Business Intelligence-Based Turnaround Strategies for Corporate Recovery

Certificate: View Certificate

Published Paper PDF: View PDF

Confirmation Letter: View

DOI: https://doi.org/10.63345/ijre.v13.i8.1

Dr. Sohrab Bharucha

Director Operations

Spica Group

Pune, Maharashtra, India

https://orcid.org/0000-0003-4195-6191

Abstract— Corporate turnaround initiatives have traditionally relied on retrospective financial analysis, expert judgment, and isolated business intelligence (BI) reporting tools, which often provide limited support for proactive decision-making during periods of organizational distress. Existing studies on business intelligence primarily emphasize operational performance improvement, while turnaround management research largely focuses on strategic restructuring and cost reduction, resulting in a limited integration of predictive analytics, enterprise-wide intelligence, and continuous recovery monitoring within a unified decision-support framework. This research addresses these gaps by proposing a Business Intelligence-Based Turnaround Strategy Framework (BI-TSF) that combines enterprise data integration, real-time BI dashboards, predictive corporate distress modeling, and an intelligent recovery strategy recommendation engine into a comprehensive corporate recovery architecture. The proposed framework consolidates financial, operational, customer, supply chain, and market intelligence to generate actionable insights that enable early identification of business decline, objective evaluation of turnaround alternatives, and continuous monitoring of organizational recovery. A quantitative simulation-based methodology is employed using publicly available financial datasets and business performance indicators, where machine learning techniques are integrated with business intelligence analytics to evaluate recovery effectiveness. Experimental results demonstrate that the proposed framework achieves 95.6% predictive accuracy, 94.7% F1-score, and an AUC of 0.97 for corporate distress prediction while significantly improving revenue growth, profitability, operational efficiency, and decision-making speed compared with conventional financial analysis and standalone analytics approaches. The framework also reduces recovery decision time and enhances strategic recommendation precision through data-driven performance evaluation. The proposed BI-TSF contributes to the literature by bridging business intelligence and corporate turnaround management into a unified intelligent decision-support system that enables proactive, scalable, and evidence-based organizational renewal. The findings demonstrate the potential of integrating business intelligence and predictive analytics to improve corporate resilience, accelerate recovery planning, and support sustainable long-term business performance.

Keywords— Business Intelligence, Corporate Recovery, Turnaround Strategy, Predictive Analytics, Decision Support System, Financial Distress Prediction, Machine Learning, Business Analytics, Organizational Renewal, Enterprise Performance Management.

References

• Z. Elbashir, P. A. Collier, and M. J. Davern, “Measuring the effects of business intelligence systems,” International Journal of Accounting Information Systems, 2008, doi: 10.1016/j.accinf.2008.03.001.
• Chen, R. H. L. Chiang, and V. C. Storey, “Business intelligence and analytics,” MIS Quarterly, 2012, doi: 10.2307/41703503.
• Gupta and J. F. George, “Toward the development of a big data analytics capability,” Information & Management, 2016, doi: 10.1016/j.im.2016.07.004.
• Akter et al., “How to improve firm performance using big data analytics capability and business strategy alignment?” International Journal of Production Economics, 2016, doi: 10.1016/j.ijpe.2016.08.018.
• F. Wamba et al., “Big data analytics and firm performance,” Journal of Business Research, 2017, doi: 10.1016/j.jbusres.2016.08.009.
• Aydiner et al., “Business analytics and firm performance,” Journal of Business Research, 2019, doi: 10.1016/j.jbusres.2018.11.028.
• Ashrafi et al., “The role of business analytics capabilities,” International Journal of Information Management, 2019, doi: 1016/j.ijinfomgt.2018.12.005.
• Mikalef et al., “Big data analytics capabilities and innovation,” British Journal of Management, 2019, doi: 10.1111/1467-8551.12343.
• J. Teece, G. Pisano, and A. Shuen, “Dynamic capabilities and strategic management,” Strategic Management Journal, 1997, doi: 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z.
• M. Eisenhardt and J. A. Martin, “Dynamic capabilities,” Strategic Management Journal, 2000, doi: 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E.
• Trahms, H. A. Ndofor, and D. G. Sirmon, “Organizational decline and turnaround,” Journal of Management, 2013, doi: 10.1177/0149206312471390.
• Schmitt and S. Raisch, “Corporate turnarounds,” Journal of Management Studies, 2013, doi: 1111/joms.12045.
• L. Barker III and I. M. Duhaime, “Strategic change in the turnaround process,” Strategic Management Journal, 1997, doi: 10.1002/(SICI)1097-0266(199701)18:1<13::AID-SMJ843>3.0.CO;2-X.
• Schweizer and M. Nienhaus, “Corporate distress and turnaround,” Management Review Quarterly, 2017, doi: 10.1007/s11301-016-0123-8.
• L. Barker III, J. Luger, A. Schmitt, and K. R. Xin, “Corporate decline and turnarounds in times of digitalization,” Long Range Planning, 2024, doi: 10.1016/j.lrp.2022.102211.