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Study on Data-Driven Decision-Making in Entrepreneurship
Published in Rajender Kumar, Rahul Sindhwani, Tavishi Tewary, J. Paulo Davim, Principles of Entrepreneurship in the Industry 4.0 Era, 2023
Vimlesh Kumar Ojha, Sanjeev Goyal, Mahesh Chand
In the context of Industry 4.0, the availability of a large amount of data generated by smart manufacturing is challenging existing decision-making approaches. Data-driven decision-making aids entrepreneurs in making competitive price decisions, expanding into new areas, and staying up to date with customer behavior changes. Based upon the reviewed articles, it is seen that the entrepreneurs of the manufacturing sector are going to see tremendous change in the near future. The data generated at each stage of the manufacturing like design, production, consumption, and decomposing, can be largely utilized in the near future, which will change the traditional decision-making approach to a data-driven decision-making one. There is strong evidence that entrepreneurs can improve industrial performance substantially via data-driven decision-making methods based on big data. Data science supported data-driven decision-making will allow entrepreneurs to make decisions automatically at a massive scale. The study advocates the utility of Data-Driven Decision-Making approach as reliable tool to unearth new facts and solve the most difficult challenges for present day entrepreneurial setups.
Introduction to Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Published in Kim Phuc Tran, Machine Learning and Probabilistic Graphical Models for Decision Support Systems, 2023
Industry 4.0 can provide automatic solutions to different sectors such as manufacturing, healthcare, automation, supply chain management. However, there are many challenges in Industry 4.0 such as shorter product life cycles, the need for resources to design, manufacture, and quality control that decision-making processes in companies are becoming extremely complex and require more and more knowledge 3. In this context, decision-making based on the data gathered from the process of data-driven decision-making is essential. Data-driven decision-making is a technology that brings a lot of benefits to the decision-making process of enterprises. As an essential tool, Decision Support System (DSS) is designed to assist companies to support the decision-making process and making more effective decisions. A DSS is an information system that analyses data from organizations and presents it so that managers can make decisions more easily4.
What Business?
Published in David C. Kimball, Robert N. Lussier, Entrepreneurship Skills for New Ventures, 2020
David C. Kimball, Robert N. Lussier
Problem-solvingis overcoming obstacles and identifying and exploiting opportunities. Decision-makingis the process of selecting a course of action that will overcome an obstacle or exploit an opportunity. Through entrepreneurial orientation (Chapter 1), entrepreneurs solve problems and make decisions to be proactive and to select innovations while accepting the risks involved. Important early decisions that entrepreneurs make, often emotionally, include: “What opportunity can I exploit profitably?” and “Should I start a new venture?” Problem-solving and decision-making go hand-in-hand and are a daily challenge facing entrepreneurs, which makes them critical skills that can lead to success or failure.4 Therefore, improving these skills can increase your performance.
The m-polar fuzzy ELECTRE-I integrated AHP approach for selection of non-traditional machining processes
Published in Cogent Engineering, 2023
The digraph plotted as shown in Figure 9 explains the pictorial presentation of the dominance of one alternative over the other alternative. Appendix-II at the end gives the overall calculations of the mFS ELECTRE-I integrated AHP approach. The decision system developed is useful for the end user that is manager (decision maker) from the manufacturing industry. It is very easy to use console to provide input. Once the decision matrix is ready, it is easy to select the best alternative from the available alternatives. All the decisions obtained with the developed decision system are in line with the decision obtained from the previous work. Furthermore, the decision system helps in improving the performance of the manufacturing process as well as reduces time for decision-making. This type of decision systems can be used in other sectors to take decisions like selection of site for thermal plant, selection of suitable material, selection of best robot, and selection of suitable FMS system.
A sustainable road pricing oriented bilevel optimization approach under multiple environmental uncertainties
Published in International Journal of Sustainable Transportation, 2022
Ying Lv, Shanshan Wang, Ziyou Gao, Guanhui Cheng, Guohe Huang, Zhengbing He
Furthermore, when a BLP method involves random and/or fuzzy features, some inputs in the optimization model may not be presented with accurate values in practice. It hence will result in multiple uncertainties existing not only in the objective function but also in the constraint (both left- and right-hand side coefficients). Those traditional BLP models associated with solution approaches apparently will not properly solve the problem. For example, pollutant amounts that are emitted from vehicles may not be evaluated precisely and can be described as fuzzy numbers; this is because various factors can affect the exhaust emissions from a transportation system, and the appearance of possible values can be more suitable to be specified by a membership function. The uncertainties involved in the pricing design model can be compounded further by multisource information and need to be presented in the combinations of multiple uncertainty formats. For example, the allowable vehicular emission amount of a road network, which could be acquired based on the local environmental capacity, would be described by using multiple uncertainties; in detail, it could be investigated from different sources with random lower, mean and upper values. Such deviations in the subjective estimations would result in both randomness and fuzziness, and thus, the emission allowance could be specified as a random-fuzzy variable, as shown in Figure 1. The uncertainties will further bring challenges to the processes of formulating and solving the decision-making model.
Toward the understanding of ‘the human' in engineering: a discourse analysis
Published in European Journal of Engineering Education, 2021
Jorge Castillo-Sepúlveda, Diana Pasmanik
Engineering contributes to the creation of how humans inhabit society (Latour 1987). In Chile, Civil Engineering has gained preponderance in hierarchical outlooks due to its association with problem-solving, proactivity, and leadership (Forcael et al. 2013). One of its multiple branches, Civil Industrial Engineering in Chile focuses on productive aspects, quality and efficiency management, and the optimisation of a wide range of industries. Thus, actions and assumptions of industrial civil engineers have a large effect on the settings and procedures of problem-solving and how humans are considered in them (Lozano 2006). Beyond the technical or strategic aspects of organisations, decision-making involves an analysis of the potential consequences of engineering work, including health risks, environmental impact, adaptative processes, caring aspects, and time management, among several others (Harris et al. 2019). In this context, ethics seems to be a problematic subject, especially for young engineers, due to tensions between the mandates of efficiency and social responsibility (Pasmanik et al. 2016). Nevertheless, these issues may be tackled using concrete experiences during educational training (Valentine et al. 2020).