Supply Chain Simulation Research Articles

A Supply Chain Simulation Model with Customer��s Satisfaction

Abstract According to the customer’s satisfaction, this paper establishes a two level supply chain simulation model through system dynamics (SD) approach, and then, Simulink tool is adopted for that model. Through the simulation, it’s shown that improvement of supply chain process’s promptness can bring out the more customers’ satisfaction, and then, the sale amount is increased. The indirect profit is gained by means of improving operation’s efficiency in supply chain. In this way, the simulation about customer satisfaction is reflected clearly, and that system dynamics simulation model can be helpful for the supply chain management project.

Development of a simulation model of biomass supply chain for biofuel production

Biofuel production from renewable biomass has experienced increased interest in order to reduce U.S. dependence on imported oil and reduce carbon emissions. Concerns regarding cost efficiency and environmental problems result in significant challenges that hinder the increased biofuel production from renewable biomass. This paper describes the development of a simulation model with an easy-to-use graphical user interface for the biofuel supply chain. The model considers key supply chain activities including biomass harvesting/processing, transportation, and storage. The model uses the delivered feedstock cost, energy consumption, and GHG emissions as system performance criteria. The utility of the supply chain simulation model is demonstrated by considering a biomass supply chain for a biofuel facility in the lower peninsula of Michigan. The results show that the model is a useful tool for supply chain management, including selection of the optimal biofuel facility location, logistics design, inventory management, and information exchange.

Simulation-optimization approach to clinical trial supply chain management with demand scenario forecast

In the pharmaceutical industry, the development activities that are required to bring a new drug to market involve considerable expense (upwards of $1 Billion) and can take in excess of 10 years. Clinical trials constitute a critically important and very expensive part of this development process as the associated supply chain encompasses producing, distributing and administering the candidate therapy to volunteer patients located in different geographic regions. A number of different approaches are being pursued to reduce clinical trial costs, including innovations in trial organization and patient pool selection. In this work, we focus our attention on improved management of the clinical supply chain. A simulation-optimization approach is presented, including patient demand simulation and demand scenario forecast, mathematical programming based planning, and discrete event simulation of the entire supply chain. Three case studies with different demand types are reported and compared to demonstrate the utility of the proposed approach.

A fuzzy-Bayesian model for supplier selection

The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making process is approached as a static one rather than a highly dynamic process. Delays, lack of capacity, quality related issues are common examples of dynamic aspects that have a direct impact on long-term relationships with suppliers. This paper presents a novel method based on the integration of influence diagram and fuzzy logic to rank and evaluate suppliers. The model was developed to support managers in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting criteria, to study the sensitivity of the behavior of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. To be effective, the computational implementation of the method was embedded into an information system that includes several functionalities such as supply chain simulation and supplier’s databases. A case study in the biodiesel supply chain illustrates the effectiveness of the developed method.

Motivations and barriers in using distributed supply chain simulation

AbstractDiscrete event simulation (DES) is a technique that is used by analysts to take informed decisions regarding an existing or proposed system of interest. DES models typically represent the processes associated with various business units. However, in the case of supply chains more than one business unit may need to be modelled as different organisations may be responsible for various supply chain operations such as manufacturing, transport and logistics, distribution, warehouse operations, etc. Organisations can be protective about their internal processes and can have concerns regarding data/information security and privacy. Thus it could be argued that creating a single DES supply chain model representing the various inter‐organisational processes is usually not an option since this will run counter to organisational privacy. Further, issues such as data transfer, model composability and execution speed may also make a single model approach problematic. A potential solution could be to create several distinct and well‐defined DES models, each modelling the processes associated with one specific supply chain business unit, linked together over the internet. We refer to this possible distributed approach as Distributed Supply Chain Simulation (DSCS). Although this approach holds great promise, there are technical barriers in using DSCS. The paper discusses the benefits and barriers of a distributed approach and then, using a healthcare DSCS, the technological feasibility is demonstrated. In conclusion, the paper argues that adopting a standardised approach to DSCS will remove a major barrier to its use.

A particle swarm optimization algorithm for solving unbalanced supply chain planning problems

This paper focuses on developing a decision methodology for the production and distribution planning of a multi-echelon unbalanced supply chain. In the supply chain system discussed here, multiple products, production loss, transportation loss, quantity discount, production capacity, and starting-operation quantity are considered simultaneously, and the system pattern is ascertained with based on appropriate partners and suitable transportation quantities. To make a quality decision in supply chain planning, we first propose an optimization mathematical model which integrates cost and time criteria. Then, a particle swarm optimization (PSO) solving method is proposed for obtaining acceptable results is called MEDPSO. The MEDPSO introduces the maximum possible quantity strategy into the basic procedure of PSO to generate the initial feasible population in a timely fashion and provides an exchange and disturbance mechanism to prevent particle lapse into the local solution. Finally, one case and two simulated supply chain structures are proposed to illustrate the effectiveness of the MEDPSO method by comparing the results of classical GA and PSO in solving multi-echelon unbalanced supply chain planning problems with quantity discount.

An extensible object-oriented and agent-based framework for modelling and simulating supply chains

We propose an extensible object-oriented agent-based framework for modelling and simulating supply chains. A problem with existing supply-chain analysis tools is that most of them are designed only for specific configurations of supply chains. The primary goal of this work is to provide an open and extensible framework for analysing supply chains with heterogeneous elements and structures. Our framework incorporates the following features: 1 It adopts an agent-based approach to handle interactions among elements of a supply chain, and an analyst can introduce new types of elements for a specific application; 2 To promote the design reusability, we propose an object-oriented type system that supports behaviour inheritance; 3 The framework formally defines a meta-model for elements of a supply chain; 4 The framework includes a discrete-event simulation algorithm, which defines interactions among elements via messages and deliveries. We also discuss SIMRISK, our Java implementation of the framework.

Modeling and Simulation of Textile Supply Chain through Colored Petri Nets

The purpose of this paper is to describe the business process modeling and simulation of a textile supply chain using Colored Petri nets. Our model takes into account both the source process and delivery logistics that exist between any two members of supply chain; moreover, we model other activities performed by the company such as manufacturing clothing. The model has been built to acquire a better understanding about the behavior of a textile company in the fulfillment of requests from costumers. The model has been built using CPN Tools. This model was built with modules for activities of the supply chain textile, e.g. receiving orders of customers, determination of production plan, procurement raw material, transportation of raw material, production and delivering products to customers. This modularized model offers some advantages to represent complex supply chains according to their structure and requirements. Thus, we can add modules easily depending on the necessary activities.

An object-oriented supply chain simulation for products with high service level requirements in the embedded devices industry

The supply chain of a case study in the embedded devices industry was examined from the perspective of a high service level for the delivery of one of its products. For this purpose, an agent-based object-oriented model of the supply chain with a quantity reorder system for the inventory management was developed. The historical demand data from the ERP system of the case study was examined and pseudo-random numbers for a Monte Carlo simulation of the supply chain was generated with it. The paper examines the performance of the supply chain by using a simulation with a stockout penalty and the percentage of items delivered from stock. Results from simulation show that no single solution for the reorder point and quantity with an optimal stockout penalty can be found. The simulation generated a solution for the minimum average penalty.

Computational Evaluation of Order Selection Methods in Dynamic Supply Chains

Previous researches proposed a dynamic supply chain model, which consists of three model components; those are clients, manufacturers, and suppliers. The model provided a numerical method to determine suitable prices and delivery times of products through the iteration of both the modification processes of production schedules and the negotiation processes among the model components. This paper proposes an effective order selection method for suppliers to find a suitable order in a number of orders sent from the lower-layer organizations. Computational experiments were carried to evaluate the effectiveness of the proposed method by using a developed supply chain simulation system.

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.

Simulation Conceptual Modelling for SCM Applications: Research Issues and Existing Practice

Evaluating supply chain problems is important yet complex; with vast amount of research and practice using a simulation based approach. However, the least understood aspect of a simulation study is the critical step of ‘conceptual modelling’. This paper argues for a conceptual modelling procedure that embeds domain-specific knowledge (i.e. SCOR for SCM applications) and incorporates existing simulation practice applicable at the conceptual modelling stage. Conceptual modelling approaches are reviewed for SCM applications, identifying that no domain-specific guidelines have been provided in the literature. It is argued that developing both comprehensive and focused methodologies would be useful in improving the rigour of supply chain simulation studies and have practical implications. The contribution builds upon Robinson (2008) seminal paper that called for further research on conceptual modelling definitions, requirements and approaches.

Economic simulation of a poultry supply chain facing a sanitary crisis

PurposeThe purpose of this paper is to study the behaviour of a food supply chain possessing two originalities, i.e. a singular structure (40‐day upstream push and 24‐hour downstream pull) and one that suffers from simultaneous fluctuations in raw material supply capacities (due to epizooty) and customer demand (due to customer anxieties and fears) caused by a sanitary crisis.Design/methodology/approachA simulation model based on the system dynamics principles of Forrester is developed and applied to the French chicken meat supply chain suffering an Avian Influenza crisis.FindingsThis model first enables one us to study the regulation mechanisms of the chain that will improve understanding of the supply chain behaviour under environmental perturbations. A what‐if analysis is then implemented to examine the supply chain stability and the influence of flexibility adjustment times, inventory coverage time, slaughtered chicken buffer size and smoothing policies on the supply chain performance in different crisis fluctuation rate scenarios in order to propose necessary logistic policy enhancements.Research limitations/implicationsThis work will improve one's knowledge about the buffer inventory problem and the global stability of this multi‐echelon push‐pull supply chain.Practical implicationsThe model can be used as a decision system support which aims to minimise the additional costs due to stock level increases as demand decreases as well as exceptional external purchasing sparked by the lack of available products when there is a sudden hike in demand. The research can help decision‐makers of fresh food push‐pull supply chains when they are facing such crises by using both cybernetic representation and computer simulation.Originality/valueThis study deals with a specific food supply chain within the context of a sanitary crisis. A system dynamics model is presented for studying the behaviour of the entire food supply chain threatened by high uncertainties in the supply capacity as well as in customer demand.

Book Review: Ciro D'Apice, Simone Göttlich, Michael Herty, and Benedetto Piccoli, Modeling, Simulation, and Optimization of Supply Chains. A Continuous Approach. Cambridge University Press, SIAM Philadelphia 2010

ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und MechanikVolume 91, Issue 8 p. 686-686 Book Review Book Review: Ciro D'Apice, Simone Göttlich, Michael Herty, and Benedetto Piccoli, Modeling, Simulation, and Optimization of Supply Chains. A Continuous Approach. Cambridge University Press, SIAM Philadelphia 2010 Christian Wieners, Christian Wieners KarlsruheSearch for more papers by this author Christian Wieners, Christian Wieners KarlsruheSearch for more papers by this author First published: 06 July 2011 https://doi.org/10.1002/zamm.201190018AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume91, Issue8August 2011Pages 686-686 RelatedInformation

Agent-Based Modeling and Simulation on Multi-Stage Supply Chain Operation

Supply chain simulation is a fundamental approach for supply chain prediction, management, evaluation, and improvement. In order to simulate member behavior, organizational strategy, and management strategy of the supply chain, an agent-based modeling and simulation approach on multi-stage supply chain operation was proposed in this paper. First, the model structure of multi-stage supply chain operation was introduced. Then, the individual agent behavior model was emphatically studied, and hierarchical colored Petri-nets were used to describe the agent behavior process and cooperative behavior process. Finally, the model was validated using a series of simulation which focused on the agent inventory strategy.

Research on Bullwhip Effect Considering Allocation Mechanism in Supply Chain

Commonly, an upstream member in a supply chain provides productions or resources for many downstream members. When the sum of retailers order quantities is beyond suppliers' inventories, suppliers might apply order allocation mechanism to solve the difficulty. The allocation mechanism is not considered in most of previous studies on the bullwhip effect. This paper develops the supply chain simulation software based browser/server technology, which can build tree-shape supply chain structure considering allocation mechanism. Two experiments are designed and implemented in order to isolate the effect of different order allocation mechanisms on bullwhip effect. The studies show that BWV differs from allocation mechanisms. For three types of supply chain members, the change tendencies of BWV with two allocation mechanism are similar. The paper also demonstrates that distributors have effect more obviously on bullwhip effect than other two roles with different allocation mechanism.

Contributors

Contributors

Modeling and distributed simulation of supply chain with a multi-agent platform

A supply chain is a complex adaptive system that is very difficult to analyze, in particular, with respect to its dynamics, stochastic, and uncertainty. The traditional analytical model is unable to cope with these characteristics of supply chain. In addition, the analytical model may not be solved due to its complexity and nonlinearity. Simulation, especially the multi-agent-based distributed simulation, is one of the most effective tools to model and analyze supply chain. In this paper, a multi-agent platform framework with multi-layers for modeling and distributed simulation of the complex supply chain is presented. In this platform, generic agents for modeling entities in supply chain are described and a novel simulation modeling approach enabled by the platform is proposed. The platform provides a flexible environment for defining, deploying, simulating, and displaying agent-based simulation models and enables users to instantiate generic agents to model their required simulation models rapidly. A three-echelon supply chain model is developed and simulated to illustrate the use of the simulation platform.

Modeling and Simulation of Lean Supply Chain with the Consideration of Delivery Consolidation

An integrated production-inventory-delivery lean supply chain model is discussed. The model is integrated with production, supply and distribution through cost. And the delivery of purchasing and distribution is consolidated. A non-linear programming model with multivariable is proposed. The length of path and the frequency of delivery are regarded as decision-making variables. The optimal delivery path and the length of path are first solved by using improved ant colony optimization algorithm. And the optimal frequency of delivery and the batch of delivery are achieved. Last, the model is validated through an example and the result indicates the model is simple and practical. Moreover it is meaningful for practice.

Contributors

Contributors