Labour KPIs – what does good look like?

UK Labour Productivity

According to the Office for National Statistics’ (ONS) most recent Labour Productivity statistical bulletin [1], the weakness of manufacturing productivity has been a ‘defining feature’ of the UK productivity puzzle since 2011. The most recent data suggests that output per hour in manufacturing is 3.4% lower than last year, while unit wage costs grew by 3.6%.

You don’t need a degree in economics to know that rising labour costs and falling output means falling profits (assuming you can’t increase prices), a situation which is just as unsustainable for your business as it is for UK plc. While government and analysts struggle to diagnose what’s wrong with UK manufacturing as a whole, there is better news for those running shop floors who want to boost their labour productivity – with the right manufacturing recording and analysis solution you can pinpoint exactly how to improve the efficiency of your human capital.

Measure what matters

To improve something, you must be able to measure it first. Aberdeen Group’s 2015 survey shows the most common metrics used to measure productivity. It is evident that there is more than one measure of productivity, and that the top measures most commonly used, which include revenue and profit per full time employee (FTE), will only really help identify high-level trends within a business.

According to this survey, it seems that most manufacturers are in the same position as the government when it comes to solving the productivity puzzle. Manufacturers are using figures for output against wages or staff numbers that provide no insight at all into how to actually improve productivity.

The key labour KPI is overall labour effectiveness (OLE), but it is the underlying components of OLE that can really help production managers to understand how to improve labour productivity. There are five key reasons why many shop floors struggle to get to grips with the constituent parts of OLE; labour utilisation, performance and quality. Only by having the capability to automatically collect detailed information, such as how long it takes individual operators to complete specific tasks, can you derive OLE data that is actionable.