An application of cost-effective fuzzy inventory controller to counteract demand fluctuation caused by bullwhip effect

Measuring Bullwhip Effect in a Single Echelon SupplyChain Using Fuzzy Approach

“…According to reference [28], the possibilistic variance of a symmetric triangle fuzzy number is defined as: (14) where the -cut of is . And the addition and multiplication by a scalar of the two fuzzy numbers A and B are defined in (19): (15) The covariance of the two fuzzy numbers are defined as: (16) The -cut of is defined as: (17) Using (14), the possibilistic variance of is calculated as: (18) After integration, the variance of is obtained as: (19) And the possibilistic variance of is: (20) Under the classical OUT policy, the is set to be 1, so the (13) becomes: (21) Using (15), the possibilistic variance of is calculated as: (22) The -cut of is defined as , and the -cut of is also defined as , so using the sum operation between the -cuts of the two fuzzy numbers: (…”

“…Using (15), the possibility variance of is obtained as: (24) International Journal of Innovation, Management and Technology, Vol. 3, No.…”

“…Accordingly, the various approaches are identified to reduce bullwhip effect, including the order information sharing, demand forecasting [6], [7], inventory management [8], and ordering policies [9]. Technically, control theories [10], [11], [12], statistical techniques [1], simulation models [13], fuzzy approaches [14], [15] and soft computing methods [16] had been applied to reduce or even eliminate the bullwhip effect.…”

Fuzzy Estimations and System Dynamics for Improving Manufacturing Orders in VMI Supply Chains

“…Other studies, which have used fuzzy approaches for improving supply chain ordering or reducing the bullwhip effect can be found in Xiong and Helo (2006), Balan et al (2007), Zarandi et al (2008), Lin et al (2010), Wangphanich et al (2010), Cannella and Ciancimino (2010), Kristianto et al (2012) and Cannella et al (2012).…”

A customer satisfaction inventory model for supply chain integration

“…During the past few decades, various literatures and models have been published and proposed for effective inventory management (Axsater, 2006;Bartmann & Beckmann, 1992;Lewis, 1997;Lu, Song, & Zhu, 2005;Pfohl, Cullmann, & Stolzle, 1999;Watts, Hahn, & Sohn, 1994;Xiong & Helo, 2006;Yung, Ip, & Wang, 2007), for example the Economic Order Quantity Model, RMSystem, Newsvendor Problem, AHM Model, Bisection Method, etc. However, most of these inventory management models only account independently for various demand patterns, quantity discount, stockout costs, lead time variations, multi-stage/multi-item situations, etc., but few concepts or models are suggested for incorporating the customer relationship into the inventory management model.…”