recommendation = optimization(prediction)
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The document discusses the importance of optimization in demand forecasting and decision-making for businesses, highlighting a conversation between an expert and a customer about improving logistics through mathematical models. It emphasizes the transition from simply providing forecasts to creating a decision support system that optimizes business KPIs and supports better decisions. Additionally, the text touches on the challenges of different programming approaches and the benefits of using custom optimization solutions tailored to specific business needs.
recommendation = optimization(prediction)
- 2. Once upon a time Wit met a Customer that needed demand forecasts to ...
- 3. Customer: I need better demand forecasts. Me: I understand. Can I have a simple question? Customer: Yes Me: Imagine I created a demand forecasting model and provided you with desired 5M forecasts (numbers). What are you going to do with them? Customer: Well… I will take the forecasts and optimize my logistics decisions using the numbers. Me: I see. Why don’t we talk about the whole decision problem? Maybe the inefficiency is not in demand forecasts but in optimization part? Customer: Can you create a math model for such complex business problem with many constraints and exceptions? I thought it was impossible. Recommendations are calculated in “Sheet” and it is a bottleneck. Me: It is possible to build decision support system that uses mathematical optimization for your problem Customer: Great, let’s talk about the details.
- 4. What has happened in the past? What is an optimal course of actions for the past? What can happen in the future? What is an optimal course of actions for the future? DataAnalyticsOptimization
- 7. Forecasts/predictions are “just” a tool for better decisions. And better decisions are based on right recommendations. And right recommendations are result of optimizing business KPIs that are linked to business decisions. Optimization model deals helps to improve robustness of decision making process robust.
- 8. Automation of the complex business process. Transition to central/global planning. Learning from best (optimal) decision.
- 9. I was selling vehicle routing solution to a logistics company. I managed to persuade manager/owner to meet and talk with the team. After 1 hour presentation of the solution to the company I got one question How can I create an invoice in this solution? This is real story that happened to me. Similar story can be read in “Being wrong with Clarke & Wright” by Robert E.D.Woolsey
- 11. There are three commercial highly efficient solvers for mixed integer problems. Solutions have been on the market for 25+ years…. and are still in development. Very resistant to parallelization and distributed computing techniques. Very sensitive to data. Tightly coupled with business. Only “auto” for mip.
- 12. There is only one good library for mixed integer programming that is open-source.
- 13. Graphics taken from SCIP solver webpage. Benchmarks from Hans Mittelmann
- 14. Most common ML libs are open-source. Open-source is very efficient compared to commercial solutions. Easy to create distributed implementations Fairly insensitive to data. Less tightly coupled with business. Quite a few AutoML solutions that work.
- 15. Problem is infeasible. Explanation is extremely difficult. Customers expect any-time feature. Debugging is hell :)
- 17. Mixed-integer programming black-box hard to customize limited applicability (still wide!) Constraint programming white-box easy to customize not limited applicability Metaheuristics custom-box easy to customize not limited applicability Easy Difficult
- 18. Mixed Integer Programming IBM CPLEX Gurobi Fico Local Solver Constraint programming Sicstus IBM CP Optimizer Commercial world Mixed Integer Programming CBC (solver) MIP (wrapper) Or-tools (wrapper) Constraint programming ECLiPSe Choco Gecode Or-tools Open-source world
- 19. Easy I can declare model using existing solver. Fairly difficult I can solve problem with a sequence of easy problems. Very Difficult I must implement custom solver.
- 20. Business requirements are almost impossible to be collected upfront. Performance is not satisfactory. Solution quality is not satisfactory. No solution found is not acceptable.
- 21. Validator Solver Optimization engines Integration layer Load balancer Validator Solver Optimization engines
- 22. Start with business process and decisions Start with small and iterate. Use real data since the first day of the project. Assume problems are infeasible or internally contradictor. Deal with must vs nice to have requirements.
- 24. Saving up to 20% of cash management costs in Deutsche Bank
- 25. Challenge • Factory throughput was too low • Upgrade or not to increase throughput Solution • Integrated planning and scheduling optimization model • Scenario generation to support investment decision • Tailor made optimisation model Effects • Ability to support investment decision with numbers Based on academic work by Roman Barták
- 26. Challenge • Dynamic and unpredictable orders flow • Complex tasks Solution • Automation by optimisation • Tailor made optimisation model Effects • In progress - feasibility tests of the working solution Collect Pay Drive Deliver Drive Collect Pay Drive Deliver Drive Collect Pay Drive Deliver Drive
- 28. Contact info ● Private: wit.jakuczun@gmail.com ● Business: ○ wit.jakuczun@fourteen33.com ○ wit.jakuczun@wlogsolutions.com