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Business Process Re-engineering: What's Old Is New Again

Sean Salleh 01 Oct 2013 Modeling methods

Many business philosophies rise and fall in popularity, and business process reengineering (BPR) is one of them. Publicized and promoted in the early 1990s, its fundamental premise was that too much of the work done in companies was of no real value, and that companies should therefore change how they did things. Since then,the popularity of  business process reengineering has waxed and waned, although it has retained diehard fans (and equally diehard critics). Perhaps because one of its leading lights was a professor of computer science, BPR sometimes also looks like it was made for a modeling software application; for example, using revenue and profitability projection models developed in Analytica.

Schematic diagram of BPR (Business Process Reengineering). Image source:


BPR – A Model in Itself

A quick definition of business process reengineering principles is the following:

  • Use outcomes as the starting point, not activities
  • Identify the processes currently underway and rank them in order of need of reengineering
  • Make information processing a part of the process that produces the information
  • Consider dispersed resources as though they were centralized
  • Link parallel activities in addition to combining their outputs
  • Give the process its own control and decision points
  • Capture information at source and just once

Some of these aspects have direct counterparts in techniques in modeling software. Models built in Analytica, for example, allow users to enter information once to be used in multiple instances, thanks to an object-oriented approach (a difference compared to conventional spreadsheet approaches). Analytica also makes it easy to shape and re-shape sub-models and their overarching top level model.

Don’t Automate, Obliterate

This catchphrase from Michael Hammer’s seminal article on BPR refers to the perceived trap into which companies fall when they try to increase the efficiency of processes that should not exist. BPR calls for ‘creative destruction’ or at least replacement of worthless processes. As corporate activities change accordingly, any modeling software used for example to project financial or marketing results from those activities needs to keep pace. Analytica’s Intelligent Array technology gives users the flexibility and rapidity to do this.

Modeling Software for Business Process Reengineering

In addition to the remarks above, desirable modeling software characteristics for effective BPR modeling include:

  • Graphical user interface that also supports efficient documentation
  • Drag and drop modeling facilities to easily build, modify and extend organizational models and relationships with objects and data
  • Object oriented technology to allow for clear naming, information and manipulation
  • Simulation within the modeling software to show how individual processes combine to give an overall result, the sensitivity of the model to the results of any particular process and the way that degrees of uncertainty may be propagated via the processes.

Needless to say (but we’ll say it anyway), Analytica is a prime example of modeling software meeting all of these criteria.

And the Layoffs and Downsizing?

While the two phenomena are not necessarily by-products of business process reengineering, many businesses have come to make associations between downsizing, layoffs and BPR. Modeling software may give initial results in terms of projected profitability, most efficient staffing levels, and best sites or location choices. However, you’ll need to go further to model any effects (positive or negative) on worker morale, or forecasts about the company’s reputation in the market place. For those interested, Analytica can equally well also be used to do this too.

If you’d like to know how Analytica, the modeling software from Lumina, can help you to model businesses, their processes and their changes, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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