Key Points

• There is a definite need to integrate big data and Lean Six Sigma in the future. 
• There is far more data available to organizations now than ever before. 
• Businesses must tackle Lean Six Sigma with big data to increase the bottom line. 

It shouldn’t be surprising that the amount of data available has grown exponentially in a digitized world. As a result, process methodologies like Lean Six Sigma have become cornerstones across any organization looking to improve its processes, find more efficiency, and eliminate as much waste as possible to increase the bottom line. 

In traditional circumstances, Lean Six Sigma relies heavily on collecting and analyzing data, though historically, this data has been more limited. Fast-forward to more recent years, and digitization has made it possible to gather data by the truckload, which has come to be known as “Big Data. ” Its availability increases the likelihood that Lean Six Sigma will be more impactful. 

The Intersection of Big Data and Lean Six Sigma

When you explore the world of Lean Six Sigma and Big Data, you must consider why this data matters. 

Improved Decision Making

When you turn back the clock and think about how Lean Six Sigma came to fruition while having access to less data than is available today, it’s far more helpful in today’s modern, data-heavy world. Big data will provide organizations and businesses with more information, especially real-time information. 

This will give teams at all levels more opportunity to analyze things like production data that might have gone unnoticed in years past. Big data has now made it more possible to be proactive with decision making, when it used to be more reactive. 

Improved Predictive Analytics 

What makes big data attractive in the Lean Six Sigma world is how well it can provide predictive analytics on the most critical pieces of information a company needs to know. This includes, but is not limited to, customer complaints before they happen, forecasting defects, or predicting downtime in the production process. 

Using historical data, this ties directly into the “Analyze” phase of the DMAIC process. One strong example is a manufacturer that could use sensor data to evaluate the possibility of a machine failure, so maintenance schedules are updated to be proactive. 

Continuous Improvement Scaling

Integrating big data and Lean Six Sigma benefits the whole concept of continuous improvement. The more data available, the more analysis can be done to identify and implement best practices or standardize practices after a retailer organizes millions of transactions. 

Different Technologies

When considering the specifics around “Big Data,” you must consider what this means. In the case of Lean Six Sigma, it could mean incorporating things like artificial intelligence and machine learning. AI will undoubtedly be able to analyze data far faster than when done manually and, as a result, put together stronger recommendations. 

Cloud computing will be equally valuable, allowing enough processing power and the ability to scale the storage for growing data sets. Accessing this information from the cloud means anyone on any team can look at this data anytime to extract insights. Whether these insights include analyzing inventory levels or customer feedback, Lean Six Sigma processes benefit. 

Challenges Of Integrating Big Data and Lean Six Sigma

Data Quality

When considering all the challenges associated with integrating big data and Lean Six Sigma, you must evaluate all possible inconsistencies. This could be as simple as examining a legacy system inside a company that isn’t yet compatible with more modern platforms. 

Separately, you can look at incomplete data sets, where inaccurate data means that the reliability of any analysis is questionable. In a vertical like healthcare, incomplete patient records can skew the quality metrics a hospital sees. As a result, there has to be an investment that requires data cleansing, as the alternative is to run analyses with incorrect data, potentially exposing an organization to flawed conclusions that could pose more harm than good. 

Lastly, you must consider whether the teams working to integrate big data have the right expertise level. It’s not uncommon to find a team familiar with Lean Six Sigma principles but lacking automation or data engineering knowledge. This means a company needs to hire or train those who can handle integrating and analyzing big data sets, which can set back timelines. 

Infrastructure and Cost

If you run a business and you’re trying to integrate big data and Lean Six Sigma, you must be prepared for some upfront costs, which might pose a big problem for small businesses. Any instance in which you need a cloud solution to store the data you want to analyze comes with a cost, so this must be considered. 

On the same thought, you must look at scaling infrastructure, which also requires additional costs. If a company fails to scale, it could experience slowdowns across systems, which could mean not having enough inventory as a retailer for a holiday or busy period. Plus, you have to consider that cloud solutions, especially if they run on-site at a big corporation, require both maintenance and upgrades, but they are mandatory costs as data has to be stored somewhere. 

Another major challenge is simply integrating old and new systems, which might require custom solutions, which speaks directly to the worries of rising costs. Any instance in which you must slow down and create a customer solution to integrate big data means that the beginning of Lean Six Sigma is slowed. 

Resistance to Change

In many instances, resistance to change will be problematic when integrating big data and Lean Six Sigma. Leaders must address and demonstrate how big data complements existing business practices and doesn’t replace them. 

Many of those resistant to change will also need to be upskilled to better understand what is happening. These upskilling efforts will be critical to understanding how to handle advanced analytics to make every business unit inside an organization as efficient as possible. 

Opportunities for Lean Six Sigma In a Big Data World

Real-Time Optimizations

Arguably, the single biggest (and best) reason to bring together the big data and Lean Six Sigma world is the opportunity for real-time optimizations. This will allow companies like airlines to incorporate changes to their flight schedules in real-time based on weather or increased air traffic. The goal would be to minimize passenger delays, as working through this in real-time will be critical. 

Real-time optimizations will also be critical when examining how companies like telecommunications firms can monitor network performance at a big sporting event. Optimizing historical patterns will help establish the baseline for optimizing in real time to handle increasing customer traffic without impacting the customer experience. 

Resource Allocation 

If you want to reduce waste in your organization, integrating big data and LSS will be invaluable. The more you can look at how to use predictive modeling from big data to optimize available resources, the more you can eliminate waste, which in turn helps grow the bottom line. This could mean avoiding overstaffing or reducing excess inventory, which costs time and money sitting in warehouses. 

Personalized Improvements

One of the biggest benefits of Lean Six Sigma is its emphasis on the customer, as big data offers an opportunity to create personalized improvements. Companies can use data to create specific customer segments that can help shape where inventory is shipped based on the weather in a specific part of the country. For the hospitality industry, determine how different service levels are determined based on frequent versus less frequent travelers. 

Simulation Modeling

Using historical data, companies can look at how to test different solutions to problems without actually rolling out processes. Simulating what would happen with more or less inventory or changing the warehouse layout will help ensure that any aspect of the “Improve” phase would be successful before any real money is spent. This reduces the level of trial-and-error, all while ensuring that a company can attempt multiple solutions using artificial intelligence tools. 

Other Useful Tools and Concepts

If you want something else to read, this is a good opportunity to learn more about Lean Six Sigma. Alternatively, you might find it a great time to look at different case studies from popular brands like Dropbox, Instagram, and Slack, and how they used lean methodologies to pivot and grow their businesses after their first attempts failed. 

Understanding where artificial intelligence fits into everything is another big winner today, as it’s arguably the most popular change in the business world. How AI affects automation, digital transformation, and a host of other tools will be super important to know how to handle these adjustments should they occur at your business in the future. 

Conclusion 

There is little question that integrating Lean Six Sigma and big data will benefit any organization. Challenges aside, the opportunities and benefits vastly outweigh the downsides, meaning that companies could see significant savings, eliminate waste, increase efficiencies, and improve the overall customer experience. 

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