Jurnal Teknik Industri

The Development of Halal Supply Chain Research in Indonesia: A Comparative Study

Sat, 31 Aug 2024 00:00:00 +0700

Halal products uphold the principles of halal throughout the entire supply chain, making managing the halal supply chain (HSC) crucial, especially for Muslim communities. Despite the importance of this topic, existing literature on halal supply chains remains limited, with most studies concentrating on the food industry. Notably, there is a significant research gap regarding the development of HSC research in Indonesia, the country with the largest Muslim population. This study aims to address this gap by conducting a comparative analysis of HSC research in Indonesia and globally. Utilizing Scopus as the data source, 355 studies on halal supply chains published between 2008 and 2024 were analyzed. A comparative study was then conducted to evaluate the development of HSC research in Indonesia based on various categories, including the number of publications, subject areas, authors, keywords, citations, paper quartiles, institutional affiliations, research objects, methods, and themes. The findings reveal that while Indonesia has made significant progress in HSC research, the research is still tiny compared to its Muslim population. These results highlight the need for further investment and focus on HSC research in Indonesia to match its global potential.

Agent-Based Modeling and Simulation for Farmer Decision Making

Nila Nurlina, Dhanan S Utomo, Tri Retno Setiyawati, Masculine Muhammad Muqorobin — Sat, 31 Aug 2024 00:00:00 +0700

Shallots are a highly demanded, perishable commodity whose fluctuating prices pose significant risks to farmers, affecting their income stability. This research aims to model and simulate the selling decisions of shallot farmers using Agent-Based Modeling (ABM). The model represents the individual behaviors of farmers and buyers, focusing on decisions to either delay or immediately sell harvested shallots. Key variables such as market prices, inventory levels, and profit expectations were incorporated into the model. Primary data were collected from observations of shallot farmers in Nganjuk, East Java. In contrast, secondary data were sourced from the Indonesian Central Statistics Agency (BPS). The simulation results indicate farmers maximize their profits by selling shallots when the minimum sales profit margin reaches 20%. Farmers tend to postpone sales when market prices fall by drying the shallots, reducing market supply, and elevating prices. The study concludes that delaying sales can be an effective strategy to stabilize prices and enhance profitability, particularly when dried shallots command higher prices than fresh ones. These findings offer valuable insights for developing agricultural policies to optimize farmers' income while mitigating price volatility risks.

Multi-Objective Portfolio Optimization Using Hybrid Ant Colony Optimization and Compromise Programming

Dinita Rahmalia, Nadya Husenti — Sat, 31 Aug 2024 00:00:00 +0700

The increasing complexity of stock trading requires effective portfolio management to optimize returns while minimizing risks. Portfolio selection is critical in determining the most suitable combination of stocks, aiming to maximize expected returns and minimize risk within a given investment limit. This study constructs a mathematical model for portfolio optimization using six different stocks, incorporating constraints such as expected return, risk, and available investment. Given the multi-objective nature of the problem, a hybrid approach is proposed, combining Compromise Programming (CP), Nadir Compromise Programming (NCP), and Ant Colony Optimization (ACO) to address both minimization and maximization objectives. The ACO algorithm is applied to minimize deviation variables, which serve as the fitness function in the optimization process. The results demonstrate the effectiveness of the hybrid method in selecting portfolios that achieve minimal deviation, providing an optimal balance between risk and return. This research offers valuable insights for investors by illustrating the trade-offs between risk and reward in stock selection, contributing to more informed decision-making in portfolio management.

Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA

Ari Andriyas Puji — Sat, 31 Aug 2024 00:00:00 +0700

Supply chains play a critical role in the operational success of organizations, impacting both costs and product quality. However, they are often exposed to various risks that can disrupt business processes. This research aims to identify supply chain risks and propose mitigation strategies using the House of Risk (HOR) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods. Through interviews, key issues were identified in a fiberglass company's supply chain, including raw material supply fluctuations, procurement cost variability, defective materials, inappropriate specifications, outdated technology, insufficient worker skills, and ambitious company targets. The novelty of this study lies in the application of MOORA, which introduces a correlation matrix for risk mitigation by considering both cost minimization and benefit maximization. The analysis identified 12 risk agents and 26 risk events, which were prioritized using HOR stage 2 with the MOORA method. The top preventive actions were ranked, providing actionable recommendations for companies to address supply chain risks more effectively. The findings of this research offer practical insights for companies in the fiberglass industry to enhance supply chain resilience by integrating cost and benefit considerations into their risk management strategies.

The Relationship Between Total Quality Management and Customer Satisfaction Through the Mediation of Employee Performance

Mario Parhan, Arfan Bakhtiar — Sat, 31 Aug 2024 00:00:00 +0700

The increasing competition in the global market, driven by globalization and technological advancements, has caused many companies to face challenges in maintaining their market position. As a result, effective quality management systems are essential to enhancing employee performance and delivering high-quality products and services to satisfy customers. This study aims to analyze the effect of Total Quality Management (TQM) implementation on employee performance, the influence of employee performance on customer satisfaction, and the mediating role of employee performance between TQM and customer satisfaction. The research was conducted on companies certified with ISO 9001:2015, using questionnaires filled out by 80 respondents. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that TQM significantly improves employee performance, positively affecting customer satisfaction. Additionally, TQM directly enhanced customer satisfaction, with employee performance as an effective mediator in this relationship. These findings suggest that improving TQM practices and employee performance can substantially increase customer satisfaction. The study provides recommendations for companies in Indonesia to optimize TQM implementation, aiming to enhance both employee performance and customer satisfaction.

Artificial Neural Network Model For Optimization of Forecasting Material Inventory

Denny Suci Prastiya, Rossi Septy Wahyuni — Sat, 31 Aug 2024 00:00:00 +0700

The increasing competition in the fast-moving consumer goods (FMCG) industry leads to demand fluctuations, negatively impacting the accuracy of demand forecasts and determining optimal lot sizes in material inventory planning. Many companies struggle to adopt appropriate forecasting models, resulting in poor accuracy and higher material costs. This study aims to develop an integrated model for forecasting and material planning using simulation. The artificial neural network (ANN) method is proposed to improve forecasting accuracy, with performance evaluated through mean percentage error (MAPE), mean absolute deviation (MAD), and mean squared error (MSE). The forecast results are then applied to optimize material inventory using the economic order quantity (EOQ) model, considering warehouse capacity constraints. The EOQ model is applied to adjust lot sizes under time-varying demand. The findings highlight the importance of integrating forecasting with inventory planning to provide accurate demand predictions and optimal lot sizing, ultimately minimizing material costs in the FMCG industry. This research contributes to better decision-making in supply chain management by enhancing forecasting accuracy and inventory optimization.