Increasing the Value of Data in Production Systems
Licentiatavhandling, 2019

A digital transformation is taking place, where information is available about almost anything, changing how work is performed and anticipated. More digitized information enabled by the digital technologies is supporting businesses to measure more about their processes and thereby also to know more. The same transformation is taking place in the manufacturing domain, which is referred to as the fourth industrial revolution. There are numerous national initiatives to approach the fourth industrial revolution and the aim is to make the manufacturing industry more digitalized and increase competitiveness. Digitalization is making more information about processes available, but it is first when data is informing decisions in an organization it will add value. 

Along with all the benefits and potential values of the digital transformation, much of the attention has been on the technologies and systems that can enable the digitalization. Less focus has been spent on how the technologies and systems should be put into practice in the organizations to fulfill the needs of the manufacturing domain. New knowledge about digital technologies in combination with already existing expertise about manufacturing processes is needed. The aim of the thesis is to identify the value of data for decision-making. The approach outlined in this thesis will identify the values gained from the raw data itself and from the further processed data to provide decision support. The distinction between these two forms of data, raw data and further processed data, is important because it is believed that these can provide different values and that they involve different challenges for the organization.

5G telecommunication and 3D laser scanning serve as digital technologies in this thesis to enable more data in digital form on a production system. 5G was used for connecting a machine enabling the collection of data about critical machine components. 3D laser scanning was used to collect the spatial data in a factory environment. The results show that more data available about the connected machine provide values to the organization to know the status of the machine, be able to compare the designed system against the behavior in the real-world setting, a better understanding of the process and to learn from data. Spatial data provide values by being able to represent the production system as-is in a very accurate and photorealistic way. The values identified from having more data available for the decision support were in the daily operations to know the condition of the machine, for the manufacturing organization to plan proactive actions, and for the production engineer to understand the behavior of the designed system in the real-world context. The spatial data could both support when making changes to the physical setup and when planning the design of the factory environment in an offline mode.

The initial studies presented in this thesis supported to build the understanding of the current practice of data as decision support in the production organization. The understanding that data should support decision-making was high, but the data availability in the current state was scarce or of poor quality. This strengthens the aim of the thesis, to provide results that can show the value of data for decision-making. “To measure more is to know more” (McAffee and Brynjolfsson, 2012) is a statement serving as a cornerstone throughout this thesis and has also been justified by the results presented to answer research question 1 and 2. Data enabled by digital technologies can support multiple roles in the manufacturing organization throughout the different phases of the production system, for example in daily operations and maintenance.

Industry 4.0

digitalization

data

Digital technologies

decision support

manufacturing

Smart Manufacturing

Virtual Development Lab, Chalmers Tvärgata 4C
Opponent: Dominic Gorecky, Head of Swiss Smart Manufacturing and Industry 4.0/Iot Research, Switzerland Innovation Park Biel/Bienne, Switzerland

Författare

Maja Bärring

Chalmers, Industri- och materialvetenskap, Produktionssystem

Challenges of Data Acquisition for Simulation Models of Production Systems in Need of Standards

Proceedings - Winter Simulation Conference,; (2018)p. 691-702

Paper i proceeding

5G Enabled Manufacturing Evaluation for Data-Driven Decision-Making

Procedia CIRP,; Vol. 27(2018)p. 266-271

Paper i proceeding

A VSM Approach to Support Data Collection for a Simulation Model

2017 Winter Simulation Conference (WSC),; (2017)p. 3928-3939

Paper i proceeding

Bärring, M., Berglund, J., Johansson, B., Stahre, J., Iupikov, O., Alayon Glazunov, A., Ivashina, M., Engström, U., Harrysson, F. & Friis, M.,Digital Twin for Factory Radio Space Design of a 5G Network

5GEM - 5G-Enabled Manufacturing

VINNOVA, 2015-11-16 -- 2017-11-15.

Morgondagens rymdproduktion

Nationellt rymdtekniskt forskningsprogram (NRFP), 2016-08-01 -- 2018-12-31.

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Systemvetenskap

Styrkeområden

Produktion

Utgivare

Chalmers tekniska högskola

Virtual Development Lab, Chalmers Tvärgata 4C

Opponent: Dominic Gorecky, Head of Swiss Smart Manufacturing and Industry 4.0/Iot Research, Switzerland Innovation Park Biel/Bienne, Switzerland

Mer information

Senast uppdaterat

2019-01-08