15
Automation, on the other hand, is positioned not directly in the center of the network, what
leads to the fact, that automation has influenced many other technologies but is not a basic
technology for the near future.
The Term “big data” has the second highest PF and TF with over 60%. However, the
TO of “big data” correlates quite low to other terms. This is due to the fact, that big data
is named in a lot of papers, but not with a special focus. Nevertheless, “big data” is seen as
a major trend in the mining industry by the consulting experts. Drones plays a significant
role in 40% of all papers with a relative high TF. This results from some special issue papers
with focus on UAVs or Drones. Terms that are directly linked to “ERP”, “data analytics” or
“drone” have a more satellite-based position, which suggests an independent development of
this technology.
4.2 How can the degree of implementation of digital trends be determined
from reliable sources?
The results from the developed algorithm show that in a short time frame a high variety
of papers can be analyzed. Compared to a normal manual literature review with conventional search engines, the considerable papers could be reduced by 44%. Besides this, the
201 manual request for every single trend in the database is not necessary which saves a lot
of time during a literature review. Nevertheless, this algorithm does not clearly identify mines
in connection with used digital technology trends. The guessed mines from the proper noun
list, generated by the algorithm are not 100% reliable, due to that the name of the mines often
come from landscapes, people’s names or regions. Additions like mine or operation helps
to identify a mine, but are not used in every case. The algorithm can be improved by using
neuronal networks as Poria et al. (2016) show for opinion mining. An overview of the implementation level of digital trends in active mining operations is not useful to create a ranking or to compare different operations, due to total different infrastructure, used equipment
and available infrastructure. This overview can rather be seen as a self-evaluation to identify
options and possible improvement for the own operation.
5 CONCLUSION
The developed text-mining method is a timesaving and therefore easily repeatable methodology for performing trend analyses independent of the data source. In this paper, recent digital
trends were extracted from consulting papers of the TOP 20 consulting agencies. The results
draw a clear picture of the leading technologies, “automation”, “internet of things” or “real
time data”, which are and will be significant for the mining industry in the next years. The
identified technologies play an important role for the manufacturing and logistics industry,
which underlines the relevance and reliability of the data source. An overview about the
implementation level in the mining sector helps to asses the value of a certain technology
for the industry. Data is rarely available and difficult to search in existing databases. For
this purpose, a text-mining algorithm was developed which searches document sets for given
keywords and associates them with the mining operation names mentioned. Future work will
focus on improvements of the proper noun recognition and drawing a detailed picture of the
implementation level of trends in the mining industry.
REFERENCES
Bassi, L. 2017. Industry 4.0. Hope, hype or revolution? In: 2017 IEEE 3rd International Forum on
Research and Technologies for Society and Industry – Innovation to Shape the Future for Society
and Industry (RTSI) 2017. Piscataway, NJ: IEEE, 1–6.
Boudreau-Trudel, B., Nadeau, S., and Zaras, K. 2015. Innovative Mining Equipment. Key Factors
for Successful Implementation. American Journal of Industrial and Business Management, 05 (04),
161–171.
Automation, on the other hand, is positioned not directly in the center of the network, what
leads to the fact, that automation has influenced many other technologies but is not a basic
technology for the near future.
The Term “big data” has the second highest PF and TF with over 60%. However, the
TO of “big data” correlates quite low to other terms. This is due to the fact, that big data
is named in a lot of papers, but not with a special focus. Nevertheless, “big data” is seen as
a major trend in the mining industry by the consulting experts. Drones plays a significant
role in 40% of all papers with a relative high TF. This results from some special issue papers
with focus on UAVs or Drones. Terms that are directly linked to “ERP”, “data analytics” or
“drone” have a more satellite-based position, which suggests an independent development of
this technology.
4.2 How can the degree of implementation of digital trends be determined
from reliable sources?
The results from the developed algorithm show that in a short time frame a high variety
of papers can be analyzed. Compared to a normal manual literature review with conventional search engines, the considerable papers could be reduced by 44%. Besides this, the
201 manual request for every single trend in the database is not necessary which saves a lot
of time during a literature review. Nevertheless, this algorithm does not clearly identify mines
in connection with used digital technology trends. The guessed mines from the proper noun
list, generated by the algorithm are not 100% reliable, due to that the name of the mines often
come from landscapes, people’s names or regions. Additions like mine or operation helps
to identify a mine, but are not used in every case. The algorithm can be improved by using
neuronal networks as Poria et al. (2016) show for opinion mining. An overview of the implementation level of digital trends in active mining operations is not useful to create a ranking or to compare different operations, due to total different infrastructure, used equipment
and available infrastructure. This overview can rather be seen as a self-evaluation to identify
options and possible improvement for the own operation.
5 CONCLUSION
The developed text-mining method is a timesaving and therefore easily repeatable methodology for performing trend analyses independent of the data source. In this paper, recent digital
trends were extracted from consulting papers of the TOP 20 consulting agencies. The results
draw a clear picture of the leading technologies, “automation”, “internet of things” or “real
time data”, which are and will be significant for the mining industry in the next years. The
identified technologies play an important role for the manufacturing and logistics industry,
which underlines the relevance and reliability of the data source. An overview about the
implementation level in the mining sector helps to asses the value of a certain technology
for the industry. Data is rarely available and difficult to search in existing databases. For
this purpose, a text-mining algorithm was developed which searches document sets for given
keywords and associates them with the mining operation names mentioned. Future work will
focus on improvements of the proper noun recognition and drawing a detailed picture of the
implementation level of trends in the mining industry.
REFERENCES
Bassi, L. 2017. Industry 4.0. Hope, hype or revolution? In: 2017 IEEE 3rd International Forum on
Research and Technologies for Society and Industry – Innovation to Shape the Future for Society
and Industry (RTSI) 2017. Piscataway, NJ: IEEE, 1–6.
Boudreau-Trudel, B., Nadeau, S., and Zaras, K. 2015. Innovative Mining Equipment. Key Factors
for Successful Implementation. American Journal of Industrial and Business Management, 05 (04),
161–171.