Monday, 11 July 2016

Completed case study: Simplifying selection and improving allocation for Charityworks


Charityworks runs a one-year graduate development programme in the third sector.

The Problem

Charityworks wanted to learn how they could streamline their selection and matching processes to make them more efficient and effective as they scaled the programme up.

The Approach
Two Pro Bono O.R. volunteers analysed the contribution of selection components by analysing how the previous years’ results would have changed had they been omitted singly or in combination.

They used data on a selection of best- and worst-performing candidates to assess the predictive power of the different assessment activities

The volunteers recognised the matching candidates to posts as an example of the Assignment Problem and after reviewing the literature decided to approach it using the Hungarian Methgod.

They developed an Excel-based prototype using VBA to produce initial allocations.

The Solution
  • The volunteers analysed the components of the selection process to show which ones were contributing to whether candidates passed the selection 
  • They also showed which ones were better predictors of performance in post 
  • They developed a prototype spreadsheet-based model to allocate candidates to posts

The Benefits

  • Charityworks decided to dispense with their second stage assessment centre
  • Charityworks were able to make more data-driven decisions about the remaining assessment activities 
  • The volunteers learnt about a classic O.R. problem in a different setting and did some fairly intensive coding
  • Charityworks are planning to take on a MSc student to carry the work on, so spreading awareness of O.R.

Charityworks commented: 'Dedicated volunteers committed to the project added great value for data/insight-driven decisions'



To view the case study slide and other completed projects with Pro Bono O.R. please visit: www.theorsociety.com/probono