The differential impact of product complexity, inventory level, and configuration capacity on unit and order fill rate performance

Closs, David J.; Nyaga, Gilbert N.; Voss, M. Douglas

doi:10.1016/j.jom.2009.04.003

Cited by 68 publications

(72 citation statements)

References 35 publications

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“…Eckstein et al (2015) attempt to test the moderating effect of product complexity on the relationship between supply chain agility and adaptability on organizational performance. Even though there are mixed views in the literature regarding the role of product complexity (see Fisher et al 1999;Closs et al 2010), we argue that product complexity may moderate sustainable supply chain performance, and hence in our study we investigate the moderating role of product complexity on sustainable supply chain performance. Therefore, H5a: Product complexity positively moderates the effect of upstream supply chain visibility on social performance; H5b: Product complexity positively moderates the effect of upstream supply chain visibility on environmental performance; H5c: Product complexity positively moderates the effect of upstream supply chain visibility on economic performance.…”

Section: Impacts Of Information Sharing On Visibilitymentioning

confidence: 99%

Upstream supply chain visibility and complexity effect on focal company’s sustainable performance: Indian manufacturers’ perspective

et al. 2017

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Understanding supply chain sustainability performance is increasingly important for supply chain researchers and managers. Literature has considered supply chain sustainability and the antecedents of performance from a triple bottom line (economic, social, and environmental) perspective. However, the role of supply chain visibility and product complexity contingency in achieving sustainable supply chain performance has not been explored in depth. To address this gap, this study utilizes a contingent resource-based view theory perspective to understand the role of product complexity in shaping the relationship between upstream supply chain visibility (resources and capabilities) and the social, environmental, and economic performance dimensions.We develop and test a theoretical model using survey data gathered from 312 Indian manufacturing organizations. Our findings indicate that supply chain visibility (SCV) has significant influence on social and environmental performance under the moderation effect of product complexity. Hence, the study makes significant contribution to the extant literature by examining the impact of SCV under moderating effect of product complexity on social performance and environmental performance.

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Section: Impacts Of Information Sharing On Visibilitymentioning

confidence: 99%

Upstream supply chain visibility and complexity effect on focal company’s sustainable performance: Indian manufacturers’ perspective

et al. 2017

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“…interconnectedness. Multiplicity is defined as the number of variants or versions of a product or the number of suppliers if evaluating at the supply chain level [40][41][42]. Diversity refers to the degree of dissimilarity seen across the elements and can be quantified by comparing the number of unique elements to the total number of elements within a system.…”

Section: Complexitymentioning

confidence: 99%

Evaluation of Techniques to Describe Device Complexity in Pre and Post Design Stages

Mountain

Bohm

Riggs

2016

Volume 11: Systems, Design, and Complexity

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Electro-Mechanical device complexity exists in everyday items from cell phones to automobiles to vacuum cleaners. Generally, product complexity is one of the least quantifiable characteristics in the design cycle with arguably some of the greatest implications. A high level of device complexity carries a negative connotation and is usually considered an attribute a designer should attempt to mitigate. Alternatively, a low level of device complexity may induce designers and marketers to question a product's usefulness. Whether complexity is a necessary aspect of a design or a hindrance needing to be minimized or eliminated, depends upon how complexity is framed. Some instances in literature attempt to measure complexity yet there is no unified measure that captures the complexity of a product or system during design phases or upon product/system realization. Complexity is defined in many ways, at different levels of abstraction, and different stages of design therefore, becoming highly contextual and subjective at best. An established and repeatable methodology for calculating complexity of existing products in the marketplace is necessary. Once a measure of complexity is agreed upon at the post design stage we can look to earlier phases in design to see whether insights are observable. Identifying complexity early in the design cycle is paramount to strategic resource allocation. This study considers the Generalized Complexity Index (GCI) measure put forth by Jacobs [1] and expands upon it to include functional modeling as a key component in determining an indicative complexity metric. Functional modeling is a method used to abstract system or product specifications to a general framework that represents a function based design solution. Complexity metrics are developed at the functional and completed design levels and used for comparison. Thirty common household products retrieved from an online design repository [2] as well as seven senior capstone design projects were evaluated using the GCI. A modification to the GCI equation is proposed and to gain a relative scale of complexity within the data, a ranked complexity metric was developed and utilized. The magnitude of the ranked complexity metric was only indicative of hierarchical status of a product within the data set and therefore is not comparable to GCI values. Though Jacobs GCI worked well in his study, the GCI does not represent a meaningful complexity measure when applied to the data in this study. This study is an initial attempt to apply an independent data set to Jacobs GCI model with perhaps greater implications, with respect to products, that complexity is multifaceted and is not accurately represented by only interconnectedness, multiplicity, and diversity.

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“…Complex products typically entail large number of components and sub-components, sophisticated design and technological requirements, variations in production and delivery processes and increased manufacturing and delivery downtimes (Closs et al, 2010). Expectedly, extant research has been able to relate high product complexity with negative operational impact: for example, increased inventory costs and decreased customer service levels (Fisher and Ittner, 1999;Lee and Tang, 1997).…”

Section: H3 the Greater The Technological Turbulence;mentioning

confidence: 99%

Operational impact of collaboration and resource specificity: the moderating role of technology context

Iyer

2014

Journal of Business &Amp; Industrial Marketing

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Purpose – The purpose of this research is to enhance understanding of the sources of relational rents in supply chains and the nature of their relationships with performance. Using the relational view framework and contingency perspective, the study develops a model and hypotheses to understand the nature of the relationships of collaboration and resource specificity with operational performance under technology context contingencies. Design/methodology/approach – Data for testing the hypothesized relationships in the conceptual model were collected through a survey of managers in the Hoover’s database of manufacturing firms. The survey sample included 115 responses from a wide variety of manufacturing forms. Findings – Findings support the conventional wisdom relating collaboration and operational improvements. Notably, technological turbulence has a differential interactive influence on collaboration and resource specificity in predicting operational performance. In the former, the strength of the performance relationship is enhanced, while in the latter, it diminishes. Product complexity enhances the collaboration–operational performance linkage. The results, however, have to be further corroborated by more confirmatory analysis in future research. Research limitations/implications – The research findings are not conclusive but of an exploratory initial evidence, as stepwise regression analysis has its limitations. Additionally, while the study specifically focused on demand-side collaboration aspects, supply chain management envelops upstream and internal collaboration as well. Investigating the performance implications and the interactive dynamics among all three partnerships in the supply chains provides a richer understanding of supply chain partnerships. Besides, more comprehensive insights could be obtained by modeling the interactive effects of other factors in the operating context. Practical implications – Firms derive performance benefits from close collaboration with downstream partners because the operational enhancements from such relationships have customer service implications. Besides, the results provide a framework to managers for understanding the technology context conditions that may be best suited for leveraging collaborative initiatives and idiosyncratic investments in pursuit of operational performance improvements. Originality/value – Much of the evidence on the rent generation capabilities in supply chain partnerships is still anecdotal and extant empirical research lacks adequate explanation. Another critical shortcoming in extant literature is research on the disentangled interactive influence of operating context factors on the supply chain sources of rent (i.e. capabilities)–performance relationships. The study contributes by addressing these issues.

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The differential impact of product complexity, inventory level, and configuration capacity on unit and order fill rate performance

Cited by 68 publications

References 35 publications

Upstream supply chain visibility and complexity effect on focal company’s sustainable performance: Indian manufacturers’ perspective

Upstream supply chain visibility and complexity effect on focal company’s sustainable performance: Indian manufacturers’ perspective

Evaluation of Techniques to Describe Device Complexity in Pre and Post Design Stages

Operational impact of collaboration and resource specificity: the moderating role of technology context

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