Predictive Analytics and the OEM Industry

The first phase of Industrial revolution was in the 1800’sled by introduction of steam engines. Later, in the early 1900’s, came the second phase, characterised by the use of fossil fuel, consumption of electricity and growth of the media; TV, radio, telephone, etc. As manufacturing became more productive with assembly lines, standardized and specialized machines. The third phase came in the late 1900’s, mostly in 1980’s, which was introduced by digitalisation and electronic usage. Today we are moving to the fourth phasecalled as "Industry 4.0", characterised by use of Internet of things (IOT) to connect physical objects to the virtual world.

This has led to companies placing the foundation for collecting whatever field data they wish and aggregating it in a central location, and with real time data processing (predictive analytics) abilities achieving rapid detection of faults and deviations, and in the process gain an upper hand bytaking corrective action immediately, enhanceproductivity by optimising the processes through:

1. Optimising the design of the service process.
2. Monitoring the units remotely and providing mobility.
3. Analysing the system failures accurately and in lesser time.
4. Dispatching orordering spare parts beforehand through condition monitoring, scheduling of preventive maintenancewith less disruption and lowering costs by effective inventory storage.

However, there is more to the way predictive analytics is being used currently in the OEM industry that meets the eye. Kotler, Philip, HermawanKartajaya, and IwanSetiawan in Marketing 3.0 talk about how collaboration can help innovation. However, if you have to include collaboration in your business model you shouldn’t ignore competition, as one will generate ideas the other will generate the zeal to succeed.

For example car manufactures work alongside with OEM supplier to create some amazing products, the BMW i8 is one such example. Cars work smoothly if, ever part in it function in a synchronous way as defined by the engineer who designed itand are susceptible to wear and tear and today are already connected to the internet. Car manufactures can gather the data from the car through the internet connection and OEM suppliers can use this data related to performance of their individual parts on the car, by embedding sensor that transmit data related to performance and parts condition. (As of now most of the performance data is only when the car rolls in for a maintenance). Now if the data is made available to every OEM manufactures, not only will it help innovate, but will also drive the eagerness among the OEM manufactures to exceed each other.

And the way the race to create driverless car is on, this is close to happening.Suppliers and the owner of the car could be informed of the probable failure of the machine and maintenance can be scheduled. This will enable the OEM companies to optimise production and logistics, and save inventory storage cost for the service provider or customer and take the car manufacturer go that extra mile, by helping the customer to create that wow factor by providing prompt service to the consumer and improve brand equity.

Thus predictive analysis that is helping companies optimise their production capabilities can similarly use the same technique to provide wow services to the consumer and enhance company’s brand image.

SaiKiran Kadam is an Analyst working with Mordor Intelligence, a market research consulting firm which specializes in Market Research Consulting, Market Research Reports, Market Sizing Reports, Industry Research Reports.