1. INTRODUCTION

India is one of the top producers of important minerals. It has huge potential in the mining industry. The country extracts many important minerals. The mineral sector in India is distinguished by a considerably lower level of maturity and limited adoption of standard frameworks (Dadhich and Kant, 2022; Purohit et al., 2022). Given the sector's issues, which include shifting need, and declining incomes, running a stable enterprise is vital (Hazra K Arnab, 2013). Through innovation, cutting-edge technology, and frameworks for process improvement for higher efficiency and safety, the industry should look into development potential in the ESG era to ensure sustainable operations.

There are frameworks that is most widely employed in a range of industries, and it has greatly increased the efficiency with which equipment, personnel, and other resources are used. The mining sector in India uses a lot of capital and needs to pay close attention to better risk management (Ade and Deshpande, 2012). (Hazra K Arnab, 2013).

In the current era of ESG, mineral segments need to look at new expansion avenues through new improvement frameworks for improved productivity and safety to ensure safe and sustainable operations.

The key for mining organisations to become competitive worldwide, achieve high ESG compliance, and run sustainable operations by adopting new and improved processes (Nemati et al., 2019). Innovative frameworks and systems are necessary to deal with changing business environment and dynamics of complex mining operations, which requires an utmost focus on safety and compliance. In order to improve safety and sustainable mining operations, AI/ML, Risk Management and other frameworks must be formulated, developed and promoted.

2. Challenges of Mining Industry, Risk Management in Mining Industry:

2.1 Challenges of the Mining Industry:

The objective of mining is to extract minerals and metals from earth and work against the nature which is an unsafe activity. By using risk management concepts, organizations can improve safety. There is a need for mining companies to make strategies to improve overall equipment effectiveness (OEE), compliance and boost essential asset utilization by strengthening the risk management system and streamlining the related processes. By adopting risk framework, companies can become more agile and responsive in a dynamic business environment. Also, implementing risk tools can improve safety and compliance over time (Ng Corrales et al., 2022).

As per EY, in coming years, increasing compliance requirements over ESG, climatic shifts, and uncertain environments will be the most crucial focus for mining CXOs. The frequent disturbance will result in uncertainty and fluctuations in costs, productivity, and workers, triggering organizations to discover new opportunities to reengineer business processes and speed up innovation (Dadhich et al., 2022; Gaurav Kumar Singh, 2022).

The ask is, how can companies traverse through these challenges? One essential and suitable tool for transformation in mining companies can be robust risk management framework implementation. Risk Management has been implemented by mining and allied industries and has given good benefits to the organization. A more structured risk management supported by AI/ML tools/ techniques, can help mining companies to ensure sustainable and safe operations.

2.2 Risk Management in Mining Industry

The level of threat posed to an organisation by a hypothetical situation or event is measured as risk. It depends on both the possibility of the event happening and its potential consequence. Determine the degree to which events could negatively influence an organisation by carefully analysing threat and vulnerability data. It also establishes the possibility that certain things will happen (Harkawat, 2023).

Risk management has been an integral part of mining and used and put into practise in the mining sector for a few years now. The mining industry has benefited from it in the past through, increased productivity and improved safety. Based on business context, demands, and internal and external environments, new frameworks have been utilised in the mining and metal industries (Dadhich et al., 2020).

Risk management in mines is the process of identifying, assessing, and controlling the hazards and risks that may arise from mining activities. Risk management is essential for preventing injury and disease, protecting the environment, ensuring compliance with regulations, and enhancing the performance and reputation of the mining industry³.

Some of the common risks that miners face include:

· Exposure to dust, noise, vibration, heat, radiation, and chemicals

· Falls from heights, slips, trips, and falls

· Struck by or caught in moving machinery or equipment

· Fire, explosion, or gas release

· Collapse of ground or structures

· Electrical shock or arc flash

· Ergonomic or manual handling injuries

· Fatigue, stress, or psychological distress

To manage these risks, mining companies should adopt a systematic and proactive approach that involves the following steps (Harkawat, 2023):

· Spotting the hazards: Identifying the sources of potential harm or damage to people, property, or the environment

· Assessing the risks: Evaluating the likelihood and consequence of each hazard, and prioritizing them according to their severity

· Making the changes: Implementing measures to eliminate or minimize the risk, such as engineering controls, administrative controls, personal protective equipment, or emergency procedures

· Monitoring and reviewing: Checking the effectiveness of the risk controls, and updating them as necessary based on new information, feedback, or changes in circumstances

Some of the benefits of risk management for mines include:

· Improved safety and health outcomes for workers and communities

· Reduced operational costs and liabilities

· Increased productivity and efficiency

· Enhanced stakeholder trust and satisfaction

· Greater innovation and competitiveness

Risk management in mining is crucial due to the inherent dangers and uncertainties associated with the industry. Safety in mining is paramount. Mining operation should have stringent safety protocols and trainings to mitigate risk related to accidents and injuries.

Following are the main impact if risks are not managed properly (Choi, 2023; Felsner, 2015; Nemati et al., 2019):

· Environment impact: mining can have adverse environmental effects. Risk management involves assessing and mitigating these impacts through a proper system.

· Regulatory compliance: noncompliance with mining regulation can lead to fines and shutdown. Rigorous compliance and monitoring are essential in mining.

· Health risk: occupational health risk search high as exposure to dust, chemicals and noise require monitoring and protective measures for workers.

Effective risk management in mining involves a holistic approach, integrating safety, environmental and operational considerations to ensure sustainable and responsible mining practices.

Risk Management is assisting businesses in reaping some benefits, but more may be done by pursuing more advancements to fulfil the organisations' and enterprises' ever-changing needs. However, there are many challenges in risk management implementation in the mining industry, which need to be addressed for the benefit of the sector (Vadim et al., 2016).

In the present aggressive environment in which corporate operates, essential resources and how they are managed are becoming increasingly important.

2.3 Risk Management in Mining Industry and AI:

Risk management using AI/ML in mining industry is a topic that has been gaining attention in recent years. Artificial intelligence (AI) can help mining companies to improve safety, efficiency, and profitability by using data analysis, machine learning, and autonomous technologies. Here are some of the ways that AI can be applied to risk management in mining (Vinuesa et al., 2020).

AI can predict failures in processes and equipment by using real-time quality data and analytics. This can prevent accidents and injuries, as well as reduce downtime and maintenance costs. For example, AI can monitor the condition of haul trucks, which are one of the most expensive and dangerous assets in mining, and alert the operators or technicians before any breakdown occurs (Nemati et al., 2019).

AI can also help to prevent accidents by using self-controlling machines that can operate in hazardous environments without putting human labor in danger. For example, AI can control mining robots that can perform tasks such as drilling, blasting, loading, and hauling in underground mines.

AI can enhance decision-making by using data-driven insights and recommendations. This can help mining managers to optimize processes, allocate resources, and plan strategies. For example, AI can analyze geological data and provide guidance on where to mine, how to mine, and what to mine.

AI can improve safety by using IoT-based suites that consist of sensors, alarms, and mobile and web applications. These suites can monitor and detect any hazardous issue, such as gas leaks, fires, or landslides, and warn the workers or authorities immediately. They can also provide easy navigation and communication for the workers in the mines.

AI innovations in mining have the potential to transform the industry and create a safer and more sustainable future. However, there are also some challenges and risks associated with AI adoption, such as ethical, legal, social, and environmental implications. Therefore, mining companies need to be careful and responsible when implementing AI solutions in their operations.

Artificial intelligence can you play significant role in risk management in mining industry by enhancing safety operational efficiency and environment sustainability some of the examples artificial intelligence in mining hour.

· Predictive maintenance- analysis sensor data from mining equipments 2 predict when maintenance is needed reducing the risk of equipment failures and accident

· Safety monitoring AI powered system can monitor mining sites food safety compliance, detecting hazard orange behaviour in real time

· Environmental monitoring monitor environmental impact helping to mitigate risk of water pollution air quality issues EC

· Risk assessment, process vast amount of data set to identify patterns and trends assisting in risk assessment and helping companies to make data driven decisions.

· Regulatory compliance can help in monitoring and ensuring compliance who is safety and environmental regulation.

· Employee health monitoring AI can monitor the help and fatigue level of workers to prevent accidents caused by exertion

Implementing AI in risk management requires a combination of data collection, machine learning models an integration with existing systems. It is important to consider data security while implementing these technologies and put a proper systems in place Effective risk management techniques not only encourage safer working conditions but also assist mining firms in adhering to legal obligations, safeguarding the environment, and enhancing operational efficiency. Mining firms may lower their exposure to potential losses and liabilities, protect their reputation, and ultimately improve their long-term profitability by putting best practises into practise.

3. Research gap and necessity of new framework:

Many businesses implement new initiatives/ frameworks to increase productivity, safety, processes, and sustainability. To have a best risk system, there is a need for a new innovation and framework for better risk management, which includes practices/procedures related to safety, sustainability and risk.

Mining activities are inherently risky due to the many risks the sector faces, such as accidents, environmental harm, and legal repercussions. Identifying, evaluating, and mitigating potential risks as well as continual monitoring and assessment are required steps in a thorough risk management framework that mining businesses must use to manage these risks. This framework will help guarantee that risk management techniques function as intended over the long term.

As of now, there is no framework available which is based on CMMI’s risk process area. The proposed new framework will be based on the CMMI’s risk assessment, practices and procedures and AI/ML tools/techniques.

4. OBJECTIVES OF THE STUDY:

This paper aims to propose a new framework for risk management. The paper suggests the usage of a framework that can be used to better risk management using AI/ML and CMMI’s Risk processes.

5. METHODOLOGY:

For building the new framework, the researcher relied upon the secondary data available from different industries. The researcher also studied the existing risk management processes and frameworks and their usage by IT organizations. The researcher decided to use the CMMI-based practices and sub-practices to define a new framework for building the new framework (Narayan Patil et al., 2016; YALÇINER et al., 2022). The CMMI’s risk management process area, related practices and AI/ML based tools/methods are used to propose the new conceptual model/framework.

The new framework will have processes, practices, and sub-practices which can be followed by mining organizations engaged in mining, excavations, extraction related work and have to handle risks on continuous basis during the operations.

6. AI/ML and CMMI’s Risk related Process Areas-based Conceptual Framework

Risk Management during mining can be done through a structured risk program using CMMI’s Risk Management (RSKM) processes. The AI/ML tools/techniques can be added to CMMI’s RSKM process areas which is proven, structured and used by many organizations.

Following table shows CMMI’s RSKM Process Area’s (PA) Practices (as defined in CMMI-Dev V1.3 Model) and proposed Risk Management Framework which can be used to design a Risk Program with AI/ML risk management in the organization –

Table 1: Mapping of CMMI RSKM Process Areas (PA) and Proposed AI/ML Risk Management Framework (AMCRMF) Processes

As per www.wibas.com, CMMI Risk Management PA’s Practices and Processes	Proposed AI/ML and CMMI-based Risk Management Framework (AMCRMF) Practices <SP Number e.g. AMCRMF SP > <Practices> <Process>
RSKM.SG 1 Prepare for Risk Management Preparation for risk management is conducted	AMCRMF.SG 1 Prepare for Risk Management Preparation for risk management conducted using Standard and AI/ML Tools
RSKM.SP 1.1 Determine Risk Sources and Categories	AMCRMF.SP 1.1 Determine Risk Sources and Categories
Determine risk sources and categories.	Determine risk sources and categories through Standard and AI/ML techniques (e.g. through AI/ML-based Taxonomy based Tools)
RSKM.SP 1.2 Define Risk Parameters	AMCRMF.SP 1.2 Define Risk Parameters
Define parameters used to analyze and categorize risks and to control the risk management effort.	Define parameters used to: analyze and categorize risks and to control the risk management effort and Define Parameters to Run/Support AI/ML tools / techniques
RSKM.SP 1.3 Establish a Risk Management Strategy	AMCRMF.SP 1.3 Establish a Comprehensive and Inclusive Risk Management Strategy
Establish and maintain the strategy to be used for risk management.	Establish and maintain the strategy including AI/ML to be used for risk management
RSKM.SG 2 Identify and Analyze Risks Risks are identified and analyzed to determine their relative importance	AMCRMF.SG 2 Identify and Analyze Risks Risks are identified and analyzed to determine their relative importance
RSKM.SP 2.1 Identify Risks	AMCRMF.SP 2.1 Identify Risks using FMEA, FTA, Decision Tree techniques
Identify and document risks.	Identify and document risks
RSKM.SP 2.2 Evaluate, Categorize, and Prioritize Risks	AMCRMF.SP 2.2 Evaluate, Categorize, and Prioritize Risks through AI/ML-techniques (using FMEA, FTA, Decision Tree techniques)
Evaluate and categorize each identified risk using the defined risk categories and parameters, and determine its relative priority	Evaluate and categorize each identified risk using the defined risk categories and parameters, and determine its relative priority
RSKM.SG 3 Mitigate Risks Risks are handled and mitigated, where appropriate, to reduce adverse impacts on achieving objectives	AMCRMF.SG 3 Mitigate Risks Risks are handled and mitigated, where appropriate, to reduce adverse impacts on achieving objectives
RSKM.SP 3.1 Develop Risk Mitigation Plans	AMCRMF.SP 3.1 Develop an AI/ML based Risk Mitigation Plans
Develop a risk mitigation plan in accordance with the risk management strategy.	Develop an AI/ML based risk mitigation plan in accordance with the risk management strategy
RSKM.SP 3.2 Implement Risk Mitigation Plans	AMCRMF.SP 3.2 Implement CMMI and AI/ML based Risk Mitigation Plans
Monitor the status of each risk periodically and implement the risk mitigation plan as appropriate.	Monitor the status of each risk periodically and implement the AI/ML based risk mitigation plan as appropriate.

Figure 1: Establishment of a CMMI and AI/ML-based Risk Management Process

Figure 2: AI-ML and CMMI based Risk Management Framework

7. CONCLUSION:

The best practices of available frameworks along with AI/ML tools/techniques are used to define a new risk management (RSKM) framework, which mining organizations can use to monitor and track the risks in more structured and scientific way. It provides an end-to-end solution to risk related activities for mining organizations. CMMI’s processes area’s practices are being mapped to the new framework for better understanding and definition of new processes/ practices. It covers all the phases/stages of risk management cycle, including risk identification, classification, categorisation, mitigation and contingency planning and closure and risk review/governance.

The framework should be customized as per the organizational context and management requirements.

8. LIMITATIONS AND FUTURE DIRECTION:

The proposed framework is based on the available literature related to CMMI, AI/ML, Risk Management and Sustainability. This conceptual framework needs to be validated by mining product/process / service companies and product designers. In future more best practices and frameworks like PMP-PMI’s risk related processes can be added to make the proposed framework more practical, wider, and robust.

Finally, researchers and practitioners may look at introducing new tools and approaches into the model for better risk management and add more AI/ML tools and methods.

9. REFERENCES:

1. Choi, Y. Interdisciplinary studies for sustainable mining. Applied Sciences. 2023; 13(7): 4621. Https://doi.org/10.3390/app13074621

2. Dadhich, M., Chouhan, V., Gautam, S. K., and Mwinga, R. Profitability and capital adequacy approach for measuring impact of global financial crisis vis-à-vis Indian Banks. International Journal of Advanced Science and Technology. 2020; 29(4): 2344–2365.

3. Dadhich, M., Hiran, K. K., Rao, S. S., and Sharma, R. Impact of Covid-19 on teaching-learning perception of faculties and students of higher education in indian purview. Journal of Mobile Multimedia. 2022; 18(4): 957–980. Https://doi.org/10.13052/jmm1550-4646.1841

4. Dadhich, M., and Kant, K. Empirical investigation of extended toe model on corporate environment sustainability and dimensions of operating performance of smes: a high order pls-ann approach. Journal of Cleaner Production. 2022; 363: 1–16. Https://doi.org/10.1016/j.jclepro.2022.132309

5. Felsner, A. Operational excellence and the applications in mining operations. 2015

6. Gaurav Kumar Singh, M. D. Assessment of multidimensional drivers of blockchain technology (bot) in sustainable supply chain management (sscm) of Indian cement industry: a novel pls-sem approach. International Journal of Logistics Systems and Management. 2022. Https://doi.org/10.1504/ijlsm.2022.10045308

7. Harkawat, P. Cmmi-based lean risk management (lrm) framework for lean implementation in mining (built on the risk management process area of cmmi). International Journal of Innovative Research in Engineering and Management. 2023; 10(1): 1–5. Https://doi.org/10.55524/ijirem.2023.10.1.1

8. Hazra K Arnab. Development of indian mining industry-the way forward non-fuel minerals ficci mines and metals division. Www.ficci.com2013

9. Narayan Patil, P., Namdev Rakhunde, H., and Hemant Jethwa, A. Cmmi-its need in the Industry. International Journal of Recent Scientific Research. 2016; 7(4) Http://www.recentscientific.com/http://www.recentscientific.com/http://www.recentscientific.com/

10. Nemati, A., Nadeau, S., and Ateme-Nguema, B. Lean mining, productivity and occupational health and safety: an expert-elicitation study. American Journal of Industrial and Business Management. 2019; 9(11): 2034–2049. Https://doi.org/10.4236/ajibm.2019.911134

11. Ng Corrales, L. D. C., Lambán, M. P., Morella, P., Royo, J., Sánchez Catalán, J. C., and Hernandez Korner, M. E. Developing and implementing a lean performance indicator: overall process effectiveness to measure the effectiveness in an operation process. Machines. 2022; 10(2). Https://doi.org/10.3390/machines10020133

12. Purohit, H., Dadhich, M., and Ajmera, P. K. Analytical study on users’ awareness and acceptability towards adoption of multimodal biometrics (mmb) mechanism in online transactions: a two-stage sem-ann approach. Multimedia Tools and Applications. 2022; 1: 1–25. Https://doi.org/10.1007/s11042-022-13786-z

13. Vadim, M. V, Yuri, R. T., Natalia, O. V, and Anna, M. V. Lean production in the coal mining industry. 2016

14. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., and Fuso Nerini, F. The role of artificial intelligence in achieving the sustainable development goals. Nature Communications. 2020; 11(1): 233. Https://doi.org/10.1038/s41467-019-14108-y

15. Yalçiner, B., Chouseinoglou, O., and Efe, Ö. M. Mapping cmmi-dev v2.0 with scrum: a project management and quality assurance perspective. SSRN Electronic Journal. 2022. Https://doi.org/10.2139/ssrn.4310530

Received on 21.05.2024 Revised on 18.12.2024

Accepted on 03.05.2025 Published on 29.07.2025

Available online from August 05, 2025

Asian Journal of Management. 2025;16(3):227-232.

DOI: 10.52711/2321-5763.2025.00034

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.