Procurement is a function that has been slow to join the digital revolution, prioritising cost-cutting rather than integrating with innovative technology to add value. In fact, Deloitte’s 2018 Global CPO survey showed that cost reduction remains the top priority for nearly four-in-five (78%) procurement leaders. Personnel in these departments are often pushed to operate more efficiently and are seen as a cost function more than anything else.
However, the same report also highlighted that data analytics is expected to have the largest impact of any technological advancement for over half (54%) of CPOs, a change that is slowly taking place. As the pressure to work more efficiently ramps up, procurement is becoming more data-driven by necessity in order to limit costly errors and control direct and indirect spend.
Historically, procurement teams dealt with diverse databases consisting of structured and unstructured data, from invoice units, price variance and fulfilment and tax information. However, such is the variety of data they are being tasked with processing that they need to consolidate and analyse this data in one place. These insights enable teams to streamline risk management and forecasting, combining expected changes in supply and demand with real-world environmental factors to create dynamic and scalable pricing models. The procurement department that is unprepared for such a change will be unable to take advantage of the best prices available and may even put a strain on supplier relationships, struggling to meet short-term requirements from contract to post-transaction evaluations.
Many companies are adopting digital procurement to mitigate the impact of global macroeconomic uncertainties such as Brexit and NAFTA negotiations. For example, advanced data analytics can help procurement departments make the best spend decisions by incorporating risk analysis into the decision-making process. By combining data related to areas such as pricing and compliance risk, future problems in the supply chain can be anticipated, allowing teams to proactively work to reduce or eradicate them.
Always have a Plan B
Making efficient use of data is all well and good, but realistically, established procurement processes do not always run according to specifications. While this may initially appear to only have a marginal effect, this divergence from the standard can have a tremendous impact on a company’s efficiency, something particularly damaging for businesses with lower margin for error. For example, if an order for materials is delayed and manufacturing comes to a standstill, customers will be waiting longer for their products.
Influences such as maverick buying can also nullify negotiated special conditions, which at first glance, could be viewed as individual cases. However, when looking at the bigger picture it quickly becomes clear that the small losses incurred can quickly add up to a significant sum and possibly also represent a compliance risk.
Managing supplier relationships
Elsewhere, data analytics can aid the help procurement teams in evaluating any risks in the supply chain, by conducting in-depth and comprehensive vendor evaluations. These can consider disparate elements such as on-time delivery, quality of goods and services, and cost. Vendors can be comprehensively evaluated and ranked on all relevant aspects of their services and compared to one another. This enables the team to find the best quality vendor solutions at the most effective prices.
However, this isn’t always easy to do, even with advanced data analytics. Imagine a company that runs a global supply chain with many different product lines and local market requirements. Identifying issues within the supply chain can be like finding a needle in a haystack for organisations, even with data-led tools at their disposal. They could be hiding in manufacturing, logistics, or the order handling process, driven both by internal and external factors. For example, a clothing manufacturer with quality issues could be faced with re-doing a lot of orders, while a logistics provider could deliver an order too late due to hold-ups in the internal approvals process.
Looking ahead, the next quantum leap in digital procurement will combine data analytics, machine learning and technologies such as process mining. This technology analyses digital traces left behind by IT-driven activities, acting as a virtual business consultant for process owners by presenting potential and actual recommendations on which action to take. By focusing on the specific processes rather than the data produced, businesses can see how efficient (or inefficient) their distribution network is and identify any causes of delays. Larger systemic weaknesses in order processing can be pinpointed, and granular details like vendor data and invoice tracking can be drilled into.
The procurement team is central to the success of the entire company, playing a critical role in balancing costs, delivering returns, and managing relationships throughout the supply chain.
Ultimately, making successful changes in procurement comes down to having full visibility and making use of the digital traces left by individual processes, enabling organisations to identify where they have deviated from the norm. Only by empowering procurement with the tools to identify issues in advance and evaluate existing processes in detail, can the department evolve its reputation from a cost function into a significant driver of business value.