Assortment Planning and Inventory Decisions Under a Locational Choice Model

Gaur, Vishal; Honhon, Dorothée

doi:10.1287/mnsc.1060.0580

Cited by 235 publications

(117 citation statements)

References 26 publications

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“…They use a local improvement heuristic based on pairwise interchanges of product variants to modify the assortment and achieve a substantial increase in profit. Cachon et al (2005) and Gaur and Honhon (2006) explicitly account for customer search in the assortment planning process and show that ignoring customer search in demand estimation can lead to sub-optimal assortment decisions and lower expected profits. Smith (2006) generalizes previous research by allowing general heterogeneous customer preferences and by incorporating customer's choice of retailer within the multinomial logit framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Rusmevichientong

Shen

Shmoys

2010

Operations Research

431

240

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The paper considers a stylized model of a dynamic assortment optimization problem, where given a limited capacity constraint, we must decide the assortment of products to offer to customers to maximize the profit. Our model is motivated by the problem faced by retailers of stocking products on a shelf with limited capacities and by the problem of placing a limited number of ads on a web page. We assume that each customer chooses to purchase the product (or to click on the ad) that maximizes her utility. We use the multinomial logit choice model to represent demand. However, we do not know the demand for each product. We can learn the demand distribution by offering different product assortments, observing resulting selections, and inferring the demand distribution from past selections and assortment decisions. We present an adaptive policy for joint parameter estimation and assortment optimization. To evaluate our proposed policy, we define a benchmark profit as the maximum expected profit that we can earn if we know the underlying demand distribution in advance. We show that the running average expected profit generated by our policy converges to the benchmark profit and establish its convergence rate. Numerical experiments based on sales data from an online retailer indicate that our policy performs well, generating over 90% of the optimal profit after less than two days of sales.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Rusmevichientong

Shen

Shmoys

2010

Operations Research

431

240

View full text Add to dashboard Cite

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“…They also discuss the implementation of the model at a supermarket chain. Gaur and Honhon (2005) study a single-period assortment planning and inventory management problem that uses a locational choice model of consumer choice. They obtain bounds on profit when customers dynamically substitute and identify conditions under which static substitution serves as a good approximation.…”

Section: Literature Reviewmentioning

confidence: 99%

Inventory Models for Substitutable Products: Optimal Policies and Heuristics

2008

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In this paper, we examine the nature of optimal inventory policies in a system where a retailer manages substitutable products. We first consider a system with two products 1 and 2 whose total demand is D and individual demand proportions are p and (1-p). A fixed proportion of the unsatisfied customers for 1(2) will purchase item 2 (1), if it is available in inventory. For the single period case, we show that the optimal inventory levels of the two items can be computed easily and follow what we refer to as "partially decoupled" policies, i.e. base stock policies that are not state dependent, in certain critical regions of interest both when D is known and random. Furthermore, we show that such a partially decoupled basestock policy is optimal even in a multi-period version of the problem for known D for a wide range of parameter values. Using a numerical study, we show that heuristics based on the de-coupled inventory policies perform well in conditions more general than the ones assumed to obtain the analytical results.The analytical and numerical results suggest that the approach presented here is most valuable in retail settings for product categories where there is a moderate level of substitution between items in the category, demand variation at the category level is not too high and service levels are high.

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“…Mahajan and van Ryzin (2001), Cachon et al (2005), Caro and Gallien (2007) and Maddah and Bish (2007) extend van Ryzin and Mahajan (1999) in various ways. Gaur and Honhon (2006) show that for a locational choice model, the products in the optimal assortment are located far from each other in the attribute space, indicating maximum differentiation, with no substitution between products in the assortment. This implies that the most popular product may not be carried in the optimal assortment, contrasting the results of van Ryzin and Mahajan (1999).…”

Section: Literature Reviewmentioning

confidence: 92%

A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations

Fisher

Vaidyanathan

2014

Management Science

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We consider the problem of choosing, from a set of N potential stock-keeping units (SKUs) in a retail category, K SKUs to be carried at each store to maximize revenue or profit. Assortments can vary by store, subject to a maximum number of different assortments. We view a SKU as a set of attribute levels and also model possible substitutions when a customer's first choice is not in the assortment. We apply maximum likelihood estimation to sales history of the SKUs currently carried by the retailer to estimate the demand for attribute levels and substitution probabilities, and from this, the demand for any potential SKU, including those not currently carried by the retailer. We specify several alternative heuristics for choosing SKUs to be carried in an assortment. We apply this approach to optimize assortments for three real examples: snack cakes, tires, and automotive appearance chemicals. A portion of our recommendations for tires and appearance chemicals were implemented and produced sales increases of 5.8% and 3.6%, respectively, which are significant improvements relative to typical retailer annual comparable store revenue increases. We also forecast sales shares of 1, 11, and 25 new SKUs for the snack cake, tire, and automotive appearance chemical applications, respectively, with mean absolute percentage errors (MAPEs) Fader and Hardie (1996).

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Assortment Planning and Inventory Decisions Under a Locational Choice Model

Cited by 235 publications

References 26 publications

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint

Inventory Models for Substitutable Products: Optimal Policies and Heuristics

A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations

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