Inventory Management and Production Planning and Scheduling¶
Silver, E. A., Pyke, & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling. Wiley.
Retired. This is not a bibliographic entry. Our extractor captured an inline prose definition from an article and stored it as a work. The concept is real; this registry record is a parsing defect. This entry is kept so the citations that pointed at it still resolve, and so the correction is visible rather than silent.
Cited by¶
8 citations across 8 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Backorder
- Blurring these reads as a single fuzzy "out of stock," but each implies a different inventory model, a different customer-service promise, and a different scorecard — fill rate, backorder rate, average backorder duration
Supported in partVerified against the work's full text
“Complete backordering. Any demand, when [out of stock], is backordered and filled as soon as”
- Blurring these reads as a single fuzzy "out of stock," but each implies a different inventory model, a different customer-service promise, and a different scorecard — fill rate, backorder rate, average backorder duration
- Economic Order Quantity
- The most practically important structural property of the solution is its flatness: total cost is very insensitive to departures from the optimum, so estimates of K and h that are rough still yield near-optimal decisions
This sourceThe textbook statement that inventory total cost is insensitive to errors in cost measurement and to selecting a size other than the exact optimum. The textbook's flat-bottomed optimum: reasonable-sized deviations from the EOQ leave total cost little changed.
Supported in partVerified against a saved copy of the source
“planning and scheduling are relatively {{{insensitive}}} to errors in cost measurement.”
- The most practically important structural property of the solution is its flatness: total cost is very insensitive to departures from the optimum, so estimates of K and h that are rough still yield near-optimal decisions
- Min–Max Inventory
- The policy is formally related to the (s, S) policy in operations research — where s is the reorder point and S the order-up-to level
This sourceStandard reference text defining the (s, S) policy with s as the reorder point and S as the order-up-to level, and treating min-max as its practitioner formulation.
- The policy is formally related to the (s, S) policy in operations research — where s is the reorder point and S the order-up-to level
- Order-Batching Distortion
- The intervention catalog ports the same way — vendor-managed inventory, continuous replenishment, EDI/shared point-of-sale signal, smaller more-frequent orders, exchange-curve batch sizing
This sourceInstructor's guide accompanying an inventory-management textbook, noting that smoother ordering facilitates more frequent orders and smaller order quantities.
Supported in partVerified against the work's full text
“will facilitate less congestion, and therefore [more frequent] orders and smaller order quantities.”
- The intervention catalog ports the same way — vendor-managed inventory, continuous replenishment, EDI/shared point-of-sale signal, smaller more-frequent orders, exchange-curve batch sizing
- Service Level
- The implication is non-linear: the resource required to move from a 95% to a 99% service level is much larger than the resource required to move from 90% to 95%, because each increment reaches further into a heavier tail of the demand distribution
This sourceThe safety-factor relation behind the sentence - reserve scales with the standard-normal quantile of the target service level - which is what makes each further increment of service cost disproportionately more. The standard treatment of inventory service levels, with the reorder policies, single-period models and safety-stock functions the bullet names set out as the field's working machinery. The safety-stock rule the worked figures apply: reserve equals the standard-normal quantile at the target cycle service level times the standard deviation of lead-time demand.
Supported in partVerified against the source
- The implication is non-linear: the resource required to move from a 95% to a 99% service level is much larger than the resource required to move from 90% to 95%, because each increment reaches further into a heavier tail of the demand distribution
- Stockout
- As an event it is binary at the SKU level — a particular item either has stock or it does not — but its cost structure is the source of most complexity in inventory theory: stockout cost is heterogeneous and partly unobservable, combining a direct lost-sale or backorder-fulfillment cost with harder-to-measure components — customer defection to competitors, lost goodwill, substitution to lower-margin items, and regulatory or safety exposure in contexts where the item is critical
This sourceThe treatment of shortage cost as a modelling parameter to be established and the item-dependence of the backorder-versus-lost-sale split (a person is unlikely to backorder a loaf of bread). The cycle service level (P1) as the probability of no stockout per replenishment cycle, with fill rate kept distinct as the percent of demand met from stock.
Supported in partVerified against a saved copy of the source
“To determine the {{{shortage}}} cost, we need to establish”
- As an event it is binary at the SKU level — a particular item either has stock or it does not — but its cost structure is the source of most complexity in inventory theory: stockout cost is heterogeneous and partly unobservable, combining a direct lost-sale or backorder-fulfillment cost with harder-to-measure components — customer defection to competitors, lost goodwill, substitution to lower-margin items, and regulatory or safety exposure in contexts where the item is critical
Mechanisms¶
- Inventory Allocation Policy
- Its failure mode is optimizing a metric that hides who is starved: a policy tuned purely to maximize overall fill rate will happily serve many small easy orders while a few critical accounts go unfilled, because fill rate counts units, not importance.
This sourceDefines fill rate as the fraction of demand units supplied directly from stock, an aggregate quantity-based service measure.
- Its failure mode is optimizing a metric that hides who is starved: a policy tuned purely to maximize overall fill rate will happily serve many small easy orders while a few critical accounts go unfilled, because fill rate counts units, not importance.
- Threshold-Based Correction
- That makes it ideal when acting is costly or disruptive and small deviations are truly benign — the logic behind reorder points and much of alarm design.
This sourceModels replenishment policies in which ordering has an explicit cost and a reorder point triggers the next order.
- That makes it ideal when acting is costly or disruptive and small deviations are truly benign — the logic behind reorder points and much of alarm design.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Read against the text for 5 of 8 citations: 5 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:a98e63fa0a4d · see in the full table