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Smart Quality: The New Paradigm 

Dive deeper into the Smart Quality approach and its potential to empower Life Sciences organizations.




Traditional Quality



A New Quality Culture Mindset




In this webinar, Filip Heitbrink, CEO at Scilife, Angel Buendía, our Knowledge Manager, together with Yan Kugel, Director at Qualistery, as moderator, uncover the main differences between Traditional Quality and Smart Quality and how the new Quality Culture Mindset can empower ownership and employee engagement toward quality. They will also showcase a practical application of Smart Quality by a Smart Quality Platform.

Q&A's from the session

What are the main differences and correlations between the Total Cost of Quality and the Cost of Poor Quality, and how do they relate to the new paradigm of Smart Quality?

In the Asberg model, there’s the overall cost of quality, the cost of good quality, and also the cost of poor quality. 

All the costs of poor quality are behind the surface. They are usually costs related to quality activities you are devoted to performing that add no value to the company and are not well seen because make you spend time and resources dealing with them.

The idea is to improve these processes, to avoid the cost of poor quality, and in the end, reduce to cost of quality and the cost of doing business in your company. The idea is to tackle your real problem—the root causes you are having in your quality systems. 


How do you implement gamification in the quality management system? Can you give some examples and the possibilities of implementing it in the organizations in terms of instructions, procedures, and training? 

We started doing a whole gamification exercise with an external partner that had a lot of experience gamifying applications for different sectors. We did a full analysis of all the actions that could be done on the platform.

The conclusion was that everything with a deadline is a candidate to be gamified to avoid late items. The first implementation of gamification we did was in anything that has a deadline: from document reviews, document approval, sign-off on deviations, non-conformities, and CAPAs. Also, change control items, change requests, and internal and external audits. The idea is that whenever there’s a due date, if you sign off on time, you get points. 

We came up with this Scilife user score which starts at zero. If you sign off on time on items, you get points. It’s always visible at the top of the application how your score increases, and you can compare your score with other users, not within the platform, but you can see the average scoring of people within the company. For example, if people typically score higher than you, you will be motivated to sign off on time. On the contrary, if you sign off late, we will not reduce your score; you will just not get points. We discussed both options, and there are reasons why we decided not to lower the score. 

One of the reasons was that we do not want to have people go under zero, which would be very demotivating. The score can always grow by signing off on items on time. But then we notice a big difference between signing off on a training that takes maybe days to finish (with many documents that need to be read, maybe assessments that need to be done) or other tasks that require reading three documents. The complexity of the training and its duration should impact the score.

Now we're implementing an improvement of how scores differ depending on the volume of the task so that scores increase more logically.

We are implementing more things, for example, a combination between gamification and education. The system has an application where you can go through questions with a yes and no option just to test your knowledge of quality. You can see a few questions popping up, and if your answer is correct, your score increases too. That helps you educate yourself a little better in quality and increases your score if you are behind. 

Gamification is not something you implement once and never look at again. It's an evolution. We're constantly going to evaluate how gamification is used to determine ways to make it more successful.


Do you have any recommendations on how to start with gamification without having some electronic tools? Is there a way to start implementing this in a company in a more manual or paper-based way?

That's really hard to do. The only thing we've seen in the past is tying bonuses to data from your quality system. In the performance reviews of employees, you can look at how many times you signed off late on documents. Even if it's in a paper-based system, you can track that. If the quality department keeps track of late items, even if it's just certain types, you can discuss them in employee review meetings and tie a bonus to it, set goals, or identify gaps in training.

But the truth is that it's really hard to do. That's why we've tried to implement it in an automated way. 


How can you implement Smart Quality into the corporate or commercial pharmaceutical distribution landscape? What are the steps? Are there any guidelines that can help with smart quality concepts? 

The first step would be to review McKinsey's articles. They explain it more academically, in a more generic way. I would try understanding these pillars of education, motivation, and data insights. You need to figure out these three pillars in your company with your processes. Figure out how you can support these pillars in a better way.

In terms of the motivation part, start by doing some analysis. Maybe late items are not a huge deal in your company. Then you can focus more on education or the other way around. Maybe education is pretty good, but motivation is not great. You need to identify the problems first and find a way to track them. If you do not find a way to track them, it will be very hard to improve. Look at the issues over time, and come up with a mitigating action, which is the typical continuous improvement cycle that we have in quality systems. 

Then, the first step is really understanding the three pillars of Smart Quality that you need to improve. 

After, follows the data insights. Everybody talks about being data-driven, and there are different levels of being data-driven, and a few companies really get a very high level of being data-driven. I think it's key to figure out what data-driven means to you, which KPIs help you with the decision-making, and which KPIs are less important. Figure out a tool to get these data points together in a dashboard with tools like Power BI or Tableau, or any other external system where you can get data from different databases and resources, and build some dashboards to help with the decision-making.

Maybe combine quality system data with manufacturing data or whatever the case is for you to create these data insights to help you make decisions. With the automation of benchmarks, we want you to be able to see how you are doing compared to others in your same industry. It's a manual process that will take more time, but it's absolutely not impossible to implement smart quality in a manual way without using a system.


Can AI help with the implementation of smart quality by tracking different processes like CAPAs implementation and timelines? And from the regulatory point of view, how do you validate it? 

We did a Scilife summit in October where validation of AI systems was one of the talks of one of our partners; you can watch the recording on our website. It's not easy, but it's absolutely not impossible nowadays to validate AI systems.

Just a side note on whether we are using AI in the smart quality system: not yet. It's true that AI is a very interesting technology used to help with Smart Quality. Actually, it's one of the critical points that McKinsey also mentions, and they do have some case studies in which they use AI to automate the follow-up on the categorization of deviations in a company that had many of them so that they can automate part of the process of managing deviations.

We haven't implemented AI yet, but we have internal projects to come up with AI solutions that will help, especially with the data insights part for now within the Scilife platform.

Regarding regulations in the second edition of GAMP 5, which was published in July this year, there's a part about artificial intelligence, and even FDA is also talking about that. The future is very promising, and it’s good to be aware of changes.

Let’s see how regulators will permit the use of artificial intelligence in quality systems.


What is the general reaction from various regulators when companies move ahead with McKinsey's approach to smart quality? 

Regulators are focused on compliance. As you saw, our system still has all the tools to be compliant: you can manage your quality actions and everything related to quality according to a traditional quality system. The auditor will typically look to prove that you're managing your quality system according to regulations.

If you have gamification on top of it, augmented learning, and more interesting dashboards to help with the decision-making, they are not going to look at that. They will see a change of focus from compliance to a competitive advantage, in which the compliance part is still the basis. Regulators and auditors are not going to have a problem with smart quality. On the contrary, they're going to be very happy to see that you're using your quality system as much more than just a system to pass the audit. 

Anything you can do to avoid nonconformities in your system, to avoid delays, anything you can do to do that and to improve your quality culture in your company is good to get a mature organization. This is why the FDA is asking organizations to increase the level of quality to be mature organizations in quality.


How do you connect smart quality to Six Sigma? 

Smart quality is a very general principle that offers different kinds of tools to automate and digitize systems. It can help very much if you are able to collect all data you have from your quality system from your business to get statistics and insightful information to make decisions regarding Six Sigma.

If you collect enough information from your Quality Control department, from your production, nonconformities, failures, and so on, you can get important insights to improve your processes. In fact, smart quality creates the tools to have everything under control.

The most powerful information for making decisions is to have the right data and implement actions to minimize the risks in the end. Smart Quality is a philosophy in which many different types of tools can be used, and even Lean and Six Sigma are into this system.


What impact does Smart Quality have on the QA team? Does it bring more work, or does it reduce it? What does it say about the amount of personnel that you need?

If we're saying that it reduces the manual work for QA departments, then people might think that we’re talking about an automated system that will replace them. That's absolutely not the case. The idea is to automate stuff that nobody wants.

Nobody wants to go behind their colleagues with a stick to have them sign off on quality actions or on documents. Nobody wants to do administrative, uninteresting, quality tasks. That's something that can be automated and replaced with Smart Quality so that the quality department can focus on higher level, more interesting tasks that actually move the needle for the company and their product and not waste 80% of their time on things that can be handled by a system, which is typically what happens in many industries with automation. 

It's not about replacing people, it’s about replacing the tasks that are uninteresting and too expensive to be held by a person. That employee will be happier to do more interesting work, and it will add more value to the company as a whole.

It is a tool that helps with engagement, a catalyst for value creation so that the quality department is freed up to do more interesting work.


How do we start the implementation? How do we go away from the quantity system to the smart quality system? What are the steps? 

The first question is, how much smart quality do you already have?
You might have systems and processes in place that you can label as smart quality, but you don't know what level of smart quality you have and what you are missing. And then identify the low-hanging fruit.

What is relatively easy to implement can be applied with the resources that we already have and the systems and tools that we already have, and start with that. And then, show the boardroom the difference between how you were doing things before and how you started this process already, and show them some benefits.

Otherwise, it will be very hard to sell them on just a promise. Having done something internally would definitely be helpful. 

Maybe the second question is, do you need help with it or not? 

Many things can be done manually without a specific tool, but you have to evaluate if you have the resources and time to invest in that.

Maybe it’s easier to outsource that or implement Scilife as a system or some other system and then get help. It still has a cost, an external cost, but doing things internally also has a cost, so you have to evaluate where you want to put that cost if it's internal or you want to acquire consultancy or tools to help you with that.

Obviously, you will need some agreement from the board to do so. Once you select the tool, at Scilife, for example, we follow a very strict implementation plan. We go through all the steps, from analysis to training and education on how the whole implementation works, with pillars like training, data import, and integrations with systems. Then the changes that you will have to make on your end for the quality system, and lastly, the validation. Scilife comes with a full validation package according to GAMP 5, and we provide all the documentation signed off and executed by us as the basis for your validation on your end. This effectively takes away 95% of the validation effort on your side. You’ll still need to do the last validation steps on your end to consider if the system fits for use, obviously. We would take you by the hand and go through the whole implementation, tackling these different pillars of training, data, import, integration of systems, and validation, so you can get up and running fast in a matter of weeks as opposed to two months, depending on everybody's needs, the size of the company, and the size of data that needs to be imported, etc.

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