This paper shows the incorporation of Value Stream Mapping (VSM) with triangular fuzzy numbers to determine variability and uncertainty in a conveyor manufacturing company. VSM is a pen and paper tool which is used to indicate wastes and bottleneck processes graphically and develop an action plan to enhance the production line. However, some weaknesses are identified in the conventional VSM where it fails to consider variability in a dynamic manufacturing environment. As such, this paper fills up the research gap by using Triangular Fuzzy Number (TFN) to illustrate time intervals, inventories and other variables of VSM operation. The purpose of this paper is to minimize total production lead time (TPLT) and total value-added time (TVAT) in the current value stream of the conveyor chain. More accurate details of variability in the dynamic manufacturing environment can be illustrated by a Triangular Fuzzy Number (TFN) of VSM.  As a result, the future value stream map shows 50% and 22% reduction in TPLT and TVAT respectively compared to the current value stream. In conclusion, this paper also recommends that in order to optimize the accuracy of VSM analysis further, a discrete event simulation can be used to examine the fuzzy VSM.

Value stream mapping (VSM) is considered as an important tool in lean manufacturing that allows analysts to visualize the actual situation that happens in the production value stream. However, in high-variety and low-volume factories, conventional VSM which is more static in nature, unable to give real vision of the variability problems concerning production process. As such, the purpose of this paper is to review the role of VSM in a dynamic context which is more efficient in today`s complex and dynamic manufacturing system.This paper discusses the conventional VSM, its applications and limitations as well as how conventional VSM integrates with computer simulation to display more dynamic behavior. Both these conventional and dynamic VSM have their own benefits and drawbacks according to their applications in the manufacturing industry. As a conclusion, this paper shows that simulation applications can help to support VSM for companies that intend to transform to lean. This work is very useful for both scholars and  companies to know how much an output can be produced within the predicted lead time before making physical changes to the system in order to save both money and time.