Application of artificial intelligence as a knowledge creation instrument in tax procedures

This study set out to find whether deep learning algorithms neural networks and self-organizing maps could be utilized in a value-adding way in the Finnish Tax Administration in the handling of income tax related claims by limited liability companies. According to research positive outcomes in artificial intelligence (AI) utilization have been attained outside Finland. The research was carried out according to the action design research method in which the focus of the research is concurrently building a suitable artifact for the organization and learning (design principles) from the creation and intervention itself. Research began with problem formulation followed by building, intervention, and evaluation. As a result, the project team consisting of three members created two functional artifacts: one based on neural networks, and another based on self-organizing maps. Creation of the artifacts was done in cycles as alpha, beta and gamma where alpha and beta were a neural network and gamma a self-organizing map. Alpha reached a macro average of 0.75–0.78 in classification and beta 0.77–0.79. Gamma gave a different point of view on the problem and was able to clearly identify the class's non-estimated customers in a topographical map. The artifacts were limited to function only as knowledge creation instruments due to legal and ethical limitations present in the context. Results suggest that it is recommendable to approach problems with more than one artifact. The preliminary results of this research were validated by applying the concept in a case organization, followed by an analysis of the results in an end-user setting.© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A unified approach for proportional-integral-derivative controller design for time delay processes

Abstract−An analytical design method for PI/PID controller tuning is proposed for several types of processes with time delay. A single tuning formula gives enhanced disturbance rejection performance. The design method is based on the IMC approach, which has a single tuning parameter to adjust the performance and robustness of the controller. A simple tuning formula gives consistently better performance as compared to several well-known methods at the same degree of robustness for stable and integrating process. The performance of the unstable process has been compared with other recently published methods which also show significant improvement in the proposed method. Furthermore, the robustness of the controller is investigated by inserting a perturbation uncertainty in all parameters simultaneously, again showing comparable results with other methods. An analysis has been performed for the uncertainty margin in the different process parameters for the robust controller design. It gives the guidelines of the M s setting for the PI controller design based on the process parameters uncertainty. For the selection of the closed-loop time constant, (τ c ), a guideline is provided over a broad range of θ/τ ratios on the basis of the peak of maximum uncertainty (M s ). A comparison of the IAE has been conducted for the wide range of θ/τ ratio for the first order time delay process. The proposed method shows minimum IAE in compared to SIMC, while Lee et al. shows poor disturbance rejection in the lag dominant process. In the simulation study, the controllers were tuned to have the same degree of robustness by measuring the M s , to obtain a reasonable comparison

Optimization Modeling of a Poultry Industry Supply Chain Network

Supply chain management is an asset to every industry globally due to its positive outcomes such as faster response time, reduction of unwanted inventory and lower sales costs with enhanced customer service. It is therefore important to focus on improving the supply chain network of any industry. The objective of this research study is to model a supply chain network for poultry Industry in Oman. The study analyzes the existing supply chain network within a poultry industry and recommended its improvement based on the identified factors while giving more emphasis on the routing and distribution network aspects of supply chain. The recommendation, in the form of optimization model, is verified and validated using Lingo optimization software. Also, heuristic method is proposed and tested to overcome the complexity of optimization model

Application of fuzzy TOPSIS framework for selecting complex project in a case company

Purpose This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions. Design/methodology/approach The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations. Findings A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works. Research limitations/implications The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity. Originality/value The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.© 2021, Ahm Shamsuzzoha, Sujan Piya and Mohammad Shamsuzzaman. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcodefi=vertaisarvioitu|en=peerReviewed

An approach for analysing supply chain complexity drivers through interpretive structural modelling

Today’s greater product variety, shorter product life cycle, and lower production costs are pushing companies to look beyond their own boundaries, thereby, creating complexity in the management of the supply chain. To manage such complexity, it is imperative that the management understand the associated complexity drivers and their interrelationships. This study identified twenty-three drivers responsible for supply chain complexity and classified them by using various criteria. In addition, the study presents a structural model using interpretive structural modelling (ISM) methodology to understand the inter-relationships between one driver to another. The research findings showed that drivers such as customer need, competitor action, and government regulation are beyond the control of supply chain partners, and have found the highest dominance with respect to supply chain complexity. Conversely, drivers related to tactical issues such as production planning and control, logistics and transportation, forecasting error, and marketing and sales are found to be the dependent drivers. Remaining drivers, such as company culture, number of suppliers, product variety, and organizational structure fall between the former two classifications. These drivers are related to strategic issues and require action from the upper level of the management hierarchy.fi=vertaisarvioitu|en=peerReviewed

Optimal maintenance for a waste-to-energy plant using DEMATEL : A case study

Waste-to-energy (WtE) plants are complex systems that requiring different types of maintenance to be reliable and available in functionality. The inadequacies of WtE plant lifetime maintenance may increase the production costs and negatively affect the competitiveness and the availability of WtE plants. To keep the efficiency of all the plant systems high and operating as expected during their lifetime, it is important to maintain them. This study focuses on the maintenance of WtE plants by analyzing the operating procedures at a case company. In the study, a multi-criterion decision-making method (MCDM) named Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to evaluate the weight and rank of twelve identified criteria for spare parts of the case WtE company. The empirical part of this study consists of a qualitative study, where data were collected from an open-ended questionnaire survey and case company data from existing documents. The respondents' rate from the questionnaire survey was 20%. Key findings from the study show that human, economic, equipment and tool related, management, and environmental factors have an important impact on the effectiveness of the maintenance and availability of the WtE plant. The study also shows that quality, lead time, price, and the severity of spare part failure are the key criteria to consider when selecting spare parts for the WtE plant. The study recommends several initiatives to improve the availability of WtE plant and spare parts which will help to reduce the costs of maintenance as well as mitigate the risks related to the maintenance.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Electronic Waste Generation by Selected Students of a University in Bangladesh : A Case Study

The creation of electronic waste (e-waste) has increased as a result of advances in recent tools and digital technologies. Consequently, there is a need to forecast the amount of e-waste generated over a specific period of time in order for society to be able handle this e-waste environmentally safely. This study focused on forecasting the generation of e-waste by a specific number of students in a university in Bangladesh––Shahjalal University of Science and Technology (SUST), Sylhet––in order to achieve this goal. The students were chosen from several departments. Estimates of the e-waste generated by the students from electronic items, such as mobile phones, laptops, desktops, pen drives, headphones, and computer components such as keyboards, motherboards, and mouses are described in this paper. The volume of the e-waste was estimated using consumption-and-use and waste-stream approaches. The total amount of electronic waste generated at SUST in 2016 was 2,494.38 kg, estimated using the linear-trend forecasting approach. Our forecasting also revealed that the e-waste generated will gradually increase, reaching 4,070.62 kg in 2024, more than double the amount generated in 2016. The basic ideas behind, and terminology of, e-waste management, as they relate to environmental sustainability, are also discussed.© 2022 American Society of Civil Engineers. This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000719fi=vertaisarvioitu|en=peerReviewed

Supply Chain Complexity Drivers and Solution Methods

Increased globalization, shorter product life cycle and rapid technological advancement in the manufacturing as well as service companies necessitates the company to have multiple supply chain partners. The partnership may be physical or virtual, thus making the chain more challenging and complex to manage. Therefore, the present supply chain network is characterized by its complexity, which requires proper management and strategy for its mitigation. In addition, the dynamic world in this complex supply chain system demands the manager to make faster and efficient decision. To manage the overall supply chain complexity and to make an efficient decision it is important that the manager understand the associated complex interactions within a supply chain, as well as, proper solution method or strategy to mitigate them. In this paper, a generic supply chain complexity drivers are identified and proposed solutions methods to manage the complexity in supply chain

Identification of Critical Factors and Their Interrelationships to Design Agile Supply Chain : Special Focus to Oil and Gas Industries

Purpose – This research attempted to identify the most critical factors and their inter-relationships to ensure designing agile supply chain, especially in oil and gas industry. This factors identification process is performed through developing a conceptual framework and the use of Interpretive Structural Modelling (ISM) tool. Design/methodology/approach – This study is conducted through an extensive literature review and questionnaires survey to identify and refine the critical factors that ensure the agile supply chain in oil and gas industry. In addition, several brainstorming sessions with the experts in the field of oil and gas industries were organized with the objective to interpret the contextual inter-relationships between the identified factors. The outcomes from the literature reviews, interview questions and experts’ opinions were used to develop a diagraph and MICMAC analysis to know the drivers of agility in supply chain. Findings –From this study, 34 enablers and 12 factors were identified, which are responsible to ensure agile supply chain in oil and gas industry. Out of these identified factors, top management commitment, strategic alignment, competency of management and integration of information and systems technology are found to be the critical drivers of supply chain agility. On the other hand, government regulations, transportation and logistics flexibility and production planning and control falls under the category of dependent factors. Originality/value – The identified factors and their interrelationships can be a valuable aid to ensure and measure the agility in supply chain, especially in oil and gas industry. These identified factors and their defined consequences will help managers and concerned authorities in oil and gas industry to take better decision to improve the agility level of their supply chain.©2020 Springer Nature. This is a post-peer-review, pre-copyedit version of an article published in Global Journal of Flexible Systems Management. The final authenticated version is available online at: http://dx.doi.org/10.1007/s40171-020-00247-5fi=vertaisarvioitu|en=peerReviewed