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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 161 Documents
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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%.  
A Postural Risk Assessment of Steamer Production Workers Using RULA and REBA Darmawan, Vertic Eridani Budi; Indra, Sofiandi Dwi; Larasati, Aisyah; Nugraha, Cahya; Fathullah, Muhammad
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.7322

Abstract

The present study examined the work posture of the worker as a basis for correcting bad postures in the workplace. Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) methods are applied to evaluate the postural risk assessment which is related to musculoskeletal disorders (MSDs). There are five workers at UD. Sidoarjo National Ship is chosen to analyze the risk posture for this study. Based on observations and calculations at UD. Sidorajo National Ship, it was found that workers were still using less than optimal methods or not supported by ergonomic workstations. The study reveals that every worker in UD. Sidoarjo National Ship workstations have a risk of getting MSDs. These indicated that the worker’s work posture was less ergonomic and required changes to lessen the risk of MDSs.
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.
Integration of Fuzzy 0/1 Knapsack Dynamic Programming and PROMETHEE Method for Vehicle Exhaust Emission Parametric Optimization and Selection in the Packing Industry Agada, Alexander Iwodi; Rajan, John; Jose, Swaminathan; Oke, Sunday Ayoola; Benrajesh, Pandiaraj; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 2 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i2.7689

Abstract

Packaging industries fabricate and transport products in wrapped, sealed, and cushioned containers and boxes on roads, often through fossil-fuelled vehicles that emit carbons. Thus, decarbonization and net zero emission drive are compelling for these vehicles. This paper proposes a robust green logistics interaction model for monitoring and reducing exhaust pipe emissions in an uncertain environment. It uses a hybrid method known as fuzzy-0/1-KDP-PROMETHEE (Fuzzy-0/1 Knapsack dynamic programming-Preference Ranking Organization Method for Enrichment Evaluation) approach to concurrently reduce uncertainty, optimize the capacity of the knapsack and establish the preferred option among the parameters of green logistic. Both PROMETHEE I and II were introduced and tested using logistics data from an Indian environment based on secondary data. The method works by first reducing the effect of uncertainty on the model outcomes. This was achieved by establishing the output space as the fuzzy state, creating fuzzy rules, and mapping degrees to rules. Then, the degrees are used to maximize, ensuring that the weighted sum is not greater than the capacity of the Knapsack. The outcome is then regarded as the element of the green logistics exhaust emission process. The results obtained from the analysis, using the replacement of fuzzy expert (triangular) with fuzzy extent (trapezoidal), fuzzy geometric mean (triangular), and fuzzy geometric mean (trapezoidal) reveal that the fuzzy-0/1-KDP-PROMETHEE method adequately represents the score obtained using the data set from the exhaust emissions.
Optimizing the Parameters of Carbon Fiber Reinforced Plastic Composite Drilling Process Using Signal-to-noise Ratio-based Grey Wolf Optimization Algorithm Taiwo, Emmanuel Oluwatobi; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.7691

Abstract

This study aims to develop an optimization scheme that contributes to the production of carbon fiber-reinforced plastics using the grey wolf optimization approach. Different from other optimization schemes such as the Taguchi method, which takes some time to compute and use, this grey wolf optimization approach introduced a fast convergence scheme to reduce computation time thereby making its implementation in the factory very interesting. Data used for the analysis was obtained from a doctoral thesis via an experimental approach. Four responses were considered in this work, namely the torque, delamination at entry and exit, eccentricity and thrust force. A spreadsheet was used to implement the computational procedure of the grey wolf optimization algorithm. In using the wolves, at the initial level, the starting point was a zero where hunting had not begun and the prey had just entered the park, which is within the territory of the grey wolves. With this in mind, real life is mimicked and such data gathered would aid precise decision-making. The results revealed the feasibility of the approach and convergence was obtained at the tenth iteration with the best fitness value at 9020785071. It is expected that the findings from this work will be useful as a method for planning in production planning and policy development for the carbon fiber-reinforced plastic industry. This study is a noteworthy contribution to the production development of CFRPs where the grey wolf algorithm is used to analyze the problem. In addition, evidence of the responses determining the quality of drilled products is provided.
Low Wear Rate Selection of Nylon 6-Boron Nitride (PA6/BN) Composite During Composite Development Using Grey Relational Analysis Through the Direct and Indirect Factors of Taguchi Method Adekoya, Abdulganiyu Adegboyega; Rajan, John; Jose, Swaminathan; Oke, Sunday Ayoola; Aderibigbe, Samuel Bolaji; Odudare, Samson Oluwaseun
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.7734

Abstract

Wear performance has been evaluated for mechanical equipment using normal load, sliding speed, and sliding distance, but aspect ratios have been traditionally ignored in the literature. Also, limited studies have analyzed wear performance with sparse information. In this study, a grey relational analysis (GRA) technique is proposed for the wear performance analysis of nylon 6/boron nitride composite using aspect ratios. A complete divergence is made from the literature where the aspect ratios of the particulate weight of the composite, normal load, sliding speed, and sliding distance are treated in direct and aspect ratios of 12 cases where the reciprocals of factors, their squares and cubes are considered. Results show that the proposed method of GRA is feasible and offers an adequate illustration of the indices of the parameters of the wear process as opposed to the present method of Taguchi that exists in the literature. A key result is from case 2, which shows that experimental trial 9 with the grey relational grade of 1.00 has the lowest wear rate. The corresponding values of the parameters are 0.05 of the 1/NL parameter, while the SD parameter is 500. This is interpreted as 0.05N-1 of the reciprocal of normal load and 500m for the sliding distance. The principal contribution of this research is the introduction of the grey relational analysis to reduce the wear rate of nylon 6-boron nitride composite. The proposed method is useful as a planning tool for the maintenance engineer to monitor the health of equipment in practice.  
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.
Improving Thermal Friction Drilling Performance of AISI 304 Stainless Steel Using the Harris Hawk Optimization Method Ogunmola, Bayo Yemisi; Alozie, Nehemiah Sabinus; Adeyinka, Oluwo; Nwankiti , Ugochukwu Sixtus; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 2 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i2.7743

Abstract

Presently, in friction drilling optimization schemes, quick convergence of solutions and simplicity of methods are still challenging. These issues are drawbacks in obtaining the maximum potential benefits from the optimization process. Therefore, this paper applies a new optimization method, Harris Hawk optimization to the thermal drilling process of AISI 304 stainless steel. The algorithm minimizes the axial force, determination error, radial force, and radial error and maximizes the bushing length as the major output of the process. The proposed approach was tested with experimental data obtained from the literature. The obtained results indicate that the optimal production is feasible. An example is given here of the results of the input parameters for the minimum axial force, which is as follows: After 500 iterations, the optimal axial force yields a tool cylindrical region diameter of 5.78593 mm, a friction angle of 60 degrees, a friction contact area ratio of 57.7082, workpiece thickness of 3 mm, feed rate of 140 mm/min and rotational speed of 3002.85 rpm, which can be applied. The results assist engineers in implementing optimal conditions for the drilling process. The outcome of this study strengthens decisions to establish thresholds of values that are less or more than expected thereby providing a basis for comparison, reward, and reprimand for workers. Thus the drilling process can be optimized.
Energy-efficient No-idle Flowshop Scheduling Optimization Using African Vultures Algorithm Risma, Yolanda Mega; Utama, Dana Marsetiya Utama; Amallynda, Ikhlasul
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.8335

Abstract

The issue of energy consumption is currently a major concern globally, especially in the industrial sector, where most of the energy demand comes from the manufacturing sector. To reduce energy consumption, one of the proposed strategies is to reduce the idle time between jobs on machines during the production process, known as No-Idle Permutation Flowshop Scheduling (NIPFSP). This research proposes the application of the African Vultures Optimization Algorithm (AVOA) as a solution to the energy consumption challenge in the case of production scheduling. The algorithm is examined in detail through a series of trials to obtain the most efficient work order in the production schedule, subject to careful setting of iteration and population parameters. The result of implementing the AVOA algorithm is then compared with the method used by the company in a scheduling case. The research findings show that AVOA significantly outperforms the method commonly used by the company, confirming its performance advantage in optimizing energy consumption in the context of production scheduling.

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