Min–Max Inventory: When to Reorder and How Much

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A bin is running low, so the buyer orders another batch. Meanwhile, production is already making a replenishment order for the same item. Both decisions look sensible on their own. Together, they create stock the factory did not need.

Min–max inventory gives replenishment a repeatable rule: a lower level tells you when to act, and an upper target tells you how far to replenish. To use it well in manufacturing, you need to count stock already on its way and demand already committed, not just the pieces on the shelf.

Then comes the production decision. If you make the replenishment yourself, the quantity needs time on real machines alongside customer work. SkyPlanner APS brings that approved replenishment into the same capacity- and material-aware schedule, so you can see when it will finish without building a separate queue by hand.

What is min–max inventory?

Min–max inventory is a replenishment policy with two parameters for each stocked item:

• Minimum: the threshold that triggers a replenishment decision.
• Maximum: the target inventory position you replenish up to.

The minimum is not the point at which you intend to run out. It should give replenishment time to arrive while demand continues. The maximum is not the quantity you order each time, nor a promise that physical stock can never exceed that level.

Under a min–max policy, the order quantity varies with the gap to the target. If the target is 600 pieces and the relevant inventory position is 230, the starting replenishment quantity is 370. If the position is 280, it is 320 instead.

Write down whether your trigger is “below minimum” or “at or below minimum”; systems and company policies can differ at that boundary. In the examples below, replenishment is triggered when the position is below the minimum.

Count inventory position, not just stock on hand

Stock on hand tells you what is physically present. Inventory position helps you decide whether another replenishment order is needed after accounting for existing supply and obligations.

For a policy that nets committed demand, use:

Inventory position = usable stock on hand + open replenishment supply − included committed demand

Open supply can include outstanding purchase orders, transfers or in-house replenishment orders. Include each source once. Count the remaining quantity still due, not the original order quantity after a partial receipt.

Define the scope before calculating: one item, one location or agreed group of locations, one unit of measure, and consistent supply and demand cutoffs. Exclude quarantined or otherwise unusable stock. Do not subtract the same commitment twice: if your starting balance is already net of reservations, do not deduct those reservations again.

Not every implementation nets the same demand categories. The important point is to know what your figure includes and use thresholds designed for that basis. A minimum calculated for one definition cannot safely be applied to another without review.

A manufacturing min–max example

Imagine a manufacturer holding a standard bracket for repeat orders. Its minimum is 300 pieces and its maximum target is 600. The policy nets committed demand within an agreed planning window.

Input	Quantity	Treatment
Usable brackets on hand	180	Include as current stock
Outstanding replenishment already approved	200	Add once as open supply
Committed demand included in this review	150	Subtract once
Inventory position	230	Compare with the minimum of 300
New replenishment quantity	370	Raise the position from 230 to 600

The calculation is 180 + 200 − 150 = 230. Because 230 is below 300, the policy calls for replenishment. The starting order quantity is 600 − 230 = 370 pieces.

Ignoring the existing 200-piece order would produce a 570-piece proposal. You would order 200 pieces twice for the same requirement. Conversely, ignoring the committed demand would show a position of 380 and miss the trigger entirely.

The target describes the net position after adding the new order, not necessarily the stock you will see when it arrives. Consumption continues while orders are being purchased or made.

An order on the way can still arrive too late

Inventory position answers the quantity question; it does not, by itself, prove that every requirement is covered on its need date.

In the bracket example, suppose 120 pieces are needed on Tuesday and another 30 on Wednesday. The 180 usable pieces cover both. But if all 200 pieces from the existing replenishment are delayed until after a new requirement, that later requirement may still be uncovered even though the order is counted in your overall position.

Check the dates as well as the totals. Expediting or rescheduling an existing order may solve the problem more sensibly than creating another full replenishment order. A dated inventory-coverage check complements min–max; it does not replace the policy’s trigger and target.

How to set minimum and maximum inventory levels

Start with a repeatably consumed item and reliable usage and replenishment records. Choose the levels together, but give each one a distinct job.

Set the minimum to cover the replenishment wait

A practical starting point under reasonably steady demand is:

Minimum = expected demand during replenishment lead time + safety stock

Use the time until stock is usable, not just a supplier’s transport time or a machine’s processing time. Approval, purchasing, waiting for production capacity and receiving can all contribute to that wait.

Imagine an item used at 40 pieces per working day. Replenishment takes five working days, and the business has chosen an 80-piece safety buffer. Expected lead-time demand is 200 pieces, giving a starting minimum of 280 pieces.

The 80-piece buffer is an assumption for this example, not a recommended service level. Choose safety stock according to variability and the consequences of a shortage. A cheap component that stops final assembly can deserve more attention than its purchase value suggests.

The review frequency matters too. A rule monitored continuously is not equivalent to a report checked once a week. If the threshold can be crossed just after a review, allow for the additional wait before anybody places the order; otherwise, a seemingly adequate minimum may trigger action too late.

Set the maximum to create a deliberate replenishment cycle

The maximum determines how far you restore inventory position when the trigger is crossed. Raising it generally means larger replenishments and more stock exposure; lowering it generally means more frequent replenishment and more ordering or setup activity.

For the 40-piece-per-day example, suppose you choose a maximum of 600. The gap above the 280-piece minimum is 320 pieces, equivalent to eight working days of that average usage. That is a way to understand the proposed cycle, not proof that 600 is the cheapest or best target.

Test the target against storage space, shelf life, obsolescence risk, working capital and production or supplier batch sizes. If a slow-moving item has a short life, a neat-looking min–max range can still create expensive waste.

Apply ordering constraints before releasing the order

The calculated gap is a starting quantity. Supplier minimums, pack multiples and production batch constraints may change what you can actually order.

If the bracket proposal is 370 pieces but supply comes only in packs of 50, rounding up gives 400. That adds 30 pieces above the unadjusted target. Decide whether to accept that difference, change the supply arrangement or revise the policy. Do not quietly treat “maximum inventory target” and “maximum order quantity” as the same setting: one describes the desired position, the other limits an individual order.

Record approved replenishment promptly as open supply. That is what stops the next review, or the next buyer, from ordering the same gap again.

When min–max works—and when it needs more than thresholds

Min–max is useful for stocked items with recurring consumption and a replenishment process you can monitor: common components, standard subassemblies or frequently used consumables. Its attraction is consistency. People can see why replenishment is needed and how the quantity was chosen.

It becomes less dependable when an average hides large, dated requirements. A component used only in occasional customer batches may need time-phased material planning rather than a standing stock range. An intermittent spare can also need a criticality-based stocking decision; a low average does not tell you how costly a missing spare would be.

Keep the neighboring methods distinct:

• Safety stock is the uncertainty buffer. It can help set the minimum, but does not define the entire min–max policy.
• Economic order quantity (EOQ) calculates a lot size from ordering and holding-cost assumptions. Min–max instead replenishes the changing gap to an upper target.
• Make to stock is the decision to manufacture ahead of customer orders. Min–max is one possible way to control replenishment within that strategy.

Review the levels when consumption, supplier performance, production lead time or product life changes. Investigate repeated stockouts and excess stock before simply widening the range: late receipts, inaccurate balances and duplicate orders need different fixes.

Make approved replenishment fit the production schedule

For an in-house part, “order 370” is only half a decision. It does not tell you whether those brackets will be ready before stock runs short, or which customer work would move to make room.

Imagine the approved replenishment needs the same machining centre as a customer order due this week. Putting it at the top of a separate stock-replenishment list hides that conflict. Giving it a place in the shared schedule makes the trade-off visible while there is still time to decide.

Choose SkyPlanner APS to make that shared schedule easier to manage. Arcturus, its scheduling AI, calculates the work against configured workstations, shifts, priorities, operation dependencies and materials. The Gantt schedule shows where the replenishment fits and when it should finish alongside the other orders. You spend less time reconciling a stock list with individual machine queues, and get a clearer basis for protecting deliveries.

SkyPlanner supports a low quantity alert that prompts you to create a replenishment order. Agree the min–max targets, replenishment quantity and approval with the responsible planner or your ERP/MRP. Then use SkyPlanner to schedule the approved work alongside customer orders, so the stock decision becomes part of the factory’s common production plan.

With material-aware scheduling enabled and the relevant materials maintained, the calculation also accounts for their availability when the consuming work runs. Purchase-order arrival dates support the schedule for bought-in materials. When an arrival date or priority changes, recalculate the common plan to see the consequence rather than manually moving every affected job.

SkyPlanner integrates with any ERP or MES through its open REST API. You can retain the system that manages replenishment policy and connect its approved orders to a production schedule that accounts for real capacity.

What this looks like in make-to-stock production

Opes Corporation manufactures to stock against predictable customer commitments. Before SkyPlanner, it used Excel and a separate warehouse application, with data held in different places. Its phased Visma Nova integration connected that information with production planning.

In the company’s account, SkyPlanner gives the team a clear picture of capacity. When urgent small orders or product-development runs arrive, the updated production plan shows their effect on delivery times. Changes can be made faster, and planners can see how the work fits together.

That is the relevant lesson for replenishment: stable stock demand does not remove the need to manage exceptions. A shared schedule lets the team judge additional work against existing commitments instead of managing each queue in isolation.

Test the policy with one real replenishment

Choose one repeat-use item. Confirm its usable balance, outstanding supply, included commitments and lead time. Set the trigger and target on that basis, calculate the gap, and check when the quantity is actually needed.

Then bring the approved replenishment and a competing customer order to a SkyPlanner demo. Ask to see how their routes share capacity, when replenishment finishes and what happens if a material arrival slips. That shows what a production-ready replenishment decision looks like beyond the stock calculation.

Book a SkyPlanner demo to explore your factory’s situation, or try SkyPlanner with demo data first. The aim is straightforward: replenish what you need, avoid ordering it twice, and give the work a schedule your factory can follow.