The current study proposes an innovative methodology for the profiling of
psychological traits of Operations Management (OM) and Supply Chain Management
(SCM) professionals. We use innovative methods and tools of text mining and
social network analysis to map the demand for relevant skills from a set of job
descriptions, with a focus on psychological characteristics. The proposed
approach aims to evaluate the market demand for specific traits by combining
relevant psychological constructs, text mining techniques, and an innovative
measure, namely, the Semantic Brand Score. We apply the proposed methodology to
a dataset of job descriptions for OM and SCM professionals, with the objective
of providing a mapping of their relevant required skills, including
psychological characteristics. In addition, the analysis is then detailed by
considering the region of the organization that issues the job description, its
organizational size, and the seniority level of the open position in order to
understand their nuances. Finally, topic modeling is used to examine key
components and their relative significance in job descriptions. By employing a
novel methodology and considering contextual factors, we provide an innovative
understanding of the attitudinal traits that differentiate professionals. This
research contributes to talent management, recruitment practices, and
professional development initiatives, since it provides new figures and
perspectives to improve the effectiveness and success of Operations Management
and Supply Chain Management professionals.

运用创新方法和工具，本研究对运营管理和供应链管理专业人员的心理特征进行了剖析，通过文本挖掘和社交网络分析，以心理特征为焦点，对一系列职位描述中相关技能的需求进行了需求分析评估，从而提供了运营管理和供应链管理专业人员所需技能的定向图谱，为人才管理、招聘实践和职业发展提供了新的观点和视角，进一步提高运营管理和供应链管理专业人员的有效性和成功率。

卓越解码：通过文本挖掘描绘运营和供应链专业人员心理特质的需求

Decoding excellence: Mapping the demand for psychological traits of  operations and supply chain professionals through text mining

Quantum computing is expected to have transformative influences on many
domains, but its practical deployments on industry problems are underexplored.
We focus on applying quantum computing to operations management problems in
industry, and in particular, supply chain management. Many problems in supply
chain management involve large state and action spaces and pose computational
challenges on classic computers. We develop a quantized policy iteration
algorithm to solve an inventory control problem and demonstrative its
effectiveness. We also discuss in-depth the hardware requirements and potential
challenges on implementing this quantum algorithm in the near term. Our
simulations and experiments are powered by \texttt{IBM Qiskit} and the
\texttt{qBraid} system.

本研究目的在于将量子计算应用于工业操作管理问题，尤其是供应链管理。我们开发了一个量化的策略迭代算法来解决库存控制问题，并展示了其有效性。同时讨论了实现该量子算法所需的硬件要求和潜在挑战。