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Volvo uses a decentralized data capturing strategy to get data from its vehicle and analyze it in the cloud. This is an efficient method because it minimizes data collection and analysis errors. Volvo has a put hundreds of sensors and CPUs throughout the car. The sensors are well connected, and they provide data-rich IT environments, for vehicle intelligence.
For an easy driving experience, the car manipulates the data captured on the brake, navigation, central locking and other systems of the car. Each vehicle system is also connected to the manufacturer wirelessly and any error or malfunction can be attended to, remotely or through a physical check. The manufacturer also upgrades the software system, in the car during regular service checks. A centralized analysis hub makes the process, of collecting and interpreting the data, efficient (Converting data into business value at Volvo, 2011).
Data Transformation to Knowledge
The data analysis hub is capable of automatically handling data and pointing out defects in a car system. However, Volvo goes to a step further and integrates various systems within its operations with the multi-terabyte vehicle-analysis system. The company transforms the data into knowledge by using the resource to create business insights. The data collected from the system is disseminated across different units of manufacturing within the company.
Each vehicle-part manufacturing unit gets feedback on the performance of the specific part on the vehicle. Therefore, parts or components can be separately analyzed for efficiency and their effectiveness of guaranteeing vehicular performance and efficiency.
For example, the company can design the air-bag component to react to accident events within a precise period. The sensors and processors fitted in the vehicle would then give the company real-time feedback of how the component operates in a real driving scenario (Converting data into business value at Volvo, 2011).
Impact of Real Time Information Systems
When the system gathers data from the vehicle, it then gives out analytic reports that are used by the manufacturer to spot inefficiencies or defects. The new knowledge is then manipulated in various ways to inform decision-making by the company. The information assists the company to collaborate with suppliers in a very detailed way. It also assists Volvo to evaluate the safety of its vehicles. The sensors are data collection points while the analysis hub delivers practical results for review and implementation.
Data linking enables the firm to learn of the potential mechanical problem likely to arise when the car is in the field. Furthermore, Volvo uses the data collected over a number of tests and actual usage to analyzing patterns of wear and tear. Thus, it is able to recommend a timely replacement of a vehicle part more frequently before the customer is exposed to the issue. The resulting knowledge is then used to adjust the manufacturing or distribution process of the particular item or component.
For example, the company would request a supplier to halt the production, of a component because it is faulty in its present formation. Moreover, Volvo could share its knowledge with the suppliers to assist them in increasing the functionality of parts. Front line staffs and distributors also obtain new insights about better performing parts and recommended driving practices that would enable one to obtain an optimal experience.
The data capturing devices also come in handy after the loss of a vehicle in an accident. Data captured before accidents shows how the vehicle responded to different driver inputs and the in-vehicle system situational awareness. The report shows the company the information that was not captured as well as how the present intelligence assisted the car.
How the Big Data Strategy Give Volvo a Competitive Advantage
The big data strategy allows Volvo to lead the industry in manufacturing safer cars. Consumers have a keen eye for safety, and the data analysis advantage of Volvo enhances its ability to fulfill their needs. The customer experience during service also improves, as mechanical diagnosis turns automatic. There is a reduction in the estimated service time for vehicles.
Moreover, the diagnosis made in one vehicle automatically affect the performance and safety improvements in all other vehicles. The already manufactured cars get a software upgrade, while those still in the manufacturing process could also get a mechanical overhaul for some of their components. The early diagnosis system and mass repair ability allow the company to avoid public recalls of vehicles. The market often views vehicle recalls as an indication of a decline in safety.
The huge data volume is beneficial for the entire value chain. Volvo could use it to dictate terms of production with suppliers since it can evaluate different components with more scrutiny. The analysis, of the information would enable Volvo to manage properly its human capital.
The HR department would evaluate car reports to inform its decision on the required technical skill mix (Vriens, 2012). Supply chain efficiencies arising from the timely interventions would results to cost savings. The company can then afford to increase the basic offerings for its cars and hence deliver a superior product to the market.
The automation of vehicle inspection and testing, which the strategy provides, would allow the company to minimize legal costs associated with unsatisfied customers and faulty vehicles. Moreover, all the performance and safety improvements enhance the Volvo brand, in the market.
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Converting data into business value at Volvo. (2011, January). Web.
Vriens, M. (2012). The insights advantage: Knowing how to win. Bloomington, IN: iUniverse.