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.