Mark Lawrenson on the science of retail optimization methods and solving real-world problems more optimally
Meet Mark Lawrenson:
Mark Lawrenson has graduated with honours in mathematical applications and computations from The Australian National University and was previously a researcher at CSIRO/Data61, the world's leading Australian research and data science agency. At HIVERY Mark is the product lead of HIVERY Curate - the world’s first truly hyper-local category management solution. Mark has several patents under his name and has published many research papers, namely "A model-based genetic algorithm framework for constrained optimization problems" where he presents a new hybrid optimization framework that can be used to solve constrained optimization problems differently.
In this podcast, Mark explains the science of optimization methods; the science of finding the best alternative from many alternatives. From wars, supply chain distribution to bakers and their bread to real category optimization.
What you will learn from this podcast:
• When optimization or applied mathematics was first used to solve real-world problems. We go back to 1947 and George B. Dantzig, when not only the first computer program was written but also new applied mathematics methods were invented, now called linear programming.
• The fascinating world of Applied Mathematics. Mark breaks down everything from Operations Research, Linear Programming (using 1 and 0.5 as inputs), Complete Methods (solving problems optimally), Heuristic Methods (solving problems faster although not always optimally), and Genetic Algorithms (inspired by nature's way of optimizing) to relatively modern methods like Integer Linear Programming (using 0, 1 or 2 as inputs). These methods have previously advanced the military to optimize the distribution of weaponry. Now, these methods are used by commercial industries and have found their way into category management to ensure the right products are in the right, specific shelf space at the right, specific store.
• Mark goes deep into explaining his fascinating research: "A model-based genetic algorithm framework for constrained optimisation problems"; and his new framework that solves real-world problems differently and more efficiently.
• Why HIVERY Curate (formally HIVERY Propel) is different to any other planogram category management optimization solution out there. Marks explains how he has applied and combined optimization techniques (i.e. how much of product X to put on that specific shelf in that specific store) with machine learning methods (what product to put (X or Y or Z) on that specific shelf in that specific store) along with the secret ingredient; the constraints that make HIVERY Curate category recommendations realistic and implementable at a store-level.
• If you are interested in learning about how our world can be run more efficiently, love maths and solving real problems. This is a must-listen.