Simulation-Based Inventory Management of Perishable Products Via Linear Discrete Choice Models

Gioia, Daniele Giovanni; Felizardo, Leonardo Kanashiro; Brandimarte, Paolo

doi:10.2139/ssrn.4207541

Cited by 1 publication

(2 citation statements)

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“…Most of them are conducted to aim for several objectives such as cost minimization, profit maximization, service level improvement, and planning accuracy. To achieve those goals, the studies did experiments on several aspects such as inventory or ordering policy and strategy for perishable items (Gioia et al, 2023;Guo et al, 2018;Gong et al, 2022), retail products (Rios and Vera, 2023;Sridhar et al, 2021), and spare parts or OEM items (Rinaldi et al, 2023;Zhang et al, 2019). The other aspects that are being changed include inventory level (Wolbert, 2013;Ohmori and Yoshimoto, 2020), inventory allocation (Bretthauer et al, 2010), product pricing (Rios and Vera, 2023), and supplier selection (Wibowo et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Guo et al (2018) utilized a risk management framework and used conditional value at risk (CVaR) in their study. Some other more popular approaches to solve inventory problems are optimization through various methods (Gong et al, 2022;Zhang et al, 2019;Dong et al, 2011), simulation (Sridhar et al, 2021;Rinaldi et al, 2023), and combination of simulation and optimization or Sim-Opt (Gioia et al, 2023;Wibowo et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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Predictive Analytics to Improve Inventory Performance: A Case Study of an FMCG Company

Suwignjo,

Panjaitan,

Baihaqy

et al. 2023

OSCM: An Int. Journal

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Predictive analytics is a methodology used to predict the outcome of future events with the use of historical data. Predictive analytics comes in very handy in various fields such as finance, manufacturing, healthcare, and even supply chain. Not only in those fields, but predictive analytics is also useful in managing inventory. However, we find that there is a lack of studies focusing on the implementation of predictive analytics to predict inventory status (overstock, understock) by considering inventory level and demand forecast. This study is inspired by a real-world problem at one of the largest FMCG companies in Indonesia. With so many product types to manage, this company often faces problems of understocked and overstocked inventory. This study attempts to solve that problem by employing big data and predictive analytics approaches. The gradient boosting model is used because it is an improvement of the decision tree model. The data that are used as predictors are inventory level, inventory week cover, historical sales, and demand forecast. The target variable for classification is inventory status which is divided into three classes, namely understock, normal, and overstock. Meanwhile, the target variable for the regression model is the amount of understock/overstock. The result of the classification model has an accuracy of 0.84 for category 1 products, 0.76 for category 2 products, and 0.74 for category 3 products. While the result of the regression model is an R 2 of 0.89 for category 1 products, 0.76 for category 2 products, and 0.74 for category 3 products. The data that comes from the prediction model are visualized in a dashboard. The visualization dashboard displays the data using heatmaps and line graphs, so the information can be used for further analysis.

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