When supply chain startup PLAIO reached out to 300 mid-sized pharmaceutical companies, it discovered that an overwhelming 95 per cent of them rely on outdated spreadsheets for supply chain planning.
In response, the Icelandic startup, founded by industry veterans and academics with over 25 years of experience in the pharmaceutical supply chain sector, uses AI to solve chronic industry pain points.
I spoke to Johann Gudbjargarson, founder and CEO of PLAIO, to learn more.
PLAIO aims to help pharmaceutical companies transition from traditional, manual, inefficient planning methods to a data-driven approach. It has developed an AI-assisted decision support platform that uses prediction algorithms and AI to assist planners and supply chain managers in optimising the sales and operations cycle.
Gudbjargarson's background is diverse, including photography, followed by a computer science degree, which led to a career in optimising supply chains from shiüpping to retail optimisation.
Gudbjargarson shared:
"I've always been driven by creating – building companies, cultures, products, and customer bases. While the concept of 'passion' for one's work is often emphasised, my passion lies in the act of creation itself."
In 2013, he co-founded Rhino Aviation, which focused on optimising inventory for airlines like Icelandair.
2021 Gudbjargarson co-founded Plaio, "leveraging prior work with Kori Pharma in Iceland and a Swiss-based pharmaceutical company, where I developed planning tools for their manufacturing processes." The efficacy of this revealed the opportunity for a suite of tools to transform — and digitise — an industry.
The realities of Industry 4.0 in 2024
We've been discussing Industry 4.0 since 2011, so it's hard to believe that legacy industries still need tech disruption. But Gudbjargarson raised an issue, I often hear, that mid-size companies frequently lag when it comes to digitisation.
The reasons vary but include the costs of adopting advanced Pharma 4.0 technologies and validating compliance for new technologies, legacy system integration, and change management – all issues familiar to anyone in the supply chain or manufacturing sectors. The result is slow manual processes prone to error, such as spreadsheets.
Digitisation — dare I say Pharma 4. 0 — has been occurring incrementally and has gained pace thanks to generative AI. However, while big pharma companies build in-house using tools like SAP and Oracle NetSuite, mid-sized companies lack comparable opportunities and competitive prices.
In response, PLAIO has developed an AI-powered decision support platform that transforms pharmaceutical supply chain planning. It aims to help producers increase manufacturing and financial efficiency, reduce waste, and minimise stockouts of end products and raw materials.
According to Gudbjargarson:
"Our platform leverages predictive algorithms and AI to assist planners and managers in optimising the entire sales and operations cycle.
At the core of PLAIO is an AI Co-planner. This intuitive platform features a natural language interface, enabling pharma professionals to easily interact with data and explore complex scenarios using simple questions like "Can we push two million tablets of this in production before March?"
The Co-planner integrates with existing systems and data sources. It leverages a "knowledge graph" to understand user queries and intelligently route them to the appropriate systems. This allows for rapid scenario analysis, streamlined order execution into campaigns, and significant time savings compared to traditional manual methods.
Gudbjargarson shared:
"By optimising planning and minimising waste, we help companies improve On-Time, In-Full (OTIF) delivery performance.
Our platform is particularly valuable for "virtual pharma" companies that outsource manufacturing, facilitating seamless data exchange and collaboration between partners. And it can be used across sites."
From the data deluge to actionable insights
When we talk about digital transformation, I'm always curious about the realities behind the promise. For example, companies can collect terabytes of data, but do they have the wherewithal to turn it into actionable insights, or does it remain in ineffective silos?
In response, PLAIO has developed a Demand Planning module. By integrating all inbound demand data—such as market forecasts and customer orders—into a unified platform, the tool provides a reliable source of truth that seamlessly aligns with manufacturing plans.
With over 25 advanced statistical methods for analysing historical data and market trends, the module enables precise forecasting at various levels, from individual SKUs to product families, helping organisations proactively manage demand.
The system supports flexible data integration from existing ERP systems and Excel, allowing businesses to leverage their current infrastructure while ensuring data accuracy with built-in exception monitoring.
Additional features, such as capacity planning and intuitive visualisation tools, help align forecasts with production capabilities and provide actionable insights for decision-making. PLAIO's Demand Planning module enhances forecast accuracy, optimises inventory levels, and improves service levels.
This startup can afford to be selective
PLAIO is in a position that many startups would envy. It raised €6.3 million and transitioned from pilots to customers in just a few years.
Gudbjargarson admits, "We have so many talks now with pharma companies, we can basically say 'we don't think you're a company for us' as appropriate."
"Usually, young companies like ours struggle to get product opportunities. We get plenty of inbound requests, which means when appropriate, we can tell companies I don't think this tool is right for you because you have requirements that should be solved in an ERP system or a BI tool."
That said, Gudbjargarson has broader goals for supply chain automation and notes, "We are still learning. We are hardening new requirements. When we have a broader client base, we want to connect systems. For example, when a virtual pharma company creates a manufacturing order, it goes straight into the contract manufacturing organisation's system and manages the quantities and delivery date.
The company has also attracted the attention of big pharma and is now servicing large companies such as EISAI for supply chain planning and distribution. It has the potential in the future to offer its solution to other industries, democratising access to its cutting-edge supply chain solutions.
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