Manufacturing – Opportunities for Innovation
CPU assembly modules, which complete the final steps of manufacturing. Using analytics
software, sensors and gateways, Intel reduced the number of machine failures and boosted
assembly line uptime and productivity. In addition, leveraging image analytics to detect defects
on the assembly line, Intel reduced time and inspection effort by 90 percent.
Examples of current and future scenarios of the fundamental impact of disruption brought about
by IIoT include:
Traditional product manufacturers are evaluating outcome-based models. Siemens AG
has designed a train-monitoring solution enabling Renfe (the Spanish national railway
company) to deliver outcome-based service. Such changes will impact the service and
capital expenditure parameters required to determine productivity.18
Traditional roles played by manufacturers are expanding. For example, John Deere has
shifted from tractor manufacturer to farming partner, leveraging IIoT. John Deere is an
incumbent in the tractor market, but it was not a player in the crop assurance/farm state
sensing market.
The intelligence gathered from machine usage and past maintenance data could be used
to prepare a JIT (Just-in-time) supply chain. A JIT supply chain could bring the spare
requirement to near zero. The manufacturer could participate in JIT by supplying the
product to the destination as and when it is required. For example, Zara has implemented
radio-frequency identification (RFID) tracking of inventory and expects to complete the
shift to wireless inventory in 201619.
Aggregation and analysis of data across a product’s life cycle can increase the uptime of
production machinery, reduce time to market and gain further insights from buyer
behavior20.
Capital expenses could be reduced by dynamically sharing data with the finance
organization enabling them to provide floating rate loans or incentives for capital usage,
leading to usage-based insurance of the assets.
Real-time pricing simulations leveraging actual factory data can drive differential product
pricing based on predictions of new product quality.
New designs, eliminating human exposure to dangers, are possible to address the
challenges of production in unsafe environments, such as areas containing hazardous
18
Data
analytics
for
smart
railways,
Accessed
on
12
June
2016,
http://www.thehindubusinessline.com/opinion/data-analytics-for-smart-railways/article8442289.ece (April 2016)
19
Brian Hartmann, William P. King, and Subu Narayanan, Digital manufacturing: The revolution will be virtualized
(August 2015), Accessed on 12 June 2016, http://www.mckinsey.com/business-fu