Abstract
One of the main challenges for the manufacturing industry is to ensure the appropriate allocation of benefits to all subsidiaries to achieve the ideal pattern of rapid and shared growth. Transfer price, which is the internal price among subsidiaries of a global manufacturing firm, is an important financial and business issue. However, most previous studies simply assume transfer prices. Our study reveals that transfer pricing can lead to different total profits of global supply chains and different profit allocation effects on subsidiaries. Furthermore, exchange rate is also considered with production planning since it is treated as an important global factor that can cause the profits of all subsidiaries to fluctuate. We add exchange constraints on both manufacturing and sales subsidiaries to reduce the inappropriate allocation of profits among subsidiaries. A profit allocation approach is proposed that considers transfer pricing and exchange rate simultaneously with production planning. The model is formulated using nonlinear programming with a nonconvex objective function and nonlinear constraints. However, we overcome modeling challenges using piecewise linear modeling and are able to obtain a mixed-integer linear programming (MILP) model, which can be solved using a linear programming solver. From numerical experiments, we highlight the importance of production planning decisions with transfer pricing and also prove the effectiveness of exchange constraints in the proposed model of the global supply chain.
Original language | English |
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Pages (from-to) | 1715-1723 |
Number of pages | 9 |
Journal | Procedia Manufacturing |
Volume | 39 |
DOIs | |
Publication status | Published - 2019 |
Event | 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, ICPR 2019 - Chicago, United States Duration: 2019 Aug 9 → 2019 Aug 14 |
Keywords
- Exchange rate
- Mathematical model
- Profit allocation
- Shared growth
- Transfer prices
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence