The Continuum of Continuous Improvement

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Imagine this: You are a plant manager at a mid-sized manufacturing firm. You’ve just finished a three-day executive retreat where the buzzword of the week was "Lean-Agile." You’ve hired a team of consultants to implement Scrum alongside 100 sticky notes on a board that vaguely resembles a value stream falling gently like the autumn leaves.

Three months later, nothing has changed. In fact, throughput is down. The "sprints" feel like forced marches. The data you need to make decisions still takes two weeks to reach your desk. Your team is frustrated, and the program is quietly abandoned. But hey, that value stream map is now a Visio file, so you can show it to the next round of consultants.
If this sounds familiar, you aren't alone. In fact, you are in the majority. Continuous Improvement (CI) programs have a failure rate that would be unacceptable in any other business function. But the reason they fail isn't "lack of management buy-in". It's likely your age of methodology doesn't match the maturity of your information.

The Crisis of Failure in Modern CI

For nearly a century, we have been obsessed with improving production efficiency. From Taylor’s scientific management of 1911, where stopwatches were used in time studies, to the automated pipelines of DevOps today, the methodology landscape is crowded. Yet, despite this wealth of knowledge, practitioners are struggling.

Findings from Albliwi et al. (2014). Systematic Literature Review
Failure Rate Context Citation
Two out of every three CI initiatives and projects fail to attain results Ringen and Holtskog (2011)
Up to 70 percent Companies that fail at implementing Lean Pedersen and Huniche (2011)
Fewer than 10 percent are successful UK organizations implementing Lean Bhasin and Burcher (2006)
Less than 50 percent of leaders are satisfied with CI Survey of aerospace companies Chakravorty (2009)
62 percent failed Six Sigma and Lean initiatives in healthcare (review of 47 other studies) Glasgow et al. (2010)

To understand why CI fails, we must first define two concepts that are rarely discussed in the boardroom but dominate the factory floor: the Iteration Cycle and Information Latency.

1. The Iteration Cycle

Edwards Deming defined CI as the repeated application of the Plan-Do-Study-Act (PDCA) cycle. The "Iteration Cycle" is the actual block of time it takes for information to travel from the "hands-on" activity (the physical change in a product) to a change agent who can modify the process. This is the very nature of scientific thinking, trying things and finding out.

2. Information Latency

Information latency is the delay between the occurrence of an event and the availability of that data for decision-making. This is closely tied to Technical Maturity. In a "Dark Factory" where everything is connected via IoT sensors and robotics, latency is near zero. In a traditional job shop where data is recorded on clipboards and entered into Excel once a week, latency is high. The latency is directly related to the technical maturity of the organization. This is evidenced by how CI methodology is formed with the "Continuum of Continuous Improvement".
The Core Thesis: The success or failure of a CI program depends on the frequency and length of the iteration cycle, and that cycle is dictated by your firm’s information latency. If you pick a methodology designed for low latency (like Scrum) but your firm has high latency (no MES, manual reporting), the program will fail.
DPS-Lean: Evolution of Improvement

DPS-Lean: Evolution of Improvement

Era 1: The Birth of Standardization

The First Industrial Revolution changed the world through Standardization and Specialization. Adam Smith’s Wealth of Nations (1776) famously described the pin factory, where breaking a task into 18 distinct operations increased productivity by orders of magnitude. Feedback loops could take months or years.

Era 2: The Giants of Quality

This is the era of the "Guru." After WWII, Deming and Juran introduced Statistical Process Control (SPC). Six Sigma followed, designed for iteration cycles of 3 to 6 months.
The Socio-Technical Exception: Toyota (TPS) achieved short cycles (hours/days) not through technology, but through visual management (Kanban, Andon, Gemba walks). They beat the technology curve with culture.

Era 3: Software Infiltrates the Shop Floor

Agile and Scrum (2001) moved the iteration cycle to two-week "Sprints." Many manufacturing firms fail when they try to run two-week sprints in environments where the physical process or information latency requires much longer.

Era 4: Industrial DevOps and Digital Twins

Industry 4.0 uses Digital Twins and Industrial DevOps. In high-tech environments like additive manufacturing, the iteration cycle drops to minutes or hours.

The Future: Hyper-automation

In "Dark Factories," the iteration cycle becomes near zero. AI agents monitor, recommend, and act on physical changes autonomously.

The DPS-Lean Method

There are few practitioners who utilize all that is available in the world of operational excellence. DPS-Lean favors the use of Theory of Constraints, Lean, Six Sigma, and Agile in order to start general and go specific at every consulting arrangement.

The DPS-Lean certification courses likewise teach with these frameworks in mind, ensuring the framework matches the information latency of the firm being analyzed, and picking the right tool for the right situation. 

The goal is to compress the time between "Problem Occurs" and "Improvement Implemented." Stop chasing the "trend." Whether you use a stopwatch from 1911 or a Digital Twin from 2024, information must flow.
The Diagnostic Framework
High Latency: Manual reporting, weekly meetings. Use Lean (TPS) and Scientific Management.
Medium Latency: ERP/MES reports reviewed in batches. Use Lean Six Sigma.
Low Latency: IoT dashboards, live assets. Use Industrial DevOps and Agile.
References
Achanga, P., Shehab, E., Roy, R., & Nelder, G. (2006). Critical success factors for lean implementation within SMEs. Journal of Manufacturing Technology Management, 17(4), 460–471. https://doi.org/10.1108/17410380610662889
Albliwi, S. A., Antony, J., Lim, S. A. H., & Van der Wiele, T. (2014). Critical success factors for Lean Six Sigma implementation: A systematic review. International Journal of Lean Six Sigma, 5(3), 257–271. https://doi.org/10.1108/IJQRM-09-2013-0147
Antony, J., Lizarelli, F. L., & Machado Fernandes, M. (2022). A global study into the reasons for Lean Six Sigma project failures: Key findings and directions for further research. IEEE Transactions on Engineering Management, 69(5), 2399–2414. https://doi.org/10.1109/TEM.2020.3009935
Babbage, C. (1832). On the economy of machinery and manufactures. Charles Knight.
Beck, K. (2000). Extreme programming explained: Embrace change. Addison-Wesley.
Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., & Thomas, D. (2001). Manifesto for agile software development. Agile Alliance. https://agilemanifesto.org/
Bhasin, S., & Burcher, P. (2006). Lean viewed as a philosophy. Journal of Manufacturing Technology Management, 17(1), 56–72. https://doi.org/10.1108/17410380610639506
Brynjolfsson, E., & McAfee, A. (2021). AI and the future of work. MIT Press.
Chakravorty, S. S. (2009). Six Sigma programs: An implementation model. International Journal of Production Economics, 119(1), 1–16. https://doi.org/10.1016/j.ijpe.2009.01.003
Crosby, P. B. (1979). Quality is free: The art of making quality certain. McGraw-Hill.
Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review Press.
Deming, W. E. (1986). Out of the crisis. MIT Press.
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The science of lean software and DevOps: Building and scaling high performing technology organizations. IT Revolution Press.
Fryer, K. J., Antony, J., & Douglas, A. (2007). Critical success factors of continuous improvement in the public sector: A literature review and some key findings. The TQM Magazine, 19(5), 497–517. https://doi.org/10.1108/09544780710817900
George, M. L. (2002). Lean Six Sigma: Combining Six Sigma with lean speed. McGraw-Hill.
Gisi, J. (2024). The dark factory and the future of manufacturing. Routledge.
Glasgow, J. M., Caziewell, S., Jill, R., & Kaboli, P. J. (2010). Guiding inpatient quality improvement: A systematic review of Lean and Six Sigma. Joint Commission Journal on Quality and Patient Safety, 36(12), 533–540. https://doi.org/10.1016/s1553-7250(10)36081-8
Goldratt, E. M., & Cox, J. (1984). The goal: A process of ongoing improvement. North River Press.
Grieves, M. (2014). Virtually perfect: Driving innovation and lean product lifecycle management through digital twin. Space Coast Press.
Johnson, S., & Yeman, R. (2023). Industrial DevOps: Build better systems faster. IT Revolution.
Juran, J. M. (1988). Juran on planning for quality. Free Press.
Kim, G., Humble, J., Debois, P., & Willis, J. (2016). The DevOps handbook: How to create world-class agility, reliability, and security in technology organizations. IT Revolution Press.
Kotter, J. P. (1995). Why transformation efforts fail. Harvard Business Review, 73(2), 59–67.
Kovach, J., Cudney, E., & Elrod, C. (2011). The use of continuous improvement techniques: A survey-based study of current practices. International Journal of Engineering, Science and Technology, 3(7), 89–100. https://doi.org/10.4314/ijest.v3i7.7S
Krafcik, J. F. (1988). Triumph of the lean production system. Sloan Management Review, 30(1), 41–52.
Kubiak, T. M. (2012). The certified Six Sigma master black belt handbook. ASQ Quality Press.
Laureani, A., & Antony, J. (2012). Critical success factors for the effective implementation of Lean Sigma: Results from an empirical study and agenda for future research. International Journal of Lean Six Sigma, 3(4), 274–283. https://doi.org/10.1108/20401461211284743
Liker, J. K. (2004). The Toyota Way: 14 management principles from the world's greatest manufacturer. McGraw-Hill.
Linder, N., & Undheim, T. A. (2022). Augmented Lean: A human-centric framework for managing frontline operations. Wiley.
Ohno, T. (1988). Toyota production system: Beyond large-scale production. Productivity Press.
Pande, P. S., Neuman, R. P., & Cavanagh, R. R. (2000). The Six Sigma way: How GE, Motorola, and other top companies are honing their performance. McGraw-Hill.
Pedersen, E. R. G., & Huniche, M. (2011). Determinants of lean success and failure in the Danish public sector: A negotiated order perspective. International Journal of Public Sector Management, 24(5), 403–420. https://doi.org/10.1108/09513551111147141
Ringen, G., & Holtskog, H. (2011). How enablers for lean product development motivate engineers. International Journal of Computer Integrated Manufacturing, 24(12), 1117–1127. https://doi.org/10.1080/0951192X.2011.593046
Rother, M., & Shook, J. (1999). Learning to see: Value stream mapping to add value and eliminate muda. Lean Enterprise Institute.
Rother, M., & Shook, J. (2003). Learning to see: Value-stream mapping to add value and eliminate muda (2nd ed.). Lean Enterprise Institute.
Shewhart, W. A. (1931). Economic control of quality of manufactured product. D. Van Nostrand.
Smith, A. (1776). The wealth of nations. W. Strahan and T. Cadell.
Sutherland, J. (2014). Scrum: The art of doing twice the work in half the time. Crown Business.
Taylor, F. W. (1911). The principles of scientific management. Harper & Brothers.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Van der Aalst, W. (2016). Process mining: Data science in action . Springer.
Van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2021). Intelligent automation: Welcome to the world of hyperautomation. Springer.
Womack, J. P., Jones, D. T., & Roos, D. (1990). The machine that changed the world: The story of lean production. Rawson Associates.
Yin, Y., Stecke, K. E., & Li, D. (2018). The evolution of production systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1–2), 848–861. https://doi.org/10.1080/00207543.2017.1403664
Jerry DeFranco is a scholar-practitioner focusing on the intersection of manufacturing, technology, and strategic management. This post is adapted from his work at the Florida Institute of Technology.
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