Prioritising the types of manufacturing flexibility in an uncertain environment

Chang, An-Yuan

doi:10.1080/00207543.2011.565156

Cited by 50 publications

(48 citation statements)

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“…Flexibility refers to the ability of a system to change (adapt) in dynamic environments. Operational system flexibility has been studied extensively in the manufacturing, service and supply chain literature from various strategic, tactical and operative perspectives (Slack 1987;Bordoloi, Cooper, and Matsuo 1999;Koste and Malhotra 1999;D'Souza and Williams 2000;Das 2001;Van Mieghem 2003;Zhang, Vonderembse, and Lim 2003;Wu, Erkoc, and Karabuk 2005;Alp and Tan 2008;Chang 2012;Jain et al 2013;Choi, Cheng, and Zhao 2016;Cobb 2016;Ivanov, Tsipoulanidis and Schönberger 2017).A substantive part of this research studies capacity flexibility, classifying investigations into two main areas, i.e. decisions on investment in dedicated versus flexible capacity (Li and Tirupati 1994;Bish, Muriel, and Biller 2005), and process flexibility (or capacity adaptability).…”

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confidence: 99%

New flexibility drivers for manufacturing, supply chain and service operations

Ivanov

Das

Choi

2018

International Journal of Production Research

112

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Increasing product proliferation, customisation, competition and customer expectations, as well as supply side disruptions, pose significant challenges to firm operations. Such challenges require improved efficiency and resilience in manufacturing, service and supply chain systems. New and innovative flexibility concepts and models offer a prospective route to such operational improvements. Several emerging issues in flexibility, such as risk and uncertainty management, environmental sustainability, optimal strategies under competition, optimal operations with strategic consumer behaviours are being examined in this regard. This overview provides a concise review of these critical research issues, and discusses related papers featured in this special issue. Four major flexibility drivers are classified: disruption risks, resilience and the ripple effect in the supply chain; digitalisation, smart operations and e-supply chains; sustainability and closedloop supply chains; and supplier integration and behavioural flexibility.Keywords: flexibility; flexible manufacturing; supply chain management; supply chain risk management; supply chain coordination; Industry 4.0; manufacturing systems; service supply chain; ripple effect Flexibility in manufacturing, service and supply chain systemsWith increasing product proliferation, reduced unit demand volumes and next-day delivery expectations, flexibility in operations, service and supply chains has become an important focal area for research and industry. This special issue of IJPR aims to present state-of-the-art research in the field of flexibility in manufacturing, supply chain and service operations.A brief background on flexibility is provided in this section. Flexibility refers to the ability of a system to change (adapt) in dynamic environments. Operational system flexibility has been studied extensively in the manufacturing, service and supply chain literature from various strategic, tactical and operative perspectives (Slack 1987;Bordoloi, Cooper, and Matsuo 1999;Koste and Malhotra 1999;D'Souza and Williams 2000;Das 2001;Van Mieghem 2003;Zhang, Vonderembse, and Lim 2003;Wu, Erkoc, and Karabuk 2005;Alp and Tan 2008;Chang 2012;Jain et al. 2013;Choi, Cheng, and Zhao 2016;Cobb 2016;Ivanov, Tsipoulanidis and Schönberger 2017).A substantive part of this research studies capacity flexibility, classifying investigations into two main areas, i.e. decisions on investment in dedicated versus flexible capacity (Li and Tirupati 1994;Bish, Muriel, and Biller 2005), and process flexibility (or capacity adaptability). Process flexibility (Jordan and Graves 1995;Chou et al. 2010) analyses the ability of a company to change manufacturing setups at flexible lines according to demand or supply changes. It relies, in part on the capability to produce multiple products on multiple production facilities or lines (Jordan and Graves 1995;Simchi-Levi and Wei 2015). In particular, the design of a flexible production network under stochastic demand has been studied by Jordan and Graves (199...

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confidence: 99%

New flexibility drivers for manufacturing, supply chain and service operations

Ivanov

Das

Choi

2018

International Journal of Production Research

112

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“…Ten articles applied the integrated AHP-multiple approaches without considering the fuzzy set theory. Among these ten articles, the DEA was most commonly used (Zhou and Chen, 2010;Zhang et al, 2012;Kengpol et al, 2014;Kengpol and Tuammee, 2016), followed by mathematical programming approaches (Meza et al, 2009;Che and Chiang, 2012;Falsini et al, 2012;Kengpol et al, 2014), QFD (Partovi, 2007;Zhou and Chen, 2010;Chang, 2012), quantitative risk assessment (QRA) (Kengpol et al, 2014;Kengpol et al, 2016), activity-based costing (ABC) (Zhang et al, 2012), analytic network process (ANP) (Partovi, 2007), entropy (Freeman and Chen, 2015), GA (Che and Chiang, 2012), GRA (Chang, 2012), K-means algorithm (Meza et al, 2009), rough sets theory (RST) (Che and Chiang, 2012), and TOPSIS (Freeman and Chen, 2015).…”

Section: Integrated Ahp and Multiple Approachesmentioning

confidence: 99%

“…Like the fuzzy-based, integrated AHP-multiple approaches mentioned above, the supplier evaluation and selection problem attracted the most attention (Che and Chiang, 2012;Falsini et al, 2012;Zhang et al, 2012;Freeman and Chen, 2015), followed by transportation route evaluation and selection (Kengpol et al, 2014;Kengpol et al, 2016), business process reengineering (Zhou and Chen, 2010), manufacturing flexibility evaluation and selection (Chang, 2012), manufacturing process evaluation and selection (Partovi, 2007), and power generation expansion planning (Meza et al, 2009).…”

Section: Integrated Ahp and Multiple Approachesmentioning

confidence: 99%

The state-of-the-art integrations and applications of the analytic hierarchy process

2018

European Journal of Operational Research

357

151

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Because of its great flexibility and wide applicability, integrated analytic hierarchy process (AHP) approaches have been studied extensively for the last 20 years. This paper, as a follow-up study to Ho (2008), reviews the literature on the integrated AHP approaches and applications published between 2007 and 2016 and compares those studies with papers published during the previous decade, i.e., 1997-2006. Based on the 88 journal articles, five questions can be answered: (1) which type of integrated AHP approaches was paid most attention to? (2) Which application areas were integrated AHP approaches primarily applied to? (3) Which specific problems were integrated AHP approaches most commonly applied to? (4) What is the trend in publications regarding integrated AHP approaches? and (5) Which international journals were integrated AHP approaches most widely published in? Finally, some new applications of new AHP integrations are proposed to assist scholars in filling the literature research gaps.

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“…The biased measurements used for flexibility performance are a similar problem. For example, existing empirical studies, such as Chang [34], have only prioritized the required flexibility dimensions in environmental uncertainty, and therefore are unable to identify actual levels of flexibility dimensions. As a result, they offer limited insights into identifying the flexibility gap to be closed.…”

Section: Literature Review-managing Multiple Dimensions Of It Flexibimentioning

confidence: 99%

Narrowing the Gaps: Assessment of Logistics Firms’ Information Technology Flexibility for Sustainable Growth

2020

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In a supply chain management context, the effective management of Information Technology (IT) flexibility has been an issue to be resolved. However, no analytical method that calculates the required and actual level of IT flexibility dimensions has been proposed. This paper aims to provide an analytical tool that measures the required and actual levels of IT flexibility dimensions to provide the best value from a logistics firm’s IT flexibility. To do so, we propose a combined Importance‒Performance Analysis (IPA) and Partial Least Squared Structured Equation Modelling (PLS-SEM) method based on a multidimensional IT flexibility model. By comparing industry-level data with client firm data, our method allows for effective identification of a client logistics company’s multiple IT flexibility gaps and indicates where particular management interventions are required. By proposing importance and performance as measurement scales, our research suggests an analytical tool that managers can utilize to assess IT flexibility and identify any gaps that exist between actual and required flexibility levels. This allows managers to effectively address areas that demand further attention. This approach also leads to an improved understanding of how organisations can extract the best value from their investment in IT flexibility to contribute to sustainable growth.

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Prioritising the types of manufacturing flexibility in an uncertain environment

Cited by 50 publications

References 50 publications

New flexibility drivers for manufacturing, supply chain and service operations

New flexibility drivers for manufacturing, supply chain and service operations

The state-of-the-art integrations and applications of the analytic hierarchy process

Narrowing the Gaps: Assessment of Logistics Firms’ Information Technology Flexibility for Sustainable Growth

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