international journal of industrial Engineering & Production Research

Pricing and Competition in Green Supply Chains Under Government Intervention

Anwar Mahmoodi

The demand for green products has increased in the past few years due to the heightened awareness of environmental issues and the increasing use of green products by consumers. Thus, choosing the best strategy for green product manufacturers is essential. At the same time, producers and retailers are likely to have their decisions influenced by government actions. In this study, we attempt to determine the product's price and greenness within two competitive supply chains. The study investigates the pricing of two substitutable and green products in which each supply chain produces a green product. Using Nash and Stackelberg Game models, we determine how supply chains and their members interact. A Nash model involves two competing supply chains with equal power, within each supply chain, however, there is a Stackelberg competition between the retailer and the manufacturer. The Stackelberg model assumes that one of the supply chains is the market leader. The results show that with increasing government intervention (government's adjustment factor and green level floor for subsidies), regardless of Nash or Stackelberg structures, the green level of the product will increase, and wholesale and retail prices will decrease. Additionally, the price changes in the retailer-Stackelberg structure are greater than those in the manufacturer-Stackelberg structure. Also, by bearing the greenness cost by the manufacturer or retailer, companies can positively impact their profits as well as the level of greenness in their products. When the manufacturer makes an investment in greenness, the retailer and consumer benefit from it, and ultimately become the main force behind the development of green products.

An intelligent Approach for Bank Customers Segmentation using time series analysis

Hossein Abbasimehr

Businesses are aware of the importance of customer relationship management and strive to gain insights into customers and their needs. Clustering and prediction are widely used data mining methods for understanding customer behavior. In this paper, we introduce an intelligent approach that utilizes time series clustering to analyze customer behavior. To segment customers, we first construct time series of their behavior and then perform preprocessing on the data. Subsequently, a set of informative features reflecting the characteristics of each time series is extracted. These features are ranked using the Laplacian Score method and employed in clustering. The proposed method is applied to time-stamped transaction data of bank customers. After constructing a customer behavior series and extracting features, four informative features —variance of all points in the time series, entropy, spikiness, and lumpiness —are selected for clustering. Customer clustering is performed using four state-of-the-art clustering algorithms: k-medoids, k-means, Fuzzy C-Means clustering (FCM), and Self-Organizing Map (SOM) algorithms. The results demonstrate that, among various clustering methods, the k-medoids algorithm outperforms others. It divides customers into four clusters with a Silhouette metric of 0.6378.

The Strategy Development of the Local Content Requirements Policy in Indonesia Paints Industry Supply Chain

Marimin Marimin

The Indonesian paint industry contributes significantly to the national economy but remains highly dependent on imported raw materials, constituting approximately 77% of the total supply. This study aims to develop strategic solutions to increase local content and reduce import dependency by applying the Analytic Hierarchy Process (AHP) and Interpretive Structural Modeling (ISM). Based on AHP results, Capacity Building and Workforce Development emerged as the top-ranked strategy, with a final priority value of 0.330, followed by Strengthening Local Supply Chain (0.262). ISM analysis identified the Centre for Increasing Local Content as a key institutional driver influencing other actors in the supply chain. The sensitivity analysis confirmed the robustness of the AHP prioritization, showing less than 5% variation in rankings across multiple expert input scenarios. This indicates that the proposed strategies are stable and reliable under varying assumptions. The research provides a structured, multi-criteria framework aligning local strategies with national policy and international trade obligations. The study offers policymakers and industry leaders practical insights by integrating technology, human capital, and supply chain optimization to support a resilient and sustainable local content policy. Integrating AHP and ISM methods presents a novel approach, contributing original insights to the industrial strategy literature.

Dynamic Inventory Risk Profiling Using PCA and Clustering: A Data-Driven Approach to Supply Chain Optimization

Ida Lumintu

Effective inventory management is critical for mitigating inefficiencies such as overproduction, excessive holding costs, and stockouts. This study leverages DBSCAN and GMM clustering methods, combined with Principal Component Analysis (PCA) for dimensionality reduction, to categorize inventory data into distinct risk-based clusters. The analysis highlights that DBSCAN outperformed GMM, achieving a silhouette score of 0.62 compared to 0.49, while identifying three meaningful inventory clusters. Each cluster reflects unique combinations of risk factors, providing actionable insights for optimizing inventory levels. The study demonstrates how these clusters enable targeted strategies to address inefficiencies and improve overall inventory management. Limitations include the reliance on historical data, which may not fully capture dynamic market conditions, and the assumption of fixed clustering parameters. The findings underscore the importance of choosing clustering algorithms suited to the data's characteristics and highlight the potential of PCA in enhancing computational efficiency. Future research should explore dynamic clustering techniques and integrate real-time data streams to refine inventory management strategies further.

Strategic Digital Leadership in Construction: Overcoming Organizational Barriers to Sustainable Competitiveness

Hanan Nazzal

Despite technological advancements (e.g., BIM, AI, IoT), the construction industry exhibits low digital maturity, hindered by persistent managerial challenges, including cultural resistance, rigid hierarchies, and institutional inertia. This study investigates Strategic Digital Leadership (CDiLe) as a catalyst for overcoming these barriers and enabling sustainable competitiveness. Employing a systematic review of 60 peer-reviewed articles (2015-2023) from Scopus, Web of Science, and ProQuest, thematic coding synthesized evidence across theoretical and regional contexts. Findings reveal that CDiLe characterized by participatory leadership, strategic visioning, digital literacy, and resource alignment, facilitates agile, data-driven, and sustainable decision-making. Organizations implementing CDiLe principles demonstrate significant gains, including project efficiency improvements (up to 30%) and reduced delays (by 25%). The study presents an empirically grounded framework for leadership-driven digital transformation, focusing on practical organizational change interventions, particularly in emerging markets. It advances scholarship by reframing digital transformation as fundamentally leadership-led, not merely technology-driven, and offers actionable pathways for firms and policymakers to embed digital strategy into construction management, guiding future empirical validation.

Enhancing Logistical Efficiency in Public Institutions through AI: A Managerial Framework for Regulatory and Technological Integration

Mohamed Hadi Al Najdawi

This study investigates regulatory gaps impeding artificial intelligence (AI) integration in public sector logistics, revealing how fragmented legislative frameworks hinder operational efficiency and innovation. Through a quantitative cross-sectional survey of 182 legal professionals, public employees, and AI/legal scholars using stratified purposive sampling and validated instruments (Cronbach’s α=0.985) we identified statistically significant stakeholder divergences (*p*<0.05) via χ² tests and Cramer’s V effect sizes. Key findings demonstrate that: (1) legal experts prioritize regulatory clarity deficits (M=4.62), while public staff emphasize institutional resistance (M=4.41); (2) human capital training is systematically undervalued (M=2.57, V=0.26) despite its theoretical importance; and (3) while regulation enhances operational efficiency (M=4.36), it paradoxically inhibits logistical innovation (M=2.48), exposing a critical innovation-governance disconnect. The study’s core contribution, a Dynamic Institutional Alignment Framework, resolves this tension through three pillars: human-centered regulatory design integrating legal-technical dimensions, adaptive policy sandboxes synchronized with AI advancement cycles, and stakeholder-specific implementation pathways. By embedding institutional adaptability within global compliance standards (EU AI Act, OECD Principles), this framework advances AI governance theory and offers public institutions actionable strategies for balancing technological advancement with accountability.

The Role of Administrative Governance in Enhancing Integrity and Transparency and Reducing Administrative Corruption in Public Institutions: An Analytical Study

Raghda Raafat

This study investigates the role of administrative governance mechanisms in enhancing institutional integrity and transparency while reducing administrative corruption in public institutions. Employing a quantitative analytical approach, primary data were collected through a structured questionnaire distributed to 50 respondents across oversight bodies, inspection departments, and academic experts in Jordan, yielding a 90% response rate (n = 45). The analysis, based on descriptive and inferential statistics (Ka² tests, α = 0.05), demonstrated that the application of governance principles, particularly transparency, accountability, and administrative oversight, was significantly associated with improved institutional performance and a measurable decline in perceived corruption. Specifically, the study found that respondents largely agreed (mean = 4.04; SD = 1.10) on the effectiveness of regulatory and legal frameworks in combating corruption, while 88% supported the role of continuous monitoring in promoting integrity. Sensitivity analyses confirmed the robustness of these relationships across stakeholder categories. From the findings, a major standing for the immediate modernization of legal-administrative frameworks, training for governance implementation, and institutionalization of oversight mechanisms emerges. Concluding that administrative governance is vital to institutional integrity and sustainability, this study's contribution lies in filling the gap by providing an evidence-based argument pertinent to anti-corruption policies, legal reforms, and modernizing the public sector within developing contexts.

A Carbon-Constrained Production Planning Model for Sustainable Manufacturing

Dwi Kurniawan

While traditional production planning focused on optimizing supply-demand balance through make-to-stock/make-to-order strategies and capacity management, the new imperative of carbon neutrality introduces critical complexities. Regulatory emission caps now require manufacturers to strategically trade carbon allowances, fundamentally transforming the challenges of production optimization. This study developed an aggregate production planning model that incorporates carbon trading constraints into operational decision-making, providing industries with a systematic approach to address both economic and environmental objectives. The model optimized multi-period production plans across alternative technologies, each with distinct cost-emission profiles, while incorporating subcontracting options. It simultaneously considered government-allocated emission permits, dynamic carbon market prices, technology-specific costs and emissions, and subcontracting expenses. Through mathematical optimization of production quantities, subcontracting levels, and carbon credit transactions, the model minimized total costs while ensuring compliance. Computational experiments with nonlinear programming solved via LINGO demonstrated the model's effectiveness in identifying optimal technology deployment strategies that achieve significant cost reductions while meeting environmental targets, offering manufacturers a powerful tool for sustainable operations in carbon-constrained markets.

The Declining Value Relevance of Accounting Measures: Evidence from the Amman Stock Exchange (2010–2019)

Osama Khader

The value relevance of financial accounting information remains pivotal, particularly in emerging markets such as Jordan, where investor confidence and market mechanisms continue to evolve. This study investigates the longitudinal impact of earnings per share (EPS), book value per share (BVS), and dividends per share (DPS) on stock prices for 247 non-financial firms listed on the Amman Stock Exchange (ASE) from 2010 to 2019. Employing the Ohlson valuation model, both pooled and annual Ordinary Least Squares (OLS) regressions were performed, alongside time-trend analyses to capture shifts in explanatory power. Results show that EPS, BVS, and DPS are significantly and positively associated with stock prices (p < 0.01), with the highest adjusted R² value of 0.77 achieved in 2011. However, the relevance of EPS and DPS declined substantially over the period, with EPS showing an annual adjusted R² decline of −0.032 and the incremental explanatory power of DPS (IEPD) falling from 0.12 in 2010 to 0.01 in 2018. Sensitivity analyses using fixed effects, winsorization, and sub-period testing confirmed the robustness of these trends. The findings suggest a structural shift in investor priorities toward forward-looking metrics, underscoring the need for corporate reporting frameworks that integrate both financial and non-financial disclosures.

Selection of Strategic Value Chain using Hybrid Entropy-TOPSIS-Fuzzy Approach

Mohammad Reza Zare Banadkouki

Nowadays, it is very important to pay attention to the influential factors and economic and social drivers in developing countries, and identifying and evaluating the performance of Strategic value chains (SVCs) is in this direction. Evaluating local VCs and planning to upgrade them to the region and the world can be part of the development programs of each country. The correct selection of SVCs, considering the economic and competitive environment of the region, causes economic and social transformation. In this research, considering the development programs of Iran and especially Yazd province, the value-creating sectors of Yazd province were considered. After studying the upstream documents, the active VCs in the Yazd province were first extracted. Then, by reviewing the literature on the subject and asking for opinions from experts, the criteria for evaluating SVCs from the economic, social, and regional perspectives were determined. The Shannon entropy method was used to weight the evaluation criteria, and the fuzzy TOPSIS method was used to select the most effective VCs. From the literature review, documents, and field observations, 20 local VCs and 8 evaluation criteria were extracted and approved by experts. The study results revealed that the evaluation criteria of “High job creation” and “Positive effect on gross domestic product (GDP)” had the highest weights, and “Tourism and handicrafts”, “Textile and clothing”, “Commercial logistics and transportation”, and “Non-metallic minerals” had the highest impacts in Yazd Province, according to the evaluation criteria known as SVCs. Moreover, a sensitivity analysis was performed to determine the stability and robustness of the proposed approach by changing the weights of the desired criteria.

The Effect of Institutional Investors on Enhancing Corporate Governance and Earnings Quality

Khaled Al-Tamimi

This study determines the effects of investment institutions on improving business earnings and governance quality in Jordanian industrial businesses for the period (2018-2023), using the descriptive analytical approach to accomplish study goals. The study participants included (46) Jordanian industrial companies while the sample amounted to (45.7%) of commercial enterprises’ representatives research community. The annual reports of industrial businesses provided data for study variables found on the Amman Stock Exchange's website and on Securities Depository Center. Researchers used the descriptive and inferential statistical method; provided by E-Views software to accomplish the study's goals. The study results indicate that investing had a favorable impact for institutions on enhancing corporate governance, and a negative effect of investment institutions on the earnings quality. The study recommended encouraging investment institutions to increase their stakes in industrial companies to enhance oversight and transparency, and motivating them to adhere to governance standards by imposing regulations requiring them to apply good governance practices to ensure the sustainability of institutional investment, in addition to adhering to accounting practices.

Clustering Based Intelligent Route Optimization Algorithm for Capacitated Vehicle Routing Problem

Md. Azizur Rahman

The Capacitated Vehicle Routing Problem (CVRP) is a significant variant of the vehicle routing problem that incorporates constraints related to customer demand and vehicle capacity. Owing to its extensive applications in logistics and transportation, CVRP has attracted substantial research attention, with numerous algorithms proposed from the perspective of intelligent search. A common solution strategy involves two phases: first, assigning customers to different vehicles to form feasible routes, and second, optimizing these routes. This paper presents a two-phase CVRP solution framework through the clustering concept with intelligent search to improve route planning. In the first phase, a set of clustering methods - fuzzy c-means, k-means, and k-medoids - combined with a nearest neighbor heuristic search, are applied to generate feasible routes for each vehicle. In the second phase, these routes are iteratively optimized using the Simulated Annealing (SA) algorithm. The process yields three distinct solution pathways: fuzzy c-means with SA, k-means with SA, and k-medoids with SA. For performance evaluation, 46 benchmark CVRP datasets from a publicly available library are used. Simulation results demonstrate that k-means with SA performs the best, surpassing the other two approaches and outperforming other clustering-based two-phase state-of-the-art algorithms in terms of solution quality.

Identifying Leading Aquaculture Commodities in West Java’s Coastal Industry: A Multimethod Regional Analysis

Sri Kaidah

The initial stage in agro-industrial development planning involves identifying high-potential raw materials. Assessing commodity competitiveness requires a comprehensive analytical approach to ensure that decisions are both accurate and sustainable. The development potential of aquaculture-based agro-industries can strengthen linkages between upstream and downstream sectors, particularly between fishery production and processing industries. However, in four coastal regencies of West Java Province, aquaculture-based agro-industries have not yet been developed, despite aquaculture recording the highest export value at the provincial level. At present, the region remains dominated by manufacturing industries. This study aims to identify priority aquaculture commodities for coastal agro-industrial development. Commodity competitiveness was assessed using five analytical methods: Location Quotient (LQ), Dynamic Location Quotient (DLQ), Sectoral Contribution Index (SCI), Growth Ratio Model (GRM), and Shift Share Analysis (SSA). A multi-method approach was employed to generate complementary results and enhance the validity of the superior commodity designation. The findings reveal that shrimp is categorized as a highly competitive commodity across all four regencies. These results serve as a strategic foundation for regional agro-industrial development policies and can be replicated in other coastal areas.

A Fuzzy Approach to Portfolio Optimization in the Light of Credibility Theory and Z-numbers: An Empirical Study of Tehran Stock Exchange

Emran Mohammadi

Portfolio optimization has emerged as a cornerstone of modern financial theory, maintaining its position as one of the field’s most dynamic and extensively studied areas. While numerous optimization models have been developed and implemented, they fundamentally grapple with the persistent challenge of market uncertainty - an inherent and inescapable characteristic of financial markets. This uncertainty necessitates practical quantification methods to improve the reliability of financial projections, among which fuzzy theory has proven particularly valuable. However, despite its advantages over conventional approaches, traditional fuzzy theory contains a fundamental flaw in its underlying assumption: the presumed absolute reliability of fuzzy number estimations. This critical limitation undermines its effectiveness in real-world applications where information quality varies significantly. To address this gap, this paper proposes a novel portfolio optimization framework that integrates Z-number theory with credibilistic Conditional Value-at-Risk (CVaR) to address both the uncertainty and reliability of asset return estimates. Traditional fuzzy portfolio models often overlook the critical dimension of information quality, potentially leading to suboptimal allocations. Our approach overcomes this limitation by incorporating expert reliability assessments as an integral component of the optimization process through Z-numbers, where the first component represents fuzzy return estimates and the second quantifies their reliability. The model incorporates practical constraints, including cardinality limits and position sizing rules, to ensure real-world applicability. Using data from the Tehran Stock Exchange, we demonstrate that the Z-number-enhanced model produces more stable and economically rational portfolios compared to conventional fuzzy approaches. The results show that considering reliability leads to different asset allocations, with improved risk-adjusted performance. A key contribution is the demonstration that information quality measurably impacts portfolio outcomes, establishing reliability assessment as a necessary element in fuzzy portfolio optimization. This framework provides individual investors and portfolio managers with a more applicated tool for decision-making under uncertainty, especially valuable in markets with varying information quality across assets.