An Integrated Framework For Configurable Product Assortment Planning

A manufacturer\u27s assortment is the set of products or product configurations that the company builds and offers to its customers. While the literature on assortment planning is growing in recent years, it is primarily aimed at non-durable retail and grocery products. In this study, we develop an integrated framework for strategic assortment planning of configurable products, with a focus on the highly complex automotive industry. The facts that automobiles are highly configurable (with the number of buildable configurations running into thousands, tens of thousands, and even millions) with relatively low sales volumes and the stock-out rates at individual dealerships (even with transshipments) are extremely high, pose significant challenges to traditional assortment planning models. This is particularly the case for markets such as the U.S. that mostly operate in a make-to-stock (MTS) environment. First, we study assortment planning models that account for exogenous demand models and stock-out based substitution while considering production and manufacturing complexity costs and economies-of-scale. We build a mathematical model that maximizes the expected profit for an Original Equipment Manufacturer (OEM) and is a mixed-integer nonlinear problem. We suggest using linear lower/upper bounds that will be solved through a Modified-Branch and Bound procedure and compare the results with commercial mixed-integer nonlinear solvers and show superiority of the proposed method in terms of solution quality as well as computational speed. We then build a modeling framework that identifies the optimal assortment for a manufacturer of automotive products under environmental considerations, in particular, Corporate Average Fuel Economy (CAFE) requirements as well as life-cycle Greenhouse Gas (GHG) emission constraints. We present a numerical experiment consisting of different vehicle propulsion technologies (conventional, Diesel, and hybrid) and study the optimal shares of different technologies for maximizing profitability under different target levels of CAFE requirements. Finally, we develop assortment planning formulations that can jointly identify optimal packages and stand-alone options over different series of the product model. Our numerical experiment reveals that product option packaging has a considerable effect on managing product complexity and profitability

Optimization Of Strategic Planning Processes For Configurable Products: Considerations For Global Supply, Demand, And Sustainability Issues

The assortment planning problem is to decide on the set of products that a retailer or manufacturer will offer to its customers to maximize profitability. While assortment planning research has been expanding in recent years, the current models are inadequate for the needs of a configurable product manufacturer. In particular, we address assortment planning for an automobile manufacturer. We develop models to integrate assortment planning and supply chain management, designed for use by a large automaker in its strategic planning phase. Our model utilizes a multinomial logit model transformed into a mixed integer linear program through the Charnes-Cooper transformation. It is able to scale to problems that contain thousands of configurations to possibly be offered, a necessity given the number of possible configurations an automaker can build. In addition, most research in assortment planning contains simplified costs associated with product complexity. We model a full supply chain and give a rich treatment of the complexity associated with product complexity. We believe that our model can significantly aid automotive manufacturers to balance their product complexity with supply chain complexity, thus increasing profitability. In addition, we study the effect of packaging on the assortment and supply chain of an automaker. We develop a new model for mathematically expressing the effect that packaging has on the way in which customers choose products. Packaging significantly complicates the search space of the assortment planning problem. We introduce a heuristic method based on our packaging model that speeds up the solve times of the models while finding reasonably good solutions. Finally, we extend our initial model to study the effects of sustainability requirements on an automaker\u27s assortment and supply chain. We introduce constraints on the vehicle program average fuel economy, greenhouse gas emissions in the supply chain, and greenhouse gas emissions in the product use phase. We dive deep into each case to glean insights about how automakers can change their decision-making process to balance making their companies more sustainable with profit maximization. While all the examples discussed are from the automotive industry, the models developed can be adapted to address assortment planning for other types of configurable products (e.g., computers, printers, phones)

Assortment Planning of Automotive Products: Considerations for Economic and Environmental Impacts of Technology Selection

A manufacturer’s assortment is the set of products that the company offers to its customers. Assortment planning considerably affects both the sales revenue and product offering costs for the company and it had experienced growing attention across different industries over recent decades. In this study, we propose a modeling framework that seeks to identify the optimal assortment for a manufacturer of configurable products (in particular, automobiles). Our model accounts for environmental considerations (Corporate Average Fuel Economy requirements, tail-pipe emissions, and greenhouse gas emissions related to the production of the fuel used to power the vehicle) during assortment planning. We formulate the economic and environmental requirements in the model through a mixed-integer programming framework and present a hypothetical product case study motivated by an American automaker that involves 120 potential configurations employing different engine technologies (gasoline, diesel, and hybrid technologies). Notwithstanding consideration for consumer perceptions and acceptance, the results of this research work show that diesel technologies are a better choice to satisfy average fuel economy requirements compared to hybrid and conventional powertrains with current technology maturity

Pricing and Assortment Selection with Demand Uncertainty.

Assortment planning and pricing are among the most important strategic questions for many firms. These decisions are particularly challenging when inventory considerations need to be taken into account. Unfortunately, the trade-offs created by the assortment, pricing and inventory decisions are complicated enough to push many firms to make these decisions separately, ignoring their synergy. This dissertation targets this gap by presenting joint assortment and pricing models with inventory considerations. The first setting studied in this dissertation is a single firm selling a configurable product (e.g. a laptop computer), formed by putting together two components: one required (e.g. processor) and one optional (e.g. DVD writer). This dissertation finds that the optimal prices are such that all variants of a component share the same effective profit margin, defined as the unit gross margin net of unit inventory-related cost, which itself depends on unit underage and overage costs, service level and demand variability. As for assortment selection, the importance of a variant's surplus is shown. When variants are put together to form a product configuration, their surpluses combine to yield the attractiveness of the product configuration, whose role in selecting the assortment is highlighted in this dissertation. Furthermore, this dissertation finds that if the optional component's assortment and margin influence the customer's decision to purchase from the firm, then the component must be priced at effective cost. This is no longer true if only the required component affects the customer's decision to purchase from the firm. The second setting studied here involves a dual-channel supply chain, where a manufacturer sells substitutable products directly to the customer and also through an independent retailer. This dissertation finds that the wholesale prices in such a supply chain exhibit a structure in which the wholesale margins weighted by a function of service levels and demand variability must be common across all variants. In addition, this work characterizes scenarios where the manufacturer's and retailer's assortment preferences are in conflict. In particular, this work shows that the manufacturer may prefer the retailer to carry items with high demand variability while the retailer prefers items with low demand variability.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75935/1/betzabe_1.pd

A Customer Choice Modeling Framework For Assortment Planning Of Configurable Products In Automotive Industry

Due to the increased competition in the auto industry, proliferation of the vehicle models and increased customer need for choice and customization, it has become more critical than ever to offer a variety of features and customization flexibility while at the same time restraining and, even better, cutting down the costs. Product complexity, in the automotive industry, can be measured by the size of the assortment offered, i.e., set of vehicle configurations a customer can choose from (e.g., for a given model of a brand). While complexity fosters growth with increased alignment of product characteristics and customer needs, it results in decreased revenue (e.g., cannibalization) and profitability (e.g., increased total supply chain costs). Companies that manage complexity by improving their products’ true profitability have seen savings of 10 percent to 15 percent on their cost of goods sold. In order to determine the optimal complexity that should be offered, the company must first understand its customers buying behavior, and their response at the instances where their primary vehicle configuration choice is not offered or is stocked out. In this thesis, we develop a customer choice modeling framework that predicts the likelihood of an average customer to buy a specific vehicle configuration in a given assortment offering. Our modeling approach utilizes neural networks to predict, based on the historical dealership level sales and inventory data, how likely a given configuration will sell when it’s offered along with a set of configurations. These configuration level sale probability estimates are then used to estimate the attraction factor for each feature included in the vehicle configuration. The attraction factor of each feature represents feature’s individual contribution to the probability of sale of the configuration as a whole. With this feature level estimation, the probability of sales for any feature combination or vehicle configuration can be estimated (including those configurations not yet built or offered). We report on the performances of several modeling and neural network based estimation approaches using historical dataset from a major US automotive OEM. Our models are parametric and thus can be used within an assortment planning model to determine the optimal product assortment that optimizes complexity by considering true profitability of the configurations in the assortment

Optimizing Consumer-Centric Assortment Planning under Cross-Selling Effects

Central to modern-time, consumer-focused retailing is the ability to provide attractive and reasonably-priced product assortments for different customer profiles. To this end, retailers can benefit from the use of data analytics in order to identify distinct customer segments, each characterized by their buying power, shopping behavior, and preferences. Further, retailers can also benefit from a careful examination of alternative procurement options and cost levers associated with products that are considered for inclusion in the assortment. Issues of assortment planning lie at the interface of operations and marketing. Profitable planning trade-offs can be identified using an optimization methodology and are simultaneously driven by consumer preferences and supply cost considerations. This dissertation proposes and investigates novel, integrated optimization models for assortment planning with the following overarching objectives: (i) To reveal insights into assortment decisions under product substitutability or complementarity and multiple customer segments; (ii) to improve the computational tractability of (nonlinear discrete) optimization models that arise in such contexts and to demonstrate their efficacy for large-scale data instances. In the first essay, we investigate the joint optimization of assortment and pricing decisions for complementary retail categories with relatively popular products having high and stable sales volumes, such as fast-moving consumer goods. Each category comprises substitutable items (e.g., different coffee brands) and the categories are related by cross-selling considerations that are empirically observed in marketing studies to be asymmetric in nature. That is, a subset of customers who purchase a product from a primary category (e.g., coffee) can typically opt to also buy from one or several complementary categories (e.g., sugar and/or coffee creamer). We propose a mixed-integer nonlinear program that maximizes the retailer\u27s profit by jointly optimizing assortment and pricing decisions for multiple categories using a deterministic maximum-surplus consumer choice model. A linear mixed-integer reformulation is developed, which effectively enables an exact solution to large, industry-sized problem instances using commercial optimization solvers. Our computational study indicates that overlooking cross-selling between retail categories can result in substantial profit losses, suboptimal (narrower) assortments, and inadequate prices. The demonstrated tractability of the proposed model paves the way for store-wide optimization of categories that exhibit significant complementarity, which retailers can infer from market basket analysis. The second essay addresses an assortment packing problem where a decision maker optimizes the assortment and release times of products that belong to different categories over a multi-period planning horizon. Products in a same category are substitutable, whereas products across categories may exhibit complementarity relationships. All products have a longevity over which their attractiveness gradually decays (e.g., electronics or fashion products), while being positively or negatively impacted by the specific mix of substitutable or complementary products that the retailer has introduced. Our proposed 0-1 fractional program employs an attraction demand model and subsumes recent assortment packing models in the literature. We highlight the effect of overlooking cross-selling and cannibalization on the profit using an illustrative example. We develop linearized reformulation that afford exact solutions to small-sized problem instances. Furthermore, a linear programming-based heuristic approach is devised and is demonstrated to yield near-optimal solutions for large-scale computationally challenging problem instances in manageable times. Model extensions are discussed, especially in the context of the movie industry where exhibitors have to decide on the assortment of movies to display and their optimal display times

A dynamic model of global natural gas supply

This paper presents the Dynamic Upstream Gas Model (DYNAAMO); a new, global, bottom-up model of natural gas supply. In contrast to most “static” supply-side models, which bracket resources by average cost, DYNAAMO creates a range of dynamic outputs by simulating investment and operating decisions in the upstream gas industry triggered in response to investors’ expectations of future gas prices. Industrial data from thousands of gas fields is analysed and used to build production and expenditure profiles which drive the economics of supply at field level. Using these profiles, a novel methodology for estimating supply curves is developed which incorporates the size, age and operating environment of gas fields, and treats explicitly the fiscal, abandonment, exploration and emissions costs of production. The model is validated using the US shale gas boom in the 2000s as a historic case study. It is found that the modelled market share of supply by field environment replicates the observed trend during the period 2000–2010, and that the model price response during the same period – due to lower capacity margins and the financing of new projects – is consistent with market behaviour