International Journal of Production Economics最新文献

A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms 数字孪生环境的混合数据驱动优化和决策方法：定制生产平台

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-26 DOI: 10.1016/j.ijpe.2024.109447

In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.

在工业 4.0 时代，先进技术正在改变制造流程和系统。此外，大数据和人工智能技术的日益普及也使得利用制造数据进行决策变得越来越重要。然而，由于资源的限制和制约，中小型企业（SMEs）在采用这些技术时遇到了巨大障碍。对于中小企业来说，由于制造系统的复杂性，选择合适的生产战略具有挑战性。为此，本文提出了一个混合模拟优化与多标准决策（SOMCDM）框架，以帮助中小企业确定有效的定制生产布局。在所提出的方法中，我们使用蜂窝制造系统对各种生产场景进行建模。使用多变量自适应回归样条曲线（MARS）创建不同生产布局的替代模型，并将其转化为基函数。随后，在遗传算法中将基函数作为拟合函数来确定最佳生产参数。然后，将优化参数应用于生产标准，并使用多标准决策技术进行排序。在一个案例研究中，所提出的框架被应用于为一家组装复杂产品的公司从三种方案中选择最佳生产平台。与现有生产平台相比，所选生产平台的整体生产绩效提高了 11.95%。这项研究证明了所提出的框架在使用数字孪生环境下的 SOMCDM 为生产高混合、小批量产品的劳动密集型中小企业确定最佳生产平台方面的有效性。通过对一家 3D 打印机装配厂的案例研究，进一步详细介绍了所提出的框架。

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引用次数: 0

Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies 区块链对范围 3 碳披露的价值：数据处理技术的调节作用

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-24 DOI: 10.1016/j.ijpe.2024.109445

Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.

披露范围 3 的碳排放量对于减轻污染和相关环境损害至关重要，而区块链可以加强披露。然而，由于缺乏实证证据，区块链对范围 3 碳披露的影响仍不明确。本文研究了区块链对范围 3 碳信息披露的价值，并探讨了是否可以通过整合数据处理技术（包括人工智能（AI）、云计算和大数据分析（BDA））来加强这一价值。借鉴协调理论，我们认为区块链作为一种记录和追踪技术，可以提高供应链成员在收集碳排放数据方面的协调性，从而促进企业的范围 3 碳披露。此外，数据处理技术可以有效利用和管理收集到的数据，并有可能与区块链协调，加强范围 3 碳披露。我们以 2021 年和 2022 年期间中国上市公司的 422 个观测值为样本，使用回归分析法检验了这些关系。结果表明，区块链的采用与企业的范围 3 碳信息披露呈正相关。此外，采用人工智能、云计算和 BDA 三种数据处理技术中的每一种都会进一步加强这种正相关关系。本研究为有关区块链和可持续供应链管理的学术知识和证据做出了贡献，为通过结合采用区块链和数据处理技术在供应链层面管理碳排放提供了实用建议。

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引用次数: 0

Contagion of corporate misconduct in the supply chain: Evidence from customers and suppliers in China 企业不当行为在供应链中的蔓延：来自中国客户和供应商的证据

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-23 DOI: 10.1016/j.ijpe.2024.109443

Non-compliance with operational procedures can significantly disrupt the functioning of supply chains. This study examines the impact of corporate misconduct by both supplier and customer firms on the corporate misconduct of the focal firm within the supply chain. Utilizing data from 723 publicly listed companies in China, we employ a difference-in-differences approach for our analysis. The results indicate that misbehavior exhibited by both supplier and customer firms contributes to an increase in corporate misconduct by the focal firm. Based on the social contagion theory, we argue that supplier misconduct leads to an increase in focus firm misconduct through a mechanism similar to “spillover effect.” Customer misconduct leads to an increase in focus firm misconduct through a mechanism similar to the “learning effect”. And this phenomenon is influenced by the cooperation intensity and the industry sensitivity. The conclusion to our research makes some theoretical contributions. First, our research focuses on silent organizational factors in supply chain contagion, providing evidence of such factors spreading unobserved in the supply chain. Secondly, we explains the different mechanisms of transmission between suppliers and customers in the dissemination of misconduct across the supply chain. Finally, our research findings provide support for managing supply chain misconduct as well as supplier and customer collaboration.

不遵守操作程序会严重扰乱供应链的运作。本研究探讨了供应商和客户企业的不当行为对供应链中焦点企业不当行为的影响。我们利用中国 723 家上市公司的数据，采用差分法进行分析。结果表明，供应商和客户企业的不当行为都会导致焦点企业不当行为的增加。根据社会传染理论，我们认为供应商的不当行为会通过一种类似于 "溢出效应 "的机制导致焦点企业不当行为的增加。客户不当行为通过一种类似于 "学习效应 "的机制导致焦点企业不当行为的增加。而这一现象受合作强度和行业敏感性的影响。我们的研究结论具有一定的理论贡献。首先，我们的研究聚焦于供应链传染中沉默的组织因素，提供了这些因素在供应链中传播的证据。其次，我们解释了不当行为在供应链中传播时，供应商和客户之间的不同传播机制。最后，我们的研究成果为管理供应链不当行为以及供应商和客户合作提供了支持。

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引用次数: 0

Drone-based warehouse inventory management of perishables 基于无人机的易腐物品仓库库存管理

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-22 DOI: 10.1016/j.ijpe.2024.109437

Warehouse inventory management is a complex process. When inventory includes perishables, the complexity of these processes is compounded with additional requirements such as appropriate ambient storage conditions and placement of one type of perishables (e.g., bananas) far away from another type of perishables (e.g., strawberries). With perishables spending a significant amount of time post-harvest in warehouses, appropriate management of warehouse inventory is necessary to reduce wastage due to spoilage. Drone-based warehouse inventory management is gaining popularity as seen in the increasing number of firms in this space as well as the number of research publications. RFID tags have been widely used for inventory management for more than two decades. While drones have been successfully used in warehouses with non-perishables, RFID and drone use in warehouses with perishables has not witnessed its fair share as evidenced by the lack of publications in this general area. This paper is a step in the direction to address this void in published literature. We consider object-level RFID tags and drones to automate warehouse inventory management of perishables. Results from our analytical model and simulation analysis indicate that such warehouse automation is beneficial to both the warehouse operators and their customers.

仓库库存管理是一个复杂的过程。当库存包括易腐物品时，这些过程的复杂性就会因额外的要求而变得更加复杂，例如适当的环境储存条件和将一种易腐物品（如香蕉）放置在远离另一种易腐物品（如草莓）的地方。由于易腐物品收获后会在仓库中存放很长时间，因此有必要对仓库库存进行适当管理，以减少因变质而造成的浪费。基于无人机的仓库库存管理越来越受欢迎，这从该领域公司数量和研究出版物数量的不断增加中可见一斑。二十多年来，RFID 标签一直被广泛用于库存管理。虽然无人机已成功应用于非易腐物品仓库，但 RFID 和无人机在易腐物品仓库中的应用还没有得到应有的重视，这一点从该领域缺乏相关出版物可见一斑。本文正是朝着解决这一文献空白的方向迈出的一步。我们考虑使用对象级 RFID 标签和无人机来实现易腐物品仓库库存管理的自动化。我们的分析模型和模拟分析结果表明，这种仓库自动化对仓库经营者及其客户都有好处。

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引用次数: 0

Can employee training facilitate production repurposing in crises? An ability-motivation-opportunity perspective 员工培训能否促进危机中的生产再利用？从能力-动机-机会的角度看问题

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-18 DOI: 10.1016/j.ijpe.2024.109444

Production repurposing is an initiative for firms to alter their manufacturing capabilities and outputs to meet new demands, particularly during times of crisis. This initiative is crucial for businesses to remain resilient to unexpected changes in the market. Despite its importance, the specific factors that drive production repurposing during crises, especially from the perspective of human capital development, are not well understood. Drawing upon the ability, motivation, and opportunity (AMO) framework, this study aims to examine the impact of pre-pandemic employee training, an ability-enhancing practice, on production repurposing during the pandemic. Based on a dataset of 4679 firm-year observations from 32 countries sourced from the World Bank, our regression results indicate that firms that engaged in pre-pandemic employee training are more likely to initiate production repurposing during the pandemic. Furthermore, we delve into the moderating roles of government wage subsides, a motivation factor, and labor shortages, an opportunity constraint. The results reveal that government wage subsidies amplify the positive effect of pre-pandemic training on production repurposing, whereas labor shortages dampen this impact. Our heterogeneity analysis further suggests that national socioeconomic features can influence these relationships. This study underscores the role of employee training in preparing firms to actively adapt during crises. It also highlights the necessity of providing adequate motivations and creating conducive opportunities to facilitate human capital. These insights are valuable for managers and policymakers aiming to enhance firm adaptability and ensure resilient operations during crises.

生产再利用是企业改变制造能力和产出以满足新需求的举措，尤其是在危机时期。这一举措对于企业保持对市场意外变化的应变能力至关重要。尽管其重要性不言而喻，但在危机期间推动生产再利用的具体因素，尤其是从人力资本发展的角度来看，却并不十分清楚。本研究借鉴能力、动机和机会（AMO）框架，旨在研究大流行前的员工培训（一种能力提升实践）对大流行期间生产再利用的影响。我们的回归结果表明，参与大流行前员工培训的企业更有可能在大流行期间启动生产再利用。此外，我们还深入研究了政府工资补贴（一种激励因素）和劳动力短缺（一种机会限制因素）的调节作用。结果显示，政府工资补贴扩大了大流行前培训对生产再利用的积极影响，而劳动力短缺则抑制了这种影响。我们的异质性分析进一步表明，国家社会经济特征会影响这些关系。本研究强调了员工培训在帮助企业做好准备以在危机期间积极适应环境方面所起的作用。它还强调了提供充分的激励和创造有利机会以促进人力资本的必要性。这些见解对于旨在提高企业适应能力和确保危机期间企业恢复性运营的管理者和政策制定者很有价值。

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引用次数: 0

Algorithm aversion during disruptions: The case of safety stock 中断期间的算法厌恶：安全库存案例

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-18 DOI: 10.1016/j.ijpe.2024.109442

Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.

当组织或个人拒绝最佳分析决策支持，而倾向于非正式的主观决策时，就会出现算法厌恶现象。这种现象已在许多实际决策场景中被观察到，并被普遍认为会对决策质量产生负面影响。然而，在波动的供应链环境中，这种现象的存在及其影响尚未在文献中得到实证检验。缓冲需求波动的安全库存是供应链管理中的一项重要决策，因此是观察算法厌恶的理想视角。在本文中，我们对安全库存背景下的算法规避行为进行了实证研究。我们从一家案例零售公司收集了一系列库存单位（SKU）的数据，包括中断前和中断后不同波动水平的时间阶段。我们引入了一个模拟模型，以确定安全库存决策是否存在算法厌恶，并评估算法的采用和适应如何影响绩效。我们的研究结果表明，在供应链中断期间会出现算法规避现象，算法决策明显优于人工判断。基于访谈结果和信息系统理论，我们提出了一种理论来解释和概括上述发现。该理论将算法厌恶行为归因于算法用户的适切感降低，以及用户和开发人员都缺乏闲置资源。该理论还提供了关于算法的采用和适应如何在破坏性事件中影响决策绩效的见解。

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引用次数: 0

Generalized multi-manufacturer multi-retailer production-delivery supply chain model and its minimum cost solution policy 广义多制造商多零售商生产-交付供应链模型及其最小成本解决策略

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-16 DOI: 10.1016/j.ijpe.2024.109439

Production of a product at multiple sources and its deliveries to multiple destinations are a common practice in business. Minimization of the integrated total cost of performing operations and transportation considering the relevant factors such as the minimum order quantity contract, capacities of transport vehicles, and times of transportation in this supply chain is essential, in supplying products to customers at reasonable lesser prices. However, such a supply chain has received little attention in terms of minimizing the integrated total cost taking into account these related factors explicitly. So, there lies a research scope on this topic to fulfill the growing need of minimizing the cost of production-deliveries of products to meet customers’ demands fruitfully. Here we develop such a generalized mathematical model to minimize the integrated total cost of carrying out operations at manufacturers and retailers, and transporting batches (sub-lots) of lots from sources to destinations considering the mentioned realistic constraints. The integrated production-delivery flow is synchronized by delivering lots with batches of equal and/or unequal sizes. First without considering transportation costs, we obtain optimal batches to minimize the total cost of the model. Each of these optimal batches is proportionally distributed at manufacturers as supplies and at retailers as demands. Then the minimum transportation cost solution from manufacturers to retailers is incorporated to the earlier solution to obtain the final result. We illustrate this solution policy with numerical example problems. Sensitivity analyses are performed to see the effect of increasing values of parameters on the minimum total cost.

在多个来源地生产一种产品并将其运送到多个目的地是企业的常见做法。要想以合理的较低价格向客户提供产品，就必须考虑到供应链中的最低订货量合同、运输车辆的能力和运输时间等相关因素，最大限度地降低运营和运输的综合总成本。然而，在明确考虑这些相关因素的情况下，如何最大限度地降低综合总成本，这种供应链很少受到关注。因此，为了满足日益增长的需求，最大限度地降低产品的生产-交付成本，以卓有成效地满足客户的需求，这一课题还存在研究空间。在此，我们建立了这样一个广义数学模型，在考虑到上述现实约束条件的情况下，最大限度地降低生产商和零售商开展业务以及将批次（子批次）产品从货源地运往目的地的综合总成本。通过等量和/或不等量的批次交货，实现生产-交货一体化流程的同步。首先，在不考虑运输成本的情况下，我们获得了最优批次，使模型的总成本最小化。每个最优批次都按比例分配给生产商作为供应，零售商作为需求。然后，将从制造商到零售商的最小运输成本方案与之前的方案相结合，得出最终结果。我们用数字示例问题来说明这一解决策略。我们还进行了敏感性分析，以了解参数值的增加对最低总成本的影响。

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引用次数: 0

Characterization of production sets through individual returns-to-scale: A non parametric specification and an illustration with the U.S industries 通过个体规模收益描述生产集的特征：非参数规范及美国工业示例

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-16 DOI: 10.1016/j.ijpe.2024.109433

This paper proposes to estimate the returns-to-scale of production sets by considering the individual return of each observation, considered as a decision-making unit through the notion of

Λ

-returns to scale assumption. Along this line, the global technology is then constructed as the intersection of all the individual technologies. Hence, an axiomatic foundation is proposed to present the notion of

Λ

-returns to scale. This new characterization of the returns-to-scale encompasses the definition of

α

-returns to scale, as a special case as well as the standard non-increasing and non-decreasing returns-to-scale models. A non-parametric procedure based on the goodness of fit approach is proposed to assess these individual returns-to-scale. To illustrate this notion of

Λ

-returns to scale assumption, an empirical illustration is provided based on a dataset involving 63 industries constituting the whole American economy over the period 1987-2018.

本文建议通过考虑每个观测值的单个收益来估算生产集的规模收益，并通过Λ-规模收益假设的概念将每个观测值视为一个决策单元。按照这一思路，全局技术就被构建为所有单项技术的交集。因此，我们提出了一个公理基础来呈现Λ-规模回报的概念。规模收益的这一新表征包含了作为特例的 α 规模收益定义，以及标准的非递增和非递减规模收益模型。本文提出了一种基于拟合优度方法的非参数程序，用于评估这些单独的规模收益。为了说明Λ-规模收益假设的概念，我们基于 1987-2018 年间构成整个美国经济的 63 个行业的数据集进行了实证说明。

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引用次数: 0

Climate policy uncertainty influences carbon emissions in the semiconductor industry 气候政策的不确定性影响半导体行业的碳排放

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-16 DOI: 10.1016/j.ijpe.2024.109436

Industry carbon emissions have been increasing, yet there remains a dearth of research on the impacts of climate policy uncertainty. This study first explored the effects of climate policy uncertainty on the carbon emissions of semiconductor enterprises. We employed the Bidirectional Encoder Representations from Transformers (BERT) model. We constructed a Chinese climate policy uncertainty index based on electronic news entries to match the enterprise panel data structure from 2011 to 2022. The results showed an increase in climate policy uncertainty, which helped to reduce semiconductor enterprises’ carbon emissions. This effect was primarily achieved via two pathways. First, climate policy uncertainty leads to companies facing stricter environmental requirements, and these companies will proactively increase their investment in environmental, social, and governance standards to cope with the potential risks. Second, climate policy uncertainty is often accompanied by shifts in government climate policy. Governments will provide green subsidies to enterprises to achieve their policy goals. Furthermore, the policy uncertainty for the semiconductor industry could amplify the reducing effect of climate policy uncertainty on the carbon emissions from semiconductor enterprises. Climate policy uncertainty has a greater impact on non-state-owned and smaller semiconductor enterprises. Our study provides a new way to measure climate policy uncertainty, finds a new perspective based on climate policy uncertainty for exploring the potential impacts of corporate carbon emission reductions, bridges the gap between previous studies on enterprise carbon reductions and climate policy uncertainty, and offers a new path for governments to manage industrial carbon emissions.

工业碳排放量一直在增加，但有关气候政策不确定性影响的研究仍然十分匮乏。本研究首先探讨了气候政策不确定性对半导体企业碳排放的影响。我们采用了变压器双向编码器表征（BERT）模型。我们基于电子新闻条目构建了中国气候政策不确定性指数，以匹配 2011 年至 2022 年的企业面板数据结构。结果表明，气候政策不确定性的增加有助于减少半导体企业的碳排放。这种效应主要通过两种途径实现。首先，气候政策不确定性导致企业面临更严格的环保要求，这些企业会主动增加在环境、社会和治理标准方面的投资，以应对潜在风险。其次，气候政策的不确定性往往伴随着政府气候政策的转变。政府会向企业提供绿色补贴，以实现其政策目标。此外，半导体产业政策的不确定性会放大气候政策不确定性对半导体企业碳排放的减少作用。气候政策的不确定性对非国有和小型半导体企业的影响更大。我们的研究提供了一种衡量气候政策不确定性的新方法，为探索企业碳减排的潜在影响找到了一个基于气候政策不确定性的新视角，弥补了以往企业碳减排与气候政策不确定性研究之间的差距，为政府管理工业碳排放提供了一条新路径。

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引用次数: 0

Time-to-Adapt (TTA) 适应时间 (TTA)

IF 9.8 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Economics

Pub Date : 2024-10-16 DOI: 10.1016/j.ijpe.2024.109432

Manufacturing operations rely heavily on external sources and supply chain (

SC

) networks, making them susceptible to material and operational risks. In response, manufacturers are investigating innovative strategies to enhance their adaptability and strengthen the resilience and viability of their value-creation systems. This shift has prompted an increased focus on integrating inherent adaptability while maintaining profitability and efficiency. Although indicators such as Time-to-Recover (

TTR

) and Time-to-Survive (

TTS

) are commonly employed to assess

SC

capabilities, the literature suggests that scholars and practitioners give less consideration to the internal factors of Mass Customization (

MC

) manufacturers and their influence on mitigating

SC

disruptions, particularly the Time-to-Adapt (

TTA

) indicator in manufacturing. This study utilizes a case study approach, complemented by a mathematical model, to analyze the role of

TTA

as a key internal controllable indicator within

MC

manufacturers and as an external controllable indicator for suppliers. The findings indicate that manufacturers can employ the

TTA

indicator to measure the adaptation period and enhance their adaptive capabilities. Moreover, it enables manufacturers to optimize profits by selecting viable production options in response to resource shortages.

制造业务严重依赖外部资源和供应链（SC）网络，因此很容易受到材料和运营风险的影响。为此，制造商正在研究创新战略，以提高其适应性，并加强其价值创造系统的复原力和可行性。这种转变促使人们更加关注在保持盈利能力和效率的同时，整合固有的适应能力。虽然通常采用恢复时间（TTR）和生存时间（TTS）等指标来评估SC能力，但文献表明，学者和从业人员较少考虑大规模定制（MC）制造商的内部因素及其对缓解SC中断的影响，特别是制造业的适应时间（TTA）指标。本研究采用案例研究法，辅以数学模型，分析了 TTA 作为 MC 制造商内部关键可控指标和供应商外部可控指标的作用。研究结果表明，制造商可以利用 TTA 指标来衡量适应期，提高适应能力。此外，它还能使制造商通过选择可行的生产方案来应对资源短缺，从而优化利润。

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引用次数: 0