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共创视角下供应链互动增值研究
中文摘要

企业竞争的根本在于供应链竞争,供应链竞争优势的产生和来源从表现性知识来看,取决于供应链运作效率和效益,其实质在于供应链价值创造系统。供应链与供应链的竞争优势差异性主要取决于供应链价值创造系统的差异性。传统的价值创造逻辑局限于企业单一主体。然而,当前供应链价值创造主体的认知过程正从单主体向双主体、链上主体向链内外主体逐步演进。 实现供应链价值最大化不仅是供应链管理追求的终极目标,而且是企业获得可持续性竞争优势的来源。如何设计有效的主体间价值增值策略,从而提升供应链的价值增值水平成为企业所面临的重要问题之一。在供应链实践过程中,各价值创造主体通过内部增值活动形成供应链价值的初始量。在此初始量的基础上,实现供应链增值的微观解释是互动,不断通过主体问互动行为实现增值是确保供应链价值最大化的关键。因此,本文基于价值共创视角,聚焦供应链互动增值问题,围绕以下几个方面展开研究,主要工作如下: (1)构建供应链互动增值系统,并对其进行定性分析。首先对供应链价值创造系统与供应链互动增值系统等概念之间的演化关系进行界定,然后在厘清供应链互动增值系统的主体构成、系统产出、系统特征及动因与目标的基础上,对子系统组成进行分析。 (2)对供应链互动增值系统进行数理模型进行定量描述,探究互动对供应链增值的影响。首先将互动关系通过参数予以表征,构建供应链互动增值无约束优化模型。然后考虑到价值创造主体间互动信息的不确定性,引入区间参数,构建不确定非线性规划模型来定量描述上述问题,设计离散PSO算法进行求解。针对解的不稳定性,进而建立鲁棒优化模型,得到具有较高稳定性的解,揭示了互动的不确定性对供应链增值的影响。 (3)深刻剖析供应链互动增值的过程,建立供应链互动增值行为网络。归结凝练价值影响因素,构建了供应链互动增值能力指标体系,实现了对供应链互动增值影响因素的量化表示,在此基础上提出供应链互动增值能力测定模型。 (4)运用机器学习的方法探究供应链互动增值因果机制。首先优化设计LSSVM算法,构建了数据驱动模型,定量描述供应链互动增值因果机制“黑箱”的供应链互动增值系统影响因素与系统价值之间的“输入-输出”关系。进一步地,给出优化的RF算法,并采用OOB估计方法挖掘“黑箱”内部起关键作用的特征影响因素。 本文的创新点主要在于: (1)基于共创逻辑下的价值创造思维,构建了供应链互动增值系统,尝试运用数理模型对其刻画,并运用离散PSO算法和鲁棒优化方法进行求解,揭示了互动对供应链增值的影响,提出了“链上主体主要创造、链外主体辅助创造”的供应链“双主体+”互动增值理论框架。 (2)从企业联盟内部互动、企业联盟与消费者群间互动、消费者群内部互动以及其他相关参与方参与互动四个方面深度挖掘了供应链互动增值过程。归结凝练出影响因素,构建了供应链互动增值能力指标体系,并对供应链互动增值能力进行测定。 (3)运用机器学习的方法,设计优化LS-SVM和ORF算法,定量描述因果机制“黑箱”的“输入-输出”关系和识别挖掘“黑箱”内部起关键作用的特征影响因素两个方面,较完整地揭示了供应链互动增值因果机制。 本研究基于共创视角,构建起“链上主体主要创造、链外主体辅助创造”的供应链“双主体+”价值创造新逻辑,提出供应链互动增值理论框架,系统研究供应链的互动增值机理,精准阐释供应链竞争优势的新来源。这一课题的深入研究,对于深化消费者以及链外价值创造主体在供应链中主体地位的认识、完善供应链价值创造理论、推动供应链管理的发展,具有重要的理论意义。通过对供应链互动增值进行系统分析与优化模型构建、过程剖析和因果机制探究,深刻把握互动增值机理及其特征影响因素的内涵,制定市场优先调整策略,对驱动实现供应链价值最大化,进而实现供应链可持续发展具有重要的实践指导意义。 关键词:价值共创;供应链互动增值;因果机制;机器学习;鲁棒优化

英文摘要

The fundamental content of enterprise competition lies in the competition of supply chain. From the expressive knowledge view, the generation and source of supply chain competitive advantage depend on the efficiency and benefit of supply chain operation. Its essence lies in the value creation of supply chain system. The supply chain and the difference of the competitive advantage between supply chains mainly depends on the difference of the value creation of supply chain system. The traditional value creation logic is confined to the single subject of the enterprise. However, the cognitive process of the current supply chain value creation subject is gradually evolving from single subject to double subject, and from the main body of the chain to the inside and outside of the chain. Realising value maximisation of supply chains is not only the ultimate goal pursued by supply chain management but also a source for enterprises to obtain sustainable competitive advantages. In practice process of supply chains, value creation subjects form initial quantity of supply chain value through internal value-adding activity, based on which realising value-adding through interactive behaviours between subjects is the key to ensuring maximisation of supply chain value. Hence, how to design effective inter-subject value-adding strategy so as to elevate value-adding level of supply chains becomes one of important problems faced by enterprises. Therefore, based on the generalized value co-creation perspective, this paper focuses on the interactive value-added problem of supply chain, mainly about four aspects: the system analysis, the optimization model research, process and capability measurement, the causal mechanism research and so on. The main work is as follows: (a) An interactive value-added system of supply chain is built, and qualitative analysis is put into. Firstly, the evolution relation between the concepts of supply chain value creation system and supply chain interactive value-added system was defined. After that, on the basis of clarifying the composition of the main structure of the supply chain interactive value-added system, its relationship, motivation, target and system output, the composition of the sub-system was analyzed. (b) In order to explore the impact of interaction on the appreciation of supply chain, a quantitative description of the supply chain interactive value-added system by the mathematical models is obtained. Firstly, the interaction was expressed as the parameter, and the unconstrained optimization model of supply chain interaction was constructed under stochastic demand. Then, considering the uncertainty of the interaction information between the entities, the interval parameter was introduced. The uncertain nonlinear programming model was constructed to describe the above problems quantitatively and the discrete PSO algorithm was designed to solve the problem. The robust optimization model was established for the sake of the instability of the solution, and then got the solution with high stability, revealing the influence of the interaction uncertainty on the value of the supply chain. (c) Based on the deep analysis of the process of supply chain interactive value-added system, the interactive value-added behavior network of supply chain was established. This paper constructed the index system of supply chain interactive value-added capability, in order to realize the quantitative analysis of the factors affecting the value-added of supply chain interaction. The determination model of supply chain interactive value-added ability was put forward on the basis. (d) The causal mechanism of supply chain interaction value-added by means of the machine learning methods is explored. Firstly, the LS-SVM algorithm was optimized to construct the data-driven model, which can be used to quantitatively describe the “input-output” relationship between the influencing factors of the supply chain interactive value-added system and the system value. Furthermore, the optimized RF algorithm was given, and the characteristic influencing factors of the "black box" was explored using the OOB estimation method. The innovation points of this paper mainly lie in: (a) Based on the value creation thinking under the generalized logic, the supply chain interactive value-added system was constructed. The mathematical model was used to describe the supply chain interactive value-added system. The discrete PSO algorithm and the robust optimization method were applied to solve the problem, revealing the impact of interaction on value-added supply chain. The paper put forward the framework of “double subject +” interactive value-added theory, which is based on the main creation on the main chain and the auxiliary creation outside the chain. (b) The value-added process of supply chain was explored from the four aspects, including the internal interactions within the enterprise alliance, the interaction between enterprise alliance and consumer group, the internal interaction within consumer groups and other relevant parties involved in interaction. This paper constructed the index system of interactive value-added capability of supply chain using the concluded influencing factors of value, and measured the interactive value-added ability of supply chain. (c) The LS-SVM and ORF algorithms were designed and optimized using the method of machine learning to quantitatively describe the "input-output" relationship of the causal mechanism “black box”, then identify and reveal the characteristics of the key function of the “black box”. These two aspects completely revealed the causal mechanism of supply chain interaction. Based on the perspective of generalized value co-creation, this paper set up the new logic of “double subject +” value creation, which is based on the main creation on the main chain and the auxiliary creation outside the chain. It also put forward the theory framework of interactive value-added supply chain, systematically investigation of the supply chain interaction value-added mechanism, and the new source of accurate supply chain competitive advantage. It is of great theoretical significance to deepen the understanding of the main body in the supply chain, improve the supply chain value creation theory and promote the development of supply chain management. Through the analysis of supply chain interactive value-added system analysis, optimization model construction, process analysis and causal mechanism study, it is necessary to deeply grasp the mechanism of value-added interaction and its influencing factors. And they also have important practical guidance for formulating the market priority adjustment strategy, driving the maximization of supply chain value and achieving the sustainable development of supply chain. Keywords: Value co-creation; Supply chain value-adding; Causal mechanism; Machine Learning; Robust optimization

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