Turning Raw Data into Planning Data
Any planning of an intralogistics system requires detailed data analysis to understand and define the requirements for the new system. For this purpose, extensive amounts of data are linked together and relevant information is derived via a series of operations as a basis for planning. The results of the data analysis are essential for communication and understanding between system providers, consultants and the company’s own logistics staff and warehouse managers. Only with solid results from a data analysis can a logistics system be planned. A good understanding of the planning data and the ability to interpret it in order to derive design criteria for the system to be planned is a prerequisite for successful planning projects.
The Concept in a Nutshell: This workshop teaches the essential techniques of data analysis and interpretation for planning automated and semi-automated intralogistics systems. Starting with raw data, participants will learn how to apply relevant analysis techniques through practical examples to create a database that will serve as the basis for planning. Initial conclusions for the planning of intralogistics systems are then drawn from the database.
Contents: Data sources for planning projects • Data quality • Dealing with erroneous or inconsistent data • The role of the customer and the consultant in data analysis • Systems for analyzing and visualizing data • Types of data analysis • Performing data analysis with Python or MS Excel and PowerQuery or MS Access or SQL (depending on the IT landscape in your company and customer preferences) • Example of an automated data analysis with Python • Interpreting the results and deriving the basis for planning.
Target Group: Sales engineers for logistics equipment or systems, sales managers for logistics equipment or systems, logistics consultants, project managers for logistics automation projects in industry and trade, junior data analysts. This seminar is ideal for onboarding new planning engineers and data analysts, as well as for warehouse automation companies looking to build data analytics expertise in-house.
Technical Requirements: Participants should have a working knowledge of Microsoft Excel and be familiar with the essential processes of warehouse operations and elements of warehouse automation. Basic knowledge of PowerQuery, MS Access and/or SQL will be learned during the course.
Language: English or German (or mixed for international groups)
Duration: 1 or 2 days
Method: Presentation and practical exercise (if possible based on data from your own projects)
Location: Locally at your premises, alternatively online via video conference
Would you like to turn this seminar into a workshop based on your own current project? That’s a great idea – let’s talk!
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