In the automotive industry, Ford is a well-known player, being one of the first to bring the assembly line to vehicle production. As technology constantly changes, incumbent players must adapt to maintain their advantage. Consequently, Ford has taken recent bets on big data investments.
In terms of maintenance, Ford utilizes data as a measure to improve downtime internally. In 2019, they developed a “Miniterms 4.0” system that notifies factory employees when manufacturing functionality reduces (2). Value capture occurs from reduced production delays as a result, leading to improved service levels and satisfaction; in the first year, the project delivered more than $1M in savings (4). As new car model launches occur at a regular cadence, this launch process and cycle continues to renew the usage of the novel predictive maintenance tool. Currently, this tool has been deployed for 15,000 machines in Ford’s Valencia plant (4). However, future challenges may arise from scaling to other sites given the complexity of vehicle parts production and the global nature of the business, with 10+ different country locations of assembly plants alone (5).
Externally, for the customer, Ford has pushed for R&D efforts with other organizations to understand how to utilize vehicle connected sensor data to detect failures before they manifest (3). This process can enable the company to proactively get parts in advance to reduce service time (3). Not only does this improve and optimize maintenance scheduling, but it also can help reduce time loss as a customer waits for their cars in service (3). Studies have shown upside can be $7M due to the downtime saved (3). The challenge is its usage might be more applicable for uncommon parts that may not typically be in stock; if the part is in stock, the time savings is not realized. The accuracy of predicting which parts may fail in the near-term may be questioned, as the study has shown a false positive rate of 2.5% with this model (3). Thus, costs can be incurred to have parts ordered by a repair shop if ultimately it isn’t needed.
Another bet Ford has taken is with its partnerships: in 2021, Ford announced that Google will provide services on AI, data analytics, and the cloud platforms for Ford; at the same time, Ford customers will be able to access Google Assistant voice technology, Google maps, and Google Play services within the car (1). With Google’s analytics and cloud services, Ford also plans to improve efficiency in vehicle development, supply chain, and manufacturing operations (6). Improved supply chain and operations will enable the company to continue to meet demand and production targets. One implemented use case has been an application that uses Google Cloud technology to monitor material inputs and process parameters for a vehicle production step (2). Part images are taken during the production process that are hosted on Google Cloud, which are then analyzed by engineers to ensure quality (2). The implications of this is improved reliability of the car as out-of-spec parts are better caught before moving further downstream.
Further value capture from the Google partnership comes from the personalization ability in-car (2) which can better meet customer needs and generate positive brand equity. Driver data collection can also enable the company to create improved safety systems (e.g. blind spot, emergency braking, lane assist) (2). Additional value capture can occur from data monetization – through user driving data, Ford can generate another revenue stream by selling data to adjacent companies like those in the insurance space (6). Challenges may stem from data privacy concerns if driving behavior data is sold to other companies, but given the rise of insurance programs that currently exist today based on usage, this seems to be less of a concern.
As an incumbent, Ford can be perceived as being a slow mover due to their scale and matrix nature. One concern is how their competition has responded, and if Ford can keep up. A core question in mind is: are big data investments now just table stakes in the automotive industry? In the examples shown above, big data has served as a means to meet service levels and reduce operational costs. Especially post-pandemic, robust operations has an even greater importance as supply chains were stress-tested. Ford’s shift towards investing in data and analytics may be a move to keep up with its peers, but challenges the in the near-term can continue with the rise of disruptive technologies by smaller, more nimble peers (e.g. autonomous technology).
- Attribution for header photo: https://www.vecteezy.com/free-photos Free Stock photos by Vecteezy