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Record W2060295976 · doi:10.1108/09600030510594549

Modeling the influence of multiple expiration dates on revenue generation in the supply chain

2005· article· en· W2060295976 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Physical Distribution & Logistics Management · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSupply chainExpiration dateFIFO (computing and electronics)Inventory controlStock (firearms)Computer scienceOperations researchRevenueSafety stockHeuristicLead timeStockoutProduct (mathematics)FIFO and LIFO accountingSupply chain managementBusinessOperations managementMarketingEconomicsMathematicsFinance

Abstract

fetched live from OpenAlex

Purpose Inventory control models for perishable products have primarily used a FIFO issuing policy with the objective of minimizing the number of outdated units. This paper aims to develop a model to evaluate an issuing policy for a single product with a fixed shelf life in single echelon inventory system. The issuing policy considers the remaining shelf life of the in‐stock inventory and the expected time that the product will spend in inventory as the decision driver. Design/methodology/approach The model developed has an objective of maximizing expected revenue over time with a budget constraint. A heuristic algorithm is proposed to iteratively arrive at the best solution to the formulation. The heuristic is tested by employing a simulation model of the system. Findings The proposed heuristic is tested against both the FIFO and the random allocation approaches and found to be superior for all the in‐stock with remaining shelf life distribution means of above 40 percent. No significant performance differences were found for the three approaches for remaining shelf life distribution. Research limitations/implications The research is focused on a single product with multiple expiration dates and further research is necessary to determine the best policies for the multi‐product multi‐expiration date environment where the items are substitutable.. Practical implications Retailers stock items with multiple expiration dates. The customer, for obvious reasons, is more likely to choose the item with the longer remaining shelf life. Therefore, the supply to the retailer's shelves and issuing policies for making available the particular items to the customers affect product outdating and related costs. Revenues will be affected by the extent to which more can be charged for items with a longer remaining shelf life or by the impact of the remaining shelf life on demand. This paper provides for a practical approach to that end. Originality/value The proposed issuing policy has not been tested before and thus makes a contribution to the body of knowledge. The flexibility of using different values for acquisition costs, selling prices, salvage value and penalty functions is a particular strength of the proposed model. Moreover, its potential application to inventory control problems for a wide range of perishable products is substantial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.272
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it