Improving Clinical Trials Supply Planning from good to world-class

The semantics of CS&OP: translating scientific thinking into operational numbers.

 

Forecasting and planning supply for clinical trials has its own idiosyncratic semantics, quite at odds with the established cross-industry standards of S&OP.

CS&OP semantics specific purpose is to provide the translation from scientists and MDs language into the operational objects that clinical supply planners are ultimately planning; sites, patients, and investigation medical products. These semantics are sensibly different from what their colleagues in commercial supply chain apply through S&OP.

First there is no market, yet.

In clinical trials there is no place for a commercial network, sales history, pricing strategies, promotions, or distributor allowances. That is just not the language of drugs development. What normally would be the “customer” planning hierarchy, linking customers with channels and markets, is replaced by a planning hierarchy that describes cohorts of patients within trials and programs.

Furthermore, the assumptions underpinning R&D plans and clinical development protocols cannot provide estimates about the products quantities required for the trials. They can only provide speculations about the trial’s key unit of count; the number of patients being recruited or discharged over a certain period. The number of patients, hierarchically classified by cohorts and treatment groups, then provides the inference for the IMPs to be available on site at a certain time.

Finally, the forecast is expressed in quantities of IMPs per site. Planners can put away their clinical trial “Rosetta Stones” and resume the familiar language of supply chain planning. They will break down the IMPs into operational quantities of physical objects to be delivered on site and, from there, into the quantities of what they are constituted by in the manufacturing process: drug products, bulk, drug substance, APIs to be produced and distributed. The planning chain is now in place, well in advance of when the first IMPs will be dispensed to patients.

 

 

Uncertainty is king: expect the unexpected.

 

Uncertainty is a key aspect of R&D, unsettling clinical supply planners with demand patterns at odds with those their colleagues in commercial supply chains have learnt to deal with.

There is no relevant historical data to leverage on. The classic “supply chain black belt planner” toolbox, made of statistical algorithms, machine learning and all that jazz, is out of its boundaries of applicability. Planners cannot imagine what even scientist don’t know yet and the unexpected occurs in the disguise of new indications, dose escalations, additional studies expanding the scope of ongoing programs, changing the assumptions behind the original IMPs forecast.

In a CS&OP context, the role market intelligence normally plays in S&OP is substituted by the R&D scientists’ experience in defining cohorts of patients, complexity and length of studies, relationships between IMPs, drug products, drug substances and active principles. But even experienced scientists face plenty of unknows, only to be discovered during the investigation.

 

 

Clinical trials applicability of the Holy Grail of supply chain planning: optimisation.

 

The objective of optimisation in supply chain is to work out the most valuable trade-offs between service level, cost of inventory, cost of distribution and the cost of running the supply chain organisation. The theory is robust and holds also in clinical trials, with some due caveats.

First, service level is not an independent variable; in clinical trials it is expected to be 100%. No compromise is acceptable during planning for the huge implications it might have both on patients and the validity of the whole trial. Secondly, the cost of running the supply chain, in terms of number of planners, is circumscribed compared to commercial supply chain organisations. Volumes, number of SKUs and distribution points are much less; there is not much margin for saving on headcount.

The only real independent variable left to play with is inventory and related costs: storage, distribution, carrying cost for capital immobilised, excess and obsoletes write offs. In clinical trials, carrying cost and write offs would generally be the most significant among the above components, since production occurs in small batches, without the economies of scale of industrial process manufacturing. Manufacturing capacity would not normally be a major constraint in the optimisation model, given the limited volumes at stake. The real limiting factor is more about the lead time required for manufacturing, since the whole process is new and takes long time to achieve the right quality, concentration, and purity even of relatively small batches. Finally, to avoid the set up costs of such complex processes, you want to produce all you need in one production run, as early as possible, only constrained by drug stability.

In these extreme make to stock settings and given as a fixed parameter the known requirements from committed trials, the key planning variable becomes the “overage”; the quantity of extra API, DS or DP behaves as a sort of insurance premium to address the unexpected.

Running a complex optimisation algorithm to eventually set one single aggregated variable, the overage, might not be worth the effort. And might even not be the best way to take strategic decisions in a context of deep uncertainty.

 

 

Scenario planning: a valuable technique for clinical trials supply planning.

 

Scenario planning is a valuable and widely used supply chain planning technique across many industries. It is even more valuable in an environment where statistical tools are not applicable and optimisation algorithms are not effective.

Two uses cases have the most applications in clinical trials; one in a strategic perspective and one in tactical planning.

In a strategic perspective, scenario planning is valuable during the design of the supply and distribution network at the very initial stages of drug development, ahead of study protocols being developed or patients being recruited. Decisions taken at this early stage are speculative in nature, merely based on assumptions.

Scenario planning allows to compare benefits, costs and risks deriving from the different networks of manufacturing sites, CMOs, distribution centres that those assumption would require to fulfil drug supply. Once decisions about supply networks are taken and implemented, it will be quite costly to reverse or amend them later in the program.

Tactical short-term simulation has a more frequent application by planners, allowing them to assess at any time the impact from changes in assumptions, like the scope of trials, or changes in drug supply, such a batch failures or delivery delays. Planners can quickly simulate the best response, resolve or mitigate the supply risk.

Where R&D supply chain heads have to choose a technological solution in the uncertain context of drug development and clinical trials, and their choice is among solutions that better perform in optimisation and those that better perform in collaboration and scenario planning, the choice should fall on the latter.

 

Conclusion

 

CS&OP, Clinical Supply and Operations Planning, is built around the idiosyncratic processes and features of clinical trials in drug development programs. Its combination of forecast semantics and the smart use of scenario planning in strategic and tactical contexts is extremely valuable for pharmaceutical development organisations.

In Tenthpin, we have developed CS&OP approach and related technological solutions as the foundation for improving supply chain planning in clinical trials and R&D programs.