The growth of the global population has facilitated the expansion of the market. However, this growth has also resulted in an increase in the number of individuals competing for a limited set of resources. The intensity of competition in production has continued to rise. In this context, the importance of efficient resource utilization has increased. As is well-established, the efficient use of resources can be defined as an increase in productivity.

Productivity is one of the important topics of engineering. It has become much more important, especially with competition increasing day by day. Accordingly, the number of engineers like me working on these issues has also increased. I am the manager of @ARGE BİLİŞİM, which produces and provides services to companies on 'Productivity and quality measurement and improvement' by using industrial, electronic and computer engineering sciences.

In this article, I will try to explain the subject of 'Productivity and quality increase' by making a general factory modeling without going into scientific details.

Let's start with the Arabic word 'bereket', which was used more frequently in the past. When we open the dictionary and look at it, words such as 'abundance, abundance, productivity' will be seen opposite the word bereket. You will remember that in the years when most of the population was engaged in agriculture, it was used to describe the abundance of the products people obtained from the field. When most of the agricultural population migrated to the cities and started working in factories, this word was replaced by the word 'yield' and then 'efficiency'.

In order to talk about efficiency, we first need to consider a transformative or generative system. In other words, we will concretize efficiency by considering a converter structure that has input energy (or material), which is converted into another energy (or product) at the output.

Since we are talking about productivity, the topic will go to factories, as you can guess.

As you know, factories are the building blocks of industry. In general, we can define factories as organizations that are established for profit (the factories mentioned here are not those established by the state and are not for profit) and produce goods. First, let's show a simple model of these organizations with an 'input' and an 'output'. Let us then give an example of this model.

Definition of efficiency for a general system; We can explain it as the amount of energy received (utilized) divided by the energy given (spent) and multiplied by one hundred, which we describe as a percentage and gives us very important information about the benefit - cost. For example, when we write the formula of efficiency for an electric lighting lamp, it will be Efficiency (%) = (Light energy received / Electric energy given) x 100. It is important to note that the energy units in the numerator and denominator are the same, because we express efficiency as (%).

The efficiency of the systems on Earth is less than 100%, in other words, there is no system with an efficiency of 100% or more. This means that there is no perfect system on Earth. If there was a perfect system on Earth, or if it could be invented, we could say that all wars would end and people would become angels. Enough energy for everyone would mean that the problem of sharing would disappear.

In fact, we have stated here that the sum of the received energies in any system is equal to the sum of the given energies. For our lamp example, the given electrical energy = received light energy + loss. The loss is heat energy for the lamp (our expectation from the lamp is light, not heat. Therefore, heat is a loss for us). When we think about the factory, there are possible losses in inputs such as energy, materials and labor. Thus, we can say that the most efficient factories are the factories with the lowest possible losses in inputs such as energy, materials and labor. This is because when losses are reduced, the numerator in the efficiency formula will increase, thus increasing efficiency.

When we define productivity for factories; we can formulate productivity over our general factory model as Productivity (%) = (Output / Input) x 100. In other words, we can define productivity as one of the most important data showing our profit and loss situation, which we express as a percentage by multiplying the output divided by the input by one hundred.
Let our example factory be a garment factory that produces a single type of pants and let's examine its productivity in terms of labor.

Assumed data

Number of operators = 100 People

Daily working time = 540 Minutes

Quantity of pants produced per day = 1500 pieces

Standard duration of pants = 25 Minutes

Productivity (%) = (Quantity x Standard Time / Daily Working Time x Number of Operators) x 100

= (1500 x 25 / 540 x 100) x 100

=  69,44

The efficiency of our sample factory is 69.44%, and the human energy lost is 100- 69.44 = 30.56%.
As can be seen here, we calculated the efficiency by making the inputs and outputs the same units.
We can do the same calculation for other inputs and outputs so that the units are equal to each other. If you wish, let's now give an example of material efficiency.

Assumed data:

Pants fabric quantity = 1.8 square meters

Amount of fabric used for trousers = 2 Square meters

Productivity (%) = (Amount of fabric for trousers / Amount of fabric used for trousers) x 100

= (1,8 / 2) x 100

= 90

The fabric productivity of our sample factory is 90%, the percentage of fabric lost is 100- 90 = 10%. This is how we calculated the efficiency of the material used.

In terms of the production sector, national and international commercial competition is increasing day by day with a rising momentum. The condition to get ahead in this competitive environment is to increase productivity. One of the most important issues (perhaps the most important) for our business people to increase and maintain their competitiveness is to continuously measure and increase their productivity. I am happy if this article I have written about productivity is useful.

Hulisi AYLUÇTARHAN

Yönetici

ARGE BİLİŞİM LTD

htarhan@argebilisim.com

 

When we observe it in terms of energy, in the Universe and the world that is part of it,there is an infinite number of living and inanimate energy transformation systems . With these systems, an infinite number of energy conversions take place at any moment. We refer by system with transformation , the energy(s) entering the system (converted energy (s)) and energy(s) coming out of the system). We can show the system in general as the following form.

in Figure 1  the system shown is an energy conversion system.The productivity of the system;

 

Productivity=(output enegry/ Input energy) x 100

It can be calculated with the formula.

If we express this formula orally;What is the percentage % of the output energy converted from the input one? We say that productivity is the answer of this question.

 

Let’s see closely the productivity formula;

 

  1. Productivity is expressed as % . It means ,if we repeat it, it is the answer to the question of what percentage of the input energy has turned into the output energy.
  2. In order to express productivity in %, the energy output from the denominator system and the energy units input the denominator system must be the same.

 

To understand the productivity concept easily let’s give an example;The electric lamp, which has an important place in all our lives, is a good example.The electric lamp is a great system invented to convert electrical energy into light energy.

 

 

Normally, lamp productivity is expressed as the generated light energy equivalent of one watt of electrical energy expended. Here we look at the light energy (lumen) obtained in this time interval in response to the electrical energy (watt) spent in a certain interval of time.

 

When we apply the productivity formula to the lamp system;

The lamp Productivity= is (Light energy/ Electric energy) x 100

We need to equalize the units so that we can express the production in % . The electrical energy unit is watt and the light energy unit is lumen.Lumen can be converted to watt by multiplying by a constant for the same system. Briefly, we can calculate the lamp productivity by converting the lumen unit to the watt unit.

When finishing the article it is useful to write the following; The energy conversion system model we showed in Figure 1 and the lamp example in Figure 2, Although it is used to understand the concept of productivity comfortably, it can cause another serious mistake. By criticizing these impressions in the next article, we will suggest the correct model to prevent misunderstanding

I wish you all the best for the future. I hope that the investments you have made in family, love, friendship and business will prove fruitful.

Hulisi AYLUÇTARHAN

 

In our previous article, we defined productivity by demonstrating modeling the energy conversion system with one energy input and one energy output.

(see WHAT IS PRODUCTİVİTY? (1)

In the same article ,we agreed about P= (Output energy / Input energy) x100 for an easy understanding of productivity. At the end of the article, we wrote that we will criticize this demonstration and redefine productivity with a new system demonstration.

In the representation in Figure 1, 1 energy enters the system and 1 energy exits.
According to the law telling that The existing energy cannot be distroyed and the one telling that it cannot be created from nothing and according to
‘Conservation Principle of Energy’ entered energies are equal to the emitted ones in a closed system.

According to this system demonstration; Input energy = Output energy Productivity; Productivity = 100%.

EIn the universe There is no such system , the PERFECT (lossless) system.

So, we can easily say that this system representation is wrong and does not fully explain the productivity.

Now we give the new system demonstration as shown on figure 2.

Again, according to the ‘Principle of Conservation of Energy’, the sum of the energies Output = the sum of the energies Input.

Let’s express it by the formula;

 

It is a very useful demonstration for understanding the subject we explained in Figure 2. Let’s make a few inferences;

1-    For an energy transformation there must be at least one type of “Input energy”

2-    At least two types of energy appear.

3-  Productivity is calculated from the target output energy.

In Figure 2, if the aim (target) energy is “output energy 1”;

 

If the “Input energy” is also one type;

PRODUCTIVITY = is (output energy 1 / input energy 1 ) x 100.

Now again let’s turn back to the lamp example like as in our previous article.

As can be seen here, electrical energy enters the lamp and (only) light energy comes out.

 

If only electrical energy was input and again only light energy was output
We would have to say that productivity is 100%. No such lamp has been invented yet.

Let’s draw the correct representation of the lamp system example;

 

Let’s adapt the inferences we made in Figure 2 to our example;

1-Electricity is the lamp entring energy.
2-At least two types of energy are released from the lamp (Light and Heat).
3-We calculate the lamp productivity using light energy because we do not expect heat from the lamp and heat energy is a loss for us.
4- Productivity is calculated from the target output energy so light energy.

 

Productivity in a system; We have proved our thesis clearly above “The short definition of the output energy / input energy x 100 is a confusing definition”.

The correct definition of productivity is the ratio of the target energy to the input energy (s) multiplied by 100 .

 

The concept of productivity is the core of engineering. We will continue our articles about productivity.

 

Hope for the family, love, friendship and business investments you have made to be efficient,for now goodbye.

 

Hulisi AYLUÇTARHAN

 

 

As Arge Bilişim, we would like to share with you the 'line balancing module' that we have designed to meet a very important need in factories.

First, let's briefly touch on what line balancing is.

Businesses have turned to assembly line production systems to more efficiently use limited production resources and reduce production costs. In these production systems, we refer to the path where the parts of the product are made at each workstation and progress as the parts are transformed into the product as a "line".

Line balancing aims to equalize the speeds of these workstations to maximize machine and labor utilization.

For each operation in the production line to be carried out, the preceding processes must be completed. If you cannot establish a balanced line, bottlenecks will occur within the line, resulting in some operators waiting idle while there may be accumulations in other areas. And this situation will lead to a loss of efficiency. The station with the most accumulations, i.e., the weakest station in the flow, will also determine the capacity of your production line. To give a simple example, let's consider producing a product that goes through a total of 5 different processes. Let's assume each of them has a production capacity of 100 units per hour. Only the 2nd station has a production capacity of 90 units per hour. In this case, if you haven't balanced your line, there will be constant accumulations from the first station to the second, while the operators after that will wait idle. And you will have a production loss of 10 units per hour.

Moreover, in this example, we assumed that the capacities of 4 stations are equal, but in reality, the durations of operations are not the same, and the speed of each operator, their competencies, skills, and the difficulty levels of tasks are different. Therefore, many parameters like these need to be taken into account when setting up the line.

Proper line balancing will ensure effective use of labor and resource capacities, reduction of idle times, and increased efficiency leading to reduced production costs.

Okay, how are businesses currently handling this?

In most existing production systems, while engineers theoretically conduct line balancing studies, operator scheduling cannot be done, or if it is done, it's not effective. This is because operator scheduling is a situation that can vary based on real-time data and is usually carried out by line supervisors who are continuously on the shop floor, rather than by engineers. When an order comes in, the engineer first establishes the operation and standard times for the model. Then, at best, by setting a target efficiency, workloads are calculated, and this list is given to the line supervisor. For example, the information that 1.5 people are needed for the side bending operation is provided by the engineer, but there is no information on which 1.5 people, or how efficiently these individuals have worked in this operation before. Therefore, this information remains purely theoretical. Line supervisors place individuals based on their experience of who can do which job or who has the capacity to do it. If anyone is absent from the line, the balance of the entire line will be disrupted, and they will have to rethink the entire setup from scratch. Redesigning is difficult and often not feasible as it leads to time loss and slows down workflow.

Without a system like ArgeMAS that measures productivity and quality, neither the standard time for the product nor individual productivity will be taken into account. Even the loss of efficiency between them may go unnoticed.

This complex decision, which cannot be achieved by observation alone, is too important to be left to individual initiative.

In systems where manual labor is intensive and variability is high, the efforts made so far regarding line balancing have been far from solving the problem completely.

Okay, how did we solve this fundamental problem that affects the future of factories?

Combining practical knowledge with theoretical knowledge in production will give much better results. Therefore, there is a need for a structure that combines real-time data collected from the field, accumulated past knowledge, and current engineering studies. With this aim in mind, Arge Bilişim engineers have developed a mathematical and specialized assignment optimization algorithm to simultaneously solve optimization problems in line setup and operator scheduling.

Firstly, with the ArgeMAS system we have implemented in factories, we can access real-time data on each operator's operational efficiency, quality, downtime, and other factors on an operational basis. We also know the standard times for each operation with the created worksheets. However, not every model and operation may be suitable for the capabilities of the line. To overcome this, we control the ability of operators to perform each operation using two complementary methods.

The first method is operation similarity classification. When defining operations, we classify them based on their methods, grouping together operations with similar execution methods. For example, the punching operation, attaching components operation, and quality control operation have completely different execution methods. While the punching operation requires alignment and rhythm skills, the component attachment operation requires alignment and finger skills. From this, it can be inferred that someone who efficiently performs the punching operation can easily learn the component attachment operation, which requires similar skills.

The other method we use to control operators' ability to perform operations is the difficulty levels of operations. With the ArgeMAS system, difficulty criteria are determined, allowing the difficulty levels of each operation to be identified. For example, criteria such as long training time, high physical strain, and high risk of quality errors are some of the criteria that make an operation difficult. By expanding on such criteria, the difficulty levels of operations are determined using analytical job evaluation methods. Taking into account the similarities between operations and their difficulties, the Arge Bilişim assignment optimization algorithm can easily predict the ability of an operator who is successful in one operation to perform another operation.

In fact, this is exactly what line supervisors on the production floor try to do, but since each line supervisor can only make evaluations based on their own experience, the margin of error is unfortunately very high. While benefiting from the managerial skills of line supervisors in the field, the decision on how to ideally set up the line is entirely made by the Arge Bilişim assignment optimization algorithm, which is a completely real engineering effort.

With the Arge Bilişim assignment optimization algorithm:

You can easily access information such as how much time your operators will spend on each operation, what percentage of your operations will be done by which operator, and obtain detailed reports based on quantity or bundle. Here, the efficiency value of each operation, operator, and production line is planned.

Taking into account the workload of operations, the model, operations, similarity groups, and difficulty levels are considered to maximize line efficiency by the algorithm. The assignment made as a result of this allocation ensures that the efficiency of the resulting line is calculated entirely based on real data. Instead of static capacity, dynamically calculated capacity based on real data is used as an important piece of information in production planning. Production planning based on completely real data replaces production planning made in the form of "If we produce an average of 1000 pieces per day, we will meet this order by Friday". This also prevents delays in deadlines due to erroneous capacity planning.

Moreover, if there is any change in the field, the line setup will need to be changed and a new ideal structure found. For example, if 3 operators do not show up for work on a given day, this situation can cause complete chaos in the field, but the Arge Bilişim assignment optimization algorithm quickly and effectively replans the most ideal line structure based on these changes and presents the real plan to the user again.

The ideal line structures where models will be produced most efficiently are also determined by the Arge Bilişim assignment optimization algorithm. For example, if there are 15 lines in your facility and you want to plan 20 models on these lines, the system treats the facility as a single line, and the most ideal 15 lines where these 20 models will be produced are recreated. Thus, an increase in efficiency can be achieved by solving the bottleneck operation on one line with a competent but idle operator found on another line, compared to lines set up without the program.

To summarize the results of the Arge Bilişim assignment optimization algorithm:

Firstly, ArgeMAS measures efficiency and quality. The "Arge Bilişim assignment optimization algorithm" then establishes the most efficient and balanced lines using real operations and operators based on these efficiency and quality results.

Secondly, it provides real production capacity information for production planning.

Another third benefit is that it reduces the workload of line supervisors, allowing them to focus more on managerial tasks.

Another benefit is that the time for line setup, shortened with the bundle system, will be further reduced with this algorithm. During model transitions, the line supervisor will not experience uncertainty about which operator to assign to which operation. They can easily organize their lines with the reports they have.

Fourthly, reconfiguring the line to adapt to production variations is quite difficult. With this algorithm, establishing the most ideal line according to current conditions will be easy and fast.

Planning multiple models on multiple lines can also be considered as another benefit, as it creates the most ideal line structures.

We can also use it in many other areas that directly support efficiency increas

With the Arge Bilişim assignment and optimization algorithm, you can prevent efficiency losses and reveal your true potential in production.

 

  • Enabling real-time measurement of productivity and quality
  • Increasing efficiency with regular workflow
  • Facilitating the implementation of a productivity and quality-based bonus system
  • Enabling the promotion of productivity and quality through a bonus system
  • Facilitating product flow and optimizing unnecessary movements
  • Reducing production costs by decreasing indirect labor
  • Making operation times accurate and predictable
  • Establishing controlled and efficiency-enhancing intermediate stocks
  • Minimizing downtime
  • Facilitating line balancing
  • Preventing operators from idle waiting
  • Facilitating process controls
  • Enabling operators to perform multiple operations
  • Enabling operators to use multiple machines
  • Implementing an improvable production system
  • Ensuring correct line structuring
  • Establishing efficiency-focused factory management
  • Developing management, production, and quality philosophies
  • Implementing an objective management and salary system
  • Ensuring accurate product pricing

Our General Manager Hulisi Ayluçtarhan, who participated as a panelist in the event organized by TİHCAD and Istanbul Nişantaşı University under the title of 'TİHCAD 5th SUSTAINABILITY MEETINGS', evaluated digitalization in the context of sustainability.

Hulisi Ayluçtarhan's findings and evaluations are as follows.

DATA IS NEW OIL

The year of wealth and struggle of the 20th century was oil.Oil brought wealth, but it also brought wars and tragedies. Because oil is a finite resource. The 21st century will be the century of data. Even though 21st century data is a field of struggle and fight, I think it will at least be a more peaceful field of struggle. There is no shortage of reserves because data multiplies as we use it. Therefore, “data is new oil”, the 21st century will be the century of data.

EFFICIENCY

The other link between digital and sustainability is efficiency. We increase the efficiency of management through digitalization. Increasing the effectiveness of management increases efficiency. This increases sustainability. Increased efficiency means using less cotton, thus less water consumption in cotton production, less chemicals, less energy, less energy, less logistics, less oil, less manpower. This is how productivity contributes to sustainability. For this reason, digitalization is one of the most important tools that enable sustainability to be realized.

TRACEABILITY, MEASURABILITY, MANAGEABILITY

In general, it is very difficult and sometimes not possible for organizations that produce with traditional methods to digitalize. Even if it is possible, it is not beneficial. Before digitalization, we build a traceable production. We build traceability with lean tools. We make traceable production measurable with hardware, communication and production software. We even facilitate management and increase its effectiveness with decision support systems. You cannot manage without measuring, but you cannot measure production without a traceable infrastructure. So we create a traceable infrastructure with lean production. Then we increase the effectiveness of management by providing measurability of hardware and software installation on it. This increases productivity. The increase in productivity brings sustainability.

-         WE NEED FLEXIBLE, EFFICIENT AND HIGH QUALITY LINES

The demands of today's customer are becoming personalized. The customer wants better quality, cheaper, faster and more personalized. The reflection of these demands on production is as follows. We need production lines that are flexible, can adapt to new products faster, have low setup times, and have high quality and efficiency.

The management of lines that produce low quantity orders requires different organizations and different managerial skills compared to lines that produce high quantity orders. In the case of low quantity orders, for example, a lot of files are executed. There is a big difference not only in where production takes place, but also in planning and management. I think the way to retain customers or find new customers is to have lines that are flexible, efficient and capable of producing quality. That is the role of our country. I am talking about the role of value-added product production. As such, digital has a lot to do.

As you know, we use software a lot in the planning and control stages of management. If we want to produce low-volume value-added products, we need very good planning. Planning solutions made with deterministic methods are slow. When the solution is reached, the solution does not work due to timeout. Instead, artificial intelligence comes to our rescue. There are many possibilities in the solution set. And if we are looking for the best, we may not always find it. Sometimes we settle for a good solution and sometimes we settle for a crappy solution. And this solution is beneficial.   This is difficult to achieve with conventional methods. As Arge Bilişim, we use digital and especially artificial intelligence a lot in the production of low-volume, high value-added products.

WHY IS ACCURATE MEASUREMENT IMPORTANT?

We have the opportunity to analyze in many factories. We recently visited a factory. As we generally encounter in companies, the biggest problem here is the wrong measurement of productivity.

Why do we normally measure standard time? To measure productivity and make improvements.

We also use it to make the basis of wage systems, to give a better price to the customer and to make better planning.

For this reason, standard time should be measured absolutely accurately so that we can do the above-mentioned jobs better and more accurately.

When we asked the company we visited, “What is your productivity”, we received the answer “85%, around 90%”.

Our second question was. “So how many of this product do you produce”?

For example, they said that they produce product X and the standard time for this product is about 50 minutes.  Because we have worked in many factories, we know that the standard time for this product is not 50 minutes, it is about 20 minutes.

When we ask, “What is your standard time measurement method?” we see that they made a wrong measurement. It is as follows; there are movements that add value in the standard time, that is, movements that the customer is really willing to pay for. Secondly, there are movements that do not add value but are obligatory. And thirdly, there are movements that do not add value. You do not add these movements that do not add value into the standard time, you eliminate them. If you add unnecessary movements into the standard time, if you show a 20-minute product as 50 minutes, you will accept your efficiency as 85%, 90%.

The sad part is that the factory thinks that it is working with high efficiency, 85%, 90%.  But the reality is that the factory is making serious losses. Therefore, the top management of the factory has decided to close it down. Given these false efficiencies, closing the factory seems like the right decision. But it is not. Why? Because when you show the standard time as 50 minutes instead of 20 minutes, you show the productivity two and a half times higher. In fact, the efficiency is around 35-40%, not 90%. The right decision to make is to measure the standard time correctly, and then to make the factory profitable by taking improvement activities, various social measures and various artistic measures together with engineering.

The data has been soaped. Even technology was used for soaping and unfortunately the standard times were inflated. Since the data that went to the top management was not the right data, the top management decided to close the factory based on this wrong data.  And the sad end. Hundreds of employees will lose their jobs. This situation is really exemplary. Soaped up inaccurate data causes the top management of the factory to make very wrong decisions.
Here, a 20 minute product is shown as 50 minutes. In other words, productivity is shown around 90% when it is actually around 35%. The top management is unable to meet the price with 90% efficiency. When the price cannot be kept, it naturally decides to close down.

What is actually the reason for this situation? The middle manager does this. He convinces himself that.  “I am absolutely successful.” How much efficiency do you need to work with to be successful? Obviously, he answers himself.  “% 85 %90.”  How many products do I produce? 2000 units. What is the time that will bring 2000 units to 90% efficiency?” He gives an inflated time. This is an unbelievable situation. So we can say that there is a kind of reverse engineering of soaping. One can only do this on purpose. This mindset unfortunately causes the closure of very important factories in our country.
We say this to company owners, especially to high level managers. Your basic data must be correct. Wrong data causes you to make very big and wrong decisions.

We have the opportunity to analyze in many factories. We recently visited a factory. As we generally encounter in companies, the biggest problem here is the wrong measurement of productivity.

Why do we normally measure standard time? To measure productivity and make improvements.

We also use it to make the basis of wage systems, to give a better price to the customer and to make better planning.

For this reason, standard time should be measured absolutely accurately so that we can do the above-mentioned jobs better and more accurately.

When we asked the company we visited, “What is your productivity”, we received the answer “85%, around 90%”.

Our second question was. “So how many of this product do you produce”?

For example, they said that they produce product X and the standard time for this product is about 50 minutes.  Because we have worked in many factories, we know that the standard time for this product is not 50 minutes, it is about 20 minutes.

When we ask, “What is your standard time measurement method?” we see that they made a wrong measurement. It is as follows; there are movements that add value in the standard time, that is, movements that the customer is really willing to pay for. Secondly, there are movements that do not add value but are obligatory. And thirdly, there are movements that do not add value. You do not add these movements that do not add value into the standard time, you eliminate them. If you add unnecessary movements into the standard time, if you show a 20-minute product as 50 minutes, you will accept your efficiency as 85%, 90%.

 

The sad part is that the factory thinks that it is working with high efficiency, 85%, 90%.  But the reality is that the factory is making serious losses. Therefore, the top management of the factory has decided to close it down. Given these false efficiencies, closing the factory seems like the right decision. But it is not. Why? Because when you show the standard time as 50 minutes instead of 20 minutes, you show the productivity two and a half times higher. In fact, the efficiency is around 35-40%, not 90%. The right decision to make is to measure the standard time correctly, and then to make the factory profitable by taking improvement activities, various social measures and various artistic measures together with engineering.

The data has been soaped. Even technology was used for soaping and unfortunately the standard times were inflated. Since the data that went to the top management was not the right data, the top management decided to close the factory based on this wrong data.  And the sad end. Hundreds of employees will lose their jobs. This situation is really exemplary. Soaped up inaccurate data causes the top management of the factory to make very wrong decisions.
Here, a 20 minute product is shown as 50 minutes. In other words, productivity is shown around 90% when it is actually around 35%. The top management is unable to meet the price with 90% efficiency. When the price cannot be kept, it naturally decides to close down.

What is actually the reason for this situation? The middle manager does this. He convinces himself that.  “I am absolutely successful.” How much efficiency do you need to work with to be successful? Obviously, he answers himself.  “% 85 %90.”  How many products do I produce? 2000 units. What is the time that will bring 2000 units to 90% efficiency?” He gives an inflated time. This is an unbelievable situation. So we can say that there is a kind of reverse engineering of soaping. One can only do this on purpose. This mindset unfortunately causes the closure of very important factories in our country.
We say this to company owners, especially to high level managers. Your basic data must be correct. Wrong data causes you to make very big and wrong decisions.