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.
Manufacturing organizations have been witnessing transformation in business strategy from mass production to lean philosophy. Value Stream Mapping (VSM) is one of the primary analytical tools for identifying waste and transforming the production environment into lean operational state. However, traditional VSM lacks the capability to handle conflicting factors in the improvement scheme and to prioritize multiple improvement initiatives. VSM enables only a static analysis of a system, and a static model does not allow assessing how the system will be affected to various scenarios with different parameters in the futurestate map. Moreover, VSM optimization is a typical multiple-attribute decision-making (MADM) problem that involves the evaluation of multiple performance metrics such as inventory levels, lead times and service levels. Therefore, this paper proposes an improved VSM procedure that incorporates simulation and MADM, using grey Taguchi method, to overcome the limitation of standard VSM. Simulation introduces a dynamic dimension to VSM, and grey Taguchi method prioritizes the scenarios with a minimum number of test series. A lean implementation program is conducted in a footwear manufacturing company to validate the improved VSM procedure. Two alternative future-state VSMs are proposed, each with nine different scenarios, and the identified optimal solution realizes the reduction in defect rate, work-in-process inventory and lead time, as well as the improvement in order fulfilment rate. The improved VSM procedure enables practitioners to determine the optimal future-state VSM according to the preference of practitioners on multiple performance criteria.
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