Assortment Planning for Multiple Quality Levels Using Locational Choice

Assortment planning is the process in which a retailer selects a product line to offer to customers and is a key determinant of a retailer\u27s profit. We consider the assortment planning problem using a locational choice model for customer product selection and allow for both horizontal and vertical product differentiation. When the distribution of customer preference is unimodal, the optimal solutions for this problem are unknown. We propose two solution philosophies for generating product assortments. First, we introduce a metaheuristic representation for the problem and test the performance of three metaheuristic techniques. We suggest that a tabu search or genetic algorithm may be the best technique for the problem depending on the parameters. Next, we introduce a combined dynamic programming and line search approach for generating optimal solutions. We use this technique to explore the properties of the optimal solution and suggest instances where this technique is preferable to the metaheuristic methods. We then propose a new model which allows for heterogeneous quality preferences among the customer population. This model allows for more realistic customer product selection but also increases the complexity of the problem. We give mathematical properties of optimal solutions to the heterogeneous model and propose a new metaheuristic representation and a genetic algorithm for solving the problem

Customer Choice Modeling For Retail Category Assortment Planning And Product-Line Extension

Growing competitiveness and increasing availability of data is generating great interest in data-driven analytics across industries. One of the areas that has gained a lot of attention is Customer choice modeling, which aims to explain the choices individual customers make in choosing from a set of products based on their preferences. While effective customer choice modeling is essential to a wide variety of application domains, including retail, it is challenging in practice due to limitations around the quality of the data available for modeling and potentially complex choice behaviors. This dissertation presents a hybrid modeling approach that relies on both parametric and non-parametric methods to derive effective recommendations for product development and assortment planning. A generic non-parametric ranking-based choice model is first derived using random utility maximization to best model revealed product-level preferences from sales transactions and inventory records. The resulting product-level ranking-based choice model is utilized to establish customer segments and derive more actionable product attribute-based parametric models that can be employed for product assortment optimization as well as product-line extension. Then, in order to leverage from the correlatedness of customers\u27 preferences toward similar attributes across multiple categories of products, we use cross category customer choice models to make the base predictions more accurate. The proposed modeling approach is validated using data from a leading global apparel retailer as well as synthetic experiments

Word of Mouth and Taste Matching: A Theory of the Long Tail

I present a model to assess the impact of demand-side factors on the concentration of sales within large product assortments. Consumers face a search problem within an assortment of horizontally differentiated products supplied by a monopolist. They may search for a product match by drawing products from the assortment or by seeking word of mouth recommendations from other consumers. Product evaluations prior to purchase and word of mouth are shown to arise endogenously, and increase the concentration of sales. I show that taste matching mechanisms such as recommender systems, which allow consumers to obtain product recommendations from others with similar preferences, reduce sales concentration by generating a long tail effect, an increase in the tail of the sales distribution. Insights are derived on the mechanisms driving concentration in artistic markets and their strategic implications for the firm. The model is suited for experience good markets such as music, cinema, literature and video game entertainment

Towards resilience through systems-based plant breeding. A review

How the growing world population can feed itself is a crucial, multi-dimensional problem that goes beyond sustainable development. Crop production will be affected by many changes in its climatic, agronomic, economic, and societal contexts. Therefore, breeders are challenged to produce cultivars that strengthen both ecological and societal resilience by striving for six international sustainability targets: food security, safety and quality; food and seed sovereignty; social justice; agrobiodiversity; ecosystem services; and climate robustness. Against this background, we review the state of the art in plant breeding by distinguishing four paradigmatic orientations that currently co-exist: community-based breeding, ecosystem-based breeding, trait-based breeding, and corporate-based breeding, analyzing differences among these orientations. Our main findings are: (1) all four orientations have significant value but none alone will achieve all six sustainability targets; (2) therefore, an overarching approach is needed: “systems-based breeding,” an orientation with the potential to synergize the strengths of the ways of thinking in the current paradigmatic orientations; (3) achieving that requires specific knowledge development and integration, a multitude of suitable breeding strategies and tools, and entrepreneurship, but also a change in attitude based on corporate responsibility, circular economy and true-cost accounting, and fair and green policies. We conclude that systems-based breeding can create strong interactions between all system components. While seeds are part of the common good and the basis of agrobiodiversity, a diversity in breeding approaches, based on different entrepreneurial approaches, can also be considered part of the required agrobiodiversity. To enable systems-based breeding to play a major role in creating sustainable agriculture, a shared sense of urgency is needed to realize the required changes in breeding approaches, institutions, regulations and protocols. Based on this concept of systems-based breeding, there are opportunities for breeders to play an active role in the development of an ecologically and societally resilient, sustainable agriculture

Search and Homophily in Social Networks

We study the formation of social ties among heteogeneous agents in a model where meetings are governed by agents' directed search. The aim is to shed light on the important issue of homophily (the tendency of agents to connect with others of the same type). The essential contribution of the model is to provide a basic microfoundation for the opportunity/meeting biases that, as the literature highlights, are a crucial element of the phenomenon. Under the assumption that search is more effective in large pools, the equilibrium is characterized by a threshold in terms of group size: large groups only search among similar agents while smaller groups search in the whole population. This threshold behavior is consistent with the empirical evidence observed in a range of social environments such as high school friendships and interethnic marriages. And assuming that search is subject to small frictions, it also generates the bell-shaped form of the so-called Coleman index observed in the data. Other implications of the model supported by the evidence concern the pattern of cross-group ties among small groups, the linearity of excess homophily for large groups, and the positive effect on it of overall population size.Homophily, search, social networks, segregation.

Assortment Optimization with Customer Choice Modeling in a Crowdfunding Setting

Crowdfunding, which is the act of raising funds from a large number of people's contributions, is among the most popular research topics in economic theory. Due to the fact that crowdfunding platforms (CFPs) have facilitated the process of raising funds by offering several features, we should take their existence and survival in the marketplace into account. In this study, we investigated the significant role of platform features in a customer behavioral choice model. In particular, we proposed a multinomial logit model to describe the customers' (backers') behavior in a crowdfunding setting. We proceed by discussing the revenue-sharing model in these platforms. For this purpose, we conclude that an assortment optimization problem could be of major importance in order to maximize the platforms' revenue. We were able to derive a reasonable amount of data in some cases and implement two well-known machine learning methods such as multivariate regression and classification problems to predict the best assortments the platform could offer to every arriving customer. We compared the results of these two methods and investigated how well they perform in all cases