Supervisory control of a multi-echelon supply chain: A modular Petri net approach for inter-organizational control

Petri nets (PNs) are frequently utilized to model system dynamics due to their ability to handle concurrencies and sequential dependence. In this paper, a portion of the supply chain operations reference (SCOR) model has been extracted and modeled using PNs for the purpose of exerting supervisory control upon a multi-echelon supply chain (SC). The activities of source, make and deliver, inherent in the SCOR model form the basis of the representation of the PN model for each echelon considered in the SC model. Two control nets are utilized: one above the base model of each echelon to exert local constraints and an enterprise level supply chain manager (SCM). The local constraints are at the tactical and operational levels while the SCM enforces additional constraints consisting of long term planning goals at the strategic level. Place invariants are used to create the supervisors. Performance measures of the total SC are formulated to determine the effectiveness of any partnership. An efficient method for finding the current state of the system is developed which is used to determine the performance measures of each echelon. This paper presents a modular approach to the overall structure and PN modeling for a SC system. It is intended to extend the use of supervisory control from a shop-floor level to an inter-organizational facility and enterprise level.     

动态供应链管理系统的风险管理及安全控制

由于动态供应链具有组成企业的动态性、地理分布性、系统管理的复杂性和蕴含高风险性等特点,动态供应链管理系统的风险管理就显得尤为重要。动态供应链管理系统的风险管理包含三个过程:风险评估、风险减缓、系统检测与再评估。风险管理的目的是为了减缓风险。通过安全控制可以达到降低风险的目的,动态供应链管理系统安全控制包括三方面内容:技术类安全控制、管理类安全控制和操作类安全控制。     

Collaborative design and analysis of supply chain network management key processes model

This study focuses on collaboratively designing a structured and comprehensive supply chain (SC) network management key processes model and analyzing the relative importance of these key processes for semiconductor industry. The collaborative design and analysis are performed by a multidisciplinary team consisting of over 20 members from both academia and industry. This research is based on experiences of these team members who joined a successful e-SCM project, used as a case study in this research, between the world's largest semiconductor foundry and the world's largest assembly and testing service provider. This study adopts focus group methodology for collaborative design and fuzzy analytic hierarchy process (FAHP) for collaborative analysis. The result of the design is a structured and comprehensive key processes model consisting of four dimensions: strategy and planning, manufacturing, logistics, and risk management (SMLR) with a total of 15 key processes included in these four dimensions. The resulting weightings from FAHP analysis can identify the most critical one dimension and four key processes since they account for approximately half of the overall weighting in their level. The SMLR model provides a structured and comprehensive reference model for future SC network management project executives, ensuring that all key processes are supported to avoid extremely costly failure. The resulting weightings provide these managers with the relative importance of these key processes and can help them make critical decisions in allocating limited resources to support the most critical processes. To confirm the results and further explore the managerial implications, a second session of focus group meeting was conducted and practices of the top three key processes in the semiconductor manufacturing industry were used to illustrate what actions can be performed to improve these processes and hence benefit the entire SC network. The research results can serve as a foundation for related academic researches.     

A statistical tolerancing approach for design of synchronized supply chains

Supply chain (SC) can be visualized as a means to fulfilling the customer's requirement with the objective to maximize the overall value generated. With the rapid change of world economy, firms need to deploy alternative methodologies to improve the responsiveness of SC. In this regard, one important issue is the synchronization and co-ordination of supply chain network (SCN). In order to ensure the dispatch of finished product to the customer, with in customers specified delivery window, with a fierce high probability, the delivery performance of any SCN is to be minimized by forcing the work in the system. These requirements can be achieved by SC coordination, where timing of the actions performed by each of the business entities must be perfectly synchronized in SCN.This research is first of its kind where a probabilistic model is conceptualized to allocate the tolerances on lead times of internal business processes of a SCN. Various linear and nonlinear constraints have been considered with an end goal of minimizing the total cost of given network. In the proposed approach, synchronization among the members of SC has been achieved in the similar fashion as that of tolerance allocation on mechanical assemblies.The effectiveness of the proposed model is illustrated with a case study. The probabilistic model for ensuring synchronization shows much promise for improving the entire performance of network in terms of greater profit, speed, flexibility, quality, and product development etc., in a long run manufacturing enterprise.     

A comprehensive decision-making model for risk management of supply chain

Risk management of a supply chain (SC) has a great influence on the stability of dynamic cooperation among SC partners and hence very important for the performance of the SC operations as a whole. A suitable decision-making model is the cornerstone for the efficiency of SC risk management. We propose in this paper a decision-making model based on the internal triggering and interactive mechanisms in an SC risk system, which takes into account dual cycles, the operational process cycle (OPC) and the product life cycle (PLC). We explore the inter-relationship among the two cycles, SC organizational performance factors (OPF) and available risk operational practice (ROP), as well as the risk managerial elements in OPC and PLC. In particular, three types of relationship, bilateral, unilateral and inter-circulative ones, are analyzed and verified. We build this dynamic relation into SC risk managerial logic and design a corresponding decision-making path. Based on the analytic network process (ANP), a methodology is designed for an optimal selection of risk management methods and tools. A numerical example is provided as an operational guideline for how to apply it to tailor operational tactics in SC risk management. The results verify that this strategic decision model is a feasible access to the suitable risk operational tactics for practitioners.     

Performance modeling and analysis of message-oriented event-driven systems

Message-oriented event-driven systems are becoming increasingly ubiquitous in many industry domains including telecommunications, transportation and supply chain management. Applications in these areas typically have stringent requirements for performance and scalability. To guarantee adequate quality-of-service, systems must be subjected to a rigorous performance and scalability analysis before they are put into production. In this paper, we present a comprehensive modeling methodology for message-oriented event-driven systems in the context of a case study of a representative application in the supply chain management domain. The methodology, which is based on queueing Petri nets, provides a basis for performance analysis and capacity planning. We study a deployment of the SPECjms2007 standard benchmark on a leading commercial middleware platform. A detailed system model is built in a step-by-step fashion and then used to predict the system performance under various workload and configuration scenarios. After the case study, we present a set of generic performance modeling patterns that can be used as building blocks when modeling message-oriented event-driven systems. The results demonstrate the effectiveness, practicality and accuracy of the proposed modeling and prediction approach.     

Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach

Supply chains are becoming perpetually complex and potentially vulnerable due to increased globalization and vertical integration. Supply chain managers are conscious that the success of any supply chain lies in its proficiency to manage risks efficiently and effectively. Supply chain risk management (SCRM) signifies proactive practices of managing vulnerability of supply chains. Various firms espouse strategic level measures for risk mitigation and implement practices for enhancing supply chain resilience. In order to enhance supply chain resilience, the enablers of supply chain risk mitigation need to be acknowledged and implemented into practice. This research emphasizes on ascertaining the major enablers of supply chain risk mitigation with emblematic focus on electronic supply chains. A blend of Grey theory and DEMATEL approaches has been employed in this research to find out cause/effect relationships among the enablers of supply chain risk mitigation. This exercise helps to find which can be the driver to initiate the effects of other driven enablers. Sensitivity analysis was also conducted of the results to ensure reliability of solutions. The results show that that the enablers of supply chain risk mitigation are intertwined and one enabler can be the cause/effect of one or more enablers of supply chain risk mitigation. Dynamic assortment planning is found to be the decisive causal enabler, as it initiates the effects of many other enablers of supply chain risk mitigation, followed by accurate demand forecasting and flexible supply contracts. Cause–effect relationships plotted facilitate managers to ascertain primary causal enablers that need imperative attention in dealing with vulnerability issues of supply chain. Managers can take proactive steps to address and implement primary causal enables of risk mitigation into practice for reducing total risk impacts of the supply chain.     

Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets

Batch deterministic and stochastic Petri nets are introduced as a tool for modeling and performance evaluation of supply chains. The new model is developed by enhancing deterministic and stochastic Petri nets (DSPNs) with batch places and batch tokens. By incorporating stochastic Petri nets (SPNs) with the batch features, inhibitor arcs, and marking-dependent weights, operational policies of supply chains such as inventory policies can be easily described in the model. Methods for structural and performance analysis of the model are developed by extending existing ones for DSPNs. As applications, an inventory system and an industrial supply chain are modeled and their performances are evaluated analytically and by simulation, respectively, using this BSPN model. The applications demonstrate that our model and associated methods can solve some important supply chain modeling and analysis issues. Note to Practitioners-This paper was motivated by the problem of performance analysis and optimization of supply chains but it also applies to other discrete event systems where materials are processed in finite discrete quantities (batches) and operations are performed in a batch way because of batch inputs and/or in order to take advantages of the economies of scale. Existing Petri net modeling and analysis tools for such systems ignore their batch features, making their modeling complicated. This paper suggests a new model called batch deterministic and stochastic Petri nets (BDSPNs) by enhancing deterministic and stochastic Petri nets with batch places and batch tokens. Methods for structural and performance analysis of the model are developed. We then show how an inventory system and a real-life supply chain can be modeled and their performances can be evaluated analytically and by simulation respectively based on the model. The model and associated analysis methods therefore provide a promising tool for modeling and performance evaluation of supply chains.     

A tri-level programming model based on Conditional Value-at-Risk for three-stage supply chain management

For the problem of supply chain management, the existing literature mainly focuses on the research of the single-stage supply chain or the two-stage supply chain that consists of a manufacturer and a retailer. To our best knowledge, little attention has been paid to the study of a more extensive supply chain that consists of a material supplier, a manufacturer and a retailer, which is a more practical and interesting case. Therefore, based on the Conditional Value-at-Risk (CVaR) measure of risk management, this paper proposes a tri-level programming model for the three-stage supply chain management. In this model, the material supplier and the manufacturer maximize their own profit while the retailer maximize his/her CVaR of expected profit. Further, we show that the proposed tri-level programming model can be transferred into a bilevel programming model, which can be solved by the existing methods. Numerical results show that the proposed model is efficient for improving the risk management of the three-stage supply chain.     

A simulation-based risk network model for decision support in project risk management

This paper presents a decision support system (DSS) for the modeling and management of project risks and risk interactions. This is a crucial activity in project management, as projects are facing a growing complexity with higher uncertainties and tighter constraints. Existing classical methods have limitations for modeling the complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This will influence the effectiveness of decisions for risk response planning and will lead to unexpected and undesired behavior in the project. Based on the concepts of DSS and the classical steps of project risk management, we develop an integrated DSS framework including the identification, assessment and analysis of the risk network. In the network, the nodes are the risks and the edges represent the cause and effect potential interactions between risks. The proposed simulation-based model makes it possible to re-evaluate risks and their priorities, to suggest and test mitigation actions, and then to support project manager in making decisions regarding risk response actions. An example of application is provided to illustrate the utility of the model.     

A First-Order Hybrid Petri Net Model for Supply Chain Management

A supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategies.     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study

In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving.     

A hierarchical distributed model predictive control approach to irrigation canals: A risk mitigation perspective

This paper presents a hierarchical distributed model predictive control approach applied to irrigation canal planning from the point of view of risk mitigation. Two levels in optimization are presented. At the lower level, a distributed model predictive controller optimizes the operation by manipulating flows and gate openings in order to follow the water level set-points. The higher level implements a risk management strategy based on the execution of mitigation actions if risk occurrences are expected. Risk factors such as unexpected changes in demand, failures in operation or maintenance costs are considered in the optimization. Decision variables are mitigation actions which reduce risk impacts that may affect the system. This work shows how model predictive control can be used as a decision tool which takes into account different types of risks affecting the operation of irrigation canals.     

供应链协同管理的绩效评估

供应链协同管理是通过对供应链系统的人力、物力、资金及信息等资源的规划、组织和控制,来实现其目标。供应链协同管理的效果如何,可通过绩效评估进行检验,他是协同管理的一个重要环节。使用平衡积分卡、粗糙集中启发式属性约简的方法和BP神经网络的理论,探索建立了平衡积分卡、启发式属性约简和BP神经网络相结合的供应链协同管理的绩效评估模型。并结合一个实例,首先对其基于平衡记分卡的指标体系进行了约简,然后将约简的评价指标输入到BP神经网络中进行智能训练,最后把评估的样本输入到训练好的BP网络中得出供应链协同管理的绩效评估值,评估结果与实际结果基本吻合。     

System dynamics of supply chain network organization structure

Information technology is providing manufacturers with additional flexibility with regard to their supply chain network choices. Our research studies supply chain network organization structures categorized by the organic and mechanistic management control structures. The structural impacts on cost and fill rate performance are studied in two-echelon and two-supply-chain network organization models under different market coordination conditions using system dynamic simulations. Our results show significant effects of demand and network structural factors, and their interactions, on these measures. As demand becomes dynamic, the cooperative interaction model, where supply chains cooperate to satisfy customer demand, is found to have better system performance than the competitive supply chain model. The analysis also suggests that increasing the responsiveness at the downstream plant is particularly important to the overall system performance improvement.     

A moment generating function based approach for evaluating extendedstochastic Petri Nets

A moment-generating-function (MGF)-based approach for performance analysis of extended stochastic Petri nets (ESPNs) is presented. The method integrates Petri nets, MGF and stochastic network concepts, and Mason's rule into a tool for evaluating various discrete-event dynamic systems. The ESPNs are modeled, given the specification of a system. Then, the state machine PN is derived, the transfer functions based on the MGFs of the related transitions are found, the network is reduced to a single transition with its transfer function for each performance measure, and system performance is calculated. Firing delays of transitions in ESPNs can be either deterministic or stochastic with an extended distribution. Three fundamental structures that can be reduced into a single transition are discussed. The machine-repairman model with a buffer is given as an example to illustrate the method for evaluating performance parameters     

Synthesis of a closed-loop combined plant and controller model

In practice, a controlled system consists of two distinct entities: the plant and the controller. These two entities are coupled by a feedback mechanism to give the closed-loop behavior of the controlled system. Under this structure, the controlled behavior of the system can be analyzed by simulation which is costly and time consuming. In this study, we synthesize a combined plant and controller model. We consider the practical issues and propose a method which combines the advantages of Petri nets (PN) and the supervisory controller design based on Formal Language Theory. The resultant combined model is a PN and represents the controlled closed-loop behavior of the system. This model can be used for the functional and the performance analysis of the system, verification of the controller and the control code generation.     

面向对象Petri网的网络传感器管理系统建模

传感器资源管理是一个复杂的系统工程，特别是在分布式传感器网络中，传感器种类繁多，使用一般的Petri网建模方法很难对系统作一形象描述。本文提出了一种应用面向对象Petri网对该系统进行建模的方法，并针对网络传感器管理特点，构造了网络传感器管理系统模型。     

Systems dynamics modelling of a manufacturing supply chain system

Supply chains are multifaceted structures focusing on the integration of all the factors involved in the overall process of production and distribution of end products to the customers. Growing interest in supply chain systems has highlighted the need to adopt appropriate approaches that can ensure the efficient management of their complexity, enormity and broadness of scope. With the main aim of supply chain management being to optimise the performance of supply chains, attention is mainly drawn to the development of modelling frameworks that can be utilised to analyse and comprehend the dynamic behaviour of supply chains. While there have been only a few supply chain modelling attempts reported in the literature, this paper proposes a modelling framework that is used to simulate the operation of a supply chain network of moderate complexity. The proposed model comprises four echelons and is build around a central medium-sized manufacturing company operating as a typical Make-to-Order (MTO) system. The developed model was built using a systems dynamics (SD) approach. The operations performed within a supply chain are a function of a great number of key variables which often seem to have strong interrelationships. The ability of understanding the network as a whole, analysing the interactions between the various components of the integrated system and eventually supplying feedback without de-composing it make systems dynamics an ideal methodology for modelling supply chain networks. The objective of the paper is to model the operation of the supply chain network under study and obtain a true reflection of its behaviour. The modelling framework is also used to study the performance of the system under the initial conditions considered and compare it with that obtained by running the system under eight different scenarios concerning commonly addressed real-life operational conditions. The modelling effort has focused on measuring the supply chain system performance in terms of key metrics such as inventory, WIP levels, backlogged orders and customer satisfaction at all four echelons. The study concludes with the analysis of the obtained results and the conclusions drawn from contrasting the system’s performance under each investigated scenario to that of the benchmark model.