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

Forecasting is the basis for planning. Good planning is based on a good prediction of what is going to happen to prepare a company, a department, and their environments for certain future developments and their intermediate states. In this context, resources are allocated to these future states in the most efficient way, given a certain set of resource conditions. Although market volatility demands the high adaptability of companies’ operations, dynamic planning is still not widespread. As a result, the alignment of planning processes with potential scenarios is not given, leading to a lack of solution preparation in the long term, suboptimal decision-making in the medium term, and corrective measures in the short term, with higher costs and a lower service level. Therefore, the aim of this research is to propose a predictive approach that will help managers develop sales and operations planning (S&OP) with higher accuracy and stability. For this purpose, a methodology combining demand scenarios, statistical analysis of the demand, forecasting techniques, random number generation, and system dynamics was developed. The goal of this predictive S&OP is to predict the supply chain system’s behavior to generate plans that prevent potential inefficiencies, thereby avoiding corrective measures. In addition, to assess the methodology, the model is applied in the software Vensim, for an automotive producer´s supply chain, to compare the predictive S&OP model with a classical approach. The results show that the proposed predictive approach can increase a manufacturer’s efficiency by increasing its adaptability through the identification of potential inefficiencies and can also be used to prepare solutions.

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An XML-centred approach to creating object-oriented supply chain simulation models

International Journal of Simulation and Process Modelling ◽

10.1504/ijspm.2006.012640 ◽

2006 ◽

Vol 2 (3/4) ◽

pp. 115 ◽

Cited By ~ 1

Author(s):

Dean C. Chatfield ◽

Terry P. Harrison ◽

Jack C. Hayya

Keyword(s):

Supply Chain ◽

Object Oriented ◽

Simulation Models ◽

Supply Chain Simulation

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A Multi-Echelon Network Design in a Dual-Channel Reverse Supply Chain Considering Consumer Preference

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094760 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4760

Author(s):

Peng Li ◽

Di Wu

Keyword(s):

Supply Chain ◽

Network Design ◽

Environmental Sustainability ◽

Rapid Development ◽

Consumer Preference ◽

Service Level ◽

Mixed Integer ◽

Reverse Supply Chain ◽

Related Factors ◽

Dual Channel

The rapid development of e-commerce technologies has encouraged collection centers to adopt online recycling channels in addition to their existing traditional (offline) recycling channels, such the idea of coexisting traditional and online recycling channels evolved a new concept of a dual-channel reverse supply chain (DRSC). The adoption of DRSC will make the system lose stability and fall into the trap of complexity. Further the consumer-related factors, such as consumer preference, service level, have also severely affected the system efficiency of DRSC. Therefore, it is necessary to help DRSCs to design their networks for maintaining competitiveness and profitability. This paper focuses on the issues of quantitative modelling for the network design of a general multi-echelon, dual-objective DRSC system. By incorporating consumer preference for the online recycling channel into the system, we investigate a mixed integer linear programming (MILP) model to design the DRSC network with uncertainty and the model is solved using the ε-constraint method to derive optimal Pareto solutions. Numerical results show that there exist positive correlations between consumer preference and total collective quantity, online recycling price and the system profits. The proposed model and solution method could assist recyclers in pricing and service decisions to achieve a balance solution for economic and environmental sustainability.

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