摘要
本研究运用探索性案例研究方法,构建了数据赋能驱动精益生产创新内在机理的整合性理论模型。研究结果发现,数据赋能驱动精益生产创新遵循"数据资源行动—数据能力生成—精益价值实现"的内在机理路径,分别从客户、流程、合作和员工层面进行赋能作用,系统推进数据化精益生产落地实现。具体而言,在客户赋能作用中,通过数据需求资源沉淀,促进客户涌现出数据设计能力,实现生产目标精准化;在流程赋能作用中,通过数据作业资源调度,促进流程构建数据制造能力,实现生产加工精密化;在合作赋能作用中,通过数据关联资源整合,促进合作商塑造数据供应能力,实现生产配套精选化;在员工赋能作用中,通过数据任务资源部署,促进员工激活数据自驱能力,实现生产治理精细化。本研究辨析了数据化精益生产相较于传统精益生产在假设、属性、模式和效果方面的系统创新变化,深化了数据赋能作用的机理研究,丰富了精益生产在大数据情境中的新解读,对指导生产企业打造数据化精益生产优势具有一定启示作用。
Since its introduction to China, the traditional lean manufacturing concept, which originated with Toyota Motor Corporation in Japan, has been widely promoted in the manufacturing sector and was once regarded as the golden rule of modern production/operations management. However, as the production environment and requirements have become more complex and varied, the shortcomings of the traditional lean manufacturing concept have come to light. On the one hand, the elimination of waste of traditional lean production is often overplayed, resulting in the neglect of the enterprising spirit of optimizing the value creation process;on the other hand, the traditional lean production concept is partial in its scope of penetration and lacks wide integration into multiple value creation activities. Data science and technology provide an opportunity to upgrade traditional lean production, enabling firms to make systematic changes in customer interaction, process control and collaboration, enabling modern firms to create a data-enabled lean production advantage.Using an exploratory case study approach, this paper constructs an integrative theoretical model of the mechanisms underlying data enablement-driven lean manufacturing innovation, using Qingdao Kutesmart Co., Ltd. as the research subject. It finds that lean production innovation with data enablement follows the intrinsic path of datafication resource action-data-enabled capability generation-lean value realization. This means that production firms need to twin conventional production resources in the form of datafication, and take specific resource actions for different types of datafication resources in different contexts. In the process, or through the reuse of datafication resources, the relevant actors generate new data-enabled capabilities with new connotations or breakthroughs or advanced features, which in turn support the creation of lean values in different production areas. Specifically, the customer enablement, through the precipitation of datafication demand resources, promotes the emergence of data-enabled design ability of customers, so as to realize the accurate production target to guide the production. The process enablement promotes to build the data-enabled manufacturing capacity by scheduling datafication operation resources, so as to realize the production in a precise processing mode. The cooperation enablement, through the integration of datafication relevance resources, promotes partners to shape the data-enabled supply capacity, so as to guarantee the production by the selected production supporting. The employee enablement, through the deployment of datafication task resources, promotes employees to activate the data-enabled self-drive ability, so as to support the production operation by refined production governance.The theoretical contributions of this paper are mainly in the following three areas. Firstly, it explores the underlying mechanisms of data enablement to drive lean production innovation. Although existing scholars have noted the role of big data technology or data itself as a series of lean enablers in production and operation scenarios, the underlying mechanisms of how data can enable lean production innovation are not yet clear. This paper uncovers the mechanism by which data enablement drives lean production innovation, fully responding to the call of existing scholars to strengthen the transformation process of big data from resource to value realization.[16] Second, the innovation of data-enabled lean production further enriches and expands the meaning of being lean in the traditional lean production. The concept of being lean in the traditional lean production focuses on the improvement and optimization of local site operations and is characterized by the act of doing everything possible to eliminate waste. With the increasing complexity of the production environment, scholars have recognized that the meaning of being lean should be further expanded and extended. Based on the practical context of lean production innovation in the era of big data, this paper explores the nature of data-enabled lean production after the implantation of the data gene, and deepens the new interpretation of lean production in the context of big data. Third, the conditions that facilitate the realization of data-enabled lean production are identified based on a phylogenetic view. Most of the existing literature is concerned with the value of big data and its technologies for a particular area of production and operation, but lacks a holistic generative approach to data-enabled lean production. This paper examines the mechanisms of enablement at the customer, process, cooperation and employee levels that drive the realization of data-enabled lean production, and identifies the role of different levels of enablement in the realization of data-enabled lean production. Customer enablement is the primary prerequisite for data-enabled lean production, process enablement is the key foundation for data-enabled lean production, cooperation enablement is the key guarantee for data-enabled lean production, and employee empowerment is the necessary support for data-enabled lean production. In the era of big data, entrepreneurs must no longer limit their knowledge of lean production to the elimination of waste, but must focus on the global data genetics and systematic optimization of the production value creation process. Furthermore, data-enabled lean production is a complex systemic project that goes beyond the introduction of advanced management tools in production processes. Firms need to design a datafication operation structure that enables them at the customer, process, cooperation and employee levels in order to build up the advantages of data-enabled lean production.
作者
张明超
孙新波
王永霞
Zhang Mingchao;Sun Xinbo;Wang Yongxia(School of Business Administration,Northeast University)
出处
《南开管理评论》
CSSCI
北大核心
2021年第3期102-114,I0020,I0021,共15页
Nankai Business Review
基金
国家自然科学基金项目(71672029)
辽宁省社科基金重点项目(L19AGL002)
中央高校基本科研业务经费(N2006008)资助。
关键词
数据赋能
精益生产
数据化精益生产
案例研究
Data Enablement
Lean Production
Data-based Lean Production
Case Study
作者简介
张明超,东北大学工商管理学院博士研究生,研究方向为数据赋能、数字化转型战略;通讯作者:孙新波,东北大学工商管理学院教授、博士,研究方向为组织与战略管理、管理哲学与本土管理、创新与创业管理等;王永霞,东北大学工商管理学院硕士研究生,研究方向为数据赋能。
