KAIZEN + IoT: A Precise Continuous Improvement Approach with Real-time Data
Currently, industrial factories face several challenges, including rising costs, fluctuating market demands, stringent quality standards, a shortage of skilled labor, and production losses that are difficult to see and measure accurately. These challenges compel organizations to seek methods for continuous performance improvement, cost reduction, and enhancing competitiveness.
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The critical core of modern improvement is Real-time data, because data received instantly at the time an event occurs helps management and frontline staff identify problems faster, make more precise decisions, and rectify issues promptly. This eliminates the need to wait for daily or weekly data, which is often delayed and inaccurate. Having accurate data is thus the crucial starting point for genuine efficiency improvement.
This is where the opportunity lies for integrating KAIZEN, the philosophy of continuous improvement, with IoT, the technology that connects devices and machines to collect Real-time data. Combining these two approaches elevates improvement from “guessing” to “decision-making based on actual data,” resulting in improvements that are precise, measurable, and systematically continuous.
Solwer will guide factories to understand the concepts of KAIZEN and IoT and clearly demonstrate how the two approaches can work together to drive Continuous Improvement in the digital age, and how they truly help transform data into tangible value in the production process.
Getting to Know KAIZEN and IoT
The Meaning of KAIZEN in Continuous Improvement
KAIZEN is a Japanese word composed of “Kai,” meaning change, and “Zen,” meaning good. Combined, KAIZEN means the concept of continuous improvement in a better direction. It is based on the principle that everyone in the organization, whether management or frontline staff, participates in finding, improving, and solving problems to enhance efficiency, reduce waste, and truly create value for the production process. KAIZEN is not about a single major project but a daily work culture that ensures development is continuous and never static. Interest
The Meaning of IoT (Internet of Things) in Industrial Systems
IoT, or Internet of Things, is the concept where various devices, such as sensors or machines, can continuously connect and communicate data via the internet network. In industrial systems (Industrial IoT or IIoT), these devices automatically collect data from machinery or production processes and send it to a central system for analysis and decision-making, thereby reducing downtime, controlling quality, and systematically increasing production efficiency.
What is Real-time Data, and Why is it Important?
Real-time data is information collected and processed as the event actually occurs, without waiting for later recording or batch processing. Examples include machine operating time data, production rates, or stoppage time, which are immediately sent to a Dashboard so management and frontline staff can see the current status instantly.
The importance of Real-time data lies in helping factories to:
- Identify problems in time, before losses accumulate and lead to major impacts.
- Analyze and make precise decisions, based on actual data, not guesswork.
- Accelerate the improvement cycle (PDCA) to occur faster than before—from months or weeks to hours or days—which is the key to achieving real KAIZEN results in the digital age.
The Intersection of KAIZEN and IoT Leading to Clear Results
The combination of KAIZEN and IoT is the drive for continuous development using actual Real-time data, which leads to tangible and clearly measurable results. The key components are as follows:
1. Insightful Data for Accurate Decision-Making
Installing IoT sensors and Real-time data collection systems allows teams to clearly see hidden operational issues (Hidden Loss), such as time used beyond the standard, abnormal operations, or variance in production rates. This clear visibility of problems is the foundation for effective planning and solving according to KAIZEN principles.
2. Accelerating the PDCA Cycle for Greater Efficiency
KAIZEN uses the PDCA (Plan – Do – Check – Act) cycle for continuous improvement, but using Real-time data allows this cycle to occur quickly and respond immediately to the actual situation. There is no need to wait for weekly or monthly data as before; results after improvement can be checked instantly, and continuous adjustments can be made meaningfully.
3. Building a Systematic Development Culture
When everyone in the organization, from management to frontline staff, can access accurate data and see the actual problems from the same Dashboard, the culture of collaborative improvement begins to deepen and become continuous. Communication about problems and solutions becomes clearer because everyone shares “the same data source” as the foundation for rational and measurable development.
Real-time Data and Continuous Improvement
Why Real-time Data is a "Game Changer" in Analysis and Improvement
Real-time data is information collected and processed as an event occurs, enabling management and operations staff to see the frontline situation instantly. This eliminates the need to wait for retrospective reports or manual data entry, which is often slow and unsuitable for real-time situations. Access to fresh data helps to:1
- Identify problems or abnormal trends immediately before they escalate into major losses.1
- Increase the accuracy of analysis and decision-making by using actual data at the point of occurrence, rather than guesswork or outdated information.1
Accelerate the PDCA (Plan-Do-Check-Act) process for continuous improvement to occur quickly and systematically.
This is essential for industrial factories that wish to achieve sustainable efficiency gains and respond to competition in the Industry 4.0 era, which Solwer supports through its IoT solutions to drive a Smart Factory based on efficient Real-time data.
The Difference Between Retrospective Data and Real-time Data
| Retrospective Data | Real-time Data |
|---|---|
|
1. Collected and analyzed after the event has occurred, e.g., end-of-day or weekly reports.
2. Suitable for reviewing the overall picture, but not timely for immediate operational issue resolution.
3. Prone to inaccuracies from data entry errors or time lag.
4. Often used for strategic analysis or long-term improvement.
|
1. Often used for strategic analysis or long-term improvement.
2. Suitable for immediate operational decision-making and rapid problem solving.
3. Reduces errors and delays because data comes directly from sensors or automated systems.
4. Applicable for both operational level and immediate Continuous Improvement.
|
Productivity vs. Losses: Analyzing with Actual Data at the Moment of Occurrence
In the production process, we are typically concerned with two major aspects: Productivity and Losses. Real-time data is the key to deeply understanding both:
Productivity
Tracking machine operation data or production processes in Real-time reveals:
- Whether the machinery is operating at its full potential.
- If the production cycle time (Cycle Time) meets the standard or if a delay is occurring.
- If there are bottlenecks in the production process.
With this real-time data, production managers can immediately adjust processes or dispatch a team to resolve issues to increase output without waiting for end-of-shift or end-of-day reports, which significantly increases output and reduces waiting time.
Losses
Production losses, such as machine downtime, machine errors, or defective parts, can be identified and measured in Real-time, allowing:
- The system is to alert supervisors when abnormalities occur.
- Faster cause analysis and resolution before accumulated losses occur.
- This data is to be used in KAIZEN activities to identify recurring loss points and systematically develop continuous solutions.
Practically, Real-time data helps factories not just to “know there is a problem,” but to know in time and know the root cause, which is the foundation of genuinely effective and sustainable improvement, the goal of the Continuous Improvement concept in the digital age supported by Solwer IoT solutions capable of comprehensive Real-time monitoring and display across the entire factory.
Solwer Solutions: Real-time Solutions for KAIZEN
Solwer develops IoT solutions designed to systematically support KAIZEN implementation in factories, focusing on collecting on-site Real-time data to ensure that analysis, decision-making, and Continuous Improvement yield the clearest results. Every solution is designed to be easy to use, suitable for actual operations, and addresses common factory challenges such as unknown loss causes, data delays, or inability to track machine status promptly.
1. On-site Data Collection System via IoT
The core of Solwer Lean IoT Solution is the use of IoT Sensors and machine connectivity devices to automatically retrieve actual data from the worksite, eliminating reliance on manual recording, which reduces inaccuracies and significantly increases data reliability.
Solwer’s IoT solutions collect data such as:
- Production speed and Cycle Time.
- Actual production quantity versus target.
- Recording of operational status in each Cycle Time.
All data is sent to the system in Real-time for processing and effective support of KAIZEN activities.
2. Loss Tracker: Real-time Loss Detection
One of Solwer’s prominent solutions is the Loss Tracker system for tracking and detecting production process “Loss” in real-time, capable of immediately classifying types of loss, such as:
- Undesired machine downtime.
- Waiting for raw materials.
- Bottlenecks in the production line.
- Machine operating slower than standard.
Loss Tracker provides a clear picture of “where, when, and why the loss occurred,” which is crucial KAIZEN data that helps teams solve problems more accurately.
3. Dashboard and Visualization for Immediate Decision-Making
Solwer uses Real-time Dashboards designed for easy comprehension, allowing key statuses to be seen at a glance, such as:
- Actual production quantity versus plan.
- Points of Loss or major issues in each shift.
Graphs and Visualization are designed to be user-friendly for frontline managers, machine operators, and executives, helping everyone make immediate decisions based on the actual situation.
Reference: Solwer Dashboards1
When abnormalities occur, such as unusually long downtime or reduced speed, they can be visualized instantly, enabling teams to reach the point of incidence and resolve issues quickly, minimizing impact before it escalates. Furthermore, the system can view a detailed Timeline of past events, such as when the machine stopped, how long it took, and who resolved the issue, for in-depth KAIZEN analysis. 5. Concrete Data Examples Supporting KAIZEN.
Real-time data from Solwer makes KAIZEN implementation concrete and problem-solving effective, such as:
- Bottleneck Analysis: Dashboard displays Cycle Time and workload per station → Clearly identifies bottlenecks → Allows for targeted line adjustment.1
- Workforce Optimization: Data on waiting for raw materials or work orders → Reduces unnecessary steps → Makes the production line flow smoothly.1
- Using Actual Data for More Accurate PDCA: No need to guess problems, but uses data to identify causes and results → Ensures the report and various KAIZEN activities have clear outcomes.1
All these examples are possible because Solwer helps factories see “what is actually happening” in Real-time, unlike manual data recording, which is delayed and inaccurate.
The New KAIZEN Loop: Plan–Do–Check–Act with Actual Data
Traditional KAIZEN uses the PDCA cycle for continuous process improvement. However, in many factories, PDCA still relies on retrospective data, end-of-shift reports, or manually collected information, causing the PDCA cycle to be slow, lack accuracy, and be untimely.1
In the digital age, having Real-time data from an IoT system like Solwer’s solution helps transform PDCA into a fast, precise cycle ready for immediate change. Actual data generated on the shop floor is the key tool that closes the gaps of traditional PDCA and elevates KAIZEN to a systematic level that creates measurable, real results.
Using Real-time Data in Each Step of PDCA
1. Plan Planning Based on Actual Data
Real-time data ensures that planning is not guesswork but based on facts, such as:1
- Machine downtime and the cause of stoppage.
- Bottlenecks in the production line.
- Actual speed compared to designed speed.
With real-time operational data, goal setting and improvement guidelines are precise and address the actual problems.
2. Do – Immediate Improvement Implementation
Once the plan is established, corrective actions can be implemented immediately, such as:
- Changing work methods.
- Adjusting machine speed.
- Optimizing personnel or material allocation.
The IoT system immediately collects the results when the improvement is implemented.
3. Check – Real-time Results Verification
Instead of waiting weekly or monthly, results can be checked instantly via the Solwer Dashboard.
Examples of what can be checked immediately:
- Changes in machine downtime.
- Production speed after adjustment.
- KPIs such as OEE.
The speed of the data allows for quick and accurate results verification.
4. Act – Standardization and Continuation
Once results are confirmed, they can be standardized immediately, such as:
- Standard Operating Procedures (Standard Procedure).
- Adjusting machine PM plans.
- Alerting for maintenance during appropriate times.
The data system helps continuously record and compare results.
Suitable Metrics for KAIZEN + IoT
When combining the KAIZEN concept with Real-time data from IoT, the indispensable element is selecting Key Performance Indicators (KPIs) that truly reflect operational results and drive continuous improvement. The main suitable metrics include:
Real-time OEE
OEE (Overall Equipment Effectiveness) is a metric that truly indicates the efficiency of machine utilization. When measured in Real-time from IoT data, it allows us to instantly see the machine’s actual Performance without delays, making it possible to:
- Continuously monitor machine efficiency.
- Instantly compare before and after improvement.
- Make targeted decisions.
OEE data derived from real-time information is, therefore a crucial metric for modern KAIZEN activities.
Downtime and Minor Stops
Downtime, or the time a machine stops operating, is considered a “major loss” in production. While Minor Stops are small, frequent stoppages that accumulate significant impact when combined.
Real-time data collection helps to:
- Instantly identify the time and reason for machine stoppages.
- Analyze the occurrence of Minor Stops to find the root cause.
- Measure the results of improvements after implementing KAIZEN.
This metric helps reveal hidden problems and solve them accurately.
Throughput / Cycle time
These three metrics reflect the quality and speed of production when using Real-time data:
- Throughput – The number of units produced within a given time.
- Cycle time – The time per single unit of production.
With data from IoT, we can see the actual limitations of the production line and set more precise improvement targets.
Dynamic KPIs and Performance Trend
In a Real-time data system, KPIs are not static figures but “metrics that change according to the situation.”
such as:
- Performance trend in each shift.
Dynamic metrics enable the KAIZEN team to:
- See trends before problems escalate.
- Plan improvements in advance.
- React to actual events immediately, which is something retrospective reporting metrics cannot do.
Case Study: Improvement with KAIZEN + IoT
Examples of On-site Problems Before and After Using Real-time Data
Before using Real-time data:
The factory found that a machine was running slower than the standard but did not know the actual cause. They had to rely on daily reports, which were often delayed, leading to “random-cause” adjustments without actual data reference.
After using Real-time data:
With the Solwer IoT system, actual operational data was collected and analyzed instantly, leading the team to find that
- Minor Stops were caused by inappropriate settings during the morning.
- Downtime increased during certain periods.
The team could determine the cause and rectify the issue immediately with actual data, which led to a reduction in machine stoppages and an increase in Throughput without waiting for end-of-day reports.
Reducing Downtime / Increasing Performance / Reducing Defects
When Real-time data is used for analysis:
- Downtime is reduced through immediate cause detection and proactive alerts.1
- Performance is increased because data helps adjust operational settings closer to the standard.
These results cannot be achieved through retrospective data analysis alone.
Business Outcomes
When organizations focus on improvement using Real-time data, tangible business outcomes are seen, such as:
- Shorter Lead Time because downtime is reduced and the process is smoother.
- Production Increase due to increased Throughput from immediate improvements.
These metrics reflect that implementing KAIZEN + IoT is not just a technology project but a creator of measurable business value.
How to Start Using KAIZEN + IoT in Your Factory
Getting started is not difficult, but it requires a plan and good coordination between various teams, as follows:
Steps for Setting up the IoT System and Data Collection
- Analyze points for installing sensors or IoT devices.
- Select a platform that supports Real-time data.
- Configure data collection to cover critical machinery and KPIs.
- Test continuous data transmission and connectivity.
The data obtained will be the foundation for every step of KAIZEN activities.
Connecting with the Lean / Production Teams
For data to be genuinely utilized, key teams should be connected:
- Lean Team – To analyze data and design improvement activities.
- Production Team – To implement results on the shop floor.
Designing KPIs and Reporting
Define clear KPIs such as OEE, Downtime, Cycle time, and dynamic KPIs from Real-time data. Also, establish a reporting system that is:
- Real-time on the Dashboard.
- Compare the before and after improvement.
Good reporting helps the team see trends and make immediate decisions.
Team Training for Data Reading and Usage
Having Real-time data will be useless if the team does not understand how to:1
- Read the Dashboard.
- Analyze KPIs.
- Use the data to solve problems.
Therefore, training should cover both technical and analytical aspects to empower the team to make confident decisions based on actual data.
In driving continuous improvement in the digital factory era, the competitive advantage is not just new machinery or increasing personnel, but using actual Real-time data in conjunction with KAIZEN.
Real-time data eliminates the need for factories to “guess” anymore, instead allowing decisions to be based on clear facts, causes, quantities, and timing. Problems can be solved at the moment of occurrence, not after the damage has accumulated. KAIZEN + IoT is not an IT project but a business strategy. Data is not just a report; it is the “competitive weapon of the modern factory”. When a factory starts collecting and using actual data, improvement becomes a systematic process that occurs hourly, every shift, and every day, rather than something done “periodically”.
To take this forward into practical factory application, you can choose Solwer’s services to design and install an IoT system for Real-time data collection, including Loss Tracking tools, analytical Dashboards, and alert systems, to create a PDCA Loop that spins instantly with actual data. When an issue occurs on the shop floor, the data is automatically sent to the system, ready for the Lean and Production teams to analyze and make precise decisions.
And to study the connection between KAIZEN + IoT in depth, including examples of practical factory applications, you can download Solwer s e-book for guidance on system setup and confidently starting Continuous Improvement in the digital age.
Reference
- ทำไมเราควรมีโซลูชันการแสดงผลแบบ Real-Time!
- เทคโนโลยี IoT ช่วยให้เกิดการ Monitoring Real Time เพื่อให้ได้ข้อมูลการปรับปรุงการผลิตในโรงงานมีความถูกต้องและแม่นยำ – idtihub
- เหตุใดความพร้อมใช้งานสูงจึงมีความสำคัญในระบบการผลิต 4.0
- Factory IoT และ QC Automation ปรับปรุงสายการผลิตให้แข่งขันได้ในโลกยุคใหม่
- IIoT คืออะไร? เรื่องต้องรู้ในโรงงานดิจิทัลและระบบอัตโนมัติ