Machine Learning Predictions and Optimization: Working together for better decisions.

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I am working on a multidisciplinary PhD project related to non-perishable product pricing in association with Tesco. We aim to develop a robust pricing algorithm by using recent advancements in Operational Research, Artificial intelligence, and Combinatorics. Pricing decisions need to satisfy both the short-term business needs and the long-lasting impact on the future growth prospects of the organisation. In a competitive market with primarily homogeneous products, price is a key incentive for any consumer when making a purchase decision. Many companies rely on manual inputs for a pricing decision. However, these human-based methods are suboptimal, expensive, and prone to behavioural bias. Developing an automated approach to set prices at all outlets of a supermarket chain is a significant challenge; current literature typically attempts this on a station-by-station basis, which ignores the inherited network structure. Furthermore, the pricing problem can be subdivided into two parts. First prediction, we require estimates of various model parameters such as demand, and competitor's price. Second optimisation, we need to get an optimal selling price satisfying all the business objectives. The most common framework to integrate these two subproblems is a sequential process. However, any projections made on historical data are subject to uncertainty, but the underlying sequential process does not include this prediction uncertainty at upstream optimisation; thus, results in a suboptimal decision. We will develop a framework to optimally price non-perishable products across the network while accounting for the uncertainty in predictions.In partnership with Tesco.