| How additive manufacturing technology changes business models?–review of literature J Savolainen, M Collan Additive manufacturing 32, 101070, 2020 | 265 | 2020 |
| Real options in metal mining project valuation: Review of literature J Savolainen Resources Policy 50, 49-65, 2016 | 108 | 2016 |
| Maintenance optimization for a multi-unit system with digital twin simulation: Example from the mining industry J Savolainen, M Urbani Journal of Intelligent Manufacturing 32 (7), 1953-1973, 2021 | 62 | 2021 |
| Analyzing operational real options in metal mining investments with a system dynamic model J Savolainen, M Collan, P Luukka The Engineering Economist 62 (1), 54-72, 2017 | 26 | 2017 |
| Contrasting digital twin vision of manufacturing with the industrial reality J Savolainen, MS Knudsen International Journal of Computer Integrated Manufacturing 35 (2), 165-182, 2022 | 20 | 2022 |
| Deep Reinforcement Learning Agent for S&P 500 Stock Selection T Huotari, J Savolainen, M Collan Axioms 9 (130), 1-15, 2020 | 17 | 2020 |
| Analyzing the returns and rate of return regulation of Finnish electricity distribution system operators 2015–2019 M Collan, J Savolainen, E Lilja Energy Policy 160, 112677, 2022 | 11 | 2022 |
| How AM technology changes business models?–review of literature J Savolainen, M Collan Addit. Manuf 32, 101070, 2020 | 10 | 2020 |
| On the trade-off between the leverage effect and real options thinking: A simulation-based model on metal mining investment J Savolainen, M Collan, K Kyläheiko, P Luukka International Journal of Production Economics 194, 43-51, 2017 | 10 | 2017 |
| Simulation-based decision-making system for optimal mine production plan selection J Savolainen, R Rakhsha, R Durham Mineral Economics 35 (2), 267-281, 2022 | 9 | 2022 |
| Investigating the effect of price process selection on the value of a metal mining asset portfolio M Collan, J Savolainen, P Luukka Mineral Economics 30, 107-115, 2017 | 7* | 2017 |
| Using a cycle reverting price process in modeling metal mining project profitability J Savolainen, M Collan, P Luukka Kybernetes 46 (1), 131-141, 2017 | 7 | 2017 |
| Industrial additive manufacturing business models—what do we know from the literature? J Savolainen, M Collan Technical, Economic and Societal Effects of Manufacturing 4.0: Automation …, 2020 | 6 | 2020 |
| Incorporating hydrologic uncertainty in industrial economic models: implications of extreme rainfall variability on metal mining investments J Savolainen, D Pedretti, M Collan Mine Water and the Environment 38 (2), 447-462, 2019 | 4 | 2019 |
| Combining system dynamic modeling and the datar–mathews method for analyzing metal mine investments J Savolainen, M Collan, P Luukka Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium …, 2016 | 4 | 2016 |
| Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features J Savolainen European Journal of Wood and Wood Products 81, 1021–1031, 2023 | 3 | 2023 |
| Screening early stage metal mining projects A simulation approach J Savolainen, M Collan, P Luukka 2015 IEEE 19th International Conference on Intelligent Engineering Systems …, 2015 | 3 | 2015 |
| A Vision for a Highly Automated Digital Local Manufacturing Network—Solutions and Challenges M Collan, J Savolainen, VM Virolainen, P Luukka Intelligent and Transformative Production in Pandemic Times: Proceedings of …, 2023 | 2 | 2023 |
| To Phase or Not to Phase : Quantifying the Effect of Phasing Construction Projects M Collan, J Savolainen Nordic Journal of Business 69 (1), 69-88, 2020 | 2 | 2020 |
| Quantifying the economic feasibility of additive manufacturing: Simulating production lifetimes in the context of spare parts production J Savolainen, M Collan Technical, Economic and Societal Effects of Manufacturing 4.0, 149, 2020 | 2 | 2020 |
Mikael CollanLappeenranta-Lahti University of Technologyכתובת אימייל מאומתת בדומיין lut.fi