Managing Perishable Inventory Systems with Age‐differentiated Demand

Chen, Shouchang; Li, Yanzhi David; Yang, Yi; Zhou, Weihua

doi:10.1111/poms.13481

Cited by 10 publications

(7 citation statements)

References 37 publications

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“…Naturally, our model and results are significantly different compared to Abouee-Mehrizi et al. (2019) and Chen et al. (2021).…”

Section: Literature Reviewmentioning

confidence: 77%

Managing Replenishment and Clearance of Perishables: Last-In, First-Out (LIFO) Issuing Policy and Age-Sensitive Demand

Goyal,

Sapra

2024

Production and Operations Management

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In supermarkets, customers choose the unit(s) they purchase, which leads to inventory being sold in a last-in, first-out (LIFO) order for perishable products. Furthermore, for such products, the demand for inventory may depend on its age since customers may choose to walk away if the freshest available inventory is too old for them. Despite the widespread occurrence of LIFO-based inventory systems, few studies have analyzed them. In this study, we contribute by developing insights on joint replenishment and clearance policy for a perishable product with a general, finite lifetime using a periodic review model such that the inventory is sold in a LIFO order and demand is age-dependent. The model seeks to optimize two decisions every period: how much of fresh inventory to order and how much of existing inventory to clear. A key objective of the model is to understand the effect of age-dependence of demand on the optimal replenishment and clearance policy. We find that the optimal clearance policy for all but the oldest inventory is a multiindex policy such that between any pair of adjacent indexes, either no inventory is cleared or all the inventory is cleared till the lower index. For the oldest inventory, we show that the optimal policy may have multiple forms depending on how sensitive demand is to inventory’s age. This is a notable result in inventory theory since parameters usually affect only the policy value(s) and not the policy structure. Since the optimal policy has a complex structure in general, which makes it difficult to compute, we develop an efficient heuristic to compute the replenishment and clearance quantities. The heuristic underperforms the optimal policy by [Formula: see text]0.92% on average.

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“…Naturally, our model and results are significantly different compared to Abouee-Mehrizi et al. (2019) and Chen et al. (2021).…”

Section: Literature Reviewmentioning

confidence: 77%

Managing Replenishment and Clearance of Perishables: Last-In, First-Out (LIFO) Issuing Policy and Age-Sensitive Demand

Goyal,

Sapra

2024

Production and Operations Management

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“…Dobson et al. (2017) and Chen et al. (2021b) point out that different in-store consumer groups have different thresholds of product freshness acceptability, while Lowalekar et al.…”

Section: Findings: Ocpi Performance Measures and Influencing Factorsmentioning

confidence: 99%

“…For in-store consumers, there are various product picking behaviors reported in the literature. Dobson et al (2017) and Chen et al (2021b) point out that different in-store consumer groups have different thresholds of product freshness acceptability, while Lowalekar et al (2016) consider the in-store consumers' freshness preferences random. More empirical data also indicate that not all in-store consumers search for and pick the freshest itemse.g.…”

Section: Order Fulfillment Process and Performance Measuresmentioning

confidence: 99%

Grocery omnichannel perishable inventories: performance measures and influencing factors

Saghiri

Aktaş

Mohammadipour

2023

IJOPM

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PurposePerishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures.Design/methodology/approachThe inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation.FindingsProduct waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects.Practical implicationsTo manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products.Originality/valueThis paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors.

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“…Ekspektasi matematika merupakan suatu nilai yang diharapkan dari hasil suatu percobaan yang dinyatakan secara matematis [11]. Dalam pengoptimalan produk dan biaya dapat dilihat berdasarkan jumlah pemesanan produk yang dapat berpengaruh terhadap besarnya biaya pemesanan dan persediaan akan berpengaruh terhadap besarnya biaya penyimpanan produk [12]. Pada penelitian ini model inventori dengan waktu diskret dan leadtime diterapkan pada permasalahan daging beku di perum BULOG Divisi Regional (Divre) Sumatera Selatan dan Bangka Belitung (Sumsel Babel).…”

Section: Pendahuluanunclassified

Penerapan Model Inventori dengan Waktu Diskret dan Leadtime Pada Permasalahan Persediaan Daging Beku

Susanti¹,

Zayanti

Cahyono³

et al. 2023

JSMS

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Manajemen persediaan diperlukan untuk menjamin ketersediaan produk dan meminimumkan resiko kerugian terutama untuk produk yang mudah rusak. Penelitian ini bertujuan untuk menentukan persediaan optimal daging beku yang meminimumkan total biaya menggunakan metode matriks analitik. Konsep Quasi-Birth-Death dengan waktu diskrit dan leadtime diterapkan untuk perhitungan matriks analitik. Diperoleh hasil adanya peningkatan terhadap tingkat persediaan di setiap periode persediaan dengan peningkatan rata-rata sebesar 2,4336% dan peningkatan rata-rata total biaya sebesar 17,2056%.

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Managing Perishable Inventory Systems with Age‐differentiated Demand

Cited by 10 publications

References 37 publications

Managing Replenishment and Clearance of Perishables: Last-In, First-Out (LIFO) Issuing Policy and Age-Sensitive Demand

Managing Replenishment and Clearance of Perishables: Last-In, First-Out (LIFO) Issuing Policy and Age-Sensitive Demand

Grocery omnichannel perishable inventories: performance measures and influencing factors

Penerapan Model Inventori dengan Waktu Diskret dan Leadtime Pada Permasalahan Persediaan Daging Beku

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