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Foreign firms in China, particularly those from the Global West, are in a difficult position as deglobalisation and strained international relations create risk. This paper offers research avenues on how operations and supply chain management scholars can analyse the risk factors, de-risking practices, and de-risking outcomes of foreign firms in China.

This paper is primarily based on interviews with managers of foreign firms’ subsidiaries and joint ventures in China between late 2019 and early 2024 and employs institutional theory to develop a conceptual framework.

Six risk factors motivating foreign firms to consider “China+1” (diversification and multi-sourcing) or “In-China-For-China” (localisation with potential bifurcation) are identified. Four pathways focusing on politics, locations, technologies, and people enable further investigation of these de-risking strategies.

This is the first paper to take a deeper look at perceptions, worries, and considerations of foreign firms in China and offers specific recommendations for impactful research.

The purpose of this paper is to provide an organizing lens for viewing the distinct contributions to knowledge production from those research communities addressing the impact of competitive strategy on company performance in general, and the influence of cost leadership and differentiation strategy on organizational performance in detail.

The research methodology was based on the PRISMA review, and thematic analysis based on an iterative process of open coding was analyzed and then the sample was analyzed by illustrating the research title, objectives, method, data analysis, sample size, variables and country.

The main factor that influenced the competitive strategy is strategic growth; strategic growth has a significant influence on competitive strategy. Furthermore, competitive strategy will boost firm network, performance measurement and organization behavior. In the same way, the internal goal factor will enhance organizational effectiveness. Also, a differentiation strategy will support management practice factors, strategic positions, product price, product characteristics and company performance.

This study contributes to the literature by identifying a framework of competitive strategy factors, company performance factors, cost leadership strategy factors, differentiation strategy factors and competitive strategy with global market factors. This study provides a complete picture and description of the resulting body knowledge in competitive strategy and organizational performance.

This study aims to investigate the impact of geopolitical risk (GPR) on supply chain concentration (SCC) and the roles of operational capabilities and resources in this relationship.

Secondary longitudinal data from multiple sources is collected and combined to test for a direct impact of GPR on SCC. We further examine the moderating effects of firms’ operational capabilities and resources (i.e. firm resilience, operational slack and cash holding). Fixed-effect regression models are applied to test the hypotheses, followed by a series of robustness tests to check the consistency of the results.

Consistent with the tenets of resource dependence theory, our analysis reveals a significant negative impact of GPR on SCC. Moreover, we find that this adverse effect is attenuated for firms with higher levels of resilience, more operational slack and greater cash holdings. Further analysis suggests that maintaining a diversified supply chain base during heightened GPR is associated with a firm’s improved financial performance.

This study contributes to the supply chain management (SCM) literature by integrating GPR into the supply chain risk management framework. Additionally, it demonstrates the roles of diversification and operational resources in addressing GPR-induced challenges.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.