Retail reliability depends on inventory accuracy and shrink control

The question behind this piece

Retailers often treat inventory accuracy and shrink as finance outcomes. Stores experience them as daily friction: customers cannot find product, pickers substitute, replenishment misfires, and teams spend time hunting instead of serving. How do you treat inventory accuracy like operational reliability, with clear standards, early-warning signals, and routines teams can sustain?

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Why this matters now

Omnichannel has raised the cost of being wrong. Inventory errors no longer stay inside the store. They cascade across channels into substitutions, cancellations, late orders, and customer service contacts.

At the same time, shrink pressure and labor constraints make it harder for stores to absorb extra process. When teams are stretched, controls degrade, counts get skipped, and exceptions pile up. The store becomes less reliable, so more time is spent managing the consequences of unreliability.

Old approaches fail because they rely on periodic correction rather than daily control.

Inventory accuracy is a reliability problem, not a data problem.

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Our perspective

Treat inventory accuracy and shrink like reliability in any complex operation: define standards, monitor signals, manage exceptions, and coach habits. The goal is not zero variance. The goal is early visibility and fewer repeatable causes.

Start by defining “inventory truth” and where it breaks. Most organizations have multiple versions of inventory across POS, perpetual, backroom, online availability, and third parties. Reliability starts with clarity on the source of truth, update frequency, and the events that create divergence. Common divergence points include receiving, transfers, returns, damages, markdowns, and fulfillment picks. Map the lifecycle simply, then focus on the few breakpoints that drive the most pain.

Next, run a tight set of leading indicators that stores can act on. Annual counts and after-the-fact shrink reporting do not create control. Stores need a short list that points to the next fix, such as phantom inventory SKUs, pickup and ship-from-store substitution rates, unusual adjustment activity, receiving discrepancies, and cycle count miss rates by product class. Each signal should have an owner and a default action.

Then replace broad counting with targeted cycle counting and exception management. Full counts can reset the system, but they do not build reliability. A better model uses targeted cycle counts driven by exception signals and risk, paired with an exception queue stores clear daily or weekly. The queue should be small, prioritized, and tagged to root causes. Over time, success is not working the queue harder. Success is shrinking the queue by eliminating repeat causes.

Finally, tighten a few control points that reduce shrink without slowing the store down. Targeted controls means risk-based controls at points of highest loss and highest error, not blanket friction everywhere. Focus on runnable routines in receiving, returns, transfers, and damages, plus clear ownership for high-value categories. Controls only work when they match the pace of store life and are reinforced through a management cadence that district and regional leaders actually run. Store signals should also trigger upstream fixes when root causes sit in item setup, vendor compliance, barcodes, or replenishment logic.

You reduce shrink by designing reliable routines, not by asking for vigilance.

How Strathen Group can help: 

We can run a daignostic engagement and build an Inventory Accuracy and Shrink Playbook plus templates to build in-house capability and governance for your team. During this engagement, we can define inventory truth, store scorecards, exception routines, and risk-based control checklists, plus a management cadence leaders can run weekly.