This is the second in a three-part series drawn from PrincetonOne’s video conversation with Scotty Kinn and Dave Malenfant on life sciences supply chain.
Catch up on What Good Outsourcing Looks Like and What Goes Wrong
or watch the full video series.
Layoffs Are the Wrong Response to Drug Pricing Pressure
When a major weight-loss drug was forced to drop its US price, Dave Malenfant, Founder and CEO of MDM Consulting, saw the same reaction he’s watched play out across the industry for years.
“What do you think the reaction to this drop in prices by a lot of pharma is going to be?” he asked.
“We’re going to have layoffs. We’re going to cut costs to accommodate it. And that’s not the right answer.“
It’s worth sitting with why that’s his answer, because “cut costs when revenue drops” is close to a reflex in most industries. Dave argues that layoffs are a blunt instrument aimed at the wrong target when the actual problem is how the supply chain is built.
The fix is visibility, velocity, and variability
“The right answer is to make our supply chain more resilient,” he said. “Increase visibility, velocity, and manage variability.” He’s explicit that these three must move together: “You’ve got to understand your variability, whether it’s demand or supply. You’ve got to have visibility across the entire supply chain. And that will enable you to increase your velocity.”
These three levers are specific functions. Visibility depends on roles like supply chain analytics and network design, the people who can see where inventory, demand, and supplier risk sit at any given moment. Managing variability is the job of demand planning and S&OP (sales and operations planning), reconciling what’s forecasted against what’s happening on the ground. And velocity depends on external manufacturing and cold chain quality roles, the people who keep product moving once a plan is set. Cut here, and the three levers Dave says must move together stop moving at all.
This reframes what a pricing shock exposes. A resilient supply chain, one with visibility into demand and supply variability, and the speed to respond to it, can absorb a margin hit without needing to cut the people who run it.
Pricing pressure is what finally reveals the brittleness.
The Layoff-Then-Rehire Cycle Costs More Than It Saves
Cutting these roles rarely saves what it appears to on paper. Most of that cost is rebuilding what the layoff threw away: supplier relationships, institutional knowledge of demand and supply variation patterns, the kind of operational trust this series has already argued takes years to build and very little time to break.
Many of these positions get rebuilt within 12 to 18 months anyway, often at a premium, once the same volatility that triggered the layoff resurfaces and the gap becomes impossible to ignore.
The savings on the org chart rarely survive contact with the rehire.
Why this is a harder problem than it sounds
None of this is easy to build, and Dave doesn’t pretend otherwise. Real visibility across a pharma supply chain means data moving cleanly between manufacturer, distributor, and pharmacy, three parties that, as he points out in this conversation, “don’t talk to each other” nearly as much as the regulatory framework assumes they do.
Managing variability means having the systems and the people to see demand changes and supply constraints as they happen, not months later in a quarterly review.
He does see the technology catching up: “We’ve got technologies out there now that are going to allow us to manage variability. AI is going to help do that.” But it’s only a tool that works if the underlying visibility and organizational structure exist for it to plug into. That caveat cuts deeper than it first appears: AI-enabled variability management still needs people who can operate it, interpret what it surfaces, and act on it.
Supply chain analytics talent is scarce and expensive, so the tool doesn’t replace the roles a layoff removes. It depends on them still being there.
The Alternative Is Restructure
The counterargument to “don’t lay off” is: then what, absorb the margin hit? It doesn’t have to be a freeze either. Redeploying supply chain talent toward the visibility and variability work Dave describes, reskilling planners into demand-and-supply analytics roles, and converting fixed headcount into flexible capacity for project and launch work are all ways to restructure without cutting the capability a company will need for the next pricing shock.
Most pharma organizations facing this kind of pressure land on a hiring freeze rather than a layoff, which is a different, more addressable problem. A freeze buys time, but a layoff removes that option.
There’s a slower cost worth naming too. Life sciences hiring hubs- Boston, New Jersey, the Research Triangle- are small, networked markets where a visible reduction in force travels fast. The organizations most exposed to that reputational cost are the ones who’ll need to hire back into that same market once launch activity resumes, right when the memory of the layoff is still fresh among the people they’re trying to recruit.
The organizations that protect their supply chain talent through a squeeze are the ones still in position to hire quickly once the market turns back toward growth. Decisions made under pressure determine how fast a company can move once the pressure lifts.
Pricing Shocks Will Keep Coming, but Layoffs Don’t Have to.
The practical value of Dave’s framing is that it turns “how do we cut costs” into a different, more useful question: are we cutting costs by removing the capability that would have prevented this problem, or are we finally investing in the resilience that makes the next price shock survivable without a layoff?
Pricing pressure in life sciences isn’t going away; regulatory and political pressure on drug pricing has intensified. Organizations that treat each pricing shock as a reason to cut deeper into supply chain capability will keep reacting the same way every time it happens again.
Those that use it to build visibility, velocity, and variability management won’t need a layoff next time
