Advances on measuring the operational complexity of supplier–customer systems

Even structurally simple supplier–customer systems can be operationally complex. This operational complexity can be colloquially defined as the uncertainty associated with managing the dynamic variations, in time or quantity, across information and material flows at the supplier–customer interface. This paper proposes a means of measuring the information demands placed on supplier–customer systems, as a result of this uncertainty.This paper mathematically models the operational complexity of supplier–customer systems from an information-theoretic perspective. A unique feature of this measure is that it captures, in relative terms, the expected amount of information required to describe the state of the system. The measure provides flexibility in the scope and detail of analysis, while at the same time allowing a systematic hierarchical approach.The application of the measure allows valuable insights to be obtained in terms of the degree of uncertainty, level of control and the detail of monitoring required to manage the operational complexity of supplier–customer systems.     

熵在供应链供需不确定性中的应用

从定量和定性的角度分析了供应链系统的不确定性，指出供应链不确定性本质和不确定性产生的因素；本文把熵理论运用到供应链不确定性的研究中，为供应链管理和控制提供了有效的方法。通过实际案例分析，我们可以看出熵理论的运用不但能够对供应链不确定性做出精确的评估，而且能有效地控制整个供应链的运营。     

A methodology for analyzing decision networks, based on information theory

This paper assumes the organization as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in a decision network, due to interdependence between decision centers.Based on this approach, new quantitative concepts and definitions are proposed in order to measure the information in a decision center, based on Shannon entropy and its complement in possibility theory, U uncertainty. This approach also measures the quantity of interdependence between decision centers and informational complexity of decision networks.The paper presents an agent-based model of organization as a graph composed of decision centers. The application of the proposed approach is in analyzing and assessing a measure to the organization structure efficiency, based on informational communication view. The structure improvement, analysis of information flow in organization and grouping algorithms are investigated in this paper. The results obtained from this model in different systems as distributed decision networks, clarifies the importance of structure and information distribution sources effect’s on network efficiency.     

Complexity of networks II: The set complexity of edge‐colored graphs

We previously introduced the concept of “set‐complexity,” based on a context‐dependent measure of information, and used this concept to describe the complexity of gene interaction networks. In a previous paper of this series we analyzed the set‐complexity of binary graphs. Here, we extend this analysis to graphs with multicolored edges that more closely match biological structures like the gene interaction networks. All highly complex graphs by this measure exhibit a modular structure. A principal result of this work is that for the most complex graphs of a given size the number of edge colors is equal to the number of “modules” of the graph. Complete multipartite graphs (CMGs) are defined and analyzed. The relation between complexity and structure of these graphs is examined in detail. We establish that the mutual information between any two nodes in a CMG can be fully expressed in terms of entropy, and present an explicit expression for the set complexity of CMGs (Theorem 3). An algorithm for generating highly complex graphs from CMGs is described. We establish several theorems relating these concepts and connecting complex graphs with a variety of practical network properties. In exploring the relation between symmetry and complexity we use the idea of a similarity matrix and its spectrum for highly complex graphs. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

基于Kriging模型的供应链优化设计

供应链优化的目的之一是确定使得总成本最小的最佳运作水平.供应链系统是复杂的动态系统,由于库存系统的复杂性和供应链本身的不确定性,利用传统优化方法往往需要耗费一定的计算成本和经济成本.而元模型则能以简单的数学表达式较精确地刻画仿真系统的输入输出关系,为研究者分析复杂系统提供了一种分析方法.针对供应链优化问题,给出了一种基于计算机试验设计中的元模型一Kriging模型的供应链优化方法,并通过一个三级供应链问题对所提方法进行了实证研究.研究结果表明了所提方法的有效性和可用性,为供应链优化提供了一种新的研究思路.     

不确定条件下应急供应链可靠性评价模型

以应急供应链可靠性为评价对象,在对其影响因素进行分析的基础上,从保障机制、信息系统、网络结构、运作流程、资金保障五个方面构建了评价指标体系,考虑应急供应链可靠性评价信息受主客观因素影响难以精确测定,依据指标特点运用语言评价值,区间值,数值多种形式测定指标值,然后将语言评价值和数值转化为区间值,运用层次分析法、熵值法分别计算一、二级指标权重,再运用联系数对指标值和权重进行集结排序。最后设计算例验证了模型的有效性和适用性。     

The Further Study of Safety Stock under Uncertain Environment

Corresponding to stochastic variable, it is a better choice to describe the market demand uncertainty of innovative products with fuzzy variable because no historical data is available. Traditionally, possibility measure is regarded as the parallel concept of probability measure. However, it is, in fact, the credibility measure that plays the role of probability measure! Based on the credibility theory, this paper studies how to evaluate the safety stock of enterprise given desired product availability when the node enterprise market demand of supply chain is described by Gauss fuzzy variable. Thereinafter, the authors discuss the impact of required product availability and demand uncertainty on safety stock, compare the correlative issues with stochastic demand, and get some useful results.     

基于ANP-灰色聚类方法的供应链风险影响因素研究

供应链网络结构日趋复杂,需要识别供应链网络风险影响因素并对其进行分析．通过分析和识别出供应链运作过程中各种风险影响因素,构建了供应链网络运作风险影响因素评价体系,利用网络分析法对供应链网络风险影响因素和运作阶段进行分析,使用灰色聚类分析对风险因素进行评分,排序并筛选出重要的风险影响因素.通过对汽车零部件供应链网络进行案例分析,得知供应链网络结构特征因素相对于其他因素对供应链网络风险影响程度最大,在供应链管理和研究过程中应对其引起重视.     

Operational complexity and supplier–customer integration: case study insights and complexity rebound

The main contribution of this paper is the demonstration that, contrary to conventional thinking, a measurable increase in the operational complexity of the production scheduling function between two companies can occur following closer supply chain integration. The paper presents the practical application of previous work carried out and validated by the authors in terms of (a) methodology for measuring operational complexity, (b) predicted implications of Supplier–Customer integration and (c) derivation of an operational complexity measure applied to before and after Supplier–Customer integration. This application is illustrated via a longitudinal case study. The analysis is based on information theory, whereby operational complexity of a Supplier–Customer system is defined as the amount of information required to describe the state of this system. The results show that operational complexity can increase when companies decide to integrate more closely, which is a fact likely to be overlooked when making decisions to pursue closer Supply-Chain integration. In this study, operational complexity increases due to reduced buffering arising from reduction in the Supplier's inventory capacity. The Customer did not change their operational practices to improve their schedule adherence post-integration, and, consequently, suffered an increase in complexity due to complexity rebound. Both the Supplier's and Customer's decision-making processes after the case study reported in this paper were enhanced by being able to quantify the complex areas to prioritise and direct managerial efforts towards them, through the use of the operational complexity measure. Future work could extend this study (in the ‘low product customisation’ and ‘low product value impact’ quadrant) to investigate Supplier–Customer integration in other quadrants resulting from further combinations between ‘product customisation’ and ‘product value impact’ levels.     

A heuristic for single-warehouse multiretailer supply chains with all-unit transportation cost discounts

In supply chain management research, transportation costs, if explicitly considered at all, are frequently assumed to be linear. These costs often have a more complex form, such as an all-unit discount structure – this piecewise cost function adds significant complexity when included in supply chain management problems and is therefore often ignored due to solution time or tractability concerns. We present and evaluate a new heuristic procedure which provides good solutions to problems involving all-unit discount cost functions while significantly reducing solution times. The general nature of this procedure does not require assumptions about the supply chain structure or policies, and is therefore applicable in a wide range of settings.     

A multi-layer demand-responsive logistics control methodology for alleviating the bullwhip effect of supply chains

This paper presents a multi-layer demand-responsive logistics control strategy for alleviating, effectively and efficiently, the bullwhip effect of a supply chain. Utilizing stochastic optimal control methodology, the proposed method estimates the time-varying demand-oriented logistics system states, which originate directly and indirectly downstream to the targeted member of a supply chain, and associate these estimated demands with estimates of different time-varying weights under the goal of systematically optimizing the logistical performance of chain members. In addition, an experimental design is conducted where the proposed method is evaluated with the two specified criteria. Numerical results indicate that the proposed method permits alleviating, to a great extent, the bullwhip effect in comparison with the existing logistics management strategies. Furthermore, the methodology presented in this study is expected to help address issues regarding the uncertainty and complexity of the distortion of demand-related information existing broadly among supply chain members for an efficient supply chain coordination.     

A method for analysing operational complexity in supply chains

This paper proposes a method for analysing the operational complexity in supply chains by using an entropic measure based on information theory. The proposed approach estimates the operational complexity at each stage of the supply chain and analyses the changes between stages. In this paper a stage is identified by the exchange of data and/or material. Through analysis the method identifies the stages where the operational complexity is both generated and propagated (exported, imported, generated or absorbed). Central to the method is the identification of a reference point within the supply chain. This is where the operational complexity is at a local minimum along the data transfer stages. Such a point can be thought of as a ‘sink’ for turbulence generated in the supply chain. Where it exists, it has the merit of stabilising the supply chain by attenuating uncertainty. However, the location of the reference point is also a matter of choice. If the preferred location is other than the current one, this is a trigger for management action. The analysis can help decide appropriate remedial action. More generally, the approach can assist logistics management by highlighting problem areas. An industrial application is presented to demonstrate the applicability of the method.     

A two-stage stochastic programming framework for transportation planning in disaster response

This study proposes a two-stage stochastic programming model to plan the transportation of vital first-aid commodities to disaster-affected areas during emergency response. A multi-commodity, multi-modal network flow formulation is developed to describe the flow of material over an urban transportation network. Since it is difficult to predict the timing and magnitude of any disaster and its impact on the urban system, resource mobilization is treated in a random manner, and the resource requirements are represented as random variables. Furthermore, uncertainty arising from the vulnerability of the transportation system leads to random arc capacities and supply amounts. Randomness is represented by a finite sample of scenarios for capacity, supply and demand triplet. The two stages are defined with respect to information asymmetry, which discloses uncertainty during the progress of the response. The approach is validated by quantifying the expected value of perfect and stochastic information in problem instances generated out of actual data.     

Robust controlling of chaotic behavior in supply chain networks

The supply chain network is a complex nonlinear system that may have a chaotic behavior. This network involves multiple entities that cooperate to meet customers demand and control network inventory. Although there is a large body of research on measurement of chaos in the supply chain, no proper method has been proposed to control its chaotic behavior. Moreover, the dynamic equations used in the supply chain ignore many factors that affect this chaotic behavior. This paper offers a more comprehensive modeling, analysis, and control of chaotic behavior in the supply chain. A supply chain network with a centralized decision-making structure is modeled. This model has a control center that determines the order of entities and controls their inventories based on customer demand. There is a time-varying delay in the supply chain network, which is equal to the maximum delay between entities. Robust control method with linear matrix inequality technique is used to control the chaotic behavior. Using this technique, decision parameters are determined in such a way as to stabilize network behavior.     

A flexible and generic approach to dynamic modelling of supply chains

In this paper, we present a new modelling approach for realistic supply chain simulation. The model provides an experimental environment for informed comparison between different supply chain policies. A basic simulation model for a generic node, from which a supply chain network can be built, has been developed using an object-oriented approach. This generic model allows the incorporation of the information and physical systems and decision-making policies used by each node. The object-oriented approach gives the flexibility in specifying the supply chain configuration and operation decisions, and policies. Stochastic simulations are achieved by applying Latin Supercube Sampling to the uncertain variables in descending order of importance, which reduces the number of simulations required. We also present a case study to show that the model is applicable to a real-life situation for dynamic stochastic studies.     

信息熵公理及其在决策分析中的应用

针对信息量是消息发生前的不确定性给出一个直观测量信息量公式.为了克服Shannon熵的局限性和分析信息度量本质,借鉴距离空间理论中度量公理定义的思路,通过非负性、对称性、次可加和极大性给出信息熵的公理化新定义.将Shannon熵、直观信息熵和β-熵等不同形式的信息度量统一在同一公理化结构下.应用直观信息熵公式仅采用四则运算进行决策树分析,避免了利用Shannon熵公式的对数运算.     

基于模糊证据推理的汽车制造产业网络环境复杂性测度

环境复杂性是汽车制造产业网络复杂性的主要诱因。利用指标波动程度测度复杂性的思想,建立表征汽车制造产业网络外部环境的指标体系,构建指标变动程度的模糊信度结构模型,运用模糊证据推理算法结合熵值理论对多指标进行信息融合,并利用灰色关联分析方法评估复杂性测度结果。通过2002~2012年汽车行业数据的实证研究,结果表明:经济环境与国际环境的波动是导致汽车制造产业网络复杂性的最主要因素,技术环境和市场环境次之;近10年来我国汽车制造产业网络外部环境经历了两次较大波动,总体复杂性呈现增长趋势。本研究可为汽车产业环境复杂性测度及应对提供思路参考。     

基于回购契约的供应链鲁棒协调模型

研究了需求不确定条件下供应链回购契约协调问题。针对未知需求具体分布形式的两级供应链系统,建立了基于回购契约的供应链鲁棒协调模型。给出了仅知需求均值和方差信息条件下,集成供应链系统的鲁棒订货策略和分散供应链系统的鲁棒契约协调策略。分析了不同契约参数条件下的供应链及其成员利润情况。最后,进行了数值计算,验证了当获得需求的真实分布形式时．供应链鲁棒回购契约协调策略的有效性。结果表明,供应链鲁棒回购契约协调策略能够有效减少需求不确定性对系统及其成员利润的影响。     

Optimizing product service system by franchise fee contracts under information asymmetry

This paper studies the problem of how to effectively provide product service system (PSS) in a service-oriented manufacturing supply chain under asymmetric private demand information. The PSS in the supply chain is operated heterogeneously and complementarily, in which the manufacturer provides the product while the retailer who possesses private demand information is responsible for adding the necessary value-added service on the basic product. We address the issue of how different contracts affect the decisions and profitability of the supply chain members. Three types of contracts are developed to help supply chain partners to make decisions and enhance the supply chain’s efficiency. The first is the franchise fee (FF) contract, under which the manufacturer provides a two-part tariff contract (wholesale price and franchise fee) to influence the retailer’s decision and to detect her private demand information. The second is the franchise fee with service requirement (FFS) contract, under which the manufacturer specifies the service level required in addition to the two-part tariff contract terms. The third is the franchise fee with centralized service requirement (FFCS) contract, which is similar to the FFS contract but that the service level specified by the manufacturer is the system optimal solution. Our analytical results show that all three contracts enable the manufacturer to detect the retailer’s private demand information, with the FFCS contract achieving the greatest channel profit. Finally, numerical examples are presented, and sensitivity analysis of service level and profit are conducted to compare the performance of the three contracts under different settings. The paper provides managerial guidelines for the manufacturer in contract offering under different conditions.