Maryam is senior lecturer in Operations Management and the PhD coordinator for the International Management and Innovation (IMI) department at Middlesex University, London. She holds a BSc and MSc in Industrial Engineering and a PhD in Business Forecasting from Brunel University and specialises in Supply Chain Forecasting and Operations Management.
Learning & Teaching Interests
Maryam has taught a range of subjects at undergraduate and postgraduate level including: Operations Management, Supply Chain Planning and Forecasting, International Logistics Management, Management Science, Research Methods and Quantitative Methods in Business.
She is external examiner of a range of Business Management programmes at Coventry University.
Research Outputs & Interests
Maryam's research interests are in intermittent demand forecasting, seasonal forecasting, aggregation in forecasting, forecasting reproducibility, and information sharing in supply chains.
J.E. Boylan, P. Goodwin, M. Mohammadipour and A. Syntetos (2015) Reproducibility in Forecasting Research, International Journal of Forecasting, 31, 79-90.
J.E. Boylan, H. Chen, M. Mohammadipour and A. Syntetos (2014) Formation of Seasonal Groups and Application of Seasonal Indices, Journal of Operational Research Society, 65 (2), 227-241.
Mohammadipour, J.E. Boylan and A. Syntetos (2012) The Application of Product-Group Seasonal Indexes to Individual Products, Foresight: International Journal of Applied Forecasting, 26, 18-24.
Mohammadipour, J.E. Boylan (2012) Forecast Horizon Aggregation in Integer Autoregressive Moving Average (INARMA) Models, OMEGA: The International Journal of Management Science, 40, 703-712.
Boylan, John E. and Chen, Huijing and Mohammadipour, Maryam and Syntetos, Aris A. (2014) Formation of seasonal groups and application of seasonal indices. Journal of the Operational Research Society, 65 (2). pp. 227-241. ISSN 0160-5682
Boylan, John E. and Goodwin, Paul and Mohammadipour, Maryam and Syntetos, Aris A. (2015) Reproducibility in forecasting research. International Journal of Forecasting, 31 (1). pp. 79-90. ISSN 0169-2070