Managing demand uncertainty: Probabilistic selling versus inventory substitution

Zhang, Yi; Hua, Guowei; Wang, Shouyang; Zhang, Juliang; Fernández, Vicenç

doi:10.1016/j.ijpe.2017.10.001

Cited by 24 publications

(12 citation statements)

References 35 publications

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“…Those problems are consumer demand, perception, and buying behavior, as well as daily demand prediction and inventory management. Consumer demand, perception, and buying behavior can be approached with a probabilistic system [ 140 , 141 , 142 ], for instance, uncertainty concerning what food products are expected to be bought. Meanwhile, daily demand prediction and inventory management can be addressed with an optimization paradigm [ 143 , 144 ].…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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“…In this vein, Dolgui et al (2013) reported that safety lead times may have a greater impact on firm performance than safety stock, especially for multi-level inventory systems, thereby contributing to firm responsiveness to market demand. Indeed, many factors may influence probabilistic demands, such as the nature of the product, the number of manufacturing system stages (single stage or multiple stages) and serving an unlimited number of customers (Zhang et al , 2018).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Supply chain management and market responsiveness: a simulation study

Aljanabi

Ghafour

2020

JBIM

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Purpose This study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement). Design/methodology/approach Simulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist. Findings The demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research. Originality/value This study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

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“…To establish the optimal quantities of inventory of a product, the demand must be considered over a period of time (days, weeks, months, and years), however, when we obtain this information, the product can have a constant or variable demand; which would hinder the process of setting the optimal quantities need to order. In any case, the responsible of these decisions aims to create and implement techniques, methods and methodologies that allow good inventory management to accurately determine the quantities to be ordered at the right time [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An optimization approach for inventory costs in probabilistic inventory models: A case study

Pulido-Rojano

Pizarro-Rada

Padilla-Polanco

et al. 2020

Ingeniare. Rev. chil. ing.

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Managing demand uncertainty: Probabilistic selling versus inventory substitution

Abstract: Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista International journal of production economics.

Cited by 24 publications

References 35 publications

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Supply chain management and market responsiveness: a simulation study

An optimization approach for inventory costs in probabilistic inventory models: A case study

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