Customer requirements based ERP customization using AHP technique

Purpose– Customization is a difficult task for many organizations implementing enterprise resource planning (ERP) systems. The purpose of this paper is to develop a new framework based on customers’ requirements to examine the ERP customization choices for the enterprise. The analytical hierarchy process (AHP) technique has been applied complementarily with this framework to prioritize ERP customization choices. \ud \ud Design/methodology/approach– Based on empirical literature, the paper proposed an ERP customization framework anchored on the customer's requirements. A case study research method was used to evaluate the applicability of the framework in a real-life setting. In a case study with 15 practitioners working on the vendor's and the client's sides in an ERP implementation, the paper applied the framework jointly with the AHP technique to prioritize the feasible customization choices for ERP implementation. \ud \ud Findings– The paper demonstrates the applicability of the framework in identifying the various feasible choices for the client organization to consider when they decide to customize their selected ERP product. \ud \ud Research limitations/implications– Further case studies need to be carried out in various contexts to acquire knowledge about the generalizability of the observations. This will also contribute to refining the proposed ERP customization framework. \ud \ud Practical implications– Very few literature sources suggest methods for exploring and evaluating customization options in ERP projects from requirements engineering perspective. The proposed framework helps practitioners and consultants anchor the customization decisions on the customer's requirements and use a well-established prioritization technique, AHP, to identify the feasible customization choices for the implementing enterprise. \ud \ud Originality/value– No previously published research studies provide an approach to prioritize customization choices for ERP anchored on the customer's requirements

Barriers to Information and Digital Technologies Adoption in Humanitarian Supply Chain Management : A Fuzzy AHP Approach

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Quantum computing challenges in the software industry. A fuzzy AHP-based approach

publishedVersio

Fuzzy-Analitycal Hirarchy Process (F-AHP) untuk Menentukan Keluarga Tidak Mampu Akibat Covid-19

Hampir setiap negara didunia ini mempunyai sekelompok masyarakat yang kurang mampu dari segi ekonomi, termasuk di Indonesia. Berdasarkan kriteria keluarga tidak mampu yang dipublikasikan oleh Badan Pusat Statistik (BPS) bulan September 2018 sampai maret 2019 menunjukkan jumlah keluarga tidak mampu mencapai sekitar 9,99 juta jiwa untuk di daerah perkotaan dan 15,15 juta jiwa untuk di daerah perdesaan. Berbagai program bantuan Pemerintah dalam upaya mengurangi keluarga tidak mampu akibat wabah corona virus disease (covid-19) sudah banyak menyalurkan bantuan terhadap keluarga miskin maupun rentan miskin, tetapi yang sampai di masyarakat banyak yang tidak sesuai target karena ada berbagai parameter yang menjadikan keluarga tidak mampu tersebut, akibatnya penyaluran bantuan tersebut menjadi kurang efektif. Dalam penelitian ini peneliti menggunakan model Analitychal Hierarchy Process (AHP) yang dikombinasikan dengan algoritma fuzzy. Metode AHP mempunyai permasalahan pada indeks konsistensi dari matriks timbal balik yang dihasilkan secara acak dari skala 9 poin dengan timbal balik yang baku. Untuk mengatasi semua kekurangan tersebut, metode F-AHP diusulkan untuk menanggulangi permasalahan hirarki kriteria, karena metode F-AHP dapat menentukan bobot kriteria utama secara maksimal. Hasil Pengukuran dalam penelitian ini diperoleh akurasi 92.78% yang dihasilkan oleh sistem pada 10 data training dibandingkan 50 data penduduk dari Kelurahan Pakisputih, Pekalongan

DevOps Ontology - An ontology to support the understanding of DevOps in the academy and the software industry

Currently, the degree of knowledge about what DevOps really means and what it entails is still limited. This can result in an informal and even incorrect implementation in many cases. Although several proposals related to DevOps adoption can be found, confusion is not uncommon and terminology conflict between the proposals is still evident. This article proposes DevOps Ontology, a semi-formal ontology that proposes a generic, consistent, and clear language to enable the dissemination of information related to implementing DevOps in software development. The ontology presented in this article facilitates the understanding of DevOps by identifying the relationships between software process elements and the agile principles/values that may be related to them. The DevOps Ontology has been defined considering the following aspects: the REFSENO formalism that uses the representation in UML was used and the language OWL language using Prótegé and HermiT Reasoner to evaluate the consistency of its structure. Likewise, it was satisfactorily evaluated in three application cases: a theoretical validation; instantiation of the continuous integration and deployment practices proposed by the company GitLab. Furthermore, a mobile app was created to retrieve information from the DevOps Ontology using the SPARQL protocol and RDF language. The app also evaluated the Ontology’s proficiency in responding to knowledge-based questions using SPARQL. The results showed that DevOps Ontology is consistent, complete, and concise, i.e.: to say: the consistency could be observed in the ability to be able to infer knowledge from the ontology, ensuring that the ontology is complete by checking for any incompleteness and verifying that all necessary definitions and inferences are well-established. Additionally, the ontology was assessed for conciseness to ensure that it doesn't contain redundant or unnecessary definitions. Furthermore, it has the potential for improvement by incorporating new concepts and relationships as needed. The newly suggested ontology creates a set of terms that provide a systematic and structured approach to organizing the existing knowledge in the field. This helps to minimize the confusion, inconsistency, and heterogeneity of the terminologies and concepts in the area of interest

Identifying and Prioritizing of Readiness Factors for Implementing ERP Based on Agility (Extension of McKinsey 7S Model)

Studies conducted by many researchers indicate high failure rate of projects of implementing ERP systems. To penetrate in global competition market, it seems necessary to carry out studies to assess organizational readiness prior to system implementation to identify weaknesses and strengths points of the organization. Furthermore, organizations should be agile to be able to respond to market changes fast and effectively to survive in competitive environment. ERP and agility are two important tools for achieving competitive advantages. The main goal of the present study was to identify and prioritize organizational readiness factors for implementing ERP based on organizational agility. In this study, along with extension of McKinsey 7S model (strategy, structure, systems, skills, style, staff, shared values) to 9S (7S+ self-evaluation and supportive factors) model, agility criteria were weighted and rated using group AHP with fuzzy logic approach; so that accountability, speed and flexibility have obtained the maximum score. The nine organizational readiness factors were ranked using integrated FAHP and TOPSIS method based on five criteria of agility. The framework was proposed to a real case of Shiraz distribution cooperative firms. Results showed that among the nine organizational dimensions based on agility, the two added to McKinsey dimensions (self-evaluation and supportive factors) are ranked in the first and fourth places. The proposed framework help the firms “to implement ERP system with agility approach” concentrate on effective empowerments and develop strategies based on their own priority

Prioritisation of requests, bugs and enhancements pertaining to apps for remedial actions. Towards solving the problem of which app concerns to address initially for app developers

Useful app reviews contain information related to the bugs reported by the app’s end-users along with the requests or enhancements (i.e., suggestions for improvement) pertaining to the app. App developers expend exhaustive manual efforts towards the identification of numerous useful reviews from a vast pool of reviews and converting such useful reviews into actionable knowledge by means of prioritisation. By doing so, app developers can resolve the critical bugs and simultaneously address the prominent requests or enhancements in short intervals of apps’ maintenance and evolution cycles. That said, the manual efforts towards the identification and prioritisation of useful reviews have limitations. The most common limitations are: high cognitive load required to perform manual analysis, lack of scalability associated with limited human resources to process voluminous reviews, extensive time requirements and error-proneness related to the manual efforts. While prior work from the app domain have proposed prioritisation approaches to convert reviews pertaining to an app into actionable knowledge, these studies have limitations and lack benchmarking of the prioritisation performance. Thus, the problem to prioritise numerous useful reviews still persists. In this study, initially, we conducted a systematic mapping study of the requirements prioritisation domain to explore the knowledge on prioritisation that exists and seek inspiration from the eminent empirical studies to solve the problem related to the prioritisation of numerous useful reviews. Findings of the systematic mapping study inspired us to develop automated approaches for filtering useful reviews, and then to facilitate their subsequent prioritisation. To filter useful reviews, this work developed six variants of the Multinomial Naïve Bayes method. Next, to prioritise the order in which useful reviews should be addressed, we proposed a group-based prioritisation method which initially classified the useful reviews into specific groups using an automatically generated taxonomy, and later prioritised these reviews using a multi-criteria heuristic function. Subsequently, we developed an individual prioritisation method that directly prioritised the useful reviews after filtering using the same multi-criteria heuristic function. Some of the findings of the conducted systematic mapping study not only provided the necessary inspiration towards the development of automated filtering and prioritisation approaches but also revealed crucial dimensions such as accuracy and time that could be utilised to benchmark the performance of a prioritisation method. With regards to the proposed automated filtering approach, we observed that the performance of the Multinomial Naïve Bayes variants varied based on their algorithmic structure and the nature of labelled reviews (i.e., balanced or imbalanced) that were made available for training purposes. The outcome related to the automated taxonomy generation approach for classifying useful review into specific groups showed a substantial match with the manual taxonomy generated from domain knowledge. Finally, we validated the performance of the group-based prioritisation and individual prioritisation methods, where we found that the performance of the individual prioritisation method was superior to that of the group-based prioritisation method when outcomes were assessed for the accuracy and time dimensions. In addition, we performed a full-scale evaluation of the individual prioritisation method which showed promising results. Given the outcomes, it is anticipated that our individual prioritisation method could assist app developers in filtering and prioritising numerous useful reviews to support app maintenance and evolution cycles. Beyond app reviews, the utility of our proposed prioritisation solution can be evaluated on software repositories tracking bugs and requests such as Jira, GitHub and so on

A Decision Support System for Investment Evaluation in Information Systems / Information Technology in Public Administration Organisations

Mestrado em Gestão de Sistemas de Informaçãoinfo:eu-repo/semantics/publishedVersio

A systematic decision-making framework for tackling quantum software engineering challenges

Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process