English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Today all small and medium size enterprises, companies and even countries (either in private, public or military domain) in the national and international business area are continuously performing activities to provide capabilities for satisfying customer needs (i.e., demand) those indeed include many sophisticated interrelated functions and processes such as decision making, management, new product development, production, marketing, logistics, finance, quality control and etc. which, all together compose dynamic, complex and chaotic structures called supply chain networks (SCNs). These complex structures with all interrelated functions have to be designed and managed perfectly pointing us to the wellknown term SCN management (SCNM). Due to the complex information flow in these systems; which consists of cumulative data about costs parameters, production activities, inventory systems and levels, logistic activities and many other related processes, we may unwaveringly express that the performance of a successful SCN directly related to the constant, accurate and appropriate demand information flow as this vital flow of information inarguably influences all decision making processes in all stages of SCNs. A well-known phenomenon of SCNs called the “Bullwhip or Whiplash Effect” (BWE) is the variability of the demand information between the stages of the SCN and the increase in this variability as the demand data moves upstream from the customer to the following stages of the SCN engendering undesirable excess inventory levels, defective labor force, cost increases, overload errors in production activities and etc. From 1952 till now many studies have been done about BWE. However very few of them interested in fuzzy and neuro-fuzzy system (NFS) approaches to BWE such as Carlssson and Fuller (1999, 2001, 2002, 2004) and Efendigil et al. (2008). Making accurate and appropriate estimation about future in decision making process is the leading activity providing bases for almost every managerial applications including SCNs. Demand forecasting and decision making are among the key activities that directly affect the SCN performance. To smoothen the undesirable variability of demand through the stages of SCN due to the chaotic nature of SCN system, appropriate demand forecasting is vital. As demand pattern varies due to the field of activity and architecture of SCNs, determining the appropriate forecasting model and adequate order/demand decision process for system interested in is snarl. As the nature of forecasting and decision making contains uncertainty or vagueness of the human judgment, they perfectly fit for the

A Hybrid Fuzzy Approach to Bullwhip Effect in Supply Chain Networks