Supply Chain Simulation Modeling Using the Supply Chain Operations Reference Model

Hermann, Jeffrey W.; Lin, Edward M.H.; Pundoor, Guruprasad

doi:10.1115/detc2003/cie-48220

Cited by 22 publications

(9 citation statements)

References 13 publications

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“…All this makes the process more immediate: a little expert user to simulate a client, or all the public to which it is presented a work by these means, he manages to get a very clear rule, if want to go in the structure of the model is necessary in some programming expertise, but in principle the production of a work detail possible with this only occurs at a later stage. The system was then developed the mapping of a restricted area of the plant but potentially applicable to the entire steel industry in this reality [8].…”

Section: Discussionmentioning

confidence: 99%

Anticipation Models for On-Line Control in Steel Industry: Methodologies and Case Study

Briano¹,

Caballini²,

Revetria³

et al. 2010

AIP Conference Proceedings

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Quadrature integration for orthogonal wavelet systems J. Chem. Phys. 110, 8309 (1999) Oscillation spaces: Properties and applications to fractal and multifractal functions Abstract. This paper describes a simulation system according to improve steelmaking's efficiency and to monitor its performances by anticipating the next period workload. Usually the production planning in those cases is made by the use of Gantt diagrams, based on operator's work. This means that if an accident occurs, the operator himself has to change in few minutes the production plan with a lower performance than the original one. The first consideration is obviously that the operator's experience itself it's not sufficient to re-plan a performing steelmaking chain. Hence the necessity of simulation as problem-solving technique in this complex situation. A brief introduction on this paper is devoted to identify the common problems in most plants about production planning, and this is indeed needed to define the boundary conditions and the framework of the problem. Then, a description of steelmaking processes and the general features of critical aspects about steelmaking planning (Paragraph 2) is given in order to understand the bonds, features, criticalities to be analyzed and implemented in the simulation model. In paragraph 3 a detailed analysis of proposed methodology and system architecture is given in order to make the reader understand the complexity that the Authors had to face in modeling the system and the solutions they found with approximations, considerations, techniques and algorithms that were the most suitable to be used in this particular situation. A short description of the likely steelmaking plant modeled and Verification and Validation (V&V) results are carried in paragraph 4. It was in fact very important in such a complex system, to define the acceptability of results in terms of verification of the correctness, validation of the results, and accreditation to the users. This is a generally valid principle in simulation, but moreover in a complex system modeling such a steelmaking process, where an error can cost millions. At last, in paragraph 5, the Authors present the conclusions of this work, with a particular attention to the savings that the simulation can bring to this real system, explaining the results of the case study to which it was applied, and the possible future developments of this research are discussed. This model can anyway be applied to other realities than the one exemplified in this paper.

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Section: Discussionmentioning

confidence: 99%

Anticipation Models for On-Line Control in Steel Industry: Methodologies and Case Study

Briano¹,

Caballini²,

Revetria³

et al. 2010

AIP Conference Proceedings

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“…Nowadays in literature there are few examples of simulated SC; moreover the number sensitively decreases if we consider the examples that apply the SCOR model [5][6] [7]. This section presents first the advantages of simulation in comparison to the analytical and mathematical models and fmally the realization of a generic architecture to simulate the VCOR model [8].…”

Section: The Simulation Of Scm and Plm Processesmentioning

confidence: 99%

Supply Chain Management Analysis: A Simulation Approach of the Value Chain Operations Reference Model (VCOR)

Domenico

Ouzrout

Savinno

et al. 2007

Advances in Production Management Systems

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The impact of globalization and worldwide competition has forced firms to modify their strategies toward a real time operation with respect to customer's requirements. This behavior, together with the possibilities of communication offered by the up to date Information and Communication Technologies, moves the top management toward the concept of extended enterprise in wich a collaborative link is established among supplier, commercial partners and customers. When the information flows involves each agent of the chain, from suppliers to the final distribution centres, the extended enterprise becomes a virtual firm, which can be defined as a set of stand-alone operational units that acts to reconfigure itself as a value chain in order to adapt to the business opportunities given by Market. The present work is intended to verify the effective quantitative advantages given by the introduction of the Value Chain concept into the supply chain management through a simulation approach. The paper, after a description of SCOR and VCOR methods, makes a comparison between the two methods by the implementation of a simulation approach which point out the main additional requirements that are added to the VCOR model for its implementation.

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“…In SCS it is always interesting to measure lead times and lead time variability, delivery accuracy and delivery speed, and to locate bottlenecks. Herrmann et al (2003) reports on the use of the SCOR model combined with discrete event simulation. Their goal was to create supply chain simulation models using the SCOR model and thus, create reusable components in general purpose discrete event simulation software.…”

Section: Introductionmentioning

confidence: 99%

Supply chain dynamics in the SCOR model — A simulation modeling approach

Persson¹,

Bartoll²,

Ganovic³

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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Supply Chain Simulation (SCS) is today a well-defined branch of discrete-event simulation applications. The differences between different applications are usually small, but in the case of SCS, models tend to be larger, take longer time to build and are harder to validate. To remedy some of these issues in SCS, we propose to use the SCOR model (Supply Chain Operations Reference Model) as a tool to speed up the simulation modeling of supply chains. The SCOR model can be useful in the conceptual phase, the modeling phase, and in the experimental phase of a simulation project. In SCOR Template, a modeling template in Arena, all level 3 processes of Source, Make, and Deliver are modeled to provide the SCS model builder a tool that is fast, follows the SCOR standards in processes and metrics, and simple to use. Here we report on the third version of the SCOR Template.

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Supply Chain Simulation Modeling Using the Supply Chain Operations Reference Model

Cited by 22 publications

References 13 publications

Anticipation Models for On-Line Control in Steel Industry: Methodologies and Case Study

Anticipation Models for On-Line Control in Steel Industry: Methodologies and Case Study

Supply Chain Management Analysis: A Simulation Approach of the Value Chain Operations Reference Model (VCOR)

Supply chain dynamics in the SCOR model — A simulation modeling approach

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Supply Chain Simulation Modeling Using the Supply Chain Operations Reference Model

Cited by 22 publications

References 13 publications

Anticipation Models for On-Line Control in Steel Industry: Methodologies and Case Study

Anticipation Models for On-Line Control in Steel Industry: Methodologies and Case Study

Supply Chain Management Analysis: A Simulation Approach of the Value Chain Operations Reference Model (VCOR)

Supply chain dynamics in the SCOR model &#x2014; A simulation modeling approach

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Product

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Supply chain dynamics in the SCOR model — A simulation modeling approach