Multi‐Methodological Research in Operations Management

Choi, Tsan‐Ming; Cheng, T.C.E.; Zhao, Xiande

doi:10.1111/poms.12534

Cited by 156 publications

(131 citation statements)

References 48 publications

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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

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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

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112

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“…This limitation could be a problem when combining an analytic model and empirical methods in a purchasing setting and readers should consider ways to overcome this issue. Additional limitations and difficulties of publishing multi-method research are also pointed out in a paper by Choi et al (2016). These authors recommend that reviewers and authors in the future should consider the rigor and contributions of the combined methods and not criticize each part of the research as if it is an independent study.…”

Section: Discussionmentioning

confidence: 99%

“…Such post-hoc analysis allows the research to further explore the relationships and sensitivity of the results in a controlled experimental setting where the analytical model is used to generate new results in addition to those gathered in the initial empirical stage (Choi et al 2016). Additionally, when empirical data are difficult to collect or are not a sufficient sample size, analytical methods offer an option for generating additional data to ensure the rigor of the analysis.…”

Section: Why Do Models Make Sense For P/sm Research?mentioning

confidence: 99%

“…Additionally, combining an analytical model with empirical methods in a multi-method approach can create several advantages and has been called for in the SCM literature (Sanders & Wagner, 2011;Cheng et al 2012;Sanders et al 2013, Choi et al 2016). There are several reasons why P/SM researchers should consider empirically informed analytical models.…”

Section: Why Do Models Make Sense For P/sm Research?mentioning

confidence: 99%

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The use of modelling in purchasing/supply management research

Matopoulos

Bell

Aktaş

2016

Journal of Purchasing and Supply Management

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The purpose of this research to explore the use of modelling in the field of Purchasing and Supply Management (P/SM). We are particularly interested in identifying the specific areas of P/SM where there are opportunities for the use of modelling based methods. The paper starts with an overview of main types of modelling and also provides a categorization of the main P/SM research themes. Our research shows that there are many opportunities for using descriptive, predictive and prescriptive modelling approaches in all areas of P/SM research from the ones with a focus on the actual function from a purely operational and execution perspective (e.g. purchasing processes and behaviour) to the ones with a focus on the organisational level from a more strategic perspective (e.g. strategy and policy). We conclude that future P/SM research needs to explore the value of modelling not just at the functional or operational level, but also at the organisation and strategic level respectively. We also acknowledge that while using empirical results to inform and improve models has advantages, there are also drawbacks, which relate to the value, the practical relevance and the generalizability of the modelling based approaches.Keywords: Purchasing and Supply Management, Modelling, Multi-method research. What is modelling and what are the generic types of modelling?Modelling is a simplified and usually mathematical representation of a process or a system to support decision-making or to simply understand the actual situation better. In terms of the purpose of the modelling exercise, models can be categorised into three types: descriptive, predictive, and prescriptive.Descriptive models provide insight into the past including what happened, and more usefully, why it might have happened. Such models usually constitute a base-case that can be used as a benchmark to make and test suggested improvements. Predictive models are aimed at understanding the future including what could happengiven what we know about a system. Regression is one of the well-known predictive models along with time series forecasting models that are used to predict the future behaviour of a variable of interest. These predictive models represent what could happen in the future, rather than what should be done to reach a desirable outcome, which is addressed by prescriptive models.Optimisation models (e.g. linear programming, integer programming, multi-objective mathematical programming) prescribe the decision maker the best course of action in order to achieve the set objective (e.g. profit / welfare maximisation, cost / time minimisation). Such

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“…Several OM scholars have recognized the importance of multi-method papers because of the potential value in their complementary angles (e.g., Boyer and Swink, 2008;Choi et al, 2016;Chandrasekaran et al, 2016). In terms of theoretical contributions, we characterize the direct impact of prior network structure and the moderating effects of firm-and industry-level characteristics on the evolution of supply chain alliance networks.…”

Section: Introductionmentioning

confidence: 99%

Structural anatomy and evolution of supply chain alliance networks: A multi‐method approach

Park

Bellamy

Basole

2018

J of Ops Management

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We investigate the evolution of supply chain alliance networks with a focus on the influence of structural, firm‐, and industry‐level mechanisms. While several structural supply chain characteristics have been found to be significant drivers of firm innovation and performance, a dearth of studies exists examining how these characteristics change over time by influencing one another. We develop and empirically test hypotheses on the impact of prior structural configurations and the moderating roles of absorptive capacity and industry growth on the temporal trajectory of supply chain alliance network structures. Adopting a multi‐method approach, we jointly use econometric analyses and simulation experiments to examine our hypotheses from complementary angles. Specifically, we characterize the dynamic relationship between the structural mechanisms on a longitudinal dataset of 2221 unique firms and 13,668 firm‐year observations spanning 25 years. We find empirical support for negative crossover effects between two key structural properties of supply chain alliance networks, a positive moderation of a firm's absorptive capacity, and a negative moderation of industry growth on the structural reinforcement. We conduct corresponding simulation experiments based on a separable temporal exponential random graph model (STERGM) to track the temporal changes in the simulated networks' key measures. The simulation results concur with most of our empirical findings and provide additional insights complementary to our econometric analysis results. By focusing on the mechanism of temporal changes in network structural properties, our study contributes to supply chain management research with a supply network perspective and interfirm alliance network research by broadening its scope into structural dynamism. Our multi‐method approach demonstrates how multiple complementary methodologies can foster a more nuanced understanding of managing supply chain alliance network management.

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Multi‐Methodological Research in Operations Management

Cited by 156 publications

References 48 publications

New flexibility drivers for manufacturing, supply chain and service operations

New flexibility drivers for manufacturing, supply chain and service operations

The use of modelling in purchasing/supply management research

Structural anatomy and evolution of supply chain alliance networks: A multi‐method approach

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