International Journal of Production Economics最新文献

In supermarkets, rapid pricing adjustments are crucial due to the short shelf life of products. Adopting a multi-case study approach, this study examines the application of dynamic pricing strategies for perishable goods through the lens of a data-driven digital transformation (DD-DT) approach. We introduce a three-phase model of DD-DT for dynamic pricing: initiation, facilitation, and strategic adaptation. In the initiation phase, we identify essential frameworks for robust data collection and analytical processes, which form the backbone of informed pricing decisions. During the facilitation phase, the study integrates sophisticated algorithms and real-time analytics to process and interpret the collected data, facilitating its seamless integration into pricing strategies. The strategic adaptation phase is critical as it focuses on the ongoing refinement and enhancement of pricing strategies, enabling supermarkets to adapt swiftly to market fluctuations and consumer behaviour changes. By presenting a comprehensive DD-DT framework, this research significantly augments the existing literature on dynamic pricing and offers actionable insights for practitioners seeking to optimize pricing strategies in a digitally transforming marketplace.

Supply chain analytics capability has recently gained more attention from both scholars and practitioners. Through the lens of information processing theory, this study examined how supply chain analytics capability impacts supply chain agility in the digital era with the associated adoption of digital technologies and platforms by companies. To obtain relevant data, a large-scale survey of Chinese manufacturing firms was conducted. Supply chain integration capability was evaluated as mediator and digital platform was evaluated as moderator. The results show that supply chain integration capability can mediate the effect of supply chain analytics capability on supply chain agility; moreover, supply chain analytics capability has a stronger impact on supply chain agility under a high level of digital platform adoption. These findings deepen the understanding of the role of supply chain analytics capability in supply chain management. This paper also provides managerial implications for enhancing supply chain agility in the digital era.

Advance selling enables consumers to order new products before their release, ensuring prompt deliveries upon release. Retailers benefit from advance selling by boosting total sales and mitigating excess or insufficient inventory costs. In practice, various advance selling policies are designed and applied. Standard Advance Selling, Advance Selling with Price Guarantee, and Advance Selling with a Deposit are often seen and successfully implemented policies by different retailers. However, given the stochastic demands and consumer valuations, whether advance selling is always desirable for retailers is unclear. If advance selling is beneficial, what are the optimal prices and ordering quantities and which policy is the best remain interesting questions. This paper aims to provide analytical solutions for optimal pricing and inventory decisions under the three advance selling policies mentioned above. The results show that the performance of the advance selling policies relies on the proportion of high-valuation consumers. The critical values of the proportion of high-valuation consumers are identified. Further, we extend the problem to consider the effects of advertising. The advertising affects the information diffusion and indirectly affects the demands of the product. Moreover, the analytical and numerical study compares the performances of different advance selling policies. Finally, multiple managerial insights are summarized.

The global manufacturing supply chain is undergoing a digital transformation (DT) powered by various digital technologies. In both stable and turbulent environments, DT helps safeguard supply chain performance by enhancing supply chain agility. While research on the use of digital technologies and their impacts on supply chains is growing, there is a lack of an overarching theoretical lens to synthesize their diverse functionalities, effects, and benefits. To address this gap, we adapt the concept of the data network effect to the supply chain context and propose that DT improves supply chain performance by enhancing supply chain resilience (SCRes) and robustness (SCRob) capabilities. To validate our hypotheses, we conducted a large-scale survey for data collection and performed Partial Least Squares Structural Equation Modelling (PLS-SEM) for data analysis. The results confirm the positive effect of DT on supply chain performance and the mediating roles of SCRob and SCRes. Our study contributes to the ongoing discussion on DT in the context of supply chains by introducing a novel theoretical perspective on the supply chain data network effect.

The literature on the antecedents of successful data-driven digital transformations needs clarity on if and how employees' cognitions and behaviours have any bearing on data-driven digital transformations with implications for operational productivity. This paper addresses this gap by drawing on socio-cognitive theory to examine how employees' cognitions (psychological safety) shape individual behaviours (employee-led process improvement) to affect organisational attainments such as data-driven digital transformations and operational productivity. A theoretical framework linking psychological safety to operational productivity through individual and serial mediations of ‘employee-led process improvement' and ‘data-driven digital transformation’ is statistically tested by collecting survey data from 183 healthcare providers in Australia. The results indicate that when employees' perceptions of interpersonal risks are allayed (psychological safety), it has a significant positive effect on operational productivity directly and indirectly through the individual and serial mediations of employee-led process improvement and data-driven digital transformations. The socio-cognitive theorisation of psychological safety as the driving mechanism that facilitates employee-led process improvement, data-driven digital transformation and operational productivity is a first in the academic literature with implications for both theory and practice.

Information technology (IT) coordination with supply chain (SC) partners improves customer and supplier integration. However, little research integrates multinational firms' cultural and geographical distance with subsidiaries' SC practice even as subsidiaries increasingly face problems engaging with their parent firms due to institutional pressures and poor SC integration. This study investigates the moderating roles of cultural and geographical distance between parent firms and subsidiaries on the relationship between subsidiaries' IT coordination with SC partners and customer and supplier integration. Three data sources were utilized to test the hypotheses. Survey data were collected from multinational firms in the retail industry across a variety of countries. Hofstede's national culture index was used to measure cultural distance, and Google Maps data were used to calculate geographical distance. We conducted structural equation modelling and hierarchical regression analysis to test the varying effects. The results indicate that when subsidiaries have higher cultural and geographical distance from their parent firms, the positive effects of IT coordination on customer and supplier integration of subsidiaries are weaker. Understanding the roles of cultural and geographical distance is important for both parent firms and subsidiaries to facilitate IT-enabled SC integration and to select the geographical locations of subsidiaries.

Green technology innovation (GTI) emerges as a critical pathway for reducing energy consumption, is crucial for semiconductor industry to address the issue of high energy consumption. Digital transformation (DT) offers opportunities to enhance GTI. There are mixed results of the relationships between DT and GTI, however, empirical studies focusing on the semiconductor industry are scant. Based on the practice-based view framework and the characteristics of Chinese semiconductor industry, we investigate how DT affects GTI. Utilizing data from 191 Chinese semiconductor firms during 2014–2022, we draw empirical evidence that DT has positive effects on GTI and the positive effects are more pronounced in non-state-owned enterprises than in state-owned enterprises. Interestingly, media coverage and executive compensation incentives weaken the positive effects. These findings advance the literatures of GTI and practice-based view and offer valuable managerial implications to enhancing GTI of Chinese semiconductor firms.

Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a Many-objective Optimization Problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events.

For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.

Working only with suppliers that meet certain social and environmental standards has become a new norm in the selection of business partners for many firms, which encourages suppliers to become more transparent to gain a competitive advantage. This study investigates whether buyer legitimacy is associated with supply chain transparency from an environmental, social, and governance (ESG) perspective. Using a dataset of 605 dyadic relationships from Chinese listed firms for the period 2011–2019, we empirically examine our conceptual framework using hierarchical linear regression models. We demonstrate that buyer legitimacy is positively associated with supplier ESG transparency in dyadic supply chains. We further find that the supplier's competition intensity negatively moderates the positive relationship between buyer legitimacy and supplier ESG transparency. Overall, our findings contribute to a better understanding of organizational legitimacy and the buyer–supplier relationship and offer practical guidelines for enhancing supply chain transparency.

To address the UN’s zero hunger goal (SDG 2), scattered and isolated initiatives by nonprofit organizations towards operating foodbanks are generally ineffective in developing countries where the foodbank ecosystem is at a preliminary stage. Establishing an integrated system comprising entities such as donors, foodbanks, food recovery and redistribution agencies (FRRA), and beneficiaries can be quite complex due to an underlying hierarchy, scale of operation, types of donors, and the severity of food insecurity of the beneficiaries. In this work, we present a strategic mixed-integer programming model to design an integrated foodbank network towards achieving an efficient, effective, and equitable food distribution mechanism for food-insecure beneficiaries while accounting for their age profile and nutritional requirements. We ensure cost-efficiency by minimizing the total system cost, effectiveness by discouraging food waste and unmet demand via charging penalties, and equity by adopting five variants of an egalitarian approach. We conduct a case study with a mix of real and realistically estimated data to design a foodbank network in Delhi (India) and present detailed analyses with insights for the practitioners. Specifically, the effects of foodbanks’ initial capacities, budget and strategic-to-operational cost constraints on the solution are identified. Among important observations, our analyses highlight when initiatives for collecting more ready-to-eat foods might be taken to relieve the pressure on the integrated system, and also help in identifying the conditions when investment in capacity building serves the beneficiaries’ interests better than direct spending.