Perfecting data-driven procurement transformations

posted by Trish Shalloe
8 November 2021

Pandemic-related business disruption has left procurement functions facing a number of challenges on the road to recovery.

The need to control costs and cash, manage supply shortages, develop a more resilient supply chain and meet demand for increased sustainability are all key priorities.

Many procurement functions are being asked to do all of this and more, with fewer people.

After a period of stagnation supply shortages, noticeably in areas such as semiconductors, are making it tougher for businesses to ramp up their production to pre-pandemic levels. This is increasingly requiring them to adopt a tactical approach to their procurement activities.

Tactics such as trying to stockpile inventory will not be a viable solution for most organisations, not least because many are cash constrained. Instead, a well-considered data-driven approach needs to be adopted.

Weaknesses in the supply chain, including the ongoing viability of suppliers, surfaced during the pandemic but the longer-term implications of this are really starting to become evident now. Businesses need early visibility of supply chain risks to enable them to limit business disruption. 

Additionally, there is increased pressure from shareholders and customers to demonstrate how they are improving the sustainability of their operations. So, how can businesses harness the power of data analytics in order to drive effective transformation?

Access to reliable data, across areas such as spend, customer forecasts, inventory and performance, is key to overcoming these challenges.

However, how this data is analysed and used will be critical. Supplementing it with external data such as suppliers’ financial performance and commodity price projections will enable real procurement transformation.

Many businesses have an absence of reliable data as it is ‘dirty’ and dispersed in silos across the organisation or, where data is available, they may lack the skills, capabilities and time needed to draw meaningful insights from it. This is the first challenge that must be overcome.

Data must first be prepared by capturing it in one location and cleansing it. It’s important that businesses develop a solution where the ongoing data cleanse is dynamic and automated, as it can be extremely time consuming to maintain data integrity.    

Once the data is cleansed, meaningful, clearly actionable insights must be drawn from it. This is only effectively achieved by ensuring that the appropriate functional and market experts know the right questions to ask of it. Understanding the individual business priorities will ensure that the insights are impactful and will enable them to be prioritised.  

It’s important to remember that the data provides one part of the story; real value is derived when combined with input from the teams that understand the business narrative around it.

This collective effort enables a prioritised plan of business improvement initiatives such as cost reduction, inventory optimisation, cash improvement and operating efficiencies to be created, which is bought into by all.   

Maintaining momentum to ensure that these initiatives are delivered to plan and are achieving the expected outcomes requires robust governance and a structured change management approach.

Finally, in order to optimise data-driven procurement transformations, it’s important to ensure that any changes made are sustainable. This can be achieved by embedding new technology or introducing specific coaching and upskilling activities. It’s vital to ensure that teams are able to continue driving positive outcomes in the long term.

In order to overcome the post-pandemic procurement challenges, businesses need to make the smartest possible use of their supply chain data. The four key steps – analyse, assess, deliver and sustain – will enable organisations to manage the current issues and prepare for any future shocks that lie ahead.

☛ Trish Shalloe is a director at management consultancy firm Vendigital.

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