The value of simulation in modeling supply chains (original) (raw)
Related papers
An Integrated Approach to Supply Chain Simulation
2018
Simulation can be a valuable tool for supply chain analysis, planning, optimization, evaluation, and risk management. Computer simulation and simulation models can be used to model intricate supply chains close to real systems, execute those models, and observe system behavior. The goal of simulation is to evaluate existing supply chain configurations, as well as to aid in design of the new supply chains. Supply chain simulation matters both supply chain design and supply chain control. In other words, it helps resolve different supply chain management (SCM) problems which can be grouped into the following categories:
How can simulation help in supply chain development
1999
The objective of this paper is to explore how simulation can be applied to support the development of responsive supply chains. We propose a stepwise approach for the use of simulation to support in evaluating appropriate supply chain designs. The role of simulation in the development process is a tool for evaluating and fmding solutions when reconsidering the chain strategy. A basic materials manufacturer case is presented to give an application example. The use of simulation is also discussed from a knowledge management perspective. It is concluded that the integration of simulation models to existing ERP systems will help supply chain development especially for the responsive chains.
Panel session: opportunities for simulation in supply chain management
Proceedings of the Winter Simulation Conference
It has become a matter of survival that many companies improve their supply chain efficiency. This presents an opportunity for simulation. However, there are many challenges that must be overcome for simulation to be a contributor to play an effective role. Four contributors discuss the opportunities that they see for simulation to play a meaningful role in the area of supply chain management.
Simulation Optimisation Methods in Supply Chain Applications: A Review
2010
T he competitiveness and dynamic nature of today's marketplace is due to rapid advances in information technology, short product life cycles and the continuing trend in global outsourcing. Managing the resulting supply chain networks effectively is challenged by high levels of uncertainty in supply and demand, confl ict objectives, vagueness of information, numerous decision variables and constraints. With such levels of complexity, supply chain optimisation has the potential to make a signifi cant contribution in resolving these challenges. In this paper, a literature review -based on more than 100 peer-reviewed articles -of state-of-the-art simulation-based optimisation techniques in the context of supply chain management is presented. A classifi cation of supply chain problems that apply simulation-optimisation techniques is proposed. The main criteria for selecting supply chain optimisers are also identifi ed, which are then used to develop a map of optimisation techniques. Such a map provides guidance for researchers and practitioners for a proper selection of optimisation techniques.
Supply Chain Management by Means of Simulation
Polibits, 2013
Several changes in the macro environment of the companies over the last two decades have meant that the competition is no longer constrained to the product itself, but the overall concept of supply chain. Under these circumstances, the supply chain management stands as a major concern for companies nowadays. One of the prime goals to be achieved is the reduction of the Bullwhip Effect, related to the amplification of the demand supported by the different levels, as they are further away from customer. It is a major cause of inefficiency in the supply chain. Thus, this paper presents the application of simulation techniques to the study of the Bullwhip Effect in comparison to modern alternatives such as the representation of the supply chain as a network of intelligent agents. We conclude that the supply chain simulation is a particularly interesting tool for performing sensitivity analyses in order to measure the impact of changes in a quantitative parameter on the generated Bullwhip Effect. By way of example, a sensitivity analysis for safety stock has been performed to assess the relationship between Bullwhip Effect and safety stock.
Simulation software as a tool for supply chain analysis and improvement
Computer Science and Information Systems, 2016
Effective decision making in the automotive supply chain is complex, due to the increasing number of suppliers and customers who form part of it. For this reason, the use of tools that allow to improve the performance of the supply chain is necessary. Simulation Software is one of these tools. Therefore, in this paper a simulation model to improve the performance of an automotive supply chain is developed. Using sensitivity analysis, this study finds the values that allow the supply chain to improve its order fulfilment indicator. In the sensitivity analysis, the variables Cycle Time, Production Adjustment Time, Delivery Time, Raw Material Inventory, and Finished Good Inventory, were modified. The results show that: 1) in the base line scenario, only the 78.85% of the orders are fulfilled, and 2) to fulfil the 100% of the orders Cycle Time, Production Adjustment Time, and Delivery Time must be reduced to one week.
Supply Chain Simulation: Experimentation without Pain
2010
Bridging the gap between theory and practice has always been a key issue for students and graduates. The magnitude and scope of subject areas that students at third level institutions have to learn in theory means that visualising them without any practical experience can be very difficult. Understanding the complexity of supply chain networks and how to manage them create a considerable level of difficulty for students and professionals. Theories and applications included in supply chain management subjects are the key to empathise the real challenges. Nevertheless, teaching these theories needs substantial efforts and new innovative approaches to deliver the concepts and assure successful transfer of the learning outcomes.
Simulation and optimization of supply chains: alternative or complementary approaches?
OR Spectrum, 2009
Discrete-event simulation and (mixed-integer) linear programming are widely used for supply chain planning. We present a general framework to support the operational decisions for supply chain networks using a combination of an optimization model and discrete-event simulation. The simulation model includes nonlinear and stochastic elements, whereas the optimization model represents a simplified version. Based on initial simulation runs cost parameters, production, and transportation times are estimated for the optimization model. The solution of the optimization model is translated into decision rules for the discrete-event simulation. This procedure is applied iteratively until the difference between subsequent solutions is small enough. This method is applied successfully to several test examples and is shown to deliver competitive results much faster compared to conventional mixed-integer models in a stochastic environment. It provides the possibility to model and solve more realistic problems (incorporating dynamism and uncertainty) in an acceptable way. The limitations of this approach are given as well.
The integrated analysis and optimization of today's complex supply chains that combine production and distribution are highly challenging both for the academic/research community and the supply chain partners. The traditional operational research models have been proven incapable to describe the complexity of global supply chains. The development and adoption of simulation based techniques seems to be the only reliable solution for modelling and testing the efficiency of a business system, as they support decision-making by studying simultaneously the effect of various critical factors. This paper aims to develop a methodological framework that analyses, models and studies the operations of a supply chain using simulation. The proposed framework is based on Petri Nets theory; Petri Nets has been used successfully applied in the literature for a valid mathematical representation of systems with discrete time transitions. Moreover, the combination of Petri Nets with the Activity Cycle Diagrams provides a valid simulation modelling and a quite simple simulation program development. The framework is presented through its implementation on the supply chain of a Ready Mixed Concrete Unit, located in Northern Greece. Specifically, the probability distributions of the stochastic variables are estimated using historical production data and the simulation model is built using Petri Nets, Activity Cycle Diagrams and the Simul8 ® software. Then, the validity and verification of the model is tested. Statistical analysis of experiments ('what-if' scenarios) is conducted and optimal decisions are proposed accordingly, regarding technological and resource investment. Finally, the paper provides future research directions.