Purpose - Investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/Methodology/Approach - This work evaluates various AM-enabled supply chain con- gurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings - This research assesses the preferred AM-enabled supply chain con guration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly a ects the preferred network con guration. The relationship between the demand volume and relative AM production capacity a ects the regions of superior network con guration performance. Research limitations/implications - This research makes several simplifying assumptions regard- ing AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity, and post-processing require- ments. Originality/value - This research is the rst study to link realistic spare part demand characteri- zation to AM supply chain design using quantitative modeling. Keywords: additive manufacturing, logistics, intermittent demand, spare parts supply chain, supply chain design 1 Introduction Additive manufacturing (AM) research is a rapidly evolving eld that pioneers, develops, and matures new materials, processes, and technology. While much of hype focuses on the future possibilities, AM research will continue to yield outputs that provide innovative and even disruptive approaches to planning and managing large logistics networks for both industrial and governmental organizations. Indeed, researchers, companies, and the government are already working hard to develop AM technologies for these applications| these include Volkswagen, Audi, and the U.S. Department of Defense, to name a few (Anonymous, 2018; Department of Defense, 2016; Department of the Army, 2016; Department of the Navy, 2017; Essop, 2020; Freedberg, 2019; Goldstein, 2019; Kosowatz, 2019; Made In Space, 2019; Oce of the Vice President for Research, 2017; Piggee, 2019; Prater et al., 2016, 2017, 2019; Randolph ,2019; T. Vialva, 2019; Volkswagen, 2018). Advances in AM technologies provide opportunities to employ this asset not only to support everyday logistical operations but to address contingency operations and disaster management. AM technologies serve as a rapidly responsive resource to address surges in demand and address logistical shortcomings as applied in response to the COVID-19 pandemic (Stratasys, 2020) and other disaster relief e orts. 1McDermott, K.C., Winz, R.D., Hodgson, T.J., Kay, M.G., King, R.E., & McConnell, B.M. 2021. Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand, Journal of Defense Analytics and Logistics , published online ahead of print, 14 Sep 2021, https://doi.org/10.1108/JDAL-08-2020-0016 These current and future AM technologies provide an opportunity for positive disruptive change. Even with matured AM technology, large corporations and governmental agencies with global supply chains are still faced with several key questions to fully realize the bene ts: When should adding AM capability into a logistics network be considered? If using AM, where should AM capability be located in the network? After establishing it, how should the AM capability best be resourced, managed, and employed? This research seeks to address the rst two questions presented above, within the framework of di erent spare part demand patterns. Spare parts supply chains stand apart from supply chains servicing other types of materials for a variety of reasons, speci cally the uncertainty of the demand patterns, criticality of parts, and high customer service level requirements (Saalmann et al., 2016). Spare parts, also referred to as service or repair parts, are those that are used to support maintenance and repair operations. Demand for spare parts occurs when a component fails, requires replacement, or is scheduled for service and as such it is di erent from a `typical' stock keeping unit (SKU) in other supply chains (Martin et al., 2010). The resulting demand patterns for spare parts are often highly intermittent and unpredictable (Fortuin & Martin, 1999; Sirichakwal & Conner, 2016). Further amplifying the challenges in managing a spare parts supply chain is that spare parts are often considered highly critical and carry high customer service level requirements. This criticality is determined by the consequences caused by the failure of a part when a replacement is not immediately available (Huiskonen, 2001). Monetarily, these consequences can be very high such as in the aviation industry where the cost per hour of downtime can be several thousands of dollars per hour (Abbink, 2015). Spare part service requirements and highly intermittent demand patterns present a variety of diculties in forecasting and inventory stock control (Syntetos & Keyes, 2009). Consequently, organizations often maintain disproportionately high inventory levels and face high inventory carrying costs, much like the U.S. Army who \accumulated billions of dollars in excess spare parts inventory against current requirements for some items and substantial inventory de ciencies in other items" (United States Government Accountability Oce, 2016). Managing the trade-o s between high inventory carrying costs and customer service levels is not a new concept in supply chain management, however, advancements in AM technologies provide new options in this eld. This paper investigates the impact of AM on the con guration and performance of spare parts supply chains. As an extension of related work in this eld, this research focuses modeling e orts on spare part demand characteristics of average inter-demand interval (ADI) and squared coecient of variation (CV2). Within the framework of the demand classi cation scheme proposed by Syntetos et al. (2005), this paper explores three distinct research areas. First, we brie y explore the performance tradeo s associated with a wide range of AM-enabled network con gurations. Next, we assess for which demand classi cation does the addition of AM technologies into a network provide the most value. Finally, we narrow our focus on two widely researched AM-enabled network con gurations, centralized and distributed, and compare their performance across a wide range of spare part demand patterns. The rest of this paper is organized as follows: Section 2 reviews applicable literature and identi es gaps, Section 3 provides a high level overview of the approach and presents our methods, Section 4 details the supply chain network and assumptions, Section 5 explores the tradeo s of various AM con gurations, Section 6 compares the performance of the centralized and distributed con gurations, Section 7 explores the relationship between total demand volume and relative AM production capacity, and Section 8 provides closing remarks and extensions for future work. 2 Literature Review The literature contains a number of studies that examine ecient con gurations of AM-enabled supply chain networks and their associated tradeo s. Table 1 summarizes these papers and compares them with this paper. While our approach is unique, this paper's emphasis on intermittent demand is common in spare parts supply chains. 2 Table 1: Previous research comparing the di erent con gurations of a supply chain when adopting AM as related to this research. Note: MILP: mixed integer linear programming. SPSC: spare parts supply chain. Type Feature This Paper Song & Zhang (2020) Kno us et al. (2019) Basto et al. (2019) Ghadge et al. (2018) Li et al. (2019) Khajavi et al. (2018) Li et al. (2017) Liu et al. (2014) Khajavi et al. (2014) Holmstrom et al. (2010) NetworkCentralized  { { { {   {   Distributed  { { {   {   Hub { { { { { { { { {  { Mixed { { { { {  { { { { { MethodConceptual { { { { { { { { { {  Analytical {  { { { { { {  { { Simulation { { { { {  { { { { { Systems Dynamics { { { {  { { { { { { Scenario Model (Monte Carlo)  { { { { {  { { { Optimization (MILP)  { { { { { { { { { SPSCGeneral   { { { { { {  { Aircraft { { { {  { { { { Maintenance { { {  { { { { { { { MetricsInventory Holdings  { {  { { { { { Cost     {  { { { Backorders  { { { { { { { { { { DemandNormal Distribution { { { {  { {  { { Poisson Process {   { { { { { { { Discrete Uniform Distribution { { { { { {  { { { Intermittent Demand Classi cation  { { { { { { { { { { 3 AM Con gurations. The network con gurations of most common interest to researchers are the cen- tralized and distributed network con gurations (Ghadge et al., 2018; Holmstrom et al., 2010; Khajavi et al., 2018, 2014; Li et al., 2019; Liu et al., 2014). Centralized con gurations consolidate AM machines at a location upstream in the supply chain and use the AM capacity to reduce the on-hand inventory. The supply chain can bene t from a pooling e ect by aggregating demand from downstream service locations and maximize the AM machine utilization. One disadvantage from this con guration is AM-produced parts still require transportation and distribution to downstream locations, which limits savings in logistics costs and lead time reductions (Holmstrom et al., 2010). As noted by den Boer et al. (2020), strategic and centralized placement of AM at key air- or seaports could exploit existing energy and infrastructure to bene t airline and shipping companies. In contrast, the distributed con guration locates AM capability at downstream service locations as much as possible which reduces logistics costs and on-hand inventory. This comes at a high initial outlay due to the current expenses for both AM machines and their associated logistics, and specialized support personnel. This approach may be appropriate in scenarios where the demand for AM-produced spare parts is high enough to ensure a high machine utilization and overcome the investment costs (Liu et al., 2014). Other studies explore con gurations between these two extremes (Khajavi et al., 2018) seeking tradeo s between their respective bene ts and weaknesses. Khajavi et al. (2018) study a hub con guration that seeks to establish an AM location that can serve demand from multiple downstream locations and realizes some of the bene ts of both the centralized and distributed designs. The bene ts of locating AM assets simultaneously at centralized distribution centers and distributed service locations, the mixed con guration, are not well known, with only one study in the literature (Li et al., 2019). Other research assumes an external AM supplier (Kno us et al., 2019). Approaches. Most of the literature is narrowly focused on aircraft spare parts supply chains where indus- trial interest in AM has led to practical innovations (Ghadge et al., 2018; Holmstrom et al., 2010; Khajavi et al., 2018; Liu et al., 2014). Modeling and solution approaches range from high level conceptual (Holmstrom et al., 2010) and systems dynamics approaches (Ghadge et al., 2018; Li et al., 2017) to scenario modeling with Monte Carlo simulation (Khajavi et al., 2018, 2014). Basto et al. (2019) present an optimization model to design an AM supply chain for elevator parts which require short lead times and high service levels but do not provide details on the demand characteristics. Their conclusions focus largely on computational re- quirements to solve the model and omit design and managerial insights. In assessing and comparing network con guration performance, the literature focuses on a variety of performance measures including inventory or safety stock levels (Ghadge et al., 2018; Liu et al., 2014), sojourn time (Li et al., 2019), and cost (Khajavi et al., 2014; Li et al., 2019; Song & Zhang, 2020). Spare part characteristics are often generalized in terms of weight (Ghadge et al., 2018; Li et al., 2019) or volume (Khajavi et al., 2018) and these features serve as the basis for AM machine selection and capability determination. As AM technologies rapidly and consistently advance, some studies not only evaluate AM employment of current technology but evaluate hypothetical future capabilities with greater print speeds or build chamber volume (Khajavi et al., 2018; Moore et al., 2018). Demand Modeling. Ghadge et al. (2018) conclude that the element which signi cantly in uences the inventory, and hence the performance of a supply chain when adopting AM, is the distribution of demand. Other studies also point to the importance of modeling the underlying demand appropriately to get accurate supply chain performance predictions when integrating AM (Li et al., 2019; Liu et al., 2014). It is well known that spare parts inventory management is often challenged by highly intermittent de- mand (Babai et al., 2020; Ghadge et al., 2018; Holmstrom et al., 2010; Li et al., 2019; Teunter & Duncan, 2009; Willemain et al., 2004) yet recent studies assume demand follows a Normal (Gaussian) distribution with little volatility (coecient of variation 0.2{0.8) (Ghadge et al., 2018; Li et al., 2017; Liu et al., 2014) or a stationary (Abbink, 2015; Li et al., 2019; Sirichakwal & Conner, 2016; Song & Zhang, 2020) or non- stationary (Kno us et al., 2019) Poisson process with low arrival rates which may not be representative of the levels of dispersion in highly uncertain environments (see Appendix A). Kno us et al. (2019) is unique in considering demand rate and AM piece price changes during the evaluation horizon. Spare part supply chains often encompass a wide array of SKUs with varying costs, di ering customer service requirements, and varying demand patterns, all of which require di erent methods of forecasting and 4 stock control (Boylan et al., 2008). There is a rich body of literature on both theory and numerical results using real data from a variety of forecasting models to address these challenges (Croston, 1972; Syntetos & Boylan, 2001, 2005; Syntetos & Keyes, 2009; Willemain et al., 2001, 2004, 2005). While previous studies address demand arrival rate and quantity with limited scope, this research o ers a new framework for evaluating AM-enabled spare parts supply chains that explicitly models realistic in- termittent demand. Most of the previous studies focus on a limited set of common network con gurations, except Basto et al. (2019) who propose a single, optimal network con guration. Busachi, Erkoyuncu, et al. (2018) provide a soft systems approach to evaluating logistics network con gurations at a high level. In addition to exploring the common con gurations (centralized and distributed), this research brie y explores tradeo s associated with all possible AM-enabled con gurations of a given network. AM Build Time Estimation. Estimating AM build times is not an easy task as it depends on the AM technology, process parameters, machine path planning, and build orientation, among other factors (Medina- Sanchez et al., 2019). In general, the literature presents three approaches to predict the AM build time: analytical, parametric, and experimental. Analytical approaches (Alexander et al., 1998; Byun & Lee, 2006; Chen & Sullivan, 1996; Giannatsis et al., 2001; Habib & Khoda, 2017; Komineas et al., 2018) closely model the AM technology dynamics, tend to be complex, and require a large quantity of speci c data. Multiple studies report average error to be less than 5% (Byun & Lee, 2006; Giannatsis et al., 2001). Parametric approaches (Campbell et al., 2008; Cheng et al., 1995; Choi & Samavedam, 2002; Kechagias et al., 2004; Xu et al., 1999) are less complex and often rely on part geometry; the increased simplicity can yield decreased accuracy but more general predictions. Campbell et al. (2008) reports less than 10% error in 80% of builds analyzed; Kechagias et al. (2004) achieves predictions within 7.6%. Experimental approaches (Ru o et al., 2006b) often rely on part properties (similar to parametric models) but t real AM build times to functions to obtain a functional representation; these may be more accurate than their parametric counterparts and simpler than analytical models. Experimental models su er from in exibility and repeatability issues (Medina-Sanchez et al., 2019). Recent experimental models include multi-factor regression (Zhu et al., 2016), Q-optimal response surfaces (Mohamed et al., 2016), Gray The- ory (Zhang et al., 2015), and arti cial neural networks (Di Angelo & Di Stefano, 2011; Munguia et al., 2009). The literature reports mean accuracy in the range 2{15% depending on the circumstances. Medina-Sanchez et al. (2019) combines analytical and experimental approaches reporting the maximum relative error below 8.5%. AM Cost Models. Estimating and modeling AM costs is dicult and complex; many researchers study the production of a few individual parts and of the studies that examine multi-part assemblies, few con- sider supply chain impacts such as transportation and inventory costs|exceptions include Pour et al. (2019), Thomas (2016), Holmstrom et al. (2010)|and/or nancial bene ts from decreased risks of dis- ruptions (Thomas, 2016; Thomas & Gilbert, 2014). Many of these studies focus on comparing AM costs with traditional manufacturing (Allen, 2006; Atzeni & Salmi, 2012; Holmstrom et al., 2010; Hopkinson & Dickens, 2003; Ru o et al., 2006a; Thomas, 2016). Most early cost models include material, machine, and labor costs (Atzeni et al., 2010; Atzeni & Salmi, 2012; Baumers et al., 2017, 2016; Hopkinson & Dickens, 2003; Mahadik & Masel, 2018; Ru o et al., 2006a; Sharma & Dixit, 2019) for a variety of AM processes. Over time, researchers have improved the models to also consider energy costs (Allen, 2006; Baumers et al., 2017, 2016; Baumers & Holweg, 2019; Piili et al., 2015; Ru o et al., 2006a), lifecycle analysis (Lindemann et al., 2012), inventory (Abbink, 2015; Khajavi et al., 2014), post-processing (Atzeni & Salmi, 2012; Baumers et al., 2017; Lindemann et al., 2012; Manogharan et al., 2016; Nagulpelli et al., 2019; Rickenbacher et al., 2013), hybrid additive plus substractive processes (Manogharan et al., 2011, 2016), 5 part screening (Lindemann et al., 2015), build failure (Baumers & Holweg, 2019; Nagulpelli et al., 2019; Schroeder et al., 2015), material removal (Nagulpelli et al., 2019), and operator experience and learning (Baumers & Holweg, 2019). The most recent survey on AM cost models (Kadir et al., 2020) concludes AM cost models are \still limited in many aspects" and that \there is still no complete satisfactory model to satisfy various AM technologies and applications." We encourage the interested reader to also consult other recent surveys on AM cost modeling focusing on the aviation industry (Gisario et al., 2019), defense sector (Busachi et al., 2017), strengths and weaknesses (Costabile et al., 2017), and the industrial sector (Fera et al., 2016). See also Thomas & Gilbert (2014). Other Relevant Studies. There are other approaches to decision support for AM-enabled supply chains. Busachi et al. (2018) present a cost model to support the Rapid Support System (Busachi, Kuepper, et al., 2018a) for deployable AM as a cloud-based decision support software (Busachi, Kuepper, et al., 2018b) to simulate supply options and perform a cost-bene t analysis, though this is limited to fused deposition melting (FDM) and wire + arc additive manufacturing (WAAM) technologies (Busachi et al., 2017). Other researchers focus on identifying AM p