Optimal pricing, production, and intelligentization policies for smart, connected products under two-level trade credit
| Autor: | Yu-Chung Tsao, Nandya Shafira Pramesti, Thuy-Linh Vu, Iwan Vanany |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | RAIRO - Operations Research. 57:121-143 |
| ISSN: | 2804-7303 0399-0559 |
| DOI: | 10.1051/ro/2022207 |
| Popis: | The development of technologies such as the Internet of Things has transformed traditional physical products into smart connected products (SCPs) that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. SCPs raise a new set of strategic choices for creating value and pricing products, how relationships with business partners such as channels are redefined, and what role companies should play as industry boundaries are expanded. This study develops an inventory model that considers optimal pricing, production, and intelligent policies for SCPs. In this model, customer demand is assumed to increase as the selling price decreases and the effort to improve product intelligence (i.e., intelligent effort) increases. In addition, a two-level trade credit is included in the SCPs supply chain channel. The manufacturer often receives a permissible delay-in-payment (trade credit) from the supplier while also offering a delayed payment to end customers to attract more sales. Trade credit is particularly important for SCPs as it can act as a payment plan to reduce the product’s price barrier. This study aims to determine the optimal selling price, lot size, and level of intelligent effort while maximizing the manufacturer’s profit under a two-level trade credit. The optimal solution is clarified, numerical examples are provided, and a sensitivity analysis is performed to illustrate the theoretical results and solution approach. The results reveal that considering the level of intelligent effort as a decision can benefit the manufacturer. Notably, as the intelligent effort coefficient increases by 55%, the total profit increases by 65.8%. |
| Databáze: | OpenAIRE |
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