AI-Driven Dynamic Pricing for High-Value Assets in Manufacturing and Services: Optimizing Finite Horizon Sales with Demand Sensitivity

Abstract

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.

Publication DOI: https://doi.org/10.1080/00207543.2024.2430447
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript version of the following article, accepted for publication in the International Journal of Production Research and published on 9th December 2024. This version is made available under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Dynamic pricing,Finite time horizon,AI-enabled manufacturing,Industrial product
Publication ISSN: 1366-588X
Data Access Statement: Some or all data and models that support the findings of this study are available from the corresponding author upon reasonable request.
Last Modified: 01 Apr 2025 07:11
Date Deposited: 22 Nov 2024 11:44
Full Text Link:
Related URLs: https://www.tan ... 43.2024.2430447 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-12-09
Published Online Date: 2024-12-09
Accepted Date: 2024-10-20
Authors: Chen, Meilan
Hu, Xiangling
Qi, Yuan
Masi, Donato (ORCID Profile 0000-0002-4553-3244)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 9 December 2025.

License: Creative Commons Attribution Non-commercial


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