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5th May 2020
Productivity is a challenge for the UK economy. Since 2008, the Office of National Statistics has reported that UK output per hour has flatlined after more than 10 years of solid growth.
Retail has additional challenges due to the structural shift as online spend has grown. So there has never been a better time to get to grips with your productivity opportunities.
Collecting and analyzing data to create actionable productivity insights is essential. Here are my top data analytics tips to power your productivity:
We all know that having the right KPI is essential to understanding your business and to generate the right focus and behaviors from your leadership teams.
Many retailers use a sales intensity measure, such as pounds per square meter, as their top line productivity measure. It is a great output measure and important as part of the set, yet it doesn’t help you to know if your inputs are right. For example, are you spending more salary than needed to achieve the sales? And is your operation efficient?
However, just moving to a salary to sales percentage is a blunt tool, unless all your stores are identical.
So what’s the right productivity measure for you? Look at your operation and find the suite of measures that link to your operating model and how it supports delivery of your strategy. If you are differentiated on the service you provide, find measures that pin down how well you are delivering great service, for example with customer experience measures. If you are a low-cost operator, look at labor invested by volume of items sold or the case rate of colleagues handling stock.
And always look at them in the context of relevant quality measures such as stock availability and shrink.
It’s long been accepted that a dashboard with a suite of relevant measures keeps the best handle on your operational productivity day to day. What do you do if you want to make a step change in your productivity? Work study can help.
What is commonly known as time and motion study can help you deep dive into the processes within your operation. By timing multiple examples of a task being completed, the data can be analyzed to identify a detailed process breakdown. This matters because you create an evidence base that shows the parts of your process to focus improvement efforts on, or even better eliminate altogether. Data analytics also shows up wasted time in the process, for example where a colleague has to wait for a system to catch up with them or where walk times within tasks are higher than needed.
This activity study creates a timed baseline for your processes. You can make operational process changes and then measure the differences. This is important as it can be easy to make changes that don’t deliver the process reduction that you expected; for example, we’ve seen automation be introduced that made no time difference at all. Our analytics showed that the team had not changed their ways of working: a coffee shop automated part of their process and instead of the barista using the freed-up time to get a cup or prepare hot milk, they stood and watched the machine at work. Without the detailed measurement, I doubt the business would have known that they weren’t getting any benefit from their investment in technology.
A useful technique called efficiency study looks at the whole operation, capturing data on what colleagues and customers are doing. This data analytics creates a detailed picture of how much time is spent with customers rather than on processes, and how well the available colleague resource matches the customer flow and demand.
Insights from this study help retailers remove wasted time from their operation and release time from tasks, to increase time with customers delivering the retail differential. One client with more than 400 UK stores has used their analysis to reduce time on essential tasks and eliminate significant wasted time in their business, to increase the proportion of time they spend with customers by almost 40% over a period of seven years. They are growing revenues and productivity measures.
Efficiency study helps retailers spot where they are not keeping up with customer demand and identifies colleague time that can be shifted to drive sales and increase productivity.
A trend within retail has been to simplify in-store leadership structures and remove layers of expensive management to create a flexible leadership team and reduce costs. A workstudy technique called day in the life role study produces a data set that can be analyzed to highlight how leaders spend their time and show whether there is a clear differentiation between the leadership roles. We’ve seen data that shows supervisors spending over 80% of their time in general assistant role and middle manager layers where there is significant overlap between the roles above and below them; both are situations you’d want to change to drive productivity. The data analytics give you a robust basis to identify opportunities to right-size leadership teams and create the role clarity that is needed for effective leadership.
Data analysis really comes into its own when you compare your own productivity measures to others. It helps retailers gain a powerful understanding of how their productivity measures stack up in their own and comparable sectors. There is nothing like seeing data analytics that shows your business is way off the pace to focus senior leaders on powering productivity. Or, knowing that you are currently best in class and others are very close behind you.
Data analytics can either create insights that power your business or leave you drowning in a mass of inconsequential numbers. Businesses that are thriving in today’s turbulent retail world choose the right internal productivity measures, use workstudy to give a wider perspective and look at comparative benchmarks to give that all-important context.
This article was published in Retail touchpoints