scholarly journals Detecting Customer Trends for Optimal Promotion Targeting

2020 ◽  
Author(s):  
Lennart Baardman ◽  
Setareh Borjian Boroujeni ◽  
Tamar Cohen-Hillel ◽  
Kiran Panchamgam ◽  
Georgia Perakis
Keyword(s):  
Optimization Model ◽  
Problem Definition ◽  
Customer Relationships ◽  
Mixed Integer ◽  
Percentage Error ◽  
Integer Optimization ◽  
Demand Model ◽  
Support Tool ◽  
Demand Models ◽  
Transactional Data

Problem definition: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data are not available to many retailers because of cost and privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data and using them to target promotions to the right customers. Academic/practical relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions. Methodology: We develop a novel customer trend demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a nonlinear mixed-integer optimization model. Though it is nondeterministic polynomial-time hard, we propose a greedy algorithm. Results: We prove that our customer-to-customer trend estimates are statistically consistent and that the greedy optimization algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the weighted-mean absolute percentage error by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3%–11%. Managerial implications: The demand model with customer trend and the optimization model for targeted promotions form a decision-support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales.

A Simulation-Based Heuristic to Find Approximate Equilibria with Disaggregate Demand Models

2021 ◽  
Vol 55 (5) ◽  
pp. 1025-1045
Author(s):  
Stefano Bortolomiol ◽  
Virginie Lurkin ◽  
Michel Bierlaire
Keyword(s):  
Discrete Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Added Value ◽  
Mixed Integer ◽  
Iteration Algorithm ◽  
Oligopolistic Competition ◽  
Integer Optimization ◽  
Equilibrium Solutions ◽  
Demand Models

Oligopolistic competition occurs in various transportation markets. In this paper, we introduce a framework to find approximate equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models, according to random utility theory. Compared with aggregate demand models, the added value of discrete choice models is the possibility to account for more complex and precise representations of individual behaviors. Because of the form of the resulting demand functions, there is no guarantee that an equilibrium solution for the given market exists, nor is it possible to rely on derivative-based methods to find one. Therefore, we propose a model-based algorithmic approach to find approximate equilibria, which is structured as follows. A heuristic reduction of the search space is initially performed. Then, a subgame equilibrium problem is solved using a mixed integer optimization model inspired by the fixed-point iteration algorithm. The optimal solution of the subgame is compared against the best responses of all suppliers over the strategy sets of the original game. Best response strategies are added to the restricted problem until all ε-equilibrium conditions are satisfied simultaneously. Numerical experiments show that our methodology can approximate the results of an exact method that finds a pure equilibrium in the case of a multinomial logit model of demand with a single-product offer and homogeneous demand. Furthermore, it succeeds at finding approximate equilibria for two transportation case studies featuring more complex discrete choice models, heterogeneous demand, a multiproduct offer by suppliers, and price differentiation for which no analytical approach exists.

A Model for Prepositioning Emergency Relief Items Before a Typhoon with an Uncertain Trajectory

2021 ◽  
Author(s):  
Joline Uichanco
Keyword(s):  
The Philippines ◽  
Problem Definition ◽  
Theory And Practice ◽  
Distribution Model ◽  
Demand Model ◽  
Probability Models ◽  
Data Set ◽  
Emergency Relief ◽  
Demand Models ◽  
Relief Distribution

Problem definition: We study the problem faced by the Philippine Department of Social Welfare (DSWD) in prepositioning relief items before landfall of an oncoming typhoon whose future outcome (trajectory and wind speed) is uncertain. Academic/practical relevance: The importance of prepositioning was a hard lesson learned from Super Typhoon Haiyan that devastated the Philippines in 2013, when many affected by the typhoon did not have immediate access to food and water. In a typhoon-prone country, it is important to build resilience through an effective prepositioning model. Methodology: By engaging with DSWD, we developed a practically relevant stochastic prepositioning model. The probability models of municipality-level demand and of supply damage are both dependent on the typhoon outcome. A linear mixed effects model is used to estimate the dependence of demand on the typhoon outcome using a large data set that includes the municipality-level impact of West Pacific typhoons during 2008–2019. The model has two objectives motivated from the practical realities of the Philippine network: prioritizing regions with high demand and prepositioning in all affected regions proportional to their total demand. Results: We find that the choice of the demand model significantly impacts the distributed relief items in the Philippine setting where it is challenging to adjust region-level supply after a typhoon. By using the historical data on past typhoons, we show that in this setting, our stochastic demand model provides the best distribution to date of any existing demand models. Managerial implications: There currently exists a gap between theory and practice in the management of relief inventories. We contribute toward bridging this gap by engaging with DSWD to develop a practically relevant relief distribution model. Our work is an effective example of collaboration with government and nongovernment agencies in developing a relief distribution model.

An optimization model for selecting wood supply contracts

2020 ◽  
Vol 50 (4) ◽  
pp. 399-412 ◽  
Author(s):  
Ali Rahimi ◽  
Mikael Rönnqvist ◽  
Luc LeBel ◽  
Jean-François Audy
Keyword(s):  
Optimization Model ◽  
Wood Fiber ◽  
Pulp And Paper ◽  
Raw Material ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Paper Mills ◽  
Wood Supply ◽  
Portfolio Strategies ◽  
Mixed Integer Optimization Model

Procurement for forest companies with pulp and paper mills aims to ensure that a sufficient volume of wood supply enters the production process. Numerous suppliers and contract types are available; however, their selection is a complex decision for procurement managers. In addition, managers typically dedicate a portion of their wood fiber demand to each group of suppliers, which is referred to as a portfolio strategy. Despite the available literature in contract selection, the consideration of contract types and their characteristics have not been addressed for the complex procurement process. In this study, a mixed-integer optimization model is proposed to best select contracts for pulp and paper procurement. The challenge was to plan deliveries in each time period to satisfy the demand of raw material at the mills. The potential of this model is demonstrated with a case study based on characteristics from a forest company in Quebec, Canada. A comparison between traditional fixed and flexible contracts is presented. Different portfolio strategies are also evaluated for groups of suppliers to investigate potential improvements.

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