A decision framework for maximising lean manufacturing performance

Ramesh, V.; Kodali, Rambabu

doi:10.1080/00207543.2011.564665

Cited by 70 publications

(39 citation statements)

References 17 publications

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“…Respecting the requirements of ISO 9001: 2008 and the results of good practice in (Nestic, Djordjevic, Puskaric, Zahar Djordjevic, Tadic, & Stefanovic, 2015;Tadić, Đorđević, Erić, Stefanović, & Nestić, 2017), the production SC performances are defined. In the paper (Ramesh & Kodali, 2012), the results obtained from the numerous literary sources were summarized, and a list of performances for the lean production SC was proposed. In this paper, the SC performance is determined according to the recommendations defined in the Supply-Chain Operations Reference (SCOR) model, and they are described in Section 3.…”

Section: Literature Reviewmentioning

confidence: 99%

Model for the supply chain management based on the interval type-2 fuzzy numbers and the TOPSIS method

Tadić¹,

Djordjević²

2017

Ekon horizonti

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The Performance improvement that leads to an increase in business efficiency, both for the enterprises integrated in the supply chain and the entire supply chain, represents one of the basic strategic management problems. A solution to this problem, among other things, can be obtained by measuring and improving the performance of the supply chain, which simultaneously represents the basic purpose of this research study. The relative importance of performances and the values of their key performance indices are assessed by decision-makers. Their assessments are described by linguistic variables, which are modelled by interval fuzzy numbers type-2. The relative importance of performance is obtained by defining the fuzzy matrix of the relative importance of each pair of performances. The weight values of performances are calculated by means of the eigenvector method. Performance values are calculated by using the fuzzy middle-value operator. The rank of the enterprises, with respect to all of the considered performances as well as their weights, is determined by applying conventional TOPSIS. The ranking of the enterprises integrated in the supply chain can be marked as the main result of the research. On the basis of the obtained rank, appropriate measures can be taken to improve the performance of those enterprises that are rated the worst by respecting all the observed performances. The proposed model has been tested on the real life data from the automotive supply chain operating in Central Serbia.

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Section: Literature Reviewmentioning

confidence: 99%

Model for the supply chain management based on the interval type-2 fuzzy numbers and the TOPSIS method

Tadić¹,

Djordjević²

2017

Ekon horizonti

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“…This leads to inconsistencies if waste is defined as seven wastes and non-value added (e.g. Hines & Rich, 1997;Rawabdeh, 2005;Ramesh & Kodali, 2012;Liu et al, 2013) ; or if the wastes used are not from the operation dimension, i.e. refer to the process (e.g.…”

Section: The Concept Of Waste As Non-value Adding Activitymentioning

confidence: 99%

“…Pheng & Hui, 1999;Canel et al, 2000;Ramesh & Kodali, 2012); or, value that the customer is willing to pay for (Oliver et al, 2007;Radnor, 2010). It is clear that the value for a customer (which is the totality of the product/service) differs both from what the customer perceives as value, which is a subset of product/service features, and what the customer is actually willing to pay for, e.g.…”

Section: The Concept Of Waste As Non-value Adding Activitymentioning

confidence: 99%

On the meaning of ‘Waste’: review and definition

Thürer

Tomašević

Stevenson

2016

Production Planning & Control

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Waste reduction is one of the main principles of lean, but it has been taken for granted that we have a common understanding of what waste means. We first present a critical, qualitative discussion that identifies four distinct waste concepts. We then conduct a systematic review of the literature that examines the different uses of these concepts. We find that only the classic concept of the seven wastes and the concept of waste as non-value adding activity are widely applied. However, both concepts are, at times, not only incompatible but used in a way that leads to open contradiction. A new definition, centered on an efficient, timely transformation process seeks to consolidate the literature. We outline two distinct waste types:(i) obvious waste, to refer to any waste that can be reduced without creating another form of waste; and, (ii) buffer waste, to refer to any waste that cannot be reduced without creating another waste. The paper has important implications for practice. To reduce waste, managers must undertake three interlinked tasks: the elimination of obvious waste; the reduction of variability to transform buffers into obvious waste; and, the balancing of remaining buffers to best achieve performance targets. The paper supports managers in their endeavors to identify waste, which is an important precursor to waste reduction/elimination.

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“…According to Sezen et al [26], although the reduction of LT is examined in some studies on lean manufacturing, it is an outcome of the efficient application of operational lean techniques such as setup reductions, pull systems, and continuous flow. This argument reinforces the need to explore the relationship between LT and variables such as setup to clarify how lean techniques should be applied to produce positive results on company performance, such as reductions in LT.…”

Section: Literature On Choosing Lean-based Improvement Programsmentioning

confidence: 99%

A framework for choosing among different lean-based improvement programs

Filho

Barco

2015

Int J Adv Manuf Technol

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This paper proposes a practical framework for choosing among different lean-based improvement programs in a one-machine environment and applies it in a company in the aeronautics industry. For the studied environment, i.e., a single machine and machining process bottleneck, the application of the proposed framework resulted in a potential 84.63 % improvement in lead time, which can be obtained by improving the mean time between failures by 1373 % and requires an effort of 5880 h. Alternatively, a 34.76 % reduction in lead time is also possible through a 1960-h investment in labor; this improves the coefficient of variation of repair time by 72 %. It is believed that the model proposed here can help managers in practice to quantify and choose between different options of lean-based improvement programs in single-machine or single-cell environments with flowshop standards and a predominant machine, which is typical of lean manufacturing systems.

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A decision framework for maximising lean manufacturing performance

Cited by 70 publications

References 17 publications

Model for the supply chain management based on the interval type-2 fuzzy numbers and the TOPSIS method

Model for the supply chain management based on the interval type-2 fuzzy numbers and the TOPSIS method

On the meaning of ‘Waste’: review and definition

A framework for choosing among different lean-based improvement programs

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