Benchmarking individual shift performance against the rest of the shifts that day, or on that project or even across the entire sector is hard but not impossible.
Not only can having access to this data address poor performance or issues affecting progress, but it also has the potential to add huge value in allowing teams to drive efficiencies and highlight and cultivate best practice.
Ranking and scoring shifts
Here is how we did it:
In June 2022, we collected 2987 rail possession Shift Records collected and scored each one on two key attributes:
- Shift Productivity Score - the adherence to planned outputs
- Access Score - the adherence to the planned possession working time.
The scores are capped at 150.
We then plotted every shift on a scatter chart and added in a constant line to both the X and Y Axis.
A score of 100 on both axis would mean that you were granted the envisaged working time and then delivered the planned output within that time.
Categorising the Shifts
Adding in the constant lines allowed for categorisation of each shift.
As a QS with a contractor background this is how I interpreted the data:
Quadrant 1 (Top Left) - Lost Opportunity
- These shifts have a Low Access Score but also demonstrate a High Productivity Score.
- Traditionally difficult to understand, these Shift Records have completed the works planned and could have potentially achieved even more if access had been as envisaged.
Quadrant 2 (Top Right) - Efficient Delivery
- These shifts are categorised by both a High Access Score and High Shift Productivity Score. Both values are greater than or equal to the envisaged time/output.
- Further analysis of Shift Records in this area are likely to highlight best practices that could be encouraged elsewhere.
Quadrant 3 (Bottom Left) - Improvement Opportunity
- These shifts have both a Low Access Score and a Low Shift Productivity Score.Both values are lesser than the envisaged time/output.
- This would normally identify the correlation between the access available and the outputs that are achievable on site. Data here could be used to support collaborative problem solving and fully understand existing access related issues affecting progress.
Quadrant 4 (Bottom Right) - Potential production Issues
- Shifts in this category all have a High Access Score which means the access provided was greater than or equal to the plan.
- However, production (Shift Productivity Score) did not achieve the envisaged output.
- The next steps here would be to look at the reasons for variance categories and commentary in the site diary so each issue could be addressed.
We firmly believe that plotting and categorising shifts in this way has huge potential to highlight best practise, reward efficient delivery teams and communicate the impact of external issues to stakeholders whilst giving project teams real time insights to collaboratively problem solve.
Asset owners and suppliers using Raildiary are capturing this data on every shift and will have access to a visualisation of performance over thousands of shifts.
We’d love to hear your feedback on how this would support commercial assurance on your projects and deliver structured commercial intelligence. How are you gaining these insights and how would you use them if you’re not?