Disrupting procurement through analytics

Supply chain management can be a tough task, owing to a lack of transparency, siloed data, organizational complexity, or over-reliance on manual processes. With vast amounts of data at our disposal and tools and technologies that turn them into informed insights, businesses now look forward to harnessing the power of procurement analytics to increase their value exponentially, generate overall productivity for the enterprise, and navigate complex sets of business drivers.

Businesses face continuous pressure to improve their productivity and drive innovation. In the competitive business world, procurement analytics apply in various use cases like accurate business forecasting and reaction to hyper-volatile conditions like demand uncertainty, supply disruptions, and rapidly evolving customer preferences.

What is procurement analytics?

Procurement analytics is the procedure of gathering and running an analysis of procurement data, helping businesses make informed decisions, and generating accurate analytics. Procurement analytics helps organizations in several ways, like analyzing their expenditure in accordance with historical data, or predicting and budgeting decisions.

The first step of procurement analytics is collecting data from diverse sources like financial software systems and ERPs. Once the system obtains the data, it classifies them into organizational standard or use-case-specific taxonomies and then presents them using visualization dashboards or business intelligence tools.

Born out of the need for a single view of spending, procurement analytics does more than just data visualization. It collects, cleans, and enriches bulk data from diverse systems, adding value to the enterprise. Procurement analytics tools get more punctual, precise, and actionable insights to help the organization understand its procurement contributions.

Vulnerable supply chain and incompetent solutions

According to a poll by McKinsey&Company, organizations using procurement analytics tools are able to find improvements in their ability to handle disruptive events. Around 50% of the organizations polled were able to react effectively and enhance their spending visibility post the COVID-19 pandemic, in comparison with the global economic crisis of 2008–09. While one-third of the companies reported slight progress in their procurement activities, one in six companies had no improvements.

Limited progress is not an option in the fast-growing and volatile business world. A majority of supply-chain systems and procurement management face unnecessary complexities and vulnerabilities. Organizations are looking to take necessary steps to respond to disruptions and obstacles by keeping themselves up to date with economic and technological developments to stay resilient and more assertive.

Though data is abundant, enterprises shy away from procurement analytics tools and implement them at a scale as the data can be naturally disorganized and fragmented. Currently, organizations store invoices in financial software systems and contracts in customer relationship management (CRM) software. With data lying in these fragmented silos, obtaining a single source of truth is a difficult task.

Enterprises find it difficult to create, control, and share category-level content and experiences. Organizations often conduct analysis manually, dampening their scalability and leaving their procurement procedures vastly unmanaged. Here, automation is a must to manage the bulk volumes of data with great accuracy and speed.

Why is procurement analytics so important?

Analytics plays a crucial part in transforming any business and industry, and the procurement sector is no exception. Analytics is one of the most disruptive tools and a vital procurement resource, arguably the most impactful technology in the industry. Over the coming decade, analytics will continue to create an ever-lasting impact on the procurement process.

Analytics touches all procurement activities like strategic sourcing, classification management, spend analysis, and procure-to-pay operations.

Analytics boosts category managers when used effectively, helping them identify savings opportunities, classify and prioritize suppliers, pinpoint sourcing possibilities, handle supply risk prospects, drive sustainability, develop supplier relationships, and foster innovation.

Procurement analytics empowers contract lifecycle management, helping the organization renegotiate contracts or providing valuable insights for the same. Analytics also helps in risk management within the supply chain and procurement, fixing complex relationships between supply, price, risks, the volatile environment, and more.

Additionally, procurement analytics boosts the source-to-pay processes, managing sustainability and corporate social responsibility (CSR) initiatives, and performance management activities.

How AI and analytics are disrupting procurement

Transforming the way you conduct spend analytics

Procurement teams handle multiple databases that contain structured and unstructured data but face the ultimate challenge of consolidating and analyzing them at a single hub. The insights help the organization study the changing supply-chain environment and determine dynamic and scalable pricing models.

Procurement analytic tools let enterprises bring together diverse data sets like invoice information, supplier and buyer information, price variation records, and tax information into a single analysis. The tools help organizations find opportunities to reduce their spending and create a long-term impact. Also, advanced procurement analytics helps the organization improve risk management and decision-making when it comes to managing its expenditure. Data analytics tools help the organization get insights into the pricing and compliance risk, geographical risk, and preventative measures, giving them better visibility.

Increase accuracy in demand forecasting

Without proper preparations to handle changes in demands, procurement teams might fail to leverage the best available prices or strain their supplier relationships while struggling to meet short-term requirements. Though companies prepare for predictable variations, it does not mean that they should only stick to the calendar. When the market alters due to various factors, enterprises must be prepared for any situation.

The recent COVID-19 pandemic did not just disrupt the supply chain, it also altered customer behavior, forcing procurement companies to change critical parts of their supply chain.

Data analytics looks at recurring and unpredictable environments together, giving businesses greater visibility over demand forecasting, and helping procurement teams and enterprises as a whole.

Strengthen supplier relationship management

Procurement analytics tools enable teams to evaluate their vendors in-depth, considering factors such as timely deliveries, quality of goods and services, and cost. An excellent data analytics system evaluates and ranks all aspects of each vendor and compares them with others to find the ones best suited for your business. Vendor management includes consolidating different vendor options or altering open market transactions to align with the enterprise’s goals.

Sources of procurement data

Generally, organizations get data for procurement analytics from internal and external sources. More organizations are using this heterogeneous mix to acquire decisive insights and make informed and well-thought-out decisions.

Internal data

Internal data consists of the information that comes from within the enterprise, like financial records, enterprise planning systems (ERPs), and data acquired from various suppliers or business units through Excel or other financial ledgers.

External data

External data consists of information that comes from outside the enterprise, such as public data available on the internet, including details about suppliers, commodity prices, or currencies. The segment might also include third-party information like supplier industry codes, risk profiles, or credit scores.

How do you use procurement analytics?

Procurement analytics brings visibility to spending, performance insights, and many more advantages to the business. However, there are a few processes that procurement analytics goes through before generating insights. Similar to an iceberg, 80% of the value created lies hidden below the surface.

The three steps of procurement analytics are:

• Data extraction
• Data cleansing and enrichment
• Analytics and report generation

Data extraction

The first step for procurement analytics is extracting data from all possible sources and bringing them under a central consolidated database. The process includes taking messy and disorganized data and preparing them for analysis.

Data cleansing and enrichment

Once the data extraction is complete, procurement analysis tools clean the data to get rid of incorrect, corrupt, duplicate, or incomplete data before classifying them into clear and well-defined categories. The tool carries out precise data classification to create compelling analyses as heterogeneous datasets make it easier for the management to handle their operations. After cleansing and categorizing the data, it is then enriched through automated translations.

Analytics and report generation

Once the tool cleans, classifies, and enhances the data, it’s time for the actual analysis. Procurement analytics tools run various artificial intelligence and machine learning algorithms on the data to generate valuable insights that help the business with procurement operations.

Conclusion

In the era of big data, artificial intelligence, and other bleeding-edge technologies, enterprises are leveraging the power of analytics to empower their procurement operations. Need to know how procurement analytic tools help your supply chain operations? Say hi to take the next step.