AI-driven dynamic pricing for high-value assets in manufacturing and services: optimizing finite horizon sales with demand sensitivity

In the context of AI-driven manufacturing and service industries, the strategic selling of high-value products within a finite time horizon is a critical challenge for maximising expected profit. This research investigates how AI can be leveraged to enhance dynamic pricing strategies, where historical prices influence each customer's offer. Employing AI algorithms, the seller dynamically adjusts the minimum acceptable prices at various time points, responding to market trends and predictive analytics. Our study reveals that in scenarios where AI anticipates an increasing trend in offered prices, sellers are inclined to delay sales to capitalise on potentially higher future offers. Conversely, in situations where AI predicts a decreasing trend in offered prices, the algorithm adjusts the minimum acceptable price to be an increasing function of the remaining sales time, optimising the timing of sales for individual product units. Additionally, when dealing with two distinct products, the AI-driven pricing strategy adapts the minimum acceptable prices based on the relative cost magnitudes of these products. This research underscores the potential of AI in transforming traditional dynamic pricing approaches, offering novel insights into how AI-enabled tools can optimise sales strategies in the manufacturing and service sectors, balancing profitability with market responsiveness.