Product modularization and effects on efficiency: An analysis of a bus manufacturer using data envelopment analysis (DEA)

Piran, Fábio Sartori; Lacerda, Daniel Pacheco; Camargo, Luís Felipe Riehs; Viero, Carlos Frederico; Dresch, Aline; Cauchick‐Miguel, Paulo A.

doi:10.1016/j.ijpe.2016.08.008

Cited by 48 publications

(32 citation statements)

References 53 publications

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“…Competition among companies suggests a need to improve the operational performance of industrial organizations (Piran et al 2016;Eidelwein et al 2018;Mansilha et al 2019). Improving operational performance in manufacturing systems requires adoption of the best production practices and appropriate measurement systems (Kenyon et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…DEA is a technique used for measuring the overall performance of systems, allowing all components to be taken into account (Cook et al 2014;Piran et al 2016). DEA has also been used to evaluate the efficiency of production systems (Liu et al 2016;Esmaeilian et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…DEA has also been used to evaluate the efficiency of production systems (Liu et al 2016;Esmaeilian et al 2016). However, DEA has limitations, such as the need for a minimum number of DMUs in relation to the number of variables used (Piran et al 2016), and a lack of standardization for replication in similar cases (Anderson and Sharp 1997). Because it is a measurement based on benchmarking, DEA does not take into account specific aspects, such as the maximum production capacity of the equipment or of the production system (Cook et al 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis

Piran

Paris

Lacerda

et al. 2020

Glob J Flex Syst Manag

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The efficiency levels at which companies turn inputs into outputs may increase their competitive capacity. Nevertheless, in the literature, there is a lack of methods allowing evaluation of the performance of operations in production systems that take into account all of their components. The objective of this study is to present an analysis of efficiency for a production system using DEA and OEE in an integrated manner (DEA/OEE). To achieve this objective, we conducted a case study of a bus manufacturer and a comparative analysis using the results obtained by DEA/OEE compared with the results of the OEE measured in an operation of the company studied. The efficiency measured by DEA/OEE presents differences in relation to OEE results. The analysis of the causes of such differences pointed out that action taken to improve the OEE indicator decreased the efficiency of the operation analyzed. This study evidences that decision-making focused on improving OEE on its own may lead to an increase in the consumption of resources during an operation. Such an increase does not necessarily increase production levels and may reduce technical efficiency.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis

Piran

Paris

Lacerda

et al. 2020

Glob J Flex Syst Manag

Self Cite

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“…Research on the development of concurrent decision support models related to product design and supply chains has not been fully explored (Chiu and Okudan 2011) and more research has been warranted in identifying supply chain, market and product factors that are important for product architecture decisions in order to understand this relationship better (Chiu and Kremer 2014). In addition, future research should focus on companies that use different product architectures and investigate the effects on manufacturing processes (Piran et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Product architecture, global operations networks, and operational performance: an exploratory study

Pashaei

Olhager

2019

Production Planning & Control

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This paper investigates the relationships between product architectures and global operations networks and their impact on operational performance, based on survey data from 82 Swedish manufacturers. We find that modular and integral product architectures are associated with different characteristics of the global operations network, and that global operations network designs are characterised by three dimensions: network, plant and supply chain. The key aspects are network structure (configuration as well as coordination), plant focus (scale or scope) and the locus of supply chain capabilities (internal or external).

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“…A modularized view of supply chains, 3 therefore, helps with decreased cost, increased profit, improved competitive positions, and increased efficiency (see e.g. Fisher, 1997, Godsell et al, 2011, Piran et al, 2016, which in turn implies effective and efficient management of supply chains. Considering the crucial role and influence of S&OP in supply chain success, a logical conclusion would be that S&OP should also reflect the specific needs of supply chains.…”

Section: Introductionmentioning

confidence: 99%

Sales and operations planning based on a modularized view of supply chains : Supporting process industries and discrete manufacturing industries

Noroozi¹

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The purpose of this dissertation is to propose a framework for sales and operations planning (S&OP), which is based on a modularized view of supply chains. The framework should support both process industries and discrete manufacturing industries in their quest for performance. S&OP has been highlighted in this framework due to its essential role in integrating different functions within a company and integrating a company with its supply chain partners (referred to as horizontal integration), and linking different planning levels in a company (referred to as vertical integration). As an integrator, S&OP influences companies' performance.Originally, S&OP was developed as a generic process but still in line with the requirements of discrete manufacturing industries. The specific requirements of process industries have not been emphasized in this process to the same extent. In order to suggest a modularized S&OP framework for both process industries and discrete manufacturing industries, a systematic literature review is performed to understand the specific characteristics of S&OP in process industries. As a result, the importance of continuity/discontinuity of materials and its influence on the required production processes are highlighted. This indicates that the production process in process industries is actually a hybrid of continuous production and discrete production, whereas the discrete manufacturing industries often only deploy discrete production. Continuous production has specific characteristics that would be beneficial to include in the S&OP process; nonetheless, this is in contrast to the view in the literature that considers S&OP a generic process. Generic here means that S&OP is independent of the context in terms of, for example, the industry in which it is implemented. This issue is investigated in this dissertation by identifying the requirements, which can be considered in the S&OP of process industries as add-ons to the generic S&OP. In addition to this, two other concepts addressing the properties of the production process are identified as important in planning and control, including S&OP. One addresses the level of repetitivity of the production process in response to the market demand, and the other concerns the trigger of the production flow. The three concepts are related to three different types of decoupling points and are the basis for a typology developed herein that provides a modularized view of supply chains. The application of the typology to S&OP leads to a modularized S&OP framework applicable to both process industries and discrete

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Product modularization and effects on efficiency: An analysis of a bus manufacturer using data envelopment analysis (DEA)

Cited by 48 publications

References 53 publications

Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis

Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis

Product architecture, global operations networks, and operational performance: an exploratory study

Sales and operations planning based on a modularized view of supply chains : Supporting process industries and discrete manufacturing industries

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