Reorder Points and Review Policies for Apparel Trims

A reorder point tells a planner when to replenish; an order quantity tells the planner how much to buy. A review policy determines when the stock position is checked. Those decisions should be explicit for each replenished trims item. Applying an EOQ calculation without a trigger, a lead-time basis and a reliable stock record leaves the main operating questions unanswered.

This guide helps inventory planners select a review policy and calculate a reorder point for repeat-use trims under stated assumptions. Buyer-specific fabric and infrequent style-dependent materials may require order-linked planning instead. No formula can make obsolete or incompatible stock usable for the next order.

Select a policy before calculating a buffer

Under continuous review, the inventory position is monitored as relevant transactions occur. A fixed-quantity policy orders Q when that position reaches or crosses a threshold s. An order-up-to policy instead replenishes toward a target S. Neither should be implemented using an unexplained physical stock balance alone.

Under periodic review, checks occur at agreed intervals. An order-up-to policy then considers demand over the review interval plus replenishment lead time. Copying a continuous-review buffer into a weekly review process can leave the interval between checks unprotected.

Policy	When it is reviewed	How quantity is determined
Continuous fixed quantity	At relevant stock changes	Approved fixed quantity Q
Continuous order up to	At relevant stock changes	Gap to target S
Periodic order up to	Every agreed review interval	Gap to target for protection period
Order linked	Against identified order requirements	Net requirement for that order

Microsoft's Business Central documentation illustrates that actual systems can use time buckets, existing supply checks, calendars and order modifiers. Verify the configured planning logic. A textbook threshold should not be described as the exact behaviour of every ERP implementation.

Use the ABC XYZ inventory segmentation guide to prioritise control effort. Segmentation helps choose where closer review is useful; it does not independently determine the correct service target or make a poor stock record reliable.

Define demand and the replenishment clock

Use one unit and time basis throughout. If demand is recorded in individual buttons per production day, the lead-time representation must be compatible with the production calendar. Calendar days and working days cannot be combined silently, particularly where receiving or suppliers operate different schedules.

Demand should represent the requirement the replenishment policy must cover. Recorded issues can understate that requirement during a stockout. Large order launches, substitutions and returns can also distort a simple historical average. Investigate those patterns before treating daily observations as independent draws from one stable distribution.

Measure replenishment until material is available for its intended use. An arrival at the gate may precede inspection and release. Keep the contributing timestamps so the planner can distinguish supplier transit delay from an internal release delay. Do not inflate every supplier parameter to conceal a receiving bottleneck.

Calculate a fixed-lead-time reorder point

For an illustrative model, assume independent daily demand with mean d, standard deviation sigma, fixed lead time L and an adequate normal approximation for demand over that period. Expected lead-time demand is dL; its standard deviation is sigma × square root of L. Reorder point is dL + z × sigma × square root of L.

MIT's stochastic-demand inventory lecture explains aggregation of demand variation and a service factor under a normal approximation. Here z represents a chosen cycle-service target, meaning a model probability of no stockout in a replenishment cycle. It is not the fraction of units filled immediately and does not guarantee observed service.

Suppose repeat-use button demand averages 400 pieces per production day, daily standard deviation is 80 pieces, and effective lead time is five production days. Select z = 1.645 as an illustrative approximately 95 percent normal-model cycle-service factor. Expected demand is 2,000 pieces and lead-time standard deviation is 80 × square root of 5 = approximately 178.885 pieces.

Safety stock is 1.645 × 178.885 = approximately 294.267 pieces. The unrounded threshold is 2,294.267 pieces. Rounding upward to whole pieces gives a reorder point of 2,295. Retain the unrounded calculation separately from any packing multiple used to decide how much to buy.

Trigger against a consistent inventory position

For this teaching example, define inventory position as usable on-hand plus valid outstanding replenishment minus backorders. This simplified definition assumes no separately deducted allocations already represented in demand or backorders. A real system needs a documented treatment of reservations, quality holds and time-phased supply.

If usable on-hand is 1,700 pieces, outstanding eligible supply is 800 pieces and backorders are 300 pieces, inventory position is 2,200 pieces. It is below the 2,295-piece threshold, so the policy triggers a replenishment review. Counting all 1,700 pieces as free while also ignoring backorders would postpone the signal.

A positive inventory position does not prove that production is protected until the next receipt. Examine supply dates and immediate requirements. An open PO arriving after a known shortage can make aggregate position look adequate while failing the production schedule. Use time-phased exception checks alongside the trigger.

Change the calculation when review is periodic

If the same illustrative item is checked every three production days, the protection period becomes L + R = eight production days. Under the same independent-demand assumptions, an order-up-to target is 400 × 8 + 1.645 × 80 × square root of 8 = approximately 3,572.221 pieces, or 3,573 rounded upward.

This is an order-up-to target for the specified periodic policy, not a replacement reorder point of identical meaning. At review, the simplified order proposal is target minus the defined inventory position, subject to nonnegative quantity and approved purchasing constraints. The treatment of outstanding orders must remain consistent.

The three extra review days materially change coverage. They cannot be omitted because the supplier's quoted lead time remains five days. Conversely, a daily automated review may still have a release or ordering delay that belongs in the effective protection period.

Treat EOQ as a quantity model with assumptions

The basic EOQ formula is square root of (2DS ÷ H), where D is annual demand, S is ordering cost per order and H is annual holding cost per unit. It assumes a stable setting with conditions such as constant demand, compatible replenishment assumptions and no quantity discount complication in the basic model.

With illustrative D = 120,000 pieces per year, S = 20 currency units per order and H = 0.10 currency unit per piece per year, EOQ is square root of 48,000,000 = approximately 6,928.203 pieces. This is a teaching cost example, not a current price quotation or a recommendation to buy that quantity.

Compare that quantity with style life, approved substitutes, minimum orders, storage and obsolescence exposure. An economical quantity under the model can still be unsuitable for a short-lived colour or buyer-specific label. Fabric committed to a particular order also needs quantity, shade and specification controls beyond generic replenishment.

Validate one item before expanding the policy

Backtest the proposed trigger using transaction history, including stockout periods and actual replenishment availability dates. Review whether the independent-demand and fixed-lead-time assumptions are credible. If lead time varies or demand is strongly correlated, do not simply retain the same square-root calculation without a revised model.

The forecast accuracy guide helps distinguish prediction error from a replenishment-policy problem. Agree the service objective, inventory-position definition and exception owner, then pilot one repeat-use item. Evaluate observed service and stock together before approving wider parameter changes.