How can we use the available wms data intelligence optimally within the company? That is the challenge faced by wms data intelligence. Using a Power BI (Business Intelligence) tool allows us to deploy data in all sorts of ways. A wms makes a lot of data available in its data warehouse; all transactions in the warehouse are stored. The wms makes dumps of the data that are loaded into a sequel database. The data is optimized for a Power BI tool in so-called “data marts.” For example, one “mart” could be “picking,” which is offered on all kinds of cross-sections, for example by order, date, order type and dock. Now, based on the selection “picking,” various complex questions can be asked that are presented in a well-organized report.
Wms data intelligence
Intelligence means having an overview and that is why a good business intelligence tool is indispensable in handling the vast quantities of data in the data warehouse. With a few clicks, we can use the Power BI tool to locate where, when and how products are stored and under what conditions. Products that do not meet the producer’s quality requirements, for example, can be safely recalled very easily.
Achieving higher efficiency in the warehouse means that we want predict the future in some sense. This is only possible through careful analysis of the available historical data. To make savings on, for example, electricity use, considered hypotheses are necessary that survey the data of the current processes over long periods. Especially because of increasing warehouse automation, the possibilities of working more efficiently through data analysis and related algorithm development are becoming more prominent. In analyzing that data, people are naturally at a disadvantage compared to the computer and the computer – besides being a tool – will increasingly be in charge of that process. A striking example of man being defeated by a computer is world champion Go player Lee Sedol’s loss when pitted against AlphaGo, a Go computer developed by scientists. Many experts did not expect that this would already be possible. The development of a “thinking” warehouse may sound futuristic, but it may not turn out to be impossible in the future.
Wms data intelligence from the cloud also offers excellent possibilities for connection between warehouses and gives people on the work floor detailed insight into the processes of which they form part. For example, if a logistics service provider has several customers, data can be requested per customer within a Power Bi tool, even if the particular customer’s goods are stored in several warehouses. This improves the connection between locations and makes insight into those locations accessible from a single business intelligence tool.