Endüstri Mühendisliği Bölümü / Department of Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11413/6819

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An Analytical Approach to Machine Layout Design at a High-Pressure Die Casting Manufacturer
(Springer-Verlag Singapore Pte Ltd., 2022) EMİR, OĞUZ; AKTİN, AYŞE TÜLİN
Sahin Metal was founded in 1975 on a 7500 m(2) area in.Istanbul to supply high-pressure aluminum casting parts to a variety of industries, especially automotive manufacturers. 64 different products are produced with various routings through 14 workstations in the plant. Being a Tier 2 company, these products are then sent to Tier 1 firms and finally reach the leading vehicle manufacturers. Nowadays, increasing competition, changing customer demands, and quality targets bring the necessity of restructuring internal processes. In this regard, Sahin Metal plans to rearrange the existing machine layout to minimize the distance traveled between departments by taking into account the material flow. This study aims to determine an efficient machine layout design by implementing analytical approaches. The study is started by visiting the production facility and meeting with the company's engineers to determine the project roadmap. Following that, the data collection process is initiated, and ABC analysis is performed to define product classes. After this identification, two approaches are utilized simultaneously. The Hollier method is employed to find a logical machine arrangement. In addition, a mathematical model based on the Quadratic Assignment Problem (QAP) is developed to obtain the optimum machine layout. The developed integer nonlinear model is solved by CONOPT using GAMS software under various scenarios. Finally, these results are compared with the existing system, and a convenient layout design is proposed to the company.
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Analyzing the Operations at a Textile Manufacturer’s Logistics Center Using Lean Tools
(Springer Science and Business Media Deutschland GmbH, 2024) GÜNAY, AHMET CAN; ÖZBEK ,ONUR; MUTLU, FİLİZ; AKTİN, AYŞE TÜLİN
Compliance with delivery times is crucial for businesses in the logistics sector. Numerous research has been conducted to improve distribution performance. Many of these studies touch on lean production as well. The strategies used in lean manufacturing are often employed by businesses and have a positive impact on performance. This study focuses on the overseas shipping department of a textile company’s logistics center. Workflow starts with product acceptance from manufacturers and ends with shipment to customers abroad. After a thorough examination, some bottlenecks that increase delivery times are observed. Value Stream Mapping (VSM), which is a lean manufacturing technique, is chosen as the main method to be used. It aims to determine value added and non-value-added activities, resulting in minimizing or eliminating the non-value-added ones. Initially, necessary data are gathered through workshops and interviews, and observations on Current State VSM are made. During these workshops, various improvements are proposed and evaluated together with the company’s engineers. After takt time and cycle time calculations, label change station is identified as the bottleneck. In the next step, Kaizens are suggested for the stations, and some lean techniques are employed to solve different workflow problems. Finally, short-term applicability of proposed improvements is discussed, and Future State VSM is drawn. It can be concluded that significant improvements are achieved especially in lead time, changeover time, productivity rate and production speed. By reducing or eliminating non-value-added activities and identifying deficiencies that slow process flow, a standard, sustainable and developable process is proposed to the company. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Analyzing the Wastewater Treatment Facility Location/Network Design Problem via System Dynamics: Antalya, Turkey Case
(Academic Press Ltd. - Elsevier Science Ltd., 2022) DEMİREL, DUYGUN FATİH; Gönül-Sezer, Eylül Damla; Pehlivan, Seyda Alperen
Wastewater treatment facility location selection and network design issues have become attractive topics in the field of wastewater management due to increasing human population, resource scarcity, environmental concerns, and rise of necessity for sustainable solutions for future policy designs. Especially in areas where the demand for wastewater treatment increases dramatically over the years because of reasons such as high migration levels, rapid industrialization, and tourism activities, the problem turns out to be more critical and dynamic. The existing studies try to deal with the issue through mathematical modeling approaches based on optimization perspectives, which require significant computational effort. In this study, an alternative approach based on system dynamics (SD) method is proposed to examine the complex dynamic and nonlinear structure of waste-water treatment facility location selection and network design problems. The proposed SD simulation model is designed for a densely populated industrial and tourism spot, the city of Antalya, located on the Mediterranean coast of Turkey. The model is capable of determining where and when to build a new wastewater treatment facility as well as generating the generic wastewater network structure to be built for the five districts situated in the city center based on cost issues for 2015-2040 period. In addition, the impacts of demand level changes for wastewater treatment due to population variations are analyzed via several scenarios to help decision makers to develop sustainable and cost-efficient management policies. Although SD is a frequently utilized approach in the water/wastewater management arena, to the best of our knowledge, this study is the first attempt to examine the complex and dynamic nature of wastewater treatment facility location selection and network design problems through SD approach.
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Cloud-Based AI Role in Intelligent Pharmaceutical Manufacturing Facilities
(Springer Science and Business Media Deutschland GmbH, 2024) DARABSEH, ESRAA; TARHAN, İBRAHİM ETHEM
Cloud-based Artificial Intelligence (AI) brings advanced computing capabilities into manufacturing facilities without capital expenditures. Cloud AI is capable of assisting turn traditional factories into intelligent factories at low cost. The disruptive innovation of cloud and artificial intelligence equipment in smart factory management leads to increased economic growth and the development of a production pipeline [1]. This article investigates how adopting Cloud-based AI services in intelligent pharmaceutical manufacturing facilities affects the factory’s internal process management. The article uses SWOT/PESTEL analysis to evaluate internal and external factors affecting such facilities based on a case study. In addition, the article uses Minitab to perform linear regression analysis on the case study to determine the outcomes of adopting Cloud-based AI in such factories. The article finds a positive effect of adopting modern technologies, such as Cloud-based AI, on creating a highly efficient production environment, reducing costs, and increasing revenues. However, further investigation is needed to conduct similar research on other pharmaceutical manufacturing facilities of different sizes to understand improved how such implementation affects these facilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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A Comparative Sectoral Analysis of Industry 4.0 Readiness Levels of Turkish SMEs
(Springer Science and Business Media Deutschland GmbH, 2021) EMİR, OĞUZ; GERGİN, ZEYNEP; YÜKSEKTEPE, FADİME ÜNEY; Dündar, Uğurcan; Gençyılmaz, Güneş M.; Çavdarlı, Ali Ihsan
The concept of Industry 4.0 brings new standards and has an essential impact on business models, work organizations, and processes. Nowadays, companies are enforced to perform such challenges as meeting personal customer requests, flexibility, responsiveness, customer-oriented solutions, and delivering continuous value to survive in the competitive market. The most innovative companies are resourceful to integrate new digital tools into their business models by following initial trends. For Small and Medium-Sized Enterprises (SMEs), adopting these new industrial challenges, and responding to them quickly is vital when it is considered that they have 99.8% of the enterprise share of Turkey. To do so, an intensive effort is needed to integrate Industry 4.0 applications within the enterprises. It has been suggested in the literature that manufacturing organizations should begin with understanding their current level of maturity by defining their strengths and weaknesses. Further, a significant effort is required with the collaboration of government, academia, and industry leaders to guide enterprises for improving their capabilities in an objective and standardized manner. Despite the diversity of approaches directed to Industry 4.0 concepts, it is not easy to find a study that measures how the manufacturing sector is adopting Industry 4.0 and compares manufacturing sectors with each other. To this end, broad research is initiated in March 2018 to identify the current situation of SMEs in Turkey. In this paper, it is intended to extend the study by providing a comparative sector analysis for manufacturing SMEs in Turkey in terms of Industry 4.0 transformation. Possible action plan suggestions are presented according to the results obtained at the end of the paper. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Customer Oriented Intelligent DSS Based on Two-Phased Clustering and Integrated Interval Type-2 fuzzy AHP and Hesitant Fuzzy TOPSIS
(IOS Press BV, 2020) Şenvar, Özlem; AKBURAK, DİLEK; Yel, Necla
Firms need to integrate multiple business functions in order to acquire, analyze, model, and evaluate information necessary for better understanding customer behaviors and making data-driven decisions to enhance the customer experience journey. This study proposes a customer oriented intelligent decision support system (IDSS) to ultimately improve the customer experience journey. Besides, a real application study is handled for a multinational company located in Turkey, considering its abrasives product sales for years of 2017 and 2018. For the data utilized in application study, the proposed methodology is constructed for customer segmentation to develop appropriate data-driven marketing strategies for customers with similar values, preferences and other factors for creating customer-centric organizations. In this regard; firstly two-phased clustering process, which involves the hierarchical multivariate average linkage clustering algorithm and partitional k-means clustering algorithm, is used to present the number of clusters on the basis of three variables (expenditure, transaction and unit cost) and then to assign the customers to the related clusters (VIP, Platinum, Gold and Bronze), respectively. Secondly, the performances of company's departments are ranked according to the preferences of customers from each segment considering 4Ps marketing mix concept via integrated methodology of interval type-2 Fuzzy AHP and hesitant fuzzy TOPSIS.
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A Decision Support Tool for Classification of Turkish SMEs’ Industry 4.0 Score Levels
(Springer Science and Business Media Deutschland GmbH, 2021) Dündar, Uğurcan; YÜKSEKTEPE, FADİME ÜNEY; GERGİN, ZEYNEP; EMİR, OĞUZ; Gençyılmaz, Güneş M.; Çavdarlı, Ali İhsan
The concept of industry 4.0 aims to enhance the employment of digitalization with the help of burst in computer usage after the third major revolution in industry. Hence, to keep up with the new trends in industry, companies should transform their processes into digital platforms. In Turkey, 99.8% of the companies are SMEs, so in order to be in the global economic competition Turkey should enable their SMEs to make the process shifts to digitalization. In this manner, in 2018 a project is initiated by KOSGEB to evaluate the readiness of SMEs and help them for Industry 4.0. As the last step of this project, a decision support tool is created with classification algorithm behind. In this paper, chosen instruments are used to predict the Industry 4.0 score of SMEs that are calculated with previously published algorithm in the scope of the project. In terms of classification, three algorithms are compared with ROC metric. CatBoost algorithm which is specifically created for categorical classification is compared with Support Vector Machine algorithms (SVM and µ-SVM) that performed best in previous research. CatBoost outperformed other algorithms and is used as base classification method in decision support tool. This decision support tool will be used in decision making process of KOSGEB to which companies to help. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Determining a New Warehouse Location for an Electrical Home Appliances Company
(Springer Science and Business Media Deutschland GmbH, 2024) KIZILKAYA, İLAYDA; KEVSER, TOLGAHAN; OFLUOĞLU, HANDE; ÖLÇÜCÜER, FEYZA; DEMİREL, DUYGUN FATİH
In today’s businesses, supply chain management is a critical factor in terms of efficiency, profitability and cost savings. The success of the supply chain is possible with the right management of the rings such as warehousing and logistics activities. Deficiencies in warehouse and logistics management can lead to inefficiencies and errors in the supply chain, which can have negative effects on business profitability and customer satisfaction. Strategic decisions regarding warehouse locations are vital for cost policies. In this study, a warehouse location selection procedure is proposed for an electrical home appliances company considering the distances to the customers, their transaction volumes, and various other costs. The approach is basically a two-stage facility layout problem that first solves a weighted Euclidean minisum model defined on a continuous plane. Then, four alternative locations that are close to the result obtained in the first step are determined. In addition, two lands owned by the firm are added to the set of alternatives. Next, a p-median (1-median) model is solved and the most suitable warehouse location is determined. Through sensitivity analysis, the changes in the solution are searched for various rent values. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations Guided by Social Influence
(Springer International Publishing, 2022) ÇİÇEKLİ, SEVRA; Gürbüz, Tuncay
Nowadays, there is an enormous increase in alternatives almost in every area with the development of technology. Thus, it has become harder for a single decision maker (DM) to completely evaluate each alternative for a problem on hand. In group decision making (GDM) problems, each DM has a different effect on the final decision because of their background. Also, it has been observed that DM's judgments are affected by those of other DMs in the group. This is defined as social influence (SI). Social Influence Network (SIN) is useful especially when there is incomplete preference information. In this paper, a proposed model has been used to demonstrate the effect of seed value choice in completion process of missing fuzzy information given by DMs.
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Examining the Effects of Forest Fires: A Framework for Integrating System Dynamics and Remote Sensing Approaches
(IGI Global, 2024) Eriş, Müjgan Bilge; DEMİREL, DUYGUN FATİH; Sezer, Eylül Damla Gönül
Forest fires have been a major concern for many countries over an extended period of time due to natural and human induced factors. In recent years, detection of forest fires has progressively shifted toward advanced technologies where the remote sensing approaches are fully operational. To enhance fire management strategies, it is crucial to gain a comprehensive understanding of the fire dynamics and its consequences on the environment, operational sources, and economic sectors. Therefore, this chapter develops an integrated framework to predict and analyze the effects of forest fires by using system dynamics approach and remote sensing technology, ultimately leading to the establishment of a conceptual model and conclusive insights. © 2024, IGI Global. All rights reserved.
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Examining the Impacts of Military Expenditures on Economic Productivity: A System Dynamics Approach
(SAGE Publications Ltd., 2023) Damla Gönül-Sezer, Eylül; DEMİREL, DUYGUN FATİH
The relationship between military expenditures and economic productivity has taken the attention of many researchers and there exist an important number of studies approaching the topic through several techniques. However, there is no consensus among the scholars whether military expenditures trigger economic growth, productivity, and other macroeconomic indicators. Such arguments are mainly due to unclear results obtained from the existing studies, in which the complex relationships between military expenditures and macroeconomics are not fully incorporated. Considering the bidirectional and nonlinear relationships among macroeconomic indicators and complex feedback mechanisms, a system dynamics (SD) model for examining the impacts of military expenditures on economic productivity in Turkey is proposed. The proposed SD model aims to reflect the complex environment surrounding the military spending–economic productivity nexus and to analyze the feedback structures that lead to miscellaneous consequences with delays. A stock–flow model is developed to represent the complex nonlinear relationships and causalities between the variables. Data from SIPRI, the World Bank, and several local statistical sources covering the years 2009–2018 are utilized to simulate the existing case, warfare in neighbors, economic shrinkage scenarios, and the combination of the latter two. The results obtained from the scenarios suggest that short fixes such as importing military products instead of national investments give rise to chronic issues like continual dependence on foreign supply, hence, leading to decrease in overall economic growth. To the best of our knowledge, this is the first attempt to integrate SD methodology with military expenditure and economic productivity analysis.
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An Excel-Based Stock Management System for a Leather Label Manufacturer
(Springer Science and Business Media Deutschland GmbH, 2024) UÇUCU, EMİNE; ERASLAN, FATMA BEYZA; KAYMAKLI, ZEYNEB NUR; EMİR, OĞUZ; AKTİN, AYŞE TÜLİN
This paper presents a proposal for an Excel-based digital inventory tracking system to address the operational challenges faced by a leather label manufacturer located in Istanbul, Turkey. The company is currently reliant on manual stock keeping and tracking methods for its leather labels and raw materials, resulting in significant functional difficulties due to the absence of a systematic and efficient inventory tracking system. The proposed system aims to enhance overall productivity and decision-making processes within the company. By implementing the new tracking system, the company can overcome the limitations of manual stock management, leading to improved efficiency and organization in inventory control. Furthermore, the installation of a barcode system is suggested to augment the tracking system’s capabilities, and the profitability of the proposed system is evaluated using three different financial analysis methods. This research contributes to the advancement of inventory management practices and provides valuable insights into the potential benefits of adopting digital solutions for stock control in the leather label industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Forecasting Greenhouse Gas Emissions Based on Different Machine Learning Algorithms
(Springer International Publishing, 2022) ÜLKÜ, İLAYDA; Ülkü, Eyüp Emre
With the increase in greenhouse gas emissions, climate change is occurring in the atmosphere. Although the energy production for Turkey is increased at a high rate, the greenhouse gas emissions are still high currently. Problems that seem to be very complex can be predicted with different algorithms without difficulty. Due to fact that artificial intelligence is often included in the studies to evaluate the solution performance and make comparisons with the obtained solutions. In this study, machine learning algorithms are used to compare and predict greenhouse gas emissions. Carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and fluorinated gases (F-gases) are considered direct greenhouse gases originating from the agriculture and waste sectors, energy, industrial processes, and product use, within the scope of greenhouse gas emission statistics. Compared to different machine learning methods, support vector machines can be considered an advantageous estimation method since they can generalize more details. On the other hand, the artificial neural network algorithm is one of the most commonly used machine learning algorithms in terms of classification, optimization, estimation, regression, and pattern tracking. From this point of view, this study aims to predict greenhouse gas emissions using artificial neural network algorithms and support vector machines by estimating CO2, CH4, N2O, and F-gases from greenhouse gases. The data set was obtained from the Turkish Statistical Institute and the years are included between 1990 and 2019. All analyzes were performed using MATLAB version 2019b software.
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Forecasting of Turkey's Total Electricity Consumption in Sectoral Bases Using Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2022) HAJJAR, MHD KHAIR; ÜLKÜ, İLAYDA
Electrical energy is a milestone in the economic growth of each country. This study forecasts the sectoral and total electricity consumption in Turkey until the year 2050. This study, utilize two distinct time series forecasting methods namely Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO) as a model to generate the forecasting formulas. The sectoral and total electricity consumption for Turkey from the year 1970 to 2020 was obtained from the Turkish Statistical Institute and fed to the models to forecast the upcoming years. The two models were evaluated and compared using determination coefficient R2 and mean absolute percentage error MAPE. It is found that MLP performed better in forecasting the commercial, governmental, illumination and other sectors and SMO performed better in forecasting the industrial and household sectors alongside the total electricity consumption. © 2022 IEEE.
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Global Impact of the Pandemic on Education: A Study of Natural Language Processing
(Institute of Electrical and Electronics Engineers Inc., 2022) AYAZ, TEOMAN BERKAY; USLU, MUHAMMED SAFA; AĞCABAY, İBRAHİM; AHMED, FARUK; KORKMAZ, ÖMER FARUK; KÜREKSİZ, MESUT; ULUÇAM, EMRE; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR
School closures due to the Covid-19 pandemic have changed education forever and we have witnessed the rise of online learning platforms. The education units of the countries made great efforts to adapt to this new order. The expanding, quick spread of the virus and careful steps have prompted the quest for reasonable choices for continuing education to guarantee students get appropriate education and are not impacted logically or mentally. Different methods were attempted to understand how students were affected by this big change. In addition to the significance of traditional surveys and consulting services, the utilization of social media analysis is used as a supportive approach. This paper analyzes the feedback of students on social media via tweets. Deep sentiment analysis is employed to identify embedded emotions such as negative, neutral, and positive. We also aimed to classify irrelevant tweets as the fourth category. Our experiments showed that the tweets are mostly biased toward negative emotions. © 2022 IEEE.
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Hospital Location Selection Using Spherical Fuzzy TOPSIS
(Atlantis Press, 2019) Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLU; Onar, Sezi Çevik; Öztaysı, Başar
The three dimensional extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets of type-2 (IFS2) or Pythagorean fuzzy sets (PFS), and neutrosophic sets (NS) aim at collecting experts' judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been introduced by Kutlu Gündoğdu and Kahraman [1]. In this paper, we develop and use spherical fuzzy TOPSIS and apply it to a hospital location selection problem.
Open Access
The Impact of Perceived COVID-19 Threat and Death Anxiety on Buying Behavior Among Academics: A Comparison Between Türkiye and Northern European Countries
(Türk Kooperatifçilik Kurumu, 2023) Turna, Gülçin Bilgin; Pekmezci, Halil; IŞIK, OKAY
Academics, one of the occupational groups exposed to isolation in the COVID-19 epidemic, tried to adapt to the remote working regulations by not being able to maintain the social environment they were accustomed to in universities. This study aimed to examine the impact of perceived COVID-19 threat and death anxiety on revenge buying behavior by comparing academics who work in Türkiye and Northern Europe (the United Kingdom, the Netherlands, and Norway). “Revenge buying” is a term popularized in the marketing field during the global COVID-19 quarantines. It refers to the surge in shopping desire observed after the lifting of pandemic-related lockdown measures. Data was collected online by using three scales: the “Perceived COVID-19 Threat Scale”, “Templer’s Death Anxiety Scale” and “Revenge Buying Behavior Scale”. The sample consisted of total 327 academics: 163 from Türkiye (TR) and 164 from Northern Europe (NE). Structural Equation Modelling (SEM) was adopted for the analyses. The results showed that academics in NE were more prone to revenge buying behavior than TR. As the perceived threat of COVID-19 and death anxiety increased, it was determined that academics working in both TR and NE have tendencies for revenge buying. However, there was insufficient evidence to support the mediating role of death anxiety in the relationship between perceived COVID-19 threat and revenge buying behavior. In this interdisciplinary study, statistically significant differences identified based on the demographic characteristics of academics working in TR and NE have also been indicated.
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The Impact of Value Engineering on Material Selection: An Example from the Construction Industry
(Springer Science and Business Media Deutschland GmbH, 2024) TARHAN, İBRAHİM ETHEM; QUFFA, BARAA
The urgent need for Value Engineering has arisen due to the enormous expenditure in the construction industry, which could be reduced by up to 25%. This substantial spending is often accompanied by poor quality, leading to a constant need for reconstruction and maintenance. The methods and concepts of Value Engineering can be applied to address this issue. By using Value Engineering, solutions can be created that fulfil the same functional purpose at a lower cost while maintaining high quality. This can be achieved through an analytical study using a specific approach conducted by a multidisciplinary team to identify and classify the functions that the project performs. Profound methods can then be utilized to find innovative alternatives without compromising the basic requirements and quality. This research is dedicated to demonstrating the methodology of Value Engineering while highlighting its impact on construction projects through a case study: Magrabi Offices in Saudi Arabia. This paper used an organized multidisciplinary methodology to identify the project’s main and secondary functions, then utilized Pareto’s law to identify higher-cost materials. It was proven that finding low-cost equivalent materials could reduce the total price by 23.13%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Implementation of MCDM Approaches for a Real-Life Location Selection Problem: A Case Study of Consumer Goods Sector
(Springer-Verlag Singapore Pte Ltd., 2022) EMİR, OĞUZ
Selecting a suitable warehouse location elevates the supply chain performance by reducing the lead times and increasing the response efficiency. Hence, location selection emerges as a strategic decision-making problem for competitive advantage. In this paper, a real decision-making problem of a multinational company operating in the consumer goods sector has been examined. The company's Turkey office is responsible for the operations in many regions such as Middle East, Africa, Central Asia, and Eastern Europe. A case study is handled by the logistics network planning team of the company to evaluate the new warehouse request coming from the regional sales team. For this decision problem, two different multi-criteria decision-making methods TOPSIS and VIKOR, are employed to evaluate four alternative scenarios. In addition, the AHP technique is also applied to determine criteria weights. The results of both methods revealed the same alternative as the best decision.
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Improving Quality Control Applications by Increasing Inspection Efficiency and Decreasing Nonconformance Percentage
(Springer Science and Business Media Deutschland GmbH, 2024) YILMAZ, BAŞAK; FIRAT, SENA; CABA, CİHAN; BEŞİR, BERNA; ÜLKÜ, İLAYDA
Quality controls are activities to evaluate the level of conformity of product attributes and optimal quality objectives. When 100% inspection is applied in quality control processes, sampling is used because it causes high costs, long control times and product damage. Acceptance sampling, which is a statistical method, determines whether the lot can be accepted or rejected in line with the tests performed on the samples taken from the lot. The acceptance sampling plan depends on multiple factors such as the level used, the degree of control applied, the lot size, and the acceptable quality level. For this reason, the use of standard sampling plans that increase the validity of quality control operations can be expressed. In this paper, acceptance process applications were studied for a the company that demonstrates textile industry studies. In this the company, the control processes entered the products in the batch of different sizes coming from the regulations used for the contract are applied. To determine the acceptance listening, the execution of the lot, the control measurement dimensions entered first, the reasons for the return of the the company’s four product groups and the AQL reports are reviewed, and the statistical evaluations of the quality controls come to an end. Next steps, using ANSI/ASQ Z-1.4, observations suitable for lot sizes and appropriate acceptance-rejection details were determined and compared with the size and decision points of the the company. Cause-effect diagrams do not take into account the reasons that cause the returns to be made so that the possible reasons for the returns can be examined. Finally, according to the results of the sampling, solutions were found to make the dimensions for these reasons. Cause-effect diagrams do not take into account the reasons that cause the returns, so that possible causes of the returns can be examined. Finally, according to the sampling results, solutions were found for sizing for these reasons. It is recommended to taken 50 samples from lot sizes between 281–500, 80 samples from 501–1200 lot sizes, and 125 samples for lot sizes between 1201 and 3200. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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