Same Budget, High Impact: Leveraging Digital Lean Six Sigma for Cost-Effective Digital Transformation.
In today's digital age, we often find ourselves wanting to achieve more without increasing our budgets.
It's crucial to understand that it's not always about doing "more" that leads to better outcomes and enhanced results. Instead, it's about how we manage our tasks and the principles we adhere to that enable growth and scalability.
In this article, I will explain the method of Digital Lean Six Sigma (DLSS) as a modern iteration of the traditional Lean Six Sigma (LSS) methodology, adapted for the digital age.
Before diving into DLSS, let's first discuss Lean Six Sigma briefly.
Lean Six Sigma (LSS)
Lean Six Sigma is a methodology that combines the principles of Lean and Six Sigma to improve performance by systematically removing waste and reducing variation. It involves five stages, referred to by the acronym DMAIC:
Define - Define the problem or the project goals.
Measure - Measure the current process to establish a baseline.
Analyze - Analyze data to identify root causes of defects or inefficiencies.
Improve - Improve the process by implementing and testing potential solutions.
Control - Control the improved process to ensure consistent performance.
The story behind Lean Six Sigma
Lean Six Sigma is a fascinating blend of two powerful methodologies, Lean and Six Sigma, both of which have rich histories of their own. The story of Lean Six Sigma is, in essence, a story of evolution in the world of process improvement.
The Origins:
The roots of Lean can be traced back to the Toyota Production System in post-World War II Japan.
After the devastation of the war, Japanese industries, including Toyota, faced significant challenges in producing high-quality products with limited resources.
This necessity led to the development of innovative processes that aimed to eliminate any form of waste, termed "muda" in Japanese.
Engineers Taiichi Ohno and Shigeo Shingo are often credited with pioneering these principles trying to emphasise maximizing customer value and ensuring that every step in a process added value, effectively creating more with less.
In contrast to Lean's origins in Japan, Six Sigma was born in the United States at Motorola in the 1980s.
Bill Smith, an engineer at Motorola, recognized that by focusing on reducing defects in a process, significant improvements in quality and customer satisfaction could be achieved.
The term "Six Sigma" was coined to describe a process so refined that it produces no more than 3.4 defects per million opportunities.
One notable advocate was Jack Welch, the former CEO of General Electric, in the 1990s, adopted Six Sigma as a core business strategy at GE, which played a crucial role in popularizing the methodology globally; the methodology was so successful that it quickly caught the attention of other businesses.
The Fusion:
By the 1990s, thought leaders in the field of process improvement began to see the potential benefits of combining Lean and Six Sigma.
They realized that while Lean focused on speeding up processes by eliminating waste, Six Sigma aimed to improve quality by reducing defects by integrating these two approaches, businesses could achieve both speed and quality, leading to the birth of "Lean Six Sigma."
This combined methodology provided organizations with a comprehensive set of tools to tackle a wide range of operational challenges, from inefficiencies in production lines to inconsistencies in service delivery.
Modern Day: Digital Lean Six Sigma (DLSS)
With the rapid evolution of technology, especially in areas like data analytics, artificial intelligence, machine learning, and IoT (Internet of Things), traditional LSS practices need an update.
DLSS incorporates digital tools and advanced data analytics into the LSS methodology.
Digital Tools Integration: Traditional LSS uses manual data collection and analysis methods, but DLSS leverages modern digital tools for real-time data collection, monitoring, and analysis.
Data Analytics: With the availability of massive datasets (Big Data), DLSS utilizes advanced data analytics techniques to gain deeper insights into processes. This can lead to more precise identification of inefficiencies and better prediction of potential problems.
Automation: One of the ways to eliminate waste in a process is through automation. DLSS focuses on automating repetitive and time-consuming tasks using technologies like Robotic Process Automation (RPA).
Enhanced Visualization: Digital platforms enable better visualization of data and processes. This aids in easier identification of bottlenecks and inefficiencies.
Cloud Computing: DLSS benefits from the scalability, flexibility, and real-time collaboration capabilities offered by cloud platforms.
IoT Integration: Internet of Things devices can provide continuous streams of data about processes, aiding in real-time monitoring and faster decision-making.
AI & Machine Learning: These technologies can be integrated into processes to optimize them continuously without manual intervention. For example, an AI system can predict when a machine might fail and schedule maintenance just in time to prevent it.
A practical example of how automation can be integrated into the Digital Lean Six Sigma (DLSS) methodology:
Invoice Processing in a Large Organization
Problem Statement:
The Accounts Payable department of a large organization receives thousands of invoices monthly. Processing these invoices manually has led to delays, errors, and a backlog.
The organization wants to streamline and automate this process to improve efficiency and accuracy. The following can be an example of a method applied, also by leveraging my experience
Digital Lean Six Sigma Approach:
Define:
Objective: Reduce invoice processing time by 50% and reduce errors by 80% within six months.
Stakeholders: Accounts Payable team, IT department, vendors.
Measure:
Current State Analysis: It's observed that the average time to process an invoice manually is 30 minutes, with a 5% error rate.
Data Collection: Track and log all invoices, the time taken for processing, and any errors or discrepancies.
Analyze:
Root Cause Analysis: Majority of the time is consumed in data entry, cross-referencing invoice details with purchase orders, and manual error rectifications.
Bottlenecks: Manual data entry, verification of invoice details against existing purchase orders, and manual routing for approvals.
Improve:
Implement Robotic Process Automation (RPA):
Automate data extraction from invoices using Optical Character Recognition (OCR).
Cross-reference invoice details automatically with purchase orders in the system.
Automate routing of invoices for approvals based on predefined rules.
Train Accounts Payable team on the new automated system.
Implement exception handling: For invoices that the RPA tool can't process due to missing details or discrepancies, route them to a human for review.
Control:
Monitor the RPA system in real-time for errors or inefficiencies.
Regularly update the RPA rules and algorithms as new scenarios or exceptions are encountered.
Use a dashboard to track key metrics, such as average processing time and error rate, to ensure continuous improvement.
Hold periodic reviews with the Accounts Payable team to gather feedback and make improvements.
Results and Measurement:
After implementing RPA with the DLSS approach, the organization observes:
A reduction in average invoice processing time from 30 minutes to 10 minutes.
A decrease in the error rate from 5% to 1%.
Enhanced visibility into the invoicing process through digital dashboards.
Here's a chart that illustrates the improvement in invoice processing time over six months:
The blue line represents the average processing time for invoices when done manually. As you can see, there's a slight reduction over time as the team becomes more efficient or as minor process tweaks are made.
The green line represents the processing time after implementing Robotic Process Automation (RPA). You can see a significant and consistent reduction in processing time as the months progress. By June, the processing time has reduced dramatically compared to January.
This chart visually demonstrates the efficiency gains that can be achieved through automation in the context of a Digital Lean Six Sigma approach.
A practical example of how an SEO (Search Engine Optimization) project can be applied to the Digital Lean Six Sigma (DLSS):
Below is an example of DMAIC methodology to systematically improve and optimize a website's search engine ranking and performance based on my experience.
Scenario: A Tech Blog's Declining Organic Traffic
Problem Statement: "TechTalk", a popular tech blog, has noticed a steady decline in its organic traffic over the past six months.
They want to identify the cause and rectify it.
Digital Lean Six Sigma Approach:
Define:
Objective: Increase organic traffic by 30% and improve keyword ranking for 50% of the targeted keywords within six months.
Stakeholders: Content team, SEO specialists, web developers.
Measure:
Current Traffic Analysis: Use Google Analytics to assess the current state of organic traffic, bounce rate, average session duration, etc.
Keyword Analysis: Use tools like SEMrush, Ahrefs and SeoZoom to gauge the current keyword rankings.
Analyze:
Technical SEO Audit: Identify issues such as slow page load times, broken links, or crawl errors.
Content Analysis: Evaluate if the content is outdated, not in-depth, or if newer competitors are offering better content.
Backlink Analysis: Check if there's been a loss of significant backlinks or if there are harmful backlinks pointing to the site.
Improve:
Website Optimization: Fix any technical issues found during the audit, such as improving site speed or rectifying crawl errors.
Content Strategy: Update outdated content, create in-depth pillar articles, and ensure content satisfies user intent.
Link Building: Reach out to high-quality websites for guest posting, reclaim lost backlinks, and disavow harmful ones.
User Experience (UX): Implement changes to improve the overall user experience on the site, ensuring it's mobile-friendly, easy to navigate, and provides value to the readers.
Integration of Digital Tools: Use AI-driven SEO tools to gain insights into content optimization, competitor analysis, and emerging trends.
Control:
Continuous Monitoring: Regularly monitor website metrics using tools like Google Search Console and Google Analytics.
Feedback Loop: Encourage user feedback on content and site usability, making adjustments based on this feedback.
Regular Audits: Conduct quarterly SEO audits to identify and fix emerging issues.
Training: Keep the content and tech teams updated with the latest SEO best practices through workshops and training sessions.
Results:
After implementing the DLSS approach to SEO:
"TechTalk" sees a 35% increase in organic traffic in six months.
60% of the targeted keywords have improved in their rankings.
The bounce rate decreases, and the average session duration increases, indicating better user engagement.
The chart above illustrates the time spent on SEO activities per week over a six-month period, both before and after implementing the Digital Lean Six Sigma (DLSS) approach:
The blue line represents the hours spent on SEO activities per week before implementing DLSS. There's a gradual decrease as the TechTalk team might have tried to optimize their processes slightly.
The green line represents the time spent on SEO activities after the DLSS implementation. Notice the steeper decline in hours spent, indicating a more efficient and streamlined process due to the systematic DLSS approach.
This visualization highlights the efficiency gains achieved when integrating the DLSS approach into the SEO process. By optimizing workflows, automating repetitive tasks, and focusing on critical areas, the team was able to achieve better results in less time.