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Telematics driven parts supply chain

Authors :

Srihari Sreedharan, Associate Consultant, MFGDCG, Infosys,
Anand Sethuraman, Senior Consultant, MFGDCG, Infosys,
Barath Ashokkumar , Senior Consultant, MFGDCG, Infosys,


A common occurrence we encounter in dealerships is the customer being informed by the dealer/service agent of a delay in procuring the spare part from the supplier or the OEM (Original Equipment Manufacturer).

In spite of being an area of high significance, OEMs have traditionally found it hard to forecast the demand of spare parts accurately. Factors like complexity, erratic demand, large SKUs have continually thwarted the accuracy of forecast. On most occasions, lack of proactive mechanisms to anticipate part failures lead to long waiting time for a customer.

With the influx of Telematics into the world of Automotive, accurate data on parts usage will be made available for the OEMs, suppliers and dealers thereby improving the forecasting mechanisms.

Spare parts market categories

The current spare parts market can be split into two major categories:

1. OE Spares - These are spare parts which are sold by the OEMs through their exclusive dealerships and are typically sold at a premium. These parts are procured from suppliers and certified by the OEM as Genuine Spares.

2. Independent After Market (IAM) - The parts sold through this channel are available at all spare parts stores. These are sold independently by the suppliers but not certified by the OEM as genuine spares. Hence they are cheaper than the OE spares.

Shortcomings of current forecasting techniques

Given the two different types of spare parts market, the next logical question would be, how demand is presently forecast. With the demand being need-driven, companies currently use any one of the traditional forecasting methods or a combination of a few to forecast their demand. 'Poor data quality costs around $600 B US dollars to US companies each year' highlights a statistic.

Albeit having some well-defined methods, forecasting is still a tough equation to crack owing to the number of assumptions made during the process. With each part having its own demand pattern, it becomes increasingly difficult to forecast, thereby impeding its accuracy. Owing to this, dealers maintain large SKUs (Stock Keeping Units) fearing the opportunity cost due to lost sales. This overstocking not only leads to over usage of real-estate space apart from hampering working capital of the dealer but also result in piling up of obsolete stock. On the flip side, maintaining very few parts that have a constant demand would often lead to stock-out situations and customer dissatisfaction.

A mere increase in serviceability levels from 95% to 97% would increase the inventory levels by two times. - Based on inventory level calculation.

Magnitude of 'cost of stock' is very high as it leads to loss of sale and customer good will. Striking a balance to maintain an optimum level of inventory and desired service level, is an area where every automotive player is looking for solutions.


Figure 1: Advantages of connected cars over unconnected cars [4]

Telematics as an enabler

With the technological advancements making automobiles more connected than ever, vehicles fitted with electronic parts and sensors are able generate huge amount of data. Breakthrough inventions in areas like Cloud Storage, Connectivity speed, Big Data Analytics are providing the means to store and process the data in a quicker and efficient manner. Similarly, sensors and other devices required to capture data have also become more affordable making the process simpler and easier

The type of data that vehicles on road can now transmit, range from overall performance indicators to the health condition of parts in use. Availability of this 'in-vehicle' data can dynamically alter the way the parts forecasting is done. It will not only improve the accuracy of forecast, but also help in ensuring 3R's (Right parts at the Right place at the Right time), the most sought after attribute by the companies. One can never guarantee the 3R's with the traditional forecasting methods as they work on the premise, "Future can be predicted by looking into the past", which most often has an element of error associated with it.

123asd.png                                     Figure 2: Benefits of vehicle connectivity [2]

The processed data can help companies gain insights on multiple parameters such as driving pattern, condition of the parts, and performance of the vehicle. This can play a great role in enhancing the customer experience. Monitoring the health of the parts in the vehicle can help a customer proactively schedule an appointment for replacement of the part and prevent unprecedented breakdowns.

For instance, consider a typical case of a sedan being continually driven on a hilly terrain resulting in brake pads wearing out sooner than the prescribed mileage/time frame. Access to this data can help OEMs gain insights and proactively store more brake pads in dealer locations on hilly regions. The data will also enable them to factor in the local geographical conditions when fixing the durability of the part.

This real-time data can help stakeholders get an idea of the kind of parts that are required to be stocked at the dealer locations. Accurate usage information could aid OEMs to do a targeted distribution aiding in optimization of inventory levels at dealer locations. Metrics like Inventory

Turnover Ratio (Times the inventory has been turned over in a particular period) will also improve from an OEM perspective due to the accuracy of the forecast.

The availability of data will also improve the communication and collaboration between the Tier I suppliers and OEMs due to the increase in transparency across the supply chain right from the dealer to the supplier, significantly reducing the buffer at each end. Suppliers functioning upstream will be able to efficiently plan their production schedules since the actual number of parts that are running on road and the number that needs to be replaced are available at any point of time, thereby drastically reducing the bull-whip effect.

Another major functional area, where 'in-vehicle' data can help reap benefits for the OEMs, is logistics.  The availability of real time data will bring down logistics costs drastically. Knowing what kind of part is needed at which place will help in considerably reducing the inter dealer transactions and ordering costs. Only a small buffer would be required to cater to unforeseen incidents like accidents.

Warranty management is an additional space where 'in-vehicle' data can provide a sizable benefit. Mining of real time data for key insights like driver behavior, handling, maintenance, location and other details will help in OEMs handle the warranty campaigns efficiently. It will help OEMs approach concerns like NTF (No Trouble Found) & Invalid claims in an informed and better manner as it saves precious time and money spent on unfruitful negotiations. Researches show that access to 'In-Vehicle data' can save warranty claim costs by 25% and also bring down recall costs by 35%


In the tightly contested space of automobiles, where OEMs are trying hard leaving no stone unturned to occupy the TOMA (Top of Mind Awareness) of the customers, leveraging the 'in-vehicle' data captured through telematics in the parts supply chain space can reduce the lead time across the chain from dealer to supplier significantly. It will also aid in improving the satisfaction levels of the customers, providing the much needed competitive advantage


[1]      Warranty management & Predictive Analytics-Tech Mahindra,2016

[2]      Connected vehicles executive summary, CISCO IBSG Research & Automotive Econometrics, 2011.

[3]      How to choose the right forecasting technique, John C Chambers, Satinder K Mullick Donald D Smith, HBR

[4]      Connected vehicle big data opportunities, SAS White paper.

[5]      Gartner, TDWI & Terra Data Reports

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