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Product deterioration is a common phenomenon and is overlooked in most contemporary research on the newsboy problem. In this study, we have considered product deterioration in a production–inventory newsboy model based on multiple just-in-time (JIT) deliveries. This model is solved by a classical optimization technique for the manufacturer production size, wholesale price, replenishment plan, and retailer order policy using a distribution-free approach. Moreover, in order to improve business and entice more customers, a return policy and a post-sale warranty policy is adopted in the model. Theoretical development and numerical examples are provided to demonstrate the validity of this approach.

Online retail of agricultural products is an emerging form of online shopping that has enormous value for researching sustainable agricultural product logistics and the sustainability of e-commerce. By reviewing these practices in China, this paper summarizes three models of online retail of agricultural products: community group buying, prepositioned warehousing, and a mixed model in which the former two are carried out simultaneously. This paper considers the uncertainty of demand and applies the newsboy model to obtain the expected profit function of the three models. The paper proves that the objective functions of the optimization models are all convex functions of the supply capacity. The optimal supply strategy and the expression for each business model are then derived. Next, the intervals for enterprises to choose the profit-optimal business model are given and visually demonstrated through graphs. These findings lead to managerial insights: in economically underdeveloped regions, it is appropriate for enterprises to conduct community group buying businesses; in economically developed regions, it is appropriate for enterprises to conduct prepositioned warehouse businesses; and in regions with average economic development, it is appropriate for enterprises to conduct both businesses. Finally, this paper verifies the optimal supply strategy for the online retail model for agricultural products through numerical experiments and sensitivity analyses for different cost parameters.

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

The present study aims to investigate the combination of the newsboy problem and the cooperative advertisement problem in the presence of uncertain demand which depends on retail price as well as both local and national advertising expenditures to coordinate pricing, ordering, and advertising decisions in a manufacturer-retailer supply chain. A game theoretic approach was adopted to determine the equilibrium values for decisions. Three different game scenarios based on the newsboy problem model were developed and analyzed: 1) Stackelberg manufacturer game in which the manufacturer as the dominant power plays the role of leader in the market and the follower retailer makes his own best decisions after considering the leader's decisions; 2) Nash equilibrium game in which both manufacturer and retailer are of equal power in the market and make their decisions simultaneously to devise their best strategies; 3) Centralized scenario in which the retailer and manufacturer make the best decisions by means of information sharing and joint cooperation. Equilibrium decisions were made exactly in these scenarios. Some corollaries were also presented and theoretically proved to show the relationships among the variables in both centralized and decentralized supply chains. Finally, some numerical examples were randomly generated and sensitivity analysis was carried out to show the capabilities of the proposed models.

Effective inventory management depends on accurate estimates of product profitability to formulate ordering and manufacturing strategies. The achievable capacity index (ACI) is a simple yet efficient approach to measuring the profitability of newsboy-type products with normally distributed demand, wherein profitability is presented as the probability of achieving the target profit under the optimal ordering quantity. Unfortunately, the ACI is applicable only to retail stores with a single demand. In the current study, we addressed the issue of measuring the integrated profitability of newsboy-type products sold in multiple locations with independent demand levels, such as own-branding-and-manufacture (OBM) companies with multiple owned channels. We began by formulating profitability in accordance with multiple independent normal demands, and then developed an integrated ACI (IACI) to simplify expression. We also derived the statistical properties of the unbiased estimator to determine the true IACI in situations where demand patterns are unknown. Finally, we conducted hypothesis testing to determine whether the integrated profitability meets a stipulated minimum level. For convenience, we tabulated the critical values as a function of sample size, confidence level, the number of channels, and the stipulated minimum level. One can make decisions simply by estimating the IACI based on historical demand data from all channels and then looking up the critical value in the corresponding tables. Consequently, the proposed methods make it possible for OBM managers to address integrated profitability evaluation, which is effective in deciding the optimal timing to pull unprofitable items from the shelves by looking up generic tables. Furthermore, we also performed numerical and sensitivity analyses for a real-world case to illustrate the applicability and some managerial implications of the proposed scheme.

This paper considers the pricing decision faced by a producer of a commodity with a short shelf or demand life. A hierarchical model is developed, and the results of the single period inventory model are used to examine possible pricing and return policies. The paper shows that several such policies currently in effect are suboptimal. These include those where the manufacturer offers retailers full credit for all unsold goods or where no returns of unsold goods are permitted. The paper also demonstrates that a policy whereby a manufacturer offers retailers full credit for a partial return of goods may achieve channel coordination, but that the optimal return allowance will be a function of retailer demand. Therefore, such a policy cannot be optimal in a multi-retailer environment. It is proven, however, that a pricing and return policy in which a manufacturer offers retailers a partial credit for all unsold goods can achieve channel coordination in a multi-retailer environment. This article was originally published in Marketing Science , Volume 4, Issue 2, pages 166–176, in 1985.

In the present contribution, the innovative nonlinear state marginal price vector model introduced in Toll and Kintzel (CEJOR 27(4):1079–1105, 2019) (plus Errata herein) is enriched to include budgeting problems under agency conflicts. Under asymmetric information, a company owner as principal can only rely on information transmitted to her from her managers as agents. In the related modeling, it is assumed that slack and capital rationing are optimal. The governing budgeting relations are integrated into a nonlinear framework furnished by a multi-period newsvendor approach and are solved numerically by means of a two-step valuation procedure based on two successive nonlinear convex optimizations. The capital market is assumed to be imperfect. As case study, the M&A-valuation case of a merger of two IT-service companies is considered subjected to optimal combined dimensioning of capacities and budgets under stochastic demand. On balance, by addressing agency conflicts within the well-established nonlinear framework, the practical application field of the valuation procedure is widened.

This article investigates a two-level supply chain consisting of a single manufacturer and a retailer. The retailer’s ordering patterns are highly influenced by his risk preferences. We discuss ordering policies when the manufacturer has limited demand information and propose a production-commitment contract, which mitigates double marginalization under imperfect information. Demand distribution is private information of the retailer and the manufacturer only assumes an educated guess about the mean and variance. Production-commitment contract is an attractive option for make-to-stock scenarios where quantity is confirmed after the demand is realized. We show that lack of information may not have an adverse effect. We also prove analytically that informational advantage may not necessarily be a supply chain advantage and also provide numerical insights for a win-win situation.

Mathematical model formulations for single-period supply chain problem depend on how to describe the demand. This paper applies uncertainty theory, which is a branch of axiomatic mathematics for dealing with human uncertainty, to model demand distribution. Uncertain decentralized management model and uncertain centralized management model are developed. Unique closed-form solutions for the two models are derived. The belief degree of “order quantity being less than the supply chain optimal order quantity” is proposed and the lower bound of the belief degree is obtained and carefully analyzed. Finally, some examples are presented to illustrate our method.