Inventory Strategy Guide

1. Make to Stock vs Make to Order: The Core Trade-off

Every SKU in your catalog falls somewhere on a spectrum between two extremes:

•       Make to Stock (MTS): Items are manufactured or purchased in advance and held in the warehouse, ready for immediate shipment. This minimises delivery lead times but requires capital commitment and carries the risk of obsolescence.

•       Make to Order (MTO): Items are only produced or ordered when a confirmed customer demand exists. This eliminates holding costs but extends the lead time the customer must accept.

The optimal policy for each SKU depends on four key factors:

•       Demand predictability and volume

•       Customer-expected lead time

•       Degree of product customisation

•       Product lifecycle stage and shelf life

2. The ABC Classification Model

The ABC model is the cornerstone of inventory prioritisation. Rooted in the Pareto principle (see Section 3), it segments your item catalog into three classes based on each item's contribution to total revenue or sales volume:

Class A — Strategic Items (the vital few)

Approximately 20% of SKUs that together account for roughly 80% of total revenue. These are your most critical products and must be managed with the highest level of attention:

•       Always held in stock (MTS) — a stock-out on a Class A item is unacceptable.

•       Positioned in prime warehouse locations — high-accessibility zones close to dispatch areas to minimise picking time.

•       Monitored continuously — stock levels reviewed daily or even in real time, with tight reorder points and safety stock calculations.

•       Forecasted rigorously — use historical trend analysis, seasonality adjustments, and year-on-year growth rates to drive purchasing decisions.

Class B — Intermediate Items

Approximately 30% of SKUs generating around 15% of revenue. These items deserve a balanced approach:

•       Maintain a minimum safety stock, but not at the same level as Class A.

•       Review stock levels weekly rather than daily.

•       Consider a mixed MTS/MTO strategy, holding a base stock while using MTO for demand spikes.

Class C — Low-Priority Items (the trivial many)

Approximately 50% of SKUs that collectively contribute only about 5% of revenue. For these items:

•       Adopt MTO or Just-in-Time (JIT) ordering — do not commit warehouse space and capital to items that move slowly.

•       If stocked at all, keep minimal quantities and accept longer replenishment cycles.

•       Review periodically (monthly or quarterly) rather than continuously.

Summary table:

3. The 80/20 Pareto Principle in Inventory Management

The 80/20 rule — also known as the Pareto Principle, named after Italian economist Vilfredo Pareto — states that roughly 80% of outcomes stem from 20% of causes. In an inventory context this translates to:

"20% of your SKUs generate 80% of your revenue."

It is important to treat these figures as directional, not exact. In your specific catalog, the split might be 75/25 or 85/15. What matters is the principle: a small subset of items has a disproportionate impact on business performance, and those items deserve disproportionate management attention.

Why the 80/20 Analysis Is a Powerful Starting Point

Performing a full statistical analysis of every SKU is time-consuming. An 80/20 analysis gives you an actionable segmentation in a fraction of the time:

•       Sort your items by annual revenue contribution (descending).

•       Calculate the cumulative percentage of revenue.

•       Draw the line: items reaching ~80% of cumulative revenue are your Class A.

•       Repeat the analysis by volume (units sold) as a cross-check.

Running the analysis on both revenue and volume axes often reveals interesting mismatches: a high-volume item may contribute little revenue (candidate for MTO), while a low-volume item may have high strategic importance (must stay in MTS). The intersection of both analyses is where ABC classification becomes most powerful.

Additional Dimensions to Enrich the Analysis

Before finalising which items belong in which class, consider overlaying the following:

•       Product bundling: which items are always sold together? A Class C component may be essential to deliver a Class A finished good.

•       Strategic customers: what do your top customers consistently buy? Customer-level 80/20 analysis can elevate certain items regardless of overall volume.

•       Margin, not just revenue: a high-revenue item with thin margins may be less strategically important than a lower-revenue item with strong contribution margins.

4. Complementary Lens: the XYZ Demand-Variability Model

While ABC measures value and volume, the XYZ model measures demand predictability. Combining both gives you a powerful 3×3 matrix for nuanced stocking decisions:

An AX item (high value, stable demand) is clearly MTS with a statistical reorder point. A CZ item (low value, erratic demand) is clearly MTO. Items in the middle of the matrix — BX, AY, BY — require more judgement and often benefit from safety stock buffers calibrated to demand volatility.

5. Additional Factors Influencing the MTS/MTO Decision

Product Lifecycle and Obsolescence Risk

•       Perishable goods (food, cosmetics) or fast-evolving technology products carry high obsolescence risk. MTS quantities should be tightly controlled and turnover maximised, or MTO preferred entirely.

•       Evergreen products with stable long-term demand are ideal candidates for MTS, as the risk of stock becoming unsellable is minimal.

Degree of Customisation

•       Standard, off-the-shelf products should always be MTS: speed of delivery is the competitive advantage and customisation is zero.

•       Custom or made-to-measure products must be MTO by nature. Customers purchasing bespoke items typically accept longer lead times in exchange for personalisation.

Customer Lead-Time Expectations

The customer's tolerance for waiting is often the deciding factor. If your customer expects same-day or next-day delivery, the item must be in stock — no other option exists. If customers regularly accept a 4–6 week lead time (e.g., in capital equipment or B2B industrial contexts), MTO may be perfectly viable even for high-value items.

6. Operationalising the Model: Detecting Stock-Outs Before They Happen

Classification is only valuable if it drives action. The real operational goal is to identify potential stock-outs on Class A (and high-priority Class B) items early enough to take corrective action. Here is a step-by-step framework:

Step 1 — Calculate Your Actual Consumption Rate

For each Class A item, derive the real consumption rate from historical sales data:

Always use net stock for the coverage calculation: gross warehouse stock minus open (unshipped) sales orders. This gives you the true available inventory.

Step 2 — Adjust for Trend

Historical averages are a baseline, not a forecast. Overlay the year-on-year sales trend to project forward:

•       Positive trend (+7% YoY): multiply your base daily/weekly need by 1.07. Your safety stock and reorder quantities should reflect the higher expected consumption.

•       Negative trend or seasonality: apply the appropriate adjustment factor and review safety stock levels at each seasonal inflection point.

Step 3 — Map Open Purchase Orders Against Coverage

Once you know your current coverage in days, map in all outstanding purchase or production orders, including their expected arrival dates. Build a simple rolling inventory projection:

•       Today's net stock ÷ daily need = current coverage (e.g., 98 days).

•       Project forward: at the current run rate, stock will reach zero on [date].

•       Add in open POs: does a replenishment arrive before the projected stock-out?

•       If not — or if it arrives with insufficient margin — trigger a corrective action immediately.

Step 4 — Define Reorder Points and Safety Stock

For each Class A item, set two formal thresholds:

•       Reorder Point (ROP): the stock level at which a new purchase order must be placed, calculated as (lead time in days × daily consumption) + safety stock.

•       Safety Stock: a buffer above the ROP that absorbs demand spikes and supply delays. For Class A/X items, size this based on service level targets (e.g., 98–99%). For Class A/Y items, use a larger buffer to account for demand variability.

Step 5 — Review Cadence

Not all items need the same monitoring frequency. Align the review cycle with the item's ABC class:

•       Class A: daily review or continuous (system-triggered) monitoring.

•       Class B: weekly review.

•       Class C: monthly or event-driven (triggered only when stock falls to near zero).

7. Translating Classification into Physical Warehouse Layout

The ABC model has a direct implication for how you physically organise your warehouse. The guiding principle is simple: the more important an item, the closer it should be to the dispatch area, the easier it should be to access, and the fewer unnecessary touches it should require.

Zone A — Prime Locations

•       Ground floor, closest to dispatch and packing stations.

•       Wide aisles, clearly marked, accessible by all handling equipment.

•       Replenishment from bulk storage should be seamless and frequent.

Zone B — Intermediate Locations

•       Moderate walking distance from dispatch.

•       May include upper rack levels for bulk reserve stock.

Zone C — Remote or High-Rack Locations

•       Furthest from dispatch, upper racking levels, or off-site storage.

•       Accessed infrequently; specialised equipment may be needed.

This layout minimises picker travel time for the items that are picked most frequently — a direct reduction in operating costs and error rates. Studies consistently show that ABC-based slotting can reduce picking time by 20–30% compared to random or SKU-code-based storage.

8. Practical Implementation Checklist

•       Export 12 months of sales data at SKU level (revenue and units).

•       Calculate each SKU's % contribution to total revenue and to total units.

•       Sort descending and compute cumulative %. Assign A/B/C classes.

•       Cross-check with volume analysis and flag mismatches.

•       Overlay XYZ demand variability classification for key items.

•       For each Class A item: calculate daily consumption, current coverage, and projected stock-out date.

•       Map all open purchase/production orders to the coverage timeline.

•       Set formal ROP and safety stock levels for all Class A and critical Class B items.

•       Reconfigure warehouse slotting to align physical location with ABC class.

•       Schedule recurring reviews: daily for A, weekly for B, monthly for C.

•       Revisit the full classification at least annually, or whenever the product mix changes significantly.

Conclusion

A well-structured inventory policy built on ABC classification and the 80/20 principle gives you a clear, data-driven answer to two fundamental questions: what should I stock, and how much? By concentrating your management effort — your monitoring, your safety stock investment, your warehouse prime real estate — on the items that genuinely drive your business, you can dramatically reduce the risk of stock-outs on critical SKUs while simultaneously freeing up capital tied up in slow-moving, low-value inventory.

The methodology is not complex, but it requires discipline: regular data pulls, honest trend analysis, and systematic follow-up on items approaching their coverage thresholds. Done consistently, it transforms inventory management from a reactive fire-fighting exercise into a proactive, strategic capability.