Three Key Methods to Unlocking Continuous Improvement Across Your Plant
Brandon Wright
Many organizations understand the “improvement” part of Continuous Improvement but struggle with the “continuous” aspect. A company might improve a process once but then assume they have solved the problem and there is no further need for improvement. Other companies know that continuous improvement can help but are unsure which strategy is appropriate. Learning about key continuous improvement methods can ensure companies know which strategies will yield the results they seek. Many companies find themselves considering many different options, and lose sight of the most important thing which is to start somewhere.
In this blog we will cover:
How Do You Evaluate Your Continuous Improvement Process?
What Is the Theory of Constraint?
What Is Genchi Genbutsu?
What Is Statistical Process Control?
Conclusion: Effective Continuous Improvement Involves Everyone
Let’s dive in.
How Do You Evaluate Your Continuous Improvement Process?
Whether implementing a new process or evaluating current policies, there are three crucial questions to ask:
1. What is your process?
DMAIC: The define, measure, analyze, improve, and control method is a way to improve existing processes but does not address designing new processes.
PDCA: Plan, do, check, and act is also known as a Deming or Shewhart cycle and is a tool with no end designed to drive continuous improvement.
Ad Hoc: The ad hoc approach is reactive and deals with issues after they arise and action becomes necessary.
2. How is your process initiated? How is it focused?
Reactive: A problem arises, and the organization finds a solution.
Proactive: An organization studies data and processes to determine where issues may appear and creates a plan to prevent or minimize the potential problem.
Scheduled: exercises are a form of proactive improvement that relies on regular calendar events and maintenance.
3. How is your process reviewed?
It takes time and data, but it is valuable to validate outcomes.
Think carefully and deeply about blind spots. Problems may not always exist where there is plenty of data.
Periodic review helps ensure the organization is productively executing the process.
What Is the Capability Maturity Model?
In the late 1980s and early 1990s, the Department of Defense (DOD) developed the Capability Maturity Model (CMM) to assess the ability of its vendors to deliver on their software projects effectively. While there are some flaws, CMM is a way to determine how well an organization can utilize data and facilitate continuous improvement. Continuous improvement is characterized by incremental progress, and CMM helps organize the small signs of improvement. When using CMM, it is essential to consider who is focusing on continuous improvement. If continuous improvement is a part of workplace culture, CMM can be a valuable barometer for progression from disorganized and reactive processes to proactive, controlled processes guided by data. When beginning to reorient to continuous improvement, CMM can provide a realistic picture of where the facility is currently.
What Is a “Good” Process Improvement Model?
Having a clearly defined continuous improvement process can ensure that facilities tackle the relevant problems. The Process Improvement Model below provides a framework for addressing the issue thoroughly from start to finish to deliver consistency and usable data.
The five steps of the Process Improvement Model are:
Step #1: Document the current state and describe the ideal state. This provides the roadmap for the remaining four steps.
Step #2: Run a root cause analysis, and select and test solutions. This step helps reveal what things are limiting the organization from improving and identifies potential solutions.
Step #3: Implement the solution identified in step #2.
Step #4: Monitor the implementation of the solution to determine whether what happened in the lab is happening on the floor. As the improvement process continues, the facility will formalize the procedures.
Step #5: Update the metrics and report on the results. A continuous feedback loop is vital to demonstrate that progress is or is not happening and to communicate throughout the organization what is happening and if it is achieving improved results.
What Is the Theory of Constraints?
It may seem counterintuitive, but the theory of constraints (TOC) highlights the most significant factor limiting the organization’s ability to achieve its goal. Introduced by Dr. Eliyahu Goldratt in 1984, TOC provides a method for focusing efforts on that limiting factor (the constraint) and identifying ways to improve the constraint’s performance to increase throughput.
Before you can improve your process, you should start your exercise by following these critical steps:
Mapping the process flow that needs improvement
Defining maximum output for each step in the process
Identifying the constraint and how improvement would impact it
TOC provides the necessary framework and approach to identify the limits of your process. Many organizations assume the work of process capability mapping has already been done, not realizing their processes are underperforming because they have never been tested and optimized for capacity. Once the assessment process reveals one constraint, facilities can often identify other constraints and drive improvement in multiple areas.
An Example of the Theory of Constraint
Company A is a larger company with many resources and an enterprise-level investment in continuous improvement. They have CI Managers at the plant level and teams producing charts and performance metrics. They decided to take their performance to the next level by implementing SafetyChain’s OEE module to digitize their effort in data collection. Part of the process of implementing SafetyChain entails documenting the theoretical maximums of the lines to create OEE models within the software. Basically, the implementation team determines how many units the line can produce per hour. Company A had traditionally performed at around 85% OEE and wanted to improve. When they turned on the system, they began to see production runs well above 100% with the platform and thought something was wrong with the software. But when the team went out on the floor with stopwatches and observed the processes, they realized their theoretical maximum was wrong—the equipment could safely run much faster than they had believed. By learning they could run their equipment faster, they achieved a 37% increase in productivity within one month, just by updating their production standards to reflect the accurate theoretical max speeds the line could run.
What Is Genchi Genbutsu?
Knowing how the process should work is one thing, but going and watching it is another. Being on the floor and seeing a process function is a valuable way to understand the process better. Also known as the “Go and See” method, Genchi Genbutsu asks people to do more than take a quick peek of the factory floor—it’s about observing over time and studying the data in the context of what is observed. By directly observing the processes, an organization can identify the true limits and determine solutions. They may even discover that what they thought were limits were actually self-imposed.
An Example of Genchi Genbutsu
Company B was post-startup with an established process, manufacturing products for architects and design firms with a specific goal of achieving over $400 per labor hour each production shift. A supervisor decided to get a new perspective one night and climbed into the rafters with a stopwatch to watch the start of the nightly process. The supervisor found the company was incentivizing people to meet the metric, but rather than getting optimized results they were getting more activity than achievement. The employees knew what the product was worth and how much time it took to make. The supervisor realized that the company had not asked the right question—they didn’t ask how quickly the product could be made. All the supervisors got together and decided to shift their perspective, changing the schedule to focus on how long it took to make the parts rather than the value of the parts being made. The company went from producing $400 per labor hour to $1,200 per labor hour overnight. Gross margins jumped from 30% to over 50%.
What Is Statistical Process Control?
An ounce of planning is worth a pound of recovery. Statistical process control (SPC) can validate the processes and make it easier to understand variability. SPC delivers a tremendous amount of focused data, and with the right tools and technology, can offer an automated collection and analysis system. With these tools in place, organizations can go beyond the data collected through SPC to understand what has caused the observed results and make changes to reduce variability.
An Example of Statistical Control
Company C is a top-five poultry processor seeking to capture data required by a customer and guided by regulations. In working with the company, there was a large amount of accumulated data. At the end of the project, the team members presenting the data felt they had achieved the objective. But members of the engineering team did not initially see the project as a success. The data revealed competing objectives—while one department was meeting regulatory requirements, another department could not achieve yields. As the temperatures rose, the yields were being “cooked out.” It started a more productive conversation about how to create objectives in different departments that do not work against each other.
Effective Continuous Improvement Involves Everyone
Strong arming employees into continuous improvement can be ineffective and create morale problems. By educating and involving employees throughout the continuous improvement cycle, organizations can create a productive continuous improvement culture. By ensuring leaders and supervisors are present and engaged beyond the special improvement project, employees see that their leaders are invested and involved in the day-to-day operations and aware that the changes they make matter. As improvements are made, management needs to update the metrics and inspect what should be happening as improvement happens. Celebrating the wins as a team engages everyone in the process and helps to solidify a continuous improvement culture.
