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International Journal of Industrial Engineering and Engineering Management
Published by Universitas Atma Jaya Yogyakarta
ISSN : -     EISSN : 26854090     DOI : https://doi.org/10.24002/ijieem
Core Subject : Engineering,
Industrial & Manufacturing Engineering
International Journal of Industrial Engineering and Engineering Management (IJIEEM) is an open access scientific journal that publishes theoretical and empirical peer-reviewed articles, which contribute to advance the understanding of phenomena related with all aspects of Industrial Engineering and Engineering Management
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 154 Documents
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An Application of Data Envelopment Analysis in the Selection of the Best Response for the Drilling of Carbon Fiber-reinforced Plastic Composites Adedeji, Wasiu Oyediran; Odusoro, Salome Ifeoluwa; Adedeji, Kasali Aderinmoye; Rajan, John; Oke, Sunday Ayoola; Oyetunji, Elkanah Olaosebikan; Nwankiti, Ugochukwu Sixtus
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.5792

Abstract

In the drilling operation, defects such as delamination at exit and entry are very disturbing responses that impact the efficiency of the drilling process. Without control, an exponential growth in the amount of drilled components with defect quantities may result. Thus, the process engineer has input in attaining the desired production levels for components in the drilling process. Consequently, this article deploys a novel method of data envelopment analysis to evaluate the relative efficiency of the drilling process in reducing the defects possible in the producing components from the CFRP composites. The high-speed steel drill bits were utilized to process the CFPs, while the responses considered are the entry and exit determination, thrust force, and torque, among others. Literature experimental data in twenty-seven experimental counts were summarized into fewer groups and processed through the data envelopment analysis method. The results show that capturing the CFRP composite responses is feasible, providing an opportunity for enhanced efficiency and a situation where undesirable defects in the CFRP composite production process may be eradicated. The article’s uniqueness and primary value are in being the foremost article in offering an updated vast representation of the comparative efficiency of CFRP composite parameters within the literature for the composite area. The work adds value to the CFRP composite literature by envisaging and understanding the comparative efficiency for the parameters, identifying and separating the best from the worst decision-making unit. It also reveals how the parameters are linked by their relative placements. The article's novelty is that using data envelopment to compare the efficiency in reducing drilling defects such as entry and exit determination, among others. The method’s utility is to provide information for cost-effective drilling operations during the planning and control phases of the operation.
Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods Bustami; Yolanda, Anne Mudya; Thahira, Nisha
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.6211

Abstract

Non-oil and gas exports could be forecasted using exponential smoothing for future periods. This study examines non-oil and gas export data in Indonesia from January 2015 to May 2021, indicating trends and seasonality. Based on the data characteristics, the obtained data were analyzed using Holt's double exponential smoothing method and triple exponential smoothing with multiplicative and additives Holt-Winters. The MAPE for all three models is less than 10%, indicating that the method is very good and could be used to forecast the next period. Using MAPE as a comparison, the best model for non-oil and gas exports is the additive Holt-Winters method triple exponential smoothing, which has the lowest MAPE of any model. The best method was employed to forecast data, making it possible for us to anticipate the pattern of non-oil and gas exports. This forecast data could be used as the basis for policymakers' decision-making. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.
Application of Fuzzy Analytic Hierarchy Process (FAHP) to Improve Precision and Certainty on Safety Conformity Evaluation in a Bottling Plant Sawyerr, Babatunde Alade; Fasina, Ebun; Adedeji, Wasiu Oyediran; Martins, Shedrach Aliakwe; Rajan, John; Oke, Sunday Ayoola
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.6498

Abstract

With the bottling plant facing safety impacts, the commitment toward zero levels of accidents needs to be evaluated. However, the perception and measurement of safety conformity by the safety manager that is subjected to imprecision and uncertainty are hardly evaluated correctly with the present dominant approach of using crisp numeric values. This article presents a fuzzy analytic hierarchy process (FAHP) approach to reduce the imprecision and uncertainty in the safety conformity multicriteria decision-making results. The method establishes and selects the best safety conformity factors in alignment with different criteria within the segments of a Nigerian bottling plant. The fuzzy synthetic extent concerning each alternative, the degree of possibility, prioritizing weights, and the choice of the best criterion were judged based on the maximum weight in the FAHP evaluation process. The average weight criterion was used to distinguish the best from the worst units within each segment. The results reveal the criteria weights as 0.4937 for haulage drillers (warehouse), 0.3038 for palletizers (manufacturing corridor), 0.3333 for syrup mixers/lab technicians for quality assurance, and no choice of the best parameter for the fleet workshop. However, the highest weight for the contractors is 0.3201, which is for contractor 1. To compare the best and worst criteria in the present study and a literature source, the optimal criteria choices of safety conformity conflicted in all the segments. The principal difference between the present method and the analytic hierarchy process approach is integrating fuzzy application to the analytical hierarchy process to provide a more accurate safety conformity assessment, yielding reliable and informative results representing the vagueness of the bottling process decision-making process. This unique approach provides an opportunity for the production workers to work more collaboratively towards attaining new solutions to the uncertainty and imprecision problem in safety conformity for the bottling plant.
Emotion Detection Research: A Systematic Review Focuses on Data Type, Classifier Algorithm, and Experimental Methods Destyanto, Twin Yoshua R.
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.7077

Abstract

There is a lot of research being done on detecting human emotions. Emotion detection models are developed based on physiological data. With the development of low-cost wearable devices that measure human physiological data such as brain activity, heart rate, and skin conductivity, this research can be conducted in developing countries like Southeast Asia. However, as far as the author's research is concerned, a literature review has yet to be found on how this research on emotion detection was carried out in Southeast Asia. Therefore, this study aimed to conduct a systematic review of emotion detection research in Southeast Asia, focusing on the selection of physiological data, classification methods, and how the experiment was conducted according to the number of participants and duration. Using PRISMA guidelines, 22 SCOPUS-indexed journal articles and proceedings were reviewed. The review found that physiological data were dominated by brain activity data with the Muse Headband, followed by heart rate and skin conductivity collected with various wristbands, from around 5-31 participants, for 8 minutes to 7 weeks. Classification analysis applies machine learning, deep learning, and traditional statistics. The experiments were conducted primarily in sitting and standing positions, conditioned environments (for developing research), and unconditioned environments (applied research). This review concluded that future research opportunities exist regarding other data types, data labeling methods, and broader applications. These reviews will contribute to the enrichment of ideas and the development of emotion recognition research in Southeast Asian countries in the future.
Business Intelligence for Decision Support System for Replenishment Policy in Mining Industry Seto, Franklin Chandra Pragnyono; Daryanto, Yosef; Diar Astanti, Ririn
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 1 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i1.7245

Abstract

The mining industry has unique characteristics in the sense that usually, the plant is located in a remote area while the headquarters are located in an urban area. These conditions pose challenges for the industry related to coordination within companies. This coordination is very important, especially in relation to the decision-making that must be carried out by the company. One of the important managerial decisions is related to the replenishment policy. To make replenishment decisions, companies need past data, such as biodiesel consumption rate, and current data, such as current stock and storage capacity, where the source of those data is in the plant. Often, decisions must be taken quickly because they have impacts on the continuousness of production operations at the plant. However, the remote location and shipping routes across rivers have created new challenges in the flow of goods and services supply because the shipment depends on the tides of the river. This research proposes a business intelligence system that collects, sorts, and visualizes data, then analyzes the replenishment decision to support decision-making in the mining industry. The system uses Microsoft Power BI software which is integrated with the company’s ERP system. To illustrate the applicability of the proposed system, it is applied to a coal mining company, especially in relation to the replenishment policy of biofuel. The result of this study indicates that the proposed system can work. In addition, it can reduce decision-making time by 220.65%.  
Analysis of Product Defect to Reduce Return Product in Flexographic Printing Apriyanti, Yunita; Zulkarnain
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 2 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i2.6244

Abstract

Product return in 2021 at PT XY increased, but the quality control implemented has not been running effectively. This study aims to analyze the failure risk that causes defects, gets the greatest failure risk in the Risk Priority Number (RPN),and gives suggestions for improvement for the next production. The focus of this study is on production defects that are returned by customers. This study used Failure Mode Effect Analysis (FMEA) methods and Problem Identification Corrective Action (PICA) table. From the gathered data, it is identified that there is one type of dominant defect that is outside the control limits. The results of data processing by multiplying the SOD value to get the RPN value found that the three largest ranking modes of failure were the engine settings did not match the RPN value of 484, negligent in the production control process with the RPN value is 230, and the compressor is not optimal with the RPN value is 210. Then an analysis was carried out using the PICA table to get suggestions for improvements, conducting periodic IK retraining, checking machine condition regularly, conducting periodic inspections during the production process, evaluating performance results, and running check sheets while carrying out the production process.
Supply Chain Performance Measurement at PT. Perkebunan Nusantara XIV Camming Sugar Factory in Bone District Saleh, Anis; Ahmad, Arfandi; Herdianzah, Yan; Lantara, Dirgahayu; Saputra, Nur Ihwan; Dahlan, Muhammad
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 2 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i2.6861

Abstract

PTPN XIV (PERSERO) of Camming Sugar Factory in Bone Regency Measuring work results in the company is one of the company's performance evaluation tasks, namely to find out if the results achieved are good enough or still need improvement. The characteristic of a weak result is the fact that the company's goal is not achieved in the desired way. The objective of this study is to identify the KPIs and measure the performance indicators at the Bone Regency of PTPN Camming Sugar Factory. Key Performance Indicator (KPI), Lean Supply Chain Management (LSCM), and Analytic Hierarchy Process (AHP) methods are used in this study. The results of this study provide 11 Lean metrics that align with business conditions. Once the indicators are obtained, the KPI weighting is carried out using the Analytical Hierarchy Process (AHP), prioritizing the performance criteria of the production and supply departments. KPIs that have a significant impact on a company's supply chain performance. This is reflected in the highest order of weight in three KPIs, namely production department performance criteria, employee productivity attributes (0.573) and delivery department criteria, on-time loading (0. 85), and delivery (0.372) attributes. with a weight percentage of 19.1, 16.20% and 12. 2%. The KPIs in the lowest weight category are collection (0.120) and delivery (0.116) with a weight percentage of .01% and 3.87%.
Defect Reduction in The Manufacturing Industry: Systematic Literature Review Mukti Ali Sadikin
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 2 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i2.7495

Abstract

Defect reduction is an important aspect of quality improvement in the manufacturing industry, as it directly impacts product quality, customer satisfaction, and operational efficiency. This paper presents a systematic literature review on defect reduction in the manufacturing industry. The study systematically reviewed articles published in the period 2012-2022 in the Google Scholar, ScienceDirect, Emerald, and Springer Link databases. The review aims to provide a synthesis of research studies, methodologies, and best practices employed to minimize defects and enhance overall product quality. This review identifies key themes, challenges, and future directions in defect reduction by analyzing the existing literature, offering valuable insights for researchers and practitioners.
Vehicle Exhausts Emission Pattern Decisions for Logistic Services and Packing Industries with Orthogonal Array-Based Rough Set Theory Agada, Alexander Iwodi; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan; Benrajesh, Pandiaraj; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 2 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i2.7740

Abstract

Precise monitoring of vehicle emissions in green logistics, focusing on the contributions of vehicles from packing industries, is crucial for many issues. It helps to understand the total emissions and gain insights into the mechanism of vehicle-associated environmental concerns. Notwithstanding, a key issue when monitoring vehicle emissions is the effective discrimination problem for different patterns generated from the parameters. Data from the packing industry are available from distribution networks but its pattern cannot be discriminated. Given this background, this article presents a new method of the orthogonal array-based rough set to discern patterns of the parametric behaviors to monitor emissions from vehicle exhausts in the packing industry. The proposed method is based on an Indian logistics network and delivery system data, which was obtained from previous work in the literature. By setting controls on the parameters of the packing industry which includes revenue obtained, packing units sold, growth rate, carbon-dioxide equivalent, materials utilized, and quantity consumed, the method was able to discern the patterns of the parametric behavior. The orthogonal arrays, which are developed, form factors (parameters) and levels to ascertain a balanced and uniform analysis of the various groups of options. Indiscernibility and approximation concepts of fuzzy sets are then applied to arrive at the outcome. Unlike previous studies, this study eliminates the need for tracking data, assumptions, and external information to establish the set membership. However, it utilizes the available information within the data. The rough set analysis indicates that there are no discernable patterns or rules that distinguish between "Yes" and "No" decisions. The method of rough set illustrated in this work shows the feasibility of the approach in the Indian packing industry. The method is useful for the logistics manager and government agencies responsible for the control of vehicle-generated greenhouse emissions.
Application of Data Envelopment Analysis for Performance Efficiency Evaluation of Oil Palm Empty Bunch Fruit Composites in The Aerospace Industry Udoibe, Ndifreke John; Oke, Sunday Ayoola; Ayanladun, Chris Abiodun; Rajan, John; Jose, Swaminathan; Adeyemi, Olusola Michael; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye
International Journal of Industrial Engineering and Engineering Management Vol. 5 No. 2 (2023)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v5i2.7741

Abstract

In this study, we propose the data envelopment analysis method as a scheme to determine the technical efficiency of a set of parametric inputs of the water absorption process when developing the oil palm particulate composite treated with an alkali solution. Although alkali-treated oil palm bunch composites have been analyzed previously for water absorption, a single parameter such as water absorption rate prevails in analyses. Unfortunately, multiple inputs and multiple outputs have been ignored and the efficiency evaluation of such composites has been missing in the literature. To address this gap, the present study exploits the linear programming theory and formulated models for each decision-making unit and solves that formulation for optimum value determination for inputs of the composites. This study investigates the technical efficiency of the water absorption in the oil palm empty fruit bunch composite development process. Overall, judging the performance of the parameters regarding the frequency of attaining 100% efficiency, analysis was performed on the average performance of all parameters in all sixteen scenarios. In this regard, the efficiency of particulate loading was 36.1%, for composite weight plus mold, it was 96.3% and for initial weight, the average efficiency score was 67.8%. It is suggestive that composite weight plus mold with an average efficiency of 96.3% is the best parameter while particulate loading with 36.1% is the worst parameter. Thus result is consistent with the result based on each scenario. From the perspective of DMUs, DMU11 with a score of 78.4% is the best ranking unit while DMU14 is the work ranking unit with an efficiency score of 60.9%. Besides, the average efficiency score for all the DMUs is 66.7%. The work is important to composite development engineers and for policy decision-making.

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