| Identification of maintenance improvement potential using OEE assessment T Ylipää, A Skoogh, J Bokrantz, M Gopalakrishnan International journal of productivity and performance management 66 (1), 126-143, 2017 | 161 | 2017 |
| An algorithm for data-driven shifting bottleneck detection M Subramaniyan, A Skoogh, M Gopalakrishnan, H Salomonsson, ... Cogent Engineering 3 (1), 1239516, 2016 | 54 | 2016 |
| Machine criticality assessment for productivity improvement: Smart maintenance decision support M Gopalakrishnan, A Skoogh, A Salonen, M Asp International Journal of Productivity and Performance Management 68 (5), 858-878, 2019 | 50 | 2019 |
| Data-driven algorithm for throughput bottleneck analysis of production systems M Subramaniyan, A Skoogh, H Salomonsson, P Bangalore, ... Production & Manufacturing Research 6 (1), 225-246, 2018 | 48 | 2018 |
| Machine criticality based maintenance prioritization: Identifying productivity improvement potential M Gopalakrishnan, A Skoogh International Journal of Productivity and Performance Management 67 (4), 654-672, 2018 | 47 | 2018 |
| Data-driven machine criticality assessment–maintenance decision support for increased productivity M Gopalakrishnan, M Subramaniyan, A Skoogh Production Planning & Control 33 (1), 1-19, 2022 | 44 | 2022 |
| Planning of maintenance activities–A current state mapping in industry M Gopalakrishnan, J Bokrantz, T Ylipää, A Skoogh Procedia CIRP 30, 480-485, 2015 | 36 | 2015 |
| Simulation-based planning of maintenance activities in the automotive industry M Gopalakrishnan, A Skoogh, C Laroque 2013 Winter Simulations Conference (WSC), 2610-2621, 2013 | 35 | 2013 |
| Cyber-physical production testbed: literature review and concept development O Salunkhe, M Gopalakrishnan, A Skoogh, Å Fasth-Berglund Procedia manufacturing 25, 2-9, 2018 | 31 | 2018 |
| Real-time data-driven average active period method for bottleneck detection M Subramaniyan, A Skoogh, M Gopalakrishnan, A Hanna International Journal of Design & Nature and Ecodynamics 11 (3), 428-437, 2016 | 27 | 2016 |
| Simulation-based planning of maintenance activities by a shifting priority method M Gopalakrishnan, A Skoogh, C Laroque Proceedings of the Winter Simulation Conference 2014, 2168-2179, 2014 | 23 | 2014 |
| Data-driven decision support for maintenance prioritisation: connecting maintenance to productivity M Gopalakrishnan PQDT-Global, 2018 | 17 | 2018 |
| Practices of preventive maintenance planning in discrete manufacturing industry A Salonen, M Gopalakrishnan Journal of Quality in Maintenance Engineering 27 (2), 331-350, 2021 | 15 | 2021 |
| Organisational Constraints in Data-driven Maintenance: a case study in the automotive industry P Savolainen, J Magnusson, M Gopalakrishnan, ET Bekar, A Skoogh IFAC-PapersOnLine 53 (3), 95-100, 2020 | 15 | 2020 |
| Analysis of the impact of process complexity on unbalanced work in assembly process and methods to reduce it K Lokhande, M Gopalakrishnan | 8 | 2012 |
| Exploring data-driven decision-making for enhanced sustainability Z Chavez, M Gopalakrishnan, V Nilsson, A Westbroek SPS2022, 392-403, 2022 | 7 | 2022 |
| Functional interaction of simulation and data analytics–potentials and existing use-cases A Skoogh, M Gopalakrishnan Simulation in Produktion und Logistik 2017, 403, 2017 | 7 | 2017 |
| Buffer utilization based scheduling of maintenance activities by a shifting priority approach-a simulation study M Gopalakrishnan, A Skoogh, C Laroque 2016 winter simulation conference (WSC), 2797-2808, 2016 | 6 | 2016 |
| Challenges and opportunities to advance manufacturing research for sustainable battery life cycles B Johansson, M Despeisse, J Bokrantz, G Braun, H Cao, A Chari, Q Fang, ... Frontiers in Manufacturing Technology 4, 1360076, 2024 | 2 | 2024 |
| Towards Effective Maintenance Planning M Gopalakrishnan Chalmers University of Technology, 2016 | 2 | 2016 |
Anders SkooghChalmers University of TechnologyPreverjeni e-poštni naslov na chalmers.se
