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.
PhD Business Forecasting in Supply Chains - (Brunel University)
MSc Business Performance Management - (Salford University)
PGD Information Technology - (Skills Development Council)
B.Eng. Mechanical Engineering - (NED University of Engg.& Tech.)
Overview
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)
Collaborators
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test
Research
Publications
Funding
- Supply Chain Information Sharing
- Forecasting for Social Good
- ARIMA Modelling
- Demand Planning and Forecasting