How data science will improve efficiency across an organisation

Every company is different, but most businesses have some common areas that they should consider when transitioning to data-centric ways of working. In this blog, our Data Science Director, Finn Wheatley, presents ways in which the Finance function can incorporate data science to improve efficiency and streamline operations.

How data science can help sweat company assets

Chief Financial Officers (CFOs) and Finance Directors are rarely thought of in data science, which tends to concentrate heavily on the operational and customer-facing aspects. However, modern finance leaders are often strong influencers in the boardroom and their buy-in can be critical to the success of data science within a corporation. To achieve this, it is vital for data science to articulate use cases that speak to the priorities of finance executives. One path can be to communicate ways for data science to help improve utilisation of a company’s asset base. Two of the largest assets on any balance sheet are

  • Inventories
  • Property, plant and equipment

Often these can be utilised more effectively using data.

Predicting revenue at specific store locations

The real estate footprint is a major expense for many companies. Although decreasing, it will largely depend on the nature of the company’s operations and strategic decisions about which markets to compete in.  A data driven approach can also play a critical role, particularly for retailers with large numbers of physical stores. By collecting large amounts of open Geographic Information Systems (GIS) data regarding building specifics, footfall/traffic and relative locations of local amenities, it is possible to build a detailed prediction of the exact revenue estimates at individual location.

For example, Pret a Manger uses data to identify that corner stores with lots of light were undervalued by landlords relative to expected footfall[1]. The exact layout of those stores can also be analysed using data, with experiments undertaken to analyse how the differences in layout can affect revenue, at a very granular level (e.g. placement of a certain SKU on one side of a display versus the other).

Optimising the office location

Physical office space is a heavy financial burden for many companies. The pandemic has highlighted the need to locate premises efficiently to maximise value. To give just one example, Whitehat Analytics undertook a project for a large public sector organisation, optimising the location of its physical estate, both at local and national levels, to minimise travel times and maximise convenience for tens of thousands of staff and millions of potential service users. Additionally, companies with large industrial equipment expenses can take a related approach with property, plant and equipment, optimising the location of capital goods according to various factors. These include distance to market, the cost and location of existing supply chains and the risk of disruption, leading to a data-driven approach for maximising risk-adjusted utilisation of machinery.

Maximising the use of physical office space

Within the office itself, optimisation algorithms can be used to efficiently plan and schedule the workforce and maximise the use of office space. This can be done while accounting for factors such as the location preferences for each employee in the company on each day of the week, as well as employees who wish to work from multiple locations. Rather than random hot-desking leaving teams scattered around, it is possible to take a more structured and efficient approach and maximise the utilisation of each square foot.

Efficient inventory management

Inventory is tightly related to demand. The key to better inventory management (especially of finished goods) is better demand forecasting, for which data science has numerous use cases. A retailer, for example, can use granular sales data (e.g. per store, per SKU, per day-level) to improve inventory planning. Deep learning techniques such as recurrent neural networks are very effective for this use case, as they can uncover complex relationships within the data.

A second focus area is reducing the inventory held by optimising the logistics and distribution process. This involves creating software models of a company’s logistics and distribution process, enabling algorithmic optimisation using data, as well as scenario planning to understand potential bottlenecks, disruption risks, and demand shocks and the trade-offs that can be made to manage these.

We have only scratched the surface of the multitude of ways to improve corporate financial performance using data science. Get in touch to learn how we can help you to build a data-driven business.

About the author

Finn Wheatley, Director of Data Science

Finn has over a decade of experience working in lead data science and quantitative roles in both the public and private sectors. Following his undergraduate degree from King’s College London, Finn worked for several years in the hedge fund industry in risk management and portfolio management roles. Subsequent to an MSc in Computer Science from University College London, he joined the civil service and helped to establish the data science team at the Department for Work and Pensions (DWP), delivering innovative analytical projects for senior departmental leaders. Since joining Whitehat Analytics, he has been involved in establishing the data science team at EDF Energy.