Assortment optimization using an attraction model in an omnichannel environment

Making assortment decisions is becoming an increasingly difficult task for many retailers worldwide as they implement omnichannel initiatives. Discrete choice modeling lies at the core of this challenge, yet existing models do not sufficiently account for the complex shopping behavior of customers in an omnichannel environment. In this paper, we introduce a discrete choice model called the multichannel attraction model (MAM). A key feature of the MAM is that it specifically accounts for both the product substitution behavior of customers within each channel and the switching behavior between channels. We formulate the corresponding assortment optimization problem as a mixed integer linear program and provide a computationally efficient heuristic method that can be readily used for obtaining high-quality solutions in large-scale omnichannel environments. We also present three different methods to estimate the MAM parameters based on aggregate sales transaction data. Finally, we describe general effects of the implementation of widely-used omnichannel initiatives on the MAM parameters, and carry out numerical experiments to explore the structure of optimal assortments, thereby gaining new insights into omnichannel assortment optimization. Our work provides the analytical framework for future studies to assess the impact of different omnichannel initiatives

A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX's performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods

On the optimal frequency of multiple generation product introductions

This paper considers a firm that introduces multiple generations of a product to the market at regular intervals. We assume that the firm has only a single production generation in the market at any time. To maximize the total profit within a given planning horizon, the firm needs to decide the optimal frequency to introduce new product generations, taking into account the trade-off between sales revenues and product development costs. We model the sales quantity of each generation as a function of the technical decay and installed base effects. We analytically examine the optimal frequency for introducing new product generations as a function of these parameters. (C) 2015 Elsevier B.V. All rights reserved

A test of inventory models with permissible delay in payment

Contrary to the long-standing view in the finance literature that firms should maximise payment delays, research in operations management suggests that long payment delays can be suboptimal. In this study, we reconcile these two views by applying a secondary data approach to established operations management theory. Based on a sample of 3383 groups of public US firms from a novel database, we find that our data are consistent with the causal relations and theoretical predictions of the operations management literature. Firm profitability is positively associated with payment delay. Payment delay, in turn, is positively associated with the capital cost difference between buyer and supplier and negatively associated with the price elasticity of demand and the deterioration rate of inventory. However, we do not observe any significant interaction effects between these factors, which raise a number of questions for future research

Item-Level RFID in the Retail Supply Chain

Analyzing the proliferation of item-level RFID, recent studies have identified the cost sharing of the technology as a gating issue. Various qualitative studies have predicted that conflict will arise, in particular in decentralized supply chains, from the fact that the benefits and the costs resulting from item-level RFID are not symmetrically distributed among supply chain partners. To contribute to a better understanding of this situation, we consider a supply chain with one manufacturer and one retailer. Within the context of this retail supply chain, we present analytic models of the benefits of item-level RFID to both supply chain partners. We examine both the case of a dominant manufacturer as well as the case of a dominant retailer, and we analyze the results of an introduction of item-level RFID to such a supply chain depending on these market power characteristics. Under each scenario, we show how the cost of item-level RFID should be allocated among supply chain partners such that supply chain profit is optimized

Designing Service Level Contracts for Supply Chain Coordination

Supply contracts are used to coordinate the activities of the supply chain partners. In many industries, service level-based supply contracts are commonly used. Under such a contract, a company agrees to achieve a certain service level and to pay a financial penalty if it misses it. The service level used in our study refers to the fraction of a manufacturer's demand filled by the supplier. We analyze two types of service level-based supply contracts that are designed by a manufacturer and offered to a supplier. The first type of contract is a flat penalty contract, under which the supplier pays a fixed penalty to the manufacturer in each period in which the contract service level is not achieved. The second type of contract is a unit penalty contract, under which a penalty is due for each unit delivered fewer than specified by the parameters of the contract. We show how the supplier responds to the contracts and how the contract parameters can be chosen, such that the supply chain is coordinated. We also derive structural results about optimal values of the contract parameters, provide numerical results, and connect our service level measures to traditional service level measures. The results of our analyses can be used by decision makers to design optimal service level contracts and to provide them with a solid foundation for contract negotiations

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterize four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company

Dynamics of UF[6] Desublimation with the Influence of Tank Geometry for Various Coolant Temperature

Mathematical model of UF[6] desublimation in a vertical immersion tank is presented in the article. Results of calculations of the filling dynamics of the tanks with 1m3 volume at various coolant temperatures, with and without ellipticity of the end walls are given. It is shown that allowance for the ellipticity of the end walls of the tanks leads to a significant increase in the time of desublimation of UF[6]