How do you solve a problem like Demand Variability?

2nd August 2017 (Last Updated July 18th, 2018 08:57)

GlaxoSmithKline's Rohan Sheth and CTA's Henry Kerali examine ways to guard against Demand Variability

How do you solve a problem like Demand Variability?

For those involved in clinical supply planning, demand variability is a common problem in clinical studies that largely goes unnoticed as a trial gets underway.

However, in a recent report by Eye for Transport, demand variability was identified as the biggest challenge facing the supply chain.

In supply chain lexicon, demand variability is "a measure of how much variability there is in demand. It is the difference between what one expects to happen and what actually happens." Or in other words, it's when things don't go according to plan.

As clinical trials become more complex and more studies move toward the adaptive design approach, the problem can become pretty profound, if not recognized early.

This then begs the question: How do you solve a problem like demand variability?

This is where the use of end-to-end (E2E) planning could guard against one of the most common uncertainties in the clinical supply chain. As clinical trial designs become increasingly complex, the problem can become pretty profound, if not recognized early.

There are many causes of demand variability (DV). From an operational standpoint, issues with patient recruitment, study protocol changes, and complex study designs can halt the progress of a trial. Sometimes DV can be attributed to process gaps where an organization's supply chain processes and systems lack robustness. That can be down to poor data discipline among users (a failure to identify the problem at the source) or a lack of communication between key partners in the supply chain.

DV could also be the result of an inherent problem in the design of the trial itself. For instance, studies with blinded designs, as well as studies that have adaptive designs both have inherent variability. In those cases, however, there's not much you can do except plan for that variability as much as you can up front.

When experiencing demand variability, companies sometimes resort to drastic actions to resolve the problem. If a sponsor waits too long to confront the issue, the demand would accumulate, putting a strain on manufacturing and supply capacity to support clinical study milestones.

Another common mistake supply chain professionals make is relying too much on historical demand trends to estimate future demand, which may not always be the right solution for titration or adaptive study designs.

A typical outcome in the midst of all this is for panic to creep in within the sponsor organization as supply chain nodes become more and more disorganized. This leads to inventory mismanagement (either too much or too little), resulting in study delays and stock outs, or resulting in waste and mismanagement of working capital.

So how do you factor in E2E planning?

E2E planning enables you to realise the demands of your clinical trial and apply it to all levels throughout your supply chain. In this model, the end patient dictates the amount of product you need for your clinical trial. By linking patient demands all the way back to manufacturing, supply plans provide end-to-end visibility. A change in clinical demand translates into revised requirements for operations.

When using E2E planning, it's important to understand the root causes of DV to enhance the quality of your demand plan. Depending on the complexity of the study, this may require rigorous data analysis, which should be enabled using IRT systems or common ERP tools in the marketplace. Crucially, make sure you recognize the capabilities of your supply chain network. Furthermore, effective collaboration with your supply chain and clinical partners, whether they're external or internal, is absolutely critical.

Running the E2E Planning process as an iterative, closed loop cycle is essential to feedback results of the demand plans into the planning process thus improving the predictability of outcomes. Supply plans must be adapted to the changing demand profiles. Buffer strategies continually need to be evaluated to ensure they are aligned with associated DV. Ultimately, timely communication with customers and suppliers can proactively identify supply issues and enable to react in a timely fashion.