Organizations across the world are undergoing a period of rapid change and transformation. At a time when businesses are looking to reduce costs, mitigate risks and align with sustainability goals, procurement processes must be an integral part of these efforts. Procurement forms a critical link between organizations and their suppliers and Chief Procurement Officers (CPOs) are ultimately responsible for the risks and benefits arising from every new partnership.
With data forming the bedrock of informed decision-making, CPOs can benefit from the insights that data can provide, with analytics capabilities driving those efforts. Predictive and prescriptive analytics can help businesses make the right procurement decisions and advances in artificial intelligence (AI) will leverage data to further shape predictions around future outcomes based on existing information.
Pain Points in Procurement
Before looking at the role of prescriptive and predictive analytics in procurement today, it’s worth examining the key pain points facing professionals. Inflation and tough macroeconomic conditions have made cost control a challenge due to fluctuating supplier cost bases, while, at the same time, organizations are closely monitoring their own internal margins. The global geopolitical situation also remains volatile, with the potential to further add to supplier costs and supply chain pressures if tensions continue to escalate, particularly in the Middle East.
Environmental, social and governance (ESG) considerations remain high on the agenda for those involved in procurement, with the United Nations having set ambitious targets that many developed countries have committed to achieving by 2030. The pressure is particularly acute on larger businesses to demonstrate they are making progress with ESG goals and procurement can play a crucial role by ensuring that suppliers meet the highest possible standards.
The Role of Prescriptive and Predictive Analytics
As the previous section highlights, the procurement and supply environments of today are far from certain and so the ability to understand and forecast what could happen is key. According to Gartner, predictive analytics can be used to envisage a series of outcomes (forecasting) or uncertainties related to outcomes (simulation). Prescriptive analytics looks at the best way to achieve an outcome. The combination of both forms of analytics can therefore help drive smarter decision-making in procurement.
Real-time data insights and predictive analytics can gather data from several third-party providers and amalgamate that information into a single source of truth, from which businesses can make informed decisions and understand risks ahead of time. Combining analytics capabilities with a list of secondary suppliers makes pivoting during times of uncertainty that much easier to accomplish and minimizes wider disruptions. Without analytics built into procurement solutions, it can become difficult to manage all of the necessary steps to make an informed decision and to take into account all of the relevant factors. This also eliminates silos and the friction they can add to the decision-making process.
Learning From the Data
AI remains an unwavering technological force and it’s beginning to make a significant impact on the procurement sector. Machine learning, a subset of AI, enables computers to learn from patterns and past data sets. Over time, the algorithms become more intelligent as more data is processed and, as a result, they can better identify trends and patterns.
The reality of today's digital economy means that the volume of data present within businesses is too much for people to link together without the right tools. AI unlocks the hidden links between various pieces of data within an organization’s data fabric, which comprises all of a business's structured and unstructured data. Gartner has estimated that data fabric reduces the time for data integration design by 30%, deployment by 30% and maintenance by 70%.
For example, a procurement team might not own contractual data, as that sits within the legal team, however, procurement is responsible for the outcomes of those agreements. AI can seamlessly make connections between pieces of information in the data fabric, to maximize outcomes for all stakeholders. This concept allows the AI to identify linkages between datasets, that would not be recognizable to the human eye at first glance.
In The Hackett Group’s 2024 Procurement Agenda and Key Issues Study, spend analytics was found to be the most important issue but scenario planning was given less importance, despite the fact it supports predictive analytics.
Over time, procurement professionals obtain a wealth of data and to fail to derive the benefits from the insights from that information is a missed opportunity to build resilience for the future and unlock the levers to growth. However, that’s not to say that organizations won’t have to overcome several hurdles on the path to AI adoption. One such hurdle is ensuring that an organization focuses on obtaining value from AI, rather than letting the process dominate. A second is focusing on the quality of the data, as if the information is not reliable and accurate, then even the best AI calculations will struggle to make any sense of the data. A lack of integration is another challenging area, as data residing in different systems limits the potential for analytical capabilities to be fully utilized. Furthermore, as with all areas of technology adoption, planning is key and rushing into deploying AI without thoroughly considering all of the pain points will only lead to additional work and disappointment.
A Procurement Analytics Revolution?
The operating environment for organizations around the world is challenging. However, there’s light at the end of the tunnel, a hope driven by machine learning and artificial intelligence. Organizations are sitting on valuable data waiting to be unlocked and AI can help procurement professionals to not only understand the present hurdles but to navigate future challenges with the use of predictive and prescriptive analytics combined with the power of AI.
