Dr Mohammad Abdollahi · Data Scientist & Operational Researcher
I build forecasting and optimisation systems that help organisations decide what to order, how to price and how to deliver when demand is uncertain. I have done this in peer-reviewed research on online grocery delivery, in a two-and-a-half-year Knowledge Transfer Partnership with a UK e-commerce business, and in a public inventory-planning competition where I placed sixth of around 180 participants.
Worked with · Published in · Ranked in
Industry delivery · KTP
Designed and built a forecasting and procurement decision system covering 1,200+ products for a UK packaging e-commerce business, as KTP Associate with the University of Kent.
1,200+ products · Priory Direct / University of Kent · 2024–2026
Read the case study →Public competition
Sixth of around 180 participants in the first public inventory-planning competition (2025). Six weekly ordering rounds, two-week lead time, scored on holding and shortage cost. Only 25 entries beat the organiser's benchmark.
6th place · independently scored
Organiser's results →Doctoral research
Developed forecast-informed pricing, slotting and routing methods for online grocery delivery; simulated profit improvements of 13.57–21.43% in experiments on real-world data.
13.57–21.43% simulated · EJOR 2023, Annals of OR 2026
Read the case study →Online grocers must decide which delivery slots to offer and at what price while orders are still arriving. This paper estimates the opportunity cost of each slot more accurately by routing forecast orders alongside confirmed ones, and introduces a dynamic slot-combination strategy that uses customers’ flexibility to make routes more efficient. Both ideas work within standard time-window routing systems and are evaluated on real data.
When a customer books a delivery slot, the retailer does not yet know which other orders will arrive. This paper forecasts the orders still to come and includes them in a dynamic routing model, so that slot availability and delivery charges reflect the true cost of each booking. The approach is tested on real-world attended-home-delivery data.
I have taught and supported teaching in artificial intelligence, machine learning, statistics, programming and operational research at undergraduate and postgraduate level, at the University of Essex and earlier as a Lecturer in Computer Science. I combine concise explanation with worked examples and coding activities, and adapt for students from different disciplinary backgrounds.
Teaching experience and a sample exercise →Completing a manuscript on global and demand-type-gated forecasting models from the KTP deployment, and revising a paper on opportunity-cost estimation for delivery pricing. Alongside this I am extending my engineering practice through an MLOps programme and studying agentic AI systems. learning in progress The agentic work is current development rather than established professional experience.
Full background →If you are hiring for data science, forecasting or optimisation roles, exploring a research collaboration, or have a forecasting or inventory problem you would like a second opinion on, send me a short note.