Ateeq Khan

A Survey of Current Challenges in Manufacturing Industry and Preparation for Industry 4.0

Evolving business needs and shorter product life cycle demand for new methods and services from production facilities. In the past few decades, research and technology has been advanced at the rapid pace on enterprise level. Technology used at production level is still quite old and has not gained much attention in comparison to enterprise level. Enterprises are interested to invest more resources on production level to overcome problems and satisfy goals of stakeholders. The paper discuss the term Industry 4.0 and why we need changes in our traditional manufacturing systems (due to new business models, companies’ competition, and innovation gap). We find-out what are the current challenges faced by organizations with the help of a survey. We also list new opportunities and applications possible by introducing new tools and technologies and provide initial feedback of a scenario from case company. At the end, we discuss how to solve these challenges, scenarios and summarize our paper.

Applying process mining in SOA environments

Process mining is an emerging analysis technique, which extracts process knowledge from data and provides various benefits to organizations. In Service Oriented Computing environment, different services collaborate with others to carry out the operations and therefore overall picture of operations and execution is not clear. Process mining extracts the information from log files of systems, as recorded during executions, and depicts the reality. In order to apply process mining, extraction of process trace data from log files is a pre-requisite step. A case study demonstrates the practical applicability of our proposed framework for extraction of the process trace data from application systems and integration portals.

Service variability patterns

Service-oriented computing (SOC) increases flexibility of IT systems and helps enterprises to meet their changing needs. Different methods address changing requirements in service-oriented environment. Many solutions exist to address variability, however, each solution is tailored to a specific problem, e.g. at one specific layer in SOC. We survey variability mechanisms from literature and summarize solutions, consequences, and possible combinations in a pattern catalogue. Based on the pattern catalogue, we compare different variability patterns and their combinations. Our catalogue helps to choose an appropriate technique for the variability problem at hand and illustrates its consequences in SOC.

The Pervasive Nature of Variability in SOC

Service-oriented computing has gained attention from researchers and industry due to various benefits. Enterprises are supported by SOC-based solutions. Enterprise requirements change with the customer needs, market conditions, and variability is often needed in SOC. Research usually focuses at variability on the business process level and ignores variability at other layers. In this paper, we show what kind of variability can occur and how it spans across all layers of SOC. We present some example scenarios to discuss different kinds of variability and their reasons in SOC environments. Finally, we classify variability to show that variability is not an isolated problem in a single layer.

A Perspective on Industry 4.0: From Challenges to Opportunities in Production Systems

Industry 4.0 and smart factory are the terms frequently used for next generation production systems. Advancement of Information technologies paved the way for evolution of production systems. To remain competitive in the market, enterprises want to utilize these technological advancements in order to solve current challenges and serve customers in new ways which were not imagined before. In order to provide new services quickly, new methods and technologies have to be introduced at manufacturing level. The paper briefly discusses industry 4.0 and settings (arrangements) for co-innovation. This paper also describes what are the current challenges faced by companies with the help of a survey. The paper proposes an approach from strategical to operational level for the implementation of industry 4.0. In this paper, we also provide new opportunities, scenarios, and applications enabled by introducing new tools and technologies for industry 4.0. At the end, the paper provides summary and glimpse of the future work.

A Holistic View of the IoT Process from Sensors to the Business Value.

Internet of things (IoT) is the focus of research, and industries are investing heavily due to potential benefits of IoT in various fields. This paper provides a holistic view of different phases in IoT covering all phases from sensor data collection to the generation of business value. In this paper, we propose to use the proven Six Sigma methodology for IoT projects. We describe each phase using a structured approach. We discuss the consequences of each phase while selecting the phase as an entry or starting point. We use predictive maintenance as a use case to demonstrate the practicability of our IoT process. Using these insights, IoT project managers can identify required activities and competencies to increase success probability. In the end, we summarise the paper findings and highlight the future work.

An Evaluation of Variability Mechanisms to Manage Cloud Services.

There is an increasing demand for customizable services by consumers in service-oriented or cloud computing domain. Service providers have to provide various kinds of variability in their services. However, there is a lack of information which illustrates how practitioners and providers cope with the variability problem in the service domain. We conduct a survey to get insights from participants what kind of variability exists and how to provide/manage variability for service-oriented domain. We also analyze the importance of criteria for variability mechanisms and discuss it in this paper. In short, we reveal the understanding of variability between survey participants and which criteria are important when choosing an approach to provide variability in services domain.

Application Software in Cloud-Ready Data Centers: A Survey

The digital transformation of the economy, as well as society in general, is in large part based on data centers’ capabilities to process, store, and transport data. As the range of application requirements, the volume of data and system complexities, as well as security requirements increase, appropriate application system landscapes to support the tasks performed in a data center become crucial to success. Within this chapter we provide an overview on application software used to manage IT service production in data centers. With the advent of cloud computing, data centers increasingly face outside competition on all layers of the IT stack. Cloud service providers, such as Software as a Service companies, may even operate their own data centers. Furthermore, the people who use the investigated application software often themselves undergo an evolution from pure administrative to engineering responsibilities. Lastly, progress in the domain of application software, in general, has influenced application software discussed here. These phenomena are examined in order to compare common practice against competitive advantage of using the software from the presented taxonomy. The presented research has implications for practitioners because it provides an overview on the essential application fields of cloud-ready data centers. Additionally, the chapter contributes to the knowledge base by providing a taxonomy that is derived from a previously conducted case study. The taxonomy may be used in further research, for instance, in application system landscape maturity models.

Variability management in Infrastructure as a Service: Scenarios in cloud deployment models

Flexible IT landscapes and efficient management of resources are key issues for enterprises. Variability is an important aspect for flexible IT landscape. The main part of the paper discusses variability characteristics in Infrastructure as a Service (IaaS). We show what kind of variability is possible at IaaS. In addition, a case study is provided that shows practical examples of infrastructure variability and how these variability issues can be managed using variability solutions and the software configuration tool Puppet. Software configuration tools enable us to treat infrastructure as code and to meet the varying requirements of customers. In the end, we summarize our paper and provide an outlook for future work.

Variability Management in IaaS

Flexible IT landscapes and efficient management of resources are key issues for enterprises. In cloud computing, varying requirements are discussed at application and platform level. In contrast, variability at cloud infrastructure level is seldom discussed. We have identified several variability requirements at infrastructure level from customer perspective. From service provider perspective, we also highlight which technical means needed to be employed to manage the variability. For elaboration, we provide examples for variability requirements and solutions. We also show with the help of a case study that how we can provide and manage variability using the software configuration tool Puppet. In the end, we summarize our paper and provide an outlook for future work.