Work Simple
Field Capacity
Let’s imagine you run an average field service organisation that manages 250 calls per day. On average, each call takes 2 hours on site to resolve. Doing a quick calculation, you find that you require 500 hours of field capacity, which, at 8 hours of work per day, means that you need to hire 63 technicians. Right?
Well, not quite. Being an average field service organisation, your first-time fix rate stands at about 80%. Which means that 20% of your work requires a second visit (the industry average is actually 1.5 extra visits but let’s keep it simple for now) which most likely yields no extra revenue.
So even though you receive 250 calls per day, you need to carry out 300 visits in order to cover the previous days’ unresolved first calls. Following the same calculations as above, you thus require 600 hours of field capacity, which is 75 technicians. Right?
Well, again, not quite. You forgot to account for travel time. Let’s take an average of 15 minutes of travel for each visit. These minutes accumulate throughout the day and must be accounted for in the 8 hours of technicians’ availability, which means they are taken away from the time technicians have, in their day, to carry out their visits. Following the same calculation, it means that you require an extra 75 hours of capacity (300 visits x 15 minutes), bringing our total up to 675 hours, which equates to 85 technicians.
So, because of unresolved first visits and travel, you had to hire an additional 22 technicians to cover your workload. Bummer. But that’s it, right? Nothing else?
One last thing, if you may.
Utilization
The above model considers that somehow, your dispatchers are able to stack the field assignments perfectly one after the other, leaving just enough time to travel and no “white” spaces at the end of the day or between visits. It also assumes that if the actual time of a visit is shorter or longer than the average 2 hours, your dispatcher immediately reacts and shuffle things around to make sure those pesky white spaces go away and your field force is always 100% utilised.
The reality is that this doesn’t always play out that way. The average field service organisation achieves a 70% utilisation rate. This means that for each two blocks of time scheduled for work or travel, there is roughly another block that is blank, or not utilised.
This is mainly due to the difficulty for human dispatchers to consider all possible allocation options when dealing with large disparate work forces but may also be due to the difficulty in reacting to continuously changing field conditions.
So, if only 70% of your field capacity is productive, it means you actually need a total of 964 hours (675 ÷ 70%) of field capacity to be able to carry out your 250 new calls per day. This comes out as 121 technicians.
Let’s also not forget the back-office administrators whose work it is to follow-up technicians, type in their forms, clean up their data, create the invoices, etc. The average ratio of technician to back-office users (dispatchers, clerks, data managers, billing, etc.) is 3 to 1.
So, for 121 field technicians, you also need 40 back office users.
Optimisation
The usual solution for field service organisation to increase their utilisation metric is the implementation of a schedule optimizer. Its functionality automates the decision making of work allocation by looking at a much wider set of constraints and conditions and play out thousands of possibilities per second. It can also repeat the decision making in very short cycles to ensure the allocation is always done with consideration of the very latest field situation.
The usual industry analysts’ findings suggest that the implementation of an optimiser can increase utilisation by 15% in most cases and up to 25% in the “best of class” organisations.
But for the purpose of our current exercise, let’s take a very conservative approach and assume that you will increase your utilisation by only 5%.
Redoing the same calculations as above, but with a utilisation rate of 75% instead of 70%, we get 900 hours of total field capacity (instead of 964) with means 113 field technicians (instead of 121). This also means 37 back-office users (instead of 40).
By simply “switching on” the schedule optimiser, you’ve shaved the cost of 11 people from a 161-person operation, or 6.8% of your human cost base. And 11 people, with on-costs, roughly comes out to 1 million dollars.
Mission accomplished. You’ve made, or rather saved, 1 million dollars.
Numbers Game
But what if you’re actually a resource bound organisation (as opposed to a revenue bound one) where you could service more calls if only you were able to get more resources… or more resource capacity.
The calculation is the same, but instead of applying the increased utilisation to reducing your required field capacity, you apply it to increasing the capacity to service calls. In terms of absolute dollar value and employee satisfaction, this will always be the better and more profitable option.
Now, close your eyes and imagine the possibilities on your bottom line if you were to use the commonly accepted utilisation increase of 15%. And then, also apply a reduction of 5% of travel time due to the same schedule optimiser working with geo-routing.
And what if the system you had was also able to help you increase your first-time fix rate by 10-15% by always allocating the right technician with the right parts and skills - not just the closest or the next available one - thus reducing your unproductive second and third visits to only 5-10% of calls.
In field service management, because of the sheer volume, a small tweak in just one metric can yield large cost savings or revenue increases. There are no better numbers games than the high-volume field service industry.
You can check out the benefits calculator here to help get a rough estimate of the annual savings you can get by tweaking some of the typical field service metrics.