Fuzzy logic based method to measure degree of lean activity in manufacturing industry

Lean manufacturing is gaining popularity as an approach that can achieve significant performance improvement in the industry. However, the application of lean manufacturing is not an easy process. To reach the level of full implementation of lean manufacturing takes a long time and during that time the continuous improvement must be made. In the process of continuous improvement, lean manufacturing assessment is required. One form of assessment is to measure the degree of lean implementation. However, it is the complexity involved in the measure of degree of leanness. This complexity arises due to (a) the inherent multi-dimensional concept of leanness (b) unavailability manufacturing practice database that can be used as a benchmark in assessing the degree of leanness and (c) the necessity for the application of subjective human judgement on lean practices which involve vagueness and bias due to variation of evaluator's knowledge and experience. In this paper a method to deal with the multi-dimensional concept, unavailability benchmark and uncertainty, which arises from the subjective and vague human judgement for the measurement of degree of leanness, is proposed. The multi-dimensional concept involving a variety of components of lean practices is measured in order to arrive at a measure for the lean activity of a given organization. It is constructed from primary and secondary data involving a comprehensive literature review and validated with interviews with a set of sample organizations representing the entire spectrum of the industry. The vagueness of subjective human judgement on degree of application of lean practices is modelled by fuzzy number in conjunction with an additional consideration related to the length of lean practice implementation and the use of multi-evaluators. Value stream mapping is used in scoring the degree of implementation of lean so the use of benchmark is not necessary. Some results from an initial survey from a sample of respondents from the manufacturing industry in Indonesia are presented to illustrate the applicability and potential strength of the proposed method.

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