Dynamic pricing and inventory control for multiple products in a retail chain

Riosa, John H.; Vera, Jorge

doi:10.1016/j.cie.2023.109065

Cited by 9 publications

(2 citation statements)

References 46 publications

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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%

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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Section: Literature Reviewmentioning

confidence: 99%

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

Suwignjo,

Panjaitan,

Baihaqy

et al. 2023

OSCM: An Int. Journal

View full text Add to dashboard Cite

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“…Because consumers have different criteria for judging the purchase price of commodities in different sales periods, their purchase behavior is not merely related to the price and quality of commodities, but also the dynamic reference price [32,33]. Rios et al [34] provided a solution for retailers' dynamic pricing in the actual environment by establishing an optimization mechanism for seasonal commodity sales to develop the optimal dynamic pricing and inventory strategy. David et al [35] studied the relationship between reference price, demand, and profit based on a dynamic pricing mechanism.…”

Section: Presale Strategies and Pricing Mechanismmentioning

confidence: 99%

Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism

Liang

Dong-jun

et al. 2023

Sustainability

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Despite the emergence of a presale mechanism that reduces manufacturing and ordering risks for retailers, optimizing the real-time pricing strategy in this mechanism and unknown demand environment remains an unsolved issue. Consequently, we propose an automatic real-time pricing system for e-retailers under the inventory backlog impact in the presale mode, using deep reinforcement learning technology based on the Dueling DQN algorithm. This system models the multicycle pricing problem with a finite sales horizon as a Markov decision process (MDP) to cope with the uncertain environment. We train and evaluate the proposed environment and agent in a simulation environment and compare it with two tabular reinforcement learning algorithms (Q-learning and SARSA). The computational results demonstrate that our proposed real-time pricing learning framework for joint inventory impact can effectively maximize retailers’ profits and has universal applicability to a wide range of presale models. Furthermore, according to a series of experiments, we find that retailers should not neglect the impact of the presale or previous prices on consumers’ purchase behavior. If consumers pay more attention to past prices, the retailer must decrease the current price. When the cost of inventory backlog increases, they need to offer deeper discounts in the early selling period. Additionally, introducing blockchain technology can improve the transparency of commodity traceability information, thus increasing consumer demand for purchase.

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WITHDRAWN: Intelligent decision support system for optimizing inventory management under stochastic events

Bao Long,

Kim,

Cuong

et al. 2024

Applied Soft Computing

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Dynamic pricing and inventory control for multiple products in a retail chain

Cited by 9 publications

References 46 publications

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

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

Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism

WITHDRAWN: Intelligent decision support system for optimizing inventory management under stochastic events

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