An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face

In today’s world competition, companies are always in search for new ways to improve the performance of their supply chain, and gather knowledge and skills to reduce time, improve quality and increase productivity. In recent years, Mongolian construction companies started to recognize the importance of supply chain. But there is a limited research and literature on the way how to successfully manage and measure the supply chain performance in Mongolia. The thesis addresses the theory and practice of factors affecting on supply chain performance on the sample of Mongolian construction companies. The study tries to define which factors influence on supply chain performance and which one has the most critical impact in the case construction companies. This study utilized 71 valid responses from 200 surveyed companies in the multiple regression analysis. The findings indicate that supply chain relationship significantly mediates information technology with supply chain performance, whereas environmental uncertainty negatively effects the performance. In addition, the results demonstrate that the supply chain relationship factor is the most critical predictor of supply chain performance. Hence, companies should control the supply chain and gain experience from their mistakes on every stage of supply chain process, and utilize the knowledge in the next phase. The answers of suggested research questions are as follows. The first research question was with which factors can the performance of the supply chain in the construction companies be measured? The results show that information technology, supply chain relationship, environmental uncertainty have positive impact on supply chain performance. The second question was with which factors are most effective in influencing supply chain performance. The results reveals that the most critical factor influencing supply chain performance in Mongolia is supply chain relationship. Since there was no formal information about supply chain performance in Mongolia, this study gives considerable information about supply chain performance in Mongolian companies. Also, it contributed to the limited research on supply chain system in Mongolia and tries to develop a framework for the relationship between affected factors and supply chain performance in case of Mongolian companies. Consequently, it provides insight for experts who would invest in supply chain systems. The result of this study pointed out critical factors for supply chain performance that claim more consideration from managers and engineers to implement supply chain performance successfully. The results of this paper reveal that companies should start the planning stage with a plan detailing the supply chain from it indicators that needed to evaluate success. Without clarifying supply chain desired it companies would not be aware that whether they implementing successfully or not. Without clear indicators and adequate parameters, it is impossible to measure supply chain. Supply chain relationship is proved that it has the strongest influence on supply chain performance. This result asserts the assessment of existing supply chain relationship is required for reaping supply chain performance. Assessments of existing supply chain relationship includes assessing whether the company has supply relation team, building stronger relationships will bring extra security because collaboration with customers and suppliers demonstrates understanding, concern and the desire to work with them over the longer term. Supply chain practitioners continue to press managers to form closer, longer-term relationships between customers and fewer suppliers as the only response to increasing market sophistication and globalization.

The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.

Contributors

PurposeThis study aims to examine the effects of behavioural antecedents of collaboration in supply chain relationships on supply chain integration and performance by developing and empirically validating a model linking these constructs.Design/methodology/approachA conceptual model was developed based on the relational exchange theory, social exchange theory and resource-based view. An international survey with supply chain/logistics managers from manufacturing focal firms based in Europe, USA and Asia was conducted; they provided input on upstream and downstream relationships based on their actual interaction and experience with supply chain partners. The collected data, which reflect supply chain managers’ perceptions on the above described phenomena, were analysed using the partial least squares method.FindingsMutuality, reciprocity, trust and commitment are instrumental for the formation of supply chain relationships characterised by higher information integration. In turn, information integration has much stronger impact on the coordination of operational decisions related to production and demand planning than on decisions related to actual production processes, but, interestingly, the latter affects supply chain performance much more than the former.Research limitations/implicationsThe research could benefit from a longitudinal rather than a cross-sectional approach, incorporating multiple respondents such as representatives of supply chain partners and senior management of the focal firm, to capture potentially varying opinions on the supply chain phenomena under examination.Practical implicationsThe results can assist supply chain decision-makers in understanding the importance of behavioural closeness between supply chain partners for the development of collaborative supply chain relationships that lead to higher integration and superior performance. Insight is provided on linkages between examined dimensions of supply chain integration. A process view of intermediate steps needed to translate collaborative relationships into higher supply chain integration and performance across the supply chain is offered.Originality/valueThe development and testing of an integrated model examining linkages between supply chain relationship antecedents, integration and performance is an original contribution. By proposing and confirming a sequential order of the influence of behavioural antecedents, integration dimensions and their impact on supply chain performance, the paper sets foundations of a roadmap for achieving higher supply chain performance from collaborative supply chain relationships. Finally, the paper contributes to the limited theoretical justification on the development of knowledge for assisting decision-making in SCM/logistics and its integration into models, processes and tasks.

Machine learning based simulation optimisation for urban routing problems

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.

Supply Chain Management (SCM) plays a critical role in the success of any business organization. Individuals involved in business activities often have to make decisions regarding different aspects of the supply chain, including planning, procurement, production, inventory management, transportation, distribution, and customer relationship management. The combination of neutrosophic logic and machine learning has gained significant attention in the field of SCM as a means to tackle uncertainties and improve decision-making. This paper highlights the potential benefits and applications of integrating neutrosophic reasoning and machine learning in SCM. Neutrosophic reasoning provides a framework for handling imprecise and uncertain information, while machine learning techniques offer powerful tools for data analysis, pattern recognition, and predictive modeling. By leveraging machine learning algorithms within the context of neutrosophic logic, SCM practitioners can enhance demand forecasting accuracy, optimize inventory management, improve transportation and routing decisions, and strengthen supply chain collaboration. The integration of neutrosophic logic and machine learning enables the handling of complex supply chain data, accommodates dynamic and uncertain environments, and supports proactive decision-making. Furthermore, the combination of these approaches can contribute to improved supply chain resilience, sustainability, and customer satisfaction. This paper applied the neutrosophic AHP method as a feature section to select the highest importance criteria as an input to machine learning. Then we applied two machine learning models named random forest and decision. The results show the random forest has the highest accuracy followed by a decision tree.

The supply chain of agricultural products is the entire production process from cultivation, processing, distribution, and marketing, to the products produced to the hands of consumers. The supply chain consists of various stakeholders who are involved either directly or indirectly. Thus, the supply chain is an integrated marketing unit that includes the integration of stakeholders and products to provide satisfaction to customers. Pinrang Regency is one of the largest rice-producing districts in South Sulawesi. The purpose of this study is to analyze the structure of the inter-island rice supply chain in rice mills in Pinrang Regency, analyze the relationship pattern in the inter-island rice supply chain in Pinrang Regency, analyze the influence of inter-island traders on the rice market in Pinrang Regency, the analysis used is descriptive analysis, panel data regression analysis. The results of this study indicate that the structure of the rice supply chain in the mill starts from farmers who supply grain to the mill, then the mill sells rice to inter-island traders, then the mill sells to wholesalers and retailers then finally the mill sells rice to direct consumers. The rice supply chain in each mill has 3 flows, namely product flow, financial flow and information flow. The pattern of relationships in the inter-island rice supply chain, namely the raw material supply channel (grain), the rice product distribution channel (milling), and the inter-island channel cooperate in completing a purchase transaction and the pattern of relationships in the supply chain uses a general trading partnership pattern. The results of the study using the Common Effect Model (CEM) show that the variable amount of production has a positive and significant effect on the volume of inter-island shipments. The destination price variable has a positive and significant effect on the volume of inter-island rice shipments. Then the variable Shipping cost has a positive and significant effect on the volume of inter-island rice shipments.

This thesis provides practical approaches to supply chain (SC) relationship assessment and monitoring for the fields of collaborative supply chain management (CSCM), supplier relationship management (SRM), customer relationship management (CRM), and partner relationship management (PRM). This work presents relationship analysis results concerning sourcing, demand planning, and logistics processes and relationships based upon a case study conducted at a multinational corporation. These results identified key relationship strengths and weaknesses across three value chains. These results, combined with results from the academic literature, are used to develop an organized list of relationship factors useful for relationship assessment and modeling purposes. The relationship factors are used to create a new Supply Chain Relationship Assessment Model (SCRAM) that incorporates the use of Plan-Do-Check-Act (PDCA) cycles and the use of statistical process control (SPC) to assess, monitor, and manage individual relationship performance. These methods can be incorporated into existing customer, supplier, or SCM software systems. This thesis extends and builds upon the existing academic literature in the fields of marketing, purchasing, and supply chain management, most importantly in developing an approach to quantify the impact of supply chain relationship factors and strategic changes upon overall supply chain performance. More accurate and precise quantification of relationship factors and relationship performance could lead to better selection of SC partners, improve SC relationships, lower SC costs, and increase value for customers.

The supply chain is the central organizing unit in today's global industries and has gained significance as one of the 21st century manufacturing paradigms for improving organizational competitiveness. In this paper, we propose a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology, a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation.

Integrating machine learning components in software systems is a task more and more companies are confronted with. However, there is not much knowledge today on how the software development process needs to change, when such components are integrated into a software system. We performed an interview study with 16 participants, focusing on emerging and changing task. The results uncover a set of 25 tasks associated to different software development phases, such as requirements engineering or deployment. We are just starting to understand the implications of using machine-learning components on the software development process. This study allows some first insights into how widespread the required process changes are.

Prioritising Visibility Influencing Factors in Supply Chains for Resilience

Purpose – The purpose of this research is to investigate the dynamics and dimensions of behaviors of supply chain employees that may impede the success of supply chain relationships. Design/methodology/approach – A grounded theory qualitative method was used to explore the concept of counterproductive work behavior in a supply chain context. Findings – Through analysis and evaluation of the data, five key supply chain counterproductive work behaviors (avoiding, withholding, emoting, confounding, and shifting) emerged. Overall, these behaviors are associated with perceived contract breaches, which undermines trust within supply chain relationships. Research limitations/implications – This work provides a basis for researchers to explore counterproductive work behaviors within supply chain management and managers to consider these behaviors in relational exchange. Future research can build on the insights provided here by applying quantitative methods to exploring the phenomenon and investigating counterproductive behaviors from the actor's perspective. Originality/value – This research provides an overarching framework for relationship management behaviors that may detract from supply chain relationships. Research has previously explored these types of behaviors in a segmented fashion. This work takes a comprehensive look at behaviors and through evaluation of the data, relational and informational contract breaches emerge. The data suggests these contract breaches may undermine the trust within supply chain relationships.

Purpose: The point of departure in this article is that the principles that underpin entrepreneurial networking also apply to the establishment of supply chain relationships. The theoretical base for the need for entrepreneurial networking can be found in Kirzner's theory of entrepreneurship that argued that entrepreneurs identify and act upon profit opportunities that exist in disequilibrium in order to equilibrate the economy. Problem investigated: This article explores the possibility of implementing entrepreneurial networking in supply chain relationships in the automotive component industry. Methodology: Kirzner's theory is used as a theoretical base to support the case for the development of supply chain relationships. The literature with regard to supply chain management and its relevance to entrepreneurial networking is first dealt with. The case for implementing entrepreneurial networking to strengthen supply chain relationships in the automotive component industry in South Africa is then presented.Findings: The findings of this article indicate that the principles underlying entrepreneurial networking could be applied to supply chain relationships in the automotive component industry in South Africa. The argument is mainly based on the sustainability and profitability potential of entrepreneurial networking and the similarities that exist between entrepreneurial networking and supply chain management relationships. Originality: This research is original as it explores the possibility that the principles that underpin entrepreneurial networking also apply to the establishment of supply chain relationships in the automotive component industry. Furthermore, there is a need for published research in South Africa on supply chain management, particularly relationships within the supply chain.Conclusion: Based on the sustainability and profitability potential of entrepreneurial networking and the similarities that exist between entrepreneurial networking and supply chain relationships, the principles underlying entrepreneurial networking can be applied to supply chain relationships in the automotive component industry in South Africa. The unique challenges facing this industry in the current global market further strengthen the case for the implementation of entrepreneurial networking.