies between the processes are represented as edges in the process network. A directional connection between unit processes i and j indicates that a physical output of process i serves as a physical input to process j. For example, if the process network involved metals or minerals, then deep within the network we would see that the manufacture of

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the main chemical used for dynamite (nitroglycerin) is an input to the process of blasting in a mining operation, and that unprocessed metal or mineral is the output of the blasting operation. Process network modularity is an attribute of a process network that indicates the degree to which it can be decomposed into clusters, where there are relatively fewer interactions between unit processes in different clusters than between unit processes in the same cluster. Additionally, each unit process has two attributes relevant to the study. Process GHG emissions captures the amount of GHG emissions resultant from operation of that unit process. In the same manner that the cost of a final product is the summation of its constituent costs, the process network GHG emissions (equivalent to the product’s GHG footprint) are the summation of the network’s constituent process GHG emissions. Each unit process also has a process classification – a category within a taxonomy that indicates the type of manufacturing activity represented. Unit processes within the same classification will be much more similar to one another than processes with different classifications. Process network commonality indicates the degree to which the unit processes within a process network are from similar process classifications. A summary of these terminologies relevant to our model and hypotheses are provided in Table 1. -Insert Table 1Consider the process network of hydrochloric acid by benzene chlorination, depicted in Figure 1. Hydrochloric acid is an intermediary product within the economy, used in industrial processes and largely the by-product of creating other intermediary chemicals for plastics manufacturing. Only a portion of the whole network is shown for simplification. The hydrochloric acid is assembled from three chemical components –

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benzene, liquid chlorine, and sodium hydroxide, and heat used in production. Liquid chlorine is manufactured from gaseous chlorine, while sodium hydroxide can be derived from three different types of processes (mercury cell, diaphragm, membrane). In this process network, heat for final production was provided by coal. Not depicted here are the sub-processes that constitute conversion of chlorine from gas to liquid (Occidental Chemical Corporation, 2013), namely, purification, heating, and operation of an electrolytic cell. These are bundled as a single unit process (manufacture of liquid chlorine) as these activities always co-occur, and measurement of these individual subprocess’s input and outputs does not exist. The network also shows that flows are not unidirectional to the ego (product) node; for example, manufacture of benzene results in waste residue that must be disposed of. -Insert Figure 1In this example, the network illustrates some degree of modularity, in that the four branches representing the three core ingredients and heat generation do not contain crossconnections. The network also shows elements of commonality. The final process and three of its four inputs are all chemical processes, and the manufacture of sodium hydroxide can occur via three different process mechanisms, each sharing a large degree of commonality. 3.2 Greenhouse gas emissions Previous theory and empirical studies suggest that process networks with greater modularity and commonality will be more productive and efficient. To link that to our research question and empirical design, we must first demonstrate that GHG emissions are highly related to process efficiency and productivity.

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Emissions of GHGs such as carbon dioxide or methane are important because they are a major driver of climate change (IPCC, 2014). GHG emissions come primarily from a few sources: burning of fossil fuels such as coal, oil, or gas, including for generation of electricity; GHGs released from land change and deforestation; and noncarbon dioxide GHGs released by some manufacturing processes, mostly in production of micro-electronics (IPCC, 2014). Methane release during livestock production is another major source, but our study does not encompass agricultural products. While some unit processes related to paper or pulp fiber may have GHG contributions related to land use and deforestation, and some unit processes related to electronics component fabrication may have GHG contributions related to use of fluorinated gases, the majority of GHG contributions within unit processes comes from the use of electricity or fuel by production equipment (Williams et al., 2012). Given that, there are three ways in which a unit process can reduce GHG emissions related to its electricity or fuel use. First, the process can use equipment that is more energy efficient. Such energy efficiency may come from less energy-intensive technology, or maintenance procedures that maintain peak operating performance. Second, the unit process can have higher productivity, yielding more output for less input. In this way, the embedded GHG emissions from the inputs to the process are reduced. Third, it can use sources of energy that generate less GHG emissions, e.g. natural gas over coal. Process changes aimed at improving productivity or efficiency will manifest themselves often as improvements in energy efficiency, whether or not energy efficiency was the intended target of the process improvement. For example, a chemical company managing a heating process may install thermal insulation to ensure minimal heat loss to

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the outside and to reduce the costs associated with fuel consumption. The company’s primary goal is to reduce cost, but in doing so, they make the underlying unit process more efficient, yielding less combustion of fossil fuel and thus lowered GHG emissions. In summary, process improvements that increase process productivity or efficiency will often lead to increased energy efficiency and thus lower GHG emissions (Corbett and Klassen, 2006; Curkovic at el., 2000). As energy is a source of production costs, cost reduction efforts will often target improving energy efficiency, and thus lower GHG emissions. 3.3 Conceptual model and hypotheses As per Simon’s notion of hierarchy and complexity, a process network would evolve and adapt over time to optimize its performance through division of labor and specialization of tasks. The outcome of this adaptation process affects the modularity and functional commonality of a process network. In this study, we do not focus on the adaptation of the network itself. As shown in Figure 2, we instead sample a range of process networks at a time point and capture its modularity, functional commonality, and GHG emissions. -Insert Figure 2The modularity of a process network depends on the degree to which process inputs and outputs are entangled together. In a process network that has high modularity, sets of unit processes (modules) will contain unit processes that significantly interact with one another, but not with other unit processes in different modules. The complex interactions that take place within a process module make it more difficult to design and operate those unit processes in an optimal fashion with respect to their energy efficiency,

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but these cross-unit process interactions may yield synergies (Ethiraj and Levinthal, 2004). For example, re-capturing lost heat from one unit process and re-using it in a downstream process requires technological coordination between the two processes, but it may reduce overall fuel consumption and thus GHG emissions. In a highly modular process network, a module (a set of interacting unit processes) will be relatively independent of other modules, implying that the decision makers who design and operate these unit processes have more autonomy to act. With fewer decision makers, process investments and improvements can be acted upon more quickly, leading to faster learning cycles and thus better efficiency. If modules are relatively independent of one another, then knowledge about how to operate them in an energy efficient manner can also be decomposed and organized into modules, e.g. energy efficiency in chemical reaction processes versus energy efficiency in metal cutting processes. The process network’s total emissions, by definition, are the sum of the emissions associated with each unit process (Weidema, et al., 2013). This does not imply however that the collective emissions result from independent actions of each unit process. Rather, per the logic above, interactions with other unit processes will create both constraints and opportunities. Thus, within a specific unit process, its calculated GHG emissions are partially the result of its interactions with other unit processes, i.e. the interactive effects are embedded in the unit process’s carbon footprint. For example, if technology A can use biofuel as energy and technology B only can use fossil fuel, then the calculated footprint of technology A will be less than that of technology B. In summary, we hypothesize:

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Hypothesis 1: Process networks that are more modular will tend to have lower greenhouse gas emissions associated with their production activities than those that are less modular. A process network that has high commonality amongst its unit process will achieve several benefits related to economies of scale. It is usually the case that the tactics used to improve energy efficiency will be common for processes that are highly similar to one another. For example, different unit processes exist for heat treating manufactured parts (e.g., annealing, quenching, tempering, hardening), but because of the commonality in the underlying physics of these processes, process efficiencies introduced to one type of heat treatment process may be able to be easily replicated with different types. An innovation in more rapid pre-heating or better vent tube maintenance is likely to be shared amongst those processes. Processes that are common in their operation are also more likely to be designed and operated by a single organization than processes that are dissimilar. As argued above, this facilitates more autonomy in decision making, and thus more rapid learning. Thus, we hypothesize: Hypothesis 2: Process networks that have higher commonality will tend to have lower greenhouse gas emissions associated with their production activities than those that have lower commonality. 4.0 METHODOLOGY 4.1 Network data A product’s environmental impact is driven by multiple physical and design factors. Consequently, there are significant challenges to empirically test our propositions. Moreover, a large sample study is needed in order to observe the effect of process network design amidst many other effects. Obtaining data to achieve this goal, however, is challenging due to a general lack of visibility in tracking process networks.

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Studies assessing products' environmental impact have examined one or several products at a time through life cycle analysis (LCA). LCA models the environmental impacts of processes from cradle (origin) to grave (end-of-life) of a particular product (Matos and Hall, 2007). In order to devise a large sample test, we put forth the use of product’s LCA model as a valid representation of the process network constituting that product. This is in line with current thinking within the discipline as exemplified by Vachon and Klassen’s (2002) consideration of product-process structures as a surrogate for supply networks. In our study, the unit of analysis is a product’s process network comprised of unit processes as identified by the EcoInvent database (Weidema et al., 2013). The EcoInvent database contains one of the most used life cycle catalogues in the world, having been developed over a number of years by a broad coalition of stakeholders. In its 2.0 version, this database contains 4,189 complete unit manufacturing processes, organized into various product categories and sub-categories. There are seven major process types, and within each category there are a number of sub-categories broken down to multiple levels (See Table 2). The Ecoinvent data timeframe spans 2000-2005 and represents processes that are primarily (82 percent) from Switzerland and the larger European Union region. The remaining 18 percent of the data is spread across the globe. -Insert Table 2 hereSome aspects of the Ecoinvent database are relevant to mention regarding the alignment between theory and data in this study. First, the LCAs and unit processes within the database are chosen to be representative of then-current operating processes, and are based on direct physical measurement of real-life unit processes in operation.

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Thus, the GHG emissions data and network configurations are real, not theoretical. Second, the content within the database are based on studies that are compliant with ISO 14044 (IOS 2006), the international standard regarding LCA (Weidema et al., 2013), and are subject to a multi-expert panel review by Ecoinvent’s operator. Thus the data has a significant degree of validity and reliability. 4.2 Network extraction and censoring The Ecoinvent database consists of a 4189x4189 matrix, where each row and column represents a unique unit process. For each pair of unit processes i and j, the cell (i,j) in the matrix represents whether unit process j is an input to unit process i. By starting at any of the 4,189 processes (nodes) and following it upstream via its input processes, one can construct a process network for each of the 4,189 processes. Ecoinvent also has data concerning the greenhouse gas emissions associated with each of the 4,189 processes, per its level of activity. Figure 3 shows the whole network and is color-coded by process type. One can see that there is a central core (consisting of material and energy related unit processes that are shared by many processes and sectors), and a periphery consisting of processing and use-phase unit processes that tend to be unique to a given process network. -Insert Figure 3 hereAfter constructing the process networks for all unit processes, we found negligible variances in measures of basic network properties such as density and reciprocity across our sample of 4,189 process networks, due to the “core” of common processes seen in Figure 3. Because of this, we chose to build process networks at different network depths to capture the process network structures that are unique to the end products. To do so,

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we took a single unit process and developed a network consisting of its first-tier inputs and the cross connections between these tier one inputs (if any). We did not consider any inputs to the tier-one unit processes themselves. We then computed the network property metrics for all the unit process networks at depth=1. Network density is defined as the ratio of the number of linked dyads divided by the total number of possible dyadic pairs in the network, and network transitivity is defined as a proportion of triads in a network that are "transitive" (i.e., A→B, B→C, C→A). The average density at depth=1 was 0.221 with a variance of 0.017 and the average transitivity at depth=1 was 0.228 with a variance of 0.044. We systematically repeated the process for increasing depths to identify the level at which the variability disappeared between the process networks. At depth=2 the variance for density and transitivity was 0.004 and 0.006 respectively, significantly smaller than the variance at depth=1. At depth=3 and depth=4 the variance converged to 0 up to three decimal places. We therefore use the depth 2 networks as the basis for our empirical tests. In Figure 4, we show a sample network extracted from the Ecoinvent dataset. We manually validated a random sample of networks with network illustrations of Ecoinvent technical networks created by SIMAPRO, a third party LCA software. The networks created using our approach were all in agreement with the ones constructed by SIMAPRO. -Insert Figure 4 here4.3 Operationalizing process network modularity We use graph theory methods to operationalize the theoretical construct of process network modularity. Modularity, or more generally, “community structure” 23

(Newman, 2006; Wasserman and Faust, 2009) is identified by partitioning a network into cluster of densely connected nodes, such that nodes belonging to different clusters are sparsely connected. There is a large variety of approaches to identifying such clusters, most of which are notoriously computationally onerous (Fortunato and Hric 2016). Researchers have proposed several community detection algorithms such as divisive algorithms (Girvan and Newman, 2002; Newman and Girvan, 2004), agglomerative approaches (Pons and Latapy, 2006) and optimization-based methods (Clauset, Newman & Moore, 2004; Newman 2006). Each of these algorithms attempt to partition the network and measure the quality of the partitioning process using a “modularity” metric. The modularity of a partition is expressed as a scalar value between -1 and 1. Among these approaches, the method proposed by Newman (2006) using a leading eigenvector method, is most widely used. The algorithm relies on the spectral properties of a network’s “modularity matrix,” in the form of the difference between the observed adjacency matrix and the matrix of ties one would expect in the absence of community structure. Specifically, the observed fraction of edges within and between clusters is compared with the expected fraction of edges of a random network conditioned on the degree sequence of the original network (Leicht and Newman, 2008). While Newman’s algorithm is both computationally efficient for large networks and generates results that align with other community finding approaches, it has some serious limitations. First, the algorithm does not take directionality of edges between nodes into account. The process networks being investigated in this study are directional in nature. Leicht and Newman (2008) modified the original algorithm to account for directional edges, however, there is a second issue with the optimization-based

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approaches. Both of the modularity optimization-based approaches suffer from the problem of “resolution limits” (Fortunato and Barthelemy, 2007). Particularly, it has been shown that modularity optimization-based methods struggle to identify communities smaller than a certain scale, thereby inducing a resolution limit on the ability to detect communities in a network. In order to account for some shortcomings of Newman’s method, we employ the Louvain method (Blondel et al., 2008) using an algorithm developed by Dugue and Perez (2015). Across the data set, the median number of clusters with more than one node was 4, ranging from 0 to 21. As an illustration of the modularity metric, in Figure 5, we depict two sample networks; one for the production of desktop computer with a CRT monitor and the second for electricity production. Both networks are comparable in size but differ in modularity. As is evident just from looking at the pictures, the network with high modularity (Electricity generation) has many more clearly defined clusters as shown by the colored process nodes. -Insert Figure 5 here4.4 Operationalizing process network commonality For a process network, commonality can be measured by calculating how similar (or dissimilar) are the constituent sub-processes. The EcoInvent database provides seven macro categories (e.g., Energy, Material, Processing, Transport) and 292 sub-categories. For each process network, we measure commonality as the opposite sign of its entropy, Entropy is measured by sum of p*log(p), across the 292 categories, where p is the fraction of times that a unit process was represented in the specific process network. 4.5 Operationalizing Greenhouse gas emissions

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In LCA modeling one must first define midpoint and endpoint categories in order to group and aggregate the various possible environmental effects of a process or activity (Finkbeiner et al., 2006). In this research, we focus on GHG emissions, as measured by equivalent measures of CO2 (i.e. CO2e). The Ecoinvent database provides corresponding CO2e emissions for each of the 4,189 unit processes. We follow the prescribed guidelines in the Ecoinvent manual to calculate the CO2e emissions for each unit process corresponding to a standardized demand load. The Ecoinvent database uses an allocation-based method to calculate the emissions of a unit process due to its various inputs. The emission of one unit process is not directly fed into another unit process (if there is a process relationship) however. Instead, based on the amount used, an allocation of the input quantity is recorded. That information is then utilized while calculating the overall emission for the unit process under considerations. For example, in order to make 1 kilogram of electronics grade silicon, 1.4 kg of hydrochloric acid is needed. While Hydrochloric acid has its own CO2e emissions, that number is not used in its raw form in calculating the CO2e emissions of the silicon production process. Instead, a matrix transformation using the economic demand data of the process inputs and ecological data related to those inputs are used independently to appropriately scale the CO2e calculations for each unit process. Thus, the final calculated CO2e emissions data for each unit process are independent of each other. Readers are referred to the Ecoinvent user’s manual for a detailed explanation of the CO2e emissions calculations (Weidema, et al. 2013, p. 52). 4.6 Control variables

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We control for the location of data collection, the general type of supply network, and the estimated pace of change of the process sub-category. The locale of data collection – i.e., Switzerland, Europe (non-Switzerland), North America, and rest of the world (Other) – may influence reported CO2e emissions because of differing standards and/or data gather methods in these regions of the world. The general type of supply network – i.e., energy, material, process, transportation, “use” (or consumption activity) and waste – is assumed to possess inherent idiosyncrasies that uniquely influence the degree of CO2e emitted. The estimated process network pace of change in product and process life cycles (also known as “clockspeed”) influences how rapidly a process network evolves and was estimated using the course grouping of slow/medium/fast based on Fine (1998) and is shown for each industry type in Table 2. We also control for basic network structural variables. While there may be relationships between these network structure variables and modularity by controlling for these, we can parse out the particular effect of modularity independent of these and thus have a strong test of the proposition. Specifically, we control for network size, density, and reciprocity (Wasserman and Faust, 1994; Borgatti and Halgin, 2011). Size is used to capture the scale of the network (compactness vs. broadness). Density and reciprocity were used for characterizing the nature of the linkages between processes in the network. In this study, since the process networks are directed graphs, reciprocity measures the extent to which two processes are both inputs into one another. 4.7 Multi-level model There are a multitude of reasons why the environmental impact of one unit process (e.g. air-dried softwood) might differ from another (e.g. chicken production); the

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effect of the process network structure, if it is present, is not likely to be large compared to some of the other sources of variation as discussed earlier. We have two strategies for dealing with this problem. First, with a large sample size even the smallest effect can be detected and found to be statistically significant because of the power associated with very large sample sizes. Second, we do comparisons within product sub-categories. Thus, from our example above, if air-dried softwood tends to have a more centralized process network than kiln dried softwood, this effect would be more directly seen in a comparison of the two rather than a comparison with air-dried softwood and chicken production. As evident from this discussion, the nested structure of this design requires consideration. According to Raudenbush and Bryk (2002), using HLM with nested data improves estimations of individual group effects, allows modeling of cross-level effects, and partitions variance-covariance components between the levels. Because we suspect a high degree of variance in unit process emissions (level-1) due to inherent idiosyncrasies of the sub-categories, and based on Snijders and Bosker’s (1999, p.22) F-test to justify multilevel modeling (F=21.777 p0.6). -Insert Table 3 here-

A Hausman specification test for multilevel modeling (Snijders and Bosker, 1999, p.52-56) detected main effects differences within- and between-groups by comparing models with and without centering within-group. Modularity has a marginally significant difference in effect and the subsequent comparisons between level-1 predictors and level-2 mean predictors shows influences exist at both levels, further justifying the utility of HLM. 1

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We begin our HLM analysis by creating an “empty” model as a base case for subsequent estimates of R-squared (as percent error reduction). In this and all subsequent models, because the number of level-2 groups is not relatively large we use restricted maximum likelihood estimation, which produces more conservative standard errors (Raudenbush and Bryk 2002). For completeness, we assess and find no difference with using full maximum likelihood estimation. Next, industry level non-network controls are entered in Model 1: process network clockspeed, process network location, and process network type. Third, network effect controls in the form of size, density, and reciprocity are entered in Model 2 – both at Level-1 and Level-2. The Level-2 metrics are averages and, if significant, indicate group-level influences. Last, network modularity and commonality at Level-1 and Level-2 are entered in Model 3. Similar to Model 2, Level-1 represents the unit process network level, while the Level-2 represents average subcategory effects. The result from the HLM analysis is shown in Table 4. In Model 1, which introduces the control variables only, the model is statistically significant compared to the baseline model with just an intercept term. The value D(df)=121.7(9), p