Supply Chain Management Tradeoffs Analysis (original) (raw)

The value of simulation in modeling supply chains

1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274)

In business today, re-engineering has taken a great deal of the cost out of internal corporate processes. Our factories and internal support organizations have become much more efficient, but there is still a great deal of unnecessary cost in the overall delivery system, or the supply chain. Although your corporation does not own all of the supply chain, the entire chain is responsible for product delivery and customer satisfaction. As one of several methodologies available for supply chain analysis, simulation has distinct advantages and disadvantages when compared to other analysis methodologies. This paper discusses the reasons why one would want to use simulation as the analysis methodology to evaluate supply chains, its advantages and disadvantages against other analysis methodologies such as optimization, and business scenarios where simulation can find cost reductions that other methodologies would miss.

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.

Improving Supply Chain Activity using Simulation

The discovery through computational modeling and simulation has become the third pillar of science, alongside theory and experimentation. As computational power increases, simulation has gained in importance and has become a major research area, where highly parallel computation is utilized. In this dissertation, we have performed the simulation by selecting a single machine which is involved in manufacturing the highest number of products. Data are collected for all the processes involved in the manufacturing processes and an input modelling analysis is been done for the data collected. After the analysis is completed, a simulation model is constructed using ARENA which involved all the manufacturing process using the simulation tools. With the help of the simulation tools we will be able to identify activities causing the bottlenecks and delays in the entire manufacturing processes. Similarly, this simulation can be carried out for each and every machine of the company so that we can identify the bottlenecks and delays. As a result, the bottlenecks and delays can be reduced and the entire supply chain can be improved. This paper aims at combining supply chain management and simulation - to give an overview of both areas and shows how supply chain management can profit from simulation and also to identify the delays and bottlenecks in the overall manufacturing process. Lastly, a sample of how a supply chain can be optimized, in the simulation development suite ARENA.

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:

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.

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 in the supply chain context:matching the simulation tool to the problem

2010

The supply chain can be a source of competitive advantage for the firm. Simulation is an effective tool for investigating supply chain problems. The three main simulation approaches in the supply chain context are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). A sample from the literature suggests that whilst SD and ABM have been used to address strategic and planning problems, DES has mainly been used on planning and operational problems., A review of received wisdom suggests that historically, driven by custom and practice, certain simulation techniques have been focused on certain problem types. A theoretical review of the techniques, however, suggests that the scope of their application should be much wider and that supply chain practitioners could benefit from applying them in this broader way.

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.