Interval disaggregate

Abstract

Business planning as well as analytics on top of large-scale database systems is valuable to decision makers, but planning operations known and implemented so far are very basic. In this paper we propose a new planning operation called interval disaggregate, which goes as follows. Suppose that the planner, typically the management of a company, plans sales revenues of its products in the current year. An interval of the expected revenue for each product in the current year is computed from historical data in the database as the prediction interval of linear regression on the data. A total target revenue for the current year is given by the planner. The goal of the interval disaggregate operation is to find an appropriate disaggregation of the target revenue, considering the intervals. We formulate the problem of interval disaggregation more precisely and give solutions for the problem. Multidimensional geometry plays a crucial role in the problem formulation and the solutions. We implemented interval disaggregation into the planning engine of SAP HANA and did experiments on realworld data. Our experiments show that interval disaggregation gives more appropriate solutions with respect to historical data than the known basic disaggregation called referential disaggregation. We also show that interval disaggregation can be combined with the deseasonalization technique when the dataset shows seasonal fluctuations.