2017

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Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is “Smart production”. Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions.

Enterprise Modeling for Business and IT Alignment – a Framework and Recommendations

Eliminating the gap between business and IT within an enterprise, i.e., solving the problem of Business and IT Alignment (BITA), requires an instrument for the multidimensional analysis of an enterprise. Enterprise Modeling (EM) is a practice that supports such analysis and therefore can be used to facilitate BITA. EM serves as a tool that can capture, visualize and analyze different aspects of enterprises. This article presents a framework that describes the role of EM in the context of BITA and presents recommendations in EM for BITA.

Research Perspective in Enterprise Architecture

The challenges of aligning IT with business triggered the attention towards Enterprise Architecture (EA). Despite the increase interest of academic scholars in EA, there is scarcity of studies that provide an up to date comprehensive research perspective view. The purpose of this study is to examine the research methodologies and theories utilized in EA studies from 2010 to 2016. The study employed Systematic Literature Review (SLR) as method to explore and analyze the literature of EA. The study revealed the research approaches and data collection methods utilized in EA. It shows that case study approach and interviews are the highly used compared to other research approaches and data collection instruments. Furthermore, it pointed out the low employment of theories in EA studies. The study is contributing to the body of knowledge by providing a foundation for novice researchers in the area of EA through detailed discussions of research methodologies and theories which are expected to support them in designing future studies.

Sustainability through Innovations Of Enterprise Architecture (EA) in Public Sector’s Management: Issues and Challenges

Innovations through Enterprise Architecture (EA) require a transformation in public sector’s management. EA has been identified as one of the prime initiatives towards public sector transformation. EA implementation is highly recommended to execute efficient and effective public service delivery. However, building upon several public sector agencies that had implemented these initiatives, EA implementation in Malaysian Public Sector (MPS) was reported as unfavourable. This study aims to identify related issues and challenges towards sustainability of EA implementation. A qualitative research approach was employed in this study. Semi structured interview was held involving five EA experts. From the analysis, six related issues such as (i) absence of the mandate from government to implement EA initiatives (ii) improper EA governance leading to difficulty in managing EA implementation; (iii) absence of EA tool to maintain EA document; (iv) lack of EA awareness (v) lack of EA readiness and (vi) limited knowledge and skills on EA among the team were discerned in sustaining EA practices. With regard to the practical implication, this paper can serve as reference in EA implementation in the public sector.

The Fatal Flaw of AI Implementation

There is no question that artificial intelligence (AI) is presenting huge opportunities for companies to automate business processes. However, as you prepare to insert machine learning applications into your business processes, I’d recommend that you not fantasize about how a computer that can win at Go or poker can surely help you win in the marketplace. A better reference point will be your experience implementing your enterprise resource planning (ERP) or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but a greater number of companies found the experience more of a nightmare. The promised opportunity never came to fruition. Why am I raining on the AI parade? Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. But eventually, the outputs of most automated processes require people to do something. As most managers have learned the hard way, computers can process data just fine, but that processing isn’t worth much if people are feeding them bad data in the first place or don’t know what to do with information or analysis once it’s provided.

Industry 4.0 paradigm: The viewpoint of the small and medium enterprises

The pervasive diffusion of Information and Communication technologies (ICT) and automation technologies are the prerequisite for the preconized fourth industrial revolution: the Industry 4.0 (I4.0). Despite the economical efforts of several governments all over the world, still there are few companies, especially small and medium enterprises (SMEs), that adopt or intend to adopt in the near future I4.0 solutions. This work focus on key issues for implementing the I4.0 solutions in SMEs by using a specific case example as a test bench of an Italian small manufacturing company. Requirements and constraints derived from the field experience are generalised to provide a clear view of the profound potentialities and difficulties of the first industrial revolution announced instead of being historically recognised. A preliminary classification is then provided in view to start conceiving a library of Industry 4.0 formal patterns to identify the maturity of a SME for deploying Industry 4.0 concepts and technologies.