Today’s quality leaders in the life sciences industry have nearly impossible charters. Long-term trends and sudden black swan events in combination can hinder an organization’s ability to exert control over product quality. Globalization, labor shortages and strikes, outsourcing, just-in-time supply chains, decentralized workforces, and whiplash-inducing changes to import and export controls make a lean and efficient supply chain break down if one or more of the steps has to change.
Our research has identified the top concerns that quality leaders struggle with the most, and we have identified how to counter their effects on overall quality:
• How do we keep a finger on the pulse of all quality data, insights, and action plans to build resilient processes?
• How do we aggregate and access organizationwide quality data to see location, product, or customer-specific metrics?
• How do we separate the signal from the noise to generate insights and decision-making and planning of next steps—be they corrective or preventive actions or continuous improvement?
• How do we best collaborate with the on-the-ground operations team to take the necessary actions without wasting time or losing goodwill?
In Pursuit of Quality Excellence
Four data layers that matter
Published: Wednesday, January 17, 2024 – 12:01
• Increase overall operational effectiveness (OOE): Enterprises can gain better insights into their operations, identify quality gaps, and improve performance on an ongoing basis.
• Harmonize processes and data: A quality control tower harmonizes all processes and data to provide enterprisewide transparency for improved decision-making.
• Integrated risk management: Predict the effects of quality events on your production, supply chain, and sales in real time, and plan ongoing risk-mitigation efforts.
• Predictive insights and resolutions: Use artificial intelligence (AI) and machine learning (ML) to spot and leverage resolution opportunities based on data patterns.
• Augment decision-making: Prescriptive data analysis can provide recommendations and augment human decision-making. Having the right data layers is the first step to making this happen.
• Intelligent workflows: Detect, display, and prioritize quality tasks in real time; make informed decisions with an AI-enabled solution that provides the next best actions and recommendations. Of course, without the right data layers, your AI will be ineffective.
• Spot early warnings: Use data to spot early warnings and resolve quality issues before they disrupt your business.
• Collaboration: Data and collaboration go hand in hand. The key is to hand over relevant data to the relevant teams so they can act.
Key questions to answer
Let’s dive into the commodity that makes the rest of this framework run smoothly—the data.
The four layers: Data that matter
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Quality leaders must act swiftly when there are quality problems or when preventive actions need to be taken. They need to monitor outcomes to make sure the steps taken deliver the required effect and ensure that all key quality metrics across product lines, suppliers, and lab locations are on track. And they must do this while staying updated on global regulatory requirements and ensuring compliance with corporate policies and industry regulations.
It serves as a centralized hub or a digital command center that offers organizations a comprehensive view of their quality-related data, insights, and action plans. The goal of a quality control tower is to provide real-time visibility, transparency, and control over an organization’s quality processes, particularly when dealing with complex and interconnected operations.
How does a quality leader keep driving a culture of quality while fighting headwinds of chaos? It’s not easy, but our take on the data needed to drive operational effectiveness can help you tame the forces driving quality down.
For all the analytics and ideas you’re getting out of data, are you truly leveraging data operationally? Photo by Headway on Unsplash
This article was provided by quality specialists from ComplianceQuest.
Imagine it as the nerve center of quality management, where data from various sources, such as manufacturing facilities, suppliers, laboratories, and even global regulatory agencies, are gathered, analyzed, and presented in a user-friendly manner. This centralized approach enables quality leaders and decision-makers to make informed and agile choices, responding promptly to quality issues, taking preventive actions, and continuously improving their processes.
Layer 1—Data visibility: Here, the key is to have visibility to all quality events in real time. Relevant data must be accessible with just a few clicks.
Layer 2—Data analytics and insights: Quality professionals want to go from data to insight as quickly as possible. Time to insight, or TTI, is an important aspect. Often, collaborative data analysis will get to you. Whatever digital solution you use must enable that.
The following traits are visible in an IDO:
• IDOs use data assets to derive a competitive advantage
• Data-driven decision-making becomes a companywide culture
• It’s critical to generate forward-looking, predictive insights in addition to analyzing past data
• Agility in the decision-making process
• Rapid adoption of new and emerging technologies and digital tools
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As you transform your quality management processes for today’s modern workforce, are you setting yourself up for continuous improvement—or just a short-term boost as you digitize processes?
• Are quality and operational teams aligned in terms of strategy and plans?
• Are they measuring the same things?
• Is the execution of quality strategy good enough to overcome the comfort and familiarity of the status quo?
• Even as you embrace data-driven decision-making, are you tracking open action items and measuring outcomes?
• Are you focused on outcomes that drive success?
To grasp how to handle your quality data, generate insights, be confident about an action plan, and deploy that plan to frontline workers, we’re going to walk through a framework that helps quality teams identify and drive rapid change in the form of a “quality control tower.”
Layer 3—Data-driven next steps: Having access to real-time data, analytics, and insights is great, but it’s not enough. The real value comes from the ability to plan next steps based on data. With high-volume data coming in, can we make some of these decisions or next-step planning semi-autonomous? That question must be asked.
We’d actually take this to the next level to ask: Is your organization insights- and execution-driven? For all the analytics and ideas you’re getting out of data, are you truly leveraging data operationally? This must be the central consideration.
Operational effectiveness: A key lever to drive quality excellence
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Having a centralized dashboard or “quality control tower” as the starting point for quality decision-makers will drive the following benefits.