The Evolution of Lean Six Sigma
If there is one thing that firms in almost every industry have in common, it is a drive to enhance performance. For any company leader, measurable progress is a common aim. And there is no better approach to enhance performance than by implementing and applying Lean Six Sigma correctly.
In simple terms, Lean Six Sigma is a performance improvement approach that depends on a collaborative team effort to eliminate waste and reduce variance.
In discrete manufacturing industries, Lean and Six Sigma set the foundation for continuous improvement techniques decades ago. The techniques that Lean and Six Sigma have been applied to are continually improving.
In recent years, Industry 4.0 and data science innovations including machine learning, big data, cloud computing, and so on have played the most important role in pushing analytic capacities well beyond their historical boundaries. While the number of processes that may be improved has grown, the technique for tackling any of these projects, or use cases, has remained consistent.
The capacity to extract fascinating information and trends about your industry or client base is not only a competitive advantage for a company, but it also opens up chances for innovation and growth. Data mining and data collecting are important, but the ability to properly evaluate the data is crucial, and a Lean Six Sigma Black Belt can help with that.
If you want to become 'that' professional who does this crucial task of deriving actionable insights from heaps of data in order to improve organizational processes, you may join a good certification to earn the required credentials. One of the suggested executive courses is the Executive Development Programme in Lean Six Sigma by IIM Kashipur.
Constantly upskilling and becoming familiar with the latest tools and platforms is very crucial to advance in the field of Lean Six Sigma. Experimental designs, which allow you to test several variables at the same time, are one of the most effective yet underutilized process improvement techniques. When processes are complex and basic tools aren't providing the results you're looking for, it's time to turn to more advanced analysis and testing approaches. These tools have the potential to lead to significant improvements!
Real-time monitoring and predictive analytics are distinguished from historical data analysis by their capacity to operationalize the outcomes of an analysis. Advanced analytics software with a live and performant link to the required data sources is needed for this. These studies provide SME information that paints a thorough picture of current performance in relation to historical and present limitations. These findings may be used to make near-real-time process modifications in order to meet the business goals of increasing quality, throughput, and dependability while reducing inventory, energy use, and waste.
While the industry is still evolving and making quick shifts under the blow of current economic conditions, it is advisable to invest in building new skill-sets.
Analyses and Lean Management, both are very crucial to an organization. Businesses that effectively recognise overboard practices and turn towards a more sensible and every-day-incremental-improvement strategy will prosper. Those who quit up and lose confidence, put off by the difficulty and failure of performance due to wrong decision making, will be left out since they have missed the point.
For decades, Lean and Six Sigma initiatives have served the process industries well, but new technologies are required to improve results. When compared to older technologies, advanced analytics software is particularly well suited to these sorts of initiatives, giving the necessary results with far less time and effort. And if you belong to the industry, it would be in your best interest to stay updated with the trends.
