We are talking about the importance of data increasingly day by day and we will continue to talk. Using data correctly offers companies great opportunities and helps them increase their productivity. However, companies can use these opportunities by collecting and cleaning the right data. Sometimes the data becomes meaningless because companies collect large amounts of data but do not know how to use it. Before starting to analyze data, the important thing is to prepare the data. Data is prepared and then analyzed with steps such as deleting unnecessary data, merging or separating fields, and transforming the data.
Especially with industry 4.0, data has become very important for companies. Companies collect large amounts of data to increase their productivity. The form of the data can be consumer data, movement data, internal data, and external data. The collected data provides many benefits such as a deeper understanding of the current market, better decision making, understanding the problems and finding solutions, and identifying successful businesses.
What we have explained so far is valid for warehouses like every sector. In warehouses, there are different types of data. Order data, stock data, machine data, and internal operation data are some of the data collected through barcodes, hardware, or software. In data-driven warehouses, it is aimed to increase the efficiency in the warehouses. Warehouses work like an organism. In repositories, one process can feed and optimize another process. With the help of the collected data, the processes that feed each other can be determined and these processes can be improved, and costs such as storage cost, labor cost, and time cost can be reduced.
As Kim explained, data-based warehouse management has four steps according to the evaluation date. These are real-time (hourly or shorter) data applications, short-term operational opportunities (weekly or daily), mid-term (several months), and long-term (for future years) analysis. Real-time data applications help ensure responsiveness for warehouse order fulfillment, while short-term data applications help maintain the efficient functioning of variable warehouse resources. Mid-term data applications enable to make predictions with data-based modeling over the installed system, and long-term data applications produce an actionable inference based on the disruptions that may occur in the warehouse in the future, with the analysis method based on predictive data.