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What motivates the geographic footprint of the supply chains that multinational firms (MNFs) deploy? Traditional research in the operations and supply chain management literature tends to recommend locations primarily based on differentials in production costs and the ramifications of physical distance ignoring the role of taxation. MNFs that strategically position parts of their supply chains in low‐tax locations can allocate the profits across the divisions to improve post‐tax profits. For the profit allocation to be defensible to tax authorities, the divisional operations must possess real decision authority and bear meaningful risks. Generally speaking, the greater the transfer of risk and control, the larger the allowable allocation of profit. These transfers may also create inefficiencies due to misalignment of business goals and attitudes toward risk. We model these trade‐offs in the context of placing in a low‐tax region a subsidiary that oversees product distribution (as a limited risk distributor commissionnaire, limited risk distributor, or fully fledged distributor). Our analysis demonstrates that the MNF's preferences regarding the operating structures are not necessarily an obvious ordering based on the amount of risk and decision authority transferred to the division in the low‐tax jurisdiction. We derive and analyze threshold values of the performance parameters that describe the main trade‐offs involved in selecting an operating structure. We find some of the optimal decisions to exhibit interesting non‐monotone behavior. For instance, profits can increase when the tax rate in the low‐tax jurisdiction increases. Numerical analysis shows that the Limited‐Risk Distributor structure is rarely optimal and quantifies when each alternative dominates it.

The main purpose of this paper is to establish some estimates for the rates of convergence in limit theorems for random sums of independent identically distributed random variables via Trotter-distance. MSC: 60F05; 60G50; 41A25

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R ecent studies have shown that the processing speed of employees in service-based queueing systems is impacted by various behavioral factors. However, there is limited analytical work to investigate how these behavioral factors affect the overall performance of different queueing system designs. In this study, we focus on the response of human servers to the design and congestion level of the queueing system in which they operate. Specifically, we incorporate two behavioral factors into multi-server analytical queueing models: (1) server speedup due to increase of workload, and (2) server slowdown due to social loafing when multiple workers share the workload. We evaluate how these factors affect the performance of both the multi-server single-queue (SQ) and multi-server parallel-queue (PQ) system and the relative superiority of each system with respect to the number of customers in queue and the expected wait time in queue. We show that the impact of workload-dependent speedup can be decomposed into a direct effect and indirect effect on system performance. The direct effect leads to a reduced queue size due to increased expected service rate, while the indirect effect decreases queue size due to the "smoothing" effect. We quantify the performance impacts associated with both behavioral factors, illustrate the conditions where each effect dominates, and derive threshold values for these behavioral effects beyond which PQ systems outperform SQ systems. We also consider strategic routing and its impact on the performance of PQ systems. Our analytical contributions and numerical analyses offer important managerial guidance regarding the choice of the queueing system design and provide a theoretical foundation for future research in behavioral queueing.