The experimental analysis on safety stock effect of chaotic supply chain attractor. (December 2020)

• Record Type: Journal Article
• Authors: Açıkgöz, Neslihan; Çağıl, Gültekin; Uyaroğlu, Yılmaz
• Is Part Of: Computers & industrial engineering. Volume 150(2020)
• Publication Date: 2020-12
• Subjects: Chaos -- Supply chain management -- Dynamical behaviour -- Chaos control -- Histogram, autocorrelation and spectral plots -- Poincare section; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106881
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The system added a control parameter transits from chaotic state to steady-state. It has been verified that the safety stock effect using the Poincare section. The higher the stability, the less autocorrelation for the production quantity. Abstract: Supply chain mechanism is a management phenomenon developed to respond to customer demands at the right time, in the right place, and the right amount. However, unpredictability in demand and associated stocks and production amounts cause chaotic behaviour in the system. In this study, a three-dimensional supply chain mathematical model has been utilized to stabilize the chaotic system by adding a linear control parameter that increases the production amount to prevent a collapse leading to dangerous instability. Through adjusting the levels of the linear control parameter, the change in system behaviour has been examined with phase portrait, histogram, autocorrelation, and spectral plots. The experimental analysis demonstrates that when the amount of production is increased, the supply chain system's chaotic state is controlled. The Poincare section and phase portraits graphics have proven that the supply chain has moved away from the chaotic situation and has become stable with the increase in production amounts. The results also reveal that the autocorrelation between the sequential simulation values of the production quantities decreased when there is a transition from chaos towards a steady state. The decrease in the similarity of the values leads to a decrease in the error. It has been shown that including a control parameter for production quantity determination improves the effectiveness of the model by means of increased flexibility to handle uncertain demand.