International Journal of Production Economics最新文献_第5页

A revisited by-production technology for energy-carbon emission nexus in the OECD countries: Measuring the green productivity gains under alternative input specifications 经济合作与发展组织（OECD）国家能源-碳排放关联的副产技术：在替代投入规格下衡量绿色生产力收益

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.ijpe.2025.109900

Zhiyang Shen , Ge Bai , Tomas Baležentis , Bin Zhang

The by-production (BP) model proposed by Murty et al. (2012) distinguishes between inputs that induce generation of the undesirable outputs (such inputs can include energy consumption) and those that do not contribute to generation of undesirable outputs (such inputs can include capital or labor). The BP model uses economic and environmental frontiers to approximate the production process and is considered to respect the materials balance principle. As the inputs contributing to generation of the undesirable outputs exhibit different roles (inequalities) across the two sub-technologies, construction of the input distance functions for construction of the productivity measures becomes cumbersome. To address this issue, the paper proposes a revision of the BP model where (i) the link between the two sub-technologies is improved and (ii) the role of the inputs that contribute to generation of the undesirable outputs is clarified. The revised model is fully compatible with popular productivity indices that incorporate input distance functions, such as the Luenberger-Hicks-Moorsteen indicator, making it practically applicable for assessing green productivity change. The empirical example of the energy-economy-environment nexus in the OECD countries is considered. The non-parametric environmental production technologies and productivity measures are compared based on the empirical example. The findings reveal substantial differences among the various methods and suggest that the proposed approach may serve as a viable alternative to existing approaches.

Murty等人（2012）提出的副产品（BP）模型区分了导致产生不良产出的投入（此类投入可以包括能源消耗）和不导致产生不良产出的投入（此类投入可以包括资本或劳动力）。BP模型使用经济和环境边界来近似生产过程，并被认为尊重物料平衡原则。由于导致产生不良产出的投入在两个子技术中表现出不同的角色（不平等），因此构建用于构建生产率度量的输入距离函数变得繁琐。为了解决这一问题，本文提出了对BP模型的修订，其中(i)改进了两个子技术之间的联系，（ii）澄清了导致产生不良产出的投入的作用。修正后的模型与Luenberger-Hicks-Moorsteen指标等包含输入距离函数的流行生产力指数完全兼容，使其实际适用于评估绿色生产力变化。本文考虑了经合发组织国家能源-经济-环境关系的经验例子。通过实证对非参数环境生产技术和生产率测度进行了比较。研究结果揭示了各种方法之间的实质性差异，并建议建议的方法可以作为现有方法的可行替代方案。

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引用次数: 0

A two-stage stochastic model for sustainable and resilient closed-loop supply chain of electric vehicle batteries 电动汽车电池可持续弹性闭环供应链的两阶段随机模型

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.ijpe.2025.109878

Danting Yang, Fei Ma, Haonan He, Yanni Long, Lujin Jia, Wenjun Liu

To address the growing need for recycling retired electric vehicle (EV) batteries, it is crucial to design a sustainable closed-loop supply chain (CLSC). Meanwhile, the EV battery SC remains vulnerable in today's volatile environment, highlighting the need for resilient designs to mitigate risks. Thus, this study contributes to develop a progressive three-tier modeling framework that evolves from a deterministic “risk-free” model to a stochastic “risk-inclusive” model, and ultimately to a resilient “risk-responsive” model. The model promotes industrial sustainability across economic, environmental, and social dimensions, and introduces five sector-specific resilience strategies to mitigate risks. A scenario-based two-stage stochastic programming approach is employed to address uncertainties in supply, demand, and recycling. The model's effectiveness is validated through a real-world case study of CATL, the world's largest EV battery manufacturer. Results show that the CLSC significantly enhances sustainability compared to one-way SCs. The resilient model improves resilience and reduces costs relative to non-resilient alternatives. The bi-objective model, balancing resilience enhancement and cost reduction, achieves substantial resilience gains with minimal cost increases. The study further demonstrates that comprehensive strategies are more effective than single-focused ones in cost control and resilience enhancement, and that optimal resilience strategies vary significantly with risk types and intensities. Finally, managerial and policy insights are provided based on the findings of the case study and sensitivity analysis.

为了满足日益增长的回收利用电动汽车电池的需求，设计一个可持续的闭环供应链（CLSC）至关重要。与此同时，电动汽车电池SC在当今动荡的环境中仍然很脆弱，因此需要有弹性的设计来降低风险。因此，本研究有助于建立一个渐进的三层建模框架，从确定性的“无风险”模型到随机的“风险包容”模型，最终到弹性的“风险响应”模型。该模型促进了工业在经济、环境和社会方面的可持续性，并引入了五种针对特定行业的弹性策略来降低风险。采用基于场景的两阶段随机规划方法来解决供应、需求和回收中的不确定性。该模型的有效性通过全球最大的电动汽车电池制造商宁德时代的实际案例研究得到了验证。结果表明，与单向复合材料相比，复合材料复合材料显著提高了可持续性。与非弹性替代方案相比，弹性模型提高了弹性并降低了成本。双目标模型平衡了弹性增强和成本降低，以最小的成本增加实现了大量的弹性增益。研究进一步表明，综合策略在成本控制和弹性增强方面比单一策略更有效，且最优弹性策略随风险类型和强度的不同存在显著差异。最后，基于案例研究和敏感性分析的结果，提出了管理和政策见解。

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引用次数: 0

Preventing drug shortages through improved demand fulfillment: The untapped potential of postponement and flexibility 通过改善需求满足来预防药品短缺：推迟和灵活性的未开发潜力

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ijpe.2025.109902

Gregor Blossey , Gerd J. Hahn , Achim Koberstein

Pharmaceutical supply chains have become increasingly fragmented and rigid, which severely impairs their ability to manage uncertainties. A symptom of this development is the growing number of drug shortages including serious implications for patient care. Against this backdrop, this article explores the use of postponement and various types of flexibilities as means to improve the management of uncertainties and, thus, to avert drug shortages. A stochastic multi-stage MILP model is developed to determine the optimal network design of a multi-tier pharmaceutical supply chain under uncertain demands. Extensive numerical studies based on a case from a global pharmaceutical company are conducted in which the impact of two uncertainty dimensions, i.e. the degree of uncertainty and the degree of anticipation, is studied. We identify four distinct network design strategies, named postponement, decentralization, redundancy, and chaining, which are driven by these dimensions. While each strategy is cost-optimal under specific circumstances, they all outperform the industry standard network design. Moreover, we find that the optimal flexibility placement in these strategies mirrors the so-called chaining guidelines proposed in the literature. Overall, this work offers a rare integrated perspective on postponement and flexibility with valuable insights for theory and practice.

药品供应链变得越来越分散和僵化，这严重损害了它们管理不确定性的能力。这种发展的一个症状是药物短缺的数量日益增加，包括对病人护理的严重影响。在此背景下，本文探讨了使用延迟和各种类型的灵活性作为改进不确定性管理的手段，从而避免药品短缺。为了确定需求不确定情况下多层药品供应链的最优网络设计，建立了一种随机多阶段MILP模型。本文以一家全球性制药公司为例进行了广泛的数值研究，研究了两个不确定性维度（即不确定性程度和预期程度）的影响。我们确定了由这些维度驱动的四种不同的网络设计策略，称为延迟、去中心化、冗余和链。虽然每种策略在特定情况下都是成本最优的，但它们都优于行业标准网络设计。此外，我们发现这些策略中的最优灵活性位置反映了文献中提出的所谓连锁准则。总的来说，这项工作为延迟和灵活性提供了一个罕见的综合视角，具有理论和实践的宝贵见解。

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引用次数: 0

A modeling and optimization approach to a tactical sugarcane harvest planning problem 战术甘蔗收获规划问题的建模与优化方法

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 10.1016/j.ijpe.2025.109896

Willy A. Oliveira Soler , Maristela Oliveira Santos , Rogério de Ávila Ribeiro Junqueira , Reinaldo Morabito

In this paper, we address a tactical sugarcane harvest planning problem commonly faced by industries that cultivate, harvest, and process sugarcane in Brazil and some other countries. The problem involves a single mill that must harvest sugarcane from several geographically dispersed areas (harvest blocks) using capacitated mechanized harvest fronts. The objective is to minimize the total costs of the harvest process while satisfying operational constraints, including capacity limits for transportation and harvesting. We propose a new mixed-integer programming (MIP) model and develop MIP-based constructive and improvement heuristic procedures. In the constructive phase, the method generates a strong dual bound and an initial feasible solution, while the improvement phase refines this solution to achieve higher quality. The efficiency of the proposed methods is evaluated through computational experiments using a dataset based on real-world scenarios. Additionally, a sensitivity analysis provides managerial insights by examining different harvest front configurations.

在本文中，我们解决了巴西和其他一些国家甘蔗种植、收获和加工行业普遍面临的战术甘蔗收获计划问题。这个问题涉及到一个单一的工厂，它必须从几个地理上分散的地区（收获区）使用有能力的机械化收获前线收割甘蔗。目标是在满足操作限制的同时，将收获过程的总成本降到最低，包括运输和收获的能力限制。提出了一种新的混合整数规划模型，并开发了基于混合整数规划的建设性和改进启发式程序。在构造阶段，该方法生成一个强对偶界和一个初始可行解，而改进阶段则对该解进行细化，以获得更高的质量。通过基于真实场景的数据集的计算实验，评估了所提出方法的效率。此外，敏感性分析通过检查不同的收获前端配置提供管理见解。

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引用次数: 0

From text to risk: Predicting repayment risk in supply chain finance with deep learning and large language models 从文本到风险：基于深度学习和大型语言模型的供应链金融还款风险预测

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.ijpe.2025.109906

Liangliang Hou, Shichun Du, Gongbing Bi

Supply chain finance (SCF) plays a pivotal role in enhancing liquidity and operational efficiency for supply chain enterprises, particularly for capital-constrained suppliers. However, repayment risk—the failure of borrowers to meet payment obligations—can propagate through supply networks, causing systemic financial disruptions and production delays. While existing studies predominantly rely on structured financial data (e.g., financial statements) and periodic reports for risk assessment, these approaches suffer from timeliness gaps and fail to reflect real-time operational vulnerabilities. To address these limitations, this study proposes DeepRRP, a novel repayment risk prediction (RRP) framework that integrates the latest disclosures (e.g., litigation announcements, executive changes) with conventional financial indicators. Leveraging advanced large language models (LLMs) and deep learning algorithms, DeepRRP captures risk signals from disclosure texts and models their temporal dependencies, enabling proactive risk mitigation. Empirical tests on Chinese listed firms demonstrate that textual features from the latest disclosures can effectively compensate for the timeliness lag of traditional financial data and improve the accuracy of RRP. In addition, interpretable analysis identifies key factors affecting repayment risk, helping practitioners optimize lending terms and suppliers manage working capital.

供应链金融在提高供应链企业，特别是资金紧张的供应商的流动性和运营效率方面发挥着关键作用。然而，还款风险——借款人未能履行付款义务——可能通过供应网络传播，导致系统性金融中断和生产延迟。虽然现有研究主要依赖结构化财务数据（如财务报表）和定期报告进行风险评估，但这些方法存在及时性差距，无法反映实时操作漏洞。为了解决这些限制，本研究提出了DeepRRP，这是一种新颖的还款风险预测（RRP）框架，将最新披露（如诉讼公告、高管变动）与传统财务指标相结合。利用先进的大型语言模型（llm）和深度学习算法，DeepRRP从披露文本中捕获风险信号，并对其时间依赖性进行建模，从而实现前瞻性风险缓解。对中国上市公司的实证检验表明，最新披露的文本特征可以有效地弥补传统财务数据的时效性滞后，提高RRP的准确性。此外，可解释的分析确定了影响还款风险的关键因素，帮助从业者优化贷款条件和供应商管理营运资金。

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引用次数: 0

Perishable inventory management under inventory level- and freshness-dependent demand and backroom effect 库存水平、新鲜度依赖需求和密室效应下的易腐库存管理

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-03-01 Epub Date: 2025-12-06 DOI: 10.1016/j.ijpe.2025.109869

Ilgin Efe Senyuva , Melvin Drent , Zumbul Atan

Managing perishable inventory in grocery retailing is challenging due to limited shelf life and consumer expectations for freshness. We develop an optimization model to guide a retailer’s in-store replenishment process, where inventory is initially stored in a backroom before being moved to the shelf. Consumer demand is stochastic and depends on shelf-life and inventory level, and the retailer wants to maximize long-term discounted profit by determining optimal time to move inventory from the backroom to the shelf. We show that the optimal policy follows a threshold structure dependent on shelf inventory levels and product lifetimes. To simplify decision-making, we propose heuristics. Our analysis indicates that the optimal time to move products from backrooms to shelves is highly dependent on the characteristics of the products. The main driver of this decision is the shelf inventory level for products with long shelf-lives and small batches. On the other hand, for products with short shelf-lives and large batch-sizes, the main driver is the product lifetime. While mixing batches on the shelf can reduce waste under ideal backroom storage, displaying a single batch is more profitable when backroom deterioration is significant.

由于有限的保质期和消费者对新鲜度的期望，管理杂货零售中的易腐库存是具有挑战性的。我们开发了一个优化模型来指导零售商的店内补货流程，其中库存最初存储在后台，然后移动到货架上。消费者需求是随机的，取决于货架寿命和库存水平，零售商希望通过确定将库存从后屋转移到货架的最佳时间来最大化长期折扣利润。我们表明，最优策略遵循依赖于货架库存水平和产品寿命的阈值结构。为了简化决策，我们提出了启发式方法。我们的分析表明，将产品从密室转移到货架的最佳时间高度依赖于产品的特性。这一决定的主要驱动因素是货架库存水平的产品与长货架寿命和小批量。另一方面，对于保质期短、批量大的产品，主要驱动因素是产品寿命。虽然在理想的后置存储条件下，将批次混合在货架上可以减少浪费，但当后置存储严重恶化时，展示单个批次更有利可图。

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引用次数: 0

Human vs. Generative AI: Strategic content creation mode choices for competing creators 人类vs.生成AI：竞争创造者的战略性内容创造模式选择

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.ijpe.2026.109913

Nenggui Zhao , Youqing Wu , Kai Li

With the rapid development of the platform economy, product-related content (e.g., short videos, copywriting, and live streaming) has emerged as a pivotal determinant of consumer decision-making. The widespread adoption of Generative Artificial Intelligence (GAI) technology enables content creators to produce product-related content more efficiently and at lower cost, yet it concurrently introduces new challenges. Specifically, the proliferation of GAI has heightened consumer skepticism regarding the authenticity and reliability of GAI-created content. Therefore, achieving a balance between GAI application and consumer trust is crucial for content creators. This paper examines a retail platform with two competing content creators who face a strategic choice between two content creation strategies: GAI-creation and human-creation, with consumers exhibiting greater trust in the latter. Our findings indicate that when the costs of GAI-creation are low, creators increase their content effort levels and set higher selling prices. Conversely, higher GAI costs result in reduced effort levels and lower pricing. Notably, under low GAI-creation costs, adopting GAI proves beneficial for creators, irrespective of their competitors’ strategies. Furthermore, if both parties adopt the GAI-creation strategy, a triple-win situation can be achieved among the platform and the two creators. However, when the GAI-creation costs are high, creators eschew GAI adoption regardless of their competitors’ decisions. In such cases, mutual reliance on human-creation yields a triple-win outcome for the entire supply chain. Finally, under hybrid strategy scenarios, the platform systematically guides creators with greater market influence toward the content creation strategy that holds relative advantages.

随着平台经济的快速发展，与产品相关的内容（如短视频、文案、直播等）已经成为影响消费者决策的关键因素。生成式人工智能（GAI）技术的广泛采用使内容创作者能够以更低的成本更高效地生产与产品相关的内容，但同时也带来了新的挑战。具体来说，GAI的扩散加剧了消费者对GAI创建内容的真实性和可靠性的怀疑。因此，实现GAI应用和消费者信任之间的平衡对于内容创造者来说是至关重要的。本文研究了一个零售平台，该平台有两个相互竞争的内容创造者，他们面临着两种内容创造策略的战略选择：人工智能创造和人类创造，消费者对后者表现出更大的信任。我们的研究结果表明，当人工智能创造的成本较低时，创作者会增加他们的内容努力水平，并设定更高的销售价格。相反，较高的GAI成本会导致工作水平的降低和价格的降低。值得注意的是，在低人工智能创造成本的情况下，采用人工智能证明对创造者是有益的，而不管其竞争对手的策略如何。此外，如果双方都采用ai创作策略，则可以实现平台与两家创作者的三赢局面。然而，当人工智能的创造成本很高时，创造者就会不顾竞争对手的决定而回避采用人工智能。在这种情况下，对人类创造的相互依赖为整个供应链带来了三赢的结果。最后，在混合策略场景下，平台系统地引导具有较大市场影响力的创作者走向具有相对优势的内容创作策略。

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引用次数: 0

Dynamic expiration date-based discounting of fresh food products 生鲜食品动态保质期折扣

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-02-01 Epub Date: 2025-10-19 DOI: 10.1016/j.ijpe.2025.109824

Rene Haijema , Lisan Duijvestijn , Renzo Akkerman , Frans Cruijssen

To reduce food waste, many supermarkets discount food products that are close to their expiration date. In practice, this is done either by discount labels put on the product or by electronic shelf labels (or digital price tags) showing the price per expiration date. Digital price tags allow to easily change the price of products and to apply different discount rates to items with different expiration dates. An important question to practitioners is when and how much discount to offer. In this study, we use Stochastic Dynamic Programming (SDP) to derive optimal expiration-date-based discounting policies for a profit-maximizing retailer who sells a product with

m

periods (e.g., days) of shelf life. We compare various discounting strategies, such as static last-day discounting, optimal dynamic last-day, and last-two-days discounting, against the no-discounting strategy.

The model allows products of different expiration dates to be in stock simultaneously, as replenishment happens every period. In the last-day discounting policies, two selling prices co-exist: the regular price and the discounted price. When applying a last-two-days discounting policy, three selling prices co-exist. Demand and product withdrawal depend on both price and product age (freshness). We consider different customer picking behavior, and divide customers into First-Expiry-First-Out (FEFO) and Last-Expiry-First-Out (LEFO) consumers (i.e, customers that pick the oldest items first and customers that take the freshest items available). For LEFO customers, we also consider that a fraction of these customers will pick discounted old items (depending on the size of discount). Finally, extra demand is attracted as long as discounted products are available.

Optimal policies are derived by SDP and evaluated by simulation to generate insights into the impact of discounting on profits, sales, fill rates, and waste. Various key factors, such as shelf life, customer picking behavior, and discount sensitivity are analyzed in detail. The results show that the last-two-days discounting policy performs well. Averaged over all experiments, this policy demonstrates a 3.8% increase in profits compared to no-discounting, and a waste reduction from 5.6% to 3.6%. Smaller, but still significant improvements are shown over simpler discounting policies.

为了减少食物浪费，许多超市对接近保质期的食品打折。实际上，这是通过贴在产品上的折扣标签或电子货架标签（或数字价格标签）来实现的，这些标签显示了每个有效期的价格。数字价格标签允许轻松更改产品价格，并对不同有效期的项目应用不同的折扣率。对于从业者来说，一个重要的问题是何时以及提供多少折扣。在这项研究中，我们使用随机动态规划（SDP）来为一个利润最大化的零售商提供基于到期日期的最优折扣策略，该零售商销售的产品有m个保质期（例如，天）。我们比较了各种折扣策略，如静态最后一天折扣，最优动态最后一天和最后两天折扣，与无折扣策略。该模型允许不同有效期的产品同时库存，因为每个周期都会进行补货。在最后一天的折扣政策中，两种销售价格并存：正常价格和折扣价。在实行最后两天折扣政策时，三个销售价同时存在。需求和产品撤回取决于价格和产品年龄（新鲜度）。我们考虑不同的顾客挑选行为，并将顾客划分为先过期先出（FEFO）和后过期先出（LEFO）消费者（即，先挑选最旧商品的顾客和先挑选最新鲜商品的顾客）。对于LEFO客户，我们还认为这些客户中的一小部分会选择打折的旧商品（取决于折扣的大小）。最后，只要有打折的产品，就会吸引额外的需求。通过SDP推导出最优策略，并通过模拟进行评估，以深入了解折扣对利润、销售、填充率和浪费的影响。详细分析了各种关键因素，如保质期、顾客挑选行为和折扣敏感性。结果表明，最后两天的折扣政策表现良好。从所有实验的平均值来看，与不打折相比，该政策的利润增加了3.8%，浪费从5.6%减少到3.6%。与简单的折扣政策相比，有了较小但仍显着的改善。

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引用次数: 0

Facilitating net-zero emissions goals through green finance: Enhancing efficiency in corporate green innovation within a two-stage value chain framework 通过绿色金融促进实现净零排放目标：在两阶段价值链框架内提高企业绿色创新效率

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-02-01 Epub Date: 2025-10-17 DOI: 10.1016/j.ijpe.2025.109832

Xiuli Liu , Jing Cui , Yukun Chang , Chunguang Bai , Xiaohang Yue , Jun Shen , Qinqin Shi

Green finance plays a crucial role in reducing corporate carbon emissions. However, the mechanisms linking green finance to emission reduction remain underexplored. This study examines 1399 Chinese listed companies from 2013 to 2022 to evaluate the carbon-reducing effects of the Green Financial Reform and Innovation Pilot Zone (GFRI). Using a two-stage value chain framework, we decompose green innovation into green technology research and development and green outcomes transformation to analyze the transmission mechanisms. The results show three key findings. First, the implementation of the GFRI significantly reduces corporate carbon emissions, and the results are robust across specifications. Second, the policy effect is stronger among firms in the central and eastern regions and in the manufacturing sector. Third, the carbon reduction effect of the GFRI is primarily driven by improvements in green innovation efficiency. Green outcomes transformation efficiency plays a more critical role than green technology research and development efficiency. These findings suggest that firms should accelerate green innovation processes and strengthen internal regulatory mechanisms to increase the effectiveness of green finance policies in promoting carbon reduction.

绿色金融在减少企业碳排放方面发挥着至关重要的作用。然而，将绿色金融与减排联系起来的机制仍未得到充分探索。本研究以2013 - 2022年1399家中国上市公司为研究对象，对绿色金融改革创新试验区（GFRI）的碳减排效果进行了评估。本文采用两阶段价值链框架，将绿色创新分解为绿色技术研发和绿色成果转化，分析绿色创新的传导机制。研究结果显示了三个关键发现。首先，GFRI的实施显著减少了企业的碳排放，并且结果在各种规格中都是稳健的。第二，政策效应在中东部地区企业和制造业中更强。第三，GFRI的碳减排效果主要是由绿色创新效率的提高驱动的。绿色成果转化效率比绿色技术研发效率更重要。这些研究结果表明，企业应加快绿色创新进程，加强内部监管机制，以提高绿色金融政策在促进碳减排方面的有效性。

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引用次数: 0

AI Platforms for Digital Servitization and Solution Delivery: Where ecosystem, production technology, and business model trajectories intersect to generate smart solutions 数字化服务化和解决方案交付的人工智能平台：生态系统、生产技术和商业模式轨迹相交，产生智能解决方案

IF 1 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2026-02-01 Epub Date: 2025-10-16 DOI: 10.1016/j.ijpe.2025.109826

Yancy Vaillant , Samuel Fosso Wamba , Rodrigo Rabetino

The rapid advancement of digitalization and artificial intelligence (AI) is transforming the manufacturing landscape, creating turbulence and complexity that challenge established managerial frameworks. Many of the theoretical models that once guided production management now require revision to remain relevant in an AI-driven environment. This special issue on AI Platforms for Digital Servitization and Solution Delivery of the International Journal of Production Economics addresses this need by integrating research on three converging trajectories—platform ecosystems, digital servitization, and solution delivery—into a unified, programmatic theoretical framework. The editorial conceptualises AI platforms as key enablers that connect these domains, facilitating the development of comprehensive smart solutions through the alignment of business models, production technologies, and value ecosystems. Collectively, the studies contribute to a deeper understanding of how AI reshapes value creation and delivery in manufacturing contexts. This framework provides a foundation for future empirical validation and offers practical insights to guide managers in navigating the evolving realities of algorithmic-driven production systems.

数字化和人工智能（AI）的快速发展正在改变制造业格局，带来动荡和复杂性，挑战现有的管理框架。许多曾经指导生产管理的理论模型现在需要修改才能在人工智能驱动的环境中保持相关性。本期《国际生产经济学杂志》关于人工智能平台数字服务化和解决方案交付的特刊通过将三个趋同轨迹——平台生态系统、数字服务化和解决方案交付——的研究整合到一个统一的、程序化的理论框架中，解决了这一需求。该社论将人工智能平台定义为连接这些领域的关键推动者，通过协调商业模式、生产技术和价值生态系统，促进全面智能解决方案的开发。总的来说，这些研究有助于更深入地了解人工智能如何在制造业环境中重塑价值创造和交付。该框架为未来的经验验证提供了基础，并提供了实用的见解，以指导管理人员在算法驱动的生产系统的不断发展的现实中导航。

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引用次数: 0