Максимизируйте рентабельность инвестиций в 2025 году: 5 проверенных шагов по оптимизации производительности пеленальной машины

Окт 29, 2025 | Новости

Аннотация

The global hygiene products market, particularly in developing economies across South America, Russia, Southeast Asia, the Middle East, and South Africa, presents a landscape of immense opportunity coupled with intense competitive pressure. For manufacturers of disposable diapers, the mechanical heart of the operation—the diaper machine—dictates profitability and market position. This document provides a comprehensive examination of diaper machine performance optimization as a strategic imperative for 2025. It moves beyond rudimentary maintenance to explore a holistic framework encompassing Overall Equipment Effectiveness (OEE) as a core metric, the implementation of predictive maintenance schedules powered by IoT and data analytics, rigorous control over raw material consumption to minimize waste, and the cultivation of an empowered, highly skilled workforce. The analysis demonstrates that achieving peak operational efficiency is not a singular project but a continuous process of improvement, demanding a cultural shift towards data-driven decision-making and proactive problem-solving to secure a sustainable competitive advantage and maximize return on investment.

Основные выводы

  • Adopt Overall Equipment Effectiveness (OEE) to measure availability, performance, and quality.
  • Shift from reactive repairs to a predictive maintenance schedule using IoT sensors.
  • Implement automated splicing and stringent quality control to reduce material waste.
  • Invest in advanced operator training to improve troubleshooting and changeover speed.
  • Use data analytics for continuous diaper machine performance optimization and improvement.
  • Standardize operating procedures (SOPs) to ensure consistent quality and efficiency.
  • Focus on reducing minor stops, as their cumulative effect significantly lowers output.

Оглавление

1. Mastering Overall Equipment Effectiveness (OEE) as a Foundational Metric

In any manufacturing endeavor, the pursuit of efficiency is the central narrative. For a diaper production facility, where machines run at incredible speeds, converting rolls of nonwoven fabric and polymers into finished products, efficiency is not merely a goal; it is the bedrock of financial viability. The language we use to speak about this efficiency must be precise, universal, and actionable. This is the role of Overall Equipment Effectiveness, or OEE. It serves as a composite metric, a powerful lens through which we can scrutinize the health and productivity of a production line. OEE tells a story in three parts: were we running when we were scheduled to run (Availability)? How fast were we running compared to our potential (Performance)? How many of the products we made were good enough to sell (Quality)? The mathematical product of these three scores gives us a single, unforgiving number that represents our true productivity. A score of 100% is a theoretical ideal—a line that runs for every scheduled minute, at its maximum designed speed, producing zero defective products. While no real-world operation achieves this, the pursuit of it is what drives meaningful improvement. Understanding OEE is the first step toward genuine diaper machine performance optimization.

Understanding the Three Pillars of OEE: Availability, Performance, Quality

Let's dissect these three pillars. Think of them as three gates through which your total potential production time must pass. Any loss at one gate diminishes the final output.

Availability is the first gate. It measures the percentage of scheduled time that the machine is actually running. The primary enemy of availability is downtime. Downtime itself can be categorized into two types: planned stops and unplanned stops. Planned stops are necessary evils—time allocated for product changeovers, scheduled maintenance, or team meetings. While they are planned, they still represent a loss from 100% availability, and a key optimization strategy is to minimize the duration of these planned events. Unplanned stops are the true villains of production. These are equipment failures, material shortages, or unexpected jams. Every minute of an unplanned stop is a direct hit to your bottom line. Calculating availability is straightforward: Availability = Run Time / Planned Production Time. If you planned to run for an 8-hour shift (480 minutes) but experienced 60 minutes of unplanned downtime, your run time is 420 minutes, and your availability is 420/480 = 87.5%.

Performance is the second gate. It accounts for the speed at which the machine operates. A machine might be running, but is it running as fast as it could be? Performance losses come from two main sources: minor stops and reduced speed. Minor stops are the brief, often unrecorded, pauses—a quick jam cleared by an operator, a sensor that needs wiping, a misaligned roll that is quickly adjusted. Individually, they seem insignificant, but cumulatively, they can represent a substantial loss. Reduced speed occurs when the machine is deliberately run slower than its ideal or designed cycle time. This might be due to poor quality raw materials, an inexperienced operator, or the fear of causing a major breakdown by running at full capacity. The formula is: Performance = (Ideal Cycle Time × Total Count) / Run Time. If your machine's ideal speed is 600 diapers per minute, but over a 420-minute run time you only produced 231,000 diapers (an average of 550 per minute), your performance score would be 550/600 = 91.7%.

Quality is the final gate. It simply measures the good products as a percentage of the total products made. It accounts for products that are rejected during production or after inspection due to defects—improper sealing, misplaced tabs, incorrect absorbent core distribution, or aesthetic flaws. Rework, if it exists in your process, is also a quality loss. The calculation is simple: Quality = Good Count / Total Count. If you produced 231,000 diapers in total, but 4,620 were rejected, your good count is 226,380. Your quality score is 226,380 / 231,000 = 98%.

To find the OEE score, we multiply the three factors: 87.5% (Availability) × 91.7% (Performance) × 98% (Quality) = 78.6%. This number tells a powerful story. While a 98% quality rate might seem excellent on its own, the combined losses from downtime and reduced speed reveal a significant gap between current performance and ideal potential.

Setting Realistic OEE Benchmarks for Diaper Production Lines

Knowing your OEE score is one thing; knowing what to do with it is another. The first step is to establish a benchmark. What constitutes a "good" OEE score? While a world-class OEE is often cited as 85%, this figure can be misleading without context. For a complex, high-speed process like diaper manufacturing, the benchmark can vary based on the age of the equipment, the product mix, and the maturity of the operational processes.

A newly installed, state-of-the-art diaper machine running a single, high-volume product might realistically target an OEE of 80-85%. In contrast, an older line that has to handle frequent changeovers between ten different diaper sizes and specifications might find an OEE of 60-65% to be a more achievable, yet still challenging, initial target. The key is not to fixate on a universal number but to use OEE as a tool for internal improvement. Your most important benchmark is your own historical performance.

The table below provides a general framework for OEE levels in a diaper manufacturing context. It helps to contextualize performance and set tiered goals.

OEE Score Performance Level Typical Characteristics in Diaper Manufacturing
Below 40% Poor High levels of unplanned downtime, frequent material jams, significant quality defects, lack of standardized procedures.
40% – 60% Average Some downtime control, but performance losses from minor stops and reduced speed are significant. Basic maintenance in place.
60% – 75% Good / Industry Typical Stable operation, planned maintenance schedules are followed, quality issues are tracked, but root cause analysis is inconsistent.
75% – 85% World-Class Low unplanned downtime, line runs close to ideal speed, high first-pass quality yield, strong culture of continuous improvement.

The goal-setting process should be collaborative, involving operators, maintenance staff, and management. Start by reliably measuring your baseline OEE. Once you have several weeks or months of data, you can identify your biggest loss category. Is it availability? Then your focus should be on analyzing and reducing unplanned downtime. Is it performance? Then you need to investigate the causes of minor stops and reduced speed. A targeted approach to improving the weakest of the three pillars will yield the fastest gains in your overall OEE score.

Practical Strategies for Improving Availability: Reducing Downtime

Availability is often the lowest of the three OEE scores in many diaper plants, making it the most fertile ground for improvement. Reducing downtime is a systematic process of investigation and action.

First, you must distinguish between planned and unplanned downtime. For planned downtime, the primary target is changeover time. The process of switching from producing a "Newborn" size diaper to a "Size 5" can involve changing cutting dies, adjusting guides, swapping raw material rolls, and re-calibrating sensors. Applying principles from Single-Minute Exchange of Die (SMED) can be transformative. The core idea of SMED is to convert as many "internal" setup steps (those that can only be done when the machine is stopped) to "external" steps (those that can be prepared while the machine is still running). For example, preparing the new raw material rolls and pre-setting the guides for the next size on a separate cart before the current run finishes is a classic externalization strategy. A well-executed SMED program can often cut changeover times by 50% or more, directly boosting availability.

For unplanned downtime, the approach must be rooted in data. Your machine control system should log every stop, tagging it with a time, duration, and, most importantly, a reason. Operators must be trained to accurately assign a reason for each stop from a predefined list. Is it a "Web Break – Nonwoven"? A "Jam at Stacker"? A "Glue System Fault"?

Once you have this data, a Pareto analysis is your best friend. This simple tool helps you identify the "vital few" causes that are responsible for the majority of your downtime minutes. You will likely find that 80% of your downtime comes from just 20% of the possible causes. Focus your problem-solving efforts on the top 3-5 reasons for downtime. For each one, form a small, cross-functional team (operator, maintenance technician, engineer) to perform a root cause analysis. Techniques like the "5 Whys" can be incredibly effective. A web break might seem like the problem, but by asking "why" repeatedly, you might discover the root cause is improper tension control, which is caused by a worn-out roller, which is caused by a lack of proper lubrication, which is caused by an unclear maintenance procedure. Fixing the procedure prevents the problem from ever recurring, which is far more valuable than just getting good at re-threading the web.

Enhancing Performance: Tackling Minor Stoppages and Reduced Speed

Performance losses are often called the "hidden factory." The machine is running, so from a distance, everything looks fine. However, the constant, brief interruptions and the failure to run at the designated speed silently steal a massive amount of potential output. Overcoming these losses requires a different mindset.

Tackling minor stoppages begins with making them visible. Many control systems do not automatically log stops under a certain duration (e.g., 30 seconds). This threshold should be lowered or eliminated. The goal is to see every single interruption. When operators have to clear a small jam or adjust a sensor multiple times an hour, they may see it as "just part of the job." It is not. Each intervention is a signal of an underlying instability in the process. Encourage operators to log these micro-events. Use high-speed cameras focused on problem areas (like the point where the elastic leg cuffs are applied) to slow down reality and see exactly what is causing the intermittent fault. Often, these issues are related to subtle variations in raw materials or slight misalignments that accumulate over time.

The issue of reduced speed is equally complex. An operator might intentionally slow down the machine because running at the full 800 pieces per minute (PPM) rate leads to more web breaks or quality defects. In their mind, running at 720 PPM with higher stability is better than frequent stops at 800 PPM. They are not wrong in their logic, but this masks the real problem. The question management should ask is not "Why are you running slow?" but "What is preventing you from running at the designed speed?" The answer could be an underperforming glue system that cannot keep up, a batch of super absorbent polymer (SAP) with inconsistent properties, or a cutting unit that vibrates excessively at high speeds. Addressing these technical bottlenecks is a core component of diaper machine performance optimization. It allows the entire line to run faster and more stably, unlocking a new level of throughput without compromising quality.

Elevating Quality: Minimizing Defects and Rework

The quality component of OEE is typically the highest of the three, but this can be deceptive. A 98% quality rate sounds great, but on a line producing 800 diapers per minute, that 2% loss equates to 16 defective diapers every minute. Over an 8-hour shift, that is over 7,500 wasted diapers. The financial cost of the raw materials, energy, and labor embodied in those defects is substantial.

Improving quality begins with a robust detection system. Modern diaper machines are equipped with vision systems that inspect every single diaper for dozens of attributes: tab placement, core integrity, leg cuff bonding, backsheet printing alignment, and more. Any diaper that fails these checks is automatically rejected. The first step is to ensure these systems are properly calibrated and maintained. A dirty camera lens or a misconfigured inspection parameter can lead to either letting bad products escape (a risk to your brand reputation) or rejecting good products (a direct financial loss).

The second, more profound step is to move from detection to prevention. The data from your vision system is a goldmine. It should not just be used to trigger a reject gate; it should be used to provide real-time feedback to the process. If the vision system detects that the absorbent core is starting to drift to the left, it should not wait until it is out of tolerance to start rejecting products. Instead, that data should be used to signal an alarm to the operator or, in a more advanced system, to automatically make a micro-adjustment to the core-forming unit to bring the process back to the center of its specification. This is the essence of Statistical Process Control (SPC). By monitoring trends and reacting to small deviations, you can prevent defects from ever occurring. This proactive approach to quality is a hallmark of a world-class manufacturing operation and a cornerstone of effective diaper machine performance optimization.

2. Implementing a Predictive Maintenance and Smart Monitoring Framework

The traditional approach to maintenance in many factories has been reactive. A component fails, the machine stops, and a maintenance technician is called to fix it. This "breakdown maintenance" model is the most expensive and disruptive way to manage equipment. It leads to long, unplanned downtime, frantic scrambles for spare parts, and often, collateral damage to other parts of the machine. The next evolution was preventive maintenance, based on a fixed schedule. "Change the oil every 3 months," "replace the cutting blades every 500 hours of operation." This is a significant improvement, as it prevents many failures. However, it is also inefficient. It often leads to replacing components that are still perfectly healthy, wasting parts and labor. Or, it can fail to predict a premature failure that occurs before the scheduled replacement.

The paradigm for 2025 and beyond is predictive maintenance (PdM). PdM uses technology to monitor the actual condition of the equipment to determine when maintenance should be performed. It is about fixing a component just before it is about to fail. This approach promises the best of both worlds: minimizing unplanned downtime while also maximizing the useful life of each component. For a high-speed, complex asset like a modern diaper machine, a PdM framework is not a luxury; it is a fundamental strategy for achieving elite levels of performance.

The Shift from Reactive to Predictive Maintenance: A Paradigm Change

Making the shift from a reactive to a predictive maintenance culture requires more than just new technology; it demands a change in mindset from the plant floor to the corner office. It is a move from being firefighters to being detectives.

In a reactive culture, the maintenance team is rewarded for speed. How quickly can they get a downed machine back up and running? Their success is measured in minutes of downtime. In a predictive culture, the team is rewarded for foresight. How many potential failures did they identify and prevent this month? Their success is measured in the absence of downtime. This is a profound shift.

This transition requires a structured approach. It starts with a criticality analysis of the diaper machine. Not all components are created equal. A failure of the main drive motor is a catastrophe. A failure of a sensor bracket is a minor inconvenience. You must identify the components whose failure would have the most severe consequences for safety, quality, and production. These critical components become the first candidates for your predictive monitoring program.

The table below contrasts the old and new paradigms, illustrating the benefits of embracing a predictive approach.

Aspect Reactive Maintenance ("Fix it when it breaks") Predictive Maintenance ("Fix it before it breaks")
Trigger Equipment failure Data indicating potential failure (e.g., vibration, temperature)
Downtime Unplanned, often long and disruptive. Planned, short, and scheduled for non-production times.
Cost High. Includes overtime labor, express shipping for parts, and lost production. Low. Maintenance is planned, parts are ordered in advance, no lost production.
Spare Parts Large inventory of "just in case" parts must be kept. Minimal inventory needed. Parts are ordered "just in time."
Component Life Parts are either run to failure (risky) or replaced too early (wasteful). Maximum useful life is extracted from every component.
Safety Higher risk of catastrophic failure and associated safety incidents. Lower risk, as developing problems are identified early.

Embracing this change means investing in training. Your maintenance technicians need to become data analysts. They need to learn how to interpret a vibration spectrum or a thermal image, not just how to turn a wrench. It's a journey, but one that leads directly to a more stable, predictable, and profitable operation.

Key Technologies: IoT Sensors, Thermal Imaging, and Vibration Analysis

Predictive maintenance is powered by data, and that data comes from a suite of technologies designed to listen to the "voice" of the machine. For a diaper machine, three technologies are particularly powerful.

1. Vibration Analysis: Every rotating component on a diaper machine—motors, bearings, rollers, cutting units—has a unique vibration signature when it is healthy. As a component begins to wear out, develop an imbalance, or become misaligned, its vibration signature changes. By placing small, wireless vibration sensors (part of the Internet of Things, or IoT) on critical rotating assets, you can continuously monitor these signatures. Sophisticated software analyzes the data, looking for tell-tale frequencies that indicate specific fault types. For example, a peak at a particular frequency might indicate a microscopic flaw on the inner race of a bearing, while a different frequency might point to a misalignment between a motor and a gearbox. This technology can often provide weeks or even months of warning before a bearing seizes, allowing you to schedule its replacement during a planned stop.

2. Thermal Imaging (Thermography): Problems in mechanical and electrical systems often manifest as heat before they lead to failure. A handheld or fixed thermal imaging camera can instantly reveal these hotspots. On a diaper machine, thermography is invaluable for scanning electrical cabinets to find loose connections or overloaded circuits, which are a major fire risk. It can identify failing motor bearings, which run hot before they seize. It can even be used to check the performance of the adhesive application systems; a clogged nozzle or a failing heater on a glue tank will show up as a cold spot in the thermal image. Regular thermal scans of the entire machine can be a quick, non-invasive way to get a health check and spot developing issues.

3. Oil Analysis and Lubrication Management: Lubrication is the lifeblood of any machine. For the numerous gearboxes and hydraulic systems on a diaper line, the condition of the oil provides a wealth of diagnostic information. Sending small samples of oil to a lab for analysis can reveal the presence of microscopic metal particles, indicating wear in gears or bearings. It can detect contamination from water or other fluids, which can degrade lubricating properties. It can also measure the depletion of critical additives in the oil. A robust lubrication management program, guided by oil analysis, ensures that components are protected, and oil is changed based on its actual condition, not just a generic schedule. This is a simple yet incredibly effective form of diaper machine performance optimization.

These technologies work best when used in concert. A rising vibration level on a gearbox might be corroborated by a hotspot detected by a thermal camera and the presence of iron particles in the oil sample. This confluence of data gives you an extremely high degree of confidence that a failure is developing, allowing you to act proactively.

Building a Data-Driven Maintenance Schedule

The output of your PdM technologies is a stream of data. The next challenge is to turn that data into a concrete maintenance plan. This requires a Computerized Maintenance Management System (CMMS). A modern CMMS acts as the brain of the maintenance operation.

When a sensor detects an anomaly—for instance, the vibration on a fan motor exceeds a preset "alert" threshold—it can automatically generate a work order in the CMMS. This work order is not an emergency request. It is a notification to the maintenance planner: "The bearing on fan motor F-101 is showing early signs of wear. Estimated time to failure is 4-6 weeks."

The planner can then look at the production schedule and see that there is a major product changeover planned in 3 weeks. They can add the replacement of the F-101 bearing to the list of tasks to be performed during that planned stop. They can check the inventory to ensure a spare bearing is available or order one with standard shipping. A maintenance technician is assigned the job. The procedure for replacing the bearing is attached to the work order.

On the day of the changeover, the technician performs the replacement. They close the work order in the CMMS, and the system records how long it took and what parts were used. The vibration sensor on the new bearing confirms that the signature is back to its healthy baseline.

This closed-loop process is the essence of data-driven maintenance. It is calm, organized, and efficient. It transforms the maintenance department from a chaotic emergency room into a well-oiled machine, systematically eliminating the sources of unplanned failure. It is a critical enabler of high OEE and a pillar of sustainable diaper machine performance optimization.

Case Study: How Predictive Maintenance Reduced Unplanned Stops by 40%

Consider the case of a mid-sized diaper manufacturer in Southeast Asia. Their primary production line was approximately seven years old and was experiencing an average of 10-12 hours of unplanned downtime per week. A Pareto analysis revealed that over half of this downtime was caused by failures of rotating components: bearings in the fluff pulp mill, rollers in the web tensioning system, and the main drive gearbox.

They initiated a pilot PdM program, focusing on these critical assets. They installed wireless vibration sensors on 30 key bearings and rollers and contracted a service to perform quarterly thermal imaging surveys and oil analysis on the main gearbox.

Within the first three months, the system flagged two issues. The vibration signature on a primary tensioning roller showed a clear bearing-wear frequency, predicting a failure within 2-3 weeks. The maintenance team replaced the bearing during the next scheduled weekend maintenance. Upon inspection, the old bearing showed significant visible pitting on the outer race; it was close to catastrophic failure. The second issue was a hotspot on a contactor in the main electrical panel, identified by the thermal scan. It was caused by a loose connection, which was tightened in minutes. Left undetected, it would have likely led to a major electrical failure and a significant fire risk.

After one year, the results were dramatic. Unplanned downtime attributed to mechanical failures on the monitored components had decreased by over 80%. The overall unplanned downtime for the entire line was reduced by 40%. The initial investment in sensors and training was paid back in less than six months through the value of the saved production time. This real-world example illustrates the tangible power of shifting from a reactive to a predictive maintenance strategy.

The Role of Machine Learning in Predicting Component Failure

The next frontier in predictive maintenance involves machine learning (ML). While traditional PdM relies on predefined thresholds (e.g., "alert if vibration exceeds X"), machine learning models can learn the normal operating behavior of a machine in a much more nuanced way.

An ML algorithm can take in data from dozens or even hundreds of sensors simultaneously—vibration, temperature, speed, web tension, glue pressure, etc. It learns the complex correlations between all these variables during normal, healthy operation. It essentially builds a highly detailed "digital twin" of the machine's behavior.

Then, the ML model continuously compares the live data stream from the machine to its learned model of "normal." When it detects a subtle deviation—a pattern that does not match what it has learned—it can flag an "anomaly." This anomaly might be a pattern that is too complex for a human to spot or for a simple threshold to catch. For example, it might learn that a small increase in the vibration of motor A, combined with a tiny drop in temperature at sensor B, and a slight increase in the current draw of drive C, is a unique precursor to a specific type of jam that occurs 30 minutes later.

By identifying these complex, multi-variate patterns, ML-based predictive maintenance can not only predict that a failure will occur but can also provide a more accurate diagnosis of what is about to fail and why. As these systems become more accessible and easier to implement, they will offer an even more powerful tool for manufacturers striving for the highest levels of diaper machine performance optimization and operational stability.

3. Optimizing Raw Material Handling and Consumption

In the economics of diaper manufacturing, raw materials represent the single largest component of the cost of goods sold, often accounting for 50-70% of the final product cost. The main ingredients—nonwoven fabrics for the topsheet and backsheet, fluff pulp and super absorbent polymer (SAP) for the absorbent core, elastics, and adhesives—are consumed at a prodigious rate. A high-speed line can consume several tons of these materials every day. Consequently, even a small percentage of waste can have a massive financial impact. Optimizing the handling and consumption of these materials is not just a peripheral activity; it is a central front in the battle for profitability. Every gram of wasted SAP, every meter of trimmed nonwoven, is profit that is literally being thrown away. An effective strategy for diaper machine performance optimization must therefore place a heavy emphasis on material efficiency.

The Financial Impact of Material Waste in Diaper Manufacturing

Let's put some numbers to this to understand the scale. Imagine a diaper line running 24/7, producing 700 diapers per minute. That's just over one million diapers per day. If the raw material cost per diaper is, for example, $0.08, the daily material consumption is $80,000.

Now, consider a waste level of 5%. This might sound acceptably low to some, but it represents $4,000 in lost material every single day. Over a year, that's nearly $1.5 million of waste for a single production line. If you could reduce that waste from 5% to 3%, you would be adding over half a million dollars directly to your annual profit.

Waste in diaper production comes from several sources:

  • Startup/Shutdown Waste: The first few hundred diapers produced after a startup or a major stop are often out of specification and must be scrapped.
  • Splice Waste: When one roll of material (e.g., nonwoven) runs out, it must be spliced to a new roll. The machine slows down or stops, and the diapers produced during this splice sequence are typically rejected.
  • Trim Waste: The process of cutting the diaper shape from the continuous web of materials generates a significant amount of trim, particularly from the leg contour area.
  • Quality Rejects: Any diaper rejected for a quality defect represents a total loss of its constituent materials.
  • Over-consumption: Using slightly more material than is specified in the product design, such as applying a thicker layer of adhesive or a heavier absorbent core, is a hidden form of waste that can add up significantly.

Tackling these sources of waste requires a combination of technology, process control, and operator diligence.

Automated Splicing Systems: The Key to Uninterrupted Production

One of the most significant sources of both waste and downtime is the roll change process. On a high-speed line, a large roll of nonwoven fabric might be consumed in less than an hour. A manual or semi-automatic splicing process requires the operator to slow or stop the machine, thread the new material, and make the splice. This process is slow, generates a significant amount of waste, and is a major cause of availability loss in the OEE calculation.

The solution is a fully automatic, zero-speed splicer. This sophisticated piece of equipment holds the new roll of material in standby. As the current "running" roll is about to expire, the splicer accumulates a buffer of the material in a "festoon" or "accumulator." This buffer allows the main process to continue running at full speed. At the precise moment the roll runs out, the splicer automatically clamps the end of the old web, cuts it, and instantly joins it to the start of the new web using tape or heat. The entire splice is made while the web entering the accumulator is momentarily stationary (hence "zero-speed"), but the web exiting the accumulator and feeding the machine never stops.

The benefits are enormous. Downtime for roll changes is completely eliminated, providing a massive boost to the Availability component of OEE. Because the splice is made automatically and at high speed, the amount of waste material is reduced to a minimum—often just one or two diaper lengths, compared to dozens or hundreds in a manual process. The splice itself is more reliable and consistent, reducing the risk of a web break after the splice. While the investment in an automatic splicer is significant, the ROI for a high-speed line is typically very rapid, often under 12 months, due to the combined savings from increased uptime and reduced material waste. Any serious effort at diaper machine performance optimization must evaluate the implementation of these systems for all primary web materials.

Quality Control for Incoming Materials: Nonwovens, SAP, and Adhesives

There is an old saying in manufacturing: "You cannot inspect quality into a product." This is especially true in diaper manufacturing. You cannot create a high-quality diaper from low-quality raw materials. Inconsistent materials are a primary cause of machine instability, downtime, and defects. A robust incoming quality control (IQC) program is therefore not just a quality function; it is a machine performance function.

Your suppliers are your partners in production. You must work with them to establish clear, measurable specifications for every material you purchase.

  • Nonwoven Fabrics: Key parameters include basis weight (grams per square meter), tensile strength, elongation, and wettability (for the topsheet). A roll of nonwoven that has inconsistent basis weight will cause problems in the core-forming unit and can lead to web breaks. You should have the equipment in your lab to test a sample from each new batch of material that arrives to verify it meets your specifications before it is ever loaded onto the machine.
  • Super Absorbent Polymer (SAP): SAP is the magic ingredient, the material that absorbs and locks away liquid. Its properties are critical. You need to test for absorption capacity, absorption speed, and particle size distribution. A batch of SAP with too many fine particles can create dust, which can clog filters and cause sensor faults. A batch with poor absorption speed will lead to a diaper that leaks, a catastrophic quality failure.
  • Adhesives: The hot-melt adhesives used for construction and elastic attachment are also critical. Their viscosity (thickness) must be consistent. If the viscosity is too low, the glue may spray or "string," contaminating other parts of the machine. If it is too high, it may not apply properly, leading to weak bonds and delamination of the diaper layers. Working with your adhesive supplier to ensure consistent quality and optimizing the application temperature and pressure on the machine are key.

When you do detect an out-of-spec material, you must have a clear procedure. The material should be quarantined and not used. You must provide clear data to your supplier to explain the rejection. A good supplier will use this feedback to improve their own processes. Over time, this collaborative approach leads to a more stable and predictable supply chain, which directly translates to a more stable and predictable production line. This is a foundational aspect of achieving long-term diaper machine performance optimization. Exploring options like comprehensive solutions for baby diaper machines can provide integrated systems that are better equipped to handle minor material variations.

Techniques for Reducing Trim Waste and Optimizing Core Formation

Even with perfect materials and uninterrupted running, the diaper design itself can be a source of waste. The "T-shape" or contoured shape of a modern diaper means that when the leg cutouts are made, the trimmed material is waste. On some products, this "trim waste" can be as high as 10-15% of the total web material.

There are several strategies to combat this. The first is design optimization. Can the shape of the diaper be slightly modified to "nest" more efficiently on the web, reducing the space between units? Can the trim from one area be repurposed for another component? Some advanced processes, for example, can reclaim the nonwoven trim, re-process it, and use it as part of the material for a non-critical layer like the acquisition distribution layer (ADL).

The second strategy is process optimization. The precision of the cutting unit is paramount. A dull or misaligned cutting die can create a ragged edge, leading to more defects. It can also drift, increasing the amount of trim. Using high-precision rotary die cutters and ensuring they are meticulously maintained is key.

Another major area for material optimization is the absorbent core. The core is a precise mixture of fluff pulp and SAP. The goal is to place this expensive material exactly where it is needed for absorption and nowhere else. Modern core-forming technology allows for "contoured" or "profiled" cores, which are thicker in the target zone and thinner at the edges. This not only improves comfort and fit but also saves a significant amount of material compared to a simple rectangular core of uniform thickness. However, this requires precise control. The forming system must be able to maintain a consistent basis weight profile across the core, and the vision system must verify this on every diaper. Any deviation can lead to either poor performance or wasted material. Fine-tuning the core-forming process to meet the absorbency target with the absolute minimum amount of pulp and SAP is a high-level form of diaper machine performance optimization.

Integrating Supply Chain Data with Production Planning

A final, more advanced strategy for material optimization is to digitally integrate your supply chain with your production floor. Imagine a system where your production schedule is directly linked to your suppliers' inventory and production systems.

When you schedule a run of 5 million "Size 4" diapers, the system automatically calculates the exact amount of each required raw material. It then checks your current inventory and communicates with your suppliers' systems to issue purchase orders and delivery schedules. This ensures that materials arrive "just-in-time," reducing the amount of capital tied up in inventory and minimizing the need for large warehouses.

This integration can also work in the other direction. If your SAP supplier informs the system that a particular batch has a slightly lower-than-average absorption capacity (but is still within an acceptable range), this information can be passed to the diaper machine. The machine's control system could then automatically make a micro-adjustment, increasing the basis weight of the core by 0.5% for the duration of that batch's use, ensuring that the final product still meets its performance specifications.

This level of digital integration creates a truly "smart" factory, where information flows seamlessly from the supply chain through the production process and back again. It allows the entire system to be more resilient, more efficient, and more responsive to the inevitable variations that occur in any real-world manufacturing environment. It represents the pinnacle of material management and a key future direction for the industry.

4. Empowering Your Workforce: Advanced Operator Training and Skill Development

In the sophisticated world of modern manufacturing, it is tempting to focus exclusively on the hardware. We celebrate the speed of the motors, the precision of the cutters, and the intelligence of the sensors. However, this focus can obscure a fundamental truth: the single most important component on any production line is the human operator. A state-of-the-art, multi-million-dollar diaper machine in the hands of an untrained or unmotivated operator will underperform. Conversely, a skilled, engaged, and empowered operator can coax surprising levels of performance out of even older equipment. Investing in your people is not a "soft" initiative; it is one of the highest-return investments you can make in your pursuit of diaper machine performance optimization. The goal is to transform operators from simple machine-minders into true process owners.

Beyond Basic Operation: Cultivating a Culture of Ownership

Traditional operator training often focuses on the "how": how to start the machine, how to stop it, how to load materials, how to clear a basic jam. This is necessary, but it is not sufficient. A culture of ownership is cultivated when training also emphasizes the "why."

Why is tension control so important? An operator who understands that incorrect tension is the root cause of web breaks, wrinkles, and registration errors is more likely to be vigilant about monitoring and adjusting it. Why must we be so careful with adhesive temperature? An operator who knows that the wrong temperature can lead to weak bonds that cause customer complaints is more likely to treat the glue system with respect. Why do we track minor stops? An operator who sees minor stops not as an annoyance but as data points that reveal process instability will be a valuable partner in problem-solving.

Cultivating this culture starts with respect. Operators are the people who spend eight hours a day with the machine. They know its sounds, its quirks, and its personality. Their insights are invaluable. Management must create channels for these insights to be heard and acted upon. Daily team huddles around a production whiteboard, formal suggestion programs, and the inclusion of operators on continuous improvement teams are all practical ways to do this.

When operators feel that their knowledge is valued and that they have the authority to make small adjustments and improvements, their relationship with the machine changes. It is no longer "the company's machine"; it becomes "my machine." They take personal pride in its OEE score, its quality rate, and its cleanliness. This sense of ownership is an incredibly powerful, self-sustaining driver of performance.

Structured Training Modules for Fault Diagnosis and Troubleshooting

While a sense of ownership is the foundation, it must be supported by technical competence. When a machine stops, the clock is ticking. The ability of an operator to quickly and accurately diagnose the cause of the stop is a critical skill that directly impacts the Availability component of OEE. Relying on "tribal knowledge" passed down from senior to junior operators is unreliable and inconsistent. A structured, competency-based training program is essential.

This program should be broken down into modules, covering each major section of the diaper machine: the web handling and unwinds, the mill and core-forming unit, the chassis application section, the elastic application systems, the cutting units, and the stacker and bagger.

For each module, the training should follow a logical progression:

  1. Theory of Operation: How does this section work? What is its purpose? What are the key process variables?
  2. Normal Operation: What does it look like, sound like, and feel like when this section is running correctly?
  3. Common Faults: What are the top 5-10 most common reasons for a stoppage or defect in this area? (This information should come from your downtime data analysis).
  4. Troubleshooting Guides: For each common fault, provide a clear, step-by-step diagnostic process. For a "Web Break" fault, the guide might start with "1. Check material roll for defects. 2. Check splice integrity. 3. Check web tension reading. 4. Inspect rollers for adhesive buildup."
  5. Assessment: At the end of each module, there should be a practical assessment where the operator has to demonstrate their ability to identify and resolve simulated faults.

This structured approach ensures that every operator receives the same high-quality training and possesses a consistent baseline of troubleshooting skill. It professionalizes the role of the operator and equips them with the confidence to solve a wider range of problems on their own, without always having to wait for a maintenance technician.

Utilizing Simulators and Augmented Reality for Safe, Effective Training

One of the challenges of training on a high-speed production line is that it is difficult—and often unsafe—to practice on the real machine. You cannot deliberately cause a major jam just for training purposes. This is where modern technology can be a game-changer.

Training Simulators: Many manufacturers of explore our advanced diaper machine technology now offer sophisticated software simulators that replicate the machine's control interface (HMI). A new operator can sit at a computer and learn to navigate the screens, respond to alarms, and practice changeover procedures in a completely safe, virtual environment. The simulator can be programmed with various fault scenarios, allowing the trainee to practice their troubleshooting skills without risking any real downtime or material waste.

Augmented Reality (AR): AR takes this a step further. An operator wearing a set of AR glasses can look at the physical machine and see digital information overlaid on their view. For a maintenance task, the AR system could highlight the exact bolts that need to be removed, show the correct torque specification, and play a video of the procedure right in their line of sight. For troubleshooting, an operator could look at a faulty sensor, and the AR system could display its live reading, its historical trend, and the relevant page from the electrical schematic. This technology can dramatically reduce the time it takes to perform complex tasks and reduce the risk of human error. It can also be used for remote assistance, where an expert in another country can see what the local operator sees and guide them through a difficult repair in real time. These technologies represent the future of industrial training.

The connection between operator skill and machine performance is direct and measurable. A well-trained operator contributes to all three components of OEE.

  • Availability: By quickly diagnosing and resolving minor stops, they reduce the "death by a thousand cuts" that kills performance. By performing efficient and correct changeovers, they minimize planned downtime. Their ability to spot the early warning signs of a developing mechanical problem (a new noise, a slight vibration) and report it can help prevent a major unplanned breakdown.
  • Performance: A skilled operator understands the delicate balance of the process and has the confidence to run the machine closer to its ideal design speed. They know how to make the fine adjustments to tension, temperature, and pressure that allow the process to run stably at high speed.
  • Quality: An operator trained in quality awareness is the first line of defense against defects. They are constantly performing visual checks, noticing subtle changes in the product, and taking corrective action before the automated vision system even has to reject a diaper. They understand the importance of proper setup and calibration to ensure that every product is made to specification.

Investing in operator competency is, therefore, a direct investment in OEE. The financial return comes in the form of more products shipped per hour, less material wasted, and a more reliable and predictable operation. This is a crucial element of a holistic approach to diaper machine performance optimization.

Creating Standard Operating Procedures (SOPs) for Fast Changeovers

Nowhere is the combination of skill and process more important than during a product changeover. As discussed earlier, changeover time is a direct hit to Availability. The key to reducing it is standardization. Every operator should perform the changeover in the exact same way—the most efficient way. This is achieved through the creation and use of Standard Operating Procedures (SOPs).

An SOP for a changeover is not just a simple checklist. It is a detailed, visual guide. It should include:

  • A list of all tools and parts that need to be prepared before the machine stops (the external setup).
  • A step-by-step sequence of tasks to be performed after the machine stops (the internal setup), with target times for each step.
  • Clear photographs or diagrams for each step, especially for complex adjustments.
  • The specific settings for the new product (e.g., "Set tension controller TC-101 to 45 Newtons," "Load recipe 'Size 3 Super' on HMI").
  • A checklist for verification and startup after the changeover is complete.

These SOPs should be developed by a team that includes the most experienced operators and maintenance technicians. Once created, they should be used for training and should be physically present at the machine during every changeover. By following the SOP, even a less experienced operator can perform a changeover efficiently and correctly. The SOP turns a complex, variable process into a standardized, repeatable routine. This standardization is the secret to achieving consistently fast changeovers and is a powerful, practical tool for boosting machine availability.

5. Leveraging Data Analytics and Process Control for Continuous Improvement

If empowered operators are the heart of an optimized production line, then data is its nervous system. In the past, manufacturing was often run on experience and intuition. A seasoned operator "knew" how to run the machine based on its sounds and feel. While that experience is still valuable, it is no longer enough to compete at the highest level. The complexity and speed of modern diaper machines generate a torrent of data every second. The ability to collect, analyze, and act on this data is what separates good manufacturers from great ones. A culture of continuous improvement, fueled by data analytics, is the final and most sustainable pillar of diaper machine performance optimization. It creates a virtuous cycle where the machine gets smarter, the process becomes more stable, and performance consistently trends upward.

Establishing a Centralized Data Collection System

You cannot manage what you do not measure. The first step in any data-driven journey is to establish a comprehensive and reliable system for data collection. On a modern diaper machine, this data comes from many sources:

  • The PLC (Programmable Logic Controller): This is the machine's brain. It contains data on cycle times, machine states (running, stopped, faulted), alarm histories, and the status of every motor and valve.
  • The HMI (Human-Machine Interface): This is the screen where operators interact with the machine. It logs operator actions, recipe changes, and the reasons entered for downtime.
  • Sensor Networks: This includes the data from your predictive maintenance sensors (vibration, temperature) as well as process sensors measuring web tension, glue temperature and pressure, and airflow.
  • Vision Systems: The quality inspection system generates a huge amount of data on the precise measurements of every diaper and the reasons for any rejects.
  • Manufacturing Execution System (MES): This higher-level system tracks production orders, material consumption, and labor.

The challenge is that this data often lives in separate "silos." The PLC has its data, the vision system has its own, and the MES has its own. A truly effective analytics strategy requires bringing all of this data together into a single, centralized data historian or database. This creates a single source of truth and allows you to correlate data from different systems. For example, you could analyze if a slight increase in web tension (from the process sensor) is correlated with a specific type of quality defect (from the vision system) that occurs 10 minutes later. This kind of cross-system analysis is impossible when data is trapped in silos.

Key Performance Indicators (KPIs) Beyond OEE

While OEE is the ultimate headline metric, a robust performance management system tracks a balanced set of Key Performance Indicators (KPIs) that provide a more detailed picture of the operation's health. These KPIs should be displayed on dashboards visible to everyone on the plant floor, providing real-time feedback. Good KPIs should be:

  • Mean Time Between Failures (MTBF): This measures the average time the machine runs before an unplanned stop occurs. A rising MTBF is a strong indicator that your maintenance and problem-solving efforts are succeeding. MTBF = Total Uptime / Number of Unplanned Stops.
  • Mean Time To Repair (MTTR): This measures the average time it takes to recover from an unplanned stop. A falling MTTR indicates that your operators and technicians are getting better and faster at troubleshooting and repair. MTTR = Total Unplanned Downtime / Number of Unplanned Stops.
  • First Pass Yield (FPY): This is a stricter measure of quality than the simple Quality component of OEE. It measures the percentage of products that are made correctly the first time, without any need for rework or special handling. It is a pure measure of process capability.
  • Material Yield: This KPI tracks the ratio of the weight of the finished, good-quality diapers to the total weight of the raw materials consumed. It is a direct measure of your efficiency in converting expensive raw materials into sellable products.
  • Changeover Time: As discussed, this should be tracked meticulously for every changeover. Plotting the times on a chart and setting aggressive reduction targets is a powerful way to focus improvement efforts.

These KPIs give your team specific areas to focus on. They break down the grand goal of "improving OEE" into more manageable sub-goals like "increase our MTBF by 10% this quarter" or "reduce the average changeover time to under 60 minutes."

Applying Statistical Process Control (SPC) to Diaper Manufacturing

Statistical Process Control (SPC) is a powerful methodology for moving from a reactive "detection" mode of quality control to a proactive "prevention" mode. The core idea of SPC is that every process has a certain amount of natural, inherent variation. An SPC chart is a tool that helps you distinguish between this "common cause" variation (the normal noise in the system) and "special cause" variation (a signal that something has changed in the process and needs investigation).

Let's take the example of the absorbent core weight. Your target weight might be 15.0 grams. Due to the nature of the fluffing and forming process, not every core will be exactly 15.0 grams. There will be some natural variation. By measuring the core weight of a small sample of diapers every 15 minutes and plotting the average on a control chart, you can establish the "voice of the process." The chart will have a center line (the average) and statistically calculated upper and lower control limits (UCL and LCL).

As long as the plotted points bounce around randomly between the control limits, the process is considered "in control" and stable. You should not react to any single point being slightly high or low. However, if you see a point fall outside the control limits, or if you see a non-random pattern (e.g., seven consecutive points all trending upwards), that is a signal of a "special cause." Something has changed. Perhaps a nozzle is starting to clog, or the density of the incoming fluff pulp has changed. The SPC chart gives you an early warning to investigate and fix the problem before the core weight goes outside the engineering specification limits and you start producing defective products.

Applying SPC to critical process parameters—core weight, glue application weight, elastic elongation, tab placement position—transforms your quality management. It allows operators to make intelligent, data-based decisions about when to adjust the process and when to leave it alone, leading to a more stable process and higher, more consistent quality.

Using Data to Optimize Machine Settings for Different Product SKUs

Most diaper factories produce a wide range of products: different sizes, different absorbency levels (day vs. night), and sometimes different brands with different features. Each of these Stock Keeping Units (SKUs) requires a unique set of machine settings, often called a "recipe." This recipe can contain hundreds of parameters: speeds, tensions, temperatures, pressures, camera inspection tolerances, and more.

Often, these recipes are developed through trial and error and then saved. However, are they truly optimal? Data analytics provides a way to answer this question. By analyzing the historical production data for a specific SKU, you can identify the set of process parameters that consistently produced the highest OEE.

For example, you could analyze all the runs of "Size 4 Premium" over the last six months. The analysis might reveal that the runs where the main web tension was set between 48-52 Newtons had a significantly lower rate of web breaks than runs where the tension was set outside this range. This data provides a clear, objective basis for updating the standard recipe for "Size 4 Premium" to specify a tension of 50 Newtons.

This process of "recipe optimization" can be applied to all key parameters and all major products. It is a systematic way of capturing the "best known way" to run each product and embedding that knowledge directly into the machine's control system. It reduces the variability that comes from different operators having different preferences for machine settings and ensures that you are always starting a run with the most optimized parameters possible.

Closing the Loop: From Data Insight to Actionable Change

The final, and most important, step is to create a culture and a process for turning data insights into concrete actions. A beautiful dashboard or a clever analysis is useless if it does not lead to a change on the factory floor. This is often referred to as "closing the loop."

This requires a structured continuous improvement process, such as a daily production meeting. The team (production manager, maintenance lead, quality lead, and operator representatives) gathers for 15-20 minutes to review the KPIs from the last 24 hours.

  • "Our OEE yesterday was 68%, 5 points below target. Why?"
  • "The downtime pareto chart shows our number one issue was 'Jam at Stacker,' accounting for 90 minutes of lost time."
  • "The operator log notes say the jams were happening with the new packaging film we started using."

Based on this brief data review, an action is assigned. "John (engineer) and Mary (operator), please investigate the stacker jams with the new film today and report back tomorrow with a countermeasure."

The next day, John and Mary report back. "We found the static bar at the stacker infeed was not effective on the new film. We adjusted its position, and we have not had a jam in the last 6 hours."

This simple, daily routine creates a relentless, problem-solving engine. It ensures that data is not just collected and admired; it is used to drive specific, measurable improvements every single day. It is this disciplined, data-driven execution that truly unlocks the full potential of the equipment and sustains a high level of diaper machine performance optimization over the long term.

Часто задаваемые вопросы (FAQ)

What is a good OEE score for a diaper machine?

A "good" OEE score is highly contextual. For a modern, high-speed diaper line running a consistent product, a world-class OEE score is generally considered to be 75% or higher. However, for older lines or operations with a high variety of product changeovers, an OEE of 60-65% might be a very strong performance. The most valuable approach is to first establish a reliable baseline OEE for your specific operation and then focus on continuous improvement from that baseline, rather than fixating on a universal number.

How can I reduce changeover times between different diaper sizes?

Reducing changeover time is best achieved using the SMED (Single-Minute Exchange of Die) methodology. The key steps are: 1) Videotape the entire changeover process. 2) Analyze the video with a team of operators and technicians to separate "internal" tasks (done while the machine is stopped) from "external" tasks (can be prepared beforehand). 3) Convert as many internal tasks to external as possible, such as preparing tool carts and pre-setting guides. 4) Streamline the remaining internal tasks by using quick-release clamps, standardized settings, and practicing the sequence.

What are the most common causes of downtime in diaper production?

While it varies by machine, common causes of unplanned downtime include: web breaks of the nonwoven or backsheet material, jams in the folding or stacking units, faults in the adhesive application system (e.g., clogged nozzles), and failures of rotating components like bearings and motors. A systematic downtime tracking system with accurate reason codes is essential to identify and prioritize the specific causes impacting your line.

How important is raw material quality for machine performance?

Raw material quality is exceptionally important. Inconsistent materials are a primary source of machine instability and downtime. For example, variations in the basis weight of nonwoven fabric can cause web tracking and tensioning problems. Inconsistent particle size in Super Absorbent Polymer (SAP) can lead to dust and clogged filters. Poor quality adhesives can fail to bond properly, causing delamination. A stringent incoming quality control (IQC) program is a prerequisite for high-performance manufacturing.

Can older diaper machines be upgraded for better performance?

Yes, older machines can often be significantly improved through strategic upgrades. Common retrofits include installing modern, zero-speed automatic splicers to eliminate roll-change downtime, upgrading to high-precision vision systems for better quality control, adding IoT sensors for predictive maintenance, and modernizing the drive and control system (PLC/HMI) for better diagnostics and data collection. A thorough audit can identify the upgrades that will provide the best return on investment.

What role does automation play in optimization?

Automation plays a vital role. On a modern diaper machine, automation handles high-speed material handling, cutting, folding, and bonding with a precision humans cannot match. Advanced automation, such as automatic splicing, robotic packaging, and process control loops that use sensor feedback to self-adjust, directly increases OEE by improving Availability (reducing stops), Performance (enabling higher speeds), and Quality (ensuring consistency).

Заключение

The journey toward superior diaper machine performance optimization is not a destination but a continuous path. It is a comprehensive endeavor that weaves together the precision of engineering with the empowerment of people, underpinned by the undeniable clarity of data. As we have explored, achieving excellence in the competitive 2025 landscape requires moving beyond the traditional reactive model of manufacturing. It demands the adoption of a holistic framework where Overall Equipment Effectiveness serves as the guiding star, illuminating the path toward greater productivity. By implementing predictive maintenance, you transform your maintenance team from firefighters into forecasters, preventing disruptions before they occur. By meticulously managing every gram of raw material, you plug the leaks that drain profitability. By investing in the skills and ownership of your operators, you unlock the human potential that no machine can replicate. Finally, by building a culture that thrives on data analytics, you create a self-improving system that learns, adapts, and relentlessly pushes the boundaries of performance. This integrated approach is the definitive strategy for manufacturers in South America, Russia, Southeast Asia, the Middle East, and beyond to not only survive but to thrive, ensuring their operations are as efficient, reliable, and profitable as possible.

Ссылки

Andritz AG. (2025). Production platforms for baby diaper production. ANDRITZ. Retrieved from

Diapermachines.com. (2024, January 8). Innovations in diaper manufacturing: Exploring the baby diaper machine revolution. Retrieved from https://www.diapermachines.com/2024/01/08/innovations-in-diaper-manufacturing-exploring-the-baby-diaper-machine-revolution/

Diapermachines.com. (2025, April 8). What is the cost of manufacturing diapers? A breakdown for new investors and manufacturers. Retrieved from https://www.diapermachines.com/2025/04/08/what-is-the-cost-of-manufacturing-diapers-a-breakdown-for-new-investors-and-manufacturers/

Hansen, R. C. (2005). Overall equipment effectiveness. Industrial Press.

Nakajima, S. (1988). Introduction to TPM: Total productive maintenance. Productivity Press.

SQ Machine. (2025, May 22). How diapers are made: Materials, machines, and process explained. Sanitary Pad Machine. Retrieved from https://sanitarypadmachine.com/how-diapers-are-made/

Sunree Hygiene. (2025, March 14). The disposable baby diaper manufacturing process: A comprehensive guide. Retrieved from

Tsarouhas, P. (2020). Improving operation of a production line by measuring OEE: A case study. International Journal of Productivity and Performance Management, 69(8), 1645–1662.

Tucker, R. (2024, September 8). Advancements in disposable diaper machine technology and user-centric innovations. Made-in-China.com. Retrieved from https://insights.made-in-china.com/Advancements-in-Disposable-Diaper-Machine-Technology-and-User-Centric-Innovations_PATGmMdOYElC.html

Williamson, R. M. (2006). Using overall equipment effectiveness to improve performance. Cost Management, 20(4), 36–42.