Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile

Rojas, Fernando; Leiva, Víctor; Lillo, Camilo; Pascual, Jimena

doi:10.1371/journal.pone.0212768

Cited by 17 publications

(21 citation statements)

References 49 publications

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“…(ii) Other distributions may be considered for approximating the distribution of the product of two normally distributed random variables; see, for example, [20,21,24,[33][34][35]. (iii) Applications of the new methodology proposed in this investigation can be of interest in diverse areas, where the distribution of the product of two normally distributed random variables is required; see, for example, [28,36]. (iv) If there are two random samples, the first one from the distribution of the product of two normally distributed random variables, and the second one from the fitted extended skew-normal distribution, and then some non-parametric tests can be used to compare them.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Approximating the Distribution of the Product of Two Normally Distributed Random Variables

Seijas-Macias

Oliveira

et al. 2020

Symmetry

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The distribution of the product of two normally distributed random variables has been an open problem from the early years in the XXth century. First approaches tried to determinate the mathematical and statistical properties of the distribution of such a product using different types of functions. Recently, an improvement in computational techniques has performed new approaches for calculating related integrals by using numerical integration. Another approach is to adopt any other distribution to approximate the probability density function of this product. The skew-normal distribution is a generalization of the normal distribution which considers skewness making it flexible. In this work, we approximate the distribution of the product of two normally distributed random variables using a type of skew-normal distribution. The influence of the parameters of the two normal distributions on the approximation is explored. When one of the normally distributed variables has an inverse coefficient of variation greater than one, our approximation performs better than when both normally distributed variables have inverse coefficients of variation less than one. A graphical analysis visually shows the superiority of our approach in relation to other approaches proposed in the literature on the topic.

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Section: Conclusion and Future Researchmentioning

confidence: 99%

Approximating the Distribution of the Product of Two Normally Distributed Random Variables

Seijas-Macias

Oliveira

et al. 2020

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

“…This particular frequency in demand could impact different company strategies (Pavlas et al, 2017;Devika et al, 2016), such as the use of inventory models (Jonkman, Barbosa-Póvoa & Bloemhof, 2019). Inventory models reduce costs and establish optimal stock levels in order to meet the demand for components and final products for customers (Rojas et al, 2019). However, in models predicting zero demand, its parameters are not calculated the same because demand is characterized by intermittency, and the particularities of the intermittent demand need to be considered to develop more accurate inventory models (Gregersen & Hansen, 2018).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand

Rojas

Coluccio

Vega-Vargas

et al. 2020

PeerJ Computer Science

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston’s method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

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“…This particular frequency in demand could impact different company strategies (Pavlas et al, 2017;Devika et al, 2016), such as the use of inventory models (Jonkman et al, 2019). Inventory models reduce costs and establish optimal stock levels in order to meet the demand for components and final products for customers (Rojas et al, 2019). However, in models predicting zero demand, its parameters are not calculated the same because demand is characterized by intermittency, and the particularities of the intermittent demand need to be considered to develop more accurate inventory models (Gregersen and Hansen, 2018).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Peer Review #3 of "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand (v0.1)"

2020

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zeroinflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

show abstract

Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile

Cited by 17 publications

References 49 publications

Approximating the Distribution of the Product of Two Normally Distributed Random Variables

Approximating the Distribution of the Product of Two Normally Distributed Random Variables

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand

Peer Review #3 of "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand (v0.1)"

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