Arge Bilisim


The increase of the world population causes the growth of the market.However, this growth increased the number of who are sharing these limited resources.The competitive environment in production has warmed up and continues to warm with the acceleration increasing .

With this competitive environment, it becomes more important to use the existing resources more effectively. As known, effective use of resources means increasing productivity.

Productivity, is one of the important topics of engineering.Especially with the competition that increases day by day , it becomes more important.According to this, the number of engineers working on these issues ,like me, has increased.

I am the manager of @ARGE BİLİŞİM, which produces’ and quality measurement, increasing’ and provides services to companies by using industry, electronics and computer engineering sciences.

In this article, I will try to explain “ and quality increasing” by making a general factory modeling without going into scientific details.If you wish let’s start with the Arabic word ‘bereket’ which means abundance, which has been more frequently used in the past.When we open and look at the dictionary, we will see words like “plentiness, abundance, ” facing the word bereket.

In the years when most of the population was envolved in agriculture,you will remember it to be used for the abundance of the product which people get from the field.With the majority of the agricultural sector workers migrating to cities and starting work in factories, this word has been replaced by ‘yield’ and then ‘productivity’.
In order to talk about productivity , we first need to consider a transformative or productive system.Means that we will concretize productivity by taking into consideration converter structure which has input energy (or material),and transform it to another energy (or product).

As we talked about , the place where the subject will go will be factories, as you can imagine.

As you know, factories are the building rocks of the industry.Generally established for profit (here the factories mentioned are non govenemental or non-profit organizations) and we can define the companies that produce goods as factories.First, let’s show these organizations a simple example model with an “input” and an “output”. After this model let’s give an example .

Definition for a general system; The amount of energy used (utilized),After dividing the given (expended) energy after it multiplying by one hundred,we describe as a percentage and benefit-We can explain it as a concept that gives us very important information about cost.

When we write as example the formul of a lightning electric lamp (%)=will be (Light energy received / Electric energy supplied ) x 100.
The point to be considered here is that the denominator and the denominator energy units are the same,
because we express the as (%).

The efficiency of systems on earth is less than 100%, in other words, there is no system with 100% or more. This means that; There is no perfect system on earth. If you have noticed, I have described the perfect system as a 100% efficient system.If there would be a perfect system on earth or if it could be invented we could say that all the wars would have ended and that people would become angels.It means that if there is enough energy for everyone, the sharing problem will disappear.

In fact, we have stated that the sum of energies received in any system is equal to the sum of the energies given.The electricity energy given for our lamp sample is = light energy received + loss. Heat energy for the lost lamp (our expectation from the lamp is light, not heat, so heat is lost for us). When we think about the factory, there are possible losses in inputs such as energy, materials and labor. BThus, we can say that the most efficient factories are the ones with the least possible losses in inputs such as energy, materials and labor. Because the part of productivity in the formula for losses will decrease, economic productivity will increase.

When we define for factories; through our general factory model (%) = We can formulate it as (Input / Output) x 100 . In other words, as purcentage we can define as one of the most important data that shows our profit-loss situation by dividing the the output by the input multiplying to one hundred .

Our sample factory, a garment factory producing one type trousers and examine its in terms of labor.

Number of workers = 100 persons

Daily work duration = 540 Minutes

Daily trousers production quantity = 1500 Pieces

Trousers standart time = 25 Minutes

Productivity(%) = (Quantity x Standart time / Daily work duration x Number of workers ) x 100

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

=  69,44

Productivity of our sample factory is % 69,44 , the lost human energy is 100- 69,44 = % 30,56.

As can be seen here, we calculated the productivity by turning the inputs and outputs into the same units.

We can make the same calculation in other inputs and outputs so that the units are equal.If you wish, let’s give an example of material now.


Default Data:  

Trousers fabric quantity = 1,8 Meters

Fabric quantity used for trousers = 2 Meters

Productivity(%) = (Trousers fabric quantity / Fabric quantity used for trousers) x 100

= (1,8 / 2) x 100

=  90

Our sample factory fabric productivity is % 90,if the lost fabric percentage 100-90= % 10.This way we calculated the of the material used.

In terms of production sector, national and international trade competition is increasing with an increasing momentum every day. The prerequisite for getting ahead and making a difference in this competitive environment is to increase productivity. One of the most important (perhaps the most important) issues that our business people need for increasing and maintaining their competition is to continuously measure and increase their productivity.I will be happy if writing this article about productivity would be useful.




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

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


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


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.


As Arge Bilişim company, we would like to share the “line balancing module” which is designed to meet very essential needs in factories.

Firstly, let’s briefly touch on what means line balancing.

Companies were headed towards line type of production systems to use limited production resources more efficiently and reduce production costs. In these production systems, we call this path as the line in which the pieces are transformed into the products.

Line balancing aims to maximize the use of machinery and labor force by synchronizing the speeds of these workstations.

In the production line for each process to be performed, the previous processes must be completed. If you couldn’t have set a balanced line, there will be bottlenecks within the line and as a result of this there will be congestion in some places whilst some operators are awaiting. This will result in a loss of productivity.The area where this congestion mostly happens, i.e the weakest workstation in the flow will also determine your production line’s capacity. With the simplest example, let’s assume that a product goes through a total of 5 separate processes while producing it. Suppose each of these has a production capacity of 100 units per hour. The workstation, which will proceed only in the second row, has a production capacity of 90 units per hour. In this case, if you have not balanced your line, the operators after the second station will be awaiting while there will be continuous congestion from the first station to the second. And you will lose 10 units of production every hour.

Moreover, in this example, we assumed that the capacities of 4 stations are equal. In reality, the duration of the operations is not the same, and the speed, competencies, skills of each operator and the difficulty level of the processes are also different. Therefore, it is necessary to consider many parameters such as these while setting up the line.

Proper line balancing causes a reduction in production costs by decreasing lost time and a rise in productivity with effective use of labor and resource capacity.

So, how do companies do this business in the current situation?

In most of the existing production systems, while the pre-study of line balancing is performed by engineers, the operator scheduling study cannot be done, even if it could be done it is not effective. Because operator assignment is a situation that can vary according to real-time data and this issue is usually done by the line supervisor who is constantly in the production area, not by the engineer.

When an order is confirmed, the engineer primarily forms operations and standard times of the model. Then at best, workloads are calculated by setting out the target efficiency, and this list is given to the line supervisor. For example, the information that 1,5 operators are required for the side seaming operation is given by the engineer. However, there is no information about which 1.5 operators are and how these operators performed in this operation before. So, this information remains only theoretical information. Line balancing which is realized by the line supervisor is based on own experiences regarding who can do which operation or have the capacity to do. In the case of operator nonattendance, the entire balance of the line will be disrupted, and the whole setup has to be thought over from the beginning. It is pretty difficult to think over again as it will cause loss of time and slow down the workflow and often it cannot be done.

If you do not have a system that measures efficiency and quality like ArgeMAS, neither the standard time of product nor the efficiency of operators will be considered. Even the loss of efficiency in between will not be noticed.

This complicated decision, which the human mind and ability cannot have by observation, is too important to be left to the initiative of the people.

In these systems which are labor-intensive and has high variability, the studies about line balancing are far from fully solving the problem.

So, how did we solve this fundamental problem affecting the future of factories?

Theoretical information which is combined with practice in production will give much better results.

For this reason, there is a need for a structure that will combine real-time data from the production area, know-how, and current engineering studies.

For this purpose, a mathematical and special assignment optimization algorithm has been developed by Arge Bilişim engineers to simultaneously solve optimized line setup and operator scheduling problems.

First of all, we can access real-time data such as efficiency, quality, lost time of each operator which are based on an operation by ArgeMAS system which we have installed in factories. We also know the standard times of each operation with the worksheet plans created. However, all models/product types and operations may not be suitable for the competency of the line. To prevent this situation, we check the operator’s ability to operate each of the operations with 2 complementary methods.

The first of these methods is “Operation Similarity Classification”. We ensure that operations are classified according to their methods while operation identification, as a result of these operations that are similar to each other in the way they are performed is taken into the same group. Namely, the way of bartack, Apertura seaming, and quality control operations are performed are completely different than each other. Apartura seaming operation requires alignment and finger dexterity, whereas bartack operation requires alignment and rhythm skills. None of these skills are expected from an operator performing the quality control operation, and it is expected to have developed the ability to distinguish differences and attention skills. From this, it can be inferred that an operator who performs the bartack operation efficiently can easily learn the buttonhole operation that requires the same skills.

The second of these methods is “The difficulty level of operations”. With ArgeMAS system, the difficulty levels of each operation can be found by determining difficulty criteria. For example; Criteria such as long training period, excessive physical burden, high risk of the quality defect are some of the criteria that make operation difficult. The difficulty levels of operations are determined with the analytical work evaluation method by determining more criteria such as these. And the ability of an operator who is successful in one of these operations to perform the other operation can be easily predicted by the Arge Bilişim assignment optimization algorithm, by taking into account the difficulty levels of the operations with the same similarity group.

This is exactly what the line supervisors in the production area are trying to do, but unfortunately, the margin of mistake is very high as each line supervisors can only make a limited evaluation with their own experiences. While utilizing the managerial skills of line supervisors in the production area, the decision on how to set up the line most ideally is made by Arge Bilişim Assignment Optimization Algorithm, which is exactly a real engineering study.

With Arge Bilişim Assignment Optimization Algorithm;

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

The assignment is made by the algorithm to maximize line efficiency by considering model, operations, similarity group, difficulty levels, and the workload of the operation. The efficiency of a line that will emerge as a result of this assignment, is calculated entirely on real data. Instead of a static capacity, a dynamic capacity that is calculated according to real data is used as important information while planning production. Production planning, which is made as “If we produce an average of 1000 pcs per day, we will deliver this order on Friday” will be replaced by a completely new production planning which is fed with real data. This also prevents delays caused by inaccurate capacity planning.

Besides, in case of any changes in the production area, it is required to change the line setup and to be found the new ideal structure. For example, when 3 operators do not come to work that day in production, this situation will cause exact chaos in the production area, whereas the Assignment Optimization Algorithm of Arge Bilişim re-plans the most ideal structure according to the variability and presents the fast, effective and a real plan to the user again.

The ideal line structures where the models/products will be produced most efficiently are also determined by Arge Bilişim Assignment Optimization Algorithm. For example, there are 15 lines in your factory and you want to plan 20 models to these lines. By the system, the factory is considered as a single line, and 15 lines which will be produced these 20 models are created again in the most ideal way. Therefore, the bottleneck operation of a line can be solved with a competent but unassigned operator compared to the lines that will set without using the program, thus productivity can be increased.

To summarize the results of Arge Bilişim Assignment Optimization Algorithm;

First of all, ArgeMAS measures productivity in terms of efficiency and quality. ‘Arge Bilişim Assignment Optimization Algorithm’ sets the most efficient and balanced lines by using real operations and operators according to these efficiency and quality results.

Secondly, it provides the actual production capacity information for production planning.

Thirdly, the workload of line supervisors is reduced, allowing them to focus more on the managerial sphere.

Fourthly, the line setup time, which is shortened with the bundle system, will be shorter with this Algorithm, the line supervisor does not have any doubt about which operator to evaluate which operation during model transitions and can easily organize the line with the report in hand.

Lastly, it is pretty difficult to think over the line again by keeping up with the production’s variability. It will be very easy and fast to think over again the line in the most ideal way according to current conditions.

We can include the other benefit of this algorithm as forming the line structures in the most ideal way whereas more than one model is planned to more than one line.

Like this, we can even use it in many areas that will directly support the rise in productivity.

Third Sector Social Economic Review

A Computer-Aided Bonus System Model Based on Productivity and Quality in Ready-Made Garment Businesses



Doç. Dr., Ege Üniversitesi İktisadi ve İdari Bilimler Fakültesi İşletme Bölümü


Doktora Öğrencisi, Ege Üniversitesi Sosyal Bilimler Enstitüsü


Today, as in many sectors, there is fierce competition in the ready-made garment sector. In labor-intensive businesses such as ready-made garment businesses, employee performance directly affects business performance and thus the competitiveness of the business. Therefore, bonus systems that build on key performance indicators such as productivity and quality in the ready-made garment sector are important to incentivize increased worker productivity. However, for such bonus systems to work effectively, there is a need for accurate measurement of employee performance and a fair distribution system. For this purpose, it is vital to use a computer-aided system that will receive accurate and timely employee performance data from the production site and make a fair and transparent distribution according to the bonus model. This study proposes a bonus system model based on productivity and quality performance criteria for direct employees in ready-made garment businesses. The model is based on the components of time, piecework and profit sharing. In the model structure, the increase in operator performance increases the profitability of the business, and the increased profit is returned to the operators as an


Extensive Summary

The most important input of labor-intensive sectors is the workforce. In this sense, high labor productivity is the main factor that directly affects businesses in gaining competitive advantage by enabling them to operate cost-effectively. In order to improve labor productivity and production quality, they must first be measured. In fact, measuring productivity and quality in real time and making improvements using instant reports is one of the essential approaches of today. For this reason, it is inevitable that the workforce performance evaluation systems to be established will be computer-aided, because collecting, storing and analyzing accurate and instant data from the field on labor performance in ready-made garment processes, where product diversity is increasing and production processes are becoming more complex, and reporting the said data in accordance with the established model can only be achieved with the support of information systems and technologies. By establishing incentive systems built on these systems, the contribution of employees to the sustainability of the business can be enhanced.

Productivity and quality in ready-made garment processes, one of the most labor-intensive sectors, are directly proportional to labor performance. For this reason, an effective workforce performance evaluation and a corresponding remuneration and bonus system with consistent, fair and achievable targets should be implemented. Since the make-up and expectations of white and blue collar employees are different, bonus systems should be created by taking these dynamics into account.

The evaluation of performance in textile and ready-made garment manufacturing is highly dependent on the performance of workers in producing quality and fast products. Therefore, accurate and fair performance evaluation studies should be one of the indispensable management systems of textile and ready-made garment sectors (Gökbulut, 2019, p:6). Ready-made garment businesses are labor-intensive businesses and have low investment costs and profit rates compared to other sectors. A low profit rate requires high productivity. This sector is a seasonal one where customers’ demand for more quality and personalized products is increasing by the day. Therefore, it is vital for these businesses to implement bonus systems that promote productivity and quality, and enhance these parameters.

This study focuses on a computer-aided bonus system model for direct employees, the most important factor in the productivity of ready-made garment businesses. In this bonus model, both individual performance based on productivity and quality and team (line) performance can be evaluated separately or together. In addition, the model provides managers with a decision support system to establish a common bonus structure that will ensure both business profitability and employee motivation.

The bonus system model is a combination of time, piecework and profit-sharing based remuneration systems. The main purpose of the bonus system is to boost profits through increased employee productivity and quality production, and to share some of this profit with the employees. First of all, a target profit is determined above the break-even point, that is, where the income and expenses of the business are equal. A portion of the profit above this target profit is shared with employees. It is expected that having employees as partners in profit will increase both personal satisfaction and sense of  responsibility. In this bonus system model, if the employee generates income above the target profit, the portion above the target profit is shared with the employee and a bonus is given. For this purpose, a break-even productivity point is determined and a bonus limit is set by adding a target profit. This target profit is taken as a proportion of the break-even productivity point. The employee receives a bonus each time they exceed this limit. The first step in the model is to determine the break-even productivity point. For this, there is a fee paid to the operator for the time worked. On the other hand, there is also a revenue received from the customer per product. If employee productivity falls, profitability will also fall. The mathematical structure of the proposed bonus model is as follows:

Monthly expenses are calculated as follows, taking into account all routine or random recurring expenses other than investment, such as energy, salaries, food, social security payments, building and machinery maintenance:

Cost per 1 minute per direct operator = Monthly cost / Number of direct workers / Average number of days per month / Daily working time (minutes)

In the model, monthly income is calculated as follows:

Products manufactured per unit time:

Product X, XT min, XQ pieces of order, TL XM labor cost.

Product Y, YT min, YQ pieces of order, TL YM labor cost.

Product Z, ZT min, ZQ pieces of order, TL ZM labor cost.


(XQ x XM + YQ x YM + ZQ x ZM)


𝑾𝒆𝒊𝒈𝒉𝒕𝒆𝒅 𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒊𝒏𝒄𝒐𝒎𝒆 𝒑𝒆𝒓 𝒎𝒊𝒏𝒖𝒕𝒆 =

                                                                                                                 (XQ x XT + YQ x YT + ZQ x ZT)



Thus, the break-even productivity point is calculated as follows:


Cost per one minute

𝑩𝒓𝒆𝒂𝒌𝒆𝒗𝒆𝒏 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒗𝒊𝒕𝒚 𝒑𝒐𝒊𝒏𝒕 =

Income per one minute    𝑥100


Bonus limit (%) = Break-even productivity point (%) + Break-even productivity point (%) x profit rate (%)

Daily bonus limit minutes = Shift minutes x bonus limit (%)

Operator daily bonus minutes = (daily shift minutes x productivity rate – daily bonus limit minutes)

Operator minute-based bonus = Operator daily bonus minutes*minute rate*quality coefficient

This described bonus model is standardized for all operators and operations. However, not every operation produced is of the same quality. Therefore, an operator may need to receive different bonuses for different operations. For this reason, an operation coefficient is determined according to the craft requirement of the operation performed by the operator, and is evaluated as a multiplier to the standard time. For example, the shoulder-fitting operation is a relatively simple one and the coefficient is set to “1”, whereas the collar-fastening operation is a very difficult one and requires mastery, so the coefficient for the collar-fastening operation is set to “1.2” and the operator who performs this operation is rewarded more. Thus, more quality operations could be incentivized more. As a result, it will be possible to establish a fair bonus system that accurately reflects the dynamics of production and will be accepted by all employees. In this case, the calculation of the final bonus per minute on an operator basis is as follows:

Operator minute-based bonus = Operator daily bonus minute*minute rate*quality coefficient*operation coefficient

The productivity and quality-based bonus model described in detail above can be illustrated with an example as follows:

The break-even productivity point for a garment line operating with 10 workers for 20 days a month and 540 minutes a day with a monthly expense of TL 50,000 can be calculated as follows:

Expense per 1 minute per direct operator: Monthly expense/number of workers/20/540

1 minute expense per direct operator = 50,000/10/20/540 = TL 0.47.

The models produced during the month, unit standard production times, order quantities and craftsmanship fees per piece agreed with the customer company are as follows:

Model X 40 min, 1,000 pcs order, TL 40 craftsmanship fee

Model Y 30 min, 10,000 pcs order, TL 35 craftsmanship fee

Model Y 50 min, 30,000 pcs order, TL 50 craftsmanship fee

(1.000×40 + 10.000×35 + 30.000×50)


𝑾𝒆𝒊𝒈𝒉𝒕𝒆𝒅 𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒊𝒏𝒄𝒐𝒎𝒆 𝒑𝒆𝒓 𝒎𝒊𝒏𝒖𝒕𝒆 =

                                                                                                   (40×1.000 + 30×10.000 + 50×30.000)

= 1,03 𝑇𝐿.


𝑩𝒓𝒆𝒂𝒌𝒆𝒗𝒆𝒏 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒗𝒊𝒕𝒚 𝒑𝒐𝒊𝒏𝒕 = 0,47 𝑥100 =46%



Considering that the target profit set by the company is 20%, the bonus limit is determined as (46 + 46*0.20%) 55.2%. In other words, when the daily working time of the operator exceeds 298 minutes (540×0,552), which is 55.2% of the daily shift duration, a bonus will be received. Based on an operator efficiency of 90% and a quality ratio of 90%, the operator’s bonus minutes for a quality coefficient of

0.5 and an operation coefficient of 1.2 are as follows:

Productivity = 540×0.90 = 486 min. Bonus limit = 540×0,552 = 298 min.

Operator’s daily bonus minutes = 486-298 = 188 minutes.

Earned bonus minutes can be monetized either as a fixed fee or as 1 minute of the operator’s net salary. Based on the operator’s salary, an operator’s fee per minute is TL 0.47. If a fixed fee is preferred, a certain amount is set by the management. In this case, when we include the quality factor and the operation coefficient in the model, the operator’s bonus fee per minute is as follows:

Daily bonus fee (based on salary) = 188×0.47×0.5×1.2 = 53 TL.

Daily bonus fee (based on flat rate (TL 0.2 per minute)) = 188×0.2×0.2×0.5×1.2 = TL 22.56.

If the operator efficiency is lower than 55.2%, the bonus amount will be negative. If the bonus amount is positive when all the days in the month are added up, the operator will earn a bonus; otherwise, the operator will not earn a bonus.


Finally, this model can work as a decision support system for the employer. The employer will be able to determine an accurate profit rate that will ensure the balance between employee motivation and sustainable profit thanks to the what if analyses to be performed with the model. In this bonus model, if the employee does not earn a bonus as a result of not working efficiently due to a specific problem, they will give feedback to their manager. Thus, in a complex process such as production, it will be possible to quickly identify where the errors and deficiencies originate and to take the necessary measures to eliminate bottlenecks. In addition, since the employees in the model are shareholders in the profit, they will have increased ownership of the business and act with a sense of belonging. Thus, instead of work pressure from senior management to employees, a need pressure will be created from employees to management. This will improve productivity and quality, and operational excellence.


Alders produces for global ready-made clothing companies in three different locations within its own structure. It is one of the leading manufacturers of the women’s clothing industry in Turkey, employing a total of 800 people and producing 800 thousand pieces per month, including contract manufacturing.

Melih Ayan – Alders Tekstil, Albrander General Coordinator

35% productivity increase

Underlining that there has been a productivity increase of nearly 35% after starting to use the @rgemas system, Alders Tekstil, Albrander General Coordinator Melik Ayan said, ‘We have been using the @rgemas system for 3 years. This system allows us to monitor every process of production in terms of both efficiency and quality. And at the same time, it is a system that allows us to take action instantly. “We can detect where there is a problem at that moment, not in the evening or at the end of that week, and develop appropriate solutions.” 

We cannot manage a company with classical methods

Drawing attention to the increasing competition and costs, Ayan said, “It is no longer possible to manage companies with classical methods.” It is necessary to have a very good data pool. “This forced us to be more careful and create a more efficient production management system,” .

As the business earns, the employee also earns

Stating that they measure everyone fairly thanks to the @rgemas system, Ayan said, “Therefore, it has become a system that we can share with our employees as our business earns.”

@rgemas is a company that leads the way

Stating that he recommends the @rgemas system to everyone, Ayan said, ‘Arge Bilişim company is not only a company that sells systems, but also a consultancy and guiding company. “It made our decision-making process very easy by providing all kinds of support,” he said.

APS Tekstil, which produces for important brands in Europe with over 50 years of experience, is one of the important companies operating in the field of textile exports in Turkey. Approximately 900 people work at APS Tekstil, whose headquarters is located in Istanbul. The monthly capacity in the production factory located in Merzifon is 600,000 units.

Kutlu ÇECELİ – General Manager of APS Tekstil

Fixing troubles is easier

APS General Manager Kutlu Çeceli stated that with the establishment of the @rgemas system, they could instantly see all critical data regarding production management and said, “In this way, it was easier to instantly monitor and correct the problems in production.”


Stating that the operators monitor their own performance in real time and that the operators benefit from it, Çeceli said, “In fact, the system is literally like the speedometer of a car; operators can instantly see that it accelerates when they press the gas, and slows down when they take off the gas. Thanks to the screens in the production area, all data regarding the line can be seen. These screens also appeal to the mental side of employees,” he said.

It also challenges management

Stating that the @rgemas system also squeezes the factory management, Çeceli continued his words this way. “As follows; When the operator experiences a decrease in performance due to reasons beyond his control, he communicates this to the management with the help of the devices in front of him.The management is dealing with the issue as soon as possible and resolving the problem,” he said.

All manufacturing businesses should definitely establish @rgemas

Underlining that he recommends the @rgemas system to all enterprises engaged in garment production, Çeceli said, “I believe that it is a system that should definitely be established.”

Erak Giyim, the largest manufacturer of Mavi, also continues to produce for many major foreign brands with its factories in Turkey in Çerkezköy and Egypt. Erak Giyim, which produces approximately 7 million annually including contract manufacturing, is one of Turkey’s well-established denim manufacturers with 37 years of experience, employing 1200 people.

Nuri Sokullu – Erak Giyim, Information Systems Manager

We have already started the digitalization process with @rgemas

Erak Giyim Information Systems Manager Nuri Sokullu said, “When we started with @rgemas 8 years ago, it means that we have already established industry 4.0” and stated that they started the digitalization process 8 years ago.

We can access data instantly with @rgemas

Stating that they could not access the data instantly in the old system they used, Sokullu said, “This made us lose something. The low efficiency of the person while performing the transaction reduced our quantity, quality and productivity that day. @rgemas added value to us the most at this point. Because we can measure efficiency in a short time and intervene in our personnel, production and stages in a short time. “We can see the number of operations based on operations in the departments and make an evaluation accordingly,” he said.

Thanks to @rgemas system our customers choose us

For 8 years, Sokullu stated that they have been using the @rgemas system in the cutting, sewing, washing, and packaging departments. He also stated that they have implemented many innovations, including automation in the washing department, in collaboration with Arge Bilişim. Sokullu mentioned, ‘When all our clients visit our company, seeing productivity measurements of employees and the performance evaluations based on this efficiency contributes positively to their preference for our company.’ Expressing satisfaction with the services received from Arge Bilişim, Sokullu concluded, ‘We recommend @rgemas.'”

“We can see our costs

Subsequently, Sokullu underlined that the greatest contribution of the @rgemas system is the timely collection and evaluation of data, ensuring that measures are taken a timely manner and stated: “The biggest factor in the following stages was in terms of staff motivation. Another impact was on the quality stage. And of course, at the end of the day, it is the cost that will keep all companies afloat. It was helpful in seeing our costs and measuring our operations.”

With my 44 years of experience,it is among the leading exporters in the field of woven ready-to-wear clothing in Turkey. The company, headquartered in Istanbul, has a monthly production capacity of 600,000 pieces, including its production factory in Sinop and production partners.

Fatih Yurdadön – Görkem Giyim manufacturing manager

There have been positive changes with @rgemas

Stating that he had prejudices about the bundle system proposed by Arge Bilişim when it was first established, Görkem Giyim manufacturing manager Fatih Yurdadön said: “It is only an 8-month project, but there have been very good changes in this period. We believe that we will do better with the @rgeMAS system,” he said.

The product workflow has changed.

Yurdadön, expressing that they are working with the bundle system for the first time, said, “Previously, our product was moving in four directions, and there were delays. Now, the product enters from a single point, and the expert focuses on a single point.”

We can see bottlenecks.

Stating that in the past, employees could not instantly see their productivity, Yurdadön said, “With the bundle system, we can see more easily what the operator cannot do. And we can ensure line balance by making actions accordingly,” he said.

It is easier to raise Jokers

Stating that they increased the competencies of each operator with the bundle system, Yurdadön said, “We have now reduced the number of jokers (persons performing multiple operations).We used to employ 7-8 jokers, but now we can use the jokers better with the bundle system,” he said.

We can see the lost times with @rgemas.

Stating that they can see time losses as data in the @rgemas system, Yurdadön said, “We can minimize these time losses. According to my calculations, there is currently a 30-40% improvement,” he said.

We don’t use matchers on the assembly line.

Yurdadön, who mentioned that they used to employ 8-9 people to match products on the assembly line, said,”We have integrated these matchers into production. When there is a missing part in the bundle, we know where it is, preventing production losses.”.

We use a 2D barcode system.

We use a 2D barcode system. Stating that they use the 2D barcode system introduced by the @rgemas system, Yurdadön said, “Now the product is identified to the bundle by attaching a 2D barcode to it before it enters production while it is in pieces. We can access the parts inside this bundle with a 2D barcode. We can easily find which bundle the missing part belongs to.Our losses have now been prevented, which has become a great gain for us,” he said.

We know what mistake each operator made.

Stating that they know which mistake each operator made with the 2D barcode application, Yurdadön said, “Since we can see the mistakes more easily, we can give Ayşe’s mistake to Ayşe and Fatma’s mistake to Fatma. This is a really nice system as it is very important in terms of traceability,” he said.

Engin Kaya – Görkem Giyim Sinop Factory Director

Real-time tracking in production;

Underlining that production management should be easy, understandable and in real-time, Görkem Giyim Sinop Factory General Director Engin Kaya explained the reasons for choosing @rgemas as “a real-time system, compatible with technology and can give us instant data and where we can intervene in production instantly. we were looking for. That’s why we started working with @rgemas.”

We gave the product an identity;

Emphasizing that they have gained an identity for each product with the 2D barcode application introduced by @rgemas, Kaya said: “When we open any box in our packaging department and look at any job, we see which lot this product was cut by, which cutter, which labeler has processed it, which sorting employee it passed through, which process it was used in production, We can fully report who performed the operation, on what day and at what time, and who did the quality control,” he said.

Automatic line installation;

Describing the assignment optimization module as a revolution, Kaya said, “We can now set up our lines automatically without the need for human intervention. It is a revolutionary work for manufacturers,” he said, pointing out the importance of assignment optimization in production.

Consulting support;

Underlining that they have at least as much knowledge and equipment on productivity as an industrial engineer with the consultancy service they received from @rgemas, Kaya said: “They are interested in how production should be in the enterprise, how lost time should be minimized, and how quality should be improved in quality control departments.We have received very serious consultancy support from @rgemas and we continue to receive it,” he said.

@rgemas a living system;

My favorite feature of @rgemas is definitely not a software program but a living productivity program. “It is a software system that instantly adapts your problems to your company with the software team,” said Kaya and continued his words as follows. “No matter what, large factories of this size definitely need to have an efficiency system. On behalf of my company, I can tell companies with peace of mind that they should work with @rgemas,” he said.

Sebur Candemir – Görkem Giyim Line Manager

I had prejudices against the @rgemas system

Stating that they had prejudices especially against the bundle (car) system when the @rgemas system was established, Sebur Canderim said, “I was worried for nothing. When I started working with the bundle system, there were many positive changes. I spend more time on line now,” he said.

Lost times (setup time) in line installations have decreased significantly

Emphasizing that before the system was established, there was a lot of lost time, especially in line installations, Candemir said, “After the @rgemas system, our time losses have decreased to a very small extent. “Sometimes we can release one product after another without wasting any time,” he said.

I do not use operators for product matching

In the bundle system, Candemir explained that they put all the parts that make up a product into a certain number of bundles. He said, “6-7 people we used for matching became unnecessary, and I assigned them to the machine.'”

I can see bottlenecks more easily

Stating that they can now see bottlenecks more easily and instantly, Candemir said, “We can immediately intervene in the bottleneck and solve the problem.”

I can use half a man

Candemir said that in the Bundle system, the free time of each operator can be seen much more clearly and said, “The operator completes his work, which I estimate as 1 hour, in 45 minutes. Since this operator has 15 minutes free, I can give him 15 minutes of work. Before the @rgemas system, I could not give that person a job,” he said.

I don’t sit at the machine to get work done.

Stating that he never sat at the machine to get work done after the @rgemas system, Candemir said, “I used to sit at the machine a lot. Now I spend more time on line. I use my time to solve bottlenecks and describe work,” he said.

I don’t experience any missing parts after the @rgemas system.

Candemir stated that since all parts of the product are in the bundle, he does not use a carrier as he used to, and said, “At the same time, I do not experience product losses.”

I recommend @rgemas

Emphasizing that the question marks in his mind were completely resolved when the system started to be installed, Candemir said, “I would definitely recommend the @rgemas system.”

Sezgin Temur – Görkem Giyim Business and Method Development Unit Manager

I recommend @rgeMAS to all manufacturers

Underlining that manufacturing companies have to work quickly, Temur said, “The @rgemas system, which has been prepared with a lot of effort with both technology and consultancy support, achieves this.”For these reasons, I recommend @rgemas to all companies engaged in production,” he said.

We can intervene in problems immediately.

Stating that he has been working on efficiency, performance and work study for 18 years, Sezgin Temur said, “@rgemas’ biggest contribution to us is providing real-time data. This enabled us to see problems immediately and intervene.” said.

No blind spots left in the factory

Stating that they received accurate data from each note with the @rgemas system, Temur said, “There is no blind spot in the factory from which we did not receive data.This was very important for us and we achieved it,” he said.

We also keep track of lost time

Emphasizing that they also track lost times in production, Temur said, “For example, when the operator scans the machine repair barcode, this notification goes to the technical service. The technical service repairs the machine without wasting any time,” he said.

We also track the quality.

Temur expressed that they monitor the quality in real-time through process, end-of-line, and final inspections.Temur said, “We can both see all the errors that occur and track how long a product goes for repair.” .

We ensure product traceability

Stating that they provide product traceability with 2D barcode and RFID application, Temur said, “We can track the product even if it is in the package.”

Consultancy support

Temur mentioned that their perspective has changed with the consultancy support they received from Arge Bilişim. He said, ‘We made the engineering part more effective. Consultancy support is provided, and they assist us all the way.”

Yusuf Yazgan – Görkem Giyim Sinop Factory Manager

I recommend @rgemas

Stating that he has a prejudice against the @rgemas system, as in every new application, Görkem Giyim Sinop Factory Production Manager Yusuf Yazgan said, “With my 37 years of textile experience, I recommend @rgeMAS to all producing companies.”

Product workflow has changed

Yazgan stated that before @rgemas, they developed the product by installing it in 4 or 5 places, but it was difficult to keep track of the product, “but now with the bundle system, trousers, dresses, skirts, jackets, whatever the product, enter from a single place and exit at the end of the line. Our expert and quality controller can control it more easily,” he said.

We can see the bottleneck

Underlining that they can see bottlenecks more easily in the bundle system, Yazgan said, “The bottleneck can be caused by either us or the operator. We now have the chance to intervene in bottleneck areas more quickly with the bundle system,” he said.

In the bundle system, everyone is a joker element.

Yazgan said that they made the entry into the Bundle system from a single point and said, “For these reasons, we increased the skill levels of the operators to higher levels. We have 2-3 extra joker members. But since everyone in the line is like a joker, we can easily produce the products,” he said.

Time losses decreased during model transitions

Yazgan stated that they consider the time losses in model transitions normal when there is no @rgemas system and said: “With the bundle system, we do not lose any time in model transitions. When the model comes with the system, we do its preliminary work and decide where to place people or the machine. At the same time, this system provides the ability to perform every operation to every person without changing our machines,” he said.

We do not experience parts loss

Yazgan said that in the bundle system, all the materials that make up a product are in a single bundle, and added, “For this reason, we do not experience any loss of parts.”

We are able to track each person in quality

Yazgan said that they reached the operator who made each mistake through sewing, process and end-of-band quality controls and added, “Even if more than one person performs the same operation, we know who made this mistake.”

H&M Hennes & Mauritz AB is an international retail clothing company based in Stockholm. The company operates in the ready-made clothing sector and produces clothing for men, women, youth and children. Starting from 2019, it provides services in 74 countries with 126,000 employees in 5,000 stores with different names.

İsmet Tayak- (H&M / COS) NQC

@rgeMAS a system that meets customer demands

Stating that he works at Görkem Giyim as NQC representing H&M, İsmet Tayak said, “@rgemas is a system that meets the demands of H&M and similar customers. It takes the factories very far in terms of both quality, quantity and performance. We experienced it live here and saw it personnaly. He drew attention to the importance of the system with the words It has contributed a lot to us.”

We can see the problems immediately.

Underlining that one of the most beautiful aspects of the @rgemas system is that they instantly see the faulty areas in the line and who made the mistake, Tayak said, “Let me make an interesting analogy. We can think of production as Istanbul traffic. With the @rgemas system, we can immediately see where it is blocked at that moment. In other words, we can see this data on the computer and intervene immediately without going into production,” he said.

2D barcode application

Saying that with the 2D barcode application developed by @rgemas, all informations about the product is included in the 2D barcode, Tayak said, “When we encounter any error during the inspection, we scan the 2D barcode and we can clearly see who made this mistake. It takes us forward both in terms of quality and quantity. Receiving instant data made us very comfortable and we can prevent errors.” he said.

Gülbebe Tekstil, which includes EMF Tekstil Konfeksiyon A.Ş., is one of the important baby clothing companies producing in Bursa city with 150 employees. Gülbebe sends its products to many domestic and international countries under the brand ‘Baby Rose’.

Fatih Gürses – Founder of Gülbebe Textile

@rgemas very positive in improving efficiency

Stating that the @rgemas system has had very positive effects on productivity and organization since the first day of their work, Gürses said, “Continuous and beneficial developments can be achieved by adding new modules to the system.”

Referring to the experience of Arge Bilişim’s technical team, software and consultants, Gürses said, “Thanks to this knowledge, they have created a successful institution that guides all companies and generates new ideas when applying in the field of production.”

Our cooperation will always be

Gürses said, “We cannot evaluate or improve what we cannot measure. This program establishes a measurement mechanism that is easy to apply in all areas of production. So far, we have benefited greatly from the system and have not experienced any problems in the application. Our cooperation with Arge Bilisim’s friendly team will always continue,” he said.

Gülce Tekstil,produces textiles in Arnavutköy, Istanbul, produces men’s and women’s trousers for top-class brands such as Massimo Dutti, Burberry and Ripley. The company produces 40-50 thousand units monthly and also exports.

 Cemal Aysel – General Manager of Gülce 

An increase of  30% in productivity and quality

The General Manager of Gülce Tekstil, Cemal Aysel, underlined the benefits of the @rgemas system with the words, “@rgemas has made noticeable and distinguishable differences after we started using it. There has been at least a 30% increase in both quality and efficiency. I am a witness to what I have seen.” Cemal Aysel is one of the best examples of medium-sized garment manufacturers using the system.

We can make quick and easy decisions with @rgemas

Stating that they can make quick and easy decisions with the @rgemas system, Aysel said: ‘The classical system and the new system are not the same. “I don’t know how to explain it, but we are not talking about the same thing,” he said.

We give bonuses to our employees with the @rgemas system

Explaining that they reward their employees with bonuses above a certain level of efficiency and quality, Aysel said that this is an important factor in increasing productivity and quality.

We can provide accurate deadlines to our customers.

Aysel, who also mentions that they provide clear feedback to their customers about deadlines, said, “You don’t say things like perhaps,however,but,on the other hand,now,like this,like that. You speak clearly. Untill this hour, the work is 1500, 1300, 1200 pieces. You can also say it could reach a certain number by the evening. The forecast here is not an estimate but is based on data.”

“I recommend @rgemas

Aysel, expressing that @rgemas has added a lot to them in terms of vision, said, “I have been using @rgemas for 3 years and I also recommend it. If we want this industry to be feasible and sustainable, we must, without a doubt, be different. We must do things differently. We must definitely change something, get rid of our yesterday’s mindset. I recommend this to my  business friends who do the job  in this industry.”

Ototrim, which has been serving in the automotive sub-industry for more than 40 years, produces at world standards. Ototrim, which operates in its Gebze facilities with 7500 closed and 20000 open areas, produces more than 250 types of sliding frames, all types and kinds of handlebars, engine and trunk covers for the automotive industry.

Şenol ATALAY – Information Technologies Manager

Solutions suitable for work

Ototrim Information Technologies Manager Şenol Atalay stated that Arge Bilişim offers solutions that are suitable for our operation and said: “Arge Bilişim focuses on creating flexible modules and solutions suitable for our operation rather than selling commercial goods to our company. Since we started using @rge-CMAS, we have been able to track our production, products and consumables instantly, starting from our purchase orders, including all production operations,” he said.

Thank you for your support

Underlining that the software requests requested by the Arge Bilişim team were fulfilled, Atalay continued his words as follows. “The efforts and special efforts they made to make the @rgemas software suitable for our wishes and needs and their subsequent support were extremely attentive. I thank them for their efforts,” he said.

Uğur Tekstil, one of the largest exporters in the Mediterranean region, has an annual production capacity of 7 million. The biggest customer of Uğur Tekstil, which mainly produces women’s upper and lower group knitted products, is Inditex. The number of employees of Uğur Tekstil, whose production areas are in Adana Mersin free zone and which continues production with its 20 thousand m2 factories in Urfa, is 440.

Akın Şimşek -Uğur Tekstil General Manager

In the textile sector, Akın Şimşek, who has been a professional manager for 32 years, expressed his reasons for choosing @rgemas as follows: “Firstly, we were looking for a company with a strong technological infrastructure. Secondly, it’s about the production philosophy. @rgemas is a production management system that I have been following for years. It’s not just software; it has significant know-how in constantly updating the production management philosophy and transferring it to the software. We wanted to transfer this know-how as well.”

@rgemas engineering software

Şimşek, underlining that they are a leading company in Turkey’s textile sector with prominent executives, highlighted another point: their desire to manage their factories through engineers. Şimşek stated, ” @rgemas is an engineering software. And thanks to @rgemas, we are providing a crucial tool to our engineers,” expressing their preference for managing factories through engineers.

Automatic line installation;

Underlining that they were excited about @rgemas’ automatic line installation module, assignment optimization, Şimşek said, “It’s an artificial intelligence-connected operation. Bringing a new phenomenon that we hadn’t really thought about before is crucial for us. It was already a module we set at the beginning of the project. I believe it’s a module that every business dealing with @rgemas should have.”

We recommend @rgemas

When asked whether you would recommend @rgemas, the experienced manager Şimşek answered, “Of course, we have been doing so for 30 years.” He expressed his thoughts with the words “We will continue to do so.”

Ünlü Tekstil, where a total of 1300 people work in two locations in Istanbul, produces H&M’s brands, ‘COS’, ‘Other Stories’, ‘Arket’, as well as ‘Lacoste’, ‘Tommy Hilfiger’, ‘Hallhuber’ and  from England Ivy&oak, Boden, Reiss, It is one of the leading women’s clothing companies in Turkey, producing for important customers in Europe such as The White Company.

Oğuz Açıkgöz – Ünlü Tekstil General Manager

A chance for our industry

For the past 10 years, the General Manager of Ünlü Tekstil Oğuz Açıkgöz used @rgemas as the production management system,said,”Actually, if you look, I’ve always said this since I started talking with @rgeMAS. This was what should be. You know how you wait for something, you wish for it, and finally, it has come. Frankly, I also think it’s a chance for our industry at the same time. That’s why, without a doubt, I not only recommend @rgemas but also think that it should be fully utilized.”.

@rgemas provides traceability

Açıkgöz continued his words as follows. “With the @rgemas system developed by Arge Bilişim, we can track all production-related data such as efficiency, quality and lost time on the system. We also ensure product traceability by executing products in bundles instead of one by one. With the 2D barcode sewn on the product along with the washing instructions, we provide backward traceability even when the product reaches the final consumer.”

Describing the ‘assignment optimization’ module of @rgeMAS developed by Arge Bilişim as a “revolution,” Oğuz Açıkgöz said, “Thanks to this optimization, as soon as we enter the criteria specific to a model into the system, the system will provide us with the best information on which line and with whom this model can be realized.”

It’s impossible to run a business without data

Underlining that it is impossible to manage businesses without data, Açıkgöz said,”So we are so used to the system -@rgemas that, believe me, it is really difficult to even imagine that there is no system. But the first thing I will say is that we will be like fish out of water,” he said.

Sinan Çalışır – Ünlü Tekstil Production, Planning and Control Manager

We have been using Industry 4.0 in our company for 11 years.

Stating that Industry 4.0 is a new concept, Çalışır said, “I can easily say that we have been using Industry 4.0 together with @rgemas from the beginning, that is, for 11 years, by collecting internal data and evaluating these data.”

Management with data;

Underlining that management with data is very important today, Çalışır said, “While this data is collected, it must be collected by a reliable system in the same way. We receive a wide variety of reliable reports from @rgeMAS. We have also made great gains in identifying inefficiency points,” he said.


Underlining that their customers really admire them when they explain their quality assurance systems, Çalışır said, “Quality is not a random result for us.”

Assignment optimization;

Emphasizing that assigning the right operation to the right person is the most crucial point in textiles, Çalışır stated, “With assignment optimization, I can confidently say that in the future of textiles, data will definitely be involved in assigning the right operation to the right person. Companies that evaluate this data correctly will be the winners. I believe that @rgemas provides this very well,” he said.

Bonus System;

Stating that the most effective and equitable method of increasing productivity is the bonus system, Çalışır said, “If employees can receive wages in proportion to their contribution when they are productive, their commitment to their jobs will increase and you can always have a certain level of productivity within.”

We can ensure product traceability.

Pointing out that product traceability is very important in planning, Çalışır said, “In addition to the bundle system in production, we have a 2D barcode system in the finish section where we track the final ironing, quality control and other products one by one. And we ensure the traceability of the products one by one,” he said.

I recommend @rgeMAS


Stating that he recommends @rgeMAS to all businesses, Çalışır said, “Today, unfortunately, it is no longer possible to make production without data in the production field or in all other business areas. That’s why I would like to thank @rgemas for providing us with this data in the most accurate way.”

Hasan Gür – Production Manager at Ünlü Tekstil Arnavutköy

 The @rgemas system makes the job easier for managers 

Hasan Gür, the Production Manager at Ünlü Tekstil Arnavutköy, who has been in the textile industry for 33 years, expressed, “I have been working with the @rgemas system for many years. It is a system that significantly makes the work easy of managers. I have seen many advantages. I can recommend it to everyone,” emphasizing the importance of the system.

Bundle System

Gür emphasizes that with the bundle system, everything related to manufacturing can be easily tracked. He said, “You can track productivity, quantity, and quality much more easily. In a traditional system, you almost have no chance of seeing these. You cannot see the bottleneck. However, with the bundle system, you can easily see this from the bundle, the device in front of the operator, and also from the reports.”

Managing the bottleneck with bundle system is easy

Gür, stating that the duration of each operation in textiles is different from each other and therefore it is difficult to balance the line, said, “You give 0.3 minutes of work to one employee, then give 1 minute to the next one, and give 2 minutes to the one before. It is impossible for someone who does a 2-minute job to catch up with someone who does a 0.3-minute job. This is a bottleneck. We can see and solve this bottleneck both from the system and the bundles. The most important feature of the bundle system is that it allows us to see and solve bottlenecks.”

We can see the error early

Stating that they caught the error before it reached the end of the line with the bundle system, Gür said, “In process (in-line) quality control, we check 7 items in each bundle. There are 20 pieces  in a bundle. A maximum of 20 mistakes can be made. In the classical system, 200 faulty products can reach the end of the line. I don’t think even this will be caught. The most important feature of the @rgemas system, together with the bundle system, is that you prevent it from getting used to the end of the line by seeing the error early and intervening early,” he said.

We can see who made the mistake.

Stating that they can easily find out who made the mistake through end-of-line quality controls, Gür said, “We do not need to search thoroughly to find out who made the mistake. He can intervene immediately,” he said.

Bonus system

Underlining that the bonus system is an indispensable part of the system, Gür said, “Bonus motivates people and means extra income for employees. I have been working in this system for many years. I work as a line manager, floor manager, and now production manager. I saw that the impact of the bonus system is very important at every stage of these,” he said.