Customer Trust Versus Retailer Profit: A Case Study on Optimising Base Stock Level Using Multi-Agent-Based Simulation

Trust plays an important role in supply chains as a binder between business partners as well as between a customer and a retailer. Customer’s trust in a retailer depends, among others, on the retailer’s ability to meet the customer’s demand. Insufficient product availability leads to customer churn and lost revenue, while overstocking increases the retailer’s operating costs. The retailer has to find the right balance between the costs of customer churn and the storage costs, but it is a demanding task due to the diversity of customers. This diversity can be modelled using a multi-agent approach. However, scientific literature contains too little research on overcoming the problem of finding the abovementioned balance using multi-agent technologies. In this paper, we examine customer’s trust in a retailer and we show how replenishing warehouse stock (reorder point) affects the retailer’s profits and customer’s trust. A multi-agent software tool in NetLogo environment was developed to study the relationship between product availability, reorder point, a retailer’s profit, and trust in a customer population. The tool allows its users to observe non-linear relationships between reorder point, profit, and trust, as well as determine the appropriate replenishment policy of the warehouse what is important for researchers dealing with customer-retailer relationships and the human factor in supply chains. It is also crucial for decision-makers responsible for inventory policy. The case study described shows how to use the tool to maximise a retailer’s profit or maximise the number of products sold.