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People Placement problems in Life Sciences Sales: Challenges and Opportunities

1 mins read

People Placement constitutes the activities related to assignment of sales reps, following any major sales force territory alignment or change in the size and structure of sales force. Placing the best available rep into each territory is critical to the success of any sales force realignment effort.

But, how to ensure "fair and unbiased" rearrangements, and mitigate business risk associated with the relocation of accounts?

At the PMSA Annual Conference 2016, David Wood, Ph.D. will present a poster in which he will describe the simple approaches in linear programming to solve classic “People Placement” problems to ensure smooth realignment, minimize disruptions and improved sales force productivity. He will compare this approach with the typical business rules used to rank the rep placements in each possible territory, followed by sequential assignment process starting with the highest-ranked territories. 

The presentation will highlight key aspects of linear programming for solving People Placement problems.  A few of them are listed below:

  • Alternative solution approaches based on linear programming
  • Usage of powerful but relatively inexpensive desktop solvers in LP
  • Advantages (and disadvantages) of each of the linear programming solutions approaches
  • Complicated business rules as constraints, and/or as variations on objective function weightings
  • Solutions to avoiding manual search for multiple swaps
  • Speedy solutions for realistic problems (300-400 reps, up to 15,000 possible rep-territory placements)

About David Wood:

David Wood, Ph.D. brings more than 35 years of experience in mathematical modeling of business problems, with over 20 years in sales and marketing analysis for Life Sciences companies.   At Axtria, David serves as a Senior Principal, with emphasis on projects in mathematical modeling of sales operations and multi-channel promotion analysis.

David holds a Ph.D. from the University of California, Berkeley, in Operations Research.

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