Combining modelling and DSS for effective business analytics

PKeenan_post

By Peter Keenan, Associate Professor at Centre for Business Analytics, University College Dublin.

Modelling has always taken advantage of the power of computing, and the enormous increase in computing power has allowed the use of ever more sophisticated modelling techniques. However, despite this growing computer power, modelling approaches have often only been capable of solving simplified versions of problems. While this simplification makes the problems computationally tractable, these simplifications often make theoretically efficient solutions of little value in the real world. To provide effective solutions for Business Analytics, we are interested not only in the theoretical power of the solution technique, but also in its actual fit with the real business problem. Faster computers will simply lead to an inappropriate answer in less time, if the problem modelled is not a useful representation of the one we actually want to solve.

What effective business analytics requires is the application of realistic modelling to appropriate data and the presentation of the output in a form useful to business decision-makers. This wish to provide a system under the control of decision-makers was the original inspiration for Decision Support Systems (DSS), which combined models, databases and user friendly interfaces to support decisions. The idea of DSS was to allow users, business decision-makers who understood the decision, interact with and control the modelling process and understand the solution presented. The greatest benefit of DSS is obtained when the scope of the problem is sufficiently complex for the decision-maker to have problems fully understanding it, and so where modelling can assist with problem solving. DSS can support such problems where the problem and solution can also be effectively visualised, allowing the user effectively intervene in the solution. Such user intervention is particularly valuable where the modelling approach does not include all of the practical constraints of interest to the decision-maker. Transportation applications were an early area of DSS application and problems like vehicle routing are both diverse and complex. However, the user can visualise the problem on a map interface and intervene in the solution, for instance by moving a customer from one route to another. There is a very wide range of decisions and so a very diverse range of DSS applications in all business domains.

DSS first emerged in the 1970s, when databases and interactive interfaces first became available. As technology has evolved, DSS has taken advantage of new developments to improve decision support. These developments have included high resolution graphics, mobile devices, and virtual reality interfaces. New technology means that better decision support can be provided. For instance, in the vehicle routing field we now have comprehensive spatial databases of road networks, geographic information system (GIS) software and global positioning system (GPS) devices to record the position of vehicles. These can be integrated in a modern DSS to enhance the value of long established modelling approaches so that solution quality is improved by more realistic modelling. If analytics modelling is to be an applied science, rather than a purely theoretical exercise, then models must be embedded in systems that actually facilitate real decisions; this is the field of DSS.

Anuncios