Tareas de mantenimiento

The current manufacturing sector in Europe and other industrialised nations is characterised by a spectrum spanning from mass production to customised, individual production. Automation is typically found in mass production, although individual production is more and more supported by adaptable tools and technologies. Mass production is not necessarily automated, in particular in countries with low labour costs, many steps are still manual labour. However, automation technology is ever more widespread and helping mass production in developing countries to catch up in the area of manufacturing quality, where manufacturing in developed countries is in the lead. Besides quality, manufacturing in developed countries can offer more flexibility and customised products through a better qualified workforce and manufacturing technologies. To develop this aspect further with technology support will be one of the key challenges in the near future.

Continuing automation is changing the classical balance between quality and price in manufacturing. The quality of mass production in low wage countries is rising. This challenges high wage countries to develop their strengths in quality and customisation further.

With the on-going integration of value chains in manufacturing—within large multi-national corporations as well as between the many players in some value chains—requirements for cooperation, such as standards and norms, market places etc. become highly important.

A study by General Electric, with data from the World Bank estimates the size of the market affected by Industry 4.0 as follows: “When traditional industry is combined with the

transportation and health services sectors, about 46 percent of the global economy or $32.3 trillion in global output can benefit from the Industrial Internet. As the global economy grows and industry grows, this number will grow as well. By 2025, we estimate that the share of the

industrial sector (defined here broadly) will grow to approximately 50 percent of the global economy or $82 trillion of future global output in nominal dollars.” (General Electric, 2012)

Industry 4.0

Seen in its historical background, the current developments in Big Data in manufacturing amount to a fourth industrial revolution. The first industrial revolution, starting around 1780, was triggered by the invention of the steam engine, the use of coal as an energy source and the introduction of the first mechanical manufacturing facilities. The second revolution, starting around 1900, was triggered by the introduction of electrical energy, mass production techniques (in particular the conveyor belt), all in large capital goods industries like steel and oil. The third revolution, starting as recently as the 1970s, was triggered by the introduction of electronic systems and computer technologies (the microcontroller), enabling automated manufacturing processes on a global scale. It had started a trend towards highly efficient production on a scope never seen before that has now matured but is still growing. Multi-national corporations are building highly automated factories on all continents and integrate small and medium enterprises within their supplier networks by expanding ever larger industrial infrastructures with the Internets of Everything (IoE), i.e., the Internet of Things, Cloud Computing, and the Internet of Services. They are creating a direct and (in many cases) real-time connection between the virtual and the physical worlds. Thus the term Cyber-Physical Systems (CPS) is used besides Industry 4.0 to describe these developments.

Industry 4.0 has a number of manufacturing and production plant specific aspects, in particular in the area of interfaces to the physical world. On the other hand, the data-related aspects apply similarly to other areas of Big Data. These aspects include the acquisition, storage, curation, analysis, and usage of data as well as corresponding issues like interfaces, visualisation, human assistance systems, integration with business processes and regulatory and legal issues. Industry 4.0 is thus a strictly larger development, including Big Data as a core aspect and extending it into the physical world of products and production.

Within existing manufacturing plants, the core challenges that can be addressed by CPS and in particular Big Data are:

 Vertical integration, i.e. the integration of the complete production process, e.g., production steps, infrastructure, logistics, human resources and human assistance

 Flexible and reconfigurable production

 “lot size 1”: customised product

 ad-hoc networking

 modularisation of production chains

 intelligent modelling and description of production plants

 Efficient and energy-saving production

 Operator qualifications, support systems, and digitising corporate (manufacturing) knowledge

Horizontal integration

Within the entire manufacturing infrastructure, the core challenges that can be addressed (and in turn are raised by) CPS and Big Data are:

 Horizontal integration, i.e. benefit from data analytics applied to all (similar) production processes, e.g., predictive maintenance based on data from all machines of one model

 Adapt and evolve the business processes

 Create new business processes and even business models by including cooperation partners

 Protection of IP

 Standards and norms as the basis for cooperation and integration

 Strategies for developing human resources

Vertical and horizontal integration as described above will—in the manufacturing sector—be centred around the engineering process. The complete life cycle of a product or product family will be driven by integrated engineering where the planning, production, service and recycling steps produce data and access data. This creates new requirements on the data models, the connection between physical and virtual world (Internet of Things, i.e., smart products with IDs and object memories), interfaces between the smart product and its production system.

7.3.2 Market Impact and Competition

In most European countries, the impact of Big Data in the context of Industry 4.0 will be the requirement to keep and extend current market advances in the areas of high-quality, high-tech and customised products. As automation will help other competitors to raise quality from manual manufacturing levels, the focus will be on high-tech and customised products.

High-tech or smart products with individual IDs and object memories (Internet of Things) will be used in integrated environments, e.g., the automobile and its life cycle.

In a changing market, the optimal strategy will be a dual strategy, combining the creation of market leaders with the establishment of leading markets. Market leaders build on top of developed and developing base technologies, i.e., Big Data technologies and other, CPS- related manufacturing technologies. In the manufacturing sector, market leaders are machine and plant engineering and construction companies, manufacturers of automation technology and corresponding integrators and service companies in the ICT sector. The leading markets in Europe and the world are the production facilities, including their network of suppliers and services, i.e., their entire value chain. Both perspectives apply to the European market and should thus be combined in a strategy for adapting the manufacturing sector to the Big Data and Industry 4.0 related changes.

7.3.3 Available Data Sources

Following a classification of data sources by BITKOM (2012), the following categories are relevant for manufacturing application scenarios: cloud computing, sensor technologies, digitization of business models. Relevant data sources for the manufacturing sector in these categories are: Cloud Computing  Logistics data  Traffic information  Weather data Sensor Technologies

 Production machinery sensors

 Temperature, pressure, light sensors

 Processing speed

 Error detection

 Monitoring of resources

 Environmental sensors (e.g. room temperature)

 Logistics data

o Vehicle location data o Load sensors

o Maintenance and wear data

 RFID (Radio Frequency Identification) data o Product and tool identification

o Machine and vehicle identificaiton

Digitization of Business Models

 Product design data

o Component and material data o Supplier data

 inventory

 factory layout and planning

o workload o traffic

Integration of these data to form an actionable context is the primary challenge. Although sensor data is generally semi-structured, data formats vary, making integration difficult. The velocity of continuous data streams from sensors is another challenge. Many applications will address it through technologies such as complex event processing.

7.4. Big Data Application Scenarios