We like to think inventory decisions are rational.
We build models.
We optimize safety stock.
We define reorder points and service levels.
And yet…
We hesitate.
We override.
We hold on to SKUs that haven’t moved in 18 months—just in case.
Because inventory isn’t just math.
Inventory is emotional. You’re not alone.
Before going further, I want to acknowledge something important.
Grief is real, deeply personal, and something many of us—including myself—experience in ways that have nothing to do with business. I’m personally navigating that in my own life right now.
This comparison isn’t meant to minimize that experience. The stakes are very different.
But the patterns—how we respond to letting go of something we value—can show up in both places. And that parallel is worth exploring.
And if you’ve ever tried to reduce it—really reduce it—you’ve probably seen something that looks a lot like the psychology of grief.
Inventory is not a dead parent. It is so much less important, less gravity so in the words of Elsa from Frozen “Let it Go”.
A Quick Primer: The “Stages of Grief” (and Why They Show Up at Work)
The idea of “stages of grief” comes from psychiatrist Elisabeth Kübler-Ross, who introduced a widely recognized framework in 1969 describing how people respond to loss. Her original model identified five common emotional responses:
- Denial
- Anger
- Bargaining
- Depression
- Acceptance
These were never meant to be a checklist or a straight line. Even Kübler-Ross later clarified that people move through these emotions in different orders—or revisit them entirely.
Over time, practitioners expanded this into more detailed versions—often referred to as the “7 stages of grief”—to better capture the full experience, adding early and late phases like:
- Shock (before denial)
- Experimentation or testing (before full acceptance)
Whether you use five stages or seven, the point isn’t precision—it’s recognition.
When people lose something they perceive as valuable, they don’t just calculate—they react.
And that applies surprisingly well to inventory.
Reframing Inventory Optimization as Letting Go
Inventory represents:
- Service protection
- Customer promises
- Past decisions
- Personal judgment
So when you ask an organization to reduce inventory, you’re not just removing stock.
You’re asking people to give up something that makes them feel safe.
And that’s where the parallels begin.
The “7 Stages” of Inventory Optimization
- Shock: “Wait… we have that much?”
The first detailed, documented visibility—true segmentation, excess identification—creates a moment of pause.
- “That number can’t be right.”
- “We’ve never looked at it like that.”
Shock isn’t resistance—it’s the realization that the problem is bigger than expected.
- Denial: “We’re actually pretty lean.”
The instinct is to normalize:
- “Our turns are fine.”
- “That’s mostly strategic stock.”
- “It looks worse than it is.”
This stage isn’t about bad intent—it’s about protecting the status quo until the data feels real. After all, we all question the data when it doesn’t support our gut.
- Anger: “The model doesn’t understand our business.”
Once the analysis sticks, pushback begins to surface:
- “The forecast is wrong.”
- “These settings don’t reflect reality.”
- “We know exceptions you don’t.”
This is where analytics meets identity. Because what the model is really saying is: “You don’t need as much as you thought.”
- Bargaining: “Let’s just trim the obvious excess.”
Now we negotiate with the math:
- “Let’s exclude critical SKUs”
- “Can we cap reductions?”
- “What if we pilot only part of this?”
This feels productive—and it is—but it often stops short of full value. Bargaining is progress… with guardrails.
- Fear (the emotional core): “What if service drops?”
This is often bundled into “depression” in the classic model, but in a business context, it shows up as risk anxiety:
- “What if we stock out?”
- “What if demand spikes?”
- “We’re going to hurt customers.”
Even when models show service is protected.
Because inventory feels like insurance –
And reducing it feels like removing protection.
- Experimentation: “Let’s test this.”
This is the turning point.
- Pilots begin
- Policies change
- Governance is introduced
And something important happens: The system holds. Service stays stable—or improves. Confidence builds.
- Acceptance (and Growth): “Why were we carrying all of that?”
With results in place:
- Excess becomes obvious
- Working capital is freed
- Teams shift behavior
Inventory becomes a managed asset – not an emotional buffer.
The Most Important Insight: It’s Not Linear. Just like real grief, organizations don’t move through this cleanly. They bounce.
Real Inventory Examples of Non-Linear Behavior
Example 1: The SKU Exception Loop
- Team reaches acceptance → agrees to rationalization
- A single customer escalation happens
- Suddenly back to anger: “We cut the wrong thing”
- Followed by bargaining: “Let’s reinstate just a few items”
How do you hold the line—and prevent “a few exceptions” from becoming the entire portfolio?
Example 2: The Forecast Miss Spiral
- Pilot shows success → experimentation → acceptance
- One demand spike creates a stockout
- Immediate shift to fear and even denial:
- “The model is too aggressive”
- “We should revert to previous levels”
Was that stockout truly detrimental—or was it demand the business could afford to lose?
Example 3: Executive vs. Operations Split
- Executives: already at acceptance (“reduce $50M inventory”—often a purely financial target without context for which $50M)
- Planners: still in anger or bargaining
- Result: misalignment, slow execution, hidden resistance, and ultimately blame travels upstream though accountability stays downstream.
How do organizations better anticipate—and bridge—this disconnect?
Example 4: Seasonal Reset
- Post-implementation: acceptance achieved
- Peak season approaching → back to fear:
- “We should build ahead just in case”
No one has a crystal ball. But we all have battle scars from seasons past—and it’s those stories (late nights, escalations, expedites) that stick with us, even when the data tells a 98% service story.
At what point do we agree to let go of that 2%?
What This Means for Leaders
If inventory optimization feels harder than it should, it’s because it’s not just an analytics problem; it’s a human one.
The best organizations don’t just build better models. They also:
- Anticipate emotional reactions
- Design safeguards (pilots, thresholds)
- Build trust over time
The Real Difference Between Inventory and Grief
There’s one important place where this analogy breaks down.
With inventory, there’s an endpoint.
You can adjust policies. Improve visibility. Build confidence. And ultimately change the outcome.
You can reduce inventory—and improve performance.
Grief doesn’t work that way.
There’s no optimization model. No service level target. No clean “acceptance” that neatly resolves the process.
It doesn’t follow a timeline. And it doesn’t always get easier in a linear way.
So why use the comparison at all? Because the reactions are familiar.
We hold on.
We question the data.
We fear the downside.
We test the waters.
We slowly rebuild confidence.
Whether we’re talking about inventory or something far more personal, letting go is hard.
A lighter way to think about it? Think Elsa.
Not everything needs to be held onto forever.
Not every scenario needs to be protected “just in case.”
Sometimes the unlock is simply: Let it go.
We don’t struggle with inventory reduction because the math is difficult.
We struggle because letting go is.
The difference? With inventory—we can actually do something about it.
—Kira Bilecky, St. Onge Company
