On-Line Analytical Processing (OLAP) is a technology/tool that allows one to go beyond the fixed, two dimensional view found in spreadsheets or printed reports. While using OLAP, one has the ability to interactively explore multi-dimensional data.

For example, a newly appointed supply chain manager wants to understand production variability in a business where a product can be produced on more than one line at more than one plant. The standard view of production data can be found in a weekly total by product, but he would like to drill into the data further to see production by product and by plant.

The data shows that production variability at one plant is noticeably greater, so the next step is to drill further down into the data to see production by line at that plant. He notices significant variability with one product on a single line. To see if related products share this variability, he then looks at production by product family for each line.

Without utilizing an OLAP tool, the supply chain manager would have to ask someone to create a new report each time he wanted to see more detail or to expand the number of standard reports to include every level of detail that might be of interest. In the first case, there is a delay between when they need the report and when they get it. In the second, the volume of reports is overwhelming.

While utilizing an OLAP tool, the supply chain manager can interactively drill into the data to generate desired views as needed. They can start at a detailed level and generate views that are more and more aggregated. Or, they may go back and forth, as in our example.

Three capabilities are required to make this kind of analysis possible:

  • The tool needs to be able to access data and perform calculations rapidly.
  • User interfaces must be flexible and intuitive.
  • The tool must provide multi-user support.

An OLAP tool pulls data from a relational database, prepares the data to speed up aggregation, and creates a data structure called a cube or hypercube in computer memory. It should be noted that data preparation may include some partial aggregation. The user then accesses the cube through an interactive interface to work with the data.

Overall, OLAP capabilities provide endless options to support visibility in your supply chain and enhance the collaborative planning processes. However, every tool has shortcomings that go along with its benefits. Large OLAP applications see a trade-off between the time required to create the cube when the OLAP application is initially loaded and the response time for working with the cube. These applications may need tuning to optimize performance. In addition, since creating the cube may involve partial aggregation of the underlying data, many OLAP tools are view only; they don’t allow editing of data from the cube because they are unable to disaggregate it into its underlying form.

Be sure to visit our blog within the coming weeks as we discuss OLAP, its future in the industry and its competitors (in-memory computing, data mining, etc).

Like this blog? Follow us on LinkedIn or Twitter and we will send you notifications on all future blogs.