Inventory pooling decisions under demand scenarios in times of COVID-19

Abstract

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. The aim of this research is to propose a novel inventory pooling model to help determine order sizes and safety inventories in local hospital warehouses. The current study attempts to portray the availability of pharmaceutical items in public hospitals facing COVID-19 challenges. Different from previous studies, this research builds upon the consecrated theory of inventory pooling, extending it to pandemic circumstances where the intractability of kurtosis and skewness in inventory models are critical issues for making sure that medicines have high availability at a low cost. These effects on the total cost of inventory are explored and compared to a supply system with no consolidation. A continuous-review model is assumed with allocation rules for centralization and regular transshipment given different skewness and kurtosis structures for the demand, describing them by the copula method. This method models a multivariate demand considering that the marginal distributions of the demand can be specified by the Generalized Additive Model for Location, Scale and Shape, which offers advantages to model demands considering virtually any marginal statistical distribution. Numerical simulations and an illustrative example show that distributions of demands with more negative skewness and high kurtosis favor to a greater extent obtaining lower total costs with regular supply transshipment systems. Our study points out important considerations for supply chain decision makers when having demands with skewness and kurtosis patterns.

Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review

“…In the reviewed papers, Shirazi et al [ 64 ] provided a four-echelon SC finding for blood collection centers and determining the allocation of temporary or permanent facilities to hospitals. Fernando Rojas also analyzed how the skewness and kurtosis of the distribution of demand data, obtained through sensors, affect the modeling of stocks of hospital pharmacies' COVID-19 treatment-related products [ 65 ]. Due to their inherent resilience, short-term disruptions have little impact on SC operations and performance but long-term disruptions can be highly devastating to supply chain efficiency, service levels, and customer satisfaction.…”

“… 25 Cristian Salazar-Concha (2021) [ 132 ] Republic of Chile Proposing an approach to prediction of blood donation intent Blood supply chain Decision Tree Algorithm - Artificial intelligence The findings demonstrate that the intention to donate blood again can be predicted with 84.17% accuracy. 26 Fernando Rojas (2021) [ 65 ] Republic of Chile Evaluating how the distribution of data collected data by sensors influence the modeling of hospital pharmacy product inventories Drug supply chain Simulation Researchers conclude that the demand statistical distribution's asymmetry does not affect hospital pharmacy product inventories. 27 Ilhaam A. Omar (2021) [ 133 ] United Arab Emirates Proposing a block chain solution by smart contracts to automate the contract process for Group Purchasing Organizations (GPO) Healthcare delivery strategy (Medical supply chain) Block chain The analysis shows that the suggested approach is feasible because the stakeholders pay a small transaction fee.…”

Robust compromise multicriteria decision model to evaluate the level of achievement of graduation profile

“…Readers interested in using copulas to describe correlated random variables can refer to Rojas et al (2021). This approach (copula) allowed us to generate 1000 random scenarios with uniform statistical distributions between an interval of 0.5 to 4.5 (rounded to 0 decimal), to obtain a vector with 5 dimensions of simulated scores on a Likert scale ordered from 1 to 4, to establish the level of achievement of the graduation profile of a cohort of 50 individuals (length of sample simulated), where 1 = acceptable level of achievement, 2 = good level of achievement, 3 = very good level of achievement, and 4 = level of achievement of excellence, qualified by each of the stakeholders evaluating the training process:…”

Impact of Lean Tools in the Supply Chain: Pharmaceutical Industry Perspective

“…Socal et al [22] demonstrate that Covid-19 led to drug shortages due to sudden and unexpected increases in the demand, which exceeded the production capacity of manufacturers. Similarly, the demand patterns for these products have undergone major changes, and have materialized, in some cases, in increases in the quantity demanded, while, in others, in abrupt decreases in the quantities patients required [23]. According to Ayati et al [24], most of these products were supplied by China and India, which are the world's largest distributors of pharmaceutical ingredients; this caused a decrease in production and contributed to drug shortages and to the increase of product prices.…”