Professor Mohammad Ali

Professor Mohammad M. Ali is a dynamic leader with over 20 years of expertise in the H.E sector and in the automotive industry. Mohammad is highly proficient in creating strategies for capability development and for the production of high quality deliverables. He is also skilled in creating teaching and learning delivery models focussed on personalised learning, technology enhanced learning and learning by doing.

Mohammad is currently the Dean of the Royal Docks School of Business and Law. Before his life as an academic, Mohammad worked first as a Consultant Engineer and then as a Supply Chain Manager in the automotive industry. During his tenure in the industry Mohammad has worked on various projects including value analysis, demand planning, inventory control and ERP/MRP implementations.
Mohammad holds a doctorate from Brunel University. His thesis discussed various new collaborative forecasting approaches in Supply Chains. He has completed a MSc in Business Performance Management, an Executive Certificate in Leadership and Management from Massachusetts Institute of Technology (MiT) and a postgraduate diploma in Information Technology. Mohammad’s first degree was in Mechanical Engineering.

Rostami-Tabar, B.,  Ali, M.M., Hong, T., Hyndman, R., Porter, M. Syntetos, A.A. (2020) “Forecasting for Social Good”, submitted for publications to International Journal of Forecasting (J) 

Rostami-Tabar, B., Babai, M.Z., Ali, M.M., Boylan J.E., (2018) “The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes”, European Journal of Operational Research, 273 (3), 920-932 (4* ABS).  (J) 
https://www.sciencedirect.com/science/article/pii/S0377221718307562 


Ali, M.M., Babai, M.Z., Boylan J.E., Syntetos, A.A. (2017) “A Forecasting Strategy for Supply Chains where Information is not Shared”, European Journal of Operational Research, 260 (3), 984-994 (4* ABS). (P) 
https://www.sciencedirect.com/science/article/pii/S0377221716309717 


Babai, M.Z., Boylan J.E., Syntetos, A.A., Ali, M.M. (2016) “Reduction of the Value of Information Sharing as Demand becomes strongly Auto-correlated”, International Journal of Production Economics, 181, Part A, 130-135 (3* ABS). (J) 

Babai, M.Z., Ali, M.M, Syntetos, A. and Boylan, J.E. (2013) “Forecasting and Inventory Performance in a Two-Stage Supply Chain with ARIMA (0,1,1) Demand: Theory and Empirical Analysis”, International Journal of Production Economics, 143 (2), 463 – 471. (3* ABS) (J) 
https://www.sciencedirect.com/science/article/pii/S0925527311003902 


Ali, M.M., Syntetos, A., Boylan J.E. (2012) “On the Relationship between Forecast Errors and Inventory performance”, International Journal of Forecasting, 28 (4), 830-841. (3* ABS) (P) 
https://www.sciencedirect.com/science/article/pii/S016920701100015X 


Babai, M.Z., Ali, M.M. and Nikolopoulos, K. (2012), “Impact of Temporal Aggregation on Stock Control Performance of Intermittent Demand Estimators: Empirical Analysis”, OMEGA: The International Journal of Management Science, 40(6), 713-721. (3* ABS) (J) 

Ali, M.M. and Boylan J.E. (2012) “Effect of Non-Optimal Forecasting Methods on Supply Chain Downstream demand inference”, IMA Journal of Management Mathematics, 23(1), 81-98. (2* ABS) (P) 
(This was included in the ‘Most Read’ article list for 2012 and 2013 by the Journal.) 


Ali, M.M, Boylan J.E. (2011) Feasibility principles for downstream demand inference in supply chains. Journal of the Operational Research Society, 62, 472 – 482. (3* ABS) (P) 

Ali, M.M. and Boylan, J.E (2010) The Value of Forecast Information Sharing in the Supply Chain, Foresight: The International Journal of Applied Forecasting, 18, 14-18. (1* ABS) (P) 

• Supply Chain Information Sharing
• Forecasting for Social Good
• ARIMA Modelling
• Demand Planning and Forecasting