Technological Disruption and Financial Risk: A Study in the Manufacturing Sector

Abstract

This study investigates the impact of technological disruption on financial risk within the manufacturing sector, with a concentrated analysis of Precot Limited, a textile and cotton-based manufacturing firm. The research explores the influence of emerging technologies specifically Artificial Intelligence (AI), the Internet of Things (IoT), and industrial automation on investment behavior, cost structures, operational efficiency, and long-term financial sustainability. By employing quantitative research methods, including trend and ratio analysis of financial statements from FY 2019 to FY 2024, the study evaluates critical financial indicators: liquidity (e.g., current ratio, quick ratio), profitability (e.g., net profit margin, ROCE), and solvency (e.g., debt-to-equity ratio). The findings show that while Precot Limited experienced periods of financial strain and volatility, particularly during the pandemic years and early digital transition, recent financial data suggest gradual improvements in liquidity and profitability metrics—signalling an adaptive response to technological integration. Key financial and operational risks identified include increased exposure to cyber security threats, capital expenditure pressures due to digital infrastructure investments, and heightened competition from tech-optimized global manufacturers. Through quantitative analysis of five years’ financial data (2019–2024), the study assesses the company’s liquidity, profitability, and capital structure using key financial ratios. Despite showing improvement in operating efficiency and net profit margins, Precot faces liquidity constraints and high capital expenditure challenges due to technology upgrades. The study also observes internal challenges such as workforce reskilling needs and supply chain recalibration. The research concludes by offering strategic recommendations, including phased digital adoption, investment in cyber security frameworks, financial planning for tech upgrades, and policy support for innovation-led growth. These findings contribute to broader discussions on financial risk management and sustainable technological transformation in traditional manufacturing environments, and can inform both managerial strategies and policymaking in similar industrial contexts.

Introduction

The manufacturing sector is experiencing a major transformation due to technological disruption driven by artificial intelligence (AI), robotics, the Internet of Things (IoT), and digitalization. While these technologies offer benefits such as increased efficiency and product quality, they also pose significant financial risks including high capital investments, supply chain disruptions, job displacement, cybersecurity threats, and asset obsolescence.

Precot Limited Company, a leading manufacturer, is facing challenges adapting to these rapid changes, which are affecting its financial performance. The COVID-19 pandemic further accelerated digital adoption, heightening both opportunities and risks.

Study Details:

Objectives:

1. Analyze the impact of technological disruption.

2. Understand employee adaptability and skill requirements.

3. Study market competitiveness due to technology.

Problem Statement:

Technological disruption is redefining operations and competitiveness in manufacturing. Precot Limited is struggling to adapt, which impacts its financial health.

Scope:

The study focuses on financial risks from technological disruption in Precot Limited, specifically relating to AI, IoT, robotics, cloud computing, and cyber-physical systems.

Limitations:

• Focuses only on Precot Limited.

• Limited access to detailed data.

• Rapid tech changes and short study duration affect depth.

• Limited industry-wide comparisons.

Literature Review Highlights:

• Cybersecurity risks increase as manufacturing digitalizes (Gartner, 2020).

• High investment costs with automation despite efficiency gains (Saks, 2020).

• Financial forecasting is essential for tech adoption (Harrison & O’Neill, 2019).

• IoT introduces security vulnerabilities (Kshetri, 2017).

• Industry 4.0 requires risk mitigation in AI and automation (Liao et al., 2017).

Methodology:

A quantitative approach using descriptive research design, analyzing Precot’s financial data (2018–2022) through financial ratios and regression analysis via SPSS. Secondary data sources included annual reports and online journals.

Data Analysis (Sample – Liquidity):

The current ratio fluctuated over five years, reflecting variability in the company’s short-term financial health:

• Ranged from 0.95 (2019–20) to 1.32 (2020–21), indicating periods of both low and healthy liquidity.

Conclusion

The profitability and liquidity analysis of Precot Limited over the years 2019-20 to 2023-24 indicates steady revenue growth, improved profitability, and better operational efficiency. The fixed asset turnover and inventory turnover ratios show effective asset utilization and inventory management, though further optimization is possible. Trend and comparative analyses highlight significant growth in net profit, largely driven by increased revenues and controlled expenses. The common-size analysis reflects a stable financial structure, with a focus on maintaining shareholder value and managing liabilities efficiently. However, areas like receivables management, cost control, and digital transformation need attention to sustain long-term profitability and competitiveness. By implementing strategic improvements, the company can further enhance its financial health and market position. By addressing these areas, the company can ensure continued growth and financial stability in the coming years.

References

[1] Saks, B. (2020). \"Impact of automation and AI on the automotive sector.\" Industry Journal, 37(4), 112–126. • Harrison, J. K., & O’Neill, A. (2019). \"Financial forecasting and risk assessment in manufacturing.\" Journal of Corporate Finance, 54, 183–203. [2] Kshetri, N. (2017). \"1 IoT in the global cyber security landscape.\" Journal of Business Research, 71, 121-132. [3] Liao, Y., et al. (2017). \"Automation and AI in the automotive industry: Impacts and challenges.\" Technology in Society, 51, 35–45. [4] Ribeiro, S., et al. (2017). \"The application of 3D printing in aerospace manufacturing: Case study and lessons learned.\" Journal of Manufacturing Science and Engineering, 139(8), 1–12. [5] Gereffi, G., & Fernandez-Stark, K. (2016). Global Value Chain Analysis: A Primer. Centre on Globalization, Governance & Competitiveness [6] Lee, H., Cho, H., & Lee, S. (2015). \"Risk management strategies for implementing smart factories.\" International Journal of Advanced Manufacturing Technology, 81(5), 947-957.

Copyright

Copyright © 2025 Mr. N. Chandru , Mr. G. Aravinth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.