International Journal of Management and Decision Making (4 papers in press)

Regular Issues

• Smart logistics with a maturity assessment perspective  
  by Elifcan Göçmen Polat, Onur Derse 
  Abstract: The logistics sector is influenced by digitalisation and technological advancement in improving sustainability and efficiency. Logistics companies, which are highly capable of incorporating innovative technologies into their business flows, have made profound impacts, ultimately leading to a transformation in logistics services. In this context, this study presents the current and target maturity levels to make an international logistics company smarter and provides recommendations as an improvement road map for the logistics sector. Within the scope of this research, nine dimensions and 36 maturity items of the smart logistics (SL) concept are discussed to assess the maturity levels using the weighted maturity score calculation model (WMSC), in which evaluation of all maturity items using the capability maturity model (CMM) maturity levels and calculation of the criteria weights using fuzzy DEMATEL (F-DEMATEL) is integrated. Computational results show that warehouse management systems and material flow control systems have lower maturity levels. Thus, product picking by voice, integrating the warehouse management system, ensures real-time updates and data integration, reducing stockouts and overstock.
  Keywords: maturity model; smart logistics; SL; Industry 4.0; fuzzy DEMATEL.
  DOI: 10.1504/IJMDM.2027.10078548
   
  
• A strategic framework to evaluate businesses in collaborative networks from the green innovation perspective  
  by Ivan De Noni, Sina Fazel Khiavi, Nezir Aydin, Mazdak Shaverdi, Hassan Mina 
  Abstract: The automotive manufacturing industry is vital to economic growth and sustainable development. However, it contributes to significant environmental challenges, including carbon emissions, energy consumption, and resource depletion. In order to assess the strengths and weaknesses of automotive manufacturing companies, this article proposes an evaluation framework based on multi-criteria decision-making methods from the green innovation perspective. In the developed approach, the independent weights of the criteria and their sub-criteria are calculated through the improved fuzzy preference programming method, and the weighted influence non-linear gauge system technique is applied to analyse the interdependencies between the criteria and determine the dependent weights of the sub-criteria. Data from Kia Motors in South Korea, SAIPA in Iran, and their collaborative network are applied to validate the proposed approach. Kia Motors and SAIPA are considered as knowledge-intensive and lagging-behind companies, respectively. The results shows that although the collaborative network performs better than SAIPA, it is weak compared to Kia Motors. Finally, the weaknesses of SAIPA and the collaborative network are identified, and strategies are presented to improve their performance.
  Keywords: green innovation; collaborative network; multi-criteria decision-making; lagging-behind and knowledge-intensive companies.
  DOI: 10.1504/IJMDM.2026.10080198
   
  
• Ridge-penalised likelihood ratio control chart for monitoring high-dimensional covariance matrix changes with limited reference data: a bootstrap-based approach  
  by Alireza Najafzadeh, Ehsan Mardan, Mohammad Reza Maleki, Hossein Eghbali 
  Abstract: Many processes involve high-dimensional data where process dimension exceeds the sample size. Under these conditions, the sample covariance matrix becomes singular, making its inverse impossible to compute. Furthermore, slow production rates and operational constraints make it impossible to collect enough observations in phase I. Hence, this paper proposes a bootstrap algorithm for estimating target covariance matrix and establishing the upper control limit (UCL) of the RPLR chart. A seven-step algorithm is developed to examine how estimation errors affect the RPLR chart. The accuracy of the algorithm in estimating the dispersion matrix is assessed through Monte-Carlo simulations. Furthermore, the impact of estimation errors in phase II is examined under seven out-of-control scenarios. The results indicate that the proposed algorithm can accurately estimate the high-dimensional covariance matrix and determine UCL, even with one reference sample. Moreover, the findings reveal that estimation errors significantly affect the run length behaviour of the RPLR chart.
  Keywords: ridge-penalised likelihood ratio; covariance matrix; sparse disturbances; bootstrap algorithm; run-length behaviour.
  
  
• A cross-method optimisation framework for automated cheese manufacturing based on digital twin-driven reinforcement learning  
  by Safiye Turgay, Sule Basar, Mehmet Burak Ceran, Samet Ozyurt 
  Abstract: This work presents integrated modelling, control synthesis, and simulation for automated production of semi-hard and hard cheeses, focusing on temperature control and grammage accuracy in kasar cheese production. To overcome the limitations of traditional rule-based and PID control in nonlinear thermal-mechanical environments, eight control and optimisation methods — PID, model predictive control (MPC), genetic algorithms (GA), particle swarm optimisation (PSO), hybrid GA-PSO, reinforcement learning (RL), and a digital twin-coupled MPC/RL approach — are evaluated in an industrial case study under nominal, high-load, and disturbance scenarios. The results show that AI-based approaches outperform traditional controllers by reducing temperature and grammage errors while improving energy efficiency and robustness. These findings demonstrate the effectiveness of combining model-based control, evolutionary optimisation, and learning-based intelligence to achieve robust, energy-efficient, and intelligent cheese manufacturing.
  Keywords: industrial cheese manufacturing; temperature control optimisation; grammage accuracy; digital twin; DT; reinforcement learning; heuristic algorithms.
  DOI: 10.1504/IJMDM.2026.10080249