Supply Chain Simulation Research Articles

Fuzzy estimations and system dynamics for improving supply chains

In this paper, we evaluate the behavior of fuzzy estimations of demand instead of demand forecasts based on exponential smoothing in a two-stage, single-item, multi-period supply chain. A system dynamics model with fuzzy estimations of demand has been constructed for supply chain simulation. Fuzzy numbers are used to model fuzzy demand estimations. With a numerical example, we show that the bullwhip effect and the amplification of the inventory variance ( NSamp) can be effectively reduced.

Logistic Game™: learning by doing and knowledge-sharing

The aim of this article is to define a new simulation game in operation management called Logistic Game™. The main objectives are to introduce a new simulation game approach in solving the different correlated subsystems based on ‘visual interactive learning’ and to verify its positive effects on the learning process with respect to the usual simulation games. The game is based on an inside plant virtual supply chain simulation and copes with the educational challenges of teaching Industrial Logistics in a new, effective way. By applying a visual interactive simulation package, the game creates a virtual dynamic scenario directly visible by participants, with an improvement of experimentation and conceptualisation phases, and offers several logistic decisions and their strategic links from a holistic point of view. The challenge goes beyond a pure theoretical setting and students learn strategies and gain experience directly by operating in a virtual supply chain and sharing knowledge. The Logistic Game has been used to train more then 300 students since December 2006 in three different Italian workshops and has been designed to encourage the employment selection process by the companies involved.

SCML: An information framework to support supply chain modeling

Abstract We develop an open information standard to assist supply chain modeling, analysis, and decision support. The Supply Chain Modeling Language (SCML) is a platform- and methodology-independent Extensible Markup Language (XML)-based markup language for storing supply chain structural and managerial information. SCML enables supply chain problem instance reuse and sharing, provides a common format for analytical software interoperability, and can improve the quality of the supply chain description. We develop several pieces of software to aid both users and developers in the utilization of SCML. We demonstrate SCML’s applicability by developing a supply chain simulation tool (SISCO) that utilizes the SCML format.

An integrated supply chain model with dynamic flow and replenishment requirements

Integration and coordination of the informational and logistical functions of the supply chain have the potential to increase a company's competitiveness. In this paper, an integrated supply chain framework is developed that considers three main dimensions of information: customer demand, transportation, and inventory data. These dynamic supply chain data are integrated in an algorithm capable of producing high-quality, minimal cost solutions for the integrated supply chain system. The integrated supply chain model's primary goal is to react effectively and quickly to supply chain disruptions and changes, thereby adding unique flexibility to current supply chain decision support systems. The performance of the proposed algorithm is tested in a dynamic environment by embedding it in the decision-making framework of a supply chain simulation model. Model results demonstrate the algorithm's ability both to improve system performance and to reduce total supply chain costs.

Improving the rigor of discrete‐event simulation in logistics and supply chain research

PurposeThe purpose of this paper is to present an eight‐step simulation model development process (SMDP) for the design, implementation, and evaluation of logistics and supply chain simulation models, and to identify rigor criteria for each step.Design/methodology/approachAn extensive review of literature is undertaken to identify logistics and supply chain studies that employ discrete‐event simulation modeling. From this pool, studies that report in detail on the steps taken during the simulation model development and model more than one echelon in logistics, supply chain, or distribution systems are included to illustrate rigor in developing such simulation models.FindingsLiterature review reveals that there are no preset rigor criteria for publication of logistics and supply chain simulation research, which is reflected in the fact that studies published in leading journals do not satisfactorily address and/or report the efforts taken to maintain the rigor of simulation studies. Although there has been a gradual improvement in rigor, more emphasis on the methodology required to ensure quality simulation research is warranted.Research limitations/implicationsThe SMDP may be used by researchers to design and execute rigorous simulation research, and by reviewers for academic journals to establish the level of rigor when reviewing simulation research. It is expected that such prescriptive guidance will stimulate high quality simulation modeling research and ensure that only the highest quality studies are published.Practical implicationsThe SMDP provides a checklist for assessment of the validity of simulation models prior to their use in practical decision making. It assists in making practitioners better informed about rigorous simulation design so that, when answering logistics and supply chain system questions, the practitioner can decide to what extent they should trust the results of published research.Originality/valueThis paper develops a framework based on some of the most rigorous studies published in leading journals, provides rigor evaluation criteria for each step, provides examples for each step from published studies, and illustrates the SMDP using a supply‐chain risk management study.

Facilitating the Analysis of a UK National Blood Service Supply Chain Using Distributed Simulation

In an attempt to investigate blood unit ordering policies, researchers have created a discrete-event model of the UK National Blood Service (NBS) supply chain in the Southampton area of the UK. The model has been created using Simul8, a commercial off-the-shelf (COTS) discrete-event simulation package (CSP). However, as more hospitals were added to the model, it was discovered that the length of time needed to perform a single simulation severely increased. It has been claimed that distributed simulation, a technique that uses the resources of many computers to execute a simulation model, can reduce simulation runtime. Further, an emerging standardized approach exists that supports distributed simulation with CSPs. These CSP Interoperability (CSPI) standards are compatible with the IEEE 1516 standard, the High Level Architecture (HLA), the de facto interoperability standard for distributed simulation. To investigate if distributed simulation can reduce the execution time of NBS supply chain simulation, this paper presents experiences of creating a distributed version of the CSP Simul8 according to the CSPI/HLA standards. It shows that the distributed version of the simulation does indeed run faster when the model reaches a certain size. Further, we argue that understanding the relationship of model features is key to performance. This is illustrated by experimentation with two different protocols implementations (using Time Advance Request (TAR) and Next Event Request (NER)). Our contribution is therefore the demonstration that distributed simulation is a useful technique in the timely execution of supply chains of this type and that careful analysis of model features can further increase performance.

A Novel Time Advancing Mechanism for Agent-Oriented Supply Chain Simulation

Among the techniques supporting a multidecision context, as a supply chain is, distribution simulation can undoubtedly play an important role in a co-operative environment. The distribution simulation for supply chain has its advantages to find and solve bottleneck of supply chain. Considering the design of distribution simulation platform for supply chain, the realization of time synchronization and time advance will be the key points. This paper proposes an advancing mechanism that integrates High Level Architecture with multi-agent distributed simulation to meet time management in supply chain simulation, i.e., a ’heterogeneous’ system is built during our research. We present some preliminary experimental results which illustrate the performance of our advancing mechanism on our platform established. Finally, the experimental results have demonstrated the novel time advancing mechanism can be successfully applied in supply chain contexts, and surely prove that it is feasible and scientific.

Collaboration stimulation through supply chain simulation

This paper aims to develop the theoretical framework underlying the Supply Chain Management Laboratory (scmLAB) set up by the authors. The overall aim of the scmLAB is to stimulate collaboration between the participating companies while, at the same time, study collaborative learning and decision-making in a laboratory setting. The method of the scmLAB combines action research and tailor-made simulation models. The theoretical model integrates conceptual, relational and action-driven components. Situated cognition causes behavioural processes to be distinct within the laboratory setting compared to the natural setting. Therefore, research questions have been developed that set the stage to explore these differences. The paper discloses that further supply chain improvement may only take place through the joint effort of the companies involved and that an intervention method such as the one described may facilitate an effective approach to change.

Controlling the bullwhip with transport capacity constraints

The bullwhip effect can be costly to companies in terms of capacity-on costs and stock-out costs. This paper examines the possibilities for controlling the bullwhip effect with transport capacity management in the supply chain. The goal is to examine how inventories and service levels react to transport capacity constraints in a simulated supply chain that is prone to the bullwhip effect. By controlling the transport capacities, the companies may be able to reduce the impacts of demand amplification and inventory variations. Thus, there may be significant practical implications of the findings for logistics managers in today's volatile business environments.

B33 Extension of Two-Layered Dynamic Supply Chain Model Considering Transportation Constraint(Advanced machining technology)

This research proposes a new model of two-layered dynamic supply chains considering transportation constraint. The new model provides a method for suppliers to estimate delivery times and prices of products based on not only production schedules but also transportation plans. The transportation plans generally include constraints about shipping times and loading capacities for transportation. A prototype of dynamic supply chain simulation system was developed and some computational experiments were carried out in order to verify the effectiveness of the new model. The prototype system is available to determine suitable shipping times and loading capacities of transportation vehicles.

Agent-Based Construction Supply Chain Simulator (CS2) for Measuring the Value of Real-Time Information Sharing in Construction

This paper presents an agent-based construction supply chain simulator (CS2) that leverages computer agents technology for modeling a virtual construction supply chain. Although construction managers and contractors do not consciously consider a supply chain and its management, they all interact with the members of a supply chain and make related decisions. Having real-time information available at any time can increase information transparency, which in turn makes it easier for project managers to identify potential risks. CS2 is a modified version of the beer game that has been widely used for illustrating the importance of real-time information in the manufacturing process. Two different types of simulations are presented in this paper, namely human-to-human and computer-to-human interactive simulation. In the simulations, a group of people play different roles, as does the current practice in construction. Results of this simulation test and verify the significance of real-time information sharing in construction. In addition, how the quantity of information affects the value of real-time information using the concept of illusion in cognitive science is investigated. The developed CS2 can be further used for educational purposes as well as for addressing the valuable lesson of real-time information sharing to practitioners.

Towards Fault-tolerant HLA-based Distributed Simulations

Large scale High Level Architecture (HLA)-based simulations are built to study complex problems, and they often involve a large number of federates and vast computing resources. Simulation federates running at different locations are subject to failure. The failure of one federate can lead to the crash of the overall simulation execution. Such risk increases with the scale of a distributed simulation. Hence, fault tolerance is required to support runtime robustness. This paper introduces a framework for robust HLA-based distributed simulations using a 'Decoupled Federate Architecture'. The framework provides a generic fault-tolerant model, which deals with failure with a dynamic substitution approach. A sender-based method is designed to ensure reliable in-transit message delivery, which is coupled with a novel algorithm to perform effective fossil collection. The fault-tolerant model also avoids any unnecessary repeated computation when handling failure. Using a middleware approach, the framework supports reusability of legacy federate code and it is platform-neutral and independent of federate modeling approaches. Experiments have been carried out to validate and benchmark the fault-tolerant federates using an example of a supply-chain simulation. The experimental results show that the framework provides correct failure recovery. The results also indicate that the framework only incurs minimal overhead for facilitating fault tolerance and has a promising scalability.

SIMULATION MODEL FOR SUPPLY CHAIN RELIABILITY EVALUATION / TIEKIMO GRANDINĖS PATIKIMUMO ĮVERTINIMO IMITACINIS MODELIS

This paper investigates problems related to supply chain risk identification and simulation‐ based risk evaluation. Accordingly, the paper is divided into two logical parts. The first part represents earlier researches about risk recognition within the sphere of supply chains. The distinction between terms “uncertainty” and “risk” is discussed. Based on the predefined supply chain functions, additional risks connected with supply chain reliability are recognized. Then, a conception of simulation‐based risk evaluation approach is discussed. Within the second part of the paper, a numerical example is provided, within which a simplified supply chain system is defined and corresponding risk evaluation is performed. Santrauka Šiame straipsnyje nagrinėjamos problemos, susijusios su tiekimo grandinės rizikos identifikavimu ir modeliavimu pagrįstu rizikos įvertinimu. Straipsnis suskaidytas į dvi logines dalis. Pirmojoje dalyje aprašomi ankstesni tiekimo grandinėse slypinčių rizikų tyrimai. Aptariami terminų neapibrėžtumas ir rizika skirtumai. Remiantis pasirinktomis tiekimo grandinės funkcijomis, nustatytos naujos rizikos, kurios veikia tiekimo grandinės patikimumą. Toliau aptariama modeliavimu pagrįsta rizikos įvertinimo koncepcija. Antroje straipsnio dalyje pateiktas skaitmeninis pavyzdys, kuriame sudaroma supaprastinta tiekimo grandinės sistema ir įvertinama su ja susijusi rizika.

A discrete event modelling approach for supply chain simulation

In today’s competitive environment, companies face increasingly diverse challenges. To survive in the next millennium, they need to improve their profit margins by cutting capital costs and creating more responsive and adaptable supply chains to meet the ever changing market demand. This paper aims to investigate how dynamic simulation tools can be used to improve and optimise supply chain performance. The general overview of the supply chain is first introduced, the importance of order release mechanisms is examined and its use as a performance measure to monitor the supply chain is proposed. Simulations were developed to investigate the customer/manufacturer relationship of the supply chain. After the running of the trials a diagnostic evaluation was formed. With the simulation results analysed, significant improvements in lowering inventory costs, increasing output and fulfilling orders on time were achieved. (Received in August 2007, accepted in June 2008. This paper was with the authors 6 months for 2 revisions.)

Managing a Supply Chain: What Communication Patterns Might Divulge about Information Availability and Team Performance

Teamwork is continually becoming more diverse and complex, with teams facing distributed and asynchronous collaborative situations. The crux of making these complex situations work successfully is the ability to convey useful information to those who need it when they need it. A fundamental question remains: how much information is enough? Decision makers need a certain level of information to make effective decisions; however, too much information can be overwhelming or even detrimental. The present study uses a modified supply chain simulation to examine the impact of information availability on overall team performance and communication. Information view and communication were manipulated to constrain information availability. The results are discussed in terms of the impact of information availability on overall performance, communication patterns, information entropy, and situation awareness of distributed teams.

Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system

Reinforcement learning (RL) appeals to many researchers in recent years because of its generality. It is an approach to machine intelligence that learns to achieve the given goal by trial-and-error iterations with its environment. This paper proposes a case-based reinforcement learning algorithm (CRL) for dynamic inventory control in a multi-agent supply-chain system. Traditional time-triggered and event-triggered ordering policies remain popular because they are easy to implement. But in the dynamic environment, the results of them may become inaccurate causing excessive inventory (cost) or shortage. Under the condition of nonstationary customer demand, the S value of ( T, S) and ( Q, S) inventory review method is learnt using the proposed algorithm for satisfying target service level, respectively. Multi-agent simulation of a simplified two-echelon supply chain, where proposed algorithm is implemented, is run for a few times. The results show the effectiveness of CRL in both review methods. We also consider a framework for general learning method based on proposed one, which may be helpful in all aspects of supply-chain management (SCM). Hence, it is suggested that well-designed ‘‘connections” are necessary to be built between CRL, multi-agent system (MAS) and SCM.

Essay on Conceptual Modeling, Analysis and Illustration of Agent-Based Simulations for Distributed Supply Chain Planning

Recent developments in supply chain planning have emerged from the field of agent technology and distributed decision making. Although several attempts have been made to exploit agent technology to design supply chain simulation tools, the integration of simulation with distributed supply chain planning tools is still a challenge. This paper aims at investigating this aspect of supply chain planning simulation through the use of theoretical contributions found in the field of simulation, systems theory, distributed decision making and agent-based software engineering. More specifically, this paper first proposes a conceptual framework for modeling “distributed agent-based Advanced Planning and Scheduling” (d-APS). Next, derived from this framework, it proposes some specific methods for understanding and modeling simulation problems at the initial phase of the modeling effort. This early phase is critical because it considerably influences the whole development process and the resulting simulation environment, but it is usually neglected in the concerned literature. Finally, a theoretical illustrative case is presented in the lumber supply chain.

Supply chain risk, simulation, and vendor selection

This paper considers three types of risk evaluation models within supply chains: chance constrained programming (CCP), data envelopment analysis (DEA), and multi-objective programming (MOP) models. Various risks are modeled in the form of probability and simulation of specific probability distribution in risk-embedded attributes is conducted in these three types of risk evaluation models. We model a supply chain consisting of three levels and use simulated data with representative distributions. Results from three models as well as simulation models are compared and analysis is conducted. The results show that the proposed approach allows decision makers to perform trade-off analysis among expected costs, quality acceptance levels, and on-time delivery distributions. It also provides alternative tools to evaluate and improve supplier selection decisions in an uncertain supply chain environment.

Application of machine learning techniques for supply chain demand forecasting

Full collaboration in supply chains is an ideal that the participant firms should try to achieve. However, a number of factors hamper real progress in this direction. Therefore, there is a need for forecasting demand by the participants in the absence of full information about other participants’ demand. In this paper we investigate the applicability of advanced machine learning techniques, including neural networks, recurrent neural networks, and support vector machines, to forecasting distorted demand at the end of a supply chain (bullwhip effect). We compare these methods with other, more traditional ones, including naïve forecasting, trend, moving average, and linear regression. We use two data sets for our experiments: one obtained from the simulated supply chain, and another one from actual Canadian Foundries orders. Our findings suggest that while recurrent neural networks and support vector machines show the best performance, their forecasting accuracy was not statistically significantly better than that of the regression model.

An XML-based modular system analysis and design for supply chain simulation

In the contemporary global market, managing the entire supply chain has become crucial for competitive advantage in different industries. However, the realization and success of supply chain management (SCM) depends significantly on the previous supply chain modeling and simulation for analyzing complex systems. This study develops a modular approach for system analysis and design of supply chain simulation, which is capable of modeling the supply chain process and the operation of related communication data and information through XML techniques and simulating them within the SIMAN simulation tool. This study performs the following tasks: (i) characterizing supply chain process and establishing data/information elements, (ii) establishing knowledge requirements for supply chain process, (iii) analyzing and designing supply chain process modules, (iv) representing and integrating specifications of supply chain process modules, and (v) generating and demonstrating modular supply chain simulation. The results of this study can be used to develop a modular system analysis and design for supply chain simulation for effectively assisting the integration of SCM and the exchange of related communication data and information in a supply chain, thus increasing business efficiency.