Retail Assortment Planning Under Category Captainship

2010 ◽  
Author(s):  
Mumin Kurtulus ◽  
Alper Nakkas
Keyword(s):  
Assortment Planning ◽  
Category Captainship

Retail Assortment Planning Under Category Captainship

2011 ◽  
Vol 13 (1) ◽  
pp. 124-142 ◽  
Author(s):  
Mümin Kurtuluş ◽  
Alper Nakkas
Keyword(s):  
Assortment Planning ◽  
Category Captainship

Capacitated strategic assortment planning under explicit demand substitution

2021 ◽  
Author(s):  
Nagihan Çömez-Dolgan ◽  
Nilgun Fescioglu-Unver ◽  
Ecem Cephe ◽  
Alper Şen
Keyword(s):  
Assortment Planning ◽  
Demand Substitution

A decision support system for retail assortment planning

2017 ◽  
Vol 45 (7/8) ◽  
pp. 808-825 ◽  
Author(s):  
Alexander Hübner
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Solution Structure ◽  
Product Variety ◽  
Substitution Effects ◽  
Assortment Planning ◽  
Shelf Space ◽  
Content Type ◽  
Solution Approach

Purpose Because increasing product variety in retail conflicts with limited shelf space, managing assortment and shelf quantities is a core decision in this sector. A retailer needs to define the assortment size and then assign shelf space to meet consumer demand. However, the current literature lacks not only information on the comprehensive structure of the decision problem, but also a decision support system that can be directly applied to practice in a straightforward manner. The paper aims to discuss these issues. Design/methodology/approach The findings were developed and evaluated by means of explorative interviews with grocery retail experts. An optimization model is proposed to solve the problem of assortment planning with limited shelf space for data sets of a size relevant in real retail practice. Findings The author identifies the underlying planning problems based on a qualitative survey of retailers and relates the problems to each other. This paper develops a pragmatic approach to the capacitated assortment problem with stochastic demand and substitution effects. The numerical examples reveal that substitution demand has a significant impact on total profit and solution structure. Practical implications The author shows that the model and solution approach are scalable to problem sizes relevant in practice. Furthermore, the planning architecture structures the related planning questions and forms a foundation for further research on decision support systems. Originality/value The planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models

2021 ◽  
Author(s):  
Xi Chen ◽  
Yining Wang ◽  
Yuan Zhou
Keyword(s):  
Choice Model ◽  
Current Knowledge ◽  
Search Algorithm ◽  
Multinomial Logit ◽  
Planning Problem ◽  
Assortment Planning ◽  
Substitutable Products ◽  
Dynamic Decisions ◽  
Mnl Model ◽  
Regret Bound

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and the customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Because all the utility parameters of the MNL model are unknown, the seller needs to simultaneously learn customers’ choice behavior and make dynamic decisions on assortments based on the current knowledge. The goal of the seller is to maximize the expected revenue, or, equivalently, to minimize the expected regret. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing policies require the estimation of mean utility for each product and the final regret usually involves the number of products [Formula: see text]. The optimal regret of the dynamic assortment planning problem under the most basic and popular choice model—the MNL model—is still open. By carefully analyzing a revenue potential function, we develop a trisection-based policy combined with adaptive confidence bound construction, which achieves an item-independent regret bound of [Formula: see text], where [Formula: see text] is the length of selling horizon. We further establish the matching lower bound result to show the optimality of our policy. There are two major advantages of the proposed policy. First, the regret of all our policies has no dependence on [Formula: see text]. Second, our policies are almost assumption-free: there is no assumption on mean utility nor any “separability” condition on the expected revenues for different assortments. We also extend our trisection search algorithm to capacitated MNL models and obtain the optimal regret [Formula: see text] (up to logrithmic factors) without any assumption on the mean utility parameters of items.

A Model for Integrated Inventory and Assortment Planning

2021 ◽  
Author(s):  
Victor Martínez-de-Albéniz ◽  
Sumit Kunnumkal
Keyword(s):  
Closed Form Solution ◽  
Optimal Solution ◽  
Integer Program ◽  
Form Solution ◽  
Assortment Planning ◽  
Dimensional Parameter ◽  
Planning Decisions ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Random Instances

Integrating inventory and assortment planning decisions is a challenging task that requires comparing the value of demand expansion through broader choice for consumers with the value of higher in-stock availability. We develop a stockout-based substitution model for trading off these values in a setting with inventory replenishment, a feature missing in the literature. Using the closed form solution for the single-product case, we develop an accurate approximation for the multiproduct case. This approximated formulation allows us to optimize inventory decisions by solving a fractional integer program with a fixed point equation constraint. When products have equal margins, we solve the integer program exactly by bisection over a one-dimensional parameter. In contrast, when products have different margins, we propose a fractional relaxation that we can also solve by bisection and that results in near-optimal solutions. Overall, our approach provides solutions within 0.1% of the optimal policy and finds the optimal solution in 80% of the random instances we generate. This paper was accepted by David Simchi-Levi, optimization.

Data mining-based algorithm for assortment planning

2020 ◽  
Vol 7 (3) ◽  
pp. 443-457
Author(s):  
Praveen Ranjan Srivastava ◽  
Satyendra Sharma ◽  
Simran Kaur
Keyword(s):  
Data Mining ◽  
Assortment Planning
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