Abstract
In this paper, value stream mapping (VSM) is integrated with fuzzy set theory to incorporate variability and uncertainty in the lean production system. VSM is one of the primary analytical tools for identifying waste and optimizing a production line. However, the standard VSM fails to consider the variability in manufacturing environments, which is, in fact, one of the root causes of waste. Therefore, this article proposes fuzzy VSM to overcome this weakness. Two alternative forms of fuzzy numbers, triangular fuzzy numbers (TFNs) and normal fuzzy numbers (NFNs), are applied, respectively, to depict time intervals, inventories, and other operating variables in VSM. An industrial case for assessing the validity of the proposed approaches is presented. Both approaches make it possible to incorporate and analyze variability in VSM and can be easily applied to industrial cases, as they only require basic algebraic operations. The obtained results are compared and the choice between TFNs and NFNs is discussed accordingly. A triangular fuzzy VSM tends to overestimate the variability of the process in complex production environment with complicated operational processes. However, it permits a more accurate description of variation in the environment where the optimistic and pessimistic values have very different variations from the core.
Highlights
Lean manufacturing, originated from the Toyota Production System, has been receiving great attention from researchers and practitioners since its production [1]. e lean production system has been widely applied in manufacturing industry worldwide and is considered as one of the most effective approaches in improving operational efficiency [2,3,4]
This paper proposes a fuzzy Value stream mapping (VSM) method to incorporate variability in value stream analysis and improvement. e same issue has been studied by Braglia et al [11] and Seyedhosseini et al [13]
A2 generates triangular and normal fuzzy total production lead time (TPLT) expressions with similar lower and upper limits. e triangular fuzzy VSM appears to be less accurate in processes such as the current and A1 states, which comprise more operational processes, as the addition of the individual lead time in each process amplifies the dispersion of the support of the triangular fuzzy TPLT. erefore, a triangular fuzzy VSM tends to overestimate the variability of the process in a more complex productive environment
Summary
Lean manufacturing, originated from the Toyota Production System, has been receiving great attention from researchers and practitioners since its production [1]. e lean production system has been widely applied in manufacturing industry worldwide and is considered as one of the most effective approaches in improving operational efficiency [2,3,4]. Value stream mapping (VSM) is a visual tool that facilitates the process of the lean production system through identifying value-added activities and eliminating wastes [6]. The uncertainty in data and their intrinsic variability acts as one of the root causes of waste [12] and is a significant noise factor in terms of processes, inputs, outputs, Mathematical Problems in Engineering random breakdowns, and random setup times for a pull system Considering these issues, the lack of consideration of real variability in the value stream is one of the main drawbacks of VSM. Seyedhosseini et al [13] applied fuzzy set theory to map the value stream and to determine the best future-state VSM These two studies both use triangular fuzzy numbers to describe values in VSM without considering and comparing the appropriateness and accuracy of different fuzzy expressions in different production environments. The application of VSM in the pharmaceutical industries was examined by Chowdary and George [30] and several opportunities were explored to reduce lead times, cycle times, and WIP inventory levels
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