Discovering new cause-effect relationships

Companies with the best processes have a clear competitive advantage. This applies not only to production and manufacturing but also to materials supply, intralogistics, transport logistics and warehousing. Progressive digitalization and automation can increase this advantage even more. By using new technology together with artificial intelligence and data analytics, it is possible to capture, analyze and optimize the flow of material and information in increasingly intelligent ways.

This enables companies to adapt and optimize their processes faster and more flexibly – either by completely reengineering them to make them more effective, or by enhancing existing processes to make them more efficient. The quicker, more automated and more data-based the modification to a process is, the leaner, more flexible, more trouble-free and better that process and thus the company’s performance will be compared to market rivals.

Does it always make sense to digitalize merely because it is possible to do so? What is the right degree of process digitalization? And does improving an individual process step really have the desired effect within the overall system? Alternatively, could a recently introduced, technology-based warehousing solution in fact lead to frustration among employees, with the result that work performance in the warehouse actually declines?

So what is genuinely worthwhile here? This question can be answered only on the basis of concrete requirements and a before/after comparison based on suitable indicators.

Studying cause and effect: How process analytics can improve company processes  

To devise a suitable concept for a data-driven process and thereby improve performance, it is first necessary to identify the precise requirements for that process – whether this be an existing one or a process that is to be newly designed.

And to improve performance, it is necessary to identify the right indicators, to know their impact on the process, and to have, if possible, highly automated processes to capture, analyze and visualize this information. Our research seeks to discover new cause-effect relationships in production and logistics processes. This will provide companies with increasingly rapid access to data of greater and greater validity, thereby enabling them to make the right decisions for their business, whether in the warehouse, on the production line or along the transport chain.

Our use of process analytics adds new, data-driven tools to the conventional methods of process analysis and combines production and logistics data in an intelligent way. This ensures that data is automatically made available for decision-making in line with process requirements and in real time.

Combining sound methodology with industry knowledge is the key

In the field of process analysis and design, however, methodological expertise is not the only factor. A profound understanding of applications in the domain itself is also vital. This is what tells us which indicators – and therefore which data – are relevant for which specific process, and how to evaluate and convert them into a requirements catalogue. For over 20 years now, the Center for Applied Research SCS has built up a store of benchmarking data and industry expertise in warehouse, production and transport logistics, with a focus on the following areas:

• Production supplies
• Container management
• Warehouse processes (from receiving to outgoing goods)
• Intralogistic transport via industrial trucks
• Intercompany transport via truck