Converting Manufacturing Companies into Data-Driven Enterprises: an Evaluation of the Transformation Model

2021 ◽  
Author(s):  
Vitaliy Mezhuyev ◽  
Martin Tschandl ◽  
Matthias Mayr
Keyword(s):  
Transformation Model ◽  
Data Driven ◽  
Manufacturing Companies

Datengetriebene Steigerung der Verfügbarkeit/Data-supported root cause analysis to increase the availability of machine tools. Data-driven availability increase

2020 ◽  
Vol 110 (07-08) ◽  
pp. 532-535
Author(s):  
Eckhart Uhlmann ◽  
Roman Dumitrescu ◽  
Julian Polte ◽  
Maurice Meyer ◽  
Deniz Simsek
Keyword(s):  
Data Analysis ◽  
Machine Tools ◽  
Critical Factor ◽  
Root Cause Analysis ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Cause Analysis ◽  
Root Cause ◽  
Comprehensive Data ◽  
Causes Of Failure

Die Zuverlässigkeit von Werkzeugmaschinen ist ein kritischer Faktor für den Erfolg produzierender Unternehmen. Durch die Analyse von Daten in der Produktplanung können Maschinenhersteller Ausfallursachen eliminieren und Maschinen systematisch verbessern. Jedoch stellt eine umfassende Datenanalyse viele Unternehmen vor große Herausforderungen. Die in diesem Beitrag vorgestellte Methodik adressiert diese Problematik und unterstützt Unternehmen bei der zielgerichteten Datenanalyse.   The reliability of machine tools is a critical factor for the success of manufacturing companies. By analyzing data in product planning, machine manufacturers can eliminate causes of failure and systematically improve machines. However, comprehensive data analysis poses great challenges for many companies. The methodology presented in this paper addresses this problem and supports companies in the goal-driven data analysis.

Practices of preventive maintenance planning in discrete manufacturing industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Antti Salonen ◽  
Maheshwaran Gopalakrishnan
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing Industry ◽  
State Of The Art ◽  
The State ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Automotive Manufacturing ◽  
Content Type ◽  
Discrete Manufacturing ◽  
State Of Practice

PurposeThe purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.Design/methodology/approachAn embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.FindingsThe studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.Practical implicationsThe results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.Originality/ValueFirst and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.

scholarly journals The Possibilities of Improving Maintenance Through CMMS Data Analysis

2020 ◽  
Author(s):  
Antti Salonen ◽  
Marcus Bengtsson ◽  
Victoria Fridholm
Keyword(s):  
Manufacturing Industry ◽  
High Standard ◽  
Production Equipment ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Time Data ◽  
Human Errors ◽  
Maintenance Program ◽  
Data Driven Decisions ◽  
Heavy Construction

Maintenance of production equipment is one of the most critical support actions in manufacturing companies for staying competitive. More recently, with the introduction of Industry 4.0, academia, as well as industry, put a lot of effort into condition monitoring in order to implement predictive maintenance. Most stakeholders agree that maintenance need to be more data-driven. However, in order to draw true advantage of data-driven decisions, it is necessary for manufacturing companies to have implemented basic maintenance to a high standard in order to reduce for example: recurring failures, human errors, unsafe machines, etc. The real-time data can then be used to improve efficiency of maintenance tasks and schedule that adds value to the processes. In manufacturing industry, maintenance actions are commonly administered in a Computerized Maintenance Management System, CMMS, still, rather few companies analyze their maintenance records. Behind these data there is often a treasure of improvement opportunities that could be used to improve basic maintenance. The purpose of this paper is to explore how historical data from a CMMS can be used in order to improve maintenance effectiveness and efficiency of activities. In order to exemplify the possibilities of analyzing CMMS records, a case study has been performed in a plant, manufacturing driveline components for heavy construction vehicles. The study shows that one major obstacle for utilizing the CMMS data is poor description of faults and failures when it comes to work order requests, mostly performed by operators and assemblers, as well as work order reporting, mostly performed by repairmen and maintenance technicians. However, by thorough analysis of well described corrective maintenance, it is possible for industry to understand the nature of the occurring breakdowns and thus, refine the preventive maintenance program in order to further increase the dependability of the production system.

Datenbasierte Produktionsoptimierung*/Data-driven production optimization: Areas of development for manufacturing companies

2018 ◽  
Vol 108 (03) ◽  
pp. 108-112
Author(s):  
D. Bauer ◽  
T. Maurer ◽  
T. Bauernhansl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Applied Research ◽  
Business Models ◽  
Big Data Analytics ◽  
Production Optimization ◽  
Data Driven ◽  
Manufacturing Companies ◽  
New Business ◽  
New Business Models

Unternehmen sehen in Big-Data-Analysen ein großes Potenzial zur Optimierung der klassischen Produktionsziele sowie zur Entwicklung neuer Geschäftsmodelle. Eine Studie des Fraunhofer IPA analysiert, welche Herausforderungen bei der Umsetzung dieser Potenziale auftreten. Darauf aufbauend werden Entwicklungsfelder für die angewandte Forschung und produzierende Unternehmen erarbeitet.   Companies expect huge benefits from big data analytics both to improve traditional production targets and to develop new business models. A study conducted by Fraunhofer IPA analyzes the upcoming challenges in exploiting these opportunities. It provides the basis for identifying areas of development for applied research and for manufacturing companies.

scholarly journals DATA-DRIVEN DESIGN AUTOMATION FOR PRODUCT-SERVICE SYSTEMS DESIGN: FRAMEWORK AND LESSONS LEARNED FROM EMPIRICAL STUDIES

2021 ◽  
Vol 1 ◽  
pp. 841-850
Author(s):  
Raj Jiten Machchhar ◽  
Alessandro Bertoni
Keyword(s):  
Design Automation ◽  
Empirical Studies ◽  
Systems Design ◽  
Lessons Learned ◽  
Service Systems ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Construction Machinery ◽  
Product Service ◽  
Product Service Systems

AbstractThe digitalization era has brought about unprecedented challenges for the manufacturing industries, pushing them to deliver solutions that encompass both product and service-related dimensions, known as Product-service Systems. This paper presents a number of lessons learned in the process of integrating the analysis of operational data as decision support in engineering design based on the empirical studies from two Swedish manufacturing companies operating in the construction machinery sector. The paper highlights the need to consider a five-dimensional perspective when collecting and analyzing data, encompassing data from the product, the service, the environment, the infrastructure, and the humans involved. Finally, a conceptual framework for data-driven design automation of Product-service Systems is proposed by leveraging the use of these data, introducing the concept of a Product-Service System Configurator as an enabler of design automation. The implementation of the proposed framework on multiple case studies in different industrial contexts shall be considered as the next step of the research.

scholarly journals Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 224
Author(s):  
Parkash Tambare ◽  
Chandrashekhar Meshram ◽  
Cheng-Chi Lee ◽  
Rakesh Jagdish Ramteke ◽  
Agbotiname Lucky Imoize
Keyword(s):  
Quality Management ◽  
Internet Of Things ◽  
Performance Measurement ◽  
Industry 4.0 ◽  
Manufacturing Industries ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Companies ◽  
Industrial Standards

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

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