A multi-period multi-product stochastic inventory problem with order-based loan

This paper investigates a multi-product stochastic inventory problem in which a cash-constrained online retailer can adopt order-based loan provided by some Chinese e-commerce platforms to speed up its cash recovery for deferred revenue. We first build deterministic models for the problem and then develop the corresponding stochastic programming models to maximize the retailers' expected profit over the planning horizon. The uncertainty of customer demand is represented by scenario trees, and a scenario reduction technique is used to solve the problem when the scenario trees are too large. We conduct numerical tests based on real data crawling from an online store. The results show that the stochastic model outperforms the deterministic model, especially when the retailer is less cash-constrained. Moreover, the retailer tends to choose using order-based loan when its initial available cash is small or facing long receipt delay length

A dynamic ordering policy for a stochastic inventory problem with cash constraints

This paper investigates a stochastic inventory management problem in which a cash-constrained small retailer periodically purchases a product from suppliers and sells it to a market while facing non-stationary demands. In each period, the retailer's available cash restricts the maximum quantity that can be ordered. There exists a fixed ordering cost for the retailer when purchasing. We partially characterize the optimal ordering policy by showing it has an $\bf s-C$ structure: for each period, when initial inventory is above the $\bf$s threshold, no product should be ordered no matter how much initial cash it has; when initial inventory is not large enough to be a $\bf s$ threshold, it is also better to not order when initial cash is below the threshold $C$. The values of $C$ may be state-dependent and related to each period's initial inventory. A heuristic policy $(s, C(x), S)$ is proposed: when initial inventory $x$ is less than $s$ and initial cash is greater than $C(x)$, order a quantity that brings inventory as close to $S$ as possible; otherwise, do not order. We first determine the values of the controlling parameters $s$, $C(x)$ and $S$ based on the results of stochastic dynamic programming and test their performance via an extensive computational study. The results show that the $(s, C(x), S)$ policy performs well with a maximum optimality gap of less than 1\% and an average gap of approximately 0.01\%. We then develop a simple and time-efficient heuristic method for computing policy $(s, C(x), S)$ by solving a mixed-integer linear programming problem and approximate newsvendor models: the average gap for this heuristic is approximately 2\% on our test bed

Comparing world regional sustainable supply chain finance using big data analytics:A bibliometric analysis

Purpose: Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement. Design/methodology/approach: A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context. Findings: The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability. Originality/value: This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF

Advancing Consumer Behavior: The Role of Artificial Intelligence Technologies and Knowledge Sharing

The increasing use of digital technologies has significantly reshaped marketing and consumer behavior (CB) as online communities and cutting-edge innovations such as artificial intelligence (AI) disrupt and advance consumer attitudes on specific products and services. As such, online communities that are supported by AI technologies creating new knowledge from consumer interactions through platforms like social media as consumers share experiences on specific products or services. Since AI is designed to “learn” and improve with data generated from digital technologies linked to consumer interactions, AI relies on consumer knowledge-sharing (KS) activities to replicate new knowledge for product and service improvement. However, given the knowledge gap in this area, this article applies the fsQCA technique to data generated from 291 participants to develop CB metaframework predicted on the concepts of AI, CB, and KS. Our results suggest that AI advances consumer attitudes and behaviors when knowledge is acquired while online communities promote curiosity and engage consumers to learn by sharing experiences about specific products or services. Furthermore, understanding the causality between AI, CB, and KS concepts offers critical decision-making insights to marketing experts across the industry

Buyer Financing in Pull Supply Chains: Zero-Interest Early Payment or In-House Factoring?

This study investigates the efficacy of zero-interest early payment financing (alternatively referred to as early payment) and positive-interest in-house factoring financing in a pull supply chain with a capital-constrained manufacturer selling a product through a capital-abundant retailer. Early payment is the prepayment of a wholesale cost to the manufacturer, whereas in-house factoring is a loan service provided to the manufacturer by a branch financing firm of the same retailer. We find that the retailer prefers early payment financing to bank financing when the manufacturer’s production cost is low. If the retailer instead offers positive-interest in-house factoring financing to the manufacturer, then the financing equilibrium domain enlarges as compared to bank financing. Interestingly, early payment financing can outplay positive-interest in-house factoring financing if the production cost is considerably low; otherwise, vice versa. When the production cost is big enough, the retailer will not provide either early payment or in-house factoring. Furthermore, our main qualitative result sustains with an identical wholesale price across all three financing schemes and the financing equilibrium domain of early payment shrinks as demand variability grows

Sourcing from Suppliers with Financial Constraints and Performance Risk

Two innovative financing schemes have emerged in recent years to enable suppliers to obtain financing for production. The first, purchase order financing (POF), allows financial institutions to offer loans to suppliers by considering the value of purchase orders issued by reputable buyers. Under the second, which we call buyer direct financing (BDF), manufacturers issue both sourcing contracts and loans directly to suppliers. Both schemes are closely related to the supplier's performance risk (whether the supplier can deliver the order successfully), which the repayment of these loans hinges upon. To understand the relative efficiency of the two emerging schemes, we analyze a game-theoretical model that captures the interactions between three parties (a manufacturer, a financially constrained supplier who can exert unobservable effort to improve delivery reliability, and a bank). We find that when the manufacturer and the bank have symmetric information, POF and BDF yield the same payoffs for all parties irrespective of the manufacturer's control advantage under BDF. The manufacturer, however, has more fexibility under BDF in selecting contract terms. In addition, even when the manufacturer has superior information about the supplier's operational capability, the manufacturer can efficiently signal her private information via the sourcing contract if the supplier's asset level is not too low. As such, POF remains an attractive financing option. However, if the supplier is severely financially constrained, the manufacturer's information advantage makes BDF the preferred financing scheme when contracting with an efficient supplier. In particular, the relative benefit of BDF (over POF) is more pronounced when the supply market contains a larger proportion of inefficient suppliers, when differences in efficiency between suppliers are greater, or when the manufacturer's alternative sourcing option is more expensive

Trade credit, risk sharing, and inventory financing portfolios

As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple financing channels (bank loans and trade credit), this paper attempts to better understand the risk-sharing role of trade credit - that is, how trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a disadvantageous position in managing default relative to a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer's financial status is relatively strong. Accordingly, trade credit is the only external source that the retailer uses to finance inventory. By contrast, if the retailer's cash level is low, the supplier offers two-part terms, inducing the retailer to finance inventory with a portfolio of trade credit and bank loans. Further, a deeper early-payment discount is offered when the supplier is relatively less efficient in recovering defaulted trade credit, or the retailer has stronger market power. Trade credit allows the supplier to take advantage of the retailer's financial weakness, yet it may also benefit both parties when the retailer's cash is reasonably high. Finally, using a sample of firm-level data on retailers, we empirically observe the inventory financing pattern that is consistent with what our model predicts