Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

Today, the manufacturing industry is aiming to improve competitiveness through the convergence with cutting-edge ICT technologies in order to secure a new growth engine. Smart Manufacturing, which is the fourth revolution in the manufacturing industry and is also considered as a new paradigm, is the collection of cutting-edge technologies that support effective and accurate engineering decision-making in real time through the introduction of various ICT technologies and the convergence with the existing manufacturing technologies. This paper surveyed and analyzed various articles related to Smart Manufacturing, identified the past and present levels, and predicted the future. For these purposes, 1) the major key technologies related to Smart Manufacturing were identified through the analysis of the policies and technology roadmaps of Germany, the U.S., and Korea that have government-driven leading movements for Smart Manufacturing, 2) the related articles on the overall Smart Manufacturing concept, the key system structure, or each key technology were investigated, and, finally, 3) the Smart Manufacturing-related trends were identified and the future was predicted by conducting various analyses on the application areas and technology development levels that have been addressed in each article.

Smart manufacturing: Past research, present findings, and future directions

The evolution and future of manufacturing: A review

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https://link.springer.com/content/pdf/10.1057%2Fjit.1991.9.pdf

Strategic planning for information systems

High competitive pressure in the global manufac- turing industry makes efficient, effective and continuously improved manufacturing processes a critical success factor. Yet, existing analytics in manufacturing, e. g., provided by Manufacturing Execution Systems, are coined by major short- comings considerably limiting continuous process improvement. In particular, they do not make use of data mining to identify hidden patterns in manufacturing-related data. In this article, we present indication-based and pattern-based manufacturing process optimization as novel data mining approaches provided by the Advanced Manufacturing Analytics Platform. We demonstrate their usefulness through use cases and depict suitable data mining techniques as well as implementation details.

/pdf/data-mining-driven-manufacturing-process-optimization-2afof1ppcx.pdf

Data Mining-driven Manufacturing Process Optimization

The ecosystem of big data technologies and advanced analytics tools has evolved rapidly in the last years offering companies new possibilities for digital transformation and data-driven solutions. Industry 4.0 represents a major application domain for big data and advanced analytics in order to exploit the huge amounts of data generated across the industrial value chain. However, building and establishing an Industry 4.0 analytics platform involves far more than tools and technology. In this paper, we report on our practical experiences when building the Bosch Industry 4.0 Analytics Platform and discuss challenges, approaches and future research directions. The analytics platform is designed for more than 270 factories as part of Bosch’s worldwide manufacturing network. We describe use cases and requirements for the analytics platform and present its architecture. On this basis, we discuss practical challenges related to analytical solution development, employee enablement, i. e., citizen data science, as well as analytics governance and present initial solution approaches. Thereby, we highlight future research directions in order to leverage advanced analytics and big data in industrial enterprises.

Building an Industry 4.0 Analytics Platform

Continuously improved business processes are a central success factor for companies. Yet, existing data analytics do not fully exploit the data generated during process execution. Particularly, they miss prescriptive techniques to transform analysis results into improvement actions. In this paper, we present the data-mining-driven concept of recommendation-based business process optimization on top of a holistic process warehouse. It prescriptively generates action recommendations during process execution to avoid a predicted metric deviation. We discuss data mining techniques and data structures for real-time prediction and recommendation generation and present a proof of concept based on a prototypical implementation in manufacturing.

Prescriptive Analytics for Recommendation-Based Business Process Optimization

The Operational Process Dashboard for Manufacturing

The digital transformation leads to massive amounts of heterogeneous data challenging traditional data warehouse solutions in enterprises. In order to exploit these complex data for competitive advantages, the data lake recently emerged as a concept for more flexible and powerful data analytics. However, existing literature on data lakes is rather vague and incomplete, and the various realization approaches that have been proposed neither cover all aspects of data lakes nor do they provide a comprehensive design and realization strategy. Hence, enterprises face multiple challenges when building data lakes. To address these shortcomings, we investigate existing data lake literature and discuss various design and realization aspects for data lakes, such as governance or data models. Based on these insights, we identify challenges and research gaps concerning (1) data lake architecture, (2) data lake governance, and (3) a comprehensive strategy to realize data lakes. These challenges still need to be addressed to successfully leverage the data lake in practice.

Christoph Gröger

Papers

Data Mining-driven Manufacturing Process Optimization

Building an Industry 4.0 Analytics Platform

Prescriptive Analytics for Recommendation-Based Business Process Optimization

The Operational Process Dashboard for Manufacturing

Leveraging the Data Lake: Current State and Challenges