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

TL;DR

This paper develops a stochastic inventory model for multi-product online retailing that incorporates order-based loans to improve cash flow management, demonstrating the model's advantages over deterministic approaches through real-data simulations.

Contribution

It introduces a stochastic programming framework for multi-product inventory management with order-based loans, addressing cash constraints and demand uncertainty.

Findings

Stochastic model outperforms deterministic model in expected profit.

Retailers prefer order-based loans when initial cash is limited.

Longer receipt delays increase the likelihood of using loans.

Abstract

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.