Deliver With Data: Leveraging AI in Pharmaceutical Manufacturing
Elliott Charbonneau Elliott Charbonneau
November 18 4 min

Deliver With Data: Leveraging AI in Pharmaceutical Manufacturing

As anyone in pharmaceutical manufacturing can attest, things don't always go exactly according to plan. Production deviations are unfortunately an inevitable cost of doing business in the pharma industry. The challenge, on the other hand, involves deviation management: thoroughly investigating any discrepancy and its cause so that corrections can be implemented quickly and efficiently.

What follows is a closer look at this critical process and the factors that make it difficult for companies to get it right, as well as a novel solution to deviation investigation and root cause analyses with Element AI Knowledge Scout.

The Problem: Too Much Data and Not Enough Time

Many pharmaceutical manufacturers struggle with efficient management of the work that's required following production deviations. Investigations are not to be taken lightly, and even experienced teams find them time-consuming and complex. If you get bogged down on the path to resolution, that could seriously impact your company's supply chain—and it could affect product compliance.

Perhaps the biggest factor preventing rapid resolution is the wide range of data sources that are needed to identify root causes and apply corrective actions. Deviation reports, batch records, and supervisory control and data acquisition system information—only together can they tell the full story behind why a product deviation occurred.

Gathering that data is one common hurdle, but then there's the additional challenge of the tacit knowledge needed to make sense of it all. If you don't have experienced specialists who can expertly connect the dots between current and longstanding production-related issues, it can be nearly impossible to get the “big picture" and truly understand what actually went wrong. The result is investigations that drag on unnecessarily and only focus on the symptoms rather than the root cause of the issue.

Our Unique Approach: Element AI Knowledge Scout

Some manufacturers simply accept this difficult trade-off and do their best with the limited resources they have. With Knowledge Scout, however, there is another way—and it's driven by automation and artificial intelligence (AI).

Integrated with a company's disparate electronic systems, Knowledge Scout software automatically organizes structured and unstructured data alike. Searching everything from ERP, MES, and quality management system data to SOP and data deviation reports, the tool pulls targeted information that may be useful and brings it together in one place.

Once the data is aggregated, the software deploys AI algorithms—namely, knowledge graphs, automated question answering, and semantic search capabilities in a natural language user interface—to turn it into information quality specialists can use.

In the end, Knowledge Scout speeds up information gathering and puts relevant facts in the hands of those responsible for the deviation management process. By eliminating the problems associated with siloed data sources, it helps quality specialists quickly identify potential root causes of deviations so that measures can be taken to prevent them from happening again.

The Results: Faster Resolution, Significant Savings

The impact that Knowledge Scout might have on any given organization will vary depending on many different factors, from the market value of the product in question to the collective expertise of its quality specialists. The results from one recent deployment, however, illustrate how the tool can potentially make a difference. Since it installed the software, the factory has seen an approximate 30 per cent reduction in average resolution time, from 37 to 25 days. This deployment is projected to have an overall impact of up to 5 per cent cost savings per deviation and about 2,700 work hours saved annually.

Those hours saved, of course, can be devoted to other work—processes better handled by humans than machines. For quality specialists, the tool buys them time; they can spend less effort finding the information they need, and more on higher-level projects like assessing CAPA plans and improving regulatory outcomes.

The bottom line with Knowledge Scout is it improves resolution times to limit production downtimes. It also helps pharma companies get their product to market faster, which, in turn, can help prevent inventory losses. It won't prevent a production deviation, but it can ensure that when a deviation occurs, there's a plan in place to get back on track.