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The purpose of this paper is to present an analysis of how different sourcing policies and resource usage affect the operational performance dynamics of warehouse processes.

The system dynamics (SD) methodology is used to model warehouse operations at the distribution centre of a leading fast‐fashion vertical retailer. This case study includes a detailed analysis of the relationships between the flow of items through the warehouse, the assignment of staff, the inventory management policy, and the order processing tasks.

Case scenario simulations are provided to define warehouse policies enabling increased efficiency, cost savings, reduced inventory, and shorter lead‐times.

The case study reaffirms that a flexible usage of human resources, outsourcing of selected warehouse operations, and sourcing from reliable manufacturers may result in important performance improvements for centralised warehousing.

It is proved that SD is a valuable tool in the field of operations management, not only to support strategic evaluations but also to execute a detailed analysis of logistical processes and make scenario‐based dynamic decisions at the operational level.

The purpose of this paper is to focus on the application and exploitation of Big Data (BD) to create competitive advantage. It presents a framework of application areas, and how they help the understanding of targeting and scoping specific areas for sustainable improvement. Empirical evidence demonstrates the application of BD in practice and tests the framework.

An exploratory approach is adopted to the secondary research which examines vendors’ offerings. The empirical research used the case study method.

The findings indicate that there is opportunity to create sustainable competitive advantage through the application of BD. However there are social, technological and human consequences that are only now beginning to emerge which need to be addressed if true long-term advantage is to be achieved.

The research develops a framework and tests it only in two dimensions. This should be expanded. The vendor analysis limitations lie within the nature of the information available and the difficulties in mitigating against bias.

The suggested framework can help academics and managers to identify areas of opportunity to do so, setting new levels of performance and new agendas for business.

This work contributes to service operations management, building on Kranzberg (1986) and the impact of technology and on Fosso Wamba et al. (2015) by developing a systems application framework to further understanding of BD from a practical perspective to extend their research taxonomy insights. The case studies demonstrate how the use of BD enhances operational performance.

The purpose of this paper is to highlight the potential of a qualitative middle-range research approach to contribute to the advancement of operations management (OM) field.

–To better signify such contribution, it takes insight from Merton's (1968) notion of middle-range theory as a means to create pathways of propositions that link substantive concepts and practices of OM in both context-specific and context-free operational environments.

The paper brings to the fore the argument that achieving the primary objective of filling the “theory-methods” void in OM can be achieved through adoption of a qualitative middle-range approach.

The originality of this paper hinges on the premise that theory-oriented qualitative field research that is able to incorporate experiences of different stakeholders of the OM intervention is highly likely to benefit OM theory advancement as well as OM practice.

This study investigates the relationships between team dynamics and performance in healthcare operations. Specifically, it explores, through wearable sensors, how team coordination mechanisms can influence the likelihood of surgical glitches during routine surgery.

Breast surgeries of a large Italian university hospital were monitored using Sociometric Badges – wearable sensors developed at MIT Media Lab – for collecting objective and systematic measures of individual and group behaviors in real time. Data retrieved were used to analyze team coordination mechanisms, as it evolved in the real settings, and finally to test the research hypotheses.

Findings highlight that a relevant portion of glitches in routine surgery is caused by improper team coordination practices. In particular, results show that the likelihood of glitches decreases when practitioners adopt implicit coordination mechanisms rather than explicit ones. In addition, team cohesion appears to be positively related with the surgical performance.

For the first time, direct, objective and real time measurements of team behaviors have enabled an in-depth evaluation of the team coordination mechanisms in surgery and the impact on surgical glitches. From a methodological perspective, this research also represents an early attempt to investigate coordination behaviors in dynamic and complex operating environments using wearable sensor tools.

The purpose of this paper is twofold. First, the authors provide a survey of operations management (OM) research applications of traditional hierarchical and nonhierarchical clustering methods with respect to key decisions that are central to a valid analysis. Second, the authors offer recommendations for practice with respect to these decisions.

A coding study was conducted for 97 cluster analyses reported in six OM journals during the period spanning 1994-2015. Data were collected with respect to: variable selection, variable standardization, method, selection of the number of clusters, consistency/stability of the clustering solution, and profiling of the clusters based on exogenous variables. Recommended practices for validation of clustering solutions are provided within the context of this framework.

There is considerable variability across clustering applications with respect to the components of validation, as well as a mix of productive and undesirable practices. This justifies the importance of the authors’ provision of a schema for conducting a cluster analysis.

Certain aspects of the coding study required some degree of subjectivity with respect to interpretation or classification. However, in light of the sheer magnitude of the coding study (97 articles), the authors are confident that an accurate picture of empirical OM clustering applications has been presented.

The paper provides a critique and synthesis of the practice of cluster analysis in OM research. The coding study provides a thorough foundation for how the key decisions of a cluster analysis have been previously handled in the literature. Both researchers and practitioners are provided with guidelines for performing a valid cluster analysis.

To the best of the authors’ knowledge, no study of this type has been reported in the OM literature. The authors’ recommendations for cluster validation draw from recent studies in other disciplines that are apt to be unfamiliar to many OM researchers.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The purpose of this paper is to provide a critical analysis of studies on strategic consensus along manufacturing competitive priorities. Based on this analysis, a new methodology to measure strategic consensus on manufacturing competitive priorities that is more consistent with mainstream operations management theory is proposed. The paper also includes novel proposals for future research.

The new methodology and proposals for research are mainly based on a literature review of previous studies on strategic consensus regarding manufacturing competitive priorities and also on relevant research and works in the field of operations management.

Previous methodologies used to measure strategic consensus regarding manufacturing competitive priorities are mainly based on studies in the business strategy field. Thus, these methodologies are deemed as inadequate in the operations management field. It is also found that there are very few studies that have analysed this topic in the operations management field. Moreover, since the methodologies used in those studies are based on previous research in the field of business strategy, the results of the reviewed papers are considered as questionable.

For academics, the paper and its results imply a change in the methodologies and research used to study the issue of strategic consensus on manufacturing competitive priorities. The lack of research into this topic is also observed. More research and studies on this theme are needed. For practitioners, the methodology proposed in the paper could be utilised in order to assess the employees' knowledge of the relationships between manufacturing capabilities inside manufacturing firms.

To the best of the author's knowledge, this is the first review paper into this under‐researched theme in the operations management field. The paper also presents the first methodology that incorporates mainstream theory and research in the field of operations management into the measurement of strategic consensus on manufacturing competitive priorities.

The purpose of this paper is to illustrate the appropriateness of system dynamics (SD) methodology as a tool for humanitarian decision makers to understand the effect of their decisions on humanitarian operations. The authors seek to present the initial stages and preliminary findings of a SD model used to analyze a well‐defined subsystem of humanitarian operations, field vehicle fleet management; then build on this example by identifying an additional area of humanitarian operations for future research using the SD methodology.

Case‐based research was combined with SD methodology to examine the appropriateness of this methodology for use in humanitarian operations.

Humanitarian operations are characterized by multiple actors, feedback loops, time pressures, resource constraints and uncertainty. The authors find that SD has the capacity to accurately represent the dynamic complexity of humanitarian operations, and is therefore an appropriate tool to study these systems.

The well‐defined issue of field vehicle fleet management in humanitarian organizations is used to illustrate an application of SD for humanitarian operations. Due to the difficulty in obtaining necessary data to build the SD model, this study uses estimations based on over three years of research into fleet management in the humanitarian sector. The authors then present an example of a broader but less well‐defined subsystem in the humanitarian sector that can be analyzed using SD methodology to the benefit of the overall humanitarian relief operation.

Decision making in humanitarian operations is usually based on intuition and experience which are not always sufficient to fully understand the global impact of these decisions. SD provides humanitarian decision makers with a method to simulate and compare the impact of alternative decisions that would not be possible in real life situations.

This paper examines the appropriateness of SD methodology to help humanitarians improve the effectiveness of their relief and development programs through better‐informed decision making.

This paper presents one of the first attempts to use SD methodology to build a model for humanitarian operations using a well‐defined subsystem, field vehicle fleet management.

Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.

Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.

Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.

Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.

This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.