Improving Product Reliability and Software Quality

Levin, Mark A.; Kalal, Ted T.; Rodin, Jonathan

doi:10.1002/9781119179429

Cited by 8 publications

(5 citation statements)

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“…suppliers from the USA, resulting in a significant negative impact on short- and long-term operations (Speier et al , 2011). Practitioners can reduce the uncertainty caused by unreliable suppliers by providing safety stocks for finished goods and raw materials and by identifying critical suppliers (Graves, 2011; Levin et al , 2019). The occurrence of critical suppliers in a JV or nonequity SA can be prevented by creating a detailed supplier audit list.…”

Section: Discussionmentioning

confidence: 99%

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How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

Bodendorf,

Feilner,

Franke

2024

SCM

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Purpose This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance. Design/methodology/approach The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling. Findings The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance. Originality/value This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

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Section: Discussionmentioning

confidence: 99%

“…The occurrence of critical suppliers in a JV or nonequity SA can be prevented by creating a detailed supplier audit list. This allows firms to be aware of the parameters that suppliers must meet and to audit specific suppliers (Levin et al , 2019).…”

Section: Discussionmentioning

confidence: 99%

How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

Bodendorf,

Feilner,

Franke

2024

SCM

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“…Analysis was carried out using the Failure Mode Effect Analysis method, where the analysis is carried out based on the effects of failure. Levin et al (2019) explained that FMEA is the most powerful tool that can be easily applied without using expensive tools.…”

Section: Fishbone Diagram Analysis (Fishbone)mentioning

confidence: 99%

Implementation of a Lean Production System using Materials and Information Flow Chart in Electroplating Services Company

Aulia,

2024

JBMS

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With the transformation of the electroplating service industry as a second-tier supplier in the automotive industry, significant changes have occurred in production processes, management, and technology to answer global market challenges. The main focus is reducing costs and increasing profitability through improving quality and productivity. In order to achieve this goal, the Lean Production System or Toyota Production System is used to analyze and reduce waste in the production process. Process flow modelling with a Material and Information Flow Chart (MIFC) helps to visualize all activities that give an additional value or not to the product. The research method involves using 7 quality management tools and Failure Modes and Effect Analysis (FMEA) to evaluate and improve areas that affect quality and productivity. This approach aims to deliver quality products to customers at low cost through systematic changes and process improvements.

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“…Combining VoC/CTQ and FMEA for Quality Improvement Levin et al (2019) adapted FMEA and VoC in Risk Management Framework in Functional Activities for Cross-Functional Reliability. Kim et al (2014) identified preventive measures for VoC management by analysing the causes and effects of factors contributing to high-risk service failure using FMEA through RPN assignment to each VoC data in the Service Failure Management field.…”

Section: Process Risk Analysismentioning

confidence: 99%

A Combined Multi-Criteria Decision-Making Framework for Process-Based Digitalisation Opportunity and Priority Assessment (DOPA)

Yıldırım

Siyahi²,

Özbek³

et al. 2022

International Journal of Business Analytics

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With the introduction of Industry 4.0 and supporting technologies, both service and manufacturing companies faced external and internal pressure for "going digital". In many cases, companies cannot decide on the digitalisation initiative due to preliminary groundwork to justify the required investment. For digitalisation priority setting under uncertain benefits, available digital technology selection methods lack the focus on process needs and do not fully utilise quality management tools in the Multi Criteria Decision Making (MCDM) framework. In this context, this study aims to propose a novel, context-independent, and process-based Digital Opportunity Priority Assessment (DOPA) methodology. The proposed approach utilizes critical to quality measures (CTQs), the causes with potential adversary effects as alternatives, and the importance, frequency, and digital control level of CTQs as the criteria in TOPSIS. AHP and Fuzzy AHP validate CTQ importance criteria. The study also presents a real industry application to validate the proposed model.

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Improving Product Reliability and Software Quality

Cited by 8 publications

References 0 publications

How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

Implementation of a Lean Production System using Materials and Information Flow Chart in Electroplating Services Company

A Combined Multi-Criteria Decision-Making Framework for Process-Based Digitalisation Opportunity and Priority Assessment (DOPA)

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