568: given their complexity and intransparency of input-output relation. This reduces the comprehensibility of the system behavior and thus also the acceptance by plant operators. Due to the lack of transparency and the stochasticity of these models, no deterministic proof of functional correctness can be achieved complicating the certification of production equipment. Given their inherent unrestricted prediction behavior, ML models are vulnerable against erroneous or manipulated data further risking the reliability of the production system because of lacking robustness and safety. In addition to high development and deployment costs, the data drifts cause high maintenance costs, which is disadvantageous compared to purely
47:
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649:(AI) with business management principles. These programs aim to prepare students for the increasing role of AI in business, equipping them with the skills necessary to apply AI technologies to areas such as predictive analytics, supply chain optimization, and decision-making. AI for business education programs are offered at both undergraduate and graduate levels by several universities globally.
509:
actions and decisions are translated back into the physical world via actuators or by human operators. This poses major challenges for the application of ML in production engineering systems. These challenges are attributable to the encounter of process, data and model characteristics: The production domain's high reliability requirements, high risk and loss potential, the multitude of
696:(ASU) offers a graduate-level program focused on AI applications in business environments. This degree explores advanced topics such as AI-driven decision-making, big data analysis, and the ethical implications of AI in business. The program is designed for professionals seeking to leverage AI technologies to transform business practices and improve efficiency.
630:) and data scraped from the open internet are frequently used for this purpose. Such datasets rarely exist in the industrial context because of high confidentiality requirements and high specificity of the data. Industrial applications of artificial intelligence are therefore often faced with the problem of data availability.
686:, the BCSAI combines foundational studies in computer science with a specialization in artificial intelligence. The program also provides a strong grounding in business principles, preparing graduates to create AI solutions for business problems and drive technological innovation in the business world.
657:
Bachelor in
Artificial Intelligence for Business (BAIB), Bachelor in Computer Science and Artificial Intelligence (BCSAI), Master of Science in Artificial Intelligence in Business (MS-AIB) – These are new programs that are still in their first cohorts and have yet to prove themselves in the industry.
633:
For these reasons, existing open datasets applicable to industrial applications, often originate from public institutions like governmental agencies or universities and data analysis competitions hosted by companies. In addition to this, data sharing platforms exist. However, most of these platforms
580:
The development of ML applications – starting with the identification and selection of the use case and ending with the deployment and maintenance of the application – follows dedicated phases that can be organized in standard process models. The process models assist in structuring the development
524:
In particular, production data comprises a variety of different modalities, semantics and quality. Furthermore, production systems are dynamic, uncertain and complex, and engineering and manufacturing problems are data-rich but information-sparse. Besides that, due the variety of use cases and data
704:
These programs typically include a combination of AI and business courses. Core subjects often cover topics such as machine learning, data science, business strategy, and financial management. The programs aim to give students a broad understanding of AI applications within a business environment,
508:
In contrast to entirely virtual systems, in which ML applications are already widespread today, real-world production processes are characterized by the interaction between the virtual and the physical world. Data is recorded using sensors and processed on computational entities and, if desired,
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The foundation of most artificial intelligence and machine learning applications in industrial settings are comprehensive datasets from the respective fields. Those datasets act as the basis for training the employed models. In other domains, like computer vision, speech recognition or language
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have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost;
421:
to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for
547:
The data collected in production processes mainly stem from frequently sampling sensors to estimate the state of a product, a process, or the environment in the real world. Sensor readings are susceptible to noise and represent only an estimate of the reality under uncertainty. Production data
604:
is a domain-specific data science methodology that is inspired by the CRISP-DM model and was specifically designed to be applied in fields of engineering and production technology. To address the core challenges of ML in engineering – process, data, and model characteristics – the methodology
533:
The domain of production engineering can be considered as a rather conservative industry when it comes to the adoption of advanced technology and their integration into existing processes. This is due to high demands on reliability of the production systems resulting from the potentially high
472:
Each application area can be further divided into specific application scenarios that describe concrete AI/ML scenarios in production. While some application areas have a direct connection to production processes, others cover production adjacent fields like logistics or the factory building.
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or insufficient product qualities. In addition, the specifics of machining equipment and products prevent area-wide adoptions across a variety of processes. Besides the technical reasons, the reluctant adoption of ML is fueled by a lack of IT and data science expertise across the domain.
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typically comprises multiple distributed data sources resulting in various data modalities (e.g., images from visual quality control systems, time-series sensor readings, or cross-sectional job and product information). The inconsistencies in data acquisition lead to low
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In addition to technical courses, many programs include practical training, such as internships, real-world AI projects, and industry case studies. This helps students gain practical experience in applying AI tools and techniques to solve business challenges.
675:
is a highly regarded institution for it's business inovation, sustainability focus and future-proof outlook. During the BBA+BAIB, students are trained to apply AI in business environments to improve efficiency, innovation, and decision-making.
1094:"Machine Learning and Artificial Intelligence in Production: Application Areas and Publicly Available Data Sets: Maschinelles Lernen und Kü nstliche Intelligenz in der Produktion: Anwendungsgebiete und öffentlich zugängliche Datensätze"
605:
especially focuses on use-case assessment, achieving a common data and process understanding data integration, data preprocessing of real-world production data and the deployment and certification of real-world ML applications.
597:) describe a generally valid methodology and are thus independent of individual domains. Domain-specific processes on the other hand consider specific peculiarities and challenges of special application areas.
552:, low data quality and great effort in data integration, cleaning and management. In addition, as a result from mechanical and chemical wear of production equipment, process data is subject to various forms of
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The undergraduate degrees are often offered in conjuction with a BBA as a 5-year double degree program, the undergraduate degrees are going through the acreditation processes in their respective countries.
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process and defining requirements that must be met in each phase to enter the next phase. The standard processes can be classified into generic and domain-specific ones. Generic standard processes (e.g.,
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Many universities offering these degrees hold accreditation from recognized educational bodies, ensuring that their programs meet rigorous academic and industry standards. For example,
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characteristics, problem-specific data sets are required, which are difficult to acquire, hindering both practitioners and academic researchers in this domain.
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Panayotov, Vassil; Chen, Guoguo; Povey, Daniel; Khudanpur, Sanjeev (2015). "Librispeech: An ASR corpus based on public domain audio books".
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Monostori, L.; Kádár, B.; Bauernhansl, T.; Kondoh, S.; Kumara, S.; Reinhart, G.; Sauer, O.; Schuh, G.; Sihn, W.; Ueda, K. (2016-01-01).
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focuses on the integration of AI and machine learning with core business disciplines such as management, marketing, and finance. The
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The challenges for ML applications in production engineering result from the encounter of process, data and ML model characteristics
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are both accredited by institutions such as EQUIS and AACSB, which evaluate the quality of business education programs. Similarly,
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data sources and the non-transparency of ML model functionality impede a faster adoption of ML in real-world production processes.
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Possible applications of industrial AI and machine learning in the production domain can be divided into seven application areas:
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customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.
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while also allowing them to specialize in areas such as supply chain management, marketing analytics, and AI-driven innovation.
484:. Collaborative robotic arms are able to learn the motion and path demonstrated by human operators and perform the same task.
65:
1027:"Machine Learning For Intelligent Maintenance And Quality Control: A Review Of Existing Datasets And Corresponding Use Cases"
430:
Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing.
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Programs that combine AI with business studies vary by institution and degree level. Below are some notable examples:
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Taxonomy of application areas and application scenarios for machine learning and artificial intelligence in production
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983:"Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation"
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838:. Lecture Notes in Production Engineering. Cham: Springer International Publishing. pp. 504–513.
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have no industrial focus and offer limited filtering abilities regarding industrial data sources.
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Wuest, Thorsten; Weimer, Daniel; Irgens, Christopher; Thoben, Klaus-Dieter (January 2016).
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Krauß, Jonathan; Dorißen, Jonas; Mende, Hendrik; Frye, Maik; Schmitt, Robert H. (2019).
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2015 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP)
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economic harm of reduced process effectiveness due to e.g., additional unplanned
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Jourdan, Nicolas; Longard, Lukas; Biegel, Tobias; Metternich, Joachim (2021).
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934:"Machine learning in manufacturing: advantages, challenges, and applications"
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1232:"Bachelor in Computer Science & Artificial Intelligence | IE University"
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1096:. In Wulfsberg, Jens Peter; Hintze, Wolfgang; Behrens, Bernd-Arno (eds.).
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holds accreditation for its graduate programs in business and technology.
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refers to the academic programs offered by universities that integrate
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Azavedo, Ana (2008). "KDD, SEMMA and CRISP-DM: a parallel overview".
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Krauß, J.; Hülsmann, T.; Leyendecker, L.; Schmitt, R. H. (2023).
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Bachelor in
Computer Science and Artificial Intelligence (BCSAI)
810:"Cognitive technologies: The real opportunities for business"
45:
665:
The
Bachelor in Artificial Intelligence for Business (BAIB)
27:
Overview of the use of artificial intelligence in industry
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Master in
Artificial Intelligence for Business (MS-AIB)
808:
Schatsky, David; Muraskin, Craig; Gurumurthy, Ragu.
1100:. Berlin, Heidelberg: Springer. pp. 493–501.
789:. The Wall Street Journal - CIO Journal - Deloitte
576:Standard processes for data science in production
643:Artificial intelligence for business education
638:Artificial intelligence for business education
496:are exemplary application scenarios from the
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8:
1098:Production at the leading edge of technology
836:Production at the Leading Edge of Technology
1208:"AI and Business Degrees - Esade Bachelors"
868:"What Does Collaborative Robot Mean ?"
764:"Reducing downtime using AI in Oil and Gas"
622:models, extensive reference datasets (e.g.
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1186:OpenAI (2023). "GPT-4 Technical Report".
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890:"Cyber-physical systems in manufacturing"
783:"Artificial Intelligence Goes Mainstream"
744:Artificial intelligence in heavy industry
476:An example from the application scenario
18:Artificial intelligence in heavy industry
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1262:Applications of artificial intelligence
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612:Machine Learning Pipeline in Production
602:Machine Learning Pipeline in Production
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981:Lu, Stephen C-Y. (1990-01-01).
478:Process Design & Innovation
66:Artificial general intelligence
682:– Offered along with a BBA by
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951:10.1080/21693277.2016.1192517
1106:10.1007/978-3-662-60417-5_49
999:10.1016/0166-3615(90)90088-7
844:10.1007/978-3-031-18318-8_51
564:ML models are considered as
425:Artificial intelligence and
1149:10.1109/icassp.2015.7178964
667:- This program, started by
442:Market & Trend Analysis
101:Natural language processing
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906:10.1016/j.cirp.2016.06.005
154:Hybrid intelligent systems
76:Recursive self-improvement
595:Team Data Science Process
498:Machinery & Equipment
445:Machinery & Equipment
727:Arizona State University
694:Arizona State University
278:Artificial consciousness
647:artificial intelligence
617:Industrial data sources
419:artificial intelligence
149:Evolutionary algorithms
39:Artificial intelligence
1143:. pp. 5206–5210.
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570:deterministic programs
550:signal-to-noise ratios
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490:preventive maintenance
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987:Computers in Industry
673:Esade Buisness School
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700:Curriculum Structure
543:Data Characteristics
492:through data-driven
482:collaborative robots
91:General game playing
628:The People's Speech
243:Machine translation
159:Systems integration
96:Knowledge reasoning
33:Part of a series on
812:. Deloitte Review.
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500:application area.
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451:Production Process
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1158:978-1-4673-6997-8
1115:978-3-662-60417-5
853:978-3-031-18318-8
653:Academic Programs
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894:CIRP Annals
585:, ASUM-DM,
554:data drifts
312:Turing test
288:Friendly AI
59:Major goals
1241:2024-09-11
1236:University
1217:2024-09-11
1193:2303.08774
1172:2023-10-18
750:References
504:Challenges
486:Predictive
434:Categories
317:Regulation
271:Philosophy
226:Healthcare
221:Government
123:Approaches
1124:213777444
1007:0166-3615
960:2169-3277
914:0007-8506
347:AI winter
248:Military
111:AI safety
1256:Category
1079:15309704
968:52037185
733:See also
624:ImageNet
583:CRISP-DM
536:downtime
457:Building
370:Glossary
364:Glossary
342:Progress
337:Timeline
297:Takeover
258:Projects
231:Industry
194:Finance
184:Deepfake
134:Symbolic
106:Robotics
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