Few industries have seen as much volatility as Pharma and Life Sciences over the last few years. The pandemic has changed everything, from how quickly companies are expected to deliver on new market demands, to how they run their trials, to how they engage with colleagues and other healthcare providers. Even now many uncertainties remain, from how pricing plans and regulations will evolve to which manufacturers will survive the post-covid patent cliff. If one thing’s clear, it’s that businesses will need to ramp up their speed and productivity levels to stay competitive in the next normal.
To this end, many have decided to reduce their reliance on a crisis-response command center and accelerate long-term digital transformation efforts instead, focusing on areas like virtual communications and workflow automation. But while these initiatives have led to a lot of progress, they haven’t brought Pharma and Life Sciences businesses up to the speed and productivity levels that match their initial ambitions.
While advanced workflow AI and automation solutions are on the rise in Pharma and Life Sciences, they don’t come without significant risk, and are unlikely to deliver the levels of value expected if the processes underlying them haven’t been addressed first.
For example, suppose you want to embark on an AI initiative in Accounts Payable by building a predictive model for flagging late payment invoices. This is a popular initiative with a tangible ROI – but one that quickly gets complicated. For one thing, because the data most companies have on the causes of late payments is anecdotal, which makes it unreliable from the outset. Then there’s the fact that most large Pharma and Life Sciences companies will be grappling with a diverse systems landscape supporting their payments process (we’ve seen as many as eight SAP systems being relied on at once…)
The truth is, a predictive model just isn’t the best place to start if you want to transform your payments processes – or indeed, any processes. Instead, the first thing you need to do is bring visibility to the data running through your systems. Only then can you form an objective view of your processes and figure out the most common causes of issues such as late payments. But here’s the catch: running this kind of initiative the “traditional” way will require a lot of compromises. For example – it’ll take a high-budget IT project to extract and transform the relevant data, plus analysis workshops and brainstorms to map out your processes and try to determine where issues (delays, errors, wastage) are arising – again, running the risk of unreliable conclusions.
This is why process mining is such a force multiplier: Unlike other solutions such as business process mapping software or a DIY predictive-modeling initiative, a process mining platform provides the best of both worlds: the rapid process analysis and optimization required to get an objective view of your operation, plus the transformative powers of machine learning and automation. Incorporating data from existing systems (from ERP and CRM to CTMS), a process mining platform can visualize all processes at once, across everything from Procurement to Clinical Trial Initiation, while using AI to uncover (and action) high-value improvement opportunities - from how to avoid late payments, to speeding up document approval across a complex decentralized trial.
Pharma and Life Sciences companies are using process mining platforms in a variety of different ways, but let’s take a look at just a few more:
Pharmaceutical deviation management involves identifying and analyzing events in the manufacturing, distribution, storage or testing of a drug or device that deviate from approved operating procedures, guidelines or specifications. For obvious reasons, the margin for error for this process in Pharma and Life Sciences is extremely low, as deviations can lead to dangerous backlogs that threaten companies’ compliance and put patients at risk. As the next normal takes shape and the speed of production and distribution in Pharma and Life Sciences is expected to accelerate, the need to improve the deviations process will become increasingly urgent.
With process mining, Pharma companies can extract data from their quality management software and evaluate events for deviations automatically, then, depending on the severity of the deviation, action appropriate protocols such as testing additional batches. This is already significantly cutting cycle times in the deviation process, including for companies like Vetter, who cut cycle times by 15% whileimproving their first-time right rate by 6%.
Fallout from the pandemic, combined with ongoing global logistics issues, weather events, material shortages, enforced measures and reliability issues has caused significant disruption to Pharma and Life Sciences supply chains. One of the teams most vulnerable to the impacts of this disruption is Order Management. For example, increased disruption makes it challenging to keep on top of order changes, let alone understand which customers are the main drivers behind them. As a result, quantifying the impact of such changes on adjacent processes like Production Planning or Procurement ends up involving a lot of guesswork.
With process mining, you can track an incoming order in real time all the way through to Production Planning, accurately calculating the financial impact of last-minute order changes. A process mining platform will also trigger intelligent actions, like alerts for last-minute order changes plus immediate, detailed recommendations on how to avoid delays. Hexion recently underwent this initiative, reducing route changes by 45%, while cutting unearned cash discounts paid out by 50%.
Following the rapid development of vaccines during the pandemic, expectations within the Pharma and Life Sciences industry (as well as among the wider public) are now higher than ever for how quickly companies can conduct clinical trials and bring drugs and medical devices to market.
Notoriously complex challenge in clinical trials operations is ensuring consistency in how medicines are being administered, the types of results being recorded, and how results are being measured. A key part of this task involves the creation of a standards request, to define the protocols that will ensure that consistency. This process isn’t easy – sometimes there are over 100 different therapeutic paths and workflows to sift through in order to define a single standard.
With process mining, you can extract and analyze data across all these paths and workflows in real time, significantly reducing the time it takes to execute a standards request.
To stay competitive and embrace major shifts such as personalized medicine, businesses are racing to ramp up their speed and productivity levels. We’ve seen how they’re doing this using process mining in a few different ways, but there are so many more use cases to explore.
Head to our dedicated Pharma and Life Sciences hub to see how the world’s top Pharma and Life Sciences brands plan to capitalize on the next normal and improve patient outcomes with intelligent process orchestration.