Activating supply chain business models' value potentials through Systems Engineering

New business opportunities, driven by smart digitalization technology and initiatives such as Industry 4.0, significantly change business models and their innovation rate. The complexity of methodologies developed in recent decades for balancing exploration and exploitation activities of digital transformation has risen. Still, the desired integration levels across organizational levels were often not reached. Systems thinking promises to holistically consider interdisciplinary relationships and objectives of various stakeholders across supply chain ecosystems. Systems theory-based concepts can simultaneously improve value identification and aligned transformation among supply networks' organizational and technical domains. Hence, the study proposes synthesizing management science concepts such as strategic alignment with enterprise architecture concepts and artificial intelligence (AI)-driven business process optimization to increase innovation productivity and master the increasing rate of business dynamics at the same time. Based on a critical review, the study explores concepts for innovation, transformation, and alignment in the context of Industry 4.0. The essence has been compiled into a systems engineering-driven framework for agile value generation on operational processes and high-order capability levels. The approach improves visibility for orchestrating sustainable value flows and transformation activities by considering the ambidexterity of exploring and exploiting activities and the viability of supply chain systems and sub-systems. Finally, the study demonstrates the need to harmonize these concepts into a concise methodology and taxonomy for digital supply chain engineering.OA-hybri

Chatbots for enterprises: Outlook

Chatbots are going to be the main tool for automated conversations with customers. Still, there is no consistent methodology for choosing a suitable chatbot platform for a particular business. This paper proposes a new method for chatbot platform evaluation. To describe the current state of chatbot platforms, two high-level approaches to chatbot platform design are discussed and compared. WYSIWYG platforms aim to simplicity but may lack some advanced features. All-purpose chatbot platforms require extensive technical skills and are more expensive but give their users more freedom in chatbot design. We provide an evaluation of six major chatbot solutions. The proposed method for the chatbot selection is demonstrated on two sample businesses - a large bank and a small taxi service.O

Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements

The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected

INFORMATION SYSTEMS EVALUATION CRITERIA BASED ON ATTITUDES OF GRADUATE STUDENTS

Importance of information systems in supporting business activities and managerial decision making is growing. Decisions related to selecting a suitable information system, including the technological background, human resources, procedures and information belong to one of the most difficult and most responsible ones. As in the case of other types of investments, assets and resources invested into information system should return in a reasonable time. There has been a lot of work done in the research and application of IS evaluation techniques to different kinds of information systems. Such evaluations involve a wide variety of technical and technological considerations made by technical experts, on the other hand impacts on management of the organization or financial impacts can be addressed. The objective of the paper is to reveal the preferences of graduate students related to their information systems evaluation and to propose a general framework for such evaluations. During the experimental period two surveys were carried out within the information systems course – at the beginning when the students were completely uninformed and at the end when the students had the knowledge of individual aspects of information systems, their role within organizations and process of information systems evaluation. The former survey used a simple scoring method whereas the latter relied on formal usage of the Analytical Hierarchy Process. The results show the differences in opinions of the students between these two surveys. Presented criteria hierarchy as well as the importance of individual evaluation criteria can be used for demonstration of attitudes of graduate students of management study programs and as a general framework for information systems evaluation

Decision support for customers in electronic environments

Due to the rapid spread of computer technologies into day-to-day lives many purchases or purchase-related decisions are made in the electronic environment of the Web. In order to handle information overload that is the result of the availability of many web-based stores, products and services, consumers use decision support aids that help with need recognition, information retrieval, filtering, comparisons and choice making. Decision support systems (DSS) discipline spreads about 40 years back and was mostly focused on assisting managers. However, online environments and decision support in such environments bring new opportunities also to the customers. The focus on decision support for consumers is also not investigated to the large extent and not documented in the literature. Providing customers with well designed decision aids can lead to lower cognitive decision effort associated with the purchase decision which results in significant increase of consumer’s confidence, satisfaction, and cost savings. During decision making process the subjects can chose from several methods (optimizing, reasoning, analogizing, and creating), DSS types (data-, model-, communication-, document-driven, and knowledge-based) and benefit from different modern technologies. The paper investigates popular customer decision making aids, such as search, filtering, comparison, ­e-negotiations and auctions, recommendation systems, social network systems, product design applications, communication support etc. which are frequently related to e-commerce applications. Results include the overview of such decision supporting tools, specific examples, classification according the way how the decisions are supported, and possibilities of applications of progressive technologies. The paper thus contributes to the process of development of the interface between companies and the customers where customer decisions take place

A data oriented framework for developing flexible information systems

As a consequence of a rapidly changing environment, the success of organizations is dependent upon the ongoing and immediate adjustments of their information systems as reactions to these changes. Therefore, flexibility becomes one of the most crucial features in information systems. This paper specifies a data model oriented framework for the development of flexible information systems. The result of such process is a system that is sufficiently general and flexible in relation to solving problems related to changing environment. From general requirements related to data layer of information systems the paper discusses the definition of major elements of logical data model (entities and their hierarchical arrangement, attributes of the entities and their important characteristics, relationships among entities and their characteristics) as well as possibilities of implementation and definition of application logic and data presentation. Proposed framework enables the organization to specify its own database structure, which best matches the situation of the organization and its environment. Because an approach similar to meta-data approaches is applied, methods for information sharing and interchange can be easily specified as well as program logic for manipulation with the data base on the application layer and data presentation

Machine Learning-Based Search for Similar Unstructured Text Entries Using Only a Few Positive Samples with Ranking by Similarity

This research was inspired by the procedures that are used by human bibliographic searchers: Given some textual, only ‘positive’ (interesting) examples, coming from one category find the most similar ones that belong to a relevant topic. The problem of categorization of unlabeled relevant and irrelevant textual documents is here solved by using a small subset of relevant available patterns labeled manually. Unlabeled text items are compared with such labeled patterns. The unlabeled samples are then ranked according their degree of similarity with the patterns. At the top of the rank, there are the most similar (relevant) items. Entries receding from the rank top represent less and less similar entries. This simple method, aimed at processing large volumes of text entries, provides practically acceptable filtering results from the accuracy point of view and users can avoid the demanding task of labeling too many training examples to be able to apply a chosen classifier. The ranking-based approach provides results that can be further used for the following text-item processing where the number of irrelevant items is already not so high as it is usually typical for, for example, only the raw browsing results provided by Internet search engines. Even if this relatively simple automatic search is not errorless, it can help process particularly very large textual unstructured data volumes. Such an approach can help also in the economics area, for example, to automatically categorize written opinions of customers (as amazon.com is collecting via the Internet), process network-based discussion groups, and so like.unlabeled text documents, one-class categorization, text similarity, ranking by similarity, pattern recognition, machine learning, natural language processing

Simulating activation propagation in social networks using the graph theory

The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks