1,721,018 research outputs found
Data Quality in Social Media Analytics for Operations and Supply Chain Performance Management
No full textSocial media analytics (SMA) is claimed to be an opportunity for practical inquiry to create new knowledge and possibilities but is only slowly finding its way into practice due to uncertain information quality. Good Operations and Supply Chain Management (OSCM) decisions are just as good as the data they are based upon. A more detailed consideration of data quality is needed, especially when natural language data is processed for decision-making. Motivated by recent calls in the domain, the purpose of this study is to investigate how big data quality is considered in SMA for operations and supply chain performance. The study employs a directed qualitative content analysis of 56 research contributions based on the re-analysis of a previous systematic literature review. The results reveal that within performance-oriented SMA literature, intrinsic and contextual data quality are not comprehensively addressed by OSCM-research to date. More particularly it is shown, that contextual data quality assessment remains a challenge for the analysis of textual social media data. The study contributes by reporting how data quality is considered for SMA in operations and supply chain performance management (OSCPM) literature from an intrinsic and contextual perspective. Based on the results of this analysis, data relevancy and data believability are identified as levers to reduce information uncertainty in SMA-aided decision-making, paving the way for future research on contextual social media data quality in OSCM
Strategic Bidding in Decentralized Collaborative Vehicle Routing
No full textCollaboration in transportation is important to reduce costs and emissions, but carriers may have incentives to bid strategically in decentralized auction systems. We investigate what the effect of the auction strategy is on the possible cheating benefits in a dynamic context, such that we can recommend a method with lower chances for carriers to cheat. We consider both a first-price auction system and a second-price auction scheme. Contrary to what was expected, a second-price auction scheme gives more room for successful strategic behaviour, while it also results in more rejected orders. A first-price auction scheme might be useful in practice if the profit shares that are allocated to the winner of an auction are selected carefully.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and LogisticsAlgorithmic
An Auction-Based Multi-Agent System for the Pickup and Delivery Problem with Autonomous Vehicles and Alternative Locations
No full textThe trends of autonomous transportation and mobility on demand in line with large numbers of requests increasingly call for decentralized vehicle routing optimization. Multi-agent systems (MASs) allow to model fully autonomous decentralized decision making, but are rarely considered in current decision support approaches. We propose a multi-agent approach in which autonomous vehicles are modeled as independent decision makers that locally interact with auctioneers for transportation orders. The developed MAS finds solutions for a realistic routing problem in which multiple pickup and delivery alternatives are possible per order. Although information sharing is significantly restricted, the MAS results in better solutions than a centralized Adaptive Large Neighborhood Search with full information sharing on large problem instances where computation time is limited.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and LogisticsAlgorithmic
Advances in thoughts and approaches for transport and logistics systems performance evaluation
No full textMaschinelles Lernen für die Patent Intelligence: Möglichkeiten und Herausforderungen
No full textThe analysis of large data volumes for decision-making has evolved from a sideline to a key driver of economic success in the business world of today. As being particularly relevant for technology-oriented organization, patent intelligence – the retrieval, pre-processing and analysis of patent information – has become a relevant means for organization-relevant decisions. This dissertation sheds light using techniques from machine learning for patent intelligence tasks. After summarizing the current literature streams of patent intelligence and machine learning, four publications outline opportunities and challenges that may arise from using supervised or unsupervised machine learning techniques for patent intelligence. For example, supervised machine learning may guide decision making by reducing noise in predictions, for example. Unsupervised machine learning may be useful to explore latent associations between patents when analyzing computationally challenging patent datasets. However, both techniques impose challenges regarding the complexity of the configuration space as well as the transparency and explainability of their underlying algorithms. Implications of this dissertation offer two trade-offs, i.e. in-house versus external procurement and high performance vs. low explanability, and relevant gaps need being addressed by this dissertation open up avenues of further research
Global Facility Location Decision making: an in-depth investigation into multilevel information alignment- relationships, structuring and boundaries
No full textGlobal facility location decisions (GFLDs) are essential for organizational strategy, involving the decision to set up manufacturing facilities, warehouses, and distribution centers. This study explores GFLDs, focusing on aligning micro-level attributes (firm priorities, strategies) with macro-level factors (labor, logistics, government incentives, transportation infrastructure).
The COVID-19 pandemic has prompted firms to consider nearshoring or reshoring, re-evaluating their global networks and recognizing hidden costs related to non-economic macro attributes (government incentives, environmental regulations, IP protection). Managers face challenges in aligning multilevel information, especially in SMEs and MNEs, which deal with diverse international markets and resource constraints.
Multilevel theory indicates that misalignments can lead to inaccuracies in specifying location constructs, acquiring comprehensive information, and structuring decision-making processes. This includes: a) Knowledge gaps on multilevel relationships for location movements, b) Lack of structured decision-making, c) Difficulty in acquiring precise location attribute information.
The thesis addresses these issues through four papers:
1. A literature review identifies dominant multilevel determinants and their relationships.
2. The second and third papers explore decision-making challenges using experiments and managerial interviews.
3. The final paper examines the impact of information volume and variety on managerial decision outcomes through simulation-based experiments.
Findings highlight critical relationships between macro capabilities (production, institutional, technological factors) and micro-level priorities (cost implications, quality). The research shows that more information isn't always better, identifying an optimal information volume for decision satisfaction. This study contributes to GFLD and multi-attribute decision analysis (MADA), emphasizing multilevel paradigms for offshoring and reshoring, challenging previous economic assumptions, and providing practical insights for firms and policymakers
Supply chain (logistics) environmental complexity
Get PDFThe spatial scope of organisations has recently been reemphasised in the context of supply
chains and supply chain management. This scope is usually accompanied by uncertainty to
organisations, especially for the extended supply chain with geographically dispersed
operations and activities, thus posing environmental complexity in the form of risks and costs
that organisations need to contend with. The main purpose of this dissertation is to create a
deep understanding of this environmental complexity facing the extended supply chain, and
the main research objective is to develop a construct, consisting of factors and measures, that
can aid in describing its state in the context of logistics.
Overall, the dissertation assumes an international business (IB) standpoint in undertaking this
task whereby it is argued that countries and borders matter, and that differences between
country environments lead to environmental complexity in the geographically dispersed
supply chain. Country-oriented constraints may then exist at macro-economic level, or the
micro-/meso- e.g. firm, network and industry levels of the business environment. In this
dissertation, supply chain (logistics) environmental complexity is developed and
operationalised in terms of the range and heterogeneity of country-oriented macro- logistics
factors that need to be considered in extended, cross-border, or global supply chain (logistics)
operations. The remainder of this dissertation is thereafter dedicated to finding these factors,
and their respective information measures, by the application of a decision-making approach.
A decision factor is one that influences the decision on selection with regards to
environmental complexity, and an information measure is a unit of measurement that aids
decision-making by providing some information on the factor.
The findings of this dissertation are based upon multiple literature reviews, content analyses
and expert opinions, and suggest the importance of 17 such decision factors and 187 different
types of information measures, which describe the state of environmental complexity in
extended, cross-border, or global supply chain operations. The study is particularly relevant
from the perspective of strategy and design issues in global supply chain management,
international operations management and international business, and more specifically for
environmental scanning and decision-making applications such as site location and transport
mode selection. By applying the results of this dissertation decision-makers may, for
example, get a preliminary idea of the environmental complexity surrounding their extended
supply chains
Global supply chain configurations using environmental complexity dimensions: An ontological examination
No full textThe configurational approach has become popular in operations and supply chain management. A number of configurations that consider the different business conditions of supply chains are published in the literature and can be usefully applied. However, as recently emphasized by Ferdows (2018), much more needs to be done within the realm of international operations. While cross-border configurations are now common in the domain from the point of view of value chains and production networks, little emphasis has been given to understanding these configurations in terms of international uncertainty, complexity, and the related international diversification strategies of firms. This chapter integrates key work within international business and operations and supply chain management and proposes an environmental complexity dimension for distinguishing cross-border supply chain configurations. This dimension aims to enhance our understanding of the detail complexity that arises in international operations. The propositions for future research pertain to the linkages and interrelationships that are posited between the already existing supply chain configurations, but also the geographic scope and complexity that is implied by each
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