A Solution Approach on Reducing Defects in Batik Tanah Liek Production Process of a Small and Medium-sized Enterprise

Small and medium enterprises (SMEs) often struggle with implementing effective quality management practices, especially in traditional industries like batik production. These challenges include ensuring consistent product quality to differentiate from competitors and attract customers. This study focuses on addressing quality control issues in small-scale Batik Tanah Liek production, where significant defects persist. The research aims to assess existing practices, identify defect causes, and propose solutions to enhance product quality and reduce rejection rates. These efforts contribute to improving production efficiency and supporting the sustainability of this traditional craft. The study employs a systematic approach combining quality management methodologies, including data collection, problem identification, brainstorming, the Failure Mode and Effects Analysis (FMEA) approach, and actionable recommendations. Data was gathered through a questionnaire to capture perspectives on defects and quality control issues in batik production. Key quality challenges identified include faded batik, torn fabric, and incorrect motifs. Analysis revealed that the primary cause of incorrect motifs is the malfunctioning canting tool, which hinders proper wax application. Additionally, defects in dyeing and boiling processes contribute to fabric fading and tearing, exacerbating quality issues. The findings underscore the need for systematic solutions, such as creating clear work instructions, designing Standard Operating Procedures (SOPs) for process consistency, and implementing preventive maintenance schedules for equipment. By addressing these issues, the study provides practical interventions to improve production quality. These measures not only enhance the economic viability of SMEs but also play a crucial role in preserving the cultural heritage of Batik Tanah Liek. The implications of this research highlight the potential for broader adoption of quality management practices in traditional industries to ensure their sustainability in competitive markets

Time Window Characteristics in a Heuristic Algorithm for a Full-Truck Vehicle Routing Heuristic Algorithm in An Intermodal Context

Intermodal container terminals handle both the pickup and delivery of containers to and from customers, with these transport activities and terminal handling comprising a significant portion of intermodal transport costs. Efficient operations are therefore essential, particularly when time window constraints limit routing flexibility. This study presents a metaheuristic incorporating time windows to plan container pickups and deliveries. The proposed algorithm operates in three phases: initial solution construction using an insertion heuristic, improvement via local search, and further refinement through a deterministic annealing metaheuristic. The presence of time windows makes the planning more difficult, as the transport company has less flexibility in constructing the transport routes and, as a result, the distance travelled and/or the cost is increased. To assess how time window characteristics affect algorithm performance andcost, the study introduces two temporal descriptors—concentration (the clustering of time windows during the day) and specialization (the dominance of short or long-time windows in specific periods). The results of the experimental runs of the algorithm are statistically analysed to identify under which conditions of concentration and specialization an effect on the cost can be identified. Experimental results reveal that increased concentration leads to a rise in both the number of routes (up to 35%) and total cost (around 2%). While concentration results in more routes, these routes remain relatively cost-efficient. Furthermore, a lack of specialization in concentrated time windows amplifies both the number of routes and the total cost. Finally, the length of time windows influences these effects, with shorter time windows having a reduced impact on concentration and specialization outcomes compared to longer ones

A Fuzzy Multi-Criteria Approach for Selecting Open-Source ERP Systems in SMEs Using Fuzzy AHP and TOPSIS

In a rapidly growing and competitive business era, selecting an open-source Enterprise Resource Planning (ERP) system is a critical step to support the efficiency and effectiveness of company operations. This research aims to propose an innovative methodology by integrating the fuzzy Analytical Hierarchy Process (fuzzy AHP) and fuzzy Technique for Order Preference by Similarity to the Ideal Solution (fuzzy TOPSIS) to improve the open-source ERP selection process. The method involves eight criteria and 26 sub-criteria to comprehensively evaluate 11 open-source ERP alternatives, specifically for SMEs in the transportation services sector in Indonesia. System quality has been identified as a critical factor in the selection of an open-source ERP system, with particular emphasis on aspects such as security and reliability. These sub-criteria are considered the most influential in determining the suitability of a system. The analysis further indicates that the 10th ERP alternative as the best choice, consistently outperforming others in meeting the defined criteria. Additionally, sensitivity analysis confirmed the robustness of this choice, demonstrating its stability and effectiveness despite changes in criteria weights. Beyond its practical implications for SMEs, this research contributes a versatile evaluation framework that can be adapted to other industries seeking effective ERP solutions. The findings emphasize the importance of structured decision-making in technology adoption, offering comprehensive and reliable guidance for organizations aiming to optimize their operations through open-source ERP systems. This study not only bridges a critical gap in ERP selection for SMEs but also establishes a methodological foundation for future research and applications across diverse industry sectors

Unveiling the Landscape of Sustainable Logistics Service Quality: A Bibliometric Analysis

In today's environmentally conscious world, where environmental sustainability and consumer demand for responsible business practices are Sustainable Logistics Service Quality (SLSQ) has emerged as a critical focus in supply chain management, driven by increasing environmental concerns and consumer demand for responsible business practices. This study conducts a bibliometric analysis of 546 Scopus-indexed documents published between 1994 and 2024, systematically uncovering key research trends, thematic clusters, and gaps in SLSQ. Findings reveal a marked increase in SLSQ research since 2013, spurred by regulatory pressures, advancements in digital technologies, and growing consumer expectations for sustainable logistics. Dominant themes include the integration of cutting-edge technologies such as artificial intelligence (AI), big data analytics, blockchain, and sustainable transportation methods, which collectively enhance logistics service quality while reducing environmental impacts. Additionally, a notable trend is the alignment of logistics services with sustainability goals, reflecting both academic interest and industry imperatives to lower carbon footprints and improve resource efficiency, particularly in sectors like e-commerce. Despite these advancements, the study identifies significant gaps, particularly the lack of multidimensional metrics capable of comprehensively evaluating SLSQ across social, environmental, and economic dimensions. This highlights an urgent need for standardized and holistic frameworks to guide logistics providers in achieving operational efficiency and sustainability objectives. By bridging service quality and sustainability, this research addresses an underexplored area and provides a foundation for future scholarly work in SLSQ. Practical implications include guiding logistics providers and policymakers in formulating sustainable practices that align with regulatory requirements and enhance customer satisfaction. For academia, it offers a pathway to develop robust SLSQ metrics and frameworks, advancing sustainable logistics strategies and fostering a more efficient, eco-friendly, and customer-centric logistics ecosystem.

Enhanced Sustainability Assessment Framework for Plywood Manufacturing: A Multi-Method Approach Using Delphi Technique, BWM, and S-VSM

Sustainable manufacturing has emerged as a critical priority in addressing the complex environmental, social, and economic challenges of modern industry. This study focuses on the plywood sector, a significant contributor to manufacturing, which faces distinct sustainability issues such as high energy consumption, material inefficiencies, and hazardous working conditions. To address these challenges, the research introduces workload and noise level as critical indicators for assessing sustainability, broadening the scope of traditional evaluation methods. A multi-method framework was employed, integrating the Delphi technique to identify key sustainability indicators, the Best Worst Method (BWM) to assign weights to these indicators, and Sustainable Value Stream Mapping (S-VSM) paired with a Traffic Light System (TLS) to evaluate and visualize the Manufacturing Sustainability Score (MSS). Applied to a plywood manufacturing case study, the framework highlighted areas requiring improvement, particularly in worker well-being and operational safety, while demonstrating the industry's moderate overall efficiency. By offering actionable insights for improving resource use, operational processes, and employee conditions, this framework provides a practical tool for industry managers aiming to enhance sustainability. Furthermore, its adaptability makes it a valuable reference for other manufacturing sectors seeking to implement resource-efficient and sustainable practices. This research not only fills critical gaps in sustainability assessment but also contributes to advancing industry practices by emphasizing holistic and innovative approaches to manufacturing efficiency

Business Incubators and Technology-Based Startups in Emerging Economies: A Bibliometric Analysis

In the context of rapid technological advancement and the global rise of entrepreneurship, business incubators have become essential mechanisms for supporting technology-based startups, particularly in emerging economies. These incubators play a strategic role in bridging resource gaps, fostering innovation, and enhancing the survival and growth of early-stage ventures. Despite their increasing importance, there remains a limited understanding of how incubator performance directly influences startup outcomes. This study addresses that gap through a comprehensive bibliometric analysis of 920 scholarly articles published between 2010 and 2022, sourced from Scopus and Google Scholar. Using VOSviewer, the analysis identifies key research trends, influential publications, and thematic clusters related to incubator performance. The findings reveal a significant increase in research activity over the past decade, with a peak in 2018, and a strong concentration of publications in journals focused on technology transfer and innovation management. Prominent themes include academic entrepreneurship, incubator performance, technology transfer offices, and the role of innovation ecosystems involving academia, industry, and government. These themes highlight the multifaceted nature of incubator success and the importance of cross-sector collaboration. The study also emphasizes the need for integrated evaluation frameworks to enhance incubator effectiveness and guide institutional and policy-level strategies. The novelty of this research lies in its synthesis of bibliometric insights to propose future research directions and methodological improvements for assessing incubator performance. By mapping the intellectual landscape of incubator research, this study contributes to a deeper understanding of how incubators can be optimized to support sustainable startup development and economic growth in emerging markets

Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning

Problems arising from suboptimal production planning can cause inventory management to be less effective and efficient in the company. The lack of integrated presentation of information also causes less efficiency in making decisions. This study aims to obtain the best kernel function forecasting model by predicting ground rod sales using the Support Vector Regression (SVR) method in order to determine the level of forecasting accuracy and the results of ground rod forecasting in the future which are presented in an optimal data visualization. This problem-solving is done with the Support Vector Regression method, which consists of linear kernel functions, polynomial kernel functions, and radial basis function (RBF) kernel functions with the Grid Search Algorithm. Based on the results of the best parameter search that has been done using the grid search algorithm, it can be concluded that the best kernel function forecasting model is a linear kernel function with a value of C = 100 and ε = 10-3. The accuracy of this forecasting model has a MAPE value of training data and testing data of 2.048% and 1.569%, where this value is the smallest MAPE value compared to the MAPE value of the other two functions. After getting the best model, forecasting was carried out within five months, obtaining an average of 6,647 monthly pieces. The results of forecasting and historical sales are reviewed in a visualization of Business Intelligence data so that it is well exposed, where the forecasting shows an increase from every month

Optimizing the Supply Chain for Recycling Electric Vehicle NMC Batteries

The rapid growth of electric vehicle production has led to increased waste batteries that can no longer be used. This increase causes environmental and economic challenges. Lithium-ion battery waste harms the environment as it contains toxic and flammable chemicals. New raw materials need to be procured economically due to the need for more infrastructure and a circular economy. Therefore, the solution to overcome the impact of the accumulation of lithium battery waste is to recycle the battery. Recycling end-of-life batteries is necessary to mitigate material supply risks, reduce demand for new materials, and mitigate harmful environmental and health impacts. This study aims to provide a conceptual model for the supply chain network design of electric vehicles' Nickel Manganese Cobalt (NMC) battery recycling process. We developed a mathematical model to determine the allocation of multi-product recycling products from multi-suppliers and other related entities such as manufacturers and landfills over multiple periods. The analysis method utilizes techno-economic investment feasibility analysis and load distance method. The problem in the recycling process supply chain network is formulated in a Mixed Integer Linear Programming (MILP) model. The MILP optimization results show that the proposed model produces a globally optimal solution for allocating NMC batteries. The application of this study is to provide a solution to the treatment of waste batteries from electric vehicle end-users in Java Island, Indonesia. In addition, it can develop economic opportunities in the waste battery recycling business in the electric vehicle industry. It is building a contribution to a sustainable electric vehicle battery management system by reducing the dependence on demand for new materials from mining and analyzing the sustainability of the NMC electric vehicle battery recycling process

Developing an Industry-Specific Lean 4.0 Readiness Assessment Tool: A Case for the Chemical Sector

In an era where digital transformation is increasingly imperative, many industries struggle to navigate the complexities of technological adoption and operational efficiency. Lean principles, which emphasize waste reduction and process optimization, provide a robust foundation for digital transformation, particularly in the chemical industry, where unique operational challenges exist. This research aims to develop an integrated Lean 4.0 readiness assessment tool to bridge the gap between leanness and Industry 4.0 readiness. The study begins with a literature review on existing lean and Industry 4.0 readiness measurement tools and integrates them to create a new framework, using the Indonesia Industry 4.0 Readiness Index (INDI 4.0) as a reference, tailored specifically to the chemical industry. Expert interviews are conducted to refine the assessment tool, ensuring alignment with real-world industry conditions and practical insights. A Delphi-based expert consensus method combined with a fuzzy approach for handling imprecision in indicator ratings is employed to validate the framework, resulting in five key dimensions and 86 indicators. By gathering expert input, the tool addresses the chemical industry’s specific challenges and simplifies readiness evaluation, helping companies assess their preparedness for digital transformation and identify areas for improvement. The resulting framework enables chemical companies to bridge readiness gaps and prioritize targeted enhancements. Furthermore, this tool has the potential to serve as a scalable model for other industries, fostering more efficient and strategic digital transformation aligned with Industry 4.0 objectives globally

Enhancing Sustainable Performance in Hotel Industry: Supplier Innovativeness and Supply Chain Integration

The hotel industry relies on supply chain to deliver value added products and services, therefore selecting suppliers significantly affects the company's competitiveness in the market to improve sustainability performance. This research is important to determine how supplier innovativeness can improve sustainable performance. It provides a new contribution in assessing the influence of supplier innovativeness and supply chain integration on sustainable performance in the hotel industry, however their combined impact remains underexplored. The study examines the effect of supplier innovativeness on sustainable performance by focusing on the mediating role of supply chain integration in the hotel industry. The study employed a non-probability sampling method using a purposive sampling technique. The sample was selected based on the criterion that respondents held managerial or equivalent positions, as they were responsible for decision-making in hotel operations. A total of 111 respondents participated in the study and the hypotheses were analysed using SmartPLS software. Supplier innovativeness has a significant effect on supply chain integration and also contributes significantly to the improvement of sustainable performance. Indirectly, supplier innovativeness also significantly impacts sustainable performance through supply chain integration. Supply chain integration partially mediates the relationship between supplier innovativeness and sustainable performance. Emphasizing these factors can help hotels to achieve their sustainability goals, offering valuable insights for managers and policymakers. Hotel managers should actively engage in partnerships with innovative suppliers and invest in strengthening integration across their supply chains. This research contributes to the growing body of literature on sustainable supply chain management, particularly within the hospitality industry