Mitigating the impact of demand disruption on perishable inventory in a two-warehouse system

Ranveer Singh Rana, Dinesh Kumar (), Kanika Prasad and K. Mathiyazhagan
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Ranveer Singh Rana: National Institute of Technology Jamshedpur
Dinesh Kumar: National Institute of Technology Jamshedpur
Kanika Prasad: National Institute of Technology Jamshedpur
K. Mathiyazhagan: Thiagarajar School of Management

Operations Management Research, 2024, vol. 17, issue 2, No 5, 469-504

Abstract: Abstract There can be various causes of demand disruption like pandemic outbreaks, war situations, natural calamities, customer preference change, etc. Recently, the world witnessed one of the biggest disruptive events: the COVID-19 pandemic. Many times, when things seemed to be returning to normal, a new wave of pandemic arrived. Another disruptive event appeared in the form of Russia Ukraine war. As people avoid going out of their homes during such events, demand for a product is likely to go down. During the COVID-19 lockdown, demand for a normal product also vanished due to stringent rules imposed by the government. The novelty of the present work lies in collaborations of realistic aspects like demand disruption, both sudden fall and rise, time-dependent demand, stochastic two-parameter Weibull distribution deterioration rate, trade credit financing, and partial backlogged demand during stock out condition and also the validation of the model with real data sets in a two-warehouse inventory system, i.e., distributor possesses two warehouses one is owned by the distributor is called as an owned warehouse (OW) and another warehouse is hired on rent by the distributor called as a rented warehouse (RW). According to the author's knowledge, this work has not been completed previously. The proposed work determines an optimum lot size and optimum backlogged quantity to maximize profit under such a disruptive environment. According to the duration of the disruption, two distinct scenarios are developed to illustrate the impact of disruption duration on total average profit. The model is validated with numerical examples, and concavity is illustrated graphically. To derive critical theoretical managerial implications, a sensitivity analysis is conducted with respect to significant parameters. This study ends with a conclusion, followed by a discussion of the limitations of the current study and the potential directions for future research.

Keywords: Demand disruptions; Covid-19 lockdown; War; Trade credit; Partial backlogging; Perishable inventory (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12063-023-00418-4

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