Market proliferation and the impact of locational complexity on network restructuring
<|reference_start|>Market proliferation and the impact of locational complexity on network restructuring: This manuscript investigates the problem of locational complexity, a type of complexity that emanates from a companys territorial strategy. Using an entropy-based measure for supply chain structural complexity ( pars-complexity), we develop a theoretical framework for analysing the effects of locational complexity on the profitability of service/manufacturing networks. The proposed model is used to shed light on the reasons why network restructuring strategies may result ineffective at reducing complexity-related costs. Our contribution is three-fold. First, we develop a novel mathematical formulation of a facility location problem that integrates the pars-complexity measure in the decision process. Second, using this model, we propose a decomposition of the penalties imposed by locational complexity into (a) an intrinsic cost of structural complexity; and (b) an avoidable cost of ignoring such complexity in the decision process. Such a decomposition is a valuable tool for identifying more effective measures for tackling locational complexity, moreover, it has allowed us to provide an explanation to the so-called addiction to growth within the locational context. Finally, we propose three alternative strategies that attempt to mimic different approaches used in practice by companies that have engaged in network restructuring processes. The impact of those approaches is evaluated through extensive numerical experiments. Our experimental results suggest that network restructuring efforts that are not accompanied by a substantial reduction on the target market of the company, fail at reducing complexity-related costs and, therefore, have a limited impact on the companys profitability.<|reference_end|>
@article{pinar-pérez2024market,
    title={Market proliferation and the impact of locational complexity on network
  restructuring},
    author={J.M. Pinar-P'erez, D. Ruiz-Hern'andez, and M.B.C. Menezes},
    journal={Applied Mathematical modelling 104 (2022)},
    year={2024},
    doi={10.1016/j.apm.2021.11.031},
    archivePrefix={arXiv},
    eprint={2402.01585},
    primaryClass={math.OC cs.DM}
}