Supply chain redesign for resilience using simulation

Supply chains are facing numerous changes that are contributing to increasing their complexity and vulnerability to disturbances, therefore, to survive, supply chains must be resilient. The paper presents a supply chain simulation study for a real case concerned with the Portuguese automotive supply chain. The subset automotive supply chain involved in the case study is a three-echelon supply chain, composed by one automaker, two 1st-tier suppliers, two 2nd-tier suppliers, and one outsource entity. The purpose of the study is to evaluate alternative supply chain scenarios for improving supply chain resilience to a disturbance and understanding how mitigation strategies affect each supply chain entity performance. Two strategies widely used to mitigate disturbance negative effects on supply chains were considered and six scenarios were designed. The scenarios differ in terms of presence or absence of a disturbance in one hand and presence or absence of a mitigation strategy in other hand. To evaluate the scenarios designed, two performance measures were defined per supply chain entity, Lead Time Ratio and Total Cost.     

Flexible configuration for seamless supply chains: Directions towards decision knowledge sharing

Supply chain excellence has a real huge impact on business strategy. Building supply chains (SC_s) as flexible system represents one of the most exciting opportunities to create value (e.g., seamless SC_s). This requires integrated decision making amongst autonomous chain partners with effective decision knowledge sharing among them. The key to success lies in knowing which decision has more impact on the supply chains performance. Knowledge sharing has immense potential to create expedient opportunities and thus retain greater value for supply chains. In this context, knowledge management (KM) can be used as an effective approach to achieve knowledge sharing and decision synchronization among supply chain partners. To maximize competitive advantage, concept of seamless supply chains is emerging with KM as key enabler. Thus, there is a need to develop demo models that can encourage chain members towards collaborative-knowledge sharing in the SC_s. This paper depicts the application of one such model based on decision knowledge sharing (DKS) for improved supply chains management. We study the impact of DKS (both partial and full DKS configuration in SC) and then compare the performance with information sharing (IS) and forecasting. By exploiting DKS and flexibility in supply chains structures better performance can be achieved. The paper develops the demo models on various supply chains scenario like (1st, 2nd and 3rd stage SC_s, forecasting, IS and DKS (full and partial). The partial and full DKS based flexibility configurations of SC_s are considered for simulation experimentation. A simulation model of a supply chains based on flexible framework is developed for demo purposes. The key results are highlighted along with the respective industry implications. Our research is continuing in this direction.     

Modelling the dynamics of supply chains

A summary of the important issues arising in supply chain research is presented. A review of the various mathematical methods used to model and analyse supply chains is then undertaken. They are categorized as: continuous time differential equation models, discrete time difference equation models, discrete event simulation models and operational research techniques. Each method is then appraised from a system dynamics perspective. To conclude, a judgement is made on whether the merits of operational research techniques justify their predominance in industry over the other methods presented here.     

Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context

In a dynamic customer-centric supply chain context, classic forecasting models turn out to have a limited applicability. In order to estimate the key performance indices of these Supply Chains and to facilitate their management, it is necessary to use more elaborate tools such as a simulation. However building simulation of customer-centric supply chains is no trivial matter. It requires the elaboration of a representative model and the execution of this model according to a set of hypotheses associated to scenarios. Due to their properties, Multi-Agent Systems seem particularly well suited for the modelling and the simulation of Supply Chains and more especially in a mass customization context. In this paper we propose an agent modelling framework for the modelling and simulation of such Supply Chains to facilitate their management. We show how this framework can be applied to a case of customer-centric Supply Chain from the golf club industry and we present an experiment plan associated.     

A service-oriented framework for agent-based simulations of collaborative supply chains

Current collaborative practices of supply chain management are limited to some known configurations where a dominant member sets the pace for the collaboration extent. Extending collaborative models to supply chains without a dominant member requires defining a fair assessment of costs and benefits and how they are distributed among members. To understand collaborative models and their mechanisms, simulation-based approaches are recommended as they can afford the complexity of real scenarios. However, building ad-hoc simulation models for studying complex supply chain interactions can be prohibitive in terms of both cost and time. Therefore, the availability of simulation frameworks, to be used easily by business managers and facilitating the development of those models, has a strong incentive in the quest of current business efforts to increase their supply chain performance. The objective of this work is to present a systematic and reusable serviceoriented framework for agent based simulation to support the analysis of collaborative interactions in supply chains. Results of a requirement analysis performed to this aim are described, and the fulfillment of identified requirements by the proposed framework, and capabilities thereof, are discussed.     

Perspectives and relationships in Supply Chain Simulation: A systematic literature review

The main purpose of this article is to develop a meta-analysis about the relationships and potential perspectives of modeling and simulation in supply chains. The research methodology used in this paper was a systematic literature review, exploring the state of the art in Supply Chain Simulation. The methodological procedures were based on a systematic literature review and statistical analysis of a sample of papers. The results indicated that modeling and simulation in supply chains can be better integrated. The models could be more sophisticated to capture the dynamics and behavior of these networks. The combination of optimization methods with agent-based simulation is an observed trend. Hybrid simulations involving normative models and empirical applications can be useful to represent the reality of supply chains, generating alternative solutions that improve supply chain performance. The relevance of this article is to analyze the interfaces related to this field of research, in order to establish a theoretical framework that improves the process of modeling, simulation and decision-making in supply chains.     

汽车数字供应链SaaS化新管理模式探究与实践

随着现代技术的发展,汽车产业孕育着新一轮升级。目前,汽车企业间的竞争逐步加大,已经逐步转化为供应链的竞争。而传统供应链生产管理仍然面临着市场高质量、高交期的巨大挑战,无法满足汽车企业对其生产效率的需求。因此,新的汽车供应链管理模式应运而生。研究了汽车数字供应链软件即服务(SaaS)化新管理模式,并基于SaaS平台实现探索供应链数字化管理的新生态模式。以某汽车公司为例,进行供应链SaaS生态平台实践研究,以数字化制造重塑供应链流程,解决供应链企业间数据协同,实现汽车全产业链互联互通、结构性降本增效,打造高效协作、互利共赢的新型行业生态模式。采用SaaS平台实现供应链生产管理后,其供应链管理数字化延伸,生产线开动率提升了1%,协同效率大幅提升。     

Optimizing dynamic supply chain formation in supply mesh using CSET model

A new e-Service model called dynamic supply chain is characterized by their dynamic nature in easily being formed and disbanded with the seamless connectivity provided by e-Marketplace. The new term “supply mesh” was coined to represent this virtual community of companies in which dynamic supply chains, as per project (also known as make-to-order), are formed across different tiers of suppliers. In a supply mesh, a dynamic supply chain can be formed vertically, from the top to the bottom layers, mediating different companies for a project. Companies that are on the same level laterally are usually competitors, and the companies that are linked vertically as supply chains are trading partners. From a global view, the companies that are connected in the supply mesh can be viewed as individual entities that have self-interest. They may compete for survival as well as collaborate with each other for jobs. Given such complex relations the challenge is to find an optimal group of members for a dynamic supply chain in the supply mesh. A multi-agent model called the collaborative single machine earliness/tardiness (CSET) model was recently proposed for the optimal formation of make-to-order supply chains. This paper investigates the possibilities of applying CSET in a supply mesh, and the corresponding allocation schemes are experimentally studied in simulations. One scheme called Cost-driven principle leads to destructive competition while the other one namely Pareto-optimal evolves into a cooperative competition that tries to mutually benefit every participant. The results, based on samples from the U.S. textile industry, show that a cooperative competition scheme is superior in terms of optimal allocation, which obtains maximum satisfaction for all participants.     

基于国密算法的车载以太网控制器身份认证方法

随着智能网联汽车的发展,车载以太网的信息安全成为汽车行业关注的重点。安全认证是保证车载以太网信息安全的关键技术之一。现有的车载以太网认证技术难以兼顾安全性和车载控制器性能要求。国密算法在车载网络信息安全的应用逐渐成为趋势,但在车载以太网领域的应用研究还处于起步阶段。为了满足车载控制器相对较低的性能和车载以太网的安全要求,提出了基于国密算法的两阶段信任链车载以太网控制器身份认证方法。该身份认证方法可适用于车载以太网不同的网络拓扑架构,通过基于认证凭证的信任链将车载以太网控制器身份认证的初始化阶段和车辆使用阶段进行关联。初始化阶段认证在整车下线首次安装控制器或售后更换控制器时,由整车厂诊断仪触发。在初始化阶段,基于复杂的SM2国密算法和身份证书建立完整认证。车辆使用阶段认证在每次车辆重新上电或网络唤醒时,由以太网网关触发。在车辆使用阶段,基于SM4对称国密算法和认证凭证信任链建立快速认证。认证凭证通过认证凭证函数生成,并且每一次认证凭证都基于之前成功认证的随机会话编号动态产生,形成信任链。基于车载以太网控制器进行了测试验证,测试结果表明,该方法资源占用较低并具有较高的安全性,能够同时满足车...     

A survey on control theory applications to operational systems,supply chain management,and Industry 4.0

Modern production and logistics systems, supply chains, and Industry 4.0 networks are challenged by increased uncertainty and risks, multiple feedback cycles, and dynamics. Control theory is an interesting research avenue which contributes to further insights concerning the management of the given challenges in operations and supply chain management. In this paper, the applicability of control theory to engineering and management problems in supply chain operations is investigated. Our analysis bridges the fundamentals of control and systems theory to supply chain and operations management. This study extends our previous survey in the Annual Reviews in Control (Ivanov et al. 2012) by including new literature published in 2012–2018, identifying two new directions of control theory applications (i.e., ripple effect analysis in the supply chains and scheduling in Industry 4.0) and analysis towards the digital technology use in control theoretic models. It describes important issues and perspectives that delineate dynamics in supply chains, operations, and Industry 4.0 networks and identifies and systemizes different streams in the application of control theory to operations and supply chain management and engineering in the period from 1960–2018. It updates the existing applications and classifications, performs a critical analysis, and discusses further research avenues. Further development of interdisciplinary approaches to supply chain optimization is argued. An extended cooperation between control engineers and supply chain experts may have the potential to introduce more realism to dynamic planning and models, and improve performance in production and logistics systems, supply chains, and Industry 4.0 networks. Finally, we analyze the trends towards the intellectualization of control and its development towards supply chain control analytics.     

Quantitative lifecycle risk analysis of the development of a just-in-time transportation network system

The automotive manufacturing industry is under financial pressure due to massive cost structure, relatively small scale operation and strong global competition. In order to improve their operational cost efficiency, companies have adopt lean principles in all their manufacturing activities, in particular, just-in-time supply chain. However, a consequence of this policy makes the transportation network from the local supply chain time critical. This paper uses an enterprise system model integrated with a quantitative method to study a manufacturing company’s logistics system re-development project. The quantitative risk analysis examines the project’s systems engineering management plan to see if it is sufficiently to mitigate risks in design, monitoring and validation of the project’s lifecycle processes. The computed risk profile shows a trend of decreasing risk and suggests areas of improvement in the systems engineering plan to ensure greater probability of success. The research assumes a single risk profile for the supply chain. Research is continuing in expanding to more accurate risk profile of the project when partners of the supply chain have individual profiles.     

Paint deposition modeling for trajectory planning on automotive surfaces

This research is focused on developing trajectory planning tools for the automotive painting industry. The geometric complexity of automotive surfaces and the complexity of the spray patterns produced by modern paint atomizers combine to make this a challenging and interesting problem. This paper documents our efforts to develop computationally tractable analytic deposition models for electrostatic rotating bell (ESRB) atomizers, which have recently become widely used in the automotive painting industry. The models presented in this paper account for both the effects of surface curvature as well as the deposition pattern of ESRB atomizers in a computationally tractable form, enabling the development of automated trajectory generation tools. We present experimental results used to develop and validate the models, and verify the interaction between the deposition pattern, the atomizer trajectory, and the surface curvature. Limitations of the deposition model with respect to predictions of paint deposition on highly curved surfaces are discussed. Note to Practitioners-The empirical paint deposition models developed herein, which are fit to experimental data, offer a significant improvement over models that are typically used in industrial robot simulations. The improved simulation results come without the computational cost and complexity of finite element methods. The models could be incorporated, as is, into existing industrial simulation tools, provided the users are cognizant of the model limitations with respect to highly curved surfaces. Although the models are based on readily available information, incorporating the models into existing robot simulation software would likely require support from the software vendor.     

The replenishment policy of agri-products with stochastic demand in integrated agricultural supply chains

Market demand of agri-products is influenced by uncertain factors, such as weather, temperature, and customer preferences. In integrated agricultural supply chains, traditional inventory models are useless because of the stochastic demand and deteriorative characteristic of agri-products. This paper provides a method to determine the optimal replenishment policy of integrated agricultural supply chains with stochastic demand. In these EOQ/EPQ models, shortages are allowed and are backlogged if market demand is stochastic. The objective function is to minimize the total cost of the supply chain in the planning horizon. The total cost includes the ordering cost, the holding cost, the shortage cost and the purchasing cost. Thinking of the nonlinear relationship and dynamic forces in models, a system dynamic (SD) simulation model is constructed to find the optimal lot size and replenishment interval. Finally, an example is given to make a sensitivity analysis of the simulation model. Compared to traditional methods (such as equalize stochastic demand), the total cost decreases by 16.27% if the supply chains adopt the new replenishment policy. The results illustrated that the new replenishment policy (with intelligent method) is beneficial to help supply chain make decision scientifically. Moreover, the intelligent method can simulate stochastic demand perfectly, and it is effectively for solving the complicated and mathematically intractable replenishment problem.     

Applicability of optimal control theory to adaptive supply chain planning and scheduling

Decisions in supply chain planning and control are interconnected and depend on tackling uncertainties and dynamics. From this perspective, control theory (CT) is an interesting research avenue for the supply chain management (SCM). In this paper, the applicability of optimal CT to SCM is investigated. Our analysis is based on the fundamentals of control and systems theory and experimental modeling. The paper describes important issues and perspectives that delineate dynamics in supply chains, identifies and systemizes different streams in application of CT to production, logistics, and SCM in the period from 1960 to 2011. It derives some classifications, performs a critical analysis, and discusses further researches. Some drawbacks and missing links in the literature are pointed out. Several crucial application areas of control theory to SCM are discussed. Subsequently, optimal program control, challenges and advantages of its application in the SCM are addressed. It is shown how optimal program control can be applied to adaptive supply chain planning. In addition, it is concluded that with the help of CT, robustness, adaptability, and resilience of supply chains can be investigated in their consistency with operations planning and execution control within a conceptually and mathematically integrated framework. However, although SCs resemble control systems, they have some peculiarities which do not allow a direct application of CT methods. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. An extended co-operation between control and supply chain experts may have the potential to introduce more realism to the dynamic planning and models and improve real-time supply chain control policies.     

Price competition in duopoly supply chains with stochastic demand

In the literature, substantial researches have been carried out on supply chain coordination. The majority of these studies suggest a mechanism that enforces the supply chain members to follow the strategies that produce the equilibrium of an integrated supply chain. Moreover, most of researches do not consider the competition among supply chains.In this study, we consider an industry consisting of two distinct supply chains which compete with each other over price. Three algorithms are presented to calculate the equilibrium of three possible industry structures. It is assumed that demand is stochastic with additive form whose random component has a uniform distribution. Furthermore, the effect of competition and demand uncertainty intensity on the Nash equilibrium of the structures and supply chains’ profits are discussed in a numerical example.     

Logistics supply chain information collaboration based on FPGA and internet of things system

The contribution of logistics in the supply chain and, at the same time, researchers and practitioners have recognized a new trend of supply chain integration and collaboration over the past two decades. This philosophy is known as supply chain management and has attracted extensive attention in logistics Development. In the existing method Deep Learning and Neural network for Logistics Supply Chain Information Collaboration. In the existing method large complexity of structure and Hard to maintain version control. So in the proposed method FPGA (Field-Programmable Gate Array) and IOT (Internet of Things) for Logistics Supply Chain Information Collaboration. The logistics Cooperation based on supply chain management reduces overall costs while improving services to end customers in the supply chain. Global progress, rapid innovation and growing regional influence have made the Supply Chain (SC) an indispensable part. Competition is now changing from the company level to the industry level. The data not only destroys the organization, but it also affects the industry as a whole supply unit. The industrial sector supports more than half of the population, and the automotive sector is the fastest-growing sector. Supply chain cooperation reduces the performance of different industries and different regions.     

Coordination mechanism of IT service supply chain: an economic perspective

Currently, there are two models of service delivery in the information technology (IT) arena: implementation agent and software-as-a-service (SaaS), and each model results in a different IT service supply chain (SSC) structure. When IT service is delivered via implementation agents, independent software vendors (ISV) form the core of the supply chain, while in SaaS chains, platform operators act as the core. It is important to clarify how to coordinate supply chain participants in their respective supply chains, because there is sparse research about supply chain coordination contracts in the IT service industry. This paper draws upon the theory of revenue sharing contracts and investigates IT SSC coordination mechanisms under both SaaS and implementation agent models. The analytical results indicate that a SaaS supply chain has an optimal IT service price that is only related to the variable costs (C _os ) of the SaaS platform operator, and so if the variable cost (C _os ) can be reduced to zero, perfect coordination would exist. In contrast, the implementation agent supply chain achieves perfect coordination when the variable cost of the implementation agent is equal to the average unit implementation fee. By comparing the two supply chain models and interpreting their respective coordination mechanisms, this study can further advance supply chain contract theory research.     

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.     

Dynamic supply chain scheduling

Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of dynamics, uncertainty, and adaptivity. Both supply chain schedule generation and execution control are represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. Hence, the problems and models of planning, scheduling, and adaptation can be consistently integrated on a unified mathematical axiomatic of modern control theory. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes. The application of optimal control for supply chain scheduling becomes possible by formulating the scheduling model as a linear non-stationary finite-dimensional controlled differential system with the convex area of admissible control and a reconfigurable structure. For this model class, theorems of optimal control existence can be used regarding supply chain scheduling. The essential structural property of this model are the linear right parts of differential equations. This allows applying methods of discrete optimization for optimal control calculation. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. The gained insights contribute to supply chain scheduling theory, providing advanced insights into dynamics of the whole supply chains (and not any dyadic relations in them) and transition from a partial “one-way” schedule optimization to the feedback loop-based dynamic and adaptive supply chain planning and scheduling.     

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.