Did you ever get someone a present that you knew you’d benefit from? A game for a sibling that you knew you’d get to “borrow”? A gift card to a restaurant that you were confident you’d be invited to share in? While it is better to give than to receive, there’s something to be said for doing both. After all, shouldn’t the recipient want you to be happy too? In the case of quality and manufacturing, what could be more perfect than a solution that will make high-level executives happy while simplifying your job?
All year we’ve been talking about connected quality data. Since we’re talking about connecting data across the organization, it makes sense that there would be benefits across the organization. Although these benefits are widely accepted, the technology that makes it possible is not. A paltry 6% of life sciences companies employ tools that provide real-time visibility into quality metrics. (1) Increasing that percentage means getting executive buy-in. Fortunately, the same tools that benefit quality managers will also benefit the C-suite.
This isn’t limited to quarterly reports. We’re essentially talking about eliminating reports altogether. When quality data is automatically connected, no one has to spend time doing a data dump into Excel to manually create a report. Think of how much time that’ll save you. Not only will you not have to compile quarterly reports, you won’t have to spend time tracking down data for specific requests. For example, if someone wants to know why overtime is up or why a line is down, you’ll be able to pull that information up quickly and have immediate, current data to demonstrate what’s happening.
Getting rid of manual reports is great news for anyone who’s ever had to create one, but it’s also great news for your executives. Now, instead of occasionally getting to see data which is days or weeks old they can immediately see real-time data on their own. This also means they’ll be sending you questions much less often. They can also have the data that they’re interested in and only that data. If overtime is up, the C-suite doesn’t really care about the details. They just want to know how it will affect the bottom line and how it can be avoided in the future.
Speaking about the details, or lack thereof, upper management doesn’t have the time to read through a lot of reports. Most people absorb information best when it’s presented in a visual way. A dashboard that can break metrics down into good, okay and critical is important for a quick overview of how things are in the company. This automated version of red light/green light quickly tells both you and your execs where to focus your efforts.
When dealing with analytics dashboards, it’s critical for the dashboard to match the role. For example, both you and the exec will want access to the high-level overview, though your high-level overview might be at the site level and the exec’s might be at the organizational level. Regardless, the exec isn’t going to do a deep dive into the data the way you will. Let’s say an exec wants to know why deviations for Site A are at a critical level. He or she probably isn’t going to drill down to find out — they’re going to ask you to fix the problem. But then your dashboard can get extremely specific so you can determine if the deviations are related to an employee, a product or a line and address the issue.
Traditionally, quality is a documents-driven endeavor. But how useful are those documents? How much do they actually contribute to quality improvement? The sad truth is that most quality documents solely exist for compliance. While there is some overlap, “compliant” and “high quality” are not the same thing. So much effort goes into maintaining compliance that not a lot of time and resources are left over to focus on actual quality improvement. Especially when that involves pouring over the aforementioned documents to try to glean data and insights.
That’s why we have machine learning (ML), a subset of artificial intelligence (AI). Natural language processing (NPL) can pull those insights from those documents and ML can put that data together in a meaningful way. You’ve undoubtedly had this alphabet soup thrown at your before, but what does it actually mean? It means an application that can tell you the ideal employees to work on a line to improve quality. It means an application that will keep employees from completing tasks until they’ve completed their training. Better quality, fewer compliance issues, and faster decision-making all lead to profits — and that’s definitely something the C-suite wants to hear about.
Moving quickly is an essential competitive advantage, but so is moving carefully. The only way to achieve both is to have a tool that can quickly get your C-suite the data they need, when they need it so they can immediately act. When everything is business as usual executives can pretty much run the business as usual. However, when faced with a dramatic change in demand, problems with suppliers, or an unforeseen catastrophe (cough, cough, COVID) business as usual will quickly put you out of business. Connected quality data isn’t just good for the quality department — it serves as the means to make data-based business decisions at every level of your organization.
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