Supply Portfolio Research Articles

A two-period model for selection of resilient multi-tier supply portfolio

A two-period decision-making model is developed for selection of resilient supply portfolio in a multi-tier supply chain under disruption risks. The planning horizon is divided into two aggregate periods: before and after the disruption. The resilience of the supply chain is achieved by selection ahead of time primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously, recovery and transshipment portfolios are determined for each disruption scenario and decisions on usage the pre-positioned inventory are made to minimise expected cost or maximise expected service level. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. The findings also indicate that the developed portfolio approach with an embedded network flow structure leads to computationally efficient stochastic mixed integer program with a strong LP relaxation.

Supplier portfolio selection based on the monitoring of supply risk indicators

This paper introduces a dynamic optimisation model for a manufacturer's optimal selection of a portfolio of suppliers. The model uses a set of indicators that measure risks imposed by suppliers on the manufacturing supply chain. These indicators measure financial stability, production stability, product quality and cost of suppliers. The model uses a combined simulation-optimisation framework to select suppliers and allocate orders to them based on real-time monitoring of supply risk indicators. This model uses a multi-period order allocation approach based on the viewpoint of a manufacturer in a manufacturing supply chain system. A system dynamics model simulates the interrelations and feedbacks among parties in the supply chain, i.e., suppliers, the manufacturer, and the manufacturing product market. It models the effect of supply risk indicators on a manufacturer's profit over a planning horizon. The result of the simulation is fed to a portfolio optimisation model to determine an optimal supplier order allocation based on the manufacturer's propensity for risk. The model informs the manufacturer to rebalance its supply portfolio in response to early changes in supply risk indicators over a planning horizon. The results show that supplier portfolio selection based on this framework provides higher expected profit and less risks to the manufacturer over the planning horizon. For instance, in our numerical example, the high-level risk averse decision maker made a profit of 5.4% and a risk of 1% less than those of low-level risk averse decision maker at the end of the planning horizon.

Designing an optimal water supply portfolio for Taiwan under the impact of climate change: Case study of the Penghu area

Water security has become a priority for adaptive policy and measures taken in response to climate change around the world. This is an opportunity and a challenge for the Taiwan government to establish a climate-resilient water supply portfolio to ensure water security. This study incorporated the costs of water production, intermittency, and carbon dioxide emission reduction, as well as the currently available water sources and how they will be affected by climate change, and applied optimal control theory to establish a cost-effective model to plan a long-term (2031) climate-resilient water supply portfolio for Taiwan. The study then uses the Penghu area as an empirical case study for this water supply portfolio. The results showed that desalination will become the major source of water, accounting for 71.81% of the water supply by 2031. However, this high proportion of desalination water will result in a significant increase in the unit cost of water production and the carbon footprint. Therefore, water demand management is also needed, and is recommended to ensure sustainable water resources in the Penghu area.

Goal-based water trading expands and diversifies supplies for enhanced resilience

In response to rising water scarcity concerns around the world, water utilities are expanding and diversifying their water supply portfolios, including attempting to value and reuse every drop throughout the water cycle. This transition is creating new hybrid infrastructure systems that combine centralized and decentralized sources at various scales. To help strategize and manage these emerging hybrid systems, we introduce a flexible goal-based water-trading framework using a combination of regulatory and market incentives. We apply this framework in a simulation of the San Francisco Bay Area, and test the effects of policy parameters, local preferences and collaboration between utilities. Results show that the option of engaging in an open trading scheme can lead to a more strategic and holistic path to higher regional resiliency through increased diversity in water portfolios, at lower costs than if utilities were tasked with pursuing goals independently. Highest benefits are observed when utilities cooperate in the exchange of information, which highlights the importance of transparency and trust operating in conjunction with regulatory and market forces. Water utilities worldwide are diversifying their portfolios of water supplies to smooth growing variation in water availability. This simulation finds that the option of engaging in an open water-trading scheme in the San Francisco Bay Area could promote regional resiliency and lowers costs.

Sustainability in supplier selection and order allocation: Combining integer variables with Markowitz portfolio theory

This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect ‘risk’ into the supplier selection problem. Secondly, since we allow for integer variables in our model—in contrast to the classical Markowitz portfolio theory—we use the ε-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution.

A resilient supply portfolio considering political and disruption risks

The need of accounting for resilience in global supply chains has been growing from practical and academic points of view. However, there is still the need for developing quantitative decision support models on this issue. In the present work, we propose a novel multi-objective mixed possibilistic, two-stage scenario-based stochastic programming model to handle supplier selection and order allocation problem in a global supply chain under operational and disruption risks. The model minimises cost and political risk, while, maximising resilience of the supply portfolio. Various risk mitigation approaches including: contracting with backup suppliers, fortification of suppliers and procurement of emergency inventory, are considered in the model. In addition, the proposed model determines recovery plans. Reservation level driven Tchebycheff procedure is incorporated in the solution procedure to find Pareto-optimal solutions. The validation of the model via computational experiments demonstrates the applicability of the proposed model and solution method in building a resilient supply portfolio under consideration of operational and disruption risks.

A resilient supply portfolio considering political and disruption risks

The need of accounting for resilience in global supply chains has been growing from practical and academic points of view. However, there is still the need for developing quantitative decision support models on this issue. In the present work, we propose a novel multi-objective mixed possibilistic, two-stage scenario-based stochastic programming model to handle supplier selection and order allocation problem in a global supply chain under operational and disruption risks. The model minimises cost and political risk, while, maximising resilience of the supply portfolio. Various risk mitigation approaches including: contracting with backup suppliers, fortification of suppliers and procurement of emergency inventory, are considered in the model. In addition, the proposed model determines recovery plans. Reservation level driven Tchebycheff procedure is incorporated in the solution procedure to find Pareto-optimal solutions. The validation of the model via computational experiments demonstrates the applicability of the proposed model and solution method in building a resilient supply portfolio under consideration of operational and disruption risks.

Rate design for a decarbonizing grid

The Western U.S. electric grid has undergone vast changes since the pre-renewables era. For example, renewables now comprise nearly 30% of California’s electric supply portfolio, and recent CA legislation mandates 50% renewables by 2030. As a consequence, intra-day variation in hourly net loads is rapidly steepening, as shown by the California Independent System Operator (CAISO)’s now famous ‘Duck Curve”. In 2015, CAISO recommended a four-season TOU pricing period design, with peak hours between 4 PM and 9 PM on summer weekdays, and a “super-off-peak” period in spring mid-day hours. We present an aggressive four-season time-of-use (TOU) rate design based on CAISO’s recommended TOU periods, and some illustrative impacts of implementing such TOU rates.In Western U.S. energy markets, high wholesale prices generally correspond to high marginal carbon emissions, and conversely. Properly designed TOU rates can help reduce GHG emissions by discouraging energy use during times of high marginal carbon emissions. However, demand charges, a feature of many commercial rate designs, can negate the benefits of TOU rates if applied during off-peak hours. We show that overuse of non-coincident demand charges can discourage customer investments that would benefit both the customer and the grid if rates were correctly aligned with cost. We recommend the following pricing strategies to support long-term efforts toward decarbonization of the electric grid: (1) More widespread use of aggressive 3- or 4-season TOU rates (or RTP where feasible); and (2) Reduced dependence on demand charges that apply during off-peak hours. Widespread utilization of retail rates that are aligned with temporal variation in carbon intensity, by both residential and nonresidential customers, is essential, for achievement of decarbonization goals. DisclaimerOpinions and recommendations stated herein should be attributed to the author alone, and not to the California Public Utilities Commission or its Staff.

Large Landscape Urban Irrigation: A Data‐Driven Approach to Evaluate Conservation Behavior

AbstractNonresidential irrigation is a unique and important yet understudied urban water sector. Knowing how urban irrigators use water is critical for projecting future demands, planning diverse supply portfolios, and designing conservation strategies. In this study, we developed a holistic, analytical approach to advance knowledge about the temporal and spatial dimensions of nonresidential outdoor water use, also known as large landscape irrigation. Our approach employed data from two forthcoming technologies: dedicated irrigation meters and smart meters (i.e., advanced metering infrastructure). We then applied our methodology to a case study city in the San Francisco Bay Area, California, from 2013 to 2016 during a historic, high‐profile drought. Importantly, we uncovered behavioral differences between customers with potable versus recycled water connections and different subsectors of large landscape irrigation. Overall, conservation patterns mimicked those across California. Although they saved at lower rates, customers with recycled water followed similar conservation trends despite receiving no mandates. A weekly water use model revealed drivers of water demand, while spatial analyses showed hot and cold spots of conservation, with those in higher‐income areas conserving less as the drought progressed. A conditional inference tree partitioned diverse customers based on their conservation rates, identifying characteristics of customers who could provide the most savings in future droughts. As increasing water scarcity and population growth prompt water suppliers to optimize resources through supply diversification and demand‐side management, large landscape irrigation presents one avenue for achieving those goals.

Designing an optimal water supply portfolio for Taiwan under the impact of climate change: Case study of the Penghu area

Water security has become a priority for adaptive policy and measures taken in response to climate change around the world. This is an opportunity and a challenge for the Taiwan government to establish a climate-resilient water supply portfolio to ensure water security. This study incorporated the costs of water production, intermittency, and carbon dioxide emission reduction, as well as the currently available water sources and how they will be affected by climate change, and applied optimal control theory to establish a cost-effective model to plan a long-term (2031) climate-resilient water supply portfolio for Taiwan. The study then uses the Penghu area as an empirical case study for this water supply portfolio. The results showed that desalination will become the major source of water, accounting for 71.81% of the water supply by 2031. However, this high proportion of desalination water will result in a significant increase in the unit cost of water production and the carbon footprint. Therefore, water demand management is also needed, and is recommended to ensure sustainable water resources in the Penghu area.

Strategies for Sourcing of Legal Services: Sector Study of Bulgarian Top 100 Non-Financial Corporations

On the background of an evolving legal market there is a debate in both theory and practice about the role of in-house general counsels (GCs). This paper aims to contribute to the discussion by studying the organization of legal departments in large Bulgarian non-financial corporations and their modes of collaboration with external providers of legal services. The research seeks to establish how Bulgarian GCs take decisions about ‘making’ or ‘buying’ legal services, to examine the factors that determine the strategic choice of sourcing and collaboration and to provide a framework for its analysis. Semi-structured interviews were conducted with GCs selected by convenience in terms of accessibility from the ‘K100’ ranking of the Bulgarian business weekly ‘Capital’ (2017 version). The panel comprises 14 respondents but due to presence of interrelations and shared service centres the sample covers over 20 companies. The analysis shows that ‘making’ is preferred over ‘buying’ when it concerns ‘blended advice’ requiring both legal knowledge and organization specific business knowledge. Market dynamics affect the strategy choice and variations in the supplier portfolio design result from the pursuit of two contradicting goals: reduced cost and increased security. Bargaining-based contracting allows better cost management but also isolates the supplier. Close collaboration and investment in relationship development mitigate uncertainty concerns permitting closer integration between legal departments and law firms. A simplified decisional matrix is proposed distinguishing between 4 generic strategies: ‘Open portfolio’, ‘Preferred supplier’, and two ‘Closed panel’ variations – with and without competition.

Supplier portfolio of key outsourcing parts selection using a two-stage decision making framework for Chinese domestic auto-maker

The Chinese domestic auto-industry has been urged to perform quality improvement practice due to the soaring warranty cost and quality loss under the “two-dimensional warranty policy” regulation. The quality of key vehicle parts provided by its outsourcing suppliers plays a significant role not only on the quality and reliability of the vehicle products, but also on the reputation, customer loyalty, warranty cost and other quality loss of the assemblers. To improve the economics of the vehicle quality, preventive quality improvement strategy through the strategic procurement studies on the key parts is addressed. This study develops a two-stage decision making framework to deal with the supplier portfolio of the key outsourcing parts (SPKOP) selection by highlighting the economics of quality within the stipulated warranty period. In stage I, on the basis of customer feedback, the extended VIKOR-based multi-criteria decision making (MCDM) technique is developed to select the key outsourcing parts, which influence the economics of the vehicles with higher quality improvement priorities. According to the sequences of the key outsourcing parts, the supplier candidates of the key parts are investigated and determined. In stage II, a nonlinear mixed 0–1 integer programming (NLMIP) model is formulated to select the optimal supplier portfolio based on the total quality-related cost, system reliability, delivery time and customer complaints. The RPN-based method considering the nonlinear characteristics of failure severity is modified to determine the relative importance of each key part subject to its reliability. To resolve the SPKOP problem, the utility function and combined weighting technique are employed to cope with multiple conflicting objectives by the weighting technique. Next, a hybrid genetic algorithm (HGA) with a local search strategy is designed to improve the search efficiency. A case study is conducted to verify the effectiveness of the proposed model and the decision-making framework.

Supply chain network equilibrium with strategic supplier investment: A real options perspective

This article develops a network equilibrium model that studies competing supply chain firms who can strategically invest in new capacity at suppliers under cost and demand uncertainty. We model the decision-making process of each supply chain firm as a stochastic optimization problem. We formulate the equilibrium conditions of all supply chain firms using the variational inequality theory. Analytical results establish connections from the supplier investment decisions to real put and call options. Option pricing theory is applied to show that the value of supplier capacity investment will increase if the demand uncertainty or the cost uncertainty increases, and will decrease if the price sensitivity or the cost factor increases. Numerical experiments are conducted to confirm and extend the analytical results, and to investigate the decision-making and profits of different supply chain firms in more complex and realistic settings. Our results also suggest that manufacturers should strategically invest in a portfolio of complementary suppliers with different risk characteristics that serve different purposes.

Two-period vs. multi-period model for supply chain disruption management

A novel two-period modelling approach is developed for supply chain disruption mitigation and recovery and compared with a multi-period approach. For the two-period model, planning horizon is divided into two aggregate periods: before disruption and after disruption. The corresponding mitigation and recovery decisions are: (1) primary supply and demand portfolios and production before a disruption, and (2) recovery supply, transshipment and demand portfolios and production after the disruption. In the multi-period model, a multi-period planning horizon is applied to account for a detailed timing of supplies and production. The primary and recovery portfolios are determined simultaneously and for both approaches the integrated decision-making, stochastic mixed integer programming models are developed. While the simplified two-period setting may overestimate (for best-case capacity constraints) or underestimate (for worst-case capacity constraints) the available production capacity, it can be easily applied in practice for a fast, rough-cut evaluation of disruption mitigation and recovery policy. The findings indicate that for both two- and multi-period setting, the developed multi-portfolio approach leads to computationally efficient mixed integer programming models with an embedded network flow structure resulting in a very strong linear programming relaxation.

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.

On the Properties of Yield Distributions in Random Yield Problems: Conditions, Class of Distributions and Relevant Applications

In this study, we propose two technical assumptions to ensure the unimodality of the objective functions in two classes of price and quantity decision problems with one procurement opportunity under supply random yield and deterministic demand in a price‐setting environment. The first class of problems involves a decentralized supply chain/assembly system under different configurations, and the second class focuses on a single firm's price and quantity decisions under different contracts, payment schemes and supplier portfolios. We provide appealing economic interpretations and easy‐to‐verify sufficient conditions for our proposed technical assumptions. We show that both assumptions are preserved under truncation and positive scale, and satisfied by most commonly used continuous yield distributions. Moreover, similar to the role that the increasing generalized failure rate (IGFR) property plays in analyzing operations problems with demand uncertainty, our Assumption 1 plays a fundamental role in regulating the behaviors of the objective functions for both classes of random yield problems. Assumption 2 is more general than both Assumption 1 and the IGFR property and is used to analyze the second class of problems. Finally, we discuss the difference between random yield and random demand problems, and explain the rationale for the need of different technical assumptions.

A business process decision model for fresh-food supplier evaluation

PurposeFrequent food safety incidents caused widespread consumer concerns. Even though food safety is one of the weakest links in the fresh food supply chain and influences consumer food choice in ways different from the quality dimension, this factor is hardly proposed as one of the key traditional supplier selection criteria (e.g. quality, delivery, and price) in the literature. The purpose of this paper is to develop a business process decision model to assess the non-compensating food safety sub-criteria in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. The preferred fresh food suppliers can minimize the risk of food safety failure and the associated huge food safety failure costs spanning from private consumer anguish to social distress that cause unbearable costs of sales loss and damage to brand image in business.Design/methodology/approachThis study proposes a novel approach that combines several well-established multi-criteria decision making (MCDM) techniques, including fuzzy AHP (FAHP), TOPSIS, and ELECTRE, and innovatively apply to analyze supplier performance and prioritize potential fresh food suppliers. This hybrid business process model can enforce compliance to all the five non-compensatory sub-criteria of food safety. Since ELECTRE is a non-compensatory MCDM method, it is therefore particularly applicable for disqualifying high risk fresh food suppliers from further full scale supplier performance evaluation by FAHP and TOPSIS. This hybrid business process decision model is able to capitalize on the strengths of these MCDM methods and offset their deficiencies.FindingsThis study uses data of an international supermarket chain to validate feasibility of the proposed model. Results indicate that this model is able to assess the non-compensating food safety sub-criteria via the ELECTRE method in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. Only the preferred suppliers with the required food safety capability can proceed to the second stage of the supplier selection process. Assessment via the TOPSIS method reveals the ranking order of those top performing suppliers according to their relative scores along all the supplier selection criteria. The TOPSIS ranking results with the selection of the suppliers C, E, A, and F are robust and consistent across all the different scenarios.Practical implicationsApplication to the fresh food industry is possible with the aid of the MCDM methods. The contribution to the body of knowledge in this teaching and research field demonstrates the importance of first identifying the order qualifier for disqualifying those suppliers that do not satisfy the food safety requirements via the ELECTRE method. The proposed assessment procedure complies with the regulatory policy on food safety, and would influence public policy in applying the best practice of food safety regulation. Without first qualifying the potential suppliers on the basis of food safety, wrong decision can be made to select those high food risk suppliers that have relatively higher overall scores in other supplier selection criteria. Using the assessment results has positive economic and commercial impact on the purchasing managers to formulate appropriate purchasing and supplier development strategy to enhance supplier’s food safety performance, whilst maximizing the overall supplier portfolio performance. The improved supplier’s food safety performance will certainly benefit the society’s quality of life as well.Originality/valueBased on the analytical MCDM methods of FAHP, TOPSIS, and ELECTRE, purchasing managers can operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. This study represents the first move to innovatively apply the FAHP, TOPSIS, and ELECTRE methods to operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. Application to the fresh food industry to validate the feasibility of the proposed model has been conceived and implemented in this study. Analysis of the data inputs of a supermarket chain via the three MCDM methods generate the results that fulfill the purpose of achieving the research objective of identifying and managing the supplier base that can deliver the best supplier performance, conditional on first passing the fresh food safety test.

Customer and Supplier Portfolios: Can Credit Risks be Managed Through Supply Chain Relationships?

While supply chain risk has been the subject of an ever‐increasing amount of research attention, the importance of credit risk has been less studied, at least by supply chain researchers. Yet, there may be risks inherent within a firm's portfolios of supply chain relationships that are manifested in the credit risk of a firm. Moreover, portfolio characteristics may serve as a signal to the external market regarding these risks. While customer and supplier portfolio characteristics may impact the firm's exposure to risks, the specific issue of how portfolio characteristics relate to credit risk has rarely been examined by supply chain scholars. This research bridges extant works in supply chain management and finance to relate supply chain characteristics to a critical reputational outcome, namely credit ratings. In this research, we utilize a sample of firms that recently underwent an initial public offering to empirically examine the theoretical predictions of Resource Dependence Theory regarding the relationships between different supply chain portfolio characteristics and credit risk.

The economic value of local water supplies in Los Angeles

Los Angeles imports water over long distances to supplement local supplies. Reduced reliability of the available imports is driving many local agencies to promote conservation and enhance local water sources. These include stormwater capture, water reuse and groundwater. But financial considerations are often a significant impediment to project development, especially when comparing new and existing sources. Here we demonstrate a comprehensive approach for evaluating the economic implications of shifting to local water reliance in Los Angeles County. We show that local water supplies are economically competitive. Results from integrated hydroeconomic modelling of urban water in Los Angeles identify cost-effective water supply portfolios and conservation targets. Considering costs across the ‘full-cycles’ of urban water supply that span agency boundaries yields better comparisons of planning alternatives. Throughout the region, many water retailers could successfully mitigate effects of imported water cuts while still supporting drought-tolerant landscapes, but some would suffer due to over-reliance on imports. Updating economic assessment methods would support needed innovations to achieve local reliance in Los Angeles, including infrastructure investments, institutional reforms, many more drought-tolerant landscapes and reallocated groundwater rights. A large-scale economic analysis of the economics of water supplies in the greater Los Angeles area, based on the ‘full-cycle’ costs of water sources such as imported water, groundwater, and reused and storm-water capture. The study showcases an updated model and framework for urban water studies that can be applied to other cities.

Financial Pooling in a Supply Chain

Trade credit is often extended by suppliers to buyers who often have easier access to external financing. Moreover, many buyers also delay paying their suppliers beyond the agreed due day. Prior literature attributes this phenomenon to quality assurance or buyer's abuse of market power. In this paper, we show that pooling could be another reason behind this practice. Using a game-theoretic model that explicitly captures the liquidity shocks faced by different supply chain partners, we analyze the total financing cost of the supply chain under endogenous supply contract and working capital policies. We show that the embedded stretching option of trade credit allows supply chain partners to pool their liquidity buffers. Due to this pooling effect, even if the supplier's financing costs are strictly higher than the buyer's, the buyer may still demand trade credit from the supplier in order to lower the total financing cost. The model reveals that trade credit is more efficient than cash on delivery when the supplier's cost for collecting trade credit is low (e.g., when the retailer trusts the supplier) or when the supplier does not have access to a low-cost financing channel when facing liquidity shocks. This pooling effect also dictates that variabilities of firms' liquidity shocks have asymmetric impact on the efficiency of trade credit: while trade credit can only be more efficient than cash on delivery when the supplier's liquidity shock has a sufficiently large variability, it could benefit the supply chain even when the buyer's liquidity shock is deterministic (e.g., an investment opportunity). The benefit of financial pooling increases as the buyer's supplier portfolio becomes more diversified. As an innovative financing scheme, reverse factoring further enhances the efficiency of this pooling effect and, as a result, reduces the overall supply chain financing cost.