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A rapid improvement event, also known as "kaizen," is a Lean manufacturing technique known for bringing about quick results. The traditional method, however, falls short in cases of limited visibility into production—a challenge particularly for process manufacturers monitoring multiple enclosed tanks, pipes and ongoing flows at a fast pace.
Analytics-infused kaizens shed light on problems previously difficult to detect. Kaizen leaders can request precise, statistically relevant data, enabling them to go beyond hunches to pinpoint root causes and design powerful solutions aligned with high performance.
With the economic recovery slower than expected, shareholders increasingly demanding cost reduction and higher revenue, plus high debt levels, lean manufacturing has attracted renewed interest. However, many manufacturers lack either the time or expertise to implement the proven Six Sigma methodology. In order to produce results quickly, many manufacturers prefer to start with kaizen, long associated with generating incremental improvements very quickly.
Kaizen events are effective in improving discrete manufacturing workflows, as well as streamlining transactional processes. Teams typically tackle problems with limited scope and suggest improvement ideas within a week or two.
The traditional kaizen process, however, can raise more questions than provide answers for process manufacturers, whether in chemicals, energy, agribusiness, or food and beverage processing. In these industries, materials are processed continuously, from raw inputs to final output, with minimal delays. Due to multiple tanks, pipes and processes, problems in the interim stages are much less visible, and undetected problems can rapidly undermine results.
Consequently, many manufacturers are using analytics to improve the performance of kaizens in process manufacturing.
Kaizen events start by asking foundational questions, such as, “What is the baseline performance of our process?” and “Where are there significant delays between steps?” Analytics-infused kaizens go beyond largely qualitative and high-level analysis to add quantitative detail.
An analytics-infused kaizen event typically does not take longer than the traditional kaizen because it draws from data that already exist. Statistical software and an analytics platform are needed, along with basic statistical training for project leaders and in-house experts. At the very least, the team requires access to individuals with statistical skills.
Analysis of data leads either to support or rejection of a hypothesis, helping teams identify root causes. Analytics are useful both in the week of preparation and during the kaizen event, when members of the broader team may call for additional data to test additional hypotheses.
One of the keys to success with kaizen is having participants with open minds and the willingness to make fact-based decisions. Being able to detect relationships between inputs, processes and output—which analytics enables—results in “aha!” moments and better prioritization of improvement ideas. Examining objective data is also a good way of defusing conflicts among team members. Fact-based data analysis has the potential to break through long-held assumptions and myths.
Continuing to rely on long-held assumptions and intuition to solve problems is analogous to piloting a ship in a storm without modern instrumentation. Such an approach leaves root-cause analysis almost to chance, wasting time and resources.
Analytics-infused kaizen events address the limitations of the traditional method, thereby driving for clearer insight and faster results. Analytics offers improved visibility to data and reporting, along with insights for better decisions that deliver improved outcomes to the bottom line.
While this paper has focused on analytics for process manufacturers hindered by poor visibility into production stages, analytics-infused kaizens can be used to help companies in a wide range of industries use hard data to support improvement hypotheses and produce tangible gains. Analytics can be infused into Six Sigma and other process improvement initiatives to bolster the quantitative detail that supports more precisely targeted strategies to improve speed and quality.
Organizations benefit by becoming more analytical over time. Combining advanced analytics with operators’ knowledge and process expertise improves the probability of implementing precisely targeted solutions. Advanced analytics is a leading-edge service that allows organizations to improve operations and pursue high performance.
July 10, 2012
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