Procurement analytics can be summarized as the process of utilizing data analysis techniques and tools to gain insights into the procurement process and spendings of an organization. Traditionally, procurement analytics uses techniques such as statistics, graphical visualization tools, mathematical algorithms, and simulation tools. However, procurement analytics has come a long way in recent years, becoming more advanced and sophisticated than ever before. Based on a company's maturity level, different data analyses can be applied or added. A prerequisite for data analysis is structure and good data quality.
The following fields apply to conventional procurement analytics: Category analysis (helps to develop sourcing strategies) Spend analysis (helps to recognize trends and identifies focus areas) Price benchmarking and should-cost analysis (helps to identify the saving opportunities) Contract analysis (helps improve supplier performance and compliance) Procurement Key Performance Indicators (KPIs) analysis (helps to monitor and track) Supply risk analysis (helps to mitigate risks)
In general, 4 different types of procurement analytics can be applied: Those 4 analytics types can explain what happened, what might happen in the future, why it happened, and how we should react.
1. Descriptive Analytics Descriptive analytics is the most sophisticated out of the four. It is fundamental to other more advanced analytics. Its purpose is to convert structured data into simple procurement dashboards (a common example) based on internal data. It answers the question of how much, where, what, and to whom I spend money. It also incorporates historical data and trends and allows procurement professionals to track KPIs
2. Diagnostic Analytics Diagnostic Analytics goes a step further and is used to determine the cause of specific trends discovered through descriptive analysis. Diagnostic Analytics uses Data drilling order Data mining methods to analyze data and determine causes which help procurement professionals in their decision making. In practice, data drilling allows more profound insight into a data set, e.g., splitting the data set into more categories to reveal more information. And data mining helps to analyze correlations within the data set. With that, procurement professionals are able to generate a clear picture of the situation and make informed decisions even in unstructured data sets.
3. Predictive Analytics Predictive analytics in procurement is defined as the practice of using data-driven insights to forecast future outcomes and to drive decisions related to procurement. Predictive analytics is used for spend forecasting derived from sales forecasts derived from production numbers, potential customers, BOM information, etc. Nevertheless, predictive analytics is also used for identifying the likelihood of future outcomes. This can be achieved by utilizing historical data in combination with statistical algorithms and machine learning (ML) paired with pattern detection for processes, commodity information, indices, and other relevant data. Procurement departments can shift from reactive to proactive when utilizing predictive Analytics.
4. Prescriptive Analytics Prescriptive analytics, on the other hand, goes beyond visualization and forecasting to support decision-with actionable insight. A prescriptive analytics approach refers to the analysis and prediction of future outcomes and trends based on historical spend, catalog, vendor and material data. Prescriptive analytics have become increasingly important, especially for business processes. For example, renegotiating with a supplier because of exchange rate fluctuation, changes in raw material cost or new regulations on import duties or tax, etc. Thus prescriptive analytics is by far the most advanced and useful procurement analytics in terms of cost savings.
Procurement Analytics as an enabler for organizations In conclusion, procurement generates more data than any procurement professionals can manage efficiently, and that's where digital procurement solutions based on Machine Learning (ML) and Artificial Intelligence (AI) come into play. Descriptive and diagnostic procurement analytics explore historical procurement data to clarify what happened in the past and why it happened. Predictive and prescriptive analytics detect masked patterns in historical procurement data and make predictions and actionable recommendations based on external data.
To thrive in a VUCA (Volatility, Uncertainty, Complexity und Ambiguity) world, procurement organizations must tackle complexity, attain mastery, and enhance agility and resilience. This can be achieved by leading profitability efforts, driving cross-functional value creation, developing market expertise, and leveraging technology for greater efficiency. Procurement analytics can revolutionize traditional procurement practices and pave the way toward excellence through digital procurement tools. In addition, procurement generates vast amounts of data, making the link between procurement and data analytics logical. Thus, procurement must utilize both internal and external data to predict outcomes, improve collaboration, and make better and faster data-driven decisions.
