Supply Chain & Inventory Management¶

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Abstractions about coordinating production, inventory and fulfillment, including order policies like just-in-time and economic order quantity, decoupling points such as make-to-order versus make-to-stock, and failure modes like stockouts and order-batching distortion.

28 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

Accelerator Effect — The macroeconomic mechanism by which a change in the level of consumer demand produces a proportionally larger swing in investment, because desired capital tracks output at a fixed ratio — so it is the rate of change of demand, not the level, that drives capital ordering.
Assemble-to-order system — An assemble-to-order system holds components in inventory and assembles products only after orders arrive, coupling shared-part stocking with demand uncertainty.
Available-to-Promise — Gate every new delivery commitment against a date-bucketed residual curve — on-hand plus scheduled receipts minus prior promises — so a request is accepted only if no future bucket goes negative, distinguishing what is physically present from what is contractually free.
Backorder — The fulfilment arrangement in which an order is accepted as a binding obligation despite a stock-out, becoming a queued claim against future supply tracked in a visible ledger and clocked against an estimated resupply, rather than being refused as a lost sale.
Build Trap — Diagnose a team that ships at high velocity yet moves no business outcome — because its measurement, calendar, and incentives are all set to what was built rather than to whether it mattered — by reading those three dials and asking what outcome the team owns.
Bullwhip Effect — Excess variability in replenishment orders relative to downstream sales or nearer-tier orders as demand information travels upstream through a supply chain.
Buzen's Algorithm — A station-by-station dynamic program for the normalization constant of a finite-population, closed product-form queueing network.
Cone of Uncertainty — Model how the plausible error range of a project estimate depends on the maturity of information defining its scope.
Cumulative Dose — Locate biological harm or benefit in the time-integral of an exposure stream rather than any single event, so a course of individually safe doses can still cross a threshold on the running stock — which reducing the present rate cannot undo.
Cutting Stock Problem — An optimization problem that chooses feasible cutting patterns for available stock and their use counts to meet item demands while minimizing a declared stock-use, cost, or waste objective.
Economic Order Quantity — Find the replenishment batch size minimizing total cost by summing a per-event ordering cost that falls with batch size and a per-unit-time holding cost that rises with it, giving a U-shaped curve whose flat-bottomed optimum is √(2DK/h).
Forecast Attainment — An aggregate planning ratio comparing realized shipments, demand, or supply for a period with the forecast snapshot selected at a policy-defined lag, used to expose overall under- or over-attainment without measuring item-level forecast accuracy.
Just-in-Time — Replenish each production stage only when the next one consumes its input, deliberately shrinking buffers so disruption propagates instead of hiding — a coupling that pays off only when paired with the variability-reduction work that earns it.
Make-to-Order — Begin conversion of inputs into a finished good only after a confirmed customer order, placing the forecast-driven/order-driven decoupling point at the upstream end of conversion, short of engineer-to-order, so finished-goods inventory vanishes and the customer absorbs the full conversion-cycle wait.
Make-to-Stock — Produce finished goods ahead of any order against an aggregate forecast, placing the decoupling point as far downstream as it will go, so customer wait collapses to the order-to-pick interval at the price of holding cost and a binding forecast bet.
Mass Customization — Mass customization uses repeatable production or delivery to meet individual customer requirements without making every order a wholly bespoke project.
Min–Max Inventory — An inventory policy that manages stock inside a declared band — replenishing up to a maximum ceiling only once on-hand falls to a minimum reorder point, and doing nothing in between — collapsing continuous order decisions into two scalars per item.
Order-Batching Distortion — The supply-chain failure in which a lumpy ordering rule converts smooth downstream consumption into artificial order spikes and troughs, which each upstream tier misreads as real demand and amplifies — one of the four canonical causes of the bullwhip effect.
Perfect Order — Read fulfillment quality as one customer-facing scalar — the product of complete, on-time, undamaged, and correctly-documented sub-rates — so multiplicative decay (four links at 95% giving 81%) becomes visible and the binding sub-rate is read off directly.
Pinch analysis — A thermodynamic design methodology that sets minimum hot and cold utility targets and restructures heat recovery around a pinch temperature constraint.
S&OP Disconnect — Diagnose a firm's downstream execution crises as an upstream planning defect — separate functions each holding an internally sound but mutually incompatible plan for the same future, because no binding forum reconciles them before commitments are taken.
Service Level — Commit in advance to fulfilling a stated quantile of stochastic demand — not its average — within a stated window with a breach consequence, and size the system's buffer against that tail, so each additional nine costs disproportionately more reserve.
Stacker Crane Problem — A least-cost closed-tour problem for one unit-load carrier serving fixed directed pickup-to-delivery requests in an order it may choose.
Stockout — Pin inventory's central failure to a precise binary event — on-hand stock for one SKU at one location reaching zero while demand still arrives — whose heterogeneous, mostly-unobservable cost, weighed against holding cost, drives every safety-stock and service-level decision.
Supplier Concentration Risk — Exposure that arises when a buyer's dependency for a critical input rests on so few suppliers that one node's disruption propagates downstream faster than alternatives can be qualified — a shape property of the dependency distribution, not of any supplier's performance.
Vendor-Managed Inventory — Shift four coupled properties — consumption data, replenishment authority, stock ownership, and risk — upstream from buyer to supplier as a single bundle, dampening the bullwhip effect at the cost of a principal-agent gap that governance metrics must close.
Warehouse Slotting Failure — Diagnose a chronically slow but findable warehouse as a layout misaligned with demand flow — fast SKUs stranded in high-access-cost positions — by checking whether the position-cost ordering and the SKU-velocity ordering have drifted apart.
Wave Picking — Release warehouse orders not on arrival but in timed, compatibility-grouped cohorts, deliberately accepting a bounded latency wait so that picking, packing, and loading run synchronously against the same wave and downstream stations stay neither starved nor overwhelmed.