Faculty of Economics and Business
The role of LSPs in Inventory Finance through the use of operational data:
a multiple-case study in The Netherlands
21st of June 2020
Word Count: 12889
C.H. Kuipers (s2588064)
Supervisors: Prof. Dr. J.T. Van der Vaart & Dr. Ir. S. Boscari Second Assessor: Dr. Ir. P. Buijs
Abstract
The importance of integrating financial flows with goods and information flows in supply chains is increasingly recognized. Practical applications of Inventory Finance (IF) is increasing, however empirical research into this field is limited. Empirical studies into the role of the LSP in IF is even more scarce. This study focuses on how LSPs can make IF possible for their customers through the operationalization of data. Employing a multiple-case study in the Netherlands involving both LSPs and FSPs, the contributions are threefold. First, it provides a theoretical model for different operational data categories that can be employed for risk assessment in IF. Second, it provides additional evidence of the importance of IT integration and standardization within supply chains as well as increased information sharing. Third, it builds upon the model of (Hofmann, 2009) by introducing alternative means of IF by accessing capital markets to reduce costs.
Table of Content
1. Introduction ... 4
2. Theoretical background ... 7
2.1 Supply Chain Finance (SCF) ... 7
2.1.1 Conflict between supplier and retailer ... 9
2.1.2 Conflict between supplier/retailer and FSP ... 9
2.1.3 Conflict between supplier/retailer and LSP ... 9
2.1.4 Resolving the conflicts ... 9
2.2 Inventory Finance (IF) ... 10
2.2.1 The role of the LSP in IF ... 11
2.2.2 LSP access to operational data to benefit IF ... 13
2.2.3 The operationalization of risk in IF ... 13
2.3 Summary of the literature ... 15
3. Research Design ... 18
3.1 Methodology ... 18
3.2 Data Collection ... 19
3.2.1 Unit of analysis ... 21
3.3 Case Selection and Description ... 22
3.4 Data Analysis... 25
4. Results ... 27
4.1 Results Phase 1: Exploration and Refining ... 27
4.2 Results Phase 2: Reflection ... 29
4.2.1 Case 4: FSP 1 ... 29
4.2.2 Case 5: FSP 2 ... 31
4.2.3 Case 6: FSP 3 ... 32
4.2.4 Case 7: FSP 4 ... 33
4.3 An Overarching View: Main Findings From Phase 2 ... 35
5. Discussion ... 37
5.1 Academic contributions... 37
5.2 Practical Implications ... 38
5.3 Limitations and future research ... 39
6. Conclusion ... 41
7. References... 42
8. Appendix ... 47
Appendix A: Questionnaire format ... 47
Appendix B: Interview protocol phase 1 ... 50
Appendix C: Interview protocol phase 2 ... 52
Acknowledgements
First of all, I would like to thank both my supervisors Prof. Dr. J.T. van der Vaart and Dr. Ir. S. Boscari from the University of Groningen, Faculty of Economics & Business for their guidance and support throughout the process of writing my thesis. Secondly, my thanks go out to Christiaan de Goeij, researcher at Windesheim University, for his active support and involvement into shaping this research project in difficult times during the COVID-19 crisis. Aside from providing relevant guidance, I could always contact him to brainstorm about research directions, interviewee suggestions or interesting literature. Thirdly, my thanks go out to Luca Gelsomino for his guidance in setting-up the framework for the semi-structured interviews.
Furthermore, I would like to thank all participants in the case studies involving the three LSPs and four FSPs. I thoroughly enjoyed the focus group interviews to approach this research area from a practical standpoint looking for meaningful applications for the theory. Furthermore, the interviews provided interesting insights into the concept of IF and SCF as a whole. Even though this might have distracted me from the research questions at hand at times, it proved for me personally that I want to pursue a career where I can combine my interests in finance and operations.
1. Introduction
Traditionally, smaller and medium-sized enterprises (SMEs) face more difficulty in acquiring financing. Due to informational asymmetries, asset structures and management experience, financial institutions have shied away from granting credit to these companies (de Boer et al., 2015). Nowadays, the situation deteriorated even more for these companies since the start of the financial crisis in 2008 (Zhou et al., 2017). Due to stricter BASEL regulations, smaller companies are required to pay significantly higher interest rates (Angelkort & Stuwe, 2011). For these smaller companies, capital is tied-up in receivables, payables and inventory. Due to globalization, the time and amount of capital that is tied up in the process of transporting goods around the world is even increasing (Gomm, 2010). For capital that is bound by inventory, opportunities exist for inventory financing (IF) to free up this capital.
IF is an instrument in the broader concept of Supply Chain Finance (SCF). The concept of SCF originally developed in response to an increased demand for financial flows to better align with product and information flows in supply chains (Fairchild, 2005). In IF a Financial Service
Provider (FSP) extends liquidity to a supplier/retailer through a loan backed by the inventory
which serves as collateral. Aside from the access to capital, there are additional accounting benefits by taking the inventory off the balance sheet of the supplier/retailer and divesting it into a Special Purpose Vehicle (SPV) in name of the FSP. A SPV is a separate legal entity with its own balance sheet. SPVs are created to isolate risk that might negatively influence the parent company financially. In other cases, the SPV is created to securitize debt so that investors can be assured of repayment.
However, entering in SCF through IF requires ‘knowledge and corresponding resources’ (Gelsomino & De Goeij, 2019; Hofmann, 2009). As portrayed by Chakuu et al. (2019) besides the suppliers/retailers and financial service providers (FSPs), a key role is reserved for logistic
service providers (LSPs). The LSPs in the supply chain may have the necessary resources as
the status and the position of inventories, the payment times of the customers, and so on.” Additionally, collaborative alternatives may also be possible where the LSP and the FSP cooperate financially in a joint-venture.
IF within the broader concept of SCF is a contemporary research subject (Chen & Cai, 2011; de Boer et al., 2015; Gelsomino & De Goeij, 2019; Hofmann, 2009). Traditionally, the literature on IF focused on the bank-led perspective (Gelsomino & De Goeij, 2019) where the FSP directly finances either the supplier or the retailer and the role of the LSP is restricted to logistics services. The introduction of the LSP as an active player in IF by Hofmann (2009) introduced new academic as well as practical implications. Practical examples of LSPs engaging in IF are SwissPost, DHL, Maersk and UPS. However, empirical academic research into this subject is still scarce. The alternative model proposed by Hofmann (2009) in his seminal paper, already approaches the strategic position of the LSP in an IF context. However, one of the assumptions made in his model is that the LSP is willing and able to take ownership of the collateralized inventory. The LSP would buy the goods from the supplier and hold interim ownership before selling it to the retailer. This would require the LSP to take on all associated risks. In reality, not all LSPs have the required resources and capabilities to engage in these type of financing activities. In response, this study further explores the potential position of the LSP within the IF framework as an enabler of IF by focusing more specifically, however not exclusively, on the offering of informational services.
operationalized for risk assessment purposes underlying IF in favour of the FSP. In this way, the LSP can thus proactively improve its strategic position to form long lasting relationships with its customers and FSPs by enabling IF through its informational services.
The first question this study aims to answer is which operational data LSPs have access to that may benefit IF. This allows for an alternative approach to the role of the LSP in IF as a facilitator by providing informational services. In the first part of this study, a theoretical model is constructed by developing different operational data categories from the perspective of the LSP. The second question addresses how LSP may operationalize this data by analysing the value-added from these informational services from the perspective of the FSP.
Contributions of this study are threefold. First, it provides a theoretical model distinguishing the various operational data categories for which LSPs have valuable information of their customers. The value-added for this information is analysed empirically from the perspective of the FSP through semi-structured interviews. The role of the LSP as a facilitator of IF instead of a provider of IF has not been studied academically before.
Second, it addresses the issue of how this data can be operationalized through further IT integration and cooperation to promote information homogeneity between all parties involved in IF. The value of information and knowledge of the LSP in an IF context is widely recognized in the literature, however empirical evidence of how this data can be operationalized in practice is scarce.
Third, it provides an alternative model to the traditional bank-led perspective by introducing alternative means to access capital through FinTechs or other financial service providers. This may prove to be especially beneficial for smaller capital constrained companies who fail to get access to capital through traditional methods.
2. Theoretical background
This section outlines related research in the field of inventory finance (IF) and starts with its origin in the concept of supply chain finance (SCF). Traditionally, academic research on supply chain management focused mainly on the physical flow of goods and materials as well as information flows (Fellenz et al., 2009; Fairchild, 2005). The study of financial flows in supply chains is a more contemporary research subject. Fairchild (2005) stated that gaps exist separating physical supply chain activities from their associated financial flows. Wuttke et al. (2013) argue that scholars only recently started to address the benefits of planning, managing and controlling financial flows along supply chains. The financial flows constitute naturally from the sales and transportation of physical goods, however these flows were always largely ignored in a supply chain optimization context. Exemplary for these models is that they pursue an optimal solution for the supply chain by considering these financial flows, in tandem with the physical flows. One example is the management of payment delays by taking suppliers’ liquidity positions and cost of capital into account. The product, information and financial flows need to be managed in a coordinated way, improving cash flow management from a supply chain perspective (Wuttke et al., 2013). For simplification purposes, this study focuses on the dyadic relationship between suppliers and retailers as the primary players in the supply chain.
2.1 Supply Chain Finance (SCF)
the supply chain as a whole. In this paper the following definition for SCF is used originally put forward by de Boer et al. (2015) (p. 21):
“SCF aims at the optimization of the flows and allocation of financial resources in a supply chain with the aim to increase value, requiring the collaboration of at least two primary supply chain members, possibly facilitated by external service providers. As such, SCF’s purpose is to improve supply chain efficiency (financial performance), effectiveness (delivery performance) and sustainability (social performance).”
SCF solutions can provide suppliers and customers with this demanded increase in liquidity without disrupting production lines (Hofmann, 2013). The key premise for SCF is the integration between customers, suppliers and service providers and aims at planning, steering and controlling financial flows along the supply chain (Chakuu, Godsell, et al., 2019). The primary members in the SCF process are the companies involved, i.e. the retailers and suppliers as specified previously. The supporting members are logistic service providers (LSPs) and financial service providers (FSPs). The relationship is depicted in the figure below by Hofmann (2009) (p. 721).
Figure 2.1: Overview main and supporting supply chain members
2.1.1 Conflict between supplier and retailer
The first conflict that arises is that the supplier wants to sell the product as fast as possible while maintaining low inventory levels. The retailer on the other hand wants the transfer of ownership to occur when it meets his demand. At the same time, for the latter to occur the inventory must be readily available at every moment in time requiring high inventory levels creating the first conflict of interest.
2.1.2 Conflict between supplier/retailer and FSP
The second conflict that arises is related to information transparency between the supplier/retailer and the FSP. For accurate measurement of counterparty risk, FSPs require detailed information about the company and financed goods (Hofmann, 2009). However, it may be in the interest of the counterparty to not fully disclose this information, creating information asymmetry leading to inefficiencies resulting in higher transaction and financing costs.
Moreover, FSPs will seek to finance goods with high marketability in case of default of the counterparty. Buzacott & Zhang (2004) emphasize that the larger the added value of the product, the harder it becomes to liquidate it in a forced sale. Therefore, even though the FSP is not directly involved in the operative value creation process, their risk preferences directly impact the supplier/retailers IF product portfolio.
2.1.3 Conflict between supplier/retailer and LSP
In the more traditional setting depicted in figure 1 the LSP neither engages in financial added services nor takes ownership of inventory. Its role is restricted to providing logistics service for which the invoice is either paid by the retailer or the supplier. It will prefer the debtor to be the party (supplier or retailer) with the most favorable financial characteristics leading to search, negotiation, hedging, control and opportunity costs (Hofmann, 2009).
2.1.4 Resolving the conflicts
related to factoring. In this context, the retailer issues a purchase order to the supplier from whom the FSP buys the invoice at a discount. Once the point of sale occurs for the retailer, it settles the invoice with the FSP (de Boer et al., 2015). The supplier receives timelier payment, the retailer is allowed delayed payment, and the FSP is compensated in the amount of the discount. The example is illustrated in figure 1 below.
Figure 2.2: Example of factoring solution
Even though these type of arrangement settle the first conflict of interest between the supplier and the retailer, informational asymmetries related to the second conflict of interest between the FSP and the supplier/retailer still remain. Furthermore, the LSP is still restricted to logistics services. In response to this, (Hofmann, 2009) introduced the addition of the logistics service provider in an inventory finance context to, among other things, promote transparency in the supply chain. This study builds further upon this model introduced by Hofmann (2009) which includes the LSP as an active participant in an IF context. First, the concept of inventory finance and its theoretical background is outlined.
2.2 Inventory Finance (IF)
extensively studied, however academic articles in the field of logistics financing were relatively scarce. The main research areas in logistics financing focused on topics such as “Financial Supply Chain Management” (FSCM) and “Supply Chain Finance” (SCF).
IF has been studied as an alternative means through which companies can access financing backed by fixed assets that serve as collateral (Buzacott & Zhang, 2004). The amount of credit extended is linked to a formula which employs hard data for a dynamically-managed estimation of the liquidation value of the collateral (Berger & Udell, 2006). This allows companies to extend their credit based on the value of their fixed assets rather than on the overall creditworthiness of the firm. Especially for SMEs and startups, inventory backed financing may serve as a substitute for lower creditworthiness (Chen & Murata, 2016).
2.2.1 The role of the LSP in IF
Academics have approached IF from a bank-led perspective as the primary source for funding (Gelsomino & De Goeij, 2019) and focused on the dyadic relationship between the FSP and the debtor (supplier/retailer). However, not all companies are able to get access to IF as FSPs might be unwilling to provide this service without accurate real-time information about inventories (Chen & Hu, 2011) (de Boer et al., 2015) (Hofmann, 2009). The FSP needs to be aware of the supply chain and logistics processes underlying the dynamics of inventory levels (Bryant & Camerinelli, 2013). Furthermore, they add that only goods which can easily be exchanged in the market in case of default are suitable for IF. De Boer et al. (2015) further explain the role of LSP in this financing construction. By not only providing transport, handling and storage services but also providing real-time information about shipments, they provide a supporting role between the supplier/retailer and the FSP.
However, empirical evidence of the LSP taking an active role in IF is scarce. In his seminal paper, Hofmann (2009) illustrates the example of a SwissPost taking over the ownership of goods in transit in a pilot project collaboration with Procter & Gamble. For P&G the LSP is a bulk buyer which resells the goods to its retailers, while processing both physical and financial flows (e.g. invoicing and debt collection). Other practical examples include DHL, Maersk and UPS (Gelsomino & De Goeij, 2019). Gelsomino et al. (2017) further investigated the motivation for LSPs to take an active role in SCF. Through their multiple case study in The Netherlands they find that LSPs venture into SCF when they operate in less stable economic conditions, or when they operate in more complex supply chains. Additionally, LSP tends to engage in SCF when they have a close relationship with a FSP and lastly when knowledge specificity in the supply chain is high. LSPs facing less stable economic conditions, negatively impacting transaction volumes, such as during the financial crisis for example tend to look into alternative solutions to increase their business. In more complex supply chains, integration of logistics and financial flows is pursued to a greater extent. Naturally, these complex supply chains are more fragmented, might cross continental borders inducing higher lead times and may include larger numbers of smaller players. Therefore, integration through SCF is more beneficial in these type of supply chains. The close relationship with a FSP as a contributing factor is straightforward and related to the complexity of implementing SCF solutions, requiring trust and established long-lasting relationships between parties involved. Knowledge specificity further emphasizes the role of the LSP as the better party to assess underlying risk as a result of their established experience dealing with specific markets and goods. For markets or goods for which this knowledge specificity is lower, the FSP is more capable to do this themselves. Gelsomino & De Goeij (2019) employ the Research Based View (RBV) to identify the necessary resources and capabilities to successfully venture into IF. They distinguish between
physical, informational, human, relational, organizational and financial resources as the
strategic resources. In order to bundle and leverage those resources, they identify integration of
physical and financial supply chains, information technology and strategic partnerships to be
the key success factors. Their findings show that even though LSPs have the required resources and capabilities, it is not a guarantee that value can be created in an IF context. In particular, they argue that “the risks involved could exacerbate required knowledge and corresponding
resources” (Gelsomino & De Goeij, 2019) (P. 17). Moreover, they suggest future research to
2.2.2 LSP access to operational data to benefit IF
In the previous section the role of the LSP in IF is outlined. The interest of this study is how operational data can be employed in this respect to strengthen the role of the LSP in the operationalization of risk. Hofmann & Kotzab (2010) address that LSPs have extensive information about sales forecasts and flows of goods in the supply chain. Furthermore, Gelsomino et al. (2017) argue that LSPs are better suited to assess price volatility, have better remarketing capabilities (i.e. the ability to salvage value in case of default) and are therefore better at assessing the percentage or value of the inventory to be financed. This knowledge specificity streams from the knowledge required in providing logistics operations for exporters for years. Steeman (2017)argues that the LSPs knowledge of local markets and customers could enable it to seek alternative buyers willing to pay more than liquidation prices in case of default. Bryant & Camerinelli (2013) argue that LSPs can lower the location risk related to the ability to seize inventory in case of default. Gelsomino et al. (2019) assess the LSP as the controller of data where their benefit streams from the control that the LSP can naturally exercise on the goods flow. Furthermore, the LSP can link the information about material flows and status of inventories to financial institutions to mitigate financial risk, enabling a reduction of credit risk Chen & Hu (2011). Chakuu et al. (2019) state that LSPs might leave the implementation of SCF (IF) solutions as well as other value added services to the FSPs, and assist in the form of collateral services and information sharing services (about inventory). The LSP excels traditional banks as a SCF (IF) partner, especially concerning complex products due to their superior information and industry insight (Wetzel & Hofmann, 2018).
2.2.3 The operationalization of risk in IF
scores, as information on financial health for these companies is generally not publicly available. This further strengthens the position of the LSP in IF as an intermediary information provider of financial information as information might not be publicly available, but they have access to this information as a result of their business relationships.
In the traditional sense, scholars have believed that financial information is the main factor influencing SMEs’ credit risk. Altman & Sabato (2007) argue that these main factors are total assets, earnings before tax, interest paid, retained earnings, short-term debt, cash and equity. Calabrese & Osmetti (2013) claim that the solvency ratio, return on equity, added value per employee, turnover per employee, cash flow, bank loans over turnover and total personnel costs are the most important factors. Fantazzini & Figini (2009) found that the SMEs’ credit risk is mainly affected by their liquidity ratio, short-term over long-term debt, debt ratio, equity over debt, short-term debt and provisions over sales.
Silvestro & Lustrato (2014) argued that the role of the FSP is to reduce risk and SCF effectiveness through the synchronization of financial information and flow of goods, to manage supply chain collaboration and improve information sharing and invisibility. This role inherently extends to that of the LSP as an intermediary in this study, as they are even better capable of contributing in these aspects. The challenge herein lies in what aspects are the most important for the assessment of the underlying risk for this relatively new financial service. In IF, the pledged inventory is evaluated to find whether its value can maintain its capability of guarantee. This is referred to as the loan-to-value ratio, or the impawn rate introduced by (Juan et al. (2012). In practice, the loan-to-value ratio is often based on experience and does not accord with the risk exposure of banks, as no suitable quantitative methods are employed to calculate these rates (Juan et al., 2012).
“The companies (agents) in a supply chain need money from banks or the capital market. These providers of capital (principles) normally stand outside of the supply chain and thus always have less information than the companies in the supply chain”
Empirical research into the operationalization of risk in IF that extends beyond credit risk is limited. In their paper, (Lyu & Zhao, 2019) use compressed sensing techniques to assess the risk in internet supply chain finance. Their risk assessment model is comprised of first, second and third level risk components. The first level contains six different factors: risk of financing enterprises, risk of focal company, risk of logistics enterprises, risk of pledge, risk of internet, risk of external environment. Zhang et al. (2015) acknowledge that default risk needs to be approached from a supply chain perspective and is not restricted to the credit risk of the company. Song et al. (2016) distinguish between regulatory risks, credit risk and legal risk. Furthermore, they identify three main factors that affect Loan-To-Value ratios: 1) fluctuations in the price of the collateral 2) the IF loan period and 3) risk preference of the capital provider.
2.3 Summary of the literature
In summary, the literature identifies defines several conflicts of interests between supply chain parties in a SCF context. The key premise for SCF is the integration between customers, suppliers and service providers and aims at planning, steering and controlling financial flows along the supply chain (Chakuu et al., 2019). There is a role reserved for the LSP as for accurate measurement of counterparty risk, financial service providers require detailed information about the company and financed goods (Hofmann, 2009).
customers that may be used in an IF context. Therefore, the first research question of this paper is:
RQ1: Which operational data do LSPs have that can benefit the IF process?
The aim is to build a theoretical framework for future operationalization of data for the benefit of risk analysis in IF from the perspective of the LSP. In this study, the focus is on the operational data that LSPs have access to. In reality, this framework may be employed by other parties willing to engage in IF.
As mentioned previously, there is ambiguity in the role reserved for LSPs in IF. The role as a provider or a facilitator is still undetermined. Not all LSPs may be able and willing to take ownership of inventory. The situation where the LSP takes on the role of a facilitator of IF by providing security through informational services is depicted in figure 2.3 below.
Figure 2.3: Overview alternative IF model with a facilitating role for the LSP
from the perspective of the FSP. This distinction between the LSP and the FSP allows to leave the capital requirement, and the ownership of inventory, in the middle to focus on the operational data as an enabler of IF. Therefore the second research question that this paper aims to answer is:
RQ2: How can LSPs operationalize this data and use it conjointly to make IF possible for their customers?
3. Research Design
This section provides an overview of the research design. In the previous section, the research questions were described. In section 3.1 the methodology to answer these questions is outlined. Section 3.2 describes the data collection procedure followed by the case selection and description in section 3.3.
3.1 Methodology
Due to the area of IF within SCF being characterized by limited previous research, this study employs a qualitative case study research approach. Case study research is preferred when the main research questions are “how” or “why” questions, and the focus of the study is a contemporary phenomenon (Yin, 2018). Karlsson (2016) provides further motivation as the case method lends itself to early, exploratory investigations where the variables are still unknown and the phenomenon not at all understood. Dubois & Gadde (2014) argue that abductive case methodology relies on matching rather than testing pre-defined hypotheses. This allows one to move back and forth between literature, data and analysis during the research process. This is characterized by the two-stage research design as will be explained in more detail below.
Figure 3.1: Overview research process design
The second implication is related to contextual validity (Ryan et al., 2002). Researchers most often employ data triangulation techniques to promote the contextual validity of their research. In this paper, data triangulation is promoted through extending the theory and findings proposed by Gelsomino & De Goeij (2019). In phase 1, data triangulation is promoted through the assessment of data by multiple researchers during the focus group interview stage. Additionally, an industry expert was present during the focus group interviews who helped build the theoretical framework for the operational data categories which is elaborated upon more in the results section. Moreover, in the second phase during the semi-structured interviews with the FSPs, the cases were carefully selected to reflect a traditional bank, a FinTech and a nonbank financial provider. This was aimed to further improve the contextual validity of the research. The methodology consists of two multiple case studies to answer the two research questions. The first research phase in figure 3.1 is aimed at answering the first research question through a multiple case study with LSPs. The second phase aims to reflect upon the findings in phase 1 through a multiple case study with FSPs and is concerned with answering research question 2. The next sections provide an overview of the data collection procedure as well as the case selection and description.
3.2 Data Collection
of operationalizing the data that LSPs have access to. Mainly the questionnaire (Appendix A) was used to design the initial operational constructs. Furthermore, the semi-structured interview protocol from Gelsomino & De Goeij (2019) can be found in Appendix B. The second phase is characterized by reflecting upon the findings from phase 1 through semi-structured interviews with FSPs. The entire data collection process, including its phases and its components is outlined previously in figure 3.1 above.
Phase 1: Exploration and Refining
As elaborated upon previously, this research extends on the study by (Gelsomino & De Goeij, 2019). During their research, all three LSPs were in a different stage of development of a SCF instrument. Their initial findings through semi-structured interviews form the basis for this paper. The interviews were exploratory in nature and were aimed to uncover the potential for financial service offerings for each of the three LSPs. The questions were designed to analyze whether there had been earlier attempts at designing financial services schemes, to what extend they had been developed and implemented, and to uncover the potential for the implementation of IF.
“We need a better idea of the role of the bank. Is the bank willing to actually lower the costs when it has more and better information? (LSP3)” (Gelsomino & De Goeij, 2019) (pp. 14)
This quote exemplifies the practical relevance for this study by addressing whether the theoretical benefits, namely that informational transparency will lead to lower costs, will hold true in reality. Furthermore, it further emphasizes the academic relevance by addressing the issue from the perspective of the FSP. What kind of data or information is relevant for the FSP? Following up on the initial research stage conducted by Gelsomino & De Goeij (2019) focus group interviews were conducted to address the following research question: “What operational
data that LSPs have access to can contribute to make IF possible for their customers?”. A focus
group interview is defined as “a research technique that collects data through group interaction on a topic determined by the researcher” (Morgan, 2004). The results of this research step are outlined in section 4.1.
Market & Price Risk 2) Obsolescence 3) Credit Risk 4) Operational Risk 5) Physical Control. The interviewees were asked to rate the risk criteria on a pre-defined scale. In IF these risks are associated with the complexity for the provider of the loan (FSP) to capture value for the collateral, in case of default of the retailer/supplier. Ultimately, this will influence the underlying risk of the IF loan and its corresponding value and interest rates. The risk categories and the corresponding questions and rating scales are presented in the table in Appendix A. To conclude, the interviewee was asked to give an estimate of “a realistic advanced payment
percentage for financing the goods, keeping in mind the time the goods are under your control and the 5 risk categories, per SKU”.
Phase 2: Reflection
The premises for phase 2 of collecting data is to reflect upon the initial model through semi-structured interviews with FSPs. The operational and financial constructs in the model were reflected upon from the perspective of the FSP. The questions of the interview were structured as follows: 1) first the interviewees were asked general questions related to supply chain finance services and more specifically inventory finance 2) more in-depth questions were asked related to the calculation of risk for these type of services and 3) questions were asked concerning the complementary role of the LSP in an inventory finance context. Lastly, the model and its constructs were presented in an open-ended manner for exploratory purposes. Without going into detail too much about the individual constructs in the model, the interviewees were asked to review the model critically based on relevance and employability in terms of a quantitative assessment. The entire interview protocol can be found in Appendix C. The results of these interviews are discussed in detail in section 4.2
3.2.1 Unit of analysis
The study consists of two multiple case-studies involving both LSPs (phase 1) and FSPs (phase 2). Therefore it is important to define the unit of analysis for this particular research. Grünbaum (2007) argues that: “The key issue in selecting and making decisions about appropriate unit of
analysis is to decide what it is you want to be able to say something about at the end of the study” (p. 83). The key issue is how LSPs can make IF possible for their customers through the
3.3 Case Selection and Description
This section elaborates on the cases that were selected and provides a description for each of them respectively. Karlsson (2016) states in case selection boundaries have to be set that define what can be studied, and need to be connected directly to the research questions. For the basis of this research, the cases were selected in response to a real-life business case involving the three logistic service providers originating from previous research by Gelsomino & De Goeij (2019).
Moreover, Karlsson (2016) argues that when building theory from case studies, case selection using replication logic is preferred. The cases for the financial service providers in this study are selected to so that it either: 1) predicts similar results (literal replication) or 2) produces contrary results, but for predictable reasons (theoretical replication) (Karlsson, 2016). With this in mind, the selection criteria for the FSPs were to find at least one traditional bank, one FinTech and an alternative financial service provider. This allows for a wider assessment of the operational data constructs defined in phase 1, while also allowing for data triangulation to promote contextual validity. The aim is to find possible similarities but also contradictions between the FSP cases to allow for a more complete view of the employability of operational data by LSPs. To maintain confidentiality for the respective cases, the details have been anonymized.
Case 1: LSP1
The first case is a LSP active in the Food and Beverage industry. Its main logistics operations are concerned with forwarding and warehousing with an annual turnover of 100-150mln€. Their IF instrument is currently still in the design phase, through which they are exploring the possibility of an alternative IF model by providing informational services and not financial services. “We do not want to finance inventory ourselves. However, we do want to provide the
bank with information to make a better estimation of risks involved so that the costs of financing the inventory go down.” (LSP 1).
Case 2: LSP2
They have successfully implemented a pilot project for IF with a partner excluding the role of the FSP. This means that they were able to set-up a pilot project which included taking ownership of the inventory, and financing the inventory themselves for one of their long-term customers. However, they do not believe this to be a scalable opportunity due to the financial constraints the LSP faces. Therefore they are looking to collaborate with banks: “We want the
bank to do the financing in the future, and us to be the provider of necessary information so that the bank can make a better estimation of the risks involved. However, the bank has ‘cold feet’ and is not cooperative in thinking with us” (LSP 2).
Case 3: LSP3
The third case is a small LSP specialized in Fashion and Retail. Its annual turnover is around 25mln€. Its main operations include forwarding, customs services and supply chain services. Exemplary for this LSP is that it does not own any physical resources such as transportation vehicles, warehouses etc. Similarly to LSP1 it foresees a role reserved for the LSP as an information provider by leveraging its existing knowledge and not engage in financial services themselves because they do not have the capital available to do so. For this to become a possibility a collaboration with a FSP is necessary. The role for the LSP is then to provide the necessary information so that the FSP can make a better assessment of the risks involved.
Company Annual turnover Industry Interviewee function/title LSP 1 €100-150 million Food and Beverages CFO
LSP 2 €25-50 million Food and Beverages Member board of advisors
LSP 3 €0-25 million Fashion and retail CEO
Table 3.1: Interview details LSPs Case 4: FSP1
Case 5: FSP2
The second case is not an exemplary FSP, but originally a consultancy services organization headquartered in the US operating in the Caterpillar dealership market. Through their acquisition of an inventory management system, they identified opportunities for inventory financing solutions through accurate processing of inventory data. They have operationalized this through 3rd party inventory financing for which they are growing their operations. An important characteristic is that they do not finance the inventory themselves.
Case 6: FSP3
The third case is a traditional bank from the US with global operations. Their IF is restricted to the recreational vehicles, motorcycles and marine markets. Key characteristics of these markets are high seasonality and long days inventory hold because of “showroom-effects”. Additional operations include IF in Tech, for which days inventory hold is shorter and more similar to the characteristics of the LSPs.
Case 7: FSP4
The fourth case is also a traditional bank headquartered in The Netherlands and well known for its focus on innovation and digitalization. Their current financing solutions offered already include IF and factoring, and is most often a combination of the two.
Company Industry Country Interviewee function/title FSP1 Working capital finance
provider
United Kingdom Managing director
FSP2 Inventory capital solutions Sweden Director working capital solutions & Director of business development
FSP3 Traditional banking United States Regional Sales Manager Benelux Commercial Distribution Finance
FSP4 Traditional banking The Netherlands Senior Customer Journey Expert/Innovation lead
3.4 Data Analysis
Phase 1
The secondary data from the semi-structured interviews and questionnaires involving the LSPs was analyzed for generalizability towards building a theoretical framework for this research. Secondary data in this sense means that the semi-structured interviews and questionnaire were originally developed for an alternative research project. The data proved to be valuable as a basis for developing a theoretical model to use in the focus group interviews and permission was given by the original data owners. Based upon the questionnaire, the initial building blocks for the theoretical framework were discerned using the five risk categories defined in that format: 1) Market & Price Risk 2) Obsolescence 3) Credit Risk 4) Operational Risk 5) Physical Control. The initial model that was constructed from the secondary data is provided in figure 3.1 below:
Figure 3.2: Initial theoretical model operational factors
model was then revisited and revised twice as a result of two additional focus group meetings. The final operationalized model is presented and elaborated on in detail in the results in section 4.1.
Phase 2
4. Results
In this section the results of the two research phases are outlined. First the results from the exploration and refining phase are elaborated upon in section 4.1, which ultimately constituted the operationalized model to be used in the testing phase involving the FSPs. This model outlines the different operational constructs for which the LSPs have access to valuable data to be used possibly in IF. In section 4.2 the results of the semi-structured interviews with each FSP respectively is outlined.
4.1 Results Phase 1: Exploration and Refining
The first result in phase 1 is the development of the theoretical framework by identifying operational data categories from the secondary data by Gelsomino & De Goeij (2019). These results were presented in the initial theoretical model in figure 3.1. To answer the first research question, which operational data do LSPs have that can benefit the IF process, focus group meetings were organized to further refine this theoretical model from the perspective of the LSP. The final theoretical model is given below in figure 4.1. An overarching view on the different operational factors is given on the next page.
Credit Risk
The first overarching risk category that was identified is related to the financial stability of the debtor. Credit risk is mainly involved with the left-hand side of the figure, as it ultimately influences the probability of default of the debtor. In the literature the credit risk is often referred to as the overall probability that the borrower or lender will not fulfil his or her legal obligations according to the debt contract (Zhu et al., 2019). In this study, credit risk predominantly constitutes in financial attributes such as Customer Creditworthiness, Customer Solvency and
Customer Profitability that ultimately influence, but do not define the probability of default of
the debtor. From the perspective of the LSP, they can add value through detailed information on Number, size and value of shipments and days sales outstanding (DSO) and signals in terms
of number of key organizational changes for more accurate forecasting of credit risk through
the use of historical data.
Market & Price Risk
The second risk category is related to the marketability of the collateral and potential losses from price variability. Hofmann (2009) argued that products with high marketability, i.e. non-perishable goods as branded commodities, have relatively lower risk as they are demanded by many retail companies. The LSPs are operating in specific industries and have more than 20 years of experience dealing with specific product markets. Ready markets is referred to the marketability of the product in case of liquidation. Costs for resale of products in second-hand markets are significantly higher for the liquidator than for the original debtor. Moreover, price
risk is related to price variation for which the LSPs have access to historical data that can be
employed for accurate forecasting.
Obsolescence
The risks related to the product becoming obsolete is related to a variety of factors, from
seasonality, to best before dates, to branding. It is also directly related to the marketability of
the product. This also requires significant knowledge of the markets in which LSPs are active.
Operational Risk
Additional operational risk is related to insurance and coverage for which the number and value
of insurance claims of the last 8 years is valuable data for forecasting. Physical Control
This risk category also influences the Loss Given Default through additional costs that might be incurred related to Customs Status and Warehouse Control. These risks are inherently related to the difficulty of claiming or seizing inventories used as collateral for providing the funds (Cao & Zhang, 2012). The LSPs have access to the documentation and are experienced in handling customs services and for example have detailed information about whether tariffs have been paid-off.
4.2 Results Phase 2: Reflection
The next step in the research process was to reflect upon the findings from phase 1, i.e. the theoretical model, from the perspective of the FSP. This is done through semi-structured interviews with FSPs to identify whether these operational data factors may be beneficial in the underlying risk assessment of IF loans from the perspective of the FSP. Furthermore, the possible future role of the LSP was discussed in more detail. Given the heterogeneity in products offered and characteristics for the FSPs the data was analyzed and the results are reported individually per case example. Section 4.3 provides an overarching view of the results.
4.2.1 Case 4: FSP 1
“We often deploy an overarching insurance blanket. We are fine with incurring credit risks. On top of that we buy an insurance for operational and obsolescence risk for example. We buy those insurances at one insurance company. Whatever happens, the investor will always receive his money either from the end-customer or the insurance company”(FSP 1)
The main reason to step away from traditional financing is that banks are restricted in their financing due to the requirements of Basel III. In response to this they stick to traditional financing means and risk calculations based on credit risk only.
“Insurance companies are way more competent at calculating complex risks. Insurance companies are familiar with fire insurances, operational insurances etc. Anything can be insured. Banks however only focus on credit risk.” (FSP 1)
In terms of findings the right investor or FSP it is important to find the party that is able and willing to understand the different risk components underlying the IF loan. He stresses that the approach of segregating the different components into risk buckets may be the right approach. Following this approach it is a matter of finding who is comfortable to take on the different risks.
From the perspective of the LSP, he adds that information transparency will promote the ability to find different investors to account for different risk components. However, finding different investors to take on different risks in one IF loan will make the entire structure more complicated.
“That is why people always end up cooperating with banks. They are willing to play a fronting role by accounting for all the risk involved. However, they charge a big premium in return.” (FSP 1)
“We are doing well when we earn a third of a percent on the volume of inventory, at a total annual volume of 10mln€ that will be around €30.000. If the legal set-up costs are €80.000 then we are not going to wait 2.5 years to break-even.” (FSP 1)
On top of that, in terms of operational risk factors, the most important are obsolescence related to seasonality and profit margins. Inventory valued at 1mln€ with a resale value of 10mln€ can easily be sold in secondhand markets for 5mln€. Furthermore, branded products are usually secure investments as the owner of the brand will make sure the products are not dumped at low prices in case debtors originally owning the inventory default.
4.2.2 Case 5: FSP 2
Exemplary for FSP 2 is their role as an information provider in their IF business, shying away from providing the capital requirements. This can have important implications for LSPs which do not have the required financial capabilities to engage in IF without the funding of a FSP. Their information focuses on risk calculation through the analysis of inventory monitoring data.
“We are getting insights into inventory where we are in the position to say: these are fast moving items with a sustainable supply side and consequently low risk. At the same time we are able to distinguish between goods with higher risk. We segregate these into risk buckets which allows us to wrap an insurance around it for the residual risk thus making it an investable product.” (FSP 2)
Furthermore, they add that the benefits of the off-balance inventory financing module still apply by taking the inventory off the balance sheet of the company and divesting it into a special purpose vehicle (SPV).
In terms of the role of the LSP they emphasize on the capabilities to underwrite performance risk. “The bank has real difficulty to underwrite performance risks, therefore insurances are
Furthermore, they use their software to prove data driven wise that there is no obsolescence risk in the business model. Any residual risk is then covered with an insurance policy in case the funder is not willing to take that risk. This stresses the importance of different product types. Their current application involves a customer in Australia supplying spare parts for the automobile industry. In case of default of their supplier, the spare parts can easily be sold off to other suppliers. This provides further evidence for the importance of marketability of products in case of default.
In response to revenue volumes they argue that through standardization and IT integration this hurdle can be diminished. “Our software can be connected to any inventory management
system, therefore we can pool different inventories across companies for which the initial set-up costs can be distributed.” (FSP 2). The set-set-up costs are related to legal and administrative
costs of setting up the SPV, what the exact cut-off point will be is difficult to tell.
Lastly, they stress that it will be a value-added service for LSPs in the future. The logistics business is a tough market and LSPs are continually adding extra services to their portfolio offering to distinguish themselves from competitors. Once you finance the goods of your customer it can be an additional means to lock them in further, it will be harder to walk away.
4.2.3 Case 6: FSP 3
The most important characteristic for this FSP is their choice to engage in IF services in a specific industry. Their motivation for the recreational vehicle, motorcycle and marine industry is related to the “showroom-factor”.
“Our supplier produces luxury consumer goods. Once production is finished, he wishes to receive money for his product to invest in the production of new products. At the same time, the product will sit in the showroom of the retailer for considerable amounts of time. The most important factor for us is that the product will retain its value for the entire period.” (FSP 3)
and 2) the company needs to be solvent. New companies are allowed to have certain start-up losses, but established companies need to be solvent and earn money.
One of the ways they believe LSPs can add value is by having access to valuable information regarding days inventory hold. For example, they are able to tell their producers approximately how long their inventory will be in hold at the retailer. Furthermore they stress the importance of segregating different types of risk. It does not matter whether in the end the insurance company or a traditional bank will use this data to do their calculations, the margins need to be big enough.
Lastly they highlight that in any case, the funder will mostly like to resume some control over the inventory. It will be difficult as an LSP to establish such a relationship with the FSP, that the FSP will accept to rely fully on the LSP’s ability to calculate the underlying risk. The FSP will always want to have some form of control. Although, the LSP can take resource intensive checks such as plant visits off the hands of the FSP which ultimately reduces costs. This furthermore stresses the importance of IT integration and product standardization in IF.
4.2.4 Case 7: FSP 4
As a traditional bank, their IF services constitute only a small part of their financial offerings towards businesses. Most often the IF loans are concerned with collateral that is stocked in a warehouse, and thus not in-transit. Aside from that, the most important characteristic of their IF service is their criterium that their clients also engage in factoring services with them.
“When there is also a factoring component coupled to that inventory, we have more control over that the debtors of our client are being paid” (FSP 4)
The first step in their assessment is to identify whether the inventory can be financed. First and foremost they look at the accessibility of the inventory. Therefore, in most cases they finance inventory that is stocked at the warehouse of their clients. In the case of the LSP, if the inventory is in the warehouse of the LSP, they might be willing to finance the LSP. However, this furthermore stresses the importance and implications of inventory ownership. “If the inventory
is not owned by the LSP, our risk is still reliant on the supplier.” (LSP 4). Aside from that, the
also that it is in balance with the level of financing from factoring agreements. Furthermore, whether the product has been sold already or not, whether it concerns raw materials, work in progress, or finished goods inventory. In terms of financial risk each company has an internal rating at the FSP, for which the calculations are classified. Lastly, another requirement is that the administration of the customer is at a pre-defined level of adequacy.
Concerning their risk assessment, they do not have prespecified lists or models to calculate the underlying risk for IF loans. Their assessment is done by the audit department based on company visits and operational criteria defined above. In collaboration with the sales department, risk experts and other stakeholders the terms of the loan will be determined. Currently, their clients provide monthly updates on inventory levels. For some clients this is done digitally, but for most clients this is done manually through Excel-files. They are investing in innovation and digitalization but this is an ongoing transformational process. The data that LSPs have access to is valuable information, however it needs to be specified whether this will be a service that they provide to their customers or to the FSP. Furthermore it can promote information transparency and reduce information asymmetry:
“For example during the current COVID-19 crisis, supply from China halted. We still financed inventory based on average monthly inventory levels. Our client continued to sell their inventory, while nothing was coming in. Essentially, we were financing inventory that was not there.” (FSP 4)
They are looking into how this can be improved by connecting this to inventory management systems, or even ERP systems so it includes sales data. However, they do propose the opportunity for the LSP to provide this as a service when the customer is not able to do so. The question still remains to whom this service is then offered.
“The question remains how much attention we can spend on monitoring our clients. If it is done manually, we do have limited resources. In that case the LSP might be better suited for the job.” (FSP 4)
though the details of this project are confidential, this is a practical example of the role for the LSP to serve as an information provider.
Lastly, they do agree that nonbanks are more flexible in terms of adjusting to new financial products. Due to stricter regulations and monitoring by the Authority for the Financial Markets and European Central Bank, innovations at traditional banks are being hampered as compliance is now the top priority. Know-your-customer procedures have become standardized and are very resource intensive. To be able to recoup the costs of these procedures, significant loan volume criteria are used in practice which makes it more difficult for smaller companies to get access to capital.
4.3 An Overarching View: Main Findings From Phase 2
Due to space constraints, the main findings focus on the results of phase 2 addressing the role of the LSP from the perspective of the FSP. The findings of phase 1 from the focus group interviews have to a certain extent been addressed in the formulation of the model’s constructs which was then reflected upon in phase 2. The role of the LSP in the future of IF is recognized across all cases. Especially the value of the knowledge and corresponding resources that LSPs possess is argued to be valuable in this financing context. All cases have experience with some form of IF service offering, however they all differ in their application. Exemplary for FSP1 and FSP2 is their alternative service offering through capitalizing on insurance companies to access capital markets. FSP3 and FSP4 as more traditional banks are characterized by more conventional or ‘safer’ service offerings. FSP3 restricts itself to fixed-asset financing, which as opposed to asset-based financing, is more safe as fixed assets such as equipment, motorcycles or real estate are virtually always uniquely identified by a serial number or a deed (Berger & Udell, 2006), therefore requiring transfer of title which induces transparency. FSP4 combines IF with factoring and requires its clients to balance their IF loans to their factoring loans to assure the client’s debtors are paid.
(DIH), seasonality, obsolescence, inventory ownership, legal costs, administration/documentation and customs status. With respect to their operationalization, large discrepancies exist between the FSPs. FSP1 aims to cover any residual risk other than credit risk by means of an insurance blanket. They claim that insurance companies are: “way more competent at calculating complex risks […] and make sure that there always is an insurance in place, or work with sufficiently strong companies” (FSP1). FSP2 uses sales and inventory data to identify obsolescence risks and covers residual risk with an insurance policy. FSP3 and FSP4 rely on periodic audits and plant/warehouse visits and monthly manual updates by means of Excel files. Furthermore, in line with (Juan et al., 2012) findings show that FSPs rely on experience to determine Loan-To-Value ratios instead of quantitative models. FSP4 for example relies on auditing and combining knowledge from the sales department and risk experts to determine the terms of loans and percentage of inventory being financed.
In terms of the risk categories identified in section 4.1, all or a subset of components are being employed currently by FSPs in their IF services. Credit Risk is often calculated through internal ratings for which the details are classified. Market & Price Risk are mainly related to the marketability of the goods in second-hand markets. Obsolescence is operationalized through best-before dates, licensing and branding. Assessment is done based on auditing and manager experience. Operational Risk is included in the calculation of the Loan-To-Value ratio. For goods with considerable additional holding and liquidation costs in case of default, it is directly reflected in the Loan-To-Value ratio which is based on the value of the goods after liquidation. Physical Control mainly concerns the FSPs ability to cease the goods in case of default. In almost all cases the goods are stored at the warehouse of the party they are financing.
5. Discussion
This section provides an overview of the academic contributions of this study, as well as the practical implications, limitations and suggestions for future research.
5.1 Academic contributions
The first contribution of this study is that it extends upon the model of (Hofmann, 2009) by assessing the role of the LSP in an IF context. Hofmann (2009) identified the role of the LSP as an active participant in IF, by providing capital and taking ownership of the inventory. This study identifies an alternative role for the LSP as a facilitator of IF by providing informational services so that the FSP can make a more accurate assessment of underlying risks. This alternative approach to identify a possible role for the LSP in IF has not been studied before. The results show that the benefits of a complementary role for LSPs in IF is widely recognized, however the operationalization is still uncertain.
Traditionally, the role of the LSP in IF has been studied from the perspective of the LSP. As mentioned before, Gelsomino & De Goeij (2019) identified necessary resources and capabilities from the perspective of the LSP. Hofmann (2009) used the LSP perspective to argue in favor of an active role in IF by providing capital instead of the traditional FSP. In phase 2 of this research, this study uniquely aims to identify the role for the LSP from the perspective of the FSP. This was used as an empirical reflection of the validity of the operational data categories in the theoretical model identified in phase 1. The results show that the practical relevance of LSP customer data is also recognized from the perspective of the FSP.
5.2 Practical Implications
The findings and practical implications of this study are threefold. First, the exact position of the LSP within the IF framework is still ambiguous with relation to the role as a facilitator or a provider of funding. In the context of the FSP, the position of the traditional bank is being contested by nonbanks and FinTechs. Due to stricter regulations traditional banks are inflexible with respect to their adaptation of IF which still relies mainly on traditional credit calculation techniques. In response to empirical evidence of LSPs being turned down by traditional FSPs for collaboration in IF related to inadequate volume, LSPs may seek to find additional financing from nonbanks or FinTechs as they are more flexible.
Third, there is a future in accessing capital markets in which the access to financing approaches infinite amounts compared to traditional banking. An important avenue for accessing capital markets is through insurance companies providing the necessary security for IF to become an investable product from the perspective of investors. It follows the current trend of alternative means of financing being developed circumventing banks and accessing capital markets directly. “Once the investment is backed by insurance company X with AAA rating, it turns into
liquid gold and you can access infinite liquidity through capital markets.”(FSP1)
5.3 Limitations and future research
The first limitation of this research is related to its exploratory nature and qualitative method to address quantitative concepts. The concept of IF within SCF is still relatively new and would benefit highly from a quantitative assessment of the underlying constructs identified in the model in section 4.1. This is the next step in the current research project involving LSP1-3, for which due to the COVID-19 crisis a quantitative data analysis approach was not possible at this time. This importance is further emphasized by the lack of quantitative models currently being employed by FSPs for their IF schemes where they rely on professional experience of management. This opens up additional avenues for LSPs to add value in the dimension of IF, and can further strengthen its position as a facilitator of IF.
Second, a natural bias exists towards the facilitating role of LSPs in IF due to the financial constraints of the LSPs being studied. Additional research should focus on the capabilities of LSPs to serve as a provider of IF by putting up the capital themselves, which opens up additional benefits in value-creation in terms of increased profits aside from the benefits previously distinguished.
6. Conclusion
This study addresses the role of the LSP in IF in an attempt to extend their service offerings towards their customers. Based on the model introduced by (Hofmann, 2009) it extends further by employing operational data to add value in the context of IF through a facilitating role. This facilitating role established naturally through the financial constraints of the LSP cases being studied having important implications for their ability to take ownership of inventory. The main research question of this study is how LSPs can contribute to make IF possible for their customers.
In phase 1 of this study a theoretical model was developed for the operational data categories for which LSP have valuable information of their customers. It answer the question which operational data do LSPs have that can benefit the IF process. The result is a theoretical model based on the Expected Loss formula segregating the operational data categories in five different risk buckets: i) Credit Risk ii) Market & Price Risk iii) Obsolescence iv) Operational Risk v) Physical Control. However, this model is still very conceptual in nature and would benefit from a more detailed quantitative analysis of the different operational data constructs.
In phase 2 of this study the theoretical developed in phase 1 was reflected upon empirically from the perspective of FSPs. The aim was to answer the question how LSPs can operationalize this data and use it conjointly to make IF possible for their customers. The role for LSPs as a facilitator of IF by providing informational services is recognized by FSPs. To be able to operationalize this data further IT integration and standardization in the supply chain is necessary.
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