💰 Reducing Slow Movers Unlocks 15% of Your Working Capital

Photo by Pascal van de Vendel / Unsplash

Nearly one third of the inventory held by mid size retailers and distributors is moving too slowly to generate cashflow when the business needs it most. These units will not correct themselves. Leaders must intervene early, reduce structural drivers, and recover the working capital already sitting on their shelves. Here is where to begin.

Slow Movers are the Biggest Hidden Cash Flow Leak in Mid-Size Inventory Businesses

Inventory accumulates due to undisciplined buying, forecast errors and assortment drift. Across the industry, 20 to 35% of inventory typically sits in slow-moving status, compared to fewer than 10% for top performers. Reducing slow movers is one of the fastest and most reliable ways to restore liquidity, stabilise operations, and limit the need for aggressive discounting.

Slow-moving inventory Creates Structural Drag on Working Capital and Operational Performance

When stock ages beyond 120 days on hand, cash becomes trapped. It is no longer available to fund replenishment, invest in strong sellers, or support growth initiatives. The operational impact is equally significant. Slow movers occupy valuable storage space, generate handling activity, and require repeated relisting or remarketing effort. None of these costs appear clearly in the P&L, yet they compound month after month. Finally, slow movers almost always end their lifecycle in clearance and scrap yards. Although a markdown may appear to be a simple commercial decision, the real effect is much deeper. Margin erosion from clearance can reach 50 to 80 % once overhead absorption and multiple discount rounds are included. In short, slow movers represent a cash flow leak that grows silently in the background until it becomes an issue that the business can't ignore.

3 Root Causes that Drive Most Slow Movers

The first driver is the forecast error at the SKU level. Many teams forecast at the category or style level because it is simpler and stable. However, SKU-level demand is volatile. Variants, sizes, colours, and seasonal patterns behave differently, and forecasting at aggregated levels masks these micro shifts. As a result, businesses build inventory assumptions that do not reflect real buying behaviour. Our work across retailers shows that only 30 - 50% of SKUs fall within a forecast accuracy band of+/- 20 %. This accuracy gap creates persistent overstock, especially on medium and long tail items.

The second driver is overbuying, driven by intuition or supplier pressure. Buyers often rely on experience, personal judgment, or a desire to avoid stockouts. Suppliers reinforce this by pushing for larger orders through minimum order quantities (MOQ) or incentives. Without a disciplined governance mechanism, order quantities drift upward. When we examine purchase orders over a 12 month period, it is common to find out that 30 to 40% of the ordered quantity has no data-based justification. Once these units enter the warehouse, they quickly translate into excessive days on hand and eventual slow movers

The third driver is assortment drift. Over time, the number of SKUs in the catalogue increases, often without deliberate strategic intent. Teams add variants because they appear incremental. Weak sellers remain because retiring them is inconvenient and costly. These decisions dilute volume concentration and push more items into low velocity status. Many mid-size businesses expand their assortment by 5 to 15 % per year while sales remain flat or grow only modestly. The result is a heavier long tail and more capital tied in items that will never turn quickly enough to justify their footprint.

Together, these three drivers explain most of the slow mover problem. They also point clearly to where intervention is most effective.

Benchmarks Differ Sharply by Category

Inventory performance varies significantly by product category. The table below summarises common ranges observed across publicly listed retailers and industry guidance. These benchmarks are directional and should be used to calibrate expectations by category rather than as fixed targets.

Days on Hand (DOH) Varies Widely by Category

Forecast Accuracy (+/- 20% at SKU level) is Strongest in FMCG and Electronics

Markdown Dependence (% of Sales)

SKU Productivity (SKUs driving 80% of Sales)

Obsolence Risk by Category

Assortment Complexity (Relative Comparison)

How to Use The Benchmark Comparison

Do not compare your Days On Hand (DOH) to another category. If you are selling home goods, do not compare yourself with an electronics retailer
If you sell apparel and your run rate is at 120-150 Days On Hand (DOH), there is an opportunity for improvement. If you sell lifestyle products, and your Days On Hand (DOH)sits at 100, you may be performing better than expected
Isolate your slow mover share by category. A blended 25% slow mover rate often highlights a 40% problem in lifestyle SKUs and a 10% problem in electronics
Link benchmarks back to the root cause
- High Days On Hand (DOH) + Low Forecast Accuracy could mean a forecasting or assortment issue
- High Slow Mover % + High Markdown % = Late Intervention Issue
- Low SKU productivity = Assortment Drift

When running an inventory audit, the benchmark comparison above serves as an anchor for your inventory performance on a category level.

The benchmark comparison above serves as the anchor for your diagnostic model. It's like the first cut on how well your inventory is performing on a category level

The Slow Mover Correction Loop Restores Cashflow Discipline

To address the issue, we use a 4-step correction loop. This model allows businesses to identify slow movers early, quantify their impact, correct underlying issues and prevent recurrence

Identification- This requires tagging all SKUs above 120 days on hand and segmenting them by category, supplier, value on hand, and age. This simple exercise provides immediate visibility into where cash is tied up.
Quantification- Calculate the working capital trapped in slow movers and estimate the true carrying cost. This includes not only the unit cost but also the storage, handling, and potential margin loss associated with markdowns. Quantification reframes slow movers from a merchandising problem into a cash flow problem.
Fix the Problem at a SKU Level- Interventions vary by category but generally include improving listing visibility, reallocating stock to higher velocity channels, activating targeted bundles, and applying controlled micro promotions. These actions often recover demand without resorting to deep markdowns.
Prevent Recurrence (Control)- This involves creating buying rules that link future purchase orders to current stock cover and forecast accuracy. It also requires setting days on hand targets and introducing quarterly SKU rationalisation. Over time, the process becomes self-correcting. Slow movers are prevented rather than managed reactively.

Case Study - Electronics Retailer

Consider a mid-size electronics retailer with $4.8 million in inventory. A diagnostic revealed that $1.2 million sat in SKUs above 150 days on hand. The root causes aligned with the industry pattern. Forecast error on accessories led to persistent overstock. Several key SKUs were listed late on online channels, missing their natural demand peaks. Buyers also over-ordered on minimum order quantities (MOQ), which inflated stock cover beyond healthy levels.

The intervention began with strict SKU-level DOH targets and a requirement that all future buys reference existing stock cover. Supplier scorecards were introduced to negotiate lower MOQs. Finally, the team implemented early interventions on the top 60 SKUs that represent most of the cash leak.

The results were significant. Within eight weeks, the business freed $620k in working captial. Slow mover percentage fell from 32%to 18 %. Clearance markdowns dropped by 40% in the following quarter. The organisation regained liquidity and improved operational discipline.

What to Do This Week

The fastest gains come from linking your immediate actions to the benchmark gaps revealed in your category. The objective for the next seven days is to move from intuition to evidence, using the benchmark table as your decision anchor.

#1- Benchmark Actuals against Category Norms

Use just 4 metrics by category:

Days On Hand (DOH)
Slow Mover Share
Forecast Accuracy
Markdown Dependence

Compare each category (apparel, lifestyle, electronics, FMCG) to the benchmark range. If you DOH is much higher than the category norm, buying or poor forecast is driving overstock.

If your slow mover share is 10-15% above benchmark, it is likely due to assortment drift or SKU Proliferation

If forecast accuracy is below 50% in a category where the benchmark 60-75%, you are overbuying without signal stability

If markdown dependence is over 20% especially in lifestyle or apparel, interventions come too late

This comparison determines where cash is actually leaking.

#2- Identify the 20 SKUs creating the Largest Cash Drag

Filter by:

High inventory value
High DOH
High Margin Exposure
Category Specific Obsolescence Risk

If you have electronics with short tech cycles, prioritise immediately. For lifestyle SKUs with long tails, intervene early. FMCG should not have any slow movers, so any item in this list is a red flag

This step isolates the cash-heavy SKUs where intervention moves the needle fastest

#3- Run Category Specific Early Interventions

Tailor your solutions by category. Do not attempt a one-size-fits-all solution

Apparel

Improve front-page or in-store visibility
Bundle with high-velocity colour/size variants
Reallocate to higher-traffic channels

Electronics

Highlight compatibility or accessory sets
Push through channels with short buying cycles
Reduce MOQ-driven future buys

Lifestyle / Home

Curated bundles
Room-style collections
Narrow variants with low productivity

FMCG

Replenishment correction
Promotion in high-frequency channels
Validate expiry windows

#4- Fix the Root Cause Using Benchmark Gaps

Tie each SKU's root cause back to the benchmark comparison

High Markdown % + High Slow Mover Share = Late Intervention
High DOH + Low Forecast Accuracy = Forecast Model needs SKU segmentation
High Obsolescence Risk + High DOH = Buying Discipline Issue

Document root causes at a SKU level. This creates the base for fixing and not repeating the problem.

#5- Introduce Category-based Buying Rules

Once you have established the issue, set buying rules to prevent recurrence of the issue and replace intuition-led buying with rules

Electronics → small lot, fast-replenishment buys
Apparel → DOH target tied to season length (no more than 8–10 weeks)
Lifestyle → SKU count limit per subcategory
FMCG → reorder points based on consumption, not supplier MOQs

You might not see an immediate impact, but it prevents next quarter's slow movers

#6- Rationalise the Bottom of the Assortment

Use SKU productivity benchmarks to guide pruning:

FMCG → Aim for 30–50% of SKUs generating 80% of sales
Electronics → 20–35%
Apparel → 15–25%
Lifestyle → often only 10–20%

Prune the weakest tail segments first. This is the quickest way to reduce slow-mover accumulation structurally.

#7- Build a 30-60-90 Day Cash Flow Forecast

Model cash unlock using

DOH improvement by category
Reduction in Slow Mover Share
Lower Markdown Dependence
Lower Ordering through Buying Rules

Reducing slow movers becomes significantly easier and faster once you anchor your decisions to category-level benchmarks; the moment you see where you diverge from the norm, the path to recovering cash becomes self-evident.

Conclusion

Slow movers are a data, governance and cash flow problem. Addressing them from a process point of view gives businesses clarity, liquidity, and control. When businesses treat slow mover management as a key process rather than an end-of-season clean-up, they unlock capital that fuels growth instead of waste.