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The Future of QA Management

The rise of Generative AI marks a significant shift, showing potential to radically transform most industries

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Introduction

Artificial Intelligence (AI) has been present in numerous industries for considerable time. Companies have been leveraging AI-based models for numerous applications. The advent of Generative AI (GenAI), however, marks a pivotal evolution, offering the potential to revolutionize entire sectors across the economy.

Give the complexity and multitude of potential applications of Gen-AI, one of the very first steps to assist a firm in better navigating and evaluating the potential positive impact of an AI-Transformation is to adopt real-life use cases that concretely demonstrate the benefits that can be obtained.

A Client Success Story Powered in AI Implementation

Our client is a global pharmaceutical company and stands as a biopharmaceutical group leader at the forefront of various healthcare domains. With a specialized focus on reproductive medicine, maternal health, gastroenterology, and urology, our client is dedicated to advancing scientific innovation with a clear commitment to a research-driven approach and a profound confidence in the transformative potential of cutting-edge investigations.

The Challenge
How to manage huge amount of info in short time in high level of precision.

The Goal
The main goal is to become the only reliable tool for exploring past deviations.

50%

Improvement in Analysis Accuracy

75%

Improvement in Search Accuracy

-30%

Reduction in Search Time

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A Four Step Approach

To support our client, we gathered and analyzed all outcomes from the preceding phases, formulated a new strategy, and presented a functional Proof of Concept to assess the actual value that a solution of this nature could bring to our client. To do this, we took a four-phase approach, during which the customer was actively involved both in a constant review activity and in a field-testing phase.

 

  1. During the initial phase, we initiated the Ignite Phase and established the groundwork for our solution, facilitated by a series of dedicated Kick-off Workshops. This phase played a pivotal role in collecting and analyzing available data, as well as defining expectations and creating a roadmap.
  2. Following this, we transitioned to the Elaboration Phase. Here, we focused on both refining the data structure and configuring the MS Azure setup to formulate the initial blueprint of our solution.
  3. The subsequent step were dedicated to the Construction Phase. Here, we concurrently developed the solution while subjecting it to constant testing by selected users from the client. The culmination of this phase was the Quality CoPilot MVP, paving the way for the final phase.
  4. In the final phase, we entered the Transition Phase, wherein we crafted a roadmap plan and conducted the Sharing Workshop to ensure a seamless transition and understanding of the developed solution.
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Gen-AI in Quality Management

One of the main advantages that an AI-led approach can provide in Quality Management is on deviation analysis. Increasing process complexity and regulation, along with a dispersion of records and recording platform, added to time pressures can make this a complex task.

Having to dedicate less time and energy to deviation research preparation could mean having more time to analyze information and structure potential strategies or solutions. This, in turn, could translate in to greater control and an enhanced ability to minimize the risk of future deviations.

AI-Transformation, Operations, and Quality: a New Era for Quality Management Across Industries

Today’s quality management systems must transcend mere deviation recording. Adopting advanced tools for data analysis in Quality Management is imperative. This will allow organizations to extract actionable insights from their quality data, enhancing CAPA processes. The ability to identify patterns and proactively address potential issues before they escalate is a critical advantage of modern analytical capabilities. Prevention has always been the ultimate objective in quality management.

To maximize their effectiveness, these tools must feature user-friendly interfaces and straightforward systems for detailed analysis. For instance, prioritizing the ability to query and analyze data in plain language can make these tools more accessible and easier to use. This not only simplifies the user experience but also ensures that the advanced features are available to a broad range of users within the rganization.

Adopting a forward-thinking approach enables organizations to go beyond merely recording data. It fosters a culture of continuous improvement. The latest generation of quality management software serves a strategical purpose: empowering businesses to proactively enhance their quality standards and sustain excellence over time.

When to Use

  • When advanced analyses need to be conducted on databases with a large amount of historical data.
  • When deviations, repetitions, and patterns need to be identified with a very high degree of precision and flexibility in formulating the problem statement.
  • When the need is to automatically identify, label and cluster group of deviations and similarities from a large pool of data.
  • When a clear and reasoned view of the data is needed to establish truly effective Corrective and Preventive Actions (CAPA).
  • When it is necessary to optimize, simplify, and expedite the workflow of analyses for reasons of timing, costs and/or team expertise

Benefits

  • Facilitate problem investigation process
  • Boost compliance to problem management standards
  • Streamline deviation search, generation and analysis
  • Generate information rich data for broader analysis
  • Make analysis and researches easier, user friendly and more intuitive

Utilizing our extensive expertise at FibonacciLab we have designed a tool we called ‘Quality Booster’. Its primary focus is to analyze quality deviations and facilitate problem investigation processes.

Essentially, it can be employed in any scenario where extensive data is available, providing quick and precise answers with minimal effort. This is particularly beneficial in situations requiring advanced and highly customizable analyses, enabling the search for specific statements within the data.

Quality Booster exemplifies how – by embracing AI-Transformation principles within Operations – Quality Management can achieve unparalleled levels of speed, precision, and efficiency.

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