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Why data and analytics are the keys to enhancing business sustainability
When large organisations grow, driving efficiencies becomes a vital part of ensuring that growth is sustainable. At The Smart Cube, we use analytics to help our clients identify areas for improvement, uncover new opportunities and make measurable savings.
When most people hear the word sustainability they probably think about environmental issues, and the continuing balancing act between population growth and resource preservation.
For our FTSE and Fortune 100 clients, each of whom has ambitious growth plans, the word has a similar connotation. In big business, sustainability means scalability – the ability to grow the business while still maintaining continuity across all areas; from the source of their raw materials to the quality of their products.
The only way for businesses to achieve this in the long term, is to continually evaluate efficiency and make improvements where possible. This can mean anything from streamlining day-to-day operations and supply chain activities, to finding more practical ways to use existing resources.
For small to medium sized businesses, it can be easier to spot room for improvement in these areas. But for large, established organisations – with a wealth of historic data sources, legacy data platforms and ubiquitous processes, and therefore more complexity – drilling down into the fine details is necessary to see where efficiency gains can be made. And that’s where analytics comes in.
As an example, The Smart Cube recently helped one of the UK’s largest national retailers to improve the efficiency of its supply chain and processing for its milk operations, resulting in significant savings, and progress towards achieving internal sustainability targets.
The retailer wanted to explore how it could work with dairy farmers in the UK to improve processes and increase the longevity of products on store shelves.
The analysis had two primary objectives. The first was to understand the scope for improving the process by understanding how various relevant metrics at each stage in the process impact the lifespan. The second was to enumerate with statistical confidence if the end product is being used optimally in all scenarios.
An exploration of efficiency using data analytics
The retailer in question procures milk from more than 250 farms across the UK, before sending it to processing units for treatment and pasteurisation.
The milk needs to be collected, transported at a particular temperature, stored and processed within a particular timeframe, and moved across silos – which in terms of analytics throws a whole hatful of variables into the equation.
Adding further complexity is the fact that the milk from each farm varies in age, bacteria levels, and a few other key factors that can affect its lifespan and quality.
It’s easy to see why you might need a data scientist to identify areas for improvement. Luckily, we know a few.
Working with the retailer, we were able to identify all the variables at each stage of the process that impact the quality and quantity of the final output. From here we were able to do the driver analysis to identify what an optimum process would look like.
Unlike the status quo, this wasn’t a ‘one size fits all’ solution. We recognised that to be optimally efficient, we’d have to incorporate factors like initial milk quality, time of year, length of supply chain and a few other factors outside of the retailer’s control.
Ultimately, we helped our client identify suppliers that were introducing inefficiencies to the supply chain, and therefore costing the most to use. Leveraging our insights, the client worked with these suppliers to improve their processes and embed more efficient ways of working.
Thanks to a vast supply of data and the robust methodology we applied, we were also able to show that the client’s milk has a longer lifespan than was initially thought. This enabled them to extend the use-by dates on the products, limiting wastage, improving efficiency and, ultimately, increasing profits.
The key to sustainability
This is just one example of how advanced analytics can be used to improve business sustainability.
It’s a trend that we see picking up steam across many of our clients, with companies collecting and analysing data on a wide range of sustainability factors – energy and resources, greenhouse gas emissions, supply chain performance – to generate the insights required to make vital efficiency gains. And what’s more, these techniques often uncover new and untapped opportunities for our clients along the way.
We believe that sustainability analytics is now providing the critical link between sustainability and business strategy. Businesses seek efficiencies but they also want to meet their sustainability goals in terms of the environment – the two aims are intrinsically linked, and with greater efficiency, companies should have more resources to ensure they meet their environmental targets.
At The Smart Cube we combine advanced analytics, data science and technology to solve our customers’ most pressing problems: from bespoke solutions such as merchandising analytics and revenue growth identification, to comprehensive Analytics Centre of Excellence support.
Ravi is a data analytics and data science professional with close to 9 years of experience in pure play analytics. He has worked extensively on a variety of client issues related to strategy, efficiency gains in operations, product propositions, promotions and customer analytics, and CRM using data analytics. Further, he is also adept at consulting and working closely with business users and stakeholders, collaborating with development teams across various formats, such as agile and prince2.