伙伴关系
长信Taps Palantir AI 链接发展和工厂反馈
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长辛店和Palantir Technologies 宣布是2026年11月4日的一个合伙公司，将Palantir创始公司和人工智能平台，即AIP，适用于鞋类制造商的开发和生产业务。 计划中的系统通过工厂生产将鞋类开发和样本规划的信息和工作流程连接起来。
部署的目的是提供端到端的能见度，使各小组能够协调决策，对问题作出更早的反应，并从工厂成果中学习。 它将发展和抽样规划置于与工厂生产相同的连通过程之内，为信息和反馈在各阶段之间旅行创造了一条道路。
从代理Camp到工厂工作流程
该伙伴关系以AgentCamp为基础，其中长辛店工程师和Palantir与现场长辛店数据合作，展示初始工作流程。 这次会议为下一阶段提供了起点，它将把发展决定与工厂规划和执行联系起来。 计划的扩展从展示工作流程和公司数据转向将负责鞋型开发和制造的团队联系起来。
Changshin CI Nam总结了依赖性：“发展方面的决定决定了我们在工厂地板上的运作方式。” 将这些决定与生产反馈联系起来的Nam 称旨在帮助各小组及早查明问题，更有效地进行协调，并将经验教训纳入下一个模式。 他还将这种伙伴关系描述为通过将数据和AI嵌入到其工作中来从长辛店的制造专业知识中建立独特能力的一种方式。
创建和AIP 提供软件层
铸币局提供数据和业务层。 Palantir 称它的Ontology为数据，分析以及业务团队提供了商业对象，行动和程序的共同代表，使得它们能够分享决策逻辑，并在条件变化时进行合作。 它可以保留日后学习的决定记录，并带入企业数据和模型，而不重复基本资产或扰乱现有真相来源。
对长信来说，这种设计的重要性在于发展与生产之间的利弊。 共享的商业对象和决定逻辑提供了将操作背景带入工厂规划与执行的方法，而记录的决定可以支持Nam描述的学习循环。
AIP覆盖AI应用和agent层。 Palantir的产品文档描述了AI应用，动作和agents的Workflow Builder，准备和改进生产工作流程的评价工具，以及暴露自动化规则和执行历史的控制。 人类经营者也可以批准AI提议的行动。 Palantir CRO和CLO Ryan Taylor 称公司正在部署Foundry和AIP跨端至端流程，同时让长信控制AI的应用方式。
长信的制造规模
长辛店带来是软件架构的庞大制造基地。 它成立于1981年，总部位于韩国釜山，1994年在越南建立了第一个海外子公司，之后扩展到中国和印度尼西亚。 该公司雇用约8万人，2025年4月报告累计达10亿双鞋。
长辛店公司网站列出4个国家和9个制造厂，并描述了一种结合数字技术，数据和AI的制造方法。 Palantir项目将该方向应用于开发与生产之间的工作流程，包括样本决定，工厂执行和后期模型的反馈。
操作测试是共享工作流程的表面工厂是否及早发布，以改善协调，并将有益的经验教训带入下一个鞋型。 这种反馈循环是衡量伙伴关系预期能见度的实际尺度：生产经验必须成为后续模式的参考，早期的问题发现必须转化为整个开发和工厂团队的协调行动。
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Partnerships
Changshin Taps Palantir AI to Link Development and Factory Feedback
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Changshin and Palantir Technologies announced a partnership on November 4, 2026 to apply Palantir Foundry and the Artificial Intelligence Platform, or AIP, across the footwear manufacturer’s development and production operations. The planned system connects information and workflows from footwear development and sample planning through factory production.
The deployment is intended to provide end-to-end visibility so teams can coordinate decisions, respond to issues earlier and learn from factory outcomes. It places development and sample planning within the same connected process as factory production, creating a path for information and feedback to travel between stages.
From AgentCamp to Factory Workflows
The partnership builds on an AgentCamp where Changshin engineers and Palantir worked with live Changshin data to demonstrate initial workflows. That session supplied the starting points for the next phase, which will connect development decisions with factory planning and execution. The planned expansion moves from demonstrating workflows with company data to linking the teams responsible for a shoe model’s development and manufacture.
Changshin CEO CI Nam summarized the dependency: “Decisions made in development define how we operate on the factory floor.” Nam said connecting those decisions with production feedback is intended to help teams identify issues earlier, coordinate more effectively and carry lessons into the next model. He also described the partnership as a way to build distinctive capabilities from Changshin’s manufacturing expertise by embedding data and AI into its work.
Foundry and AIP Provide the Software Layers
Foundry supplies the data and operational layer. Palantir says its Ontology gives data, analytics and operational teams a common representation of business objects, actions and processes, allowing them to share decision logic and collaborate as conditions change. It can retain a record of decisions for subsequent learning and bring in enterprise data and models without duplicating underlying assets or disturbing existing sources of truth.
For Changshin, the significance of that design lies in the handoffs between development and production. Shared business objects and decision logic provide a way to carry operational context into factory planning and execution, while recorded decisions can support the learning loop described by Nam.
AIP covers the AI application and agent layer. Palantir’s product documentation describes a Workflow Builder for AI apps, actions and agents, evaluation tools for preparing and improving production workflows, and controls that expose automation rules and execution history. Human operators can also approve AI-proposed actions. Palantir CRO and CLO Ryan Taylor said the companies are deploying Foundry and AIP across end-to-end processes while keeping Changshin in control of how AI is applied.
Changshin’s Manufacturing Scale
Changshin brings a substantial manufacturing base to that software architecture. Founded in 1981 and headquartered in Busan, South Korea, it established its first overseas subsidiary in Vietnam in 1994 before expanding to China and Indonesia. The company employs approximately 80,000 people and reported reaching one billion cumulative pairs of shoes in April 2025.
Changshin’s corporate website lists four countries and nine manufacturing plants and describes a manufacturing approach that combines digital technology, data and AI. The Palantir project applies that direction to the workflows between development and production, including sample decisions, factory execution and feedback for later models.
The operational test is whether the shared workflows surface factory issues early enough to improve coordination and carry useful lessons into the next shoe model. That feedback loop is the practical measure of the partnership’s intended visibility: production experience must inform subsequent models, and earlier issue detection must translate into coordinated action across development and factory teams.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.