The main task of this paper is to exemine how simulation is being used in logistics and supply chain (LSC) in relation to planing and control. In order to understand how simulation is being used to analyse the supply chain performance, a literature review was critically analyse. The main focus of this reserch was to analyse the various methods being used in simulation as regard the supply chain planing and control in order to analyse the benefit and contrain of the various method ( ).
A sample of problem in SC was identify and treated with one of the methods of simulation in order to measure the efficiency and effectivenes within SC. Conclusion was drawn which compares and contras both approch to measure the weakness and streagnth in diffrent circumstancies.
Problem Identification and Description 问题识别和描述
Litrature Review 文献综述
A review recently by Cid Yanez et al., (2009) Identified more comprehensive taxonomy of the different types of simulation wich ara: continuous, discrete, System Dynamics, Gaming, Agent, Artificial Intelligence, Virtual Reality etc. However, thier are quite of number of methods or techniqures used in planing accross supply chain, namely XML, Spreadsheet, Mathematical Modelling, Java, potal Software, Matlab, etc. In contrast Skiadas and Skiadas, (2009) argue that methods or techniqures like Spreadsheet, XML, Java and Genetic Algorithm were not actual simulation techniqure but rather are merely simple analysis tools and programming methods wich is categorised under Discrete Mathematical Model wich are static in nature. In the same vein Small et al., (2013) suggests that Discrete mathematical model is diffrent in nature because it try to find an optimal solution to a problem in the present rather than in the future. In view of DAVIS, EISENHARDT and BINGHAM, (2007) agrees that mathematical modelling techniqures cannot deal with dynamic and complex situiation expecially in gaining competative advantage through the supply chain network. This means that mathematical model or techniques is not considered as the best in dealing with real phenomenon. Example product demand forecasting. Conversely, Dynamic simulation modelling is regarded as poor modelling techniqures as it lacks the ability to handle too detailed LSC micro problems such as job sequencing (Longo, 2011). However as ponted out by Chopra and Mendl (2007) there are three basic supply chain simulation in planinig and control wich are: System Dynamics (SD), Discrete Event Simulation (DES), and Agent Based Modelling (ABM). Accordingly, Lorenz and Jost (2006) have identified these three basic types of simulation in supply chain planing and optimization, each problem has a particular approach being used to find optimal solution. In view of the defination provided by copra and mendl (2011) in relation to supply chain management decision making, identified two main problem namely; strategic and opertational wich can be tackled by these approch (i.e. SD, DES ABM). ( ) suggest that In order to make the right decision it is important to prepare them with appropriate planning and control.
Cid Yanez等人最近的一篇综述对不同类型的模拟进行了更全面的分类：连续、离散、系统动力学、游戏、代理、人工智能、虚拟现实等。然而，在跨供应链规划中使用了相当多的方法或技术，即XML、电子表格、数学建模、Java，potal Software、Matlab等。相比之下，Skiadas和Skiadass认为，电子表格、XML、Java和遗传算法等方法或技术不是实际的模拟技术，而只是简单的分析工具和编程方法，属于本质上静态的离散数学模型。同样，Small等人认为，离散数学模型本质上是不同的，因为它试图在当前而不是未来找到问题的最优解。鉴于DAVIS，EISENHARDT和BINGHAM，一致认为，数学建模技术无法处理动态和复杂的情况，尤其是在通过供应链网络获得竞争优势方面。这意味着数学模型或技术在处理真实现象时并不被认为是最好的。产品需求预测示例。相反，动态模拟建模被认为是较差的建模技术，因为它缺乏处理过于详细的LSC微观问题（如工作顺序）的能力。然而，正如Chopra和Mendl提出的那样，在规划和控制中有三种基本的供应链模拟：系统动力学（SD）、离散事件模拟（DES）和基于代理的建模。因此，Lorenz和Jost已经确定了供应链规划和优化中的这三种基本模拟类型，每个问题都有一种特定的方法来寻找最优解。鉴于copra和mendl对供应链管理决策的定义，确定了两个主要问题：；这些方法（即SD、DES ABM）可以解决战略和操作问题。建议为了做出正确的决定，重要的是要为他们做好适当的规划和控制。
Similarly, Arne Schuldt (2010) state that the primary supply chain function are applied in order to tackle challenges usually, each of the function is not sufficient alone to tackle the challenges instead multiple functions contributed by multiple techniques or model must be combine to supply network in order to make the right decision. For instance, Baril et al. (2016) use both approch to improve health care service delivery by reducing patient delay using DES, SD to improve patient pathways and ABM for quick implementation process. As can be seen both method can be used to achieve SC strategic and planning objectives
However, Lean thinking simulation and implementation has emerged in various segment of the suppy chain particularly in manufacturing and recently in the healthcare because, recent litrature has shown significant benefit such as increased patient and management throughput( ). In view of ( ) every oranisation has similar process of simulation implemetation wich must start with define value, measure, analyse, improve and control as indicated in the following figure 1
Figure 1 Organisational Approch of Simulation
Source: (Baril et al. 2015)
STRENGTHS AND WEAKNESSES OF THE THREE (3) APPROACHES 三种方法的优缺点
System Dynamics 系统动力学
Despite the potential benefit and broad range of applications of SD in supply chain domains, the SD techniques is often regarded as a means suited to handle only macro SC problems and relational policy problems before implemetation ( ). Conversely, SD is regarded as a poor modelling technique but, as agued by ( ) that System Dynamics need to be complemented by micro modelling techniques in many situation, such as credible models which lies in the body domain of Descret event simulation that is strong and beneficial for analysing detailed and complex situaions. However, from its earliest to the recent reserch in SC ( ) has identified that feedback flow of information has been a core central features of SD and the techniques has been used to analyse many SC problems where studies finding suggested that SD is a continuous and syncronose procesess where operations are aggregated together rather than being as seprate or discrete entities. For This reason it has being suggests that the the SD techniques may be less effective to tackle problems where human behaviour or individul are observed. For example shoppers habit in Tesco. In the same vein, ( ) that one of the constraint of SD, is not effective to tracking movement of individual in space and location. In other word, one of the major aim of SC is targeted at individaul customer but, SD being a cyclical process may not model the movement of individual as it tend to varies from one point in time to another. For instance, the Impact of Brexit policy in the uk to Eu may have negative impact on various supply chain networks (Viner, 2016). Therefore, SD is aims only at addressing policy problems that are strategic in nature rather than tactical or operational level particularly dealing with human interaction in SC ( ). In contrast, many scholars in the field of SC claims that ABM can model detailed distinct behaviour where spatial relationships co-exist. And ABM can be used to model the behaviour of distict supply chain or distinct suppy node ( ). For example SD deals with problems like:
Supply chain redesign 供应链重新设计
Quality perception and Quality Control 质量感知与质量控制
Impact of demand amplification on transport cost 需求放大对运输成本的影响
Cycle time compression and Performance metrics 循环时间压缩和性能指标
The effect of batching on bullwhip 配料对牛鞭的影响
Discrete Event Simulation (DES) 离散事件仿真
Base on litrature reviews, claims that DES is very effective to handle the problem of variation in time intervals ( ) In similar vein, DES in supply chain planing and control can be used to measure relationships between entities in it strategic form. For example……………………………. ( ) argue that DES is not very effective for modelling policy in it generic level. Of course in reality SC problems under critical study ranging from planing, operations, and strategy, a more embodied discription by many leading scholar in the field of SC emphasised the need of of DES in the operational and planing level whereby the DS is more practicable in application to strategic level problems ( )
Agent base Simulation 基于Agent的仿真
Agent Based Modelling ABM is an emerging method of simulation capable of application throughout the range from the strategic to operational level in the supply chain Arvitrida, Robinson and Tako, (2015) . As an emerging model, a number of views have been made for ABM. For instance, agent base model of simulation can be use to exemine distinct behaviour where spatial interconnection of relationships core exiest ( ). Similarly, agent based simulation can be used to model the behaviour of individual characteristics and it has being term as the only techniqures that can model certain features such as “agilty” to respond to ever increasing level of volitality of deman, agility in the sense of quick response to what ever situation under study (Critopher, 2011 ). ABM deals with supply chain ploblem in the following:
Market dynamics 市场动态
Modelling control elements 建模控制元素
Human behaviour on bullwhip effect 牛鞭效应下的人类行为
Human behaviour and trust 人类行为和信任
Collective customer collaboration 集体客户协作
Table 1 Agent Base Simulation Source: 表1基于Agent的模拟来源：
Discussion and Conclusion 讨论和结论
The review of the litrature in this paper point out the benefit and constraint. In other words, it differenciate the various method of simulation in sc perspective. It shows that the various method varies and differs in scope both in customs and in practice or reality. In particular, SD is predominantly regarded as static in nature as it involves in continuous process or vertual techniques to tackle macro supply chain problems that are strategic in nauture. On the other hand, both DES and ABM are complementry to model entities in the same supply chain to varioius supply networks.
However, the major challengies to be fully and critcally analyse by researchers remains on the ability to match DS and ABM to work together in order to tackle companies problem internally and externally.
Thus, most supply network constrain related to timing is that, the time intended to find optimal solution mostly exceed the parameter of the time considered for executing the the SC respective function. This is more so for the fact that, static planning cannot be applied externally (I.e. outside the organisation) but rather internally, within the organisational boundaries of control as it involves continues or virtual process. Furthermore, the nature of ABM, in decision-making cannot be achieve using centralised approach because, not all information are centrally available.
Finally, it may be possible in the future to identify maximal self-contained system within lager systems that can be centrally control at the same time re-planning in the continuous process. But most likely that system of computational complexity is likely to exists within the boundaries of one company.
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