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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
To link to this article: https://doi.org/10.1080/24725854.2021.1939466
Published online: 06 Aug 2021.
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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
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, …
Special Issue: Analytical Methods for Detecting, Disrupting, and Dismantling Illicit Operations IISE Transactions: Focused Issue on Operatio
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