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A review of research in illicit supply-chain networks and new directions to thwart them

Rashid Anzoom, Rakesh Nagi & Chrysafis Vogiatzis

To cite this article: Rashid Anzoom, Rakesh Nagi & Chrysafis Vogiatzis (2021): A review of research in illicit supply-chain networks and new directions to thwart them, IISE Transactions, DOI: 10.1080/24725854.2021.1939466

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A review of research in illicit supply-chain networks and new directions to thwart them

Rashid Anzoom , Rakesh Nagi , and Chrysafis Vogiatzis

Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

ABSTRACT Illicit trades have emerged as a significant problem to almost every government across the world. Their gradual expansion and diversification throughout the years suggests the existence of robust yet obscure supply chains as well as the inadequacy of current approaches to understand and disrupt them. In response, researchers have been trying hard to identify strategies that would suc- ceed in controlling the proliferation of these trades. With the same motivation, this article con- ducts a comprehensive review of prior research in the field of illicit supply-chain networks. The review is primarily focused on the trade of physical products, ignoring virtual products and serv- ices. Our discussion includes analyses of their structure and operations, as well as procedures for their detection and disruption, especially from the perspective of operations research, manage- ment science, network science, and industrial engineering. We also address persisting challenges in this domain and offer future research directions to pursue.

ARTICLE HISTORY Received 27 November 2020 Accepted 26 May 2021

KEYWORDS Illicit trade; supply-chains; disruption of illicit networks; literature review; future directions

1. Introduction

Over the last few decades, the world has experienced unprece- dented growth in commerce, spanning across different coun- tries and continents. This growth has outpaced the existing governance mechanisms, resulting in the proliferation of illicit trades. Despite the adoption of numerous measures, govern- ment entities have fallen short of halting the growth of such trades, which now make up approximately 8–15% of the glo- bal GDP (Mashiri and Sebele-Mpofu, 2015). This calls for a better understanding of illicit trade and its operations. To aid in this ensuing battle, researchers from different disciplines have come forward to contribute to this field. Aligned with this perspective, we are presenting a literature review on the operation and disruption of illicit trade, which we believe will prove useful to the policymakers and fellow researchers in the field of operations research, management science, and indus- trial engineering.

The field of illicit trade is a vast one that can be catego- rized by product, market, or trade characteristics. Existing works have mostly focused on a particular category of illicit trade, e.g., literature review of counterfeit trade by Staake et al. (2009). Others have focused on a specific aspect of the trade. Bichler et al. (2017) reviewed the literature related to the network structure of drug trafficking organizations. Kammer-Kerwick et al. (2018) outlined the application of operation research and data science in combating human trafficking. In contrast, we intend to discuss the domain of illicit trade in a holistic manner, comprising both qualitative and quantitative aspects. It is done in two ways. First, we try to picture the operations of illicit trade from two perspectives:

supply chain and network analysis. Second, we present meth- odologies, especially in the field of operations research and data science that have been proposed to help combat the pro- liferation of these trades. We also look into the research gaps and suggest future research directions to pursue.

The organization of this article is as follows. We start with a presentation of the selection criteria of the literature and general statistics in Section 2. Section 3 provides a big picture discussion on illicit trades. Sections 4 and 5 review illicit activities from the supply chain and the network per- spective, respectively. Section 6 is devoted to different meth- odologies used to identify entities related to illicit trade. Section 7 discusses strategies to combat illicit activities and their associated networks. Section 8 is a critical analysis into the research gaps and possible directions for future research. Finally, Section 9 concludes the discussion with summary statements. Following this sequence is not mandatory; in fact, one can move from Section 3 to any of the other sec- tions based on your interests. For example, a reader more interested in the network perspective and less in supply chain aspects can skip Section 4 and directly proceed to Section 5.

2. Review methods and statistics

To our best knowledge, there has not been any review paper discussing illicit trades/supply chains on such a broad scale. The topics addressed in the review comprise research from multiple disciplines (industrial engineering, management sci- ence, criminology, network science). As a result, we could

CONTACT Rakesh Nagi [email protected] Copyright � 2021 “IISE”

IISE TRANSACTIONS https://doi.org/10.1080/24725854.2021.1939466

not set any specific strategy for searching the papers. Instead, we had to rely on searching through Google Scholar for a set of keywords related to illicit trades (e.g., illicit/ illegal trade, illicit/illegal supply chain/network). We also searched for keywords relevant to individual trade categories (drugs, counterfeit, arms, wildlife). The initial screening was done through reading the abstract. While reading the selected manuscripts in detail, additional papers were dis- carded or included in the literature as per relevance. The final tally of the articles cited in this article stands at 239 and their sources include journals, conference proceedings, online and technical reports, and books. A summary of the descriptors of the literature reviewed is shown in Figure 1 (Year-wise statistics), Figure 2 (Trade-wise statistics), and Figure 3 (Journal statistics).

As seen in Figure 1, research in illicit trades has increased gradually over time, with approximately a 40% increase over the last 5 years. In terms of trade categories, the top position is reserved, as expected, for narcotics. However, the glaring gap between narcotics and the other trades shows the skew- ness of research advancement and focus in illicit supply chains. Figure 3, on the other hand, indicates the highly diverse perspectives of illicit supply chain analysis, including criminology, operations research, data science, network sci- ence, risk analysis, and so on. The highest number of cita- tions from a single journal (Crime, Law, and Social Change) was only six, which is only 2.5% of the total number of articles.

3. Illicit trade

3.1. Definition of illicit trade

According to the World Economic Forum (2012), illicit trade involves the process of gaining money, goods, or value gained from illegal and generally unethical activity causing harm to the economy, society, environment, or politics. Feige (1997) described illicit trades comprising non-compli- ant economic behaviors like evasion, avoidance, circumven- tion, and corruption of rules as well as efforts to hide these behaviors from public authority surveillance. On the

contrary, governmental institutions try to suppress these trades leading to social, economic, and organizational fric- tion between the two entities. Crotty and Bouch�e (2018) mentioned two key risks distinguishing illicit markets from its licit counterparts. First, buyers and sellers carry the risk of getting arrested. Second, they cannot rely on state or legal institutions to enforce market rules. These risks prevent illicit businesses from adapting to changes in the economic environment, rendering their failure to match the efficiency of licit trades (Dean et al., 2010). Despite such inefficiency, illicit trade is still maintaining an annual turnover of 2.2 tril- lion dollars (Coke-Hamilton and Hardy, 2019).

3.2. Reasons behind illicit trade

While investigating the factors facilitating illicit trade, one must first acknowledge that significant demand exists for its associated products. This demand, along with the lucrative payoff for successful transactions, creates strong economic incentives for participation in illicit trades (Basu, 2014a). Poor socio-economic condition adds stability to the market, as people start treating this as a profession. And the growth or decline of the market is dictated by risk in operation, which in turn is dependent upon the government’s ability (or will) to detect and prosecute criminals (Helbling et al., 2012; Grant Thornton, 2013; Hauenstein et al., 2019). Apart from these, regional influence, trade regulation, tax policy, and lack of awareness also contribute to its growth (Helbling et al., 2012; Basu, 2014a; Patel et al., 2015). However, one should not consider this list as exhaustive since there can be factors specific to a particular trade cat- egory or a country. Statistical hypothesis testing could be one way to identify these additional factors. Gonz�alez Ordiano et al. (2020a) recently proposed another approach using node embedding and clustering.

Researchers have provided several quantitative models regarding the growth of the illicit market. Caulkins and Padman (1993) pictured the narcotics market growth as a function of the associated utility and risk, whereas Baveja

Figure 1. Summary of research on illicit supply chain over time. Figure 2. Summary of research on illicit supply chain by trade.

2 R. ANZOOM ET AL.

et al. (2004) based it on the enforcement level and economic hardship. Koen et al. (2017) attempted to predict wildlife trafficking through causal modeling, but had to rely on expert judgment for the modeling, due to data scarcity. The Economist Intelligence Unit Limited (2018) developed an index to indicate the vulnerability of a country to illicit trade. The index, however, was linear in nature and did not consider possible interrelationships between the factors.

3.3. Classification of illicit trades

Classification of illicit trades is roughly based on two prem- ises: product and trade characteristics. Researchers and gov- ernment agencies have mostly adopted the former approach, although their categorization has not been uniform (Basu, 2014a; WCO, 2017; Transnational Alliance to Combat Illicit Trade, 2019). The products are usually considered as either physical or virtual. However, illicit trades also involve the

egregious act of human and wildlife trafficking, which does not go with the definition of products and thus require add- itional categories. Illegality may also arise from a particular aspect of the trade: the product, its acquisition, exchange, or other regulation breaches (Beckert and Wehinger, 2013). These characteristics form the basis of an alternate classifica- tion approach. Fuzing both approaches, Staake et al. (2009) outlined a comprehensive classification scheme for illicit products and services. The current article adopts a similar approach, but overlooks services and virtual products. This intensifies our focus to the trade of physical goods, which is further classified into four categories: contraband trade, counterfeit trade, fencing, and parallel/illicit import. Figure 4 denotes the classification scheme.

The first two categories to appear in our classification are the trade of contrabands and counterfeits. Contrabands are products with an embargo or restriction on production (e.g., narcotics) or/and distribution (e.g., arms). Counterfeits, on

Figure 3. List of journals with multiple publication on illicit trades.

IISE TRANSACTIONS 3

the other hand, mimic the characteristics of a brand prod- uct. They can be of two types: deceptive and non-deceptive (Staake et al., 2009; Cho et al., 2015). Non-deceptive coun- terfeits can be distinguished from the brand products and are sold at a discount. In contrast, deceptive counterfeits are hard to detect and sold at the same price as brand products. The third category, fencing, represents the trade of stolen products (Johns and Hayes, 2003). The product traded can be used or new, and occasionally with an alteration (e.g., car parts). The final category, parallel import, is the sourcing of a legal product without authorization of the intellectual property owner. Products subjected to excise tax (e.g., cigar- ette, alcohol) commonly dominate this trade (opportunity to evade tax by importing from lower tax region). The legality of these products, however, is a matter of controversy and often hinges upon the law of exhaustion of intellectual prop- erty rights (Williams, 2020). For simplicity, we consider all such trades as illegal. It might also be possible for the same product to be distributed through different types of trades. For example, tobacco products in the market can be coun- terfeits, illegally imported, or even fenced. Moreover, trade can also occur physically as well as virtually. All these varia- tions make the general analysis of illicit trades somewhat challenging.

3.4. Impact of illicit trades

The impact of illicit trades is multi-faceted, traversing across different viewpoints. Illicit trade undermines human rights, upsets ecological balance, and circumvents law and order of the country. Another possible consequence is the loss of consumer welfare. Some of the products traded (e.g., nar- cotics, counterfeit drugs) can directly harm one’s well-being. The lack of commitment to maintain quality or provide ser- vice raises the risk of mismatch between actual and expected utility. Proactive consumers who assume this risk may opt not to buy such products or expect a lower utility from the product (Cho et al., 2015). These hurt the profitability of legal organizations in terms of lost sales, decreased brand value, and increased R&D expenditure. As a result, the gov- ernment collects less tax revenue. This, coupled with the poor performance of licit organizations, can cause unemployment creating further incentives for participation in illicit trades. For further information, we refer to the

work by Hintsa and Mohanty (2014) and Transnational Alliance to Combat Illicit Trade (2019), which discuss the implications of illicit trades from socio-economic and global sustainable development perspectives.

3.5. Sources of data on illicit trade

Acquisition and analysis of data is useful for developing a realistic understanding of illicit operations and their associ- ated network. However, significant challenges need to be overcome during their collection and application (e.g., avail- ability, accuracy, completeness), hindering development of quantitative studies. Researchers have nevertheless attempted to produce them using both conventional and innovative sources. For convenience of the readers, some of these data- bases are listed in Table 1.

Based on accessibility, one can categorize data sources as open, closed, and classified. Data provided by open sources are publicly accessible, whereas those in closed sources require permission for access. Classified sources contain vital informa- tion that are restricted from sharing. From a format perspec- tive, data provided by these sources can be classified as unstructured, semi-structured, and structured. Unstructured data contain a swarm of information, whereas structured data systematically categorize the information. Organization in semi-structured data falls somewhere between these two. The following subsections introduce five major sources of data and discuss their utility and limitations. These include: news and media, law enforcement, court proceeding, organizational database, and miscellaneous sources.

News and media: Whenever a significant event such as seiz- ure or arrest occurs, it appears in the media. They also pro- vide investigative reports occasionally. Coscia and Rios (2012) used such reports from online newspapers and blogs to identify the mobility of drug traffickers. Important infor- mation can also be obtained from different websites (Crotty, 2015; Patel et al., 2015; Farrugia et al., 2020). In recent times, researchers have turned to social media for data accu- mulation (Mackey and Kalyanam, 2017; Zhao et al., 2020). Although easy access is a definite advantage for these sour- ces, most of the data available are unstructured and require further processing. Moreover, the reliability of these data is questionable.

Figure 4. Classification of illicit trade.

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Law enforcement: Due to direct engagement with criminals, law enforcement agencies possess substantial information regarding illicit networks. These information are mostly unstructured and available in multiple forms including arrest records (Morselli and Petit, 2007; Duijn et al., 2014), phone records (Agreste et al., 2016), wiretap transcripts (Natarajan, 2006), intelligence reports (Malm and Bichler, 2011; Coutinho et al., 2020; Toledo et al., 2020), and co-offense and financial transaction reports (Levitt and Venkatesh, 2000). However, they are not readily attainable and classified in some cases, and often depend on the extent of the investigation and might include bias. Nevertheless, this remains a major data source for researchers.

Court proceedings: Researchers have also used court proceed- ings as a source of data for illicit trade, often in the format of the prosecutor’s file and court records (Fuentes, 1998; Becucci, 2004). These involve the summary of law enforcement investi- gation (Agreste et al., 2016; Cavallaro et al., 2020), in addition to witness statements, transcripts of trial, and judges’ sentenc- ing comments (Bright and Delaney, 2013; Bright et al., 2019). The advantage of using this source is that it contains several types of information. And since they are made available after closure of the case, access is relatively easy. However, the time difference between the event occurrence and data availability can be long, depending upon the pace of prosecution. And similar to law enforcement data, sampling bias is possible, since available data mostly represent information of failed (busted) enterprises, not necessarily the successful ones.

Organizational databases: Various organizations are work- ing nationally and internationally on different aspects of illicit trades (e.g., OECD, WCO, EMCDDA, UNCTAD, UNODC, WHO). Many of these maintain databases that are mostly structured and closed. Researchers have often used these data in their research (e.g., UNODC Individual Seizure Data by Giommoni et al. (2017), Consolidated Counterdrug Database by Magliocca et al. (2019)). Business organizations can also keep databases to track their products and detect possible counterfeits. Gonz�alez Ordiano et al. (2020b) used such a dataset for analyzing licit and illicit supply chains.

Miscellaneous: This includes any sources that do not fall under the above categories. An interesting instance was Tsirogiannis and Tsirogiannis (2016) using the book of Watson and Todeschini (2007) to derive illicit antiquity traf- ficking networks. Personal interviews are also often used for

information extraction (Stevenson and Forsythe, 1998; Bradshaw, 2016; Caulkins et al., 2016;).

As previously mentioned, data on illicit trade is far from being perfect. Existing flaws relate to four major issues: incompleteness, boundary specification, dynamics, and coord- ination. The first one, incompleteness, is not surprising, given the concealment of illicit networks. Without complete infor- mation, analysis of these data may suffer from lower credibil- ity. Researchers, however, have made progress in predicting some of the missing entities (see Section 6.2). The second issue, also known as the boundary specification problem, addresses the confusion regarding the extent of the network to consider. In response to this question, Bouchard (2007) presented three viewpoints: research intent, member outlook, and social interactions. Campana and Varese (in press) coun- tered by suggesting five strategies regarding boundary specifi- cation. However, the information available is collected from the perspective of law enforcement agencies and thus may not meet these conditions, leading to possible bias. The third con- cern is about the incorporation of dynamics, which requires frequent updating of the database. Generally only organiza- tional databases happen to do so, since other sources provide event-specific data. Understandably delayed access may make the data obsolete, diminishing the value of its insights. The last concern we raise is the coordination of data among differ- ent organizations. Nowadays, multiple agencies gather data on distinct features of illicit trades. Often there is an overlap in the trade or jurisdiction. Data sharing would accelerate this data accumulation process. However, agreements must be made on terminologies to use. Haas and Ferreira (2015) illus- trated the development of such a database on wildlife traffick- ing. Another possible coordination approach could be the amalgamation of different types of data (Kammer-Kerwick et al., 2018).

4. Supply chain view of illicit trade

A typical supply chain consists of five stages: supplier, manufacturer, distributor, retailer, and consumer (Chopra and Meindl, 2019). For illicit supply chains, though, no such exact number has been universally agreed upon. Kilmer and Hoorens (2010) proposed the existence of four stages (pro- duction, distribution, retail, and consumers) for narcotics supply chain, whereas Basu (2014b) listed six for wildlife trafficking. The latter further split the stages into three phases: upstream activities, concealment, and distribution. Most of the activities described in these stages were congru- ent with a conventional supply chain model. This led us to

Table 1. Databases related to illicit trade.

Database Context Author/Year Accessibility

MAGLOCLEN Survey Drug Caulkins (1995) Closed Medicine Quality Database Countereit Isah et al. (2015) Closed HealthMap Wildlife Trade Wildlife Patel et al. (2015) Open Drug Retail Price Data Drug Caulkins et al. (2016) Open UNODC Individual Drug Seizure Cases Drug Giommoni et al. (2017) Closed Consolidated Counterdrug Database Drug Magliocca et al. (2019) Closed Network Disruption Data Mafia Cavallaro et al. (2020) Open Link of Occurrence Database Criminal Toledo et al. (2020) Closed Etharscamdb Crypto-currency Farrugia et al. (2020) Open

IISE TRANSACTIONS 5

adopt the aforementioned five stages in the illicit supply chain model and we added a new stage on smuggling. A fundamental requirement in the illicit supply chain is the veiled flow of products and services, which is satisfied by smuggling. Turner and Kelly (2009) defined smuggling as the clandestine and unlawful transportation of goods between different jurisdictions. Viewed simplistically, its function is the same as logistics, i.e., transferring products from one location to another. However, it also involves tasks (e.g., product concealment, evasive route selection, docu- ment forgery, corruption of officials) deemed indispensable for success. Thus, smuggling can be considered a core com- petence in the illicit supply chain (Basu, 2013). The pursuit of excellence in this domain has eventually contributed to the emergence of smugglers, i.e., independent professionals specializing in smuggling (Morselli, 2001; Basu, 2013). Acknowledging these unique attributes, we consider smug- gling as a distinct stage in the illicit supply chain.

As mentioned in Section 3.1, not all stages in the supply chain need to be illegal. Here we define a step to be illicit if its actors participate consciously in the supply chain. Based on this definition, legal status of the stages might vary across trades. For example, suppliers are illicit in fencing, but licit in parallel trade (since the product manufactured is legal). One or more stages might be missing for some trades as well (e.g., unaltered fencing). Such disparity generates differ- ent flow configurations within the supply chain, as shown in Figure 5. Finally, variation may also stem from the direction of flows. The usual norm is the forward flow of products

and the reverse flow of funds. However, some cases might involve bi-directional flow of products (e.g., exchange of products in fencing), or an entirely different network for fund flow (Johns and Hayes, 2003; Brown and Hermann, 2020). Researchers have tried to incorporate these differen- ces through multiple models, which we discuss in the fol- lowing section.

4.1. Modeling perspectives of the illicit supply chain

Existing modeling approaches for the illicit supply chain have been conducted from the perspective of a particular trade rather than in a comprehensive manner. In this regard, the field of narcotics has received by far the most attention. Caulkins (1997) developed a model to describe the domestic distribution network of narcotics. It represented the number of customers to serve (branching factor) as a function of the quantity discount factor for price markup and the ratio of selling costs to product costs. Other modeling approaches of narcotic supply chains include network flow representation by Helbling et al. (2012), cyclic view by Caulkins et al. (2016), and global production network framework (Dicken, 2003) by Miltenburg (2018). Markowski et al. (2009) devel- oped a probabilistic multi-channel supply chain model to demonstrate the trade of illicit small arms. To explain the robustness of the supply chain, they presented the concept of tie and cut set, which denoted the minimum number of elements required to connect and disconnect the supply chain, respectively. For analyzing the trafficking of nuclear

Figure 5. Supply chain flow in different illicit trades.

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products, Bradshaw (2016) proposed Illicit Non-state Nuclear and Radiological Trafficking network model. The model incorporated features of three existing models: loca- tional model, network model, and enterprise model. The locational model looks into the factors that facilitate the ori- gination of a network, the network model visualizes the flow within the network, and the enterprise model outlines four major components governing the flow: supply, regulators, competition, and customer. Apart from these, Stevenson and Forsythe (1998) discussed four disposal methods of stolen goods (supply chain configuration) in fencing. Recently, Gonz�alez Ordiano et al. (2020b) used a variable-state reso- lution Markov Chain to model both licit and illicit (counter- feit) supply chains. Here they identified the optimal set of states (geographic areas) across three levels of analysis (country, region, continent) that best fit the supply chain model. Comparison of the limiting distributions for licit and illicit supply chain led to the discovery of potential hotspots of counterfeit activities.

4.2. Associated revenue and cost

As discussed in the previous section, illicit trade is quite profitable, with the price markup of some products reaching 600%. However, the distribution of revenues across different stages is not properly demarcated. A substantial share of the revenue is thought to be enjoyed by the distributors, grant- ing them power over manufacturers and retailers (Caulkins et al., 2016; Miltenburg, 2018).

Caulkins and Padman (1993) discussed five areas of expenditure in narcotic supply chains: procurement, trans- portation, inventory (holding and stock-out), and risk of arrest. Clemons et al. (1993) categorized them into two components: coordination cost and cost due to transaction risk. The first one is incurred in activities between trade partners. And the second one is due to risks regarding detection of operations, seizure of products and equipment, and arrest of members. To mitigate these risks, investment is made in concealment, corruption, and evasion. Basu (2014b) adopted these as the three elements of transaction cost. Williamson (1975) listed four drivers influencing these costs: asset specificity, operational uncertainty, frequency of operation and interdiction, and human behavioral aspects. Significant cost also occurs in money laundering, i.e., 10 to 17% of the smuggled amount (Soudijn and Reuter, 2016).

4.3. Performance drivers and strategies

Like legal business organizations, stakeholders in illicit trade also aim for efficiency to increase profit. However, pressure from law enforcement agencies forces them to incorporate strategies to secure their supply chain operations, creating a tradeoff between the two objectives. Chopra and Meindl (2019) listed six drivers of traditional supply chain perform- ance: facility, inventory, transportation, sourcing, pricing, and information. Basu (2014b) introduced three additional features specific to an illicit supply chain: transnational smuggling, money laundering, and corruption. We, however,

consider smuggling as a stage rather than a performance driver. Along with the remaining eight, we introduce two new drivers: concealment, and innovation and technology. In the following sections, we discuss the strategies regarding these 10 drivers.

1. Facility: Facilities in illicit trades are discreet and mobile, creating difficulty in their detection (Basu, 2014a). Despite this limitation, researchers have attempted to investigate their location strategies. Stevenson and Forsythe (1998) identified different stor- age facilities used in fencing. Zhao (2019) analyzed the facilities involved in the supply chain of chemicals that are precursors to narcotics in China. They noted the geographic location of manufacturers to be concen- trated in border provinces and rural settings, which indicate the consideration of efficiency and security, respectively. Crotty and Bouch�e (2018) went ahead and studied the locational strategies of illicit massage parlors linked with sex trafficking. The authors used Gi� clusters (Mitchel, 2005) to identify the spatial clus- ters of these facilities, whereas ordinary least squares regression and geographically weighted regression were applied to determine the variables impacting their configurations.

2. Inventory: Similar to legal trades, illicit trades also face the risk of a mismatch between demand and sup- ply, leading to the necessity of inventory management. Inventory is also needed to “cool off” stolen products to avoid detection by law enforcement agencies (Johns and Hayes, 2003). Not much work is found concerning the inventory management issues of illicit supply chains. Basu (2013) stated different responsibilities in narcotics inventory management that are carried out by stash managers. Caulkins et al. (2016) observed an increase in inventory turnover ratio down the drug supply chain. Magliocca et al. (2019), on the other hand, found the lot-size to be inversely related to interdiction risk. Miltenburg (2018) calculated an inventory level equal to 2 years of productions for opi- ate and heroin supply chains. However, his empirical study found a lower inventory level in practice, along with the absence of safety stock. These combined with seizure by law enforcement and variation in annual production can exacerbate the risk of shortage. For some narcotics such as heroin, this shortage is miti- gated by reducing the purity of the product.

3. Transportation: Success in illicit trade depends heavily on the execution of its logistics function, especially in concealment and evasion. While selecting an appropri- ate strategy, criminals have to make decisions regard- ing the modes and routes of transportation, as well as the number of transshipment points. Two factors gov- ern these decisions: profit and risk. For example, limit- ing the number of transshipment points allows members to enjoy a higher profit share. However, it also increases the distance of a shipment leg, and hence, the risk of interdiction. Being well versed in

IISE TRANSACTIONS 7

these affairs, illicit traders adopt strategies befitting dif- ferent scenarios (Basu, 2014a).

Researchers have characterized the distribution of smuggling routes to be non-uniform and clustered across a small number of paths (Boivin, 2013; Arroyave et al., 2020). Using exponential random graph modeling, Giommoni et al. (2017) identified four factors behind such configurations: risk, profit, geographic and social proximity. The presence of adversaries (e.g., rival organizations, law enforcement agencies) can also play a role in shaping the routes. Several attempts have been made to model the routing decision of smugglers. Sin and Boyd (2016) applied Dijkstra’s algorithm (Dijkstra, 1959) to define the potential route of nuclear traffickers that maximized their probability of evasion: specifically, they used a proxy for probability of successfully traversing an edge as its corruptibility. Meneghini et al. (2020) created a geographical likelihood network (where edges denoted the likelihood of flow between two node) based on seizure and consumption data of illicit cigarettes. The best set of paths in this network was identified using Yen’s algorithm (Yen, 1970). Tsirogiannis and Tsirogiannis (2016) tested three additional algorithms to infer the smuggling routes of antiquities between two given locations: shortest path algorithm, local expansion algorithm, and maximum weight arbor- scence method. The characteristic of the optimal path in these three methods were: minimum number of links, presence of most probable links, and maximin transaction volume, respectively. Two criteria were used to evaluate their accuracy: number of correct pre- dictions and modified Levenshtein distance (Wagner and Fischer, 1974), i.e., the minimum number of node operations required in the predicted path to match the correct one. Shortest path algorithms appeared to be the better performer. Magliocca et al. (2019) developed an agent-based model to simulate the shift of routes in response to government interdiction.

4. Pricing: Most of the studies addressing illicit product pricing take place in the field of narcotics. One of the earliest works regarding this was done by Brown and Silverman (1980), which estimated the retail price of heroin based on quantity and purity. Caulkins (1995) found drug prices to be an increasing function of the distance between sales point and source, as well as a decreasing function of market size and transaction quantity. However, their findings were limited to local markets only. Miltenburg (2018) dismissed this reser- vation by stating that the retail price was determined solely by local factors. Despite these formulations, drug prices exhibit high volatility, due to low inventory lev- els and uncertain lead times (Caulkins and Baker, 2010).

Cho et al. (2015) presented a game-theoretic model to simulate the pricing decision of counterfeit products in retail and wholesale stages. Here, equilibrium strat- egies (subgame-perfect Nash) were derived for

counterfeiters in response to the quality and price of brand products. Other factors taken into consideration for determining the optimal price involved functional quality and market share of the counterfeit product, as well as the risk and cost of product confiscation or dealer incarceration.

5. Sourcing: Sourcing of precursor materials plays a piv- otal role in ensuring the viability of the illicit supply chain. These materials may be legal or illegal. Legal ones are known as dual goods, since they have applica- tion in both businesses. Criminals often use deceptive tactics to procure these from suppliers without expos- ing their intended use (Albright et al., 2010; Zhao, 2019). However, suppliers can also conspire with the criminals. The number of suppliers to include may vary depending on the sourcing strategy. However, there exist risks of incompetence and betrayal since no legal binding is applicable. Johns and Hayes (2003) discussed this issue in fencing and categorized the sup- pliers into four groups based on their competence and reliability: good thieves, in-between thieves, walk-in thieves, and dopers. Liu et al. (2004), on the other hand, modeled the sourcing decision of a distributor as a shocked multi-item newsvendor problem, where both genuine and counterfeit products could be pur- chased. In this model, optimal purchase quantity was determined by inspection rate and penalty.

6. Information: The performance of a supply chain is highly dependent on its communication structure. Better information sharing can improve coordination across different stages. The same goes for the illicit supply chain. However, the constant threat of interdic- tion makes the dissemination of information risky here. Illicit traders, hence, face a tradeoff between effi- ciency and security while determining the extent of information sharing. One way to accomplish this is to compartmentalize operations, restricting the spread of its information only to specific groups. This way, if any member is exposed, only information about that particular operation will be imperiled.

Regular success in extracting information from phone record and wiretap denote that criminals use conventional technologies like cellphones. However, the use of advanced communication technologies is also increasing (Deville, 2013). Over the last few years, the internet has appeared as an essential communica- tion medium. Criminals are observed using chat rooms and private messaging services to exchange informa- tion about illicit operations (Motoyama et al., 2011). Social media and darknet are also facilitating commu- nication between vendors and consumers.

7. Concealment: Criminals employ different concealment strategies to avoid detection by adversaries (e.g., law enforcement agencies, licit suppliers and retailers). The bulk of their efforts focus on the clandestine transpor- tation of illicit products. This involves embedding them into licit products, hiding in vehicles, or using misleading packaging (Williams and Godson, 2002;

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Decker and Chapman, 2008; Zhao, 2019). Basu (2013) documented different concealment strategies used in drug smuggling. The adoption of a particular conceal- ment strategy depends on its implementation cost as well as the cost inflicted to government agencies for their detection. If the second cost exceeds the budget allocated for law enforcement, the detection risk will reduce for the illicit supply chain, increasing its profitability in turn. For these reasons, concealment capability can be considered a key driver in the illicit supply chain.

8. Corruption: The aim of corruption is to dissuade law enforcement agencies from disrupting illicit trade (Basu, 2014b; Shelley, 2018). In exchange, the corrupt personnel receives periodic bribes or a cut from unit revenue. Criminals see this as a competitive advantage, as it allows them to use cost-efficient transportation routes, enjoy lenient or no inspection, and protect ter- ritorial integrity (Michael, 2012). In addition, corrupt agents can also act as double agents providing infor- mation about law enforcement plans (Van Der Veen, 2003). This relationship between criminals and officials can be short-term and activity-specific, or long-term and institutionalized. Criminals favor the latter since it allows them greater control over operations. And at its extreme state, criminals can even influence national policies toward their advantage (Greenhill, 2009). These advantages, however, are weighed against costs affiliated with corruption. Basu (2014b) discusses these costs as well as their drivers.

9. Innovation and Technology: Every time law enforce- ment agencies catch a shipment of illicit products, its trafficking strategy is compromised. Criminals, there- fore, have to conjure a new strategy for sustaining their operations. Thus, innovation plays a crucial role in strengthening the illicit supply chain, especially in the concealment of goods and finances (Basu, 2013). The rapid pace of technological advancement has remarkably enhanced these capabilities, e.g., sales and marketing by social media and darknet, money laun- dering through crypto-currencies (Seddon, 2014; Dittus et al., 2018). However, these amenities also pro- vide law enforcement agencies information to trace their activities. Mackey and Kalyanam (2017) detected illicit fentanyl sale sites from twitter using text filtering and biterm topic model. For similar purpose, Zhao et al. (2020) used methods based on a support vector machine and convolutional neural networks. Di Minin et al. (2018) suggested using deep learning algorithms to detect illegal wildlife trades. All these approaches were implemented in contraband trade, where the products are unique. However, in case of other illicit trades, the classifier may face difficulty in differentiat- ing between legal and illegal products because of high similarity (see, e.g., the study by Sam et al. (2007) to identify pornographic websites).

10. Money Laundering: Finance is considered a critical resource in an illicit supply chain and thus holds the

key to organizational viability. Understanding this, law agencies try to intercept its flow in illicit supply chains (Bright et al., 2017). Close surveillance by officials deters the use of traditional transaction channels for this purpose. However, criminals circulate funds either by skirting security measures in conventional mediums or through innovative mechanisms (Godspower- Akpomiemie and Ojah, 2019; Brown and Hermann, 2020). This illegal flow of money, i.e., money launder- ing, consists of three stages: disassociation from source, obfuscation of money trail, and legitimization of the fund (Naylor, 2004). Brown and Hermann (2020) denoted three primary methods of money laundering: banking system, non-banking system (e.g., hawala, cash courier), and geographically oriented system (e.g., offshore company, free trade zone).

Although non-cash payments are not uncommon, cash remains the preferred payment method in illicit trade, due to low traceability (Tammaro, 2014; Zhao, 2019). Of late, cryptocurrencies are steadily gaining popularity with the provision of anonymity and swift convertibility, greatly enhancing the security of an illicit supply chain (Fanusie and Robinson, 2018). In response, researchers have invested in methods to detect and thwart money laundering. Chen et al. (2018) provided a detailed review of machine learning methods used to detect suspicious bank transactions. On the other hand, Hirshman et al. (2013) and Farrugia et al. (2020) attempted to identify accounts conducting illegal transactions of bitcoins and etherium. To do so, the former used k-means clustering and RolX algorithm, and the latter employed the XGBoost algorithm.

5. Network view of illicit trade

The frequent association of illicit trade enablers with organ- ized criminal groups and terrorists has created the need for viewing and interpreting illicit trade operations in terms of the interactions between the entities involved. This, in turn, has led to a network representation of illicit supply chains, commonly referred to as illicit or dark networks (Duijn et al., 2014). Researchers have leveraged different tools from network science and analysis for analyzing the properties of these networks. In particular, social network analysis (Wasserman and Faust, 1994) is commonly employed in the literature on illicit networks (see, e.g., Kinsella (2008); Bright et al. (2012); Arroyave et al. (2020)). Ideally, one would like to know the full network in advance. However, this informa- tion is often unavailable at the outset and has to be obtained through a discovery process: these mechanisms are discussed later in Section 6. For the remainder of this section, we assume complete network knowledge, i.e., consider the com- pletion of discovery process. In the following subsections, we explore different features of illicit networks and present studies relevant to them.

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5.1. Structure of illicit networks

Based on their structure, one may classify networks as ran- dom Erd}os-R�enyi (ER) (Erd}os and R�enyi, 1959), small-world (Watts and Strogatz, 1998), or scale-free (Barab�asi and Albert, 1999). Random ER networks possess links with the same probability of existence. Small-world networks are associated with high clustering and small characteristic path length (diameter). Although in these networks most nodes are not adjacent, they tend to share neighbors. In a similar vein, most nodes in scale-free networks also follow this characteristic. A small number of nodes, referred to as hubs, show high connectivity. This property is consistent with the power-law degree distribution.

Illicit supply-chain networks are not typically represented by ER networks. Instead, Kinsella (2008) and Malm and Bichler (2011) mentioned the scale-free structure of illicit arms and narcotics markets. Tsirogiannis and Tsirogiannis (2016), on the other hand, found conformity of both scale- free and small-world characteristics in antiquity trafficking networks. However, they refrained from confirming it as scale-free, citing an insufficient number of nodes. Another concept of criminal networks was provided by Borgatti and Everett (2000), who split it into two node sets: core and per- iphery. The former consists of densely interconnected nodes, whereas the latter includes nodes that are sparsely con- nected. In another sense, the core and periphery sets can be considered as planners and executioners in the network, respectively. Gimenez-Salinas Framis (2011) found evidence of this structure in multiple Spanish cocaine traffick- ing networks.

Recent advancements in network science have prompted the adoption of complex networks in diverse disciplines. One of its variants, multiplex or multi-relational networks, has sparked interest among researchers working on illicit trade. Bianconi (2013) defined multiplex networks as the aggregation of networks with identical nodes but different links. Each constituent network, i.e., layer or plex, specifies the base of connectivity between actors (e.g., monetary, kin- ship, resource). Incorporation of relational multiplexity allows analysis from multiple perspectives, resulting in a bet- ter understanding of illicit networks (Bright, 2015). Section 5.2 provides further discussion regarding this point. Bahulkar et al. (2018a) and Baycik et al. (2018), on the other hand, adopted interdependent network structures. Duijn et al. (2014) offered a two-tiered drug trafficking network. The first tier represented individual networks operating in a particular market, whereas the second indicated a combin- ation of all such networks. Bichler et al. (2017) termed them as group and market structure, respectively. The representa- tions mentioned above are quite similar in nature. In fact, they all fall under the realm of multilayered networks. For interested readers, we refer to the review of Kivel€a et al. (2014), which provides a more precise definition of these networks.

A variety of metrics are available in the literature for explaining different aspects of a network, the simplest ones being node and edge frequency. Perera et al. (2017) defined their implications in supply chains. Bichler et al. (2017), on

the other hand, provided a list of metrics used in structural analysis of drug supply networks. In the current article, we will limit our discussion to the metrics separating illicit net- works from others. Agreste et al. (2016) found the average clustering coefficient in mafia networks to be higher than that in social networks. Xu and Chen (2008) differentiated drug trafficking networks from terrorist networks in terms of higher path length, clustering coefficient, and efficiency measures. In general, illicit networks are characterized by low density and high centralization (Baker and Faulkner, 1993; Morselli, 2009a; Bright et al., 2012). Some, however, disagree with this notion (Enders and Su, 2007). Bichler et al. (2017) mentioned the centralization and density in drug trafficking networks to be lower than legitimate and co-offending networks, but greater than terrorist networks.

5.2. Ties in illicit network

Xu and Chen (2004) described organized criminal groups as a set of offenders connected through various types of rela- tionships. Factors facilitating these connections include trust, triadic closure, roles in the supply chain, and network per- formance (efficiency and security) (Morselli, 2009b; Bright et al., 2019). They may also originate from already estab- lished social networks of kinship, friendship, or ties (McCarthy et al., 1998). Law enforcement agencies pursue these links employing various techniques. However, it is unlikely to discover all links within the network. Some of the missing ties can be predicted using link analysis, which will be discussed in Section 6.2.

While examining the associations among members in illicit supply chains, one should acknowledge the diversity or multiplexity present in them, which, until recently, was ignored by traditional social network analysis (Everton, 2009; Papachristos and Smith, 2012). In the study of a drug trafficking network by Bright et al. (2015), about 48% of the nodes were involved in multiple types of relations. Assuming the exchange of each particular resource as a dis- tinct relationship category, they identified eight types of links: drugs, money, precursors, premises, skills, informa- tion, equipment, and labor. Krebs (2002) considered four categories of criminal ties: trust, task, money and resource, strategy and goal. Papachristos and Smith (2012) provided a different classification scheme involving criminal, personal, and legitimate contacts. None of these papers, however, con- sidered adversarial relations representing rivalry or enmity.

Although incorporating multiplexity into networks does provide for better analysis, considering too many categories may also cause the constituent networks to be sparse and incomplete. To avoid that, one might aggregate them into groups or layers (Krebs, 2002). The question that arises then is how many layers to use. Gera et al. (2017) related its answer to the quality of community detection in the result- ing networks. They also provided an index combining four quality detection metrics referred to as: Normalized Mutual Index, Purity, Rand Index, and Adjusted Rand Index.

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5.3. Roles and positions in illicit network

Position or role in a network can be defined as the set of nodes that are structurally substitutable (Xu and Chen, 2005). In the case of illicit networks, these are associated with specific skills (i.e., human capital) and may vary depending on activity or market niche (Bichler et al., 2017). Reported indicators of complex roles include high degree and betweenness centrality, as well as low clustering coeffi- cient (Malm and Bichler, 2011; Calderoni, 2012). To identify these roles, Xu and Chen (2005) suggested using positional analysis, i.e., investigation of how similarly two nodes con- nect to other network members.

Among the different types of roles and positions in net- works, two are featured frequently in the literature: hubs and brokers (Everett and Borgatti, 1999; Borgatti and Everett, 2006). Hubs maintain higher connectivity, whereas brokers control the flow between different nodes. Kinsella (2008) pro- vided a detailed classification of brokerage roles in the arms trade. It identified a broker as a coordinator or liaison depend- ing upon whether the connection is between members of the same or different organizations, respectively. Based on the flow direction, they again classified liaison into gatekeepers (resource inflow) and representatives (resource outflow) Divi�ak et al. (2019) provided another node classification scheme based on position: visible, strategically positioned, marginal, and cen- tral. We shall review it in Section 5.5.

5.4. Key nodes in illicit network

Gathering information on illicit networks is a difficult task. With limited resources it becomes infeasible for law enforce- ment agencies to surveil or disrupt the whole network. Researchers, therefore, suggest focusing on key or central nodes instead (Shaikh and Jiaxin, 2008). Several propositions are available for their characterization in illicit networks. Ballester et al. (2006) identified them as the ones with the largest contribution to criminal activities. Baycik et al. (2018) suggested targeting the highest-ranked criminals, whereas Carley (2006) opted for removing emergent leaders. Borgatti and Everett (2000) assumed key nodes to occupy core positions in the network. However, this is not necessar- ily the case since central actors might assume peripheral positions to avoid detection (Baker and Faulkner, 1993; Agreste et al., 2016). Schwartz and Rouselle (2009) suggested two more perspectives for determining node importance: human and social capital. Human capital denotes the posses- sion of certain resources or skills, and associated nodes are usually hard to replace (Robins, 2009). Based on this, Hastings (2012) mentioned two types of actors in nuclear trafficking networks: have and have-not. Social capital, on the other hand, indicates the social connections with other nodes. Nodes with high social capital (i.e., hubs and brokers) are more capable of sharing information or resources with others. For better identification, Bichler et al. (2017) sug- gested using both human and social capital of nodes in the investigation.

Centrality measures have been a popular choice among researchers for identifying central nodes in illicit networks

(Bichler et al., 2017). Although several metrics exist (see Das et al. (2018) for details), three have prevailed: degree, closeness, and betweenness centrality (Shaikh and Jiaxin, 2008). The first two are global network measures, whereas the last one is a local measure. Patel et al. (2015) identified the key exporter and importer in wildlife trafficking using out-degree and in- degree centrality, while betweenness centrality designated top intermediary countries. Kinsella (2008) used the same approach to distinguish mediators in arms trades. Grassi et al. (2019) used seven variants of betweenness centrality to identify crim- inal leaders and found different results. They suggested choos- ing a measure according to the nature of data, as since each alternative captured discrete features of the nodes. Memon (2012), on the other hand, recommended inclusion of tie strength in centrality measurement.

Hussain and Ortiz-Arroyo (2008) applied Bayesian prob- ability theory alongside social network analysis to detect key nodes in criminal networks. Here, they ranked nodes based on the change in entropy upon their removal from the net- work. Farasat et al. (2016) provided two social network approaches (hop count weighted and path salience) to iden- tify high-valued terrorists from fused data. Taha and Yoo (2016, 2017) provided two more approaches for deriving prime nodes. The first one created a minimum spanning tree of criminal networks from existing data. In that tree, nodes received importance scores based on the number of vertices that were dependent on them for existence. The second method computed the relative individual influence of nodes over the network. It aimed at capturing the immedi- ate leaders of lower-level criminals.

While examining the positional importance of nodes across different network layers, Bright (2015) found fluctua- tions in the central position. No metric, however, was used to resolve it. Toledo et al. (2020) applied node diversity ana- lysis in a multiplex crime network to determine critical nodes. They considered two types of diversity: connection of node in various layers and heterogeneity of relationships in the system.

Bichler et al. (2017) considered network topology and detection algorithm to be determinants of quality of critical node identification. Researchers usually test the effectiveness of their approaches by comparing the result with a known network. These measures are: sensitivity, specifity, accuracy, recall, precision, F1 score, area under the ROC curve.

5.5. Network vulnerability, resilience and adaptability

A prerequisite for disrupting any network is to understand its strengths and weaknesses. In the case of illicit networks, these two are often intertwined. According to Bouchard (2007), resilience has two features: resistance to disruption and adaptation following the interruption. Cavallaro et al. (2020) cited three factors governing these capabilities: struc- ture, nodal position, and human capital. Scale-free and multiplex networks contain structural redundancy, i.e., diversity of links, which grants them tolerance to random disruption. However, this also makes critical nodes highly susceptible to targeted disruption. Nodal positions can also

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inform about the vulnerability or resilience of a node. Morselli (2010) used a scatter plot of betweenness against degree centrality to describe this situation. Betweenness cen- trality denoted the brokerage position of a node, whereas degree centrality indicated its visibility or vulnerability. The mean centrality scores divided the graph into four quad- rants, which Divi�ak et al. (2019) mentioned as the location for central, strategically positioned, marginal, and visible nodes, respectively. Central nodes have high betweenness and degree centrality. As a result, these nodes are valuable and vulnerable to surveillance or interdiction by law enforcement agencies. Strategically positioned nodes also have high betweenness centrality, but lower visibility reduces their detection probability. Bright (2015) assumed a node to be strategically positioned if its betweenness and degree cen- trality were at least one standard deviation above and below the respective mean network score.

Besides resistance to disruption, illicit networks are also capable of adapting and recovering from interruption, increasing the number of steps for their destabilization. Basu (2013) viewed their adaptation process as a cat and mouse game. Law enforcement agencies try to put pressure on illicit operations either by increasing surveillance or through offensive actions. Traffickers respond by changing conceal- ment techniques and transportation modes or shifting to alternative routes. The latter phenomenon is known as the balloon and cockroach effect, i.e., law enforcement actions in one location results in the displacement of trafficking activities to another location (Kleiman, 2011; Reuter, 2014). Changes can occur in network size and reach as well. To recognize these mutations, researchers use simulation (Caulkins et al., 1993; Rydell et al., 1996; Dray et al., 2008; Magliocca et al., 2019).

Networks often experience loss of active members due to interdiction attempts by law enforcement agencies. In such circumstances, one has to look for replacements. Trust plays a crucial role in their selection process. Thus, substitutes often come from short social distances from the cohesive core (Duijn et al., 2014; Bright et al., 2017). However, for replacing a specialist, one might have to go beyond the existing network. Two significant risks arise here. First, it may require cooperation with personnel whose reliability is unknown. Second, the search increases network communica- tion, raising network visibility as well as vulnerability (Lindelauf et al., 2009). Duijn et al. (2014) provided a quan- titative model for explaining the recovery mechanism in illicit networks. According to it, when a particular node is removed from the network, its adjacent (orphan) nodes look to recover the link by connecting to replacement nodes. These replacement nodes are selected by three methods: ran- dom recovery, preference by distance recovery, and prefer- ence by degree recovery. The first mechanism does not have any preference and treats all nodes equally. The second pro- cedure evaluates the distance of candidate nodes from the orphan actor for selection, while the last one judges on their degree centrality. Interestingly, the network came out to be more efficient than before in all three mechanisms. However, for that, they had to sacrifice their security,

leading to more scope for interdiction. In case of repetitive disruption, the network eventually becomes completely vis- ible, sinking its adaptation capability and causing failure.

5.6. Network dynamics

Carley et al. (2002) stated that failure to account for net- work dynamics and adaptation can lead to erroneous policy- making. However, not many studies are available concerning the dynamics of illicit networks. One significant reason behind this is the static nature of the acquired data, which we discussed in Section 3.5. Nevertheless, this highly-antici- pated field, often dubbed as the holy grail of network ana- lysis, has been receiving increased attention over the last few years. The prime objective of these studies is to comprehend how different aspects of illicit networks change over time. Bright and Delaney (2013), for example, discussed the struc- tural changes that took place in a drug trafficking network. In their study, centralization of an organization increased when it became more profit-oriented, indicating a preference for efficiency over security. However, this does not necessar- ily imply the disregard of security. Bright et al. (2019) men- tioned that actors optimized security through triadic closure, trust, and communication through brokers. They based the conclusion on a 2-year study of a drug trafficking organiza- tion that evolved from a small social network into a large profit-oriented corporation. A stochastic actor-oriented model was applied to understand the formation of links, whereas the Jaccard index was used to identify network sta- bility over time. Recently, Meneghini et al. (2020) con- structed a dynamic transnational cigarette trafficking network that reflected its activity from 2008 to 2017. Broccatelli et al. (2016), on the other hand, suggested bi- dynamic line graph representation of illicit networks and demonstrated its application on three case studies.

6. Uncovering illicit supply chains

Sections 4 and 5 provided a detailed review on the structure and operations of the illicit supply chain and methods to char- acterize them. However, different concealment and evasion strategies by criminals create difficulty in the identification of the supply chain. Researchers nevertheless endeavor to achieve greater success in exposing them. Their efforts concentrate on the detection of three principal entities: products, activities, and networks. Section 4 has already addressed the second issue. Therefore, this section will discuss the remaining two topics.

6.1. Detection of products

Land and sea ports of entry are regularly used by criminals to smuggle illicit products across borders. Port authorities try to identify these products using different inspection measures. Although manual unpacking is the surest way to identify illicit products, resource and budget restrictions impede its complete implementation (Martonosi et al., 2007). Therefore, the detection system typically follows a risk-based approach instead. Generally, shipments are

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assigned to different risk categories based on available infor- mation and additional intelligence. The higher risk contain- ers pass through subsequent detectors for further inspection. If this increases the risk, the cargo is unpacked and physic- ally checked. Otherwise, it is released. Decisions involved in the design of an inspection system include the choice of inspection strategies and tools, the number of detectors to use, their arrangement, and the conditions for further screening. And the system performance is assessed through the detection rate and efficiency. Intuitively, one can increase the detection rate by adopting a stringent inspec- tion policy. However, this also increases lead time and cost, reducing operational efficiency in turn. Thus, inspection strategies have to be devised in a way that satisfies these conflicting objectives.

The first issue to examine is the choice of inspection methodology. Kantor and Boros (2010) described three major inspection categories: document screening, scanning test, and manual unpacking. Among these, document screening is the least expensive. It attempts to detect docu- ment fraud by analyzing patterns in trade (Hua et al., 2006; Digiampietri et al., 2008; Yaqin and Yuming, 2010) or itin- erary (Camossi et al., 2012; Dimitrova et al., 2014). Triepels et al. (2018) included both approaches in their analysis, which employed a Bayesian network model to predict the presence of goods in the container. The second method, scanning test, employs Non-Intrusive Inspection (NII) equipment (e.g., Radiation Portal Monitor (RPM), gamma- ray scanners) to examine the consignments. Since these technologies need to be purchased and maintained, cost for this method exceeds the previous one. Dimitrov et al. (2011) mentioned several factors affecting the performance of the detection equipment (e.g., material and container type, geo- metric attenuation, shielding, background, sensing time). Gaukler et al. (2012) evaluated three schemes for detecting nuclear materials in the cargo: the existing Automated Targeted System (ATS), radiography-based Hardness Control System, and Hybrid Inspection System. In terms of maximizing the minimum detection probability, the latter two outperformed ATS under a wide range of conditions. However, the decision was highly dependent on the reliabil- ity of available data. Apart from this, operating costs and the rate of false alarm are important as well. Especially, the latter issue is quite significant since it can cause user dissat- isfaction and port congestion, leading to lower competitive- ness. Bak ir (2008) suggested comparing these issues against the perceived security risk. He also advised against invest- ment in new technologies unless the security risk becomes high. Kantor and Boros (2010) developed an index to exam- ine the cost-effectiveness of different inspection strategies. It incorporated four factors: sensitivity, specificity, cost, and detection rate.

Bakker et al. (2020) stressed on having more assets rather than having better assets. This raises another salient ques- tion: how many detectors to use in the system. Intuitively, one would think of increasing the number as much as pos- sible, as it would increase the detection rate. However, cost- efficiency also needs to be considered in this decision.

Jacobson et al. (2006) compared the cost-effectiveness of using single and two-device explosive detection architectures at airports. Their analysis recognized the single device archi- tecture as the better performer. Kretschmann and M€unsterberg (2017), on the other hand, used a discrete- event simulation framework to evaluate different combina- tions of NII technologies in a border crossing. Here, they identified the existence of a tradeoff between Type I (false- clear) and Type II (false-alarm) errors in increasing identifi- cation devices. Besides, adding more detectors did not necessarily increase lead time and utilization rate. Instead, it depended on the logical structure of the detection architec- ture. Wein et al. (2007) optimized the number of RPMs in a port considering budget capacity as well as queue length and time.

Researchers have also investigated the location strategies for detectors, often using stochastic network interdiction models (Pan et al., 2003; Nehme, 2009; Dimitrov et al., 2011). Here, the smuggler selects the path maximizing the probability of undetected transportation, whereas the authority (interdictor) installs detectors to minimize this probability. Stochasticity arises in smuggler’s origin and des- tination, type of product smuggled, detector probability, and manner of shielding. Nehme (2009) provided three versions of this problem: Stackelberg game, Cournot game, and a hybrid game. In the first game, the smuggler is aware of the detector locations. The second game allows both the inter- dictor and the smuggler to act simultaneously. In the third game, the smuggler knows some of the detector locations. All three problems were formulated as mixed-integer pro- gramming models and solved using branch-and-bound algo- rithms. Wein et al. (2007) also optimized the spatial positioning of RPMs in three designs of border inspection systems. For that, they used a mathematical model consist- ing of a queuing model, a detection model, and a cost model. The objective was to minimize the mean detection threshold for 95% detection probability under budget con- straints. Under the operating assumption that the position- ing of weapons within containers affects the detection rate, both best-case and worst-case placement scenarios were con- sidered. The optimal design reduced the existing detection threshold to one-third, which was effective in detecting plu- tonium, but not uranium.

As previously mentioned, inspection processes may involve multiple steps, where the outcome of a particular stage determines whether to elevate a consignment to its subsequent stage or not. Since each stage might consist of multiple detectors, one has to decide on the number of device alarms that will lead to further screening. McLay and Dreiding (2012) formulated this as a knapsack problem model (Multilevel Knapsack Screening Problem), aiming to maximize the detection rate within a fixed budget. For com- parison, they introduced another threshold-based model (Multilevel Threshold Knapsack Screening Problem). The analysis showed that threshold-based policy was not optimal under all conditions, but its detection probability was near- optimal. Boros et al. (2009), on the other hand, developed a large-scale linear model to determine the optimal inspection

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sequence. Here, they considered multiple thresholds for each sensor. However, some have cautioned against using a pure strategy since adversaries can learn of this and take adaptive measures. An alternative can be using mixed strategies, i.e., random assignment of containers to different paths through multiple detectors, which maintains constant risk for smug- glers (Boros et al., 2009; Kantor and Boros, 2010). Apart from these, Sherman et al. (2012) used a set of simulation tools (Scenario Analysis, Decision Trees, Monte Carlo Simulation) to identify the most cost-efficient inspection rates. Extensive research also exists in design and analysis of aviation security systems, which is summarized in the survey by Lee et al. (2008) and Albert et al. (2021). Although the papers focused mostly on the passenger transportation sys- tem, they could also be applicable to freight transportation and other transportation modes.

6.2. Identification of illicit networks

Section 5 presented different properties of known illicit net- works and their analyses. In this section, our focus shifts toward the discovery of these networks. Existing literature offer multiple techniques to facilitate this endeavor. For example, Diesner and Carley (2004) combined network text analysis (Popping, 2000) with meta-matrix modeling to obtain the representation of a Middle-Eastern illicit network. Anwar and Abulaish (2014), on the other hand, used a social graph-based text mining framework (n-gram tech- nique and hyperlink-induced topic search) on criminal chat logs to derive their network. Ozgul et al. (2012) developed four network detection models that looked for node similar- ity in different sets of features (e.g., crime location, date, modus operandi, surname, hometown, co-offending data). Testing the models on several databases, they observed that narcotics trafficking network was the hardest to detect.

One might also be interested in the discovery of a par- ticular element of the network rather than the whole. In that sense, the detection techniques fall under three categories: nodes, edges, and communities. The following subsections provide a brief discussion on these issues.

Node detection: Members of illicit networks tend to exhibit interactions that are suspicious and different from their peers (Chandola et al., 2009). In other words, they are con- sidered anomalies in a social network. Bindu et al. (2017) applied an unsupervised anomaly detection method in a multilayered social network to identify such anomalies. In that paper, they defined nodes with near-star or clique neighborhood topology as anomalous. Suehr and Vogiatzis (2018), on the other hand, used integer programming and a k-club path-like formulation to identify nodes that might misrepresent themselves in social media. In this case, the network was single-layered.

Link analysis: Link analysis/prediction involves estimating the likelihood of a link existing between two nodes based on observed ties and attributes of the nodes. Researchers have proposed different algorithms over time for this purpose

(see Al Hasan and Zaki (2011) and L€u and Zhou (2011) for details). However, our focus is limited to the studies in illicit (and criminal) networks. Schroeder et al. (2007) discussed four principal methods for criminal link analysis: heuristics- based, template-based, similarity-based, and statistical. Link detection in these methods depend on decision rules, pre- defined template, similarity between entities, and lexical sta- tistics, respectively.

Xu and Chen (2004) proposed two shortest path algo- rithms (priority-First-Search (PFS) and two-tree PFS) to identify associations between criminal entities. Logarithmic transformation converted the link weights into distances. When applied to a drug network, PFS fared better in terms of execution time. Schroeder et al. (2007) combined co- occurrence analysis, shortest path algorithm, and a heuristic approach for automatic link analysis. Isah et al. (2015) used a bipartite network model to infer hidden ties between actors in the illicit medicine supply chain. Initially, they con- structed a network with two node sets: actors and resources. Later, it got converted into an actor–actor network, where an edge connected two nodes if they had a common neigh- bor in the previous network. The model was validated using a standard network algorithm for structural and community analysis. Lim et al. (in press) compared a link prediction model based on reinforcement learning against machine learning methods. The former model provided better accur- acy with a smaller dataset. Marciani et al. (2017) investigated different social network metrics for detecting and predicting links in an evolving criminal network. Moreover, the appli- cation of data stream processing approach allowed the extraction of valuable information in real-time. For that, they also introduced three similarity social network metrics. Calderoni et al. (2020) applied several link prediction algo- rithms on a mafia network and observed that algorithms using the full graph topology had better accuracy and con- sistency. They also investigated the impact of different data attributes on the prediction accuracy. Results from simula- tion showed that the link predictions remain robust as long as information on the network was fairly complete, and the unobserved edges followed a generative law.

Community detection methods: Communities, also known as clusters or modules, are independent compartments in a network that exhibit a higher concentration of edges within themselves than with members of other sections (Fortunato, 2010). Calderoni and Piccardi (2014) mention communities as a natural phenomenon in illicit networks. Thus, their dis- covery can provide useful insights regarding the network structure. Xu and Chen (2004) used a hierarchical clustering approach for automatic detection of subgroups in criminal networks. For this purpose, they used a Reciprocal Nearest Neighbor-based complete-link algorithm. Isah et al. (2015) applied four community detection algorithms (Girvan Newmann, Clauset Newmann Moore, Wakita Tsurumi, Walktrap) to a network of medicine counterfeiters. Neither of the aforementioned approaches allowed the presence of a particular node in multiple groups. Robinson and Scogings (2018) and Zhao et al. (2020) included this provision in

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their works. The former developed a novel graph mining method (GraphExtract algorithm) that detected the sub- graphs of entities involved in atomic criminal events. Zhao et al. (2020), on the other hand, provided a matrix factoriza- tion method for detecting drug vendor communities across three social platforms. Besides these, Calderoni and Piccardi (2014) applied local and global (max modularity) commu- nity detection approaches to investigate clustering in mafia networks. For better performance, Bahulkar et al. (2018b) suggested predicting hidden links first and then performing community detection on the augmented network. Recently, Sangkaran et al. (2020) surveyed graph-analytics-based com- munity detection methods used in criminal networks. Apart from that, one may also look into algorithms used in other domains (see works by Parthasarathy et al. (2011) and Malliaros and Vazirgiannis (2013)).

7. Disruption of illicit trade

The key to subdue illicit trades lies in the disruption of their associated networks. According to Carley et al. (2003), net- work disruption is the act of reducing, or possibly eliminat- ing a network’s capability to disseminate resources efficiently. Usual targets for such interruption are the peo- ple, activity, or assets in the network (Bright, 2015; Bright et al., 2017). Rydell et al. (1996) discussed four approaches to intervene in the narcotics trade: source-country control, interdiction, domestic enforcement, and treatment. The first three methods focused on curbing the supply, whereas the last one concentrated on repressing demand. Clifton and Rastogi (2016), on the other hand, discussed four social net- work intervention strategies: individual, segmentation, induction, and alteration. The majority of these strategies were designed from the perspective of law enforcement agencies; however, some are available for business organiza- tions as well. Capitalizing on these techniques, different institutions try to confront illicit trade.

However, disrupting illicit trades is not easy. The first difficulty arises in their detection due to various conceal- ment measures. Second, even after disruption, networks can spring back through adaptive measures or spread to new regions. Third, actions to disrupt illicit trades can hurt per- formances of licit trades as well (Cedillo-Campos et al., 2014). These have led some researchers to question the rationale for disruptive measures, especially in drugs (Duijn et al., 2014; Magliocca et al., 2019). The proponents of inter- diction, however, stress upon its symbolic and moral value. In their view, the current interdiction approach is inad- equate, not ineffective. And this inefficiency stems from resource limitations, inadequate coordination, and corrup- tion (Basu, 2014a). Considering both perspectives, we have divided the disruption approaches of illicit trades into two broad categories: invasive and noninvasive. Invasive approaches involve a direct engagement with the illicit net- work stakeholders. Noninvasive approaches look for alter- nate efforts to influence the trade indirectly. The following sections discuss both approaches in detail, along with the methodologies involved.

7.1. Measurement of disruption

Researchers have suggested different measures to evaluate disruption in illicit trade and its associated networks. Traditional scales for illicit trade disruption include the change in number of retailers, sales volume, and retail price (Caulkins and Padman, 1993; Crane and Rivolo, 1997; Caulkins and Hao, 2008). Schneider (2008) added several indicators specific to fencing: change in the market, disposal time, and burglary frequency. In the network interdiction literature, two poplar measures for disruption are the reduc- tion in flow or increase in cost. Apart from these, several network-based metrics have been used by researchers, including the average path length, degree centralization, number of connected components, size of the largest con- nected component (Tsvetovat and Carley, 2003; Agreste et al., 2016). Patel et al. (2015) suggested two new measures in wildlife trade disruption: fragmentation index and weighted reach index. The former denoted the proportion of nodes isolated after the removal of interdicted nodes, whereas the latter represented the weighted distance of non- key nodes to key nodes.

7.2. Invasive disruption techniques

An invasive approach to disrupt an illicit supply chain typic- ally involves law enforcement agencies adopting an offensive stance against the illicit supply chain members. Three major disruption approaches are found in the literature: social net- work analytic methods, network interdiction, and crack- down. Each approach is discussed separately in the following subsections.

Social network analytic methods: Section 5.4 discussed the significance of key nodes in network disruption as well as the techniques to identify them. In this segment, we intend to address their removal strategies. Generally, there are two ways of removing nodes in a network: random or targeted. Random removal, in essence, is not a concerted strategy. It resembles opportunistic law enforcement interventions against criminal networks, e.g., stop and search, vehicle stops, opportunistic seizures (Bright et al., 2017). Targeted removal, as the name suggests, attacks nodes with specific attributes and with a certain goal in mind. Bright et al. (2017) discussed five targeting methods: three focused on social capital (based on betweenness, degree, cut-set), and two focused on human capital (possession of money and precursor chemicals). Results from simulation identified betweenness and money as the best attributes to use. Duijn et al. (2014) presented two new disruption strategies for the cannabis supply chain: value chain degree and specific value chain role.

Agreste et al. (2016), on the other hand, classified disrup- tion strategies as parallel or serial, depending on whether they are removed simultaneously or sequentially. Although serial interdiction performed better in their experiment, it may allow the network to reorganize between successive attacks. From this perspective, the authors found parallel interdiction to be more realistic. Patel et al. (2015) noted

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that removing members based on traditional centrality measures does not necessarily cause the highest disruption. Instead they employed the key player problem approach (Borgatti, 2006) to identify the set of optimal nodes whose removal maximized the fragmentation in wildlife trafficking network, increasing the number of connections required to travel from one node to another.

Network interdiction: Section 6.1 introduced network inter- diction for detecting illicit products. In this section, we dis- cuss further its application in illicit network disruption. Research on network interdiction perhaps started with Wollmer (1964) who attempted to identify the best set of arcs to remove. Later, McMasters and Mustin (1970) intro- duced a model to interdict the opponent’s supply network subject to budget constraints. Over time, the literature has been enriched with multiple studies (see, e.g., the recent sur- vey of Smith and Song (2020)). In a recent study on illicit supply chains, Jabarzare et al. (2020) categorized network interdiction methods into four major categories: shortest path, facility assignment, minimum cost flow, and maximum flow. The last category has been mostly applied to disrupt illicit supply chains. Wood (1993) developed a deterministic network interdiction model for analyzing the actions against narcotics flow across South America and showed it to be NP-complete. Washburn and Wood (1995) formulated the problem as a two-person zero-sum game. Meng (2013) opti- mized border patrol routes using a strictly mixed strategy Nash equilibrium in a two-player game. Guo et al. (2016) introduced a novel Stackelberg game model, and proposed a column and constraint generation algorithm to solve it. Jabarzare et al. (2020), on the other hand, studied the dynamic maximum flow interdiction of an illicit supply net- work with multiple commodities, sources, and sinks. Here, they proposed two reformulations of the min–max bi-level mathematical model into a mixed-integer model, and pro- vided a solution method based on the Benders decompos- ition and different accelerating strategies (e.g., Super Valid Inequalities). Stochastic models have also been proposed by researchers with frequent application in nuclear material detection systems (Pan et al., 2003; Morton et al., 2007). In addition, Zhang et al. (2018) applied a stochastic shortest path network interdiction model to a network of illicit path- ways along the Arizona–Mexico border. Sadeghi and Seifi (2019) applied a two-stage maximum flow network interdic- tion problem with endogenous uncertainty. Tezcan and Maass (2020) used single and multi-stage stochastic network interdiction models to tackle human trafficking. Recently, researchers have also attempted to represent and interdict illicit networks as complex networks. Baycik et al. (2018) applied network interdiction in a system comprising two interdependent networks: physical and information. The tar- get was to minimize the maximum flow of physical prod- ucts, and novel multi-step dual-based reformulation technique was developed to solve it. Inspired by this, Bahulkar et al. (2018a) proposed a framework for interdict- ing three interdependent networks: smuggling, money, and money laundering.

Crackdown: Crackdown denotes the sudden intensification in law enforcement activities in a particular area to increase the perceived or actual threat of apprehension among crimi- nals (Davis and Lurigio, 1996). In illicit supply chains, its application is mostly found in the retail stage. Caulkins (1993) introduced a model to describe the impact of crack- down on the narcotics market. That paper also addressed questions regarding the features of an optimal crackdown strategy (e.g., target selection, enforcement level). Based on his analysis, Caulkins suggested targeting one market at a time and identified the threshold enforcement level to col- lapse the market. However, some may intend to minimize the market size rather than causing complete collapse. In such cases, the market may attempt to recuperate. To avoid this, Caulkins prescribed maintenance measures as a follow- up to the disruption. The resource required in this phase is much less than the disruption phase and depends on the number of dealers at the end of the disruption (Baveja et al., 2000). Baveja et al. (1993) introduced two dynamic enforce- ment policies: the first policy decreased the enforcement level gradually over time, whereas the second strategy mod- eled the enforcement level as a function of the number of dealers in the market. Market size and characteristics also play a significant role in determining the crackdown policy (Baveja et al., 1993; Kort et al., 1998). Kort et al. (1998) developed an optimal control model to identify the crack- down rate in three market settings: buyers, sellers, and mixed market. Using the maximum principle, they showed that it was easier to disrupt a sellers’ market than a buyers’ market. For the mixed scenario, the fate of the market depended on its initial size. If sufficiently small, the market collapses; otherwise, the number of dealers converges to a stable saddle point equilibrium. Baveja et al. (2000) devel- oped another optimal control model consisting of two states (number of users and budget) and one control variable (enforcement). The authors recommended tailoring enforce- ment policy according to the risk nature of dealers. They also assumed that the outcome of the crackdown should be apparent within a short time, such as a week.

7.3. Noninvasive disruption approach

In addition to invasive strategies, governments are also look- ing into alternate policies that can hurt illicit trades. Moreover, licit organizations affected by illicit trades can also play a part in disruption, especially in the case of coun- terfeiting. These have given rise to different noninvasive approaches over the last few years. Among these, marketing strategy is frequently used by government authorities and licit organizations to create awareness among consumers against illicit trades (Schneider, 2008; Chaloupka et al., 2015). Konrad et al. (2017) suggested optimizing media investment to maximize awareness about human trafficking. Such marketing approaches are also applicable to illicit trad- ers. Patel et al. (2015), for instance, suggested disseminating educational messages to encourage refraining from wildlife trafficking. Another interesting idea is the optimization of

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sentencing policy to deter people from participating in illicit trades (Caulkins and Padman, 1993).

To tackle counterfeiting, a brand company can also adopt strategies regarding pricing, quality, and technology: e.g., price rebates, quality enhancement, and R&D investments. Cho et al. (2015) evaluated the effectiveness of these approaches from three perspectives: profit of brand compa- nies, profit of counterfeiters, and consumer welfare. They also showed that the same strategy is not applicable for countering both deceptive and non-deceptive counterfeiting. Using an innovative idea, Kumar and Tripathi (2019) pro- posed a block chain model for securing medicine supply chain from counterfeits.

Often, the targeted network might be out of a country’s jurisdiction. Shan and Zhuang (2015) presented a similar scenario regarding the WMD supply chain where terrorists plan to attack with weapons sourced from another criminal organization. To repeal this threat, the country at risk can reach out to the supplier’s native country with an offer to subsidize the market disruption. The collaboration within the governments and criminal groups was determined by the amount of subsidy and payoff, along with additional var- iables (e.g., attacking cost, preparation cost, proliferation cost). Using a game-theory approach, the authors modeled their interactions as two individual sub-games (proliferation and subsidization) and integrated them into a four-player game. Under different combinations of the variable states, the model provided the optimal policies to follow.

7.4. Scheduling of disruption

Naik et al. (1996) introduced the Crackdown Scheduling Problem (CSP), an analytical framework to identify the opti- mal sequence of crackdowns on drug markets. Two phases were considered: crackdown and maintenance. An exponen- tial-time algorithm was employed to solve the general case of the problem. They also provided an approximation algo- rithm to solve a specific scenario. Cai et al. (1998) developed a quasi-polynomial time approximation algorithm to solve the CSP with a monomial cost function. Baveja et al. (2004) developed a sequential crackdown model where enforcement decisions were made daily. The model used a probabilistic framework, where the probabilities of drug dealing and dealer incarceration depended on several factors. The opti- mal strategy was a cyclic one, which resembled a crack- down-backoff strategy.

Malaviya et al. (2012) developed a multi-period network interdiction problem that focused on scheduling the activ- ities of law enforcement for successfully interdicting crimi- nals in a narcotics supply chain. Enayaty-Ahangar et al. (2019) solved this problem with a logic-based decomposition approach along with constraint programming.

7.5. Disruption policies: Evaluation and selection

With the increase in the number of intervention strategies, it is reasonable to examine their efficacy. Rydell et al. (1996) assessed the cost-efficiency of four narcotics control

strategies and found addiction treatment to be the best choice. Mazerolle et al. (2007) conducted a systematic review of five drug enforcement policies. Their results found pro- active interventions involving partnerships between the police and third parties rendering the highest disruption. Kovari and Pruyt (2012) used a system dynamics simulation model to provide insights regarding the effects of proposed policies to combat human trafficking. Data envelopment analysis can be another possible mechanism.

It is also possible to adopt multiple strategies simultan- eously. For example, in narcotics control, efforts are required in both interdicting the supply as well as the treat- ment of the users or victims. Questions may arise regarding how to allocate budget to these policies. Tragler et al. (2001) modeled this dilemma as an optimal control problem with the objective to minimize social costs due to drug use and control. Their solution implied that if the problem was in its early stage, then it would be optimal to invest highly in both enforcement and treatment to possibly eradicate it. Otherwise, they suggested moderating it initially with enforcement and later with a gradual increase in treatment control. Recently, Baycik et al. (2020) proposed a Markov Decision Process framework to analyze a resource allocation problem for law enforcement that aimed to balance intelli- gence and interdiction decisions to combat narcotics traf- ficking. They applied a column generation technique and a heuristic to solve this problem.

7.6. Coordination in disruption

Section 3.5 discussed the importance of coordination among different organizations in combating illicit trade. However, research in this domain is still relatively unexplored. Using data envelopment analysis and cooperative game theory, Lozano (2012) showed that data sharing among different organizations improves cost-efficiency for everyone, although not uniformly. Sharkey et al. (2015) considered three coordination settings: centralized, decentralized, and information-sharing. The first setting involves comprehen- sive planning by a single central authority. In the second environment, each agency decides on its own without con- sideration of others. The third scenario also provides inde- pendence in decision making but requires disclosure of the decisions to others. Sharkey noted centralized environment as the ideal state, yet backed the information-sharing envir- onment under realistic conditions.

Wilt and Sharkey (2019) developed a maximum flow net- work interdiction model to quantitatively assess the impact of coordination in illicit supply chain interdiction. Their study identified that the importance of coordination increases when the network gets sparse. Moreover, return (disruption inflicted) on investment was greater for a coor- dinated than for an uncoordinated environment. This result, however, was observed for a smaller budget level. The model was applied to a five-tiered drug supply chain for identifying the optimal configuration of coordination. Out of six combi- nations between five agencies, the one between federal, state, and municipal law provided the best result.

IISE TRANSACTIONS 17

8. Research gap and opportunities

The previous seven sections presented and discussed differ- ent aspects of illicit trades and relevant literature. While reviewing them, we identified several research gaps. These are not intended to be comprehensive; instead, they will serve to thwart illicit trade through IE/OR methods and hopefully advance the state-of-knowledge of the latter. The following paragraphs expand on and summarize them with a hope to offer potential directions.

8.1. Illicit trade activity index

Although all sorts of illicit trade count toward criminal offense, it is not practical for law enforcement agencies to treat each of them with the same urgency or intensity. Therefore, it becomes essential to critically examine the trades from multiple perspectives before committing resour- ces to their disruption. One possible scheme involves com- paring the impacts of illicit businesses on a specific sector (e.g., society, economy) or across all streams. Once the desired number of significant categories are selected, focus should shift toward identifying the factors that most affect their performance. Quantifying their influence could lead to the development of a vulnerability index, which implies the proliferation risk of illicit trades in a particular region. Although there exists one such index in the literature, it does not differentiate between different trade categories. In addition, its calculation involves a simple weighted average of factor scores based on subjective opinions. In reality, illicit trades are opportunistic crimes, and their growth, extent, and impact are dynamic with time and dependent upon the prevailing environmental conditions. There may also exist connections between prevalence of different trade categories (e.g., human traffickers may use their victims for smuggling drugs). Efforts are needed to characterize the influence of these conditioning factors on each trade, prefer- ably through complex, probabilistic, and data-driven model- ing. The resultant models may be of great value to policy- makers, assessing the efficacy of different strategies. They might also assist in quantitative assessment of illicit supply chain performance, which has not received much attention in the past. Development of performance metrics for the whole supply chain and its drivers is a potential task for the future. It is also worth investigating whether indices in licit supply chain literature apply to the illicit one. Research advancement in this arena is expected to improve the design of supply chain disruption strategies. Finally, it is worth mentioning that research on illegal supply chains is biased toward a select few trade categories (e.g., contraband and counterfeit by trade characteristics, narcotics by product). Initiatives are required in the future to mitigate this gap and ensure uniform progress in research across all categories.

8.2. Data source management and quality assessment

Since data on illicit trades are subject to a variety of short- comings, it might be a good idea to assess their quality

before employing them for decision-making. The evaluation can encompass multiple criteria (e.g., provenance, complete- ness, dynamics) depending on the research objective. This should also lead to the discovery of relationships between data quality and performance of different analytic methods. Implementation of data imputation techniques might be one way to alleviate some deficiencies, i.e., incompleteness. Another approach involves the generation of synthetic net- works that resemble the characteristics of real-life illicit net- works (Elsisy et al., 2020). One can also make the data accumulation process more efficient by developing a shared database. However, its design framework must address secur- ity concerns, i.e., protection from adversarial interception (corrupt agents and illicit network members). Fusion of het- erogeneous data sources is another possible research direction for the future. The classic tradeoff between investing resour- ces to acquire new data for improved decision-making and the interdiction process itself remains relevant, and modeling and optimization methods can be advanced further.

8.3. Complex network analysis with rivalry

Analysis of illicit supply networks hitherto has been limited to a few familiar models. Recent developments in network science have brought forward further novel and intricate network structures (Kivel€a et al., 2014). Applicability of these models is a possible avenue to explore. Moreover, most of the analyses have focused on a single organizational network, whereas in reality, there might be multiple. Excluding occasional truces, these organizations naturally compete with each other, denoting adversarial relations between their members. It is also possible to have rivalry within the same organization. Consideration of these ties can have paradoxical implications for law enforcement agen- cies. On one end, rivalry reduces the resources of the net- work members, making them easier to interdict. However, disrupting one party would not necessarily eliminate/weaken the trade since it augments the power (trade volume) of its rivals. In such contexts, one would need to rethink network analysis and disruption strategies (critical node identifica- tion, whether to attack one network or both, sequentially or simultaneously). Multi-player game-theoretic models may have potential application here. Implementation of social network analytic techniques should also be worthwhile, as long as it incorporates the additional network features in its metrics.

It might also be beneficial for law enforcement agencies to instill or intensify rivalry between a cluster of nodes in illicit networks. The decision depends on the expected util- ity, i.e., impact on network resilience and the cost involved. If approved, the task would then be to identify the optimal node-set for instilling this rivalry. One possible approach is to target nodes performing similar roles. More advanced community detection methods can be developed to assist the search procedure, especially those that consider side con- straints that reflect specific patterns based on information from previously interdicted illicit supply chains. In the case of rivalry, it is important that side constraints effectuate in-

18 R. ANZOOM ET AL.

fighting in the cluster and can cause a significant part of the network to fail alongside. Moreover, dynamic network ana- lysis could be employed for measuring network resilience.

8.4. Further concerns in detection and disruption

The last few decades have seen significant research advance- ments in the detection and disruption of illicit supply chains. However, there remain avenues to explore. One is to incorporate both positive and negative consequences of dis- ruption strategies in their design. Researchers also need to consider the displacement of trades resulting from disrup- tion. Pattern analysis and facility location models could be useful for predicting these future destinations. Furthermore, resources need to be deployed to these areas for arresting the spread, adding complexity to the resource allocation decision. We can also represent the problem through dynamic network interdiction models, where the cost of interdiction or movement across edges is variable over time. It is also of interest to predict the life expectancy of illicit markets through survival analysis.

Coordination and corruption play critical roles in illicit supply chain disruptions. However, only a handful of stud- ies have addressed them through modeling (Geller et al., 2011; Wilt and Sharkey, 2019). Researchers should also put more focus on noninvasive disruption strategies. For instance, optimization of regional tax policy can contribute to a decline in parallel imports. Redesign of incentive poli- cies can also deter distributors and retailers from engage- ment in illicit trades. As for detection of supply chain entities, anomaly detection methods can be useful, especially for exposing corrupt government agents as well as deceitful suppliers and consumers. Furthermore, incorporation of Natural Language Processing and image recognition meth- ods has the potential to increase the detection rate of sales, advertisement, or discussion regarding illicit products. A particular challenge here is the accurate differentiation between legal and illegal products, where AI/ML methods are most befitting.

9. Conclusion

There is no question about the threat illicit trade poses to our economy, society, and environment. Its defiant presence despite disruptive measures is a matter of concern across the whole world. The complex and diverse nature of illicit trade occludes its detection and interpretation, resulting in limited success from interruption. Overcoming this challenge requires further participation from researchers across differ- ent disciplines, especially from the field of operations research and data analytics. Inspired by this, this article attempted to provide a comrehensive review of illicit supply chains. Here, we outlined the general concepts in illegal trade and its supply chain/network, summarized the advancements in research so far, and pointed out future directions for research. However, by no means should this review be considered exhaustive. The first reason is the exclusion of virtual products and services from our

discussion. Second, due to space limitations, we were not able to include all the works that are ongoing in this field. Nevertheless, we are hopeful that our work will assist researchers engaged in this topic as well as encourage others to join the fight against these reprehensible activities.

Acknowledgments

The authors would like to thank two anonymous reviewers and the Associate Editor for their helpful comments that have led to an improved paper.

Notes on contributors

Rashid Anzoom is a PhD student in the Department of Industrial and Enterprise Systems Engineering at University of Illinois, Urbana- Champaign. He received his MSc (2019) and BSc (2017) degrees in industrial and production engineering from Bangladesh University of Engineering and Technology. Rashid’s research interest includes opera- tions research, data analytics, and supply chain.

Rakesh Nagi is Donald Biggar Willett Professor of Engineering at the University of Illinois, Urbana-Champaign. He served as the Department Head of Industrial and Enterprise Systems Engineering (2013-2019). He is an affiliate faculty in CS, ECE, CSL, and CSE. Previously he served as the Chair (2006-2012) and Professor of Industrial and Systems Engineering at the University at Buffalo (SUNY) (1993-2013). He has more than 200 journal and conference publications. Dr. Nagi’s academic interests are in big graphs/data, social networks, GPU-accelerated computing, graph algorithms, production systems, applied/military operations research and data fusion using graph theoretic models.

Chrysafis Vogiatzis is a Teaching Assistant Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois, Urbana-Champaign. Previously he was an assist- ant professor of Industrial and Systems Engineering at North Carolina A&T State University. He received his PhD (2014) and MS (2012) degrees in industrial and systems engineering at the University of Florida, and his Dipl. Eng. (2009) degree in Electrical and Computer Engineering at the Aristotle University of Thessaloniki in Greece. His academic interests include network optimization and analysis, decom- position techniques for combinatorial optimization, and applied opera- tions research.

ORCID

Rashid Anzoom http://orcid.org/0000-0003-4699-6379 Rakesh Nagi http://orcid.org/0000-0003-4022-6277 Chrysafis Vogiatzis http://orcid.org/0000-0003-0787-9380

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