Jeong Yujin, Jang Hyejin, Yoon Byungun
Department of Industrial and Systems Engineering, College of Engineering, Dongguk University, 3-26, Pil-dong 3ga, Chung-gu, Seoul, 100-715 South Korea.
Scientometrics. 2021;126(5):3697-3722. doi: 10.1007/s11192-021-03945-8. Epub 2021 Mar 20.
Firms today face rapidly changing and complex environments that managers and leaders must navigate carefully because confronting these changes is directly connected with success and failure in business. In particular, business leaders are adopting a new paradigm of planning, dynamic adaptive plans, which react adaptively to uncertainties by adjusting plans according to rapid changes in circumstances. However, these dynamic plans have been applied in larger-scale industries such as wastewater management in longer-range time frames. This paper follows the dynamic adaptive plan paradigm but transfers it to the technology management context with shorter-range action plans. Based on this new paradigm of risk management and technology planning, we propose a risk-adaptive technology roadmap (TRM) that can adapt to changing complex environments. First we identify risk by topic modeling based on futuristic data and then by sentiment analysis. Second, for the derived risks, we determine new and alternative plans by co-occurrence of risk-related keywords. Third, we convert an existing TRM to network topology with adaptive plans and construct a conditional probability table for the network. Finally, we estimate posterior probability and infer it by Bayesian network by adjusting plans depending on occurrence of risk events. Based on this posterior probability, we remap the paths in the previous TRM to new maps, and we apply our proposed approach to the field of artificial intelligence to validate its feasibility. Our research contributes to the possibility of using dynamic adaptive planning with technology as well as to increase the sustainability of technology roadmapping.
如今，企业面临着迅速变化且复杂的环境，管理者和领导者必须谨慎应对，因为应对这些变化直接关系到企业的成败。特别是，企业领导者正在采用一种新的规划范式——动态适应性计划，该计划通过根据环境的快速变化调整计划来对不确定性做出适应性反应。然而，这些动态计划已在诸如废水管理等更大规模的行业中，在更长的时间范围内得到应用。本文遵循动态适应性计划范式，但将其应用于技术管理背景下，并采用更短时间范围的行动计划。基于这种风险管理和技术规划的新范式，我们提出了一种能够适应不断变化的复杂环境的风险适应性技术路线图（TRM）。首先，我们通过基于未来数据的主题建模，然后通过情感分析来识别风险。其次，对于所导出的风险，我们通过与风险相关的关键词的共现来确定新的和替代计划。第三，我们将现有的TRM转换为具有适应性计划的网络拓扑结构，并为该网络构建条件概率表。最后，我们估计后验概率，并通过贝叶斯网络根据风险事件的发生情况调整计划来进行推断。基于此先验概率，我们将先前TRM中的路径重新映射到新的地图上，并将我们提出的方法应用于人工智能领域以验证其可行性。我们的研究有助于实现将动态适应性规划与技术相结合的可能性，并提高技术路线图的可持续性。
