Integration of smart data analytics with lean six sigma in an auto manufacturing company

Despite the many benefits of lean six sigma in manufacturing, there are several downsides, such as the tiring decision-making process and the difficulty in detecting defects caused by the often enormous and complex manufacturing data (Thomas, et al., 2009). In addition, the lack of substantial evidence on the business model for lean six sigma implementation with Smart Data hinders policymakers from making good design, implementation and evaluation of policies and practices (IBM, 2010). Hence, there is a need for continuous process improvement in vehicle manufacturing companies through the uninterrupted or non-disruptive integration of Smart Data and Lean Six Sigma through simulation.

Therefore, the main aim of this study is to investigate the integration of Smart data analytics with lean six sigma technique within the manufacturing processes of a vehicle manufacturing company to optimise productivity. To achieve this, the research employed a mixed method research approach. Quantitative smart data was gathered from two case studies of companies in the vehicle manufacturing industry. For Case Study 1, data was collected from Anglia Ruskin University’s lecture manual. Whereas for Case Study 2, primary data was collected from the company's real-time data monitoring system and validated by observing and recording the manufacturing process using a stopwatch. In addition, qualitative data was collected from relevant company documents and observations. Lastly, the researcher utilised Witness Discrete Event Simulation to model, analyse and optimise process performance in automobile manufacturing businesses using Smart Data and Lean Six Sigma.

In conclusion, the research findings demonstrate that integrating Smart data with Lean Six Sigma process within the manufacturing processes of a vehicle manufacturing company optimises manufacturing productivity and reduces waste. Through the case studies, it was revealed that intermixing the methodologies helped speed up identifying root causes. Overall, the new framework resulted in a significant increase in throughput, with case study 1 improving by 8.23% and case study 2 by 2.4%, 27.7% and 106% for scenario one, scenario two and scenario three respectively. In addition, the study established that auto manufacturing firms adopt Smart Data in various ways. Some of the ways are outlined but not limited to RFIDs for manufacturing information systems and MTM decision support systems. Finally, the research shows that the globe is increasingly becoming a digital community at an alarming rate. As a result, Smart Data is being adopted by industries in all sectors around the world and the manufacturing sector, on the other hand, is trailing.