Why both S-data and O-data are needed
— By Nico Ros
During the past two decades, the value of pharmaceutical goods traded globally has grown sixfold, from $113 billion in 2000 to $629 billion in 2019¹. As a result of this growth, supply chains have become increasingly global and complex, with current regulations being too focused on managing a ‘black box’ without visibility on the status of the shipment. More companies are also outsourcing production to contract manufacturers and exploring novel ways to reach patients. For some products, this results in supply chain steps that are so complex that the shipments ultimately end up crossing several continents before arriving at their final destination. Leading pharma companies assess and plan for risks that come with both internal and external disruptions, but without a robust understanding of the nature of the risks, the subsequent disruptive impact on the chain will be hard to minimize. This is a massive opportunity for pharma to better drive growth, reduce operational risk and manage costs. But with $35B still lost annually as a result of failures in temperature-controlled logistics worldwide, it’s clear that there is still a lot of work to be done².
When my co-founder Richard Ettl and I first founded SkyCell, we didn’t just want to build a better mousetrap, we wanted to innovate a space that had remained largely stagnant for decades. When we first spoke to pharma companies it became obvious that many of the supply chain visibility challenges they were facing were simply not addressed by current hardware and software solutions. They were all transporting and managing the proverbial ‘black box’. That’s why our second hire back in 2012 was an Internet of Things (IoT) engineer. SkyCell was the first company in the temperature-controlled air freight (TCAF) space to integrate sensors in our containers. This was a unique approach at the time and meant that we could track and trace all our containers regardless of where they were in the world. Having access to the right data at the right time has always been a key part of why we’ve been consistently able to deliver a safer and more secure service with <0.1% temperature deviations (which is more than 7x better than our closest competitor³). To date, we’ve collected over one billion data points which we’re able to leverage in our back end systems to both successfully operate our own fleet and plan smarter transport routes for the benefit of our clients.
With tens of thousands of shipments having been planned and run on our internal systems for almost a decade now, we have a 30,000 foot view of what’s happening in the pharma supply chain management and why. As we work with our clients, we came to realize that many of the top pharma companies in the world are tackling very similar supply chain challenges. Most of them are looking to release product faster, reduce operational risk, take corrective actions as needed and optimize shipment lanes. But at the end of the day, it all falls under the same umbrella of needing complete end-to-end supply chain visibility platform — in real-time. These challenges are all tackled differently depending on the company but I see the same trends affecting their businesses and appearing over and over again.
Trend 1: Optimizing based on what you have and know
One of the main challenges with optimizing something is lack of data and over-optimizing areas where we have a lot of data (or where it is easy to capture new data). This is a common engineering problem and applies to supply chains as well as packaging solutions. We know from IATA studies, that an unbroken cold chain requires three key factors:
- Correct handling, assembly and loading
- Protection of the packaging against external physical and temperature impact
- A properly planned and executed transport process
Measuring and optimizing step B is the easiest as that’s where companies usually have the most data (i.e. lab tests, simulations, packaging optimization, etc.) because the variables are internal and thus controllable. The problem is that 50% of excursions occur within steps A and C³ and that’s where data capture is the most challenging. Simulating runtime of a packaging in specific temperature ranges is easy. Simulating the same while taking into account operational realities like improper handling and customs processes is a completely different story. This is also the reason why A and C are much more demanding to optimize. For example, using different configurations for optimizing packaging can give us 10% better performance but if handling mistakes go up by 50% (due to lack of handling data) it may ultimately result in worse overall performance than before. Simulation data (S-data) alone is not enough.
Conversely, the same issues apply if we don’t look at the end-to-end transport process and don’t have the right operational data (O-data). Consider for example, only having access to S-data but not having real-time O-data in a scenario where instead of going direct, a routing gets optimized with additional airport unloading steps. S-data may have told us that the packaging runtime will be sufficient, but without operational data it will not be possible to assess the unloading risk and evaluate how many additional excursions this may cause.
There’s a visibility gap between what we think is going on, and what’s actually happening. Pharma companies have been trying for years to close the gaps, but either have the wrong kind of data, not enough of it and/or can’t put it all together. Companies that are truly closing the gap understand that both types of data are needed: simulation data and operational data. Both S-data and O-data are needed to help companies understand the past, present and future to drive better decision-making.
S-data: Simulation data are things like lane risk assessment simulation, temperature profile simulation, packaging simulation, thermal footprint simulation, performance simulation — simulated records of simulated activities. Most companies do this to some extent but simulation data can only tell you what may happen in a simulated future. It needs to be augmented with operational data (O-data) to be truly useful.
O-data: Operational data is the ‘real life’ data. I.e. temperature data, tracking data, milestone data, handling data, image comparison data etc. It’s what actually happens during the shipment and points to the gaps between what was simulated and what’s really happening.
Most companies are O-data rich but S-data poor and even fewer are able to put it all together. This is why there are so many visibility gaps in the supply chain.
S-data tells you what could happen.
O-data tells you what happened.
Together it tells you why it’s happened, which is the first step towards closing your visibility gaps.
Trend 2: The need for a single view
In early 2021 I had an interesting discussion with one of the largest North American pharma companies about their digitalization efforts and how they manage their supply chain. They had multiple systems for monitoring their shipments but the process itself was still very manual, time-consuming and resulted in a very reactive approach. Put simply, the company lacked the ability to react to changing circumstances. And today being reactive is no longer enough. Disruptions vary in severity and frequency but they do occur with regularity. In other words, change is the only constant in the supply chain business. They had some S-data and they had a lot of O-data but ultimately struggled to put it all together. When they looked at their operational data (in this case, temperature and location) it only told them what had happened to the shipment after arrival.
The data sources that are collected cannot be independent. They need to be interdependent. Any one data source alone isn’t enough. You have to combine all data sources in a purposeful way or you’ll never thrive.
Trend 3: Transportation digitalization
When we started SkyCell, it was all about who had the best hardware to transport pharma products. Real-time transportation visibility was simply not considered a critical capability by many pharma companies until relatively recently. Today, the situation has changed. Pharma companies’ portfolios are growing in size and diversity, and shifts in product technology to biologicals has resulted in products that are more effective and more expensive, with the average pallet value being $1–2M. In addition, stricter regulation imposed by governments requires more monitoring to verify product efficacy. We quickly realized that software was going to be the bedrock of our business but so far, however, the systems and capabilities in pharma lag behind.
We believe that the seamless combination of hardware and software can make a big difference in the pharma supply chain. Bringing all shipments under one platform helps to accelerate product release, automatically detect counterfeit and theft of goods, and significantly increase workflow efficiency. The main challenge for pharma is the consolidation of existing supply chain tools and techniques for continuous improvement. The companies that succeed in adopting new technologies are the ones who are able to close the visibility gaps. And the ones that are able to combine S-data with O-data will stay one step ahead.
Learn how SkyCell’s SkyMind platform can help you close visibility gaps, schedule a demo:
1UN Comtrade Database, United Nations, February 2020, comtrade.un.org; McKinsey Global Institute analysis.
2IQVIA Institute for Human Data Science estimates that the biopharma industry loses approximately $35 billion annually as a result of failures in temperature-controlled logistics — from lost product, clinical trial loss and replacement costs, to wasted logistics costs and the costs of root-cause analysis.
3Comparative performance of selected pharmaceutical airfreight containers – Insights in temperature control, CO2 profile and economic efficiency, University of St. Gallen 2021
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