# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Pig Fattening
#### 4.3.2 State and Potential of Industry 4.0 Benefits  
**Digital individualization**: In pig farming, individualization tends to refer to a group of animals. Examples of digital individualization include the animal-specific nutrient supply of sows during on-demand feeding and the optical recording of the weight of individual animals during fattening [62, 63].  
In the research area, research is increasingly being carried out into digital individualization. So far, this has rarely been applied in practice, mostly for cost reasons.  
**Flexibilization**: In its current orientation, pig farming is a rather inflexible system overall. It is a fixed-line production dependent on biological cycles.  
The entire production is currently designed for large one-sided quantities. Individualization based on the individual animal does not currently take place. This production method is also a problem in the current social discussion [64]. The corresponding approaches from Industry 4.0 could help here [65].  
**Information transparency**: In terms of the basic structure, pig production is actually predestined for comprehensive information transparency. In the individual sub-systems such as climate control and feeding, data are also recorded and used accordingly. However, so far there are no general and open data interfaces or fair rules to make the data sufficiently transparent and to share them with other systems. The first signs of certain information transparency can be seen in the salmonella monitoring between the slaughterhouse and the fattener [66]. The degree of vertical integration in this sector is varying. There are meat production chains like in poultry, organized by external services.  
**Demand Orientation (\"X-as-a-Service\")**: The structure of demand-oriented provision is usually only available in the form of wage work such as cleaning stables or other things in pig farming.  
**Sustainability**: In pig farming, concentration on certain areas with advantageous production factors can be observed. The concentration of animal stocks can have a negative impact on sustainability if the economic and ecological assessment of the individual production factors is not balanced [67].  
**Consistent process orientation**: Pig farming is very process-oriented due to its structure. The individual production processes are strictly geared towards the production of meat.  
Despite this clear process orientation in production, this continuity cannot be found in the area of data management. There is often a lack of data transfer between the individual partners in the production chain, such as the transfer of health data from piglet rearing to the fattening area. As a result, measuring points and links are not optimally recognized, which makes the use of decision models and their implementation more difficult. Industry 4.0 concepts would offer basic approaches to improve process data orientation.  
**Automated knowledge and learning**: In the field of automated knowledge and learning, pig production offers great potential due to the relatively quickly repeated production steps in which data series are continuously generated. This knowledge is still often used in the individual production steps, but it is often missing beyond the individual production steps [68].  
**Collaboration skills and productivity optimization**: In pig farming, the networking of different production stages would result in many approaches for Industry 4.0. For example, the growth behavior of the single piglet could provide important information for stall management in the fattening barn, or the lung and liver findings in the slaughterhouse could provide parameters for health and climate management in piglet rearing and fattening. However, there is hardly any implementation at the moment. There are already software tools available for improved data management along the value chain, such as, e.g., from the Dutch company ChainPoint B.V. Such tools may facilitate and increase digital data transfer for quality assurance.