Data analytics for time constraint adherence prediction in a semiconductor manufacturing use-case

Semiconductor manufacturing represents a challenging industrial environments, where products require more than several hundred operations, each representing the technical state-of-the-art. Products vary greatly in volume, design and required production processes and, additionally, product portfolios and technologies change rapidly. Thus, technologically restricted rapid product development, stringent quality related clean room requirements and high precision manufacturing equipment application enforce operational excellence, in particular time constraints adherence. Product specific time constraints between two or more successive process operations are an industry-specific challenge, as violations lead to additional scrapping or reworking costs. Time constraint adherence is linked to dispatching and currently manually assessed. To overcome this error-prone manual task, this article presents a data-based decision process to predict time constraint adherence in semiconductor manufacturing. Real-world historical data is analyzed and appropriate statistical models and scoring functions derived. Compared to other relevant literature regarding time constraint violations, the central contribution of this article is the design, generation and validation of a model for product quality-related time constraint adherence based on a real-world semiconductor plant

Classification of the Existing Knowledge Base of OR/MS Research and Practice (1990-2019) using a Proposed Classification Scheme

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOperations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context the findings of our keyword analysis. Towards this we first present a classification scheme that relies on keywords that appeared in articles published in important OR/MS journals from 1990-2019 (over 82,000 articles). Our classification scheme applies a methodological approach towards keyword selection and its systematic classification, wherein approximately 1300 most frequently used keywords (in terms of cumulative percentage, these keywords and their derivations account for more than 45% of the approx. 290,000 keyword occurrences used by the authors to represent the content of their articles) were selected and organised in a classification scheme with seven top-level categories and multiple levels of sub-categories. The scheme identified the most commonly used keywords relating to OR/MS problems, modeling techniques and applications. Next, we use this proposed scheme to present an analysis of the last 30 years, in three distinct time periods, to show the changes in OR/MS literature. The contribution of the paper is thus twofold, (a) the development of a proposed discipline-based classification of keywords (like the ACM Computer Classification System and the AMS Mathematics Subject Classification), and (b) an analysis of OR/MS research and practice using the proposed classification

Complex Industrial Sociotechnical Systems Dynamics Modeling and Ramp-up

The aim of this research is to study emerging ramp up scenarios in the context of complex sociotechnical dynamic systems. These represent industrial and manufacturing companies that are facing fierce competition due to globalization and free trade, and the race to be in the market first with new products. Furthermore, for every manufacturer to launch their newly designed products in market and introduce the latest functionality attributes, or improve quality of their products, effective and fast ramp up is necessary for capturing a good market share. This makes the production ramp up a back bone in modern manufacturing; as its effective management enables faster ramp-up every time a change is brought in the quality, quantity features and fabrication at design, system and process level while integrating systems logical and physical enablers. In this context, models of ramp up scenario have been explored by setting up nonlinear system dynamic models in order to understand complex trends and behaviours for large and complex systems. Apart from that, novelty of these introduced system dynamic models is the set-up of an analogy to understand what impact they can produce when the respective parameters are perturbed and how this will affect the whole system and related sub-systems when they together form a system of systems (SOS). Prior research has demonstrated that variety, due to mass customization and personalization, introduces complexity in the design as well as in manufacturing process due to production mix. Complexity is modelled and implemented, not only at the system and sub system levels but also at machine level and product level, by improving design for assembly (DFA) and design for manufacturing (DFM). In the end, sociotechnical aspects and risk assessment involving triple bottom line impact factor analysis have been explored with respect to new product design by studying utility function and trigonometry. Finally, a comprehensive model is developed and analyzed with human behavior core attributes by applying Porter\u27s theory of motivation and system dynamic. This model highlights major impacts of motivation theory, by providing intrinsic and extrinsic rewards impact on labor which enables an understanding of behavior pattern of labour in relation to work assigned. Lastly, but not the least, this dissertation has contributed and demonstrated the potential usefulness of modeling complex industrial sociotechnical systems by using system dynamic approach for ramp-up

Revisiting cooperation dynamics: implications for opportunism and value creation when firms compete and cooperate simultaneously.

Referring to simultaneous competition and cooperation between firms, coopetition is emerging in practice as a promising source of value creation. However, the scholarly literature is dominated by a widespread assumption that opportunism, a core behavioral assumption of transaction cost economics, hinders value creation and requires formal safeguards in coopetition. The assumption of heightened opportunism in coopetition is at odds with rising adoption in practice, which often proceeds without formal safeguards. This raises concerns about the utility of existing theory for explaining coopetition dynamics and their implications for value creation. Building from theories of competitive dynamics and the resource-based view, my research challenges the dominant assumption of heightened opportunism and develops an alternative explanation to better explain coopetition dynamics. I identify and test informal market-based safeguards which reduce opportunism in coopetition. This provides theoretical resolution for conflicting findings in the literature and develops a nuanced understanding of the factors affecting opportunism in coopetition at multiple levels. It addresses the failure of extant research to explain coopetition dynamics and establishes foundations for systematic analysis of coopetition benefits and costs in future research. For managers, my findings move beyond simplistic perceptions that have emphasized instability, knowledge leakage, and the resultant need for formal safeguards in coopetition. Instead, I identify an efficient and effective alternative for constraining opportunism. This indicates that establishing, maintaining, and ultimately achieving value creation in coopetition relationships may be less challenging and costly than the literature assumes. Given the benefits of coopetition for both firms and society, this has important economy-level implications

Additive Manufacturing in After-Sales Service Supply Chains

Additive Manufacturing (AM, also known as 3D printing) is developing into a powerful complement to more conventional manufacturing (CM) methods. In comparison to CM methods such as milling, drilling, casting and forging, AM technologies build complete parts by adding materials layer upon layer without using any dedicated tooling. The resulting ability to produce complex structures without lengthy and expensive setup procedures could turn out particularly valuable for the low-volume spare parts business. Short AM lead times are likely to significantly improve the balance between spare parts inventory investment and system downtime. Generic AM processes could relax the dependence on suppliers and therefore decrease risks and costs associated with supply disruptions. Ultimately, AM could even enable the implementation of a decentralized production concept that holds the promise of increased supply chain responsiveness at low costs. However, it is necessary to deconstruct these concepts and to separate the hype from reality to leverage the potentials of AM technology in after-sales service supply chains. In this dissertation, we aim to contribute to this undertaking by offering a scientific perspective on how and to what extent after-sales service supply chains can benefit from AM technology. To that end, we develop and apply techniques from the field of Operations Research to learn from the various case studies that were conducted at different organizations throughout this research

Reliability Abstracts and Technical Reviews January-December 1969

No abstract availabl

Quantum systems engineering

With the aim of defining a Quantum Systems Engineering paradigm, we show that the systems engineering of quantum technologies is materially different from systems engineering in general. The thesis is based upon a two pronged mixed-methods research approach considering: (a) a comprehensive theoretical analysis of the difficulties in deriving systems engineering modelling tools; (b) identifying systems engineering challenges in practical quantum technology development through direct observation and case-study methods. We show a modified systems approach should benefit early stage quantum technologies design and development, a stage characterised by a low Technology Readiness Level (TRL), with the aim of accelerating capitalisation. The research showed that systems engineering applied to quantum technologies will require processes that are both more complex, and different from, those used for conventional systems technology development. This is fundamentally caused by the quantum properties of the system. Furthermore, the research evidenced that applying systems methods, tools, and approaches to low Technology Readiness Level development, both quantum and classical, is very likely to accelerate development, increase the quality of deliverables, and improve the alignment of early research to end-user needs and natural technology pull. Based on these results we have developed a series of recommendations, and a selection of systems tools, which together constitute a light-weight systems approach for low Technology Readiness Level development (some of which also apply to non-quantum domains). These are contained within the concluding chapter of the report. Findings are presented both as a verbal narrative and with full mathematical derivations

An Investigation into Factors Affecting the Chilled Food Industry

With the advent of Industry 4.0, many new approaches towards process monitoring, benchmarking and traceability are becoming available, and these techniques have the potential to radically transform the agri-food sector. In particular, the chilled food supply chain (CFSC) contains a number of unique challenges by virtue of it being thought of as a temperature controlled supply chain. Therefore, once the key issues affecting the CFSC have been identified, algorithms can be proposed, which would allow realistic thresholds to be established for managing these problems on the micro, meso and macro scales. Hence, a study is required into factors affecting the CFSC within the scope of Industry 4.0. The study itself has been broken down into four main topics: identifying the key issues within the CFSC; implementing a philosophy of continuous improvement within the CFSC; identifying uncertainty within the CFSC; improving and measuring the performance of the supply chain. However, as a consequence of this study two further topics were added: a discussion of some of the issues surrounding information sharing between retailers and suppliers; some of the wider issues affecting food losses and wastage (FLW) on the micro, meso and macro scales. A hybrid algorithm is developed, which incorporates the analytic hierarchical process (AHP) for qualitative issues and data envelopment analysis (DEA) for quantitative issues. The hybrid algorithm itself is a development of the internal auditing algorithm proposed by Sueyoshi et al (2009), which in turn was developed following corporate scandals such as Tyco, Enron, and WorldCom, which have led to a decline in public trust. However, the advantage of the proposed solution is that all of the key issues within the CFSC identified can be managed from a single computer terminal, whilst the risk of food contamination such as the 2013 horsemeat scandal can be avoided via improved traceability