Ultimate Guide to Proactive Risk Management for Better Quality & Safety

Every organization is responsible for managing various risks associated with safety and quality. Each industry has its risks to handle. For instance, the pharmaceutical industry has quality risks that can greatly affect consumer health and safety. Similarly, the manufacturing industry is prone to safety risks due to the nature of the materials and equipment used.

ISO 31000 Regulation for Risk Management

ISO 31000 is a risk-management framework that applies to all organizations regardless of the industry, size, or operation. It helps organizations increase the likelihood of achieving objectives, identifying opportunities and threats, and effectively allocating and using resources for risk treatment.

Here are other regulations that focus on risk management -

ISO 45001 is solely focused on the health and safety of employees. It takes a proactive approach to evaluate and remedy hazard risks before they cause accidents and injuries
ISO 27000 offers risk management best practices for information security, in compliance with the requirements of an Information Security Management System (ISMS)

Safety and Quality in Risk Management

When it comes to risk management, safety, and quality both are equally important and go hand-in-hand. Although it is possible to produce a quality product without safety management, the company will pay a high cost in case of a disaster. While construction, automobile, and manufacturing industries have very high-risk rates, there are industries like aerospace and roads where the risk rates are lower, but casualties are higher should accidents happen. Sectors such as pharmaceuticals, life sciences, and medical devices have reputation risks to consider.

In case of an accident or product failure, every company must implement a fundamental change in its thought process by adopting a risk-based approach. Businesses define both quality and safety standards and risks that are acceptable and those as per their industry and requirements, document these risks and put metrics and practices in place to measure them. This practice also helps with compliance with legal and regulatory requirements and the external and internal standards that the company follows.

Implementing Integrated Risk Management Strategy for Change Management

Businesses usually approach risks in silos, with different teams handling them without dependencies. However, this fragmented approach can become a bottleneck while taking a risk-based approach to setting quality and safety standards in the organizations. Leaders do not have any means to spot the connection between various risks.

To create an optimal risk management strategy, businesses need a unified view of the data to identify potential quality, safety, and environmental issues impacting production. Integrated Risk Management (IRM) approach allows quality and safety leaders to assess the potential impact of change management initiatives and ensures preventive controls are in place while change is implemented. The idea is to spot opportunities amidst risk and be agile enough to drive competitive advantage with a better risk mitigation strategy. They can monitor relevant KPIs and fine-tune their quality and safety management systems to protect business and stakeholder interests. Therefore, a more detail-oriented, collaborative approach to risk management must include -

A dedicated process in place to uncover all potential risks in the business across all departments and areas of operations.
Focused risk management efforts around workplace safety and employee well-being
Robust risk analytics framework with data at the core
A “continuous” and constantly revisited risk assessment plan
Risk-aware approach to quality management
Risk assessment framework established across areas, including business and strategy, finance, compliance, people and talent, brand, supply chain, and industry shifts

Role of AI and Analytics in Risk Management

Quality performance and workplace safety are integral to the success of the business. However, paper-based systems are increasingly becoming unsustainable as no central data deposit exists. This situation is changing as business leaders identify the power of AI in risk management. With the right AI model, decision-makers and stakeholders can access data collected from various sources, use these insights, and take a call on the next steps based on their own experience. This convergence of AI and people helps create a better and more effective risk-based decisions making process. The need for AI in risk management has increased the need to implement a next-generation AI-enabled EQMS solution such as the one from ComplianceQuest.

As a business scale, it has become essential for businesses to mitigate the major risks, establish metrics and measures to monitor them, and then track and report the effectiveness of these measures. Read our whitepaper to understand the role of AI and analytics in risk management.

ai and analytics role in risk management

How is Training Related to Risk Management for Better Quality and Safety?

Regulatory authorities such as the FDA and ISO require organizations to maintain specific standards across quality processes and safety measures. Businesses must focus on quality and safety training to improve the workforce’s overall quality performance and safety. It gives employees the right skills, information, know-how, and behavioral skills to help them decide what to do and what not to do at work. Educating and training employees is critical to proactively identifying, analyzing, and mitigating risks. It helps them look for common quality and safety issues and plans control measures.

Automation in Risk Management Workflows

Risk management has three approaches - reactive, proactive, and predictive. Periodic risk assessments and implementation of risk controls are the two pillars of creating a proactive culture of safety and quality. However, businesses with a paper-based risk management system cannot access effective risk assessment tools. The manual process of identifying, sorting, assessing, and prioritizing risks leads to higher chances of uncertainty and can deeply impact the business in terms of compliance and business performance.

Adopting newer technology is imperative for a business to embrace the three approaches to risk management truly. If risk management is done in a silo, it becomes ineffective. To that end, automation is considered the next big thing in risk assessment and management. Maintaining a centralized repository of identified risks at the project, department, or company level helps understand the existing risks within the organization, take necessary mitigative steps, and drive strategic decisions on potential threats.

With automation becoming a driving force for risk management, solutions such as CQ Risk Management Software leverage artificial intelligence to spot potential risks based on history and trending data. Organizations can now take a proactive and predictive approach to risk by using the solution’s Einstein Analytics to identify various quality and operational risks in a unified manner.

ComplianceQuest Risk Management Software

ComplianceQuest’s Risk Management Software is integral to Enterprise-wide Quality Management System. The software has unique AI and Einstein Analytics to identify potential risks based on history and trending data. With CQ Risk Management Software, you can:

Maintain a centralized repository of identified risks at the project, department, or company level
Drive strategic decisions on potential threats with a solution that is aligned with the latest standards, including ISO 31000, ICH Q9, ISO 14971, and various risk principles such as ISO 9001:2015, ISO 45001, ISO 13485:2016, and ISO 14001
Identify the right approaches that contribute to process efficiency, improved risk management, and internal controls with the CQ risk framework.
Accelerate assessments, identify and analyze risk trends and drive risk mitigation activities.
Use the reporting and dashboard features to spot potential risks based on history, trending data, and key metrics.
Leverage Einstein Analytics and a centralized registry of risks to identify patterns and gather insights from quality data across the enterprise.

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