Omega-international Journal of Management Science最新文献

This study examines how customer firms’ digital transformation affects the performance and value creation of their suppliers in the Chinese context. We use text mining to construct digital transformation indicators for customer firms based on data from Chinese A-share-listed firms from 2011 to 2021. We find that customer firms’ digital transformation has a positive and significant impact on supplier performance, and this effect is robust to various sensitivity tests. We also find that customer firms’ digital transformation influences supplier performance through two mechanisms: demand push and innovation spillover. Moreover, the heterogeneity analysis reveals that the effect of customer firms’ digital transformation on supplier performance varies by different factors, such as geographic distance, customer concentration, ownership type, and industry type. This study contributes to the literature on supply chain value from a customer-driven perspective and provides practical implications for supply chain firms to leverage digital transformation to enhance their performance and competitive advantage.

Online retailers throw out food that has not yet expired. This gives rise to the question whether online retailers generate more food waste than offline retailers, who typically throw out food only after it has expired. We focus on the food waste at the retailer which inherently ensues from the logistical set-up. We provide a theoretical discussion of the potential increase in food waste as a result of the inventory control policy and throwing out food that has not yet expired, as is the case for online retailers. Note that the online retailer has some advantages over offline retailers as well. Online retailers benefit from full control of order picking, which is instead often done by the consumer in offline retail. Moreover, the online retailer often benefits from the pooling effect, as offline retailers might use multiple stores to satisfy the same demand volume as an online retailer from a single warehouse. Our numerical experiments with a base-stock policy suggests that online retail actually yields less food waste for many products, despite throwing out food before expiration.

Among recent methodological proposals for efficiency measurement, machine learning methods are playing an important role, particularly in the reduction of overfitting in classical statistical methods. In particular, Support Vector Frontiers (SVF) is a method which adapts Support Vector Regression (SVR) to the estimation of production technologies through stepwise frontiers. The SVF estimator is convexified in a second stage to deal with convex technologies. In this paper, we propose SVF-Splines, an extension of SVF for the estimation of efficiency in multi-input multi-output production processes which uses a transformation function generating linear splines to directly estimate convex production technologies. The proposed methodology reduces the computational complexity of the original SVF and does not require a two-step estimation process to obtain convex production technologies. A simulated experiment comparing SVF-Splines with standard DEA and (convexified) SVF indicates better performance of the proposed methodology, with improvements of up to 95 % in mean squared error when compared with DEA. The computational advantages of SVF-Splines are also observed, with runtime over 70 times faster than SVF in certain scenarios, with better scaling as the size of the problem increases. Finally, an empirical illustration is provided where SVF-Splines is calculated with respect to various typical technical efficiency measures of the literature.

This paper proposes an innovative reverse logistics network (RLN) to manage kitchen waste (KW) transportation and resource treatment. The network employs battery electric (BE) trucks for transportation, and the challenge lies in determining the distribution of various KW treatment centers and establishing the optimal transportation routes for KW and its residues. The proposed RLN is self-sufficient, because the electricity produced by the centers within the network is adequate to power the BE trucks. We develop a matched mixed-integer programming model to optimize the entire process, with the goal of minimizing the total potential economic and environmental costs. Notably, the model considers comprehensive cost components and employs a carbon trading policy to translate carbon emissions into carbon costs. We use robust optimization to generate optimal solutions that remain viable even under the worst-case scenario concerning uncertain parameters. We then test the feasibility of the proposed methodology in a real-world case. We conduct specific scenario analyses on capacity and mode of trucks to offer practical KW transportation strategies and recommendations. We found that the larger the capacity of a BE truck, the greater the economic and environmental benefits for the KW RLN. The self-sufficient KW RLN using BE trucks proved to be the least costly, followed by the ordinary KW RLN using BE trucks, while the KW RLN using diesel trucks was the most expensive and environmentally detrimental.

With the booming development of social e-commerce platforms, an increasing number of brand owners are embarking on short video marketing activities through these channels. Nevertheless, brand owners encounter numerous quandaries in determining the optimal short video creation effort and traffic investment when they opt to rely on their own capabilities or enlist Key Opinion Leaders (KOLs) for their short video marketing campaigns. Hence, this paper delves into the sensitive demand function for short video creation effort and traffic investment. Subsequently, it formulates optimization decision frameworks based on the brand owner's self-built or KOL's short video channel. Accordingly, we delineate the brand owner's short video channel preference, and further design coordination mechanisms for the KOL short video channel. The research results first indicate that a stronger KOL influence and a lower signing fee will help the brand owner gain more profit after introducing the KOL short video channel. Secondly, the KOL is inclined to invest less in short video creation and traffic investment when engaging in short video marketing compared to the brand owner. Fortunately, the ex-ante traffic investment cost-sharing and cooperative decision can incentivize them to intensify their endeavors in short video creation and investing in traffic. Finally, we design the ex-post traffic investment cost-sharing and co-creation value distribution mechanisms under the cooperative decision situations, both of which can realize Pareto improvement, and it suggests that using the latter one would be fairer for the brand owner and KOL.

Evaluating the performance of public service providers is often complicated by the fact that they must choose input levels before demands for their services are known. We consider an even more complicated situation in which service providers have no opportunity to directly influence demands. This means that their predetermined inputs may be more than what is required to meet realised demands. In such cases, conventional measures of revenue efficiency will generally mis-classify rational and efficient managers as inefficient. We develop a more appropriate measure of revenue efficiency that accounts for exogenously-determined demands. We explain how data envelopment analysis (DEA) methods can be used to estimate our measure. The methodology is applied to hospital and health service networks in Queensland (Australia). We find that most of these networks were able to maximise the revenue they could obtain from their predetermined inputs.

Customer returns are a major problem for online retailers due to their economic and environmental impact. This paper investigates a new concept for handling online returns: customer-to-customer (C2C) returns logistics. The idea behind the C2C concept is to deliver returned items directly to the next customer, bypassing the retailer’s warehouse. To incentivize customers to purchase C2C return items, retailers can promote return items on their webshop with a discount. We build the mathematical models behind the C2C concept to determine how much discount to offer to ensure enough customers are induced to purchase C2C return items and to maximize the retailer’s expected total profit. Our first model, the base model (BM), is a customer-based formulation of the problem and provides an easy-to-implement constant-discount-level policy. Our second model formulates the real-world problem as a Markov decision process (MDP). Since our MDP suffers from the curse of dimensionality, we resort to simulation optimization (SO) and reinforcement learning (RL) methods to obtain reasonably good solutions. We apply our methods to data collected from a Dutch fashion retailer. We also provide extensive numerical experiments to claim generality. Our results indicate that the constant-discount-level policy obtained with the BM performs well in terms of expected profit compared to SO and RL. With the C2C concept, significant benefits can be achieved in terms of both expected profit and return rate. Even in cases where the cost-effectiveness of the C2C returns program is not pronounced, the proportion of customer-to-warehouse returns to total demand becomes lower. Therefore, the system can be defined as more environmentally friendly. The C2C concept can help retailers financially address the problem of online returns and meet the growing need for reducing their environmental impact.

This paper considers a supplier who offers a customized product to multiple potential customers, each with uncertain demand. The supplier’s end items consume common raw materials, which must be ordered far in advance of the selling season due to long procurement lead times, limiting the supplier’s capacity to meet realized demands. As a result of the customization, each customer negotiates an individual agreement with the supplier, leading to customer-specific prices and loss of goodwill costs in case of unsatisfied demands. The supplier aims to select a portfolio of customers and a raw material procurement quantity to maximize its expected profit. We formulate the problem based on echelon stockout costs, and establish optimality conditions and bounds for the newsvendor solution. This formulation, moreover, elegantly generalizes the analysis of the so-called selective newsvendor problem to address settings with lost sales and an uncertain spot market price for expediting. As customer requirements are oftentimes interdependent, we further extend our models to handle correlated customer demands. Lastly, we analyze the setting in which capacity can be reserved with the supplier prior to the selling season. Such contracts may serve as coordination mechanisms to improve overall supply chain profitability. Since the resulting models are in general difficult to solve exactly, we propose conic quadratic programming-based heuristics as well as sampling-based methods. We validate our approach through an extensive numerical study, illustrating the effectiveness of the proposed solution methods, the importance of explicitly considering correlation among customers due to risk pooling effects, and the impact of capacity reservation on both supplier and customer decisions. Finally, to support real-world applicability, we demonstrate the remarkable performance of the solutions obtained assuming normally distributed demands even when the true distributions deviate from this assumption.

This paper considers a single-machine scheduling problem to minimize the total weighted completion time with a weight modifying activity, after which the job weights are discounted by a given factor. The problem is known to be ordinary NP-hard. We propose two mixed integer linear programs (MILPs) and a dynamic programming algorithm to optimally solve the problem. Optimality properties are established and then formulated as pruning constraints to improve the problem-solving efficiency of the MILPs. Special cases are discussed and shown to be solvable by polynomial time algorithms. Complexity status of the studied problem with several instance characteristics is shown. Computational experiments indicate that the optimality properties can reduce the computing efforts and that one of the proposed MILPs can solve instances of 200 jobs in a few seconds.

In this paper we introduce and study the rescheduling problem to trade off between global disruption of the original jobs with flexibility and the scheduling cost of the new jobs. A set of original jobs has been scheduled on a single machine. But before the processing of original jobs begins, a set of new jobs arrives unexpectedly. The scheduler needs to adjust the existing schedule with a view to finding a cost-efficient schedule for the new jobs without causing too much disruption of the original schedule. We make three assumptions that are different from those in the literature: (i) the original jobs are regarded as a unified whole (a big job) and the global disruption of the original jobs is considered, (ii) the original jobs can be split into small pieces in a schedule, which enables effective control of the global disruption, and (iii) the cost of the original jobs depends on the global disruption, while the cost of the new jobs is expressed as a regular scheduling criterion, such as the maximum lateness, the total weighted completion time, and total weighted number of tardy jobs. We analyze the computational complexity of variants of the rescheduling problem.