Procurement data quality: the must-have to optimize spend management
Businesses are collecting large amounts of data in their daily operations. In procurement only, data is circulating through different forms, is coming from different sources (external and internal) and is used by many stakeholders (even suppliers). That’s why, it can be an issue for the Procurement department to know its amount of spends. Will it be in a transparency matter, to collaborate with Finance or to reduce costs, businesses must always be able to easily collect and analyze their data. This article in 3 parts, written with « Procurement Leader », will explain you why is it important to clean your procurement data. It will also give you an insight on the methods used by companies, with concrete examples and testimonies.
Episode 1 : Why is it important to clean our procurement data? With the Vodafone case
Vodafone Group situation
At multinational telecoms company Vodafone Group, the ability to drive value by getting better business insights through a single consistent view of global spend data has been a long-held ambition. In early 2016, explains Virginie Vast, Vodafone’s head of cognitive procurement and digital sourcing, work finally began on transforming that ambition into reality.
A new digital supply chain unit was created, headed by Vast and tasked with building a data-driven procurement organisation strategy – from data cleansing to automation to augmentation. The digital supply chain management team worked with Vodafone’s business intelligence unit to consolidate data from across the group, clean it, and combine it with externally sourced data to enable category managers to make better decisions more quickly. ”We always knew that we wanted this one clear version of the truth and the data to transform our business, and now that we’ve got it, it’s mind-blowing,” says Vast. “With all our spend data in one place, we can immediately see our performance at any time, review our spend and understand the fluctuations in the markets in real time – making it clear where we need to prioritise our efforts. Next, we’ll be applying artificial intelligence to the data, to discover new business insights.”
Data quality and consistency
Nor is Vodafone unique. The fact is that data availability – and data quality – remain significant challenges for many larger enterprises wishing to perform spend analysis. The more that purchase transactions take place across multiple systems and through different third-party specialist providers, the greater the challenge of consolidating that data into a meaningful picture. “For spend analytics, data quality can be a huge problem – especially for larger companies, or those with a strategy of growing by acquisition,” says Soroosh Saghiri, senior research fellow at the Centre for Strategic Procurement and Supply Chain Management at Cranfield University School of Management. “The goal is to have a single view of spend across the business but, in practice, the reality is a combination of multiple systems, with incomplete and inconsistent data.”
In such circumstances, companies face a subtle irony: the fact spend data is distributed across multiple systems serves to simultaneously make spend analytics not only more difficult, but also more alluring. It’s more difficult, because not only is that data coming from different source systems, it may also be coded in many forms. So different systems may use different product codes and item codes for the same item, and different names and codes for the same supplier. In other words, consolidation is only part of the task: once consolidated, a significant process of data cleaning remains. And it’s more alluring, precisely because of the difficulty of cleansing and consolidating. Simply put, achieving a single view of an organisation’s procurement spend opens up a new range of possibilities. The ‘dirtier’ – and more distributed – the raw spend data, the more likely it is that exploring those possibilities will prove profitable.
From a narrow procurement point of view, for instance, a single view of spend data makes it much easier to quantify spend with specific suppliers, helping to maximize pricing leverage. And in the case of identical items being bought from multiple suppliers, it becomes possible to undertake strategic sourcing initiatives, consolidating that spend on fewer suppliers. Price harmonization becomes much easier, as well: are identical items being bought from the same supplier, but at different prices? More than likely.
Advantages beyond the Procurement Department
More broadly, obtaining this single view of procurement spend can also help to inform a wider set of initiatives. In terms of innovation, for instance, it may be possible to specify and incorporate into new products a number of components that are already being purchased for other purposes, thereby eliminating the need to re-qualify those components, and improving time-to-market. Furthermore, consolidating the purchase of identical items from multiple suppliers usually helps to deliver inventory reductions, as well as stripping complexity from products’ bills-of-material, thereby helping improve resilience while driving down product cost.
Nor are such benefits fanciful. Sprawling British defence contractor GEC-Marconi (now part of BAE Systems) became the poster child for most of them, when it carried out just such a spend data consolidation exercise in the late 1990s, obtaining a single view of spend from no fewer than 168 ERP and procurement systems. Product cost, time-to-market, and inventory holdings all decreased – and not by insignificant amounts.
But before having these results, the way can be long. Data cleaning requires times, resources and a clear organisation. We will see it next week in the second episode.