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Analytics-Driven Supply Chain Management: A Future Research Agenda Based on the XAIOR Framework: 2025

Analytics-Driven Supply Chain Management: A Future Research Agenda Based on the XAIOR Framework: 2025

Interesting News . Jan 04, 2025

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Supply chain management (SCM) involves coordinating various functions across a multi-echelon system to manage the flow of goods, services, and processes that transform raw materials into finished products. This article explores the evolution of SCM toward analytics-driven approaches, framed through the three dimensions of the XAIOR framework: Performance Analytics (PA), Attributable Analytics (AA), and Responsible Analytics (RA). This framework offers a pathway for researchers to advance SCM methodologies, addressing challenges and opportunities in the field.

Performance Analytics (PA): Enhancing Efficiency and Effectiveness

Research in SCM has long focused on improving operational efficiency and decision accuracy. Bayesian decision theory, in particular, has emerged as a robust tool for integrating information into supply chain decisions.

Key Studies:

  • Iyer & Bergen (1997) applied Bayesian conjugate pair theory to model responsive supply chain operations, quantifying the impact of information updates on supply chain performance and achieving Pareto improvement. [1]
  • Aronis et al. (2004) leveraged Bayesian information updating for inventory management, optimizing inventory policies by updating parameters based on failure rates. [2]
  • Choi, Li, & Yan (2006) extended Bayesian models to scenarios with varying variances, emphasizing the importance of sophisticated models and well-chosen observation targets. [3]

Future Research Directions:

Researchers could explore adaptive Bayesian models tailored to dynamic supply chain environments, incorporating real-time data to further enhance predictive accuracy. Extending Bayesian applications to perishable goods or volatile markets could also provide valuable insights.

Did You Know? Analytics Is Transforming Supply Chain Management

Bayesian models are used for supply chain management since they help to make better decisions and learn from new data.

In addition, the employ of Explainable AI is increasing the supply chain’s transparency so that risks can be managed and ethical decisions can be made.

Such change is making supply chains smarter and more responsible all over the world!

Analytics-Driven

Attributable Analytics (AA): Improving Interpretability and Actionability

As computational power and data availability grow, there is a rising need for interpretable and actionable models in supply chain risk analysis. Bayesian networks (BNs) have proven instrumental in this regard, offering probabilistic graphical models that provide clarity in risk relationships.

Key Studies:

  • Garvey, Carnovale, & Yeniyurt (2015) used Bayesian network analysis (BNA) to model risk propagation and dependencies in supply chains. [4]
  • Liu et al. (2021) adopted dynamic Bayesian networks (DBNAs) to address disruptions under worst-case probability scenarios, creating explainable optimization models. [5]
  • Sakib et al. (2021) applied BNA to forecast and manage challenges in oil and gas supply chains, identifying critical factors influencing performance. [6]

Challenges and Opportunities:

Many AI-driven tools in SCM are still opaque, functioning as “black boxes” that limit their usability for operations managers. The integration of explainable AI (XAI) techniques, such as SHAP (Shapley Additive Explanations), has shown promise in improving model transparency.

Future Research Directions:

Researchers could focus on integrating XAI methodologies with AI-driven SCM tools to bridge the gap between automation and interpretability. This could involve developing user-friendly dashboards that translate complex analytics into actionable insights for supply chain managers.

Responsible Analytics (RA): Ensuring Ethical and Sustainable Practices

Responsible analytics ensures that SCM practices align with ethical, legal, and financial standards. While relatively underexplored, this area holds immense potential for advancing trust and compliance in supply chains.

Key Studies:

  • Westerski et al. (2021) developed explainable AI models to detect procurement fraud, enabling severity ranking and transaction scoring to improve auditing. [7]
  • Senoner et al. (2022) used AA to enhance process quality in semiconductor manufacturing, incorporating SHAP to explain correlations and streamline operations. [8]
  • Choi et al. (2022) proposed an analytical framework to achieve sustainable social welfare (SSW), balancing human welfare, environmental impact, and corporate benefits. The study emphasized policymaker involvement in incentivizing ethical practices. [9]

Future Research Directions:

Researchers could investigate how RA can be embedded into Industry 5.0 supply chains, where human-machine collaboration takes precedence. Developing frameworks to assess the long-term social and environmental impacts of disruptive technologies in SCM could guide policymakers and organizations in achieving sustainability goals.

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Conclusion and Call to Action

The XAIOR framework offers a comprehensive lens through which researchers can address pressing challenges in SCM. By advancing methodologies in performance, attributable, and responsible analytics, future studies can contribute to the creation of scalable, interpretable, and ethical supply chains.

Examples of Future Applications:

  1. Industry-Specific Studies: Applying AA to healthcare or renewable energy supply chains to manage risks and optimize resource allocation.
  2. Ethical AI in Procurement: Extending RA to model fraud detection across diverse industries, ensuring fairness and accountability.
  3. Dynamic Decision Systems: Combining PA with real-time analytics for responsive supply chains in e-commerce and perishable goods sectors.
By exploring these dimensions, researchers can shape the future of SCM to be more efficient, transparent, and socially responsible, ensuring its relevance in an increasingly complex global landscape.