Maximizing ROI with Spend Analytics: A Human-Machine Collaboration

Maximizing ROI with Spend Analytics: A Human-Machine Collaboration

By Manoj Kumar

In essence, Spend Analytics involves utilizing systematic computational analysis to extract valuable insights from spend and savings data across various categories of expenses. This process can help organizations achieve their desired business outcomes, including cost savings, cost avoidance, spend forecasting, spend leakage management, and identifying spend anomalies. By gaining a better understanding of where and how money is being spent, organizations can make informed decisions about where to reduce costs.

Spend Analytics can be used to identify both short-term and long-term savings opportunities. In the short term, it can be used to identify one-time savings by negotiating better prices with suppliers. In the long term, spend analytics can be used to develop strategic sourcing plans that result in sustained cost savings.

As part of spend analytics, there are several key aspects of spend and savings that can be analysed to reveal meaningful patterns. These aspects include:

  • Category spend: analysing spend across different categories to identify areas where cost savings can be achieved
  • Supplier spend: analysing spend across different suppliers to identify opportunities for consolidation and negotiations
  • Contract compliance: analysing compliance with existing contracts to identify potential savings and minimize maverick spend
  • Payment terms: analysing payment terms to identify opportunities to negotiate discounts or optimize cash flow
  • Purchase order data: analysing purchase order data to identify spending patterns and opportunities for process improvements.

The following are examples of questions that can be answered through spend analytics:

  • Identifying categories and suppliers with major spend
  • Determining maximum and minimum spend categories
  • Consolidating suppliers for tail spend
  • Analysing spend and savings by preferred suppliers
  • Categorizing direct and indirect spends
  • Identifying spend by contract and non-contract
  • Setting procurement spend thresholds, analysing spend across different regions and countries
  • Tracking purchase order (PO) activity, forecasting spend for specific categories, classifying maverick spending
  • Identifying potential savings opportunities, detecting instances of spend anomalies, and evaluating the percentage of spend managed through catalog buying across different categories.

In addition to the above, data scientists can collaborate with data visualization experts and analytics engineers to develop suitable dashboards and advanced analytics solutions. Category managers strive to maximize cost savings while creating innovative data products.

The following are some approaches that can be utilized to achieve this objective:

  • Explore the possibility of eAuctions, an online marketplace technology that can help organizations manage their spend by facilitating competitive bidding and driving down prices.
  • Payment terms optimization, which involves reviewing and adjusting payment conditions and timing to suppliers to improve a company’s working capital position, optimize spend, and maintain supplier relationships. By analysing past payment data, companies can identify patterns and negotiate more favourable payment terms with suppliers.
  • Volume discounts and supplier consolidation leading to discounts.
  • Optimal demand management and inventory planning, resulting in better spending and increased savings.
  • Preventing maverick spends and tail-spend management
  • Cost bundling of items purchased across different regions and business units to reduce costs.

Why Spend Analytics?

Spend analytics plays a crucial role in procurement analytics due to several reasons, including:

  • Savings opportunities identification: Spend analysis to identify spend categories where 80% of spend is made, procurement teams can negotiate with suppliers to find savings opportunities.
  • Tail spend analysis: It can also help identify spend areas that can be optimized for greater operational efficiency, which is especially valuable for tail spend.
  • Supplier relationship management: Spend analysis enables teams to analyse supplier relationships and categorize suppliers as strategic or non-strategic. This categorization helps maintain appropriate supplier relationships and leverage them to obtain greater discounts through spend consolidation.
  • Spend analysis by regions and countries: This provides insights into spend by regions and countries, which helps track spending and detect any anomalies.
  • Spend leakage prevention: Spend analysis helps avoid spend in categories that are mandated to be avoided.
  • Spend internalization: Procurement teams can identify spend that can be internalized to avoid costs.
  • Volume bundling: Spend analysis enables the identification of opportunities to bundle spend on the same items across different business units and regions at different costs.

Exploring Descriptive and Predictive Use Cases for Spend Analytics

Below are some different use cases for spend analytics, including descriptive and predictive analytics that can inform decision-making regarding spending:

Descriptive Spend Analytics: Descriptive spend analytics provides insight into past spending patterns and can be used to answer questions such as:

  • Analysing spending in different categories to identify major spending areas and suppliers using various key performance indicators (KPIs). This information can be used to identify strategic or preferred suppliers to target for cost savings.
  • Identifying categories where tail spend occurs (5% of spending) to improve operational efficiency. Traditionally, 70-75% of procurement effort is focused on tail spends. By redirecting this effort towards strategic spending, greater cost savings can be achieved. Machine learning-based clustering techniques can be used to identify tail spends and steer them towards preferred suppliers rather than multiple suppliers.

Predictive Spend Analytics: Predictive spend analytics involves using historical data and machine learning techniques to forecast future spending patterns and identify opportunities for cost savings, such as:

  • Forecasting future spending in different categories based on historical data to better plan procurement activities and reduce the risk of stockouts or overspending.
  • Predicting the likelihood of maverick spending (spending outside of established procurement policies or with unauthorized suppliers) and implementing controls to prevent it.
  • Identifying suppliers who are at risk of financial distress or bankruptcy and developing contingency plans to ensure continuity of supply.
  • Using predictive analytics to optimize payment terms and improve working capital management, leading to cost savings and improved supplier relationships.
  • Another important use case for spend analytics is the classification of spend based on commodity codes such as the United Nations Standard Products and Services Code (UNSPSC) or custom taxonomy. This classification allows for better tracking and reporting of spend data, ensuring that it is accurately categorized and recorded for audit and compliance purposes. By using spend analytics to identify and correct any misclassified spend, organizations can avoid potential compliance issues and ensure accurate reporting of their procurement activities.

Want to learn more? Please contact Impendi.

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