• Subject Name : Management

Quality Management Systems

Introduction to Daimler Trucks Case Study

Daimler Trucks Asia (DTA), a subsidiary of Daimler AG, one of the world's largest truck makers, has a clear vision of leading the commercial vehicle market into the future. Nonetheless, new challenges are emerging, requiring rapid change, creativity and changing ways of thinking and working. Disruption is not only possible but also unavoidable for DTA. For DTA, disruption started in an unexpected location — the department of quality control, with its abundant amount of data. Data flows across quality control systems from more than 150 countries, but traditional research by the department was time-intensive, obsolete and dependent on internal siloed sources (Deloitte 2017). Basically, DTA was collecting data but was not using it in such a manner that facilitates better decision making. It was the desire to lay the necessary foundation for the digital transformation of DTA that led to Deloitte's partnership

Problem with The Data at DTA

As mentioned earlier, DTA gathers copious amounts of data through quality management systems form more than 150 countries. So, a lack of quality data was last of DTA’s concerns. The key problem was that DTA was unable to effectively analyse the data to support its decision making. As DTA’s Chief Information Officer (CIO), Lutz Beck mentioned, the company is collecting data but is not using it to create business models or make decisions. Moreover, Ashwin Patil, the managing director at Deloitte Consulting LLP, agreed with Beck and stated that quality management at DTA has access to a wealth of siloed data but the department did not necessarily know how to analyse it efficiently. Basically, DTA had a reactive strategy towards quality and safety problems and only examined retrospective coverage of the warranty results.

Impact of The Data Problem

The data problem at DTA resulted in a reactive approach to identification and investigation of quality and safety issues within the organization. It also led to the inability of the company in analysing unstructured data and recognizing critical data patterns early. Moreover, because of this lack of effective data analysis, DTA had to bear high warranty costs and recall delays which adversely affected the brand image. The data problem also exacerbated the quality management issue by extending the issue resolution time. As the Deloitte (2017) mentioned the time taken for investigation of quality and safety issues in DTA used to take up to two years, before the implementation of the data analytics project. This time translated into lost profit, poor brand image and lost profits.

Benefits of Data Analytics Project for DTA

In order to deal with the poor quality management and data analysis, DTA partnered with Deloitte to implement cognitive technology solutions to improve DTA's ability to predict, detect and remediate repairs. The data analytics project integrates data from over GPS monitors and 400 sensors to provide much better real-time truck status and activity information. The project led to a 50% reduction in issue detection time and approximately $8 million in savings during the first 24 months (Deloitte 2017). 65% of the IT workforce was transformed to be more productive because of the data analytics project, along with quality risk management in thousands of trucks due to proactive sensing solution.

Risks of The Data Analytics Project for DTA

Although the cognitive technology project resulted in numerous benefits to DTA, there are also several risks associated with the implementation of the project. Such risks include unorganized data, data storage and retention problem, cost management, data privacy, and incompetent analytics (Gupta and George 2016). Big data is really powerful and versatile. These come from various sources, and from different forms and enterprises are ineffective in successfully handling these unorganized and siloed data sets (Moller and Hass 2019). Moreover, one of big data's most evident threats. As data accumulates at such a rapid pace and in such huge volumes, its preservation is a big concern for DTA. In addition to this, the company also has to deal with the risk of data privacy and analytics (Taleb, Serhani and Dssouli 2018).

Future of Disruptive Innovations at DTA

The DTA's journey isn't ending here though. The on-going process of change continues, not just for the company and market, but also for the ever-changing environment that is increasingly dependent on connectivity, data and the Internet of Things. As Mr Beck stated, proactive sensing has helped the company to shape the overall business, but the company has to ensure constant innovation and implementation of disruptive innovation to mitigate future risks. By continuing innovation and its new commitment to the power of data, Daimler Trucks Asia is positioned to help drive the industry into the future, develop new technologies and assert its place as a genuinely creative and forward-looking global organisation.

Future Competitiveness of DTA

DTA plans to stay competitive in future through constant innovation and implementation of disruptive technology. Because the quality and safety technologies that reach across many functional areas, the chief operating officer or the CEO need to take responsibility for their creation and implementation. Otherwise, the exploitation and profit generated from technical advances may be sub-optimal or may be exploited by societies that do not embrace a data-driven approach (Loc 2017). Given the continuous stream of technology advances and the complexities of processing vast new data sources, this revolutionary path is a long road to completion. DTA is entering a period of widespread and continuing progress in technology and management approaches to quality and safety.

Conclusion on Daimler Trucks Case Study

Although developments can continue in the future, the implementation of current leading practices and well-established technologies can help businesses to achieve effective quality management. Organizations that tend to see quality and safety as a sluggish, backward-looking discipline will suffer from market recognition and poor performance in the sector. Previously DTA was challenged — like the industry as a whole — to detect quality problems early. DTA had a reactive approach to quality and safety problems, and only examined retrospective coverage of the warranty results. However, beginning in 2016 Daimler rolled out a global program called the Common Telematics Platform for linked trucks. It combines data from over GPS monitors and 400 sensors to provide much better real-time truck status and activity information. This led to effective data analysis and quality management in the organization. Moreover, DTA is positioned to help push the industry into the future, create innovative innovations and claim its role as a genuinely imaginative and forward-looking global company by promoting innovation and its innovative dedication to data analytics.

References for Daimler Trucks Case Study

Deloitte. 2017. A deeper perspective. Available at: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/about-deloitte/us-about-deloitte-daimler-truck-manufacturing-case-study.pdf [Accessed 19 April. 2020]

Gupta, M. and George, J.F., 2016. Toward the development of a big data analytics capability. Information & Management53(8), pp.1049-1064.

Loc, V.B., 2017. Determinants of operational efficiency at Mitsubishi Fuso Truck and Bus Corporation (MFTBC) in Japan.

Möller, D.P. and Haas, R.E., 2019. Guide to Automotive Connectivity and Cybersecurity. Springer International Publishing.

Taleb, I., Serhani, M.A. and Dssouli, R., 2018, July. Big data quality: A survey. In 2018 IEEE International Congress on Big Data (BigData Congress) (pp. 166-173). IEEE.

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