Current clinical supply forecasting tools were not developed to adapt to the increasingly complex world of clinical trials, with unpredictable enrollment and study design changes.
Excel, the industry standard, is easy-to-use however it is slow, labor-intensive, at risk of human error and not automated. Commercial tools are incredibly costly and complex, requiring outsourcing to consultants or relying on supply manager’s knowledge in statistical algorithms.
There is an industry need for an improved solution.
Early-Stage Planning (24 – 12 months to study start)
Best practice, in an early stage of protocol development, the supply chain function is consulted for assessments on supply capabilities. These are typically high-level feasibility and risk assessments and often requires multiple What-If scenarios. The clinical supply planner (CSP) seeks input from clinical operations to understand critical study assumptions such as number of patients, treatment schedule, number of sites, countries, enrollment plan, etc. Combined with product characteristics and supply network assumptions, the CSP builds a high-level model and calculates rough-cut product volumes overtime. As margin of error on the assumptions at this stage are generally high, an overage of 50% and often higher is added in the feedback to clinical operations and manufacturing.
Getting closer to study start, the CSP needs to start planning comparator sourcing, Investigational Medicinal Product (IMP) manufacturing and distribution. A model needs to be built at the appropriate detailed level to assess supply needs overtime at site, depot network and manufacturing levels. At this stage, it is critical to agree on a detailed set of assumptions to build the demand forecasting model and a matching supply plan. The complexity of the study determines the complexity of the model including but not limited to the following variables:
The CSP is expected to define how much inventory needs to be held at site level, at depot network level and at central storage level taking all these variables into account. The added challenge that is demand is extremely unpredictable.
Traditionally, buffer levels (re-supply triggers) are static and are typically defined manually during the specification process. Values are input (into whatever tool you are using) and you hope for the best. This does not account for unexpected or unknown demand or current enrollment rates, but rather based on expected enrollment. This presents challenges to supply managers to achieve accurate forecasts and leads to wasted supply from overly conservative forecasts.
4G Clinical’s clinical supply forecasting calculates the total demand for sites and depots by combining buffer levels, enabled by dials, with dynamically updated demand for existing patients.
The system displays the demand for each site so you have complete transparency. Also, the trigger level per site is clear, so you know that when a site’s current available inventory falls below that number of kits, a shipment request will be triggered. And you can control the size and frequency of shipments per enrollment group using the long window, again with visibility per site so you can see exactly how big the site’s next shipment will be.
Buffer Levels for Unknown Patients
This is the greatest variable. You don’t know how many or where patients will enroll, so you need to balance your supply constraints (available drug, expense) against your assumed demand (random bursts of enrollment, steady enrollment, or a trickle). The confidence dial allows you to find that balance, and see the resulting buffer levels at your sites.
Given the current limitation of supply tools, CSPs focus primarily at the study-level so there is little to no time spent coordinating demand over time. Aggregation to the compound level is critical to make the right decisions, especially when multiple CSPs manage studies using the same drug product.
Bottom Line: End-to-end clinical supply planning is critical to the success of the business, and tools are needed that enable the CSP to forecast based on available information to enable decisions from early-stage feasibility planning, 1–3 years ahead of study start, all the way through study completion.
Supply Chain Planning is the process of coordinating assets to optimize the delivery of goods, services and information from supplier to customer, balancing supply and demand. The integrated Supply Chain Planning model includes a hierarchy of planning cycles which aggregate up and down between operational, business and strategic planning. Each of these cycles have their own focus and purpose.
At 4G Clinical, we believe CSPs should have full transparency and control over supply decisions without having a Ph.D. in math or relying on expensive consultants. That’s why we have revolutionized site and depot forecasting through intuitive confidence dials with a fully integrated RTSM/supply forecasting engine.
We aren’t stopping there. We understand the need for a tool to aggregate between the levels of operational, business supply chain and strategic planning – that adapts to the increasingly complex world of clinical trials.
4C utilizes natural language processing (NLP) to enable clinical supply planners (CSP) to optimally manage supply and dynamically adapt to new information, focusing on business need rather than on interpreting complex calculations. 4C brings speed, simplicity and control over supply decisions.
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