Joint Inventory and Pricing Decisions for an Assortment

Aydın, Göker; Porteus, Evan L.

doi:10.1287/opre.1080.0562

Cited by 158 publications

(87 citation statements)

References 23 publications

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“…The above theorem uses the unimodal concept (see, for example, Aydin and Porteus (2008)) to establish the necessary and sufficient condition for q * . See proof of Theorem 1 for details.…”

Section: Dual Sourcingmentioning

confidence: 99%

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Wang

Gilland

Tomlin

2010

M&SOM

325

202

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Surveys suggest that supply chain risk is a growing issue for executives and that supplier reliability is of particular concern. A common mitigation strategy observed in practice is for the buying firm to expend effort improving the reliability of its supply base. We explore a model in which a firm can source from multiple suppliers and/or exert effort to improve supplier reliability. For both random capacity and random yield types of supply uncertainty, we propose a model of process improvement in which improvement efforts (if successful) increase supplier reliability in the sense that the delivered quantity (for any given order quantity) is stochastically larger after improvement. We characterize the optimal procurement quantities and improvement efforts, and generate important insights. For random capacity, improvement is increasingly favored over dual sourcing as the supplier cost heterogeneity increases but dual sourcing is favored over improvement if the supplier reliability heterogeneity is high. In the random yield model, increasing cost heterogeneity can reduce the attractiveness of improvement, and improvement can be favored over dual sourcing if the reliability heterogeneity is high. A combined strategy (improvement and dual sourcing) can provide significant value if suppliers are very unreliable and/or capacity is low relative to demand.

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“…The above theorem uses the unimodal concept (see, for example, Aydin and Porteus (2008)) to establish the necessary and sufficient condition for q * . See proof of Theorem 1 for details.…”

Section: Dual Sourcingmentioning

confidence: 99%

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Wang

Gilland

Tomlin

2010

M&SOM

325

202

View full text Add to dashboard Cite

show abstract

“…Both of two papers use newsboy setting to search for optimal assortment. The difference is that Maddah and Bish (2007) perform MNL method to model static customer choice [21], while Maddah, Bish and Tarhini (2014) consider a multiplicative-addictive demand model. Aydin and Porteus (2008) also assume a "multiplicative" demand in the assortment and the demand counts on products' attractiveness whose function is its own price, so the parameter of the "multiplicative" demand model is independent of pricing decision.…”

Section: Pricing Inventory and Assortmentmentioning

confidence: 99%

Review on Assortment Problems in Retailing Supply Chain Management

Ji¹,

Liang²

2018

dtssehs

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In this paper, we are aimed at reviewing assortment problems integrating pricing strategies, inventory levels and distribution network planning in retail. Through the literature study, we present a comprehensive and updated review and put forward the future directions for assortment study. The main methodology we used is literature study. We classified related papers and reviewed them by different themes. We find that a great number of existing papers related to assortment problems mostly combine the problem of pricing decision, inventory control and distribution design. However, those papers tend to study two or three points above. In other words, there is no literature studying all points simultaneously. Given our findings, we finally propose that it is innovative and valuable to put efforts to study assortment problems combined with pricing, inventory and distribution decisions.

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“…There are some exceptions: Mahajan and van Ryzin (2001) show that when substitution is stockout-based, the problem quickly becomes intractable and profits are no longer concave in inventory levels; Honhon et al (2010) propose a dynamic programming formulation to find the optimal stocking levels when there is a fixed proportion of customer types and random demand; Bernstein et al (2010) dynamically adjust the assortment over time, depending on each customer's preferences and the remaining inventory, so as to "hide" products with low inventories to reserve them for future customers. Furthermore, pricing decisions have also been considered: Aydin and Porteus (2008) study the joint inventory and pricing decision for an assortment. The competitive aspect of these assortment and price decisions has been studied in Besbes and Sauré (2010), under the multinomial logit (MNL) model.…”

Section: Literature Reviewmentioning

confidence: 99%

The Assortment Packing Problem: Multiperiod Assortment Planning for Short-Lived Products

2014

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Motivated by retailers' frequent introduction of new items to refresh product lines and maintain their market shares, we present the assortment packing problem in which a firm must decide, in advance, the release date of each product in a given collection over a selling season. Our formulation models the trade-offs among profit margins, preference weights, and limited life cycles. A key aspect of the problem is that each product is short-lived in the sense that, once introduced, its attractiveness lasts only a few periods and vanishes over time. The objective is to determine when to introduce each product to maximize the total profit over the selling season. Even for two periods, the corresponding optimization problem is shown to be NP-complete. As a result, we study a continuous relaxation of the problem that approximates the problem well, when the number of products is large. When margins are identical and product preferences decay exponentially, its solution can be characterized: it is optimal to introduce products with slower decays earlier. The structural properties of the relaxation also help us to develop several heuristics, for which we establish performance guarantees. We test our heuristics with data on sales and release dates of woman handbags from an accessories retailer. The numerical experiments show that the heuristics perform very well and can yield significant improvements in profitability.

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Joint Inventory and Pricing Decisions for an Assortment

Cited by 158 publications

References 23 publications

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Review on Assortment Problems in Retailing Supply Chain Management

The Assortment Packing Problem: Multiperiod Assortment Planning for Short-Lived Products

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