To measure productivity you simply divide outputs by inputs over a set period of time. As a formula that’s productivity = output ÷ input. It’s a simple equation – yet measuring productivity in the real world is never so straightforward; nor is making any meaningful improvement.
In this article we’ll first look at practical ways to measure productivity in the workplace, and then at some of the practical metrics and KPIs that help business owners and managers lift productivity.
To keep things simple we’ll be looking mostly at manufacturing productivity. Most of what we’ll touch on, though, will be equally relevant to wholesalers and retailers, who each have outputs and inputs that can be optimised. And we’ll also look at how to measure knowledge worker productivity (and improve it), as part of measuring multifactor productivity.
In this article:
- How to measure productivity
- How to measure labour productivity
- What does measuring labour productivity tell you?
- What does measuring labour productivity NOT tell you?
- How do you improve labour productivity?
- How to measure multifactor productivity
- An example of multifactor productivity in practice
- How to improve multifactor productivity
- Measuring knowledge worker productivity
- 6 practical productivity KPIs
To improve labour productivity you need to first measure it.
How to measure productivity
There are two main approaches to measuring productivity: measuring labour productivity, and measuring multifactor productivity. We’ll look at the difference later in the article – and at the pros and cons of both – but first let’s take a moment to look at the core components of productivity, ‘inputs’ and ‘outputs’.
Let’s say a toy making company decides they want to lift their productivity. Their first task is to work out a standard way to measure this in their business that’s relevant to what they do – and that gives them a meaningful metric by which they can chart their progress.
Deciding on an output
The management team decides that the output measure will simply be ‘individual toys produced’. That’s a relatively blunt measure – after all they sell lots of different kinds of toy, some of which take more time and money to create. But they figure that over a long enough time-frame – say three months – this variability will cease to matter, compared to the convenience of having such a simple unit to measure.
Deciding on an input
Our toy maker decides that they want to keep things similarly simple at the input end, and choose ‘number of direct labour hours’ as their measure of input.
By ‘direct labour’ they mean ‘staff directly involved in running the toy-making production-line’. This will include the assembly line workers, the machinists, the parts handlers who keep the assembly lines stocked, plus the shift supervisor – but, for example, not the cleaners, nor the toy design team that comes up with each season’s new range – nor the management team themselves.
They decide on a quarterly measurement period.
How to measure labour productivity
Our toy makers, therefore, will be dividing the number of toys produced by the number of direct hours of labour, to reach a number. What that number is isn’t relevant – what’s relevant is how much it changes quarter by quarter.
Here’s how Q1 pans out:
The toy factory has 25 ‘direct’ staff on per shift, working a conventional 8 hours, for 5 days a week.
In Q1 therefore their inputs are:
- 25 staff x 8 hours = 200 labour hours per shift
- 5 shifts per week = 1000 labour hours week
- 12 weeks per quarter = 12,000 labour hours per quarter
Inputs = 12,000
In Q1 they produce 20,000 toys. Outputs therefore = 20,000
- So their Q1 productivity is 20,000/12,000 = 1.6
- Then in Q2 they produce 22,000 toys: their productivity is 22,000/12,000 = 1.8
- And in Q3 they’re struck by a bout of illnesses. They run the same number of shifts, and by pulling through on reduced staff numbers produce 20,000 toys – but with only 11,000 labour hours. Their productivity in Q3 is therefore 20,000/11,000 = 1.8
As you can see, basic labour productivity can be altered by changing either side of the equation – inputs or outputs. Producing more with the same labour inputs is an increase in productivity. And producing the same with reduced labour is also an increase in productivity.
Measuring labour productivity alone won’t give you the whole picture.
What does measuring labour productivity tell you?
Our toy makers now have a valuable metric for understanding productivity in their factory. By choosing to focus on labour inputs alone they avoid getting bogged down in the details of measuring all sorts of other, less tangible, factors. From here they can make practical steps towards addressing productivity
What happened in Q2, for example? What motivated staff to produce more? Can it be replicated? And why was output down in Q1?
As for Q3 – is that ‘let pull through this together’ attitude something that could be expanded on, or would it just exhaust their staff and lead to higher turnover?
This is where a good management team will think beyond the obvious to build a manufacturing business that’s more productive – and therefore more valuable and competitive.
What does measuring labour productivity NOT tell you?
Equally relevant – and equally important to think about before leaping into a new course of action – is what measuring labour productivity doesn’t tell you.
By focusing on the simplest element of the productivity equation – labour – managers can miss many other harder-to-measure, yet highly relevant inputs that affect productivity. These can include:
- The effectiveness of management
- The suitability of machinery used
- The suitability of the working space
- How well capital in the company is being used
- And much more
We’ll touch on these below when we look at measuring multifactor productivity. But first…
How do you improve labour productivity?
Sticking with the simple toy factory example above – and considering only the labour productivity elements being measured, we can see several ways to improve productivity; some obvious, others less so.
Consider these scenarios, for example.
Looking at Q2’s results, management decides that staff are fully capable of working at a faster rate. The conveyor belt in the production line is marginally sped up – and a team member hosts a pre-shift stretch-and-exercise routine, followed by coffee. Staff begin working at the faster rate as part of the new normal, with no complaints, thereby lifting labour productivity.
Decreasing staff levels
Looking at Q3’s results, management decides they can get by with only one parts handler per shift, rather than two. They go through a redundancy process and one of the handlers opts to go part-time to pursue university study. Labour inputs drop with no reduction in output, lifting labour productivity.
Looking at Q1’s results, management realise the lower output was due to decreased demand for toys. Yet with staff on full-time fixed-hour contracts, labour hours had remained the same. They decide to move staff on to rostered hours, so that in future input hours can be matched to output, with no reduction in productivity.
Taking a blunt approach to lifting labour productivity can have unintended consequences.
A caveat about measuring labour productivity
Experienced managers are well aware of most of the basic approaches above. Naturally they need to be in the back of every manager or owner’s mind, as resets are sometimes required. However relying on rudimentary ‘work faster with fewer staff’ approaches to productivity improvements can have unintended consequences – and risks overlooking some of the most potent ways to increase productivity.
Which is where multifactor productivity comes in.
How to measure multifactor productivity
Multifactor productivity (MFP) looks beyond labour to include other inputs, such as capital and materials. All of these are added together, with the basic MFP formula looking like this:
Multifactor Productivity = units of output ÷ (units of labour + units of capital + units of materials)
In order to compare apples with apples, labour, capital and materials are often all reduced to their dollar value. It’s possible to not simplify things in this way – indeed a whole industry of productivity consultants makes a living from creating complex weighted models for measuring the relative productivity value of all sorts of hard-to-quantify inputs. However this approach almost always sacrifices practicality in the name of accuracy.
For the purposes of practicality, let’s go back to our hypothetical toy maker and see how they could measure productivity with the MFP approach using dollar values – and make potentially radical improvements.
An example of multifactor productivity in practice
Our happy toy-making company has a successful first experiment with measuring productivity and decides to move to a more sophisticated MFP approach.
While their outputs are still measured in toys (they make another 22,000 in Q4), their inputs now consist of:
- 12,000 direct labour hours per quarter, @ $20 per hour = $240,000
- 5 managers working 2400 hours per quarter @ $40 per hour = $96,000
- Plant, insurance, other labour and machinery costs per quarter = $30,000
They then benchmark their new productivity formula in Q4 at 22,000/366,000 = 0.06
This allows them to use all of the above approaches – and more – to interrogate their performance over time.
Investing in plant equipment can be an effective route to higher productivity
How to improve multifactor productivity
With our toy makers now considering far more factors in their productivity, they can now make more sophisticated and effective changes to improve their productivity. Consider the following scenarios.
Are they middle-management heavy? Or are they spending too little on employees that can make a disproportionate impact on performance? Should they splash out on that expensive productivity consultant (no – they can just read this article instead), or should they hire an HR team so that future hires are more effective – and valuable current staff are kept content? (probably).
Looking at their data, management realise the value of smart design. They invest in their innovation team and tools and start designing toys that not only sell well, but are faster to assemble.
Making more sales doesn’t raise productivity in and of itself. But selling more effectively can. Management decides that their on-the-road sales teams will work better with a mobile sales app, and sees sales improve per agent. That’s a better use of their labour capital expenditure (i.e. their wage spend on salespeople has a better return). But more importantly, the improved sales pipeline means the factory is sitting idle less often. Factory overhead relative to output improves, meaning better productivity.
Similarly, investing in technology can have dramatic effects on productivity. For example our toy makers could:
- Deploy inventory management software: to reduce stock loss and wastage, reduce admin time, and prevent production stoppages thanks to automated low stock alerts.
- Invest in robotics: with output lifting, the warehouse manager needs more help. Instead of employing three new staff however, he buys a stock-picking robot and employs one new staff member – a robotics technician. It’s a more productive long-term use of capital, because fixed wages are down relative to output.
- Invest in RFID sensors and GPS: by tracking their delivery fleet, plus movement around their factory and warehouses, the firm can design more efficient layouts for their assembly lines – and reduce their fuel costs too.
Capital expenditure into targeted training can have a disproportionate improvement on productivity. For one thing, staff are more engaged if they feel they are being invested in – and higher staff engagement almost always equates to improved productivity. And what the staff learn can also be applied back to the workplace.
Our toymakers, for example, decide to send their assembly line workers on an agile methodology training course. Not only do the staff now have a valuable external qualification, but they decide of their own volition to trial a sprint-based approach to production. Management backs the trial and it’s a success – with production runs completed in record time by the team.
Measuring knowledge worker productivity
The challenge of measuring knowledge worker productivity is not easily solved. Naturally, a multifactor approach is needed, as most knowledge workers can’t be considered ‘direct labour’, in that their day-to-day work doesn’t result in product units created. However simply measuring their input by wage spend can result in a blunt assessment of their effect on productivity.
Consider the example of an HR manager who is hired into a growing manufacturing business where previously there was none. Their impact could be transformational – yet not well reflected by their salary, if broken down into hourly pay.
To measure the impact on productivity of such knowledge workers in any practical way, you need to set KPIs for their roles that are in turn tied clearly back to productive work.
Below are some examples of practical KPIs that businesses can use to track the productivity of their workplaces.
Set KPIs for your knowledge workers that are tied to productivity gains.
6 practical productivity KPIs
The ratio of on-time to late assemblies is a useful metric for supervisors, department managers, production managers and the like. Improving this metric is highly likely to improve overall productivity – so it’s an appropriate KPI for these roles.
Value of Wastage
Minimising the value of waste created in a production process invariably lifts productivity, as goods out relative to capital in improves. Food manufacturers in particular need to keep a tight eye on this and related metrics.
Average Days Until Goods Sold
This KPI is particularly relevant to sales and marketing managers tasked with moving goods on before they become obsolete. Our toy maker, for instance, would do well to set an average days until goods sold target – of, say, 90 days – for their head of marketing: toy sales are notoriously trend-based, so shifting valuable stock before it becomes dated is critical to their productive use of capital.
The proportion of overtime hours paid relative to regular hours can be a useful indicator of inefficient use of labour capital. Having the flexibility in your workforce to deal with sudden surges in production is important. But leaning on overtime work too often is a sign of either poor planning, or insufficient staffing. Address either (or both) for productivity improvements.
Staff turnover & Net Promoter Scores
A low staff turnover reflects an engaged workforce: which itself is strongly linked to high productivity. It’s also expensive to hire and then train new staff, so the less frequently you have to do this, the more productive your use of capital. Staff turner figures are therefore effective KPIs for HR employees – as are employee Net Promoter Scores (eNPS), which are generated by regular anonymous surveys within the company.
Similarly the performance of support or customer service staff within your business can be measured via external NPS.
Sales and Marketing KPIs
The regular sales targets that you set for your sales team are themselves a good indicator of productivity: by dividing targets achieved (outputs) by staff hours (inputs) you are in essence measuring productivity at a department level. This in itself is desirable, but as mentioned above it can also have flow-on effects to your overall productivity by ensuring factory production is kept high, and downtime low, thanks to a steady throughput of work.
Meanwhile in the digital world, where activity can be measured to the click, marketing departments can be set similar productivity KPIs. At the macro level ‘leads generated’ is an appropriate KPI for marketers tasked with supporting sales teams, as is ‘sales generated’ when digital sales can be made.
Meanwhile the list of more nuanced KPIs by which marketing teams can be measured is endless: Email open rates, website conversion rates, and even the number of target keywords appearing on the front page of Google (“best toys for kids” for example) are all now easily measurable via marketing technology tools.
Article by Greg Roughan in collaboration with our team of inventory management and business specialists. Greg has been writing, publishing and working with content for more than 20 years. His writing motto is ‘don’t be boring’. His outdoors motto is ”I wish I hadn’t brought my headtorch’, said nobody, ever’. He lives in Auckland, New Zealand, with his family.