In this article the impact of micro-stops on machinery efficiency and product quality is analyzed. The main causes of micro-stops and possible techniques to reduce them are listed.
Today, companies are engaged in the pursuit of the improvement of the production cycle, i.e. the efficiency of the manufacturing process at 360 degrees.
Regardless of the product sector and the technologies adopted in their departments, all companies have to deal with ever lower margins and entrepreneurs are struggling to optimize productivity. All refer to the OEE as the main indicator of good machinery set-up and working time management (equipment and maintenance).
The OEE is a synthetic indicator that encompasses the availability of a plant, its production capacity compared to the theoretical one and describes the quality of products through the relationship between good parts and produced parts.
Like all summary information, the OEE alone does not allow a thorough understanding of all aspects of a production process, especially if it is complex, and for this reason it is useful to integrate it with other machinery performance indicators (KPI = Key Performance Indicators).
In this article we want to focus attention on micro-stops, i.e. those machine stops that last a few minutes, often 1 or 2 minutes and are neither tracked nor justified by the operators.
The causes of micro-stops are obviously of a different nature and depend on factors that are often inherent to the type of production process and specific types of machinery. Not being able to indicate all the possible industrial processes, let's try to divide the stops into different categories using some examples.
We refer to cases in which the machine, after having produced automatically for a certain time, stops for a few minutes and then starts working again. These stops occur without the operators being aware of them because they are often unsupervised production processes.
The cause of the downtime is due to machine parameters that are outside the working conditions and the control PLC takes corrective action to bring the parameters back into their correct operating range.
Example 1: if the oil temperature is outside the programmed thresholds, the machine switches on the heating/cooling circuits to have oil in the right operating conditions. In this case the problem is not the machine, but the oil system (oil circuit) which is not configured/sized correctly.
Example 2: if the temperature of a motor is above a programmed threshold, the PLC waits for the motor to cool down before resuming machining. In this case the problem is due to a component that is deteriorating and therefore its performance has deteriorated over time and will continue to deteriorate until some kind of maintenance is carried out.
Example 3: a shared resource in the production plant is below the required levels for production (air from compressors, vacuum circuit, components to be processed with pneumatic circuits in the vibrating tables of machinery, etc.) and the machine is waiting for the resource it needs to return to the required minimum values.
This typically happens at times when all the machines in the department are working and the resources are not enough to meet the demand of the machinery.
In these three cases, the number of waste parts does not increase compared to production times without stoppages. The decrease in OEE is due to the lower productivity index of the machinery.
These micro-stops occur when the equipment of the machinery is made in a way that does not conform to the type of item to be produced or because the machinery has a damaged component that causes a repetitive error that cannot be easily identified.
Example 1: the machine has been equipped to produce with one or more incorrect parameters. Common causes are:
Example 2: a robot is programmed to execute trajectories that touch certain parts of the machine and sometimes hits the surface of it, generating movement errors.
In these cases the machine stops because the product is discarded or because the machine parts detect a configuration error. In the machines serviced by robots for automatic loading, these stops lead to a huge increase in the number of discarded items. Only when the number of consecutive rejects is greater than a set value, the machines stop drawing the attention of the operators.
The decrease in OEE is due to the combined effect of the decrease in productivity and quality indexes of the machinery.
These micro-stops take place with the machinery to be loaded/unloaded by the operators. Let's not forget that the operators are human beings and can make mistakes, especially when the following conditions are present:
I want to underline the last of the items in the list: human operators are often asked to work at the rhythms imposed by a robot or similar automatic systems capable of repeating movements in a few moments.
Entrepreneurs' desire to meet the schizophrenic demands of some markets leads to work at such a tight pace that operators are misled. In addition, staff with less experience often need more time than their older colleagues, so they may occasionally feel the need to stop the machinery because they cannot keep up with the pace of work.
This problem is very common in typical food production chains and coating lines. These are production sectors that also suffer from a strong labour turnover also for this reason.
These micro-stops occur when operators have to load/unload the incoming/outgoing pallets of the machines. If the operator is not ready at the right time, the workpiece presence sensors stop the machine until the operator has completed the task.
These stops result in a drop in OEE, causing the machine's productivity index to drop. The quality of the machined items is not affected by this problem. Many companies have successfully adopted the use of intelligent palletizers to have "lungs" in and out of the machines. The correct sizing of these buffers is crucial to eliminate the need for micro-stops.
Why are the micro-stops listed above not tracked by companies? Comparison with many entrepreneurs and maintenance managers shows that everyone is aware of the problem, but underestimate the impact of these stops on overall production efficiency.
These stoppages are almost never justified by the operators because the personnel on board the machine is busy reaching its production targets and therefore does not considers it important to stop to justify the micro-stops. How often do you hear people say that the time to justify downtime is greater than the machine downtime!
Let's add the fact that micro-stops due to human errors are hardly justified by those who have made mistakes, for fear of being taken over by their superior (a more than understandable behavior).
It should also be considered that machinery is increasingly complex and operators can rarely fully understand the operation of all the components of a production line (for example at a molding cell) and so operators, even the most qualified, are unable to assess the reason why the machine sometimes "throws a tantrum".
Often problems with machine parts occur at random and therefore even maintenance workers find it difficult to investigate the causes of downtime.
The main reason why micro-stops are not tracked is due to a lack of culture of production optimization that leads operators, technicians and process managers to consider stops lasting a few minutes negligible.
It is ignored that a one-minute standstill can lead the machine to change its condition, for example, the mould of a press cools down or the chemical characteristics of the components involved in the deformation process change, etc., and therefore each standstill affects the quality of the products being manufactured.
Micro-stops cannot be reliably measured by human operators for several reasons:
The only reliable system to track downtime is to use an electronic machine status detection system.
The simplest and most economical systems use machine status relays (1 = machine on, 0 = machine off). The most modern systems detect the status of the machine directly from the PLC, also collecting a lot of other information, useful to understand how the machine has come to the interruption of work.
You can present the table of stops to operators at the end of their shift and ask them to justify those stops of which they have useful information. It turns out that there are always more stops than operators remember!
It is important to use detailed justification lists and to teach operators to justify the causes of downtime precisely, otherwise you will not get useful data: nobody needs a history of downtime where the most appropriate justification is "OTHER".
The analysis of downtime always starts from Pareto diagrams in which the data are organized by type and frequency, as in the following example.
Analysis of the micro-stops organized by type and duration with the Pareto Diagram
Typically, companies address the issues that cause the longest lasting shutdowns. These stops are usually caused by problems with a significant solution cost, for example, a bearing failure requires replacement of the component and can last several hours.
Micro-stops, on the other hand, are due to causes that can be managed with a different working model, and finding out what causes them can help to size the plants correctly, to better organize the loading and unloading logistics of the production lines and to make the staff more aware of their responsibilities.
The micro-stops can be reduced by organizing a team of 3 or 4 people involving operators, maintainers and production managers.
The first thing to do is to measure the frequency of the micro-stops and give each of them a correct cause. The operators must feel that they are the protagonists of the improvement process and not the object of a process to those who are more to blame, otherwise they will not cooperate and their fundamental role will be lost.
Conscious operators help colleagues, share best practices and report all anomalies and causes of micro-stops, even the most unusual ones, without fear of being sanctioned.
After running a micro-stop detection campaign for a few weeks, the team has to analyze the causes and choose those that can be acted on immediately, changing some organizational aspects of the work, often there are no extra costs for this type of improvements.
The team has to implement corrective strategies and define a reasonable target, for example reducing the micro-stops of causal A by 20% and causal B by 30%. Typically it takes 3 to 4 months to achieve the desired results.
Automatic measurement systems will help the team to understand if the strategies identified are effective or if it is necessary to work in a different way involving machine suppliers or other colleagues.
This continuous improvement approach, of which Japanese industries are masters and have spread methodologies such as kaizen and lean manufacturing around the world, is the model that all companies can adopt to gain competitiveness and improve the work of their workers.
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