Getting it Right from the Start: Simulation Optimizes Supply Chain Planning for Clinical Trials
Clinical supply chains are inherently dynamic and ambiguous, contributing to a high degree of uncertainty when trying to match drug supply to research needs at every stage of clinical development. By modelling this uncertainty, simulation tools can provide robust forecasts to help ensure investigational therapies get to patients when they are needed, and to reduce waste and development costs.
Benedict Hirth |
sponsored by Patheon
Managing clinical trial supply chains has always been challenging. Any delay or unexpected issue upstream in manufacturing or sourcing can impact available bulk quantities for every single downstream step – adding time and cost to clinical studies. The supply chain pressures exerted by the pandemic have exacerbated these challenges by an order of magnitude, as companies have had to adapt their supply strategies in real time when safety stocks run out, when plants shut down, or when shipments to clinical sites are blocked or delayed.
In the face of these uncertainties, early planning and demand forecasting using simulation tools helps companies to manage supply chain problems by setting the right priorities from the outset, anticipating the likelihood of disruption, and finding the best balance between risk and budget. A solid picture of demand supports management of expectations, steers communication with internal and external stakeholders, and assists with the setting of timelines and calculation of the right budget to feed the study. It also helps the bulk manufacturer plan for sufficient volumes and arrange appropriate timings for resupplies if batch sizes are too small.
We all know that a rushed, unplanned supply chain will have consequences – the biggest being higher costs and higher risks. You may need to pack less material because of deadline pressure or because of supply shortages at the time of packaging. And that can lead to increased packaging activities, higher distribution efforts, and, ultimately, higher costs.
Further, many internal and external stakeholders are involved in the planning and execution of supply chains, meaning there are multiple interdependencies to manage and navigate. Having to do so in an environment of uncertainty and under extreme pressure increases the risk of error, which can negatively affect trial milestones and potentially compromise patient safety.
Deterministic tools – which can be as simple as an Excel spreadsheet – can assist with supply chain planning, but these tools can only calculate demand based on fixed parameters; they cannot show the potential impact of different patient enrolment scenarios, or of scenarios where different numbers of patients arrive for treatment each month, for example. When used at an early planning stage, a deterministic tool can also tie you down to a specific scenario based on early data – and early data are not always the most accurate representation of the future. In addition, deterministic tools tend not to consider shipment factors, such as shipment lead times and cost, Interactive Response Technology (IRT) settings, or the risk of late shipments, which can all have a significant impact on the clinical supply chain.
Ultimately, managing clinical supplies is a far more complex task than many realize. Patient enrolment and dropout are known to be highly dynamic, but there are also many other factors that can impact the supply chain. In my view, you can be better prepared if you consider your envisaged scenario and then investigate the effect of variability in different areas using simulation tools.
Simulation tools consider a large number of influencing factors on a supply chain model by executing hundreds of runs using different variables. For example, simulation can show what happens if all patients in the study attend in one month, and then zero.
The runs can cover different country setups, enrolment plans, distribution setups, supply plans, expiry dates, available material, label groups, titrations, cohorts, packaging designs, bulk limitations, and more. What you’re presented with is the likely outcomes – and the biggest risks. The simulation process can uncover potential issues that a standard human evaluation may miss, including their impact on the supply chain and likelihood of occurring. The data can produce suggested IRT settings, supply plans, shipment frequency, and quantities. Simulation also provides data to assist with depot shipment quantities and inventory management, and helps evaluate options based on risk and cost, while also considering any constraints, such as limited drug supply or limited storage capacity at sites.
The data output from simulation is only an assumption of what could happen. It’s impossible to budget for every eventuality, but simulation allows you to see the biggest problems facing your trial, such as the risks of insufficient bulk quantities, unoptimized packaging designs, incorrect timelines, and so on. The data can then be used to inform supply strategy and to pivot resources to maintain the best balance between cost and risk. the next month.
Learning from the data
Recognizing the benefits, Thermo Fisher Scientific has invested in simulation and the expertise necessary to digest the data so that it can be read and understood by supply chain manufacturers without the need for modeling experience. We use Monte Carlo simulation, which brings variability within a defined framework into every single run. Monte Carlo is also considered a best practice approach for studying complex supply chains. There are many variables in how our clients set up their clinical trials it is important for us to use a tool that is flexible enough to work with different input data, including live data from the IRT, to re-evaluate supply strategies using real-time data.
Understanding the data output from the simulations requires fundamental knowledge of both clinical studies and the supply chain, including the distribution networks and IT systems, as well as a good understanding of the underlying statistical methods. Drawing the correct conclusion from the results is key to establishing a solid supply strategy. Our clients often want to investigate some very specific points or scenarios, so we work them to define the goals of the simulation and answer the questions they have about their supply chains. Because the quality of the models depend on the quality of the input data, a significant amount of data gathering is required at the outset. After the simulation, we create a report and discuss the results with the client and supply team to help them understand the simulation and how to adapt their supply chain as a result.
While deterministic tools are quicker to set up and more digestible for a supply chain manager, they do not offer line of sight into how variation or evolving reality may affect the supply chain, whereas simulation tools create models based on multiple scenarios to inform planning. Knowing the potential risks in advance is a key component of a strong supply chain. It allows companies to prepare alternative strategies to keep study timelines on track and maintain patient treatment if a serious issue should arise. Simulation is the most elegant and sophisticated way to gain such knowledge. Put simply, simulation is the next best thing to hindsight.
Top Benefits of Simulation
Cost versus risk analysis
- cost and supply chain risk/effort to execute the study
Material demand analysis
- trial execution strategy if drug is limited
- impact of expiry date on demand plan
Evaluate “What If” scenarios
- different enrolment forecasts
- different countries participating in the trial
- different pack/label/ distribution strategies