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Showing 4 results for Modelling
Deployment of Interpretive Structural Modelling Methodology in Supply Chain Management –An overview
Pramod Shahabadkar,
Volume 23, Issue 3 (9-2012)
Abstract
Abstract
 Purpose-The purpose of this paper is to review a sample of the literature relating to Interpretive Structural Modelling (ISM) and its deployment for modelling purposes in the area of supply chain management (SCM).
Design/methodology/approach- The literature is examined from the three perspectives. First, concept of ISM and examines ISM as modelling technique. Second, use of ISM by the various researchers in their research for modelling. Third, use of ISM for modelling in the area of supply chain management. Findings- ISM is a systematic application of some elementary graph theory in such a way that theoretical, conceptual and computational advantage are exploited to explain the complex pattern of conceptual relations among the variables. From the literature review, we can conclude that many researchers have used ISM for modelling the variables of: reverse logistics, vendor managed inventory, IT enabled supply chain management etc.
Research limitation/implications-The scope of this literature review is by design limited to ISM and it does not cover in investigating other modelling techniques. Literature review investigates sample of important and influential work in the area of application of ISM in the research.
Originality/Value-This study reviews a sample of recent and classic literature in this field and in doing so this paper provides some comprehensive base and clear guidance to researchers in developing, defining and presenting their research agenda for applying ISM methodology in a systematic and convincing manner.
 Key words: Interpretive Structural Modelling, SCI, SMEs, SCM
An artificial neural network approach to prediction of surface roughness and material removal rate in CNC turning of C40 steel
Saadat Ali Rizvi, Wajahat Ali,
Volume 32, Issue 3 (9-2021)
Abstract
The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (vc), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (Ra and Rq) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method.  ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (Ra and Rq) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.
Non-Identical Parallel Machine Scheduling Problem with Worker Fatigue Consideration
Achmad Pratama Rifai, Suma Samala Jiwangga, Andri Nasution,
Volume 37, Issue 3 (9-2026)
Abstract
Machine scheduling critically influences production efficiency. This study focuses on the Non-Identical Parallel Machine Scheduling Problem (NIPMSP) and explicitly incorporates human factors, namely worker fatigue, into an exact optimization framework. A mixed-integer nonlinear programming (MINLP) model where fatigue accumulation and recovery dynamically affect processing times. The model is verified and validated using secondary datasets and manual calculations, and solved with Gurobi across multiple configurations (2–3 machines; 6–20 jobs). Results show that the model consistently produces feasible solutions; higher fatigue rates increase makespan, whereas higher recovery rates reduce it. Sensitivity analyses on the fatigue rate, recovery rate, and percentage of jobs influenced by fatigue further confirm these trends. Incorporating worker fatigue enables more realistic makespan minimization and provides actionable managerial insights to balance throughput and workforce well-being.

A FACTS–SEM Framework for National Radiosonde Standardization in Indonesia under Tropical Operating Conditions
Hogan Eighfansyah Susilo, Iqbal Vernando, Amy Reimessa,
Volume 37, Issue 3 (9-2026)
Abstract
Indonesia’s dependence on imported radiosondes poses challenges for ensuring data consistency, calibration traceability, and operational resilience in tropical meteorological conditions. This study proposes an integrated framework combining the Framework for Analysis, Comparison, and Testing of Standards (FACTS) with Structural Equation Modelling (SEM) to develop a stakeholder-driven national radiosonde standard. The FACTS methodology was applied through a five-phase workflow standards inventory, comparison, gap analysis, requirements translation, and validation to systematically benchmark 45 technical clauses from WMO, ITU-R, and ETSI against Indonesia’s tropical operating conditions, while SEM was used to quantitatively validate adoption drivers across 80 respondents from government agencies, research institutions, and industry. The PLS-SEM results confirm that measurement accuracy (β = 0.58, p = 0.011) is the most significant determinants of standard acceptance, followed by adaptability, testing reliability (β = 0.34, p = 0.008), technical compliance (β = 0.31, p = 0.011), and radiosonde performance (β = 0.24, p = 0.006). Model robustness is supported by contemporary fit indices (SRMR = 0.067; NFI = 0.934) and PLSpredict cross-validation confirming positive predictive relevance (Q² > 0). The proposed model successfully explains 76% of the variance in national standard adoption (R² = 0.76). The integrated FACTS–SEM approach provides a data-validated foundation for designing radiosonde standards compatible with WMO, ITU, and ETSI guidelines, while being optimized for tropical performance and local manufacturing capabilities. This study contributes an actionable framework to strengthen Indonesia’s SNI-based meteorological standardization and support industrial self-reliance.

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