The future of the procurement function is in data extraction and analytics that inform decision-making for the present and long-term planning. Moving in this direction is a journey that builds on legacy systems.-Deborah Jenkins

As the procurement function evolves, analytics plays an increasingly important role in their success. By leveraging data-driven insights, organizations can make more informed decisions, streamline processes, and improve overall efficiency. Procurement should analyze structured and unstructured data throughout the procurement and supply chain processes to maximize efficiency and profitability. By leveraging improved analytics, businesses can drive higher value and make more informed decisions. So much data is available through various sources that it can be challenging to develop and implement a data collection and analysis process that delivers the actionable insights needed to create real value. Most companies cannot afford to quit their existing systems too quickly, so building a system that captures the desired insights and supports supply chain transparency is a step-by-step process that includes leveraging existing systems while building a new one.

Collecting Data That Delivers Insights

Procurement analytics is the end result of collecting and analyzing relevant data for business insights that improve decision-making. Today, structured and unstructured data is collected from internal and external sources. The amount of data available with the right collection system is massive, so a primary goal is to collect, sort, and cleanse the data and turn the data into analytics that describe, predict, and guide decision-making and supply chain management. The simplest systems describe the past, and the most advanced systems deliver prescriptive analytics that assist decision-making. In between are diagnostic analytics and predictive analytics.

Legacy systems needing upgrading are commonly found in procurement, because in many organizations it is one of the last functions targeted for technology improvements. The existing systems are often piecemeal, too, with past upgrades and add-ons. Some companies can retain existing systems, like spreadsheet reporting and spend analytics, while moving towards a data-driven procurement transformation.

A data-driven transformation includes all the activities and transactions related to strategic sourcing, procurement, supplier relationship management, corporate social responsibility, sustainability, contract and purchase order management, and risk management. The first step is identifying the benefits the organization wants to realize by moving to data collection that delivers valuable insights. Software company Sievo portrayed a closed cycle of opportunities flowing from procurement data analytics. They are improving prioritization and strategic focus, identifying changes in spend, negotiating better contracts, improving forecasting and budget, improving risks and disruption coverage, and gaining insights into quality and performance. Procurement executives must prioritize collecting and analyzing data to stay ahead, explains Sievo. It is not just about the structured data but also the unstructured data that holds key insights. There are four dimensions for assessing the complexity of needs: connectivity between internal and external sources of data, adaptability to the organization’s sourcing processes, culture, structure, and workflows, transparency based on data that can be trusted, and speed or how often analysis updating occurs and how actively analytics are used in the organization.

Data From All Directions

The examples Sievo gives for procurement analytics include analytics for spend, supplier, diversity, sustainability, contract, market, savings lifecycle, CO2, and spend forecasting. Business spend intelligence is “the process of statistical procurement spend analysis” to support informed business decisions. It includes data mining, data visualization, and reporting. Enterprise procurement analytics data is continuously analyzed and influences procurement strategies and goals. This type of analytics looks at past spend data but also includes discovery, interpretation, and communication of procurement data patterns. There is also category analytics. A balanced scorecard helps track the execution and consequences of procurement activities from a financial, customer, internal, and talent learning and growth perspective.

Procurement data is structured and unstructured both internally and externally. Some data is easier to access and integrate. Deloitte “brings it all together,” meaning connecting physical and digital inputs to improve efficiency, drive better decision-making, produce insights and strategies, enhance process excellence, operational efficiency, and promote collaboration. A digital procurement system benefits the entire organization. The inputs include a wide array of sources: physical documents, unstructured databases, goods tracking, and third-party data. Third-party data includes supplier data, social media, commodity trends, country risk, duties and tariffs, and third-party payment clearing. The third-party data enriches the internal data. Goods tracking is enabled through data from demand, deliveries, material consumption, and receipt of goods.

Deloitte’s procurement analytics system model incorporates decision support, including crowdsourcing, cognitive computing, advanced costing, collaboration networks, advanced visualization, and supplier performance dashboards. Blockchain, RPA solutions, intelligent content extraction, artificial intelligence, sensors, analytics models, and cyber tracking are some tools that support procurement data collection and analytics. How does procurement begin and sustain such a complex journey? Deloitte recommends assessing core technology's status and deciding what maturing and emerging technologies should accomplish. If there is little investment in core technology, the advanced technologies should leapfrog the process by offsetting some core investments. For example, cognitive spend and optical character recognition solutions can extract valuable information from unstructured data and metadata, and provide predictive insights and replace the need for spend analytics that look backward. Suppose there has been a moderate investment in core digital technologies. In that case, the investments in predictive analytics and enhanced costing solutions can complement the existing sourcing platforms, by adding enhancements that deliver more detailed information, such as segmented global sourcing data and anticipating supplier changes. The new investments plug data and analytics gaps. Companies with an advanced investment in core technologies and are early adopters of enhanced technologies can accelerate their investments and choose to go in any direction desired, i.e., automating transactional processes, automated risk sensing, etc.

Identify the Challenges and Then Move Forward

Started by procurement professionals, Buyers Meeting Point strives to stay on top of information and solutions for procurement professionals. The organization lists nine challenges procurement must address as the function begins the data analytics journey. First is data quality, followed by complex and labor-intensive cleansing to detect and remove inaccuracies and redundancies and classification of data categories. Other challenges include a lack of data-driven leadership, unrealistic goals, using the wrong tools, and limited analytics capabilities that do not meet procurement needs. The last three challenges are a lack of domain competence, fear of losing control over data, and analytics as a one-time effort instead of being a long-term part of the procurement transformation journey. Add to these challenges a lack of understanding of technologies like blockchain, AI, machine learning, and RPA, and the transition to a procurement analytics-based function can seem overwhelming. This is a complex transformation, made even more so by the non-stop advancement of technologies. Despite the challenges and the complexity of moving towards a data-driven procurement function, starting the effort is critical to remaining competitive. The organizations that try to operate on inadequate data and data analytics are not likely to succeed over the long term. The companies that commit to and invest in the procurement transition are the ones that will thrive in the future. They will have easy access to the information needed to make good decisions and be forward-looking, crucial in the global business environment filled with opportunities and risks.