PyData Meetup Demand Forecasting and Logistic Optimization @ EyeOn

It is time for another edition of the PyData Eindhoven meetup. Join us on the 26th of January at EyeOn for an evening of technical talks for technical people. Please note that this event will take place at EyeOn which is actually located in a Castle.

We were humbled by the large interest at the PyData Conference in December 2022. To keep the momentum going we are organising this event around the theme: Demand Forecasting and Logistic Optimization.

This PyData Eindhoven Meetup will be hosted by EyeOn at their campus in Aarle-Rixtel. If you would like help getting there or getting a taxi from train station send Gareth a message.

Agenda:
17:30 - Doors open
18:00 - Welcome and Introduction
18:15 - Organizing order fulfillment using mathematical optimization -Victoria Guerrero Mestre & Arturo Pérez Rivera from IKEA
18:45 - Dinner/Drinks
19:30 - Driver-based demand forecasting: beyond existing frameworks - Mark Ramakers from EyeOn
20:00 - Is there a next step in warehouse logistics optimization? -Wouter Leenen & Lennart van Ham from Pipple
20:30 - Drinks and networking
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Title: Organizing order fulfillment using mathematical optimization
Description:
The increasing popularity of e-commerce brings new selling opportunities for retailers. However, selling online also brings new logistical challenges. In this talk, we will discuss a particular challenge for IKEA: organizing the fulfillment of e-commerce orders. We will give an overview on how fulfillment decisions are made today using business rules and how they will be made tomorrow using mathematical optimization models and data analytics. We will illustrate how the new decision paradigm is implemented (as a microservice in Python) and what potential value it can bring to our customers and operations.
Presenter:
Victoria Guerrero Mestre (Data Scientist) and Arturo Pérez Rivera (Data & Analytics Leader)
LinkedIn:
Company:
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Title: Driver-based demand forecasting: beyond existing frameworks
Description:
Time series can be tricky to handle, especially when there are hundreds of thousands of them, like in supply chain demand forecasting. Forecasts are essential for an efficient supply chain in order to make informed decisions and optimize inventory and customer service levels. Despite the potential of machine learning in this area, it is not a simple task. In fact, it has only been in the past few years that machine learning has been able to outperform traditional statistical methods in this context (source). During this talk, we will explore the application of machine learning for supply chain demand forecasting, as well as our own unique framework that can be used to achieve this goal.
Presenter: Mark Ramakers, Data Scientist
LinkedIn:
Company:

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Title: Is there a next step in warehouse logistics optimization?
Description:
The growth of online web shops over the past decade has been accompanied by the arrival of many e-fulfilment centers. An incredibly dynamic operation, if only because of the short delivery times. Ordered today, delivered tomorrow.

Increasing mechanization and automation are necessary given the tight labor market and consequently rising personnel costs. The result: an extremely dynamic and complex process. Human intelligence can no longer manage these operations efficiently and optimally.

Heuristics have been used in Operations Research (OR) for decades and are capable of making quick and - in general - good decisions. But are they adaptive enough as well?

With its worth already proven in complex gaming environments, Reinforcement Learning (RL) learns through a simulated version of reality. As such, it is able to anticipate constant changes in a dynamic environment, without having to re-calculate a thing. But a warehouse is no game of chess, and is RL cut out to fully grasp its immense complexity?

We believe we can get the best of two worlds. Real-time response in a dynamic environment whenever required, while reducing complexity using heuristics where they are best suited. Not replacing, but combining OR-heuristics with innovative Machine Learning technologies.

In this talk, we will introduce the dynamics in warehouse logistics, show how RL can be used to accelerate OR-techniques and give practical examples of applications in real-world scheduling problems.
Presenter: Wouter Leenen: Operational Director at Pipple & Lennart van Ham: Data Scientist at Pipple
LinkedIn:
Company: www.pipple.nl

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Attendee profile:

• People who want to learn about Python, Julia, R in the context of Data
• Interested in seeing how others use Python for solving real problems
• Interested in being part of the Python Community in Eindhoven
• No Recruiters
• No Sales
• Only people who speak Data